From 21ea86388ae6857f8abadb25add4ecc584a63664 Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Thu, 16 Jan 2025 13:44:58 -0700 Subject: [PATCH 01/48] minor clean-ups to floris.py (#419) * removed writing wind data to file in floris.py and added verbose as attr * minor clean up to applying losses in floris execute function * added floris clean-up to RELEASE.md * patch for readthedocs build using sphinx * fixed typo in config filepath from previous commit * fixed typo in config filepath from previous commit again --- .readthedocs.yaml | 3 +++ RELEASE.md | 3 +++ hopp/simulation/technologies/wind/floris.py | 23 ++++++++------------- 3 files changed, 15 insertions(+), 14 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 02abf7463..e1f1f632d 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -4,6 +4,9 @@ # Required version: 2 +sphinx: + # Path to your Sphinx configuration file. + configuration: docs/conf.py # Set the version of Python and other tools you might need build: diff --git a/RELEASE.md b/RELEASE.md index 48070d1f0..36a2f0a24 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,5 +1,8 @@ # Release Notes +## Unreleased, TBD ++ minor clean up to floris.py - removed unnecessary data exportation and fixed bug in value() + ## Version 3.1.1, Dec. 18, 2024 * Enhanced PV plant functionality: added tilting solar panel support, improved system design handling, and refined tilt angle calculations. diff --git a/hopp/simulation/technologies/wind/floris.py b/hopp/simulation/technologies/wind/floris.py index a507c706e..50339b311 100644 --- a/hopp/simulation/technologies/wind/floris.py +++ b/hopp/simulation/technologies/wind/floris.py @@ -20,6 +20,7 @@ class Floris(BaseClass): site: SiteInfo = field() config: "WindConfig" = field() + verbose: bool = field(default = True) _operational_losses: float = field(init=False) _timestep: Tuple[int, int] = field(init=False) @@ -44,14 +45,6 @@ def __attrs_post_init__(self): self.wind_resource_data = self.site.wind_resource.data self.speeds, self.wind_dirs = self.parse_resource_data() - save_data = np.zeros((len(self.speeds),2)) - save_data[:,0] = self.speeds - save_data[:,1] = self.wind_dirs - - with open('speed_dir_data.csv', 'w', newline='') as fo: - writer = csv.writer(fo) - writer.writerows(save_data) - self.wind_farm_xCoordinates = self.fi.layout_x self.wind_farm_yCoordinates = self.fi.layout_y self.nTurbs = len(self.wind_farm_xCoordinates) @@ -90,7 +83,7 @@ def value(self, name: str, set_value=None): """ if set_value = None, then retrieve value; otherwise overwrite variable's value """ - if set_value: + if set_value is not None: self.__setattr__(name, set_value) else: return self.__getattribute__(name) @@ -119,8 +112,9 @@ def parse_resource_data(self): return speeds, wind_dirs def execute(self, project_life): - - print('Simulating wind farm output in FLORIS...') + + if self.verbose: + print('Simulating wind farm output in FLORIS...') # find generation of wind farm power_turbines = np.zeros((self.nTurbs, 8760)) @@ -138,14 +132,15 @@ def execute(self, project_life): power_turbines[:, self.start_idx:self.end_idx] = self.fi.get_turbine_powers().reshape((self.nTurbs, self.end_idx - self.start_idx)) power_farm[self.start_idx:self.end_idx] = self.fi.get_farm_power().reshape((self.end_idx - self.start_idx)) + operational_efficiency = ((100 - self._operational_losses)/100) # Adding losses from PySAM defaults (excluding turbine and wake losses) - self.gen = power_farm * ((100 - self._operational_losses)/100) / 1000 # kW + self.gen = power_farm * operational_efficiency / 1000 # kW self.annual_energy = np.sum(self.gen) # kWh self.capacity_factor = np.sum(self.gen) / (8760 * self.system_capacity) * 100 - self.turb_powers = power_turbines * (100 - self._operational_losses) / 100 / 1000 # kW + self.turb_powers = power_turbines * operational_efficiency / 1000 # kW self.turb_velocities = self.fi.turbine_average_velocities - self.annual_energy_pre_curtailment_ac = self.annual_energy + self.annual_energy_pre_curtailment_ac = np.sum(self.gen) # kWh def export(self): """ From 68393204d03db5d10cba2d66c76caf4c7dc8d7ac Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Wed, 22 Jan 2025 13:04:54 -0700 Subject: [PATCH 02/48] feature: option to load wind and solar resource data from HPC (#414) * added ability to grab wind and solar resource data off the HPC and updated upstream functions * updated doc strings and comments for wtk_data and nsrdb_data scripts * added comments to site_info for added options, fixed bug of resource year being set to 2012 * updated formatting and docstrings for previous commit * updated RELEASE.md with recent changes * changed input variable name in HPCWindData to align with WindResource and updated doc strings * added high level test for hpc resource data * added sphinx config path in readthedocs to fix build * updated doc strings and minor updates to hpc resource functions * updated docstrings and added valid year check and tests for hpc resource data * changed site_info api doc file to markdown * added doc files for wind and solar resource * minor updates to doc strings in hpc resource classes * updated string formatting * added tests for wtk and nsrdb resource if filepath is provided * split long comments into multiple lines --- RELEASE.md | 2 + docs/_toc.yml | 7 + docs/api/resource/index.md | 39 +++ docs/api/resource/solar_api.md | 10 + docs/api/resource/solar_hpc.md | 11 + docs/api/resource/wind_api.md | 10 + docs/api/resource/wind_hpc.md | 11 + docs/api/{site_info.rst => site_info.md} | 10 +- .../technologies/resource/__init__.py | 2 + .../technologies/resource/nsrdb_data.py | 231 ++++++++++++++++++ .../resource/wind_toolkit_data.py | 206 ++++++++++++++++ .../technologies/sites/site_info.py | 46 +++- pyproject.toml | 3 +- tests/hopp/test_resource_download.py | 48 +++- 14 files changed, 620 insertions(+), 16 deletions(-) create mode 100644 docs/api/resource/index.md create mode 100644 docs/api/resource/solar_api.md create mode 100644 docs/api/resource/solar_hpc.md create mode 100644 docs/api/resource/wind_api.md create mode 100644 docs/api/resource/wind_hpc.md rename docs/api/{site_info.rst => site_info.md} (62%) create mode 100644 hopp/simulation/technologies/resource/nsrdb_data.py create mode 100644 hopp/simulation/technologies/resource/wind_toolkit_data.py diff --git a/RELEASE.md b/RELEASE.md index 36a2f0a24..965578a60 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,6 +1,8 @@ # Release Notes ## Unreleased, TBD +* Added option and functionality to load wind and solar resource data from NSRDB and Wind Toolkit data files if user-specified. +* Fixed a bug in site_info that set resource year to 2012 even if otherwise specified. + minor clean up to floris.py - removed unnecessary data exportation and fixed bug in value() ## Version 3.1.1, Dec. 18, 2024 diff --git a/docs/_toc.yml b/docs/_toc.yml index 05da9a2d2..eff23f32e 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -14,6 +14,13 @@ parts: chapters: - file: api/hopp_interface - file: api/site_info + sections: + - file: api/resource/index + sections: + - file: api/resource/solar_api + - file: api/resource/wind_api + - file: api/resource/solar_hpc + - file: api/resource/wind_hpc - file: api/hybrid_simulation - file: api/technology/index sections: diff --git a/docs/api/resource/index.md b/docs/api/resource/index.md new file mode 100644 index 000000000..1728c3e58 --- /dev/null +++ b/docs/api/resource/index.md @@ -0,0 +1,39 @@ +# Resource Data + +These are the primary methods for accessing wind and solar resource data. + +- [Solar Resource (API)](resource:solar-resource) +- [Wind Resource (API)](resource:wind-resource) +- [Solar Resource (NSRDB Dataset on NREL HPC)](resource:nsrdb-data) +- [Wind Resource (Wind Toolkit Dataset on NREL HPC)](resource:wtk-data) + +## NREL API Keys + +An NREL API key is required to use the functionality for [Solar Resource (API)](resource:solar-resource) and [Wind Resource (API)](resource:wind-resource). + +An NREL API key can be obtained from [here](https://developer.nrel.gov/signup/). + +Once an API key is obtained, create a file ".env" in the HOPP root directory (/path/to/HOPP/.env) that contains the lines: + +```bash +NREL_API_KEY=key +NREL_API_EMAIL=your.name@email.com +``` + +where `key` is your API key and `your.name@email.com` is the email that was used to get the API key. + +## NREL HPC Datasets + +To load resource data from datasets hosted on NREL's HPC, HOPP must be installed and run from the NREL HPC. Currently, loading resource data from HPC is only enabled for [wind](resource:wtk-data) and [solar](resource:nsrdb-data) resource. + + +(resource:resource-base)= +## Resource Base Class + +Base class for resource data + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.resource.Resource + :members: + :exclude-members: copy, plot, _abc_impl +``` diff --git a/docs/api/resource/solar_api.md b/docs/api/resource/solar_api.md new file mode 100644 index 000000000..84f64867f --- /dev/null +++ b/docs/api/resource/solar_api.md @@ -0,0 +1,10 @@ +(resource:solar-resource)= +# Solar Resource (API) + +By default, solar resource data is downloaded from the NREL Developer Network hosted National Solar Radiation Database (NSRDB) dataset [Physical Solar Model (PSM) v3.2.2](https://developer.nrel.gov/docs/solar/nsrdb/psm3-2-2-download/). Using this functionality requires an NREL API key. + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.resource.solar_resource.SolarResource + :members: + :exclude-members: _abc_impl, check_download_dir +``` diff --git a/docs/api/resource/solar_hpc.md b/docs/api/resource/solar_hpc.md new file mode 100644 index 000000000..35ba6e5e0 --- /dev/null +++ b/docs/api/resource/solar_hpc.md @@ -0,0 +1,11 @@ +(resource:nsrdb-data)= +# Solar Resource (NSRDB Dataset on NREL HPC) + +If enabled, solar resource data can be loaded from the NREL HPC (Kestrel) hosted National Solar Radiation Database (NSRDB) dataset. This functionality leverages the [NREL REsource eXtraction (rex) tool](https://github.com/NREL/rex). Information on NREL HPC file systems and datasets can be found [here](https://nrel.github.io/HPC/Documentation/Systems/Kestrel/Filesystems/#projectfs). + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.resource.nsrdb_data.HPCSolarData + :members: + :undoc-members: + :exclude-members: _abc_impl, check_download_dir, call_api +``` \ No newline at end of file diff --git a/docs/api/resource/wind_api.md b/docs/api/resource/wind_api.md new file mode 100644 index 000000000..ee8f2ab98 --- /dev/null +++ b/docs/api/resource/wind_api.md @@ -0,0 +1,10 @@ +(resource:wind-resource)= +# Wind Resource (API) + +By default, wind resource data is downloaded from the NREL Developer Network hosted Wind Integration National Dataset (WIND) Toolkit dataset [Wind Toolkit Data - SAM format (srw)](https://developer.nrel.gov/docs/wind/wind-toolkit/wtk-srw-download/). Using this functionality requires an NREL API key. + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.resource.wind_resource.WindResource + :members: + :exclude-members: _abc_impl, check_download_dir +``` \ No newline at end of file diff --git a/docs/api/resource/wind_hpc.md b/docs/api/resource/wind_hpc.md new file mode 100644 index 000000000..9196ad730 --- /dev/null +++ b/docs/api/resource/wind_hpc.md @@ -0,0 +1,11 @@ +(resource:wtk-data)= +# Wind Resource (Wind Toolkit Dataset on NREL HPC) + +If enabled, wind resource data can be loaded from the NREL HPC (Kestrel) hosted Wind Integration National Dataset (WIND) Toolkit dataset. This functionality leverages the [NREL REsource eXtraction (rex) tool](https://github.com/NREL/rex). Information on NREL HPC file systems and datasets can be found [here](https://nrel.github.io/HPC/Documentation/Systems/Kestrel/Filesystems/#projectfs). + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.resource.wind_toolkit_data.HPCWindData + :members: + :undoc-members: + :exclude-members: _abc_impl, check_download_dir, call_api +``` \ No newline at end of file diff --git a/docs/api/site_info.rst b/docs/api/site_info.md similarity index 62% rename from docs/api/site_info.rst rename to docs/api/site_info.md index 2465c51d9..0b429ea8a 100644 --- a/docs/api/site_info.rst +++ b/docs/api/site_info.md @@ -1,11 +1,9 @@ -.. _SiteInfo: - - -Hybrid Plant Site Information -============================== +# Hybrid Plant Site Information The purpose of this class is to house all site specific data, e.g., weather data. +```{eval-rst} .. autoclass:: hopp.simulation.technologies.sites.SiteInfo :members: - :undoc-members: \ No newline at end of file + :undoc-members: +``` diff --git a/hopp/simulation/technologies/resource/__init__.py b/hopp/simulation/technologies/resource/__init__.py index 7a8ea8f0b..0d3601457 100644 --- a/hopp/simulation/technologies/resource/__init__.py +++ b/hopp/simulation/technologies/resource/__init__.py @@ -5,3 +5,5 @@ from hopp.simulation.technologies.resource.resource import Resource from hopp.simulation.technologies.resource.greet_data import GREETData from hopp.simulation.technologies.resource.cambium_data import CambiumData +from hopp.simulation.technologies.resource.nsrdb_data import HPCSolarData +from hopp.simulation.technologies.resource.wind_toolkit_data import HPCWindData diff --git a/hopp/simulation/technologies/resource/nsrdb_data.py b/hopp/simulation/technologies/resource/nsrdb_data.py new file mode 100644 index 000000000..0b4b1180e --- /dev/null +++ b/hopp/simulation/technologies/resource/nsrdb_data.py @@ -0,0 +1,231 @@ +from rex import NSRDBX +from rex.sam_resource import SAMResource +import numpy as np +from hopp.simulation.technologies.resource.resource import Resource +from typing import Optional, Union +from pathlib import Path +import os +from hopp.utilities.validators import range_val +NSRDB_DEP = "/datasets/NSRDB/deprecated_v3/nsrdb_" + +# NOTE: Current version of PSM v3.2.2 which corresponds to /api/nsrdb/v2/solar/psm3-2-2-download +NSRDB_NEW = "/datasets/NSRDB/current/nsrdb_" + +# Pull Solar Resource Data directly from NSRDB on HPC +# To be called instead of SolarResource from hopp.simulation.technologies.resource +class HPCSolarData(Resource): + """ + Class to manage Solar Resource data from NSRDB Datasets. + + Attributes: + nsrdb_file: (str) path of file that resource data is pulled from. + site_gid: (int) id for NSRDB location that resource data was pulled from. + nsrdb_latitude: (float) latitude of NSRDB location corresponding to site_gid. + nsrdb_longitude: (float) longitude of NSRDB location corresponding to site_gid. + + """ + + + def __init__( + self, + lat: float, + lon: float, + year: int, + nsrdb_source_path: Union[str,Path] = "", + filepath: str = "", + ): + """Class to pull solar resource data from NSRDB datasets hosted on the HPC + + Args: + lat (float): latitude corresponding to location for solar resource data + lon (float): longitude corresponding to location for solar resource data + year (int): year for resource data. must be between 1998 and 2022 + nsrdb_source_path (Union[str,Path], optional): directory where NSRDB data is hosted on HPC. Defaults to "". + filepath (str, optional): filepath to NSRDB h5 file on HPC. Defaults to "". + - should be formatted as: /path/to/file/name_of_file.h5 + Raises: + ValueError: if year is not between 1998 and 2022 (inclusive) + FileNotFoundError: if nsrdb_file is not valid filepath + """ + + # NOTE: self.data must be compatible with PVWatts.SolarResource.solar_resource_data + # see: https://nrel-pysam.readthedocs.io/en/main/modules/Pvwattsv8.html#PySAM.Pvwattsv8.Pvwattsv8.SolarResource + super().__init__(lat, lon, year) + + if filepath == "" and nsrdb_source_path=="": + # use default filepath + self.nsrdb_file = NSRDB_NEW + f"{self.year}.h5" + elif filepath != "" and nsrdb_source_path == "": + # filepath (full h5 filepath) is provided by user + if ".h5" not in filepath: + filepath = filepath + ".h5" + self.nsrdb_file = str(filepath) + elif filepath == "" and nsrdb_source_path != "": + # directory of h5 files (nsrdb_source_path) is provided by user + self.nsrdb_file = os.path.join(str(nsrdb_source_path),f"nsrdb_{self.year}.h5") + else: + # use default filepaths + self.nsrdb_file = NSRDB_NEW + f"{self.year}.h5" + + # Check for valid year + if self.year < 1998 or self.year > 2022: + raise ValueError(f"Resource year for NSRDB Data must be between 1998 and 2022 but {self.year} was provided") + + # Check for valid filepath for NSRDB file + if not os.path.isfile(self.nsrdb_file): + raise FileNotFoundError(f"Cannot find NSRDB .h5 file, filepath {self.nsrdb_file} does not exist") + + # Pull data from HPC NSRDB dataset + self.download_resource() + + # Set solar resource data into SAM/PySAM digestible format + self.format_data() + + + def download_resource(self): + """load NSRDB h5 file using rex and get solar resource data for location + specified by (self.lat, self.lon) + """ + + # Open file with rex NSRDBX object + with NSRDBX(self.nsrdb_file, hsds=False) as f: + # get gid of location closest to given lat/lon coordinates + site_gid = f.lat_lon_gid((self.latitude,self.longitude)) + + # extract timezone, elevation, latitude and longitude from meta dataset with gid + self.time_zone = f.meta['timezone'].iloc[site_gid] + self.elevation = f.meta['elevation'].iloc[site_gid] + self.nsrdb_latitude = f.meta['latitude'].iloc[site_gid] + self.nsrdb_longitude = f.meta['longitude'].iloc[site_gid] + + # extract remaining datapoints: + # year, month, day, hour, minute, dn, df, gh, wspd,tdry, pres, tdew + + # 1) NOTE: datasets have readings at 0 and 30 minutes each hour, + # HOPP/SAM workflow requires only 30 minute reading values -> filter 0 minute readings with [1::2] + # 2) NOTE: datasets are not auto shifted by timezone offset + # -> wrap extraction in SAMResource.roll_timeseries(input_array, timezone, #steps in an hour=1) to roll timezones + # 3) NOTE: solar_resource.py code references solar_zenith_angle and RH = relative_humidity but I couldn't find them + # actually being utilized. Captured them below just in case. + self.year_arr = f.time_index.year.values[1::2] + self.month_arr = f.time_index.month.values[1::2] + self.day_arr = f.time_index.day.values[1::2] + self.hour_arr = f.time_index.hour.values[1::2] + self.minute_arr = f.time_index.minute.values[1::2] + self.dni_arr = SAMResource.roll_timeseries((f['dni', :, site_gid][1::2]), self.time_zone, 1) + self.dhi_arr = SAMResource.roll_timeseries((f['dhi', :, site_gid][1::2]), self.time_zone, 1) + self.ghi_arr = SAMResource.roll_timeseries((f['ghi', :, site_gid][1::2]), self.time_zone, 1) + self.wspd_arr = SAMResource.roll_timeseries((f['wind_speed', :, site_gid][1::2]), self.time_zone, 1) + self.tdry_arr = SAMResource.roll_timeseries((f['air_temperature', :, site_gid][1::2]), self.time_zone, 1) + # self.relative_humidity_arr = SAMResource.roll_timeseries((f['relative_humidity', :, site_gid][1::2]), self.time_zone, 1) + # self.solar_zenith_arr = SAMResource.roll_timeseries((f['solar_zenith_angle', :, site_gid][1::2]), self.time_zone, 1) + self.pres_arr = SAMResource.roll_timeseries((f['surface_pressure', :, site_gid][1::2]), self.time_zone, 1) + self.tdew_arr = SAMResource.roll_timeseries((f['dew_point', :, site_gid][1::2]), self.time_zone, 1) + + self.site_gid = site_gid + + + def format_data(self): + # Remove data from feb29 on leap years + if (self.year % 4) == 0: + feb29 = np.arange(1416,1440) + self.year_arr = np.delete(self.year_arr, feb29) + self.month_arr = np.delete(self.month_arr, feb29) + self.day_arr = np.delete(self.day_arr, feb29) + self.hour_arr = np.delete(self.hour_arr, feb29) + self.minute_arr = np.delete(self.minute_arr, feb29) + self.dni_arr = np.delete(self.dni_arr, feb29) + self.dhi_arr = np.delete(self.dhi_arr, feb29) + self.ghi_arr = np.delete(self.ghi_arr, feb29) + self.wspd_arr = np.delete(self.wspd_arr, feb29) + self.tdry_arr = np.delete(self.tdry_arr, feb29) + # self.relative_humidity_arr = np.delete(self.relative_humidity_arr, feb29) + # self.solar_zenith_arr = np.delete(self.solar_zenith_arr, feb29) + self.pres_arr = np.delete(self.pres_arr, feb29) + self.tdew_arr = np.delete(self.tdew_arr, feb29) + + # round to desired precision and convert to desired data type + # NOTE: unsure if SAM/PySAM is sensitive to data types and decimal precision. + # If not sensitive, can remove .astype() and round() to increase computational efficiency + self.time_zone = float(self.time_zone) + self.elevation = round(float(self.elevation), 0) + self.nsrdb_latitude = round(float(self.nsrdb_latitude), 2) + self.nsrdb_longitude = round(float(self.nsrdb_longitude),2) + self.year_arr = list(self.year_arr.astype(float, copy=False)) + self.month_arr = list(self.month_arr.astype(float, copy=False)) + self.day_arr = list(self.day_arr.astype(float, copy=False)) + self.hour_arr = list(self.hour_arr.astype(float, copy=False)) + self.minute_arr = list(self.minute_arr.astype(float, copy=False)) + self.dni_arr = list(self.dni_arr.astype(float, copy=False)) + self.dhi_arr = list(self.dhi_arr.astype(float, copy=False)) + self.ghi_arr = list(self.ghi_arr.astype(float, copy=False)) + self.wspd_arr = list(self.wspd_arr.astype(float, copy=False)) + self.tdry_arr = list(self.tdry_arr.astype(float, copy=False)) + # self.relative_humidity_arr = list(np.round(self.relative_humidity_arr, decimals=1)) + # self.solar_zenith_angle_arr = list(np.round(self.solar_zenith_angle_arr, decimals=1)) + self.pres_arr = list(self.pres_arr.astype(float, copy=False)) + self.tdew_arr = list(self.tdew_arr.astype(float, copy=False)) + + self.data = { + 'tz' : self.time_zone, + 'elev' : self.elevation, + 'lat' : self.nsrdb_latitude, + 'lon' : self.nsrdb_longitude, + 'year' : self.year_arr, + 'month' : self.month_arr, + 'day' : self.day_arr, + 'hour' : self.hour_arr, + 'minute' : self.minute_arr, + 'dn' : self.dni_arr, + 'df' : self.dhi_arr, + 'gh' : self.ghi_arr, + 'wspd' : self.wspd_arr, + 'tdry' : self.tdry_arr, + 'pres' : self.pres_arr, + 'tdew' : self.tdew_arr + } + + @Resource.data.setter + def data(self,data_dict): + """ + Sets data property with formatted solar resource data for SAM + data (dict): + :key tz (float): Time zone is for standard time in hours ahead of GMT + :key elev (float): Elevation is in meters above sea level + :key lat (float): degrees north of the equator + :key lon (float): degrees East of the prime meridian + :key year (list(int)): year + :key month (list(float)): number associated with month (1 = January) + :key day (list(float)): number indicating the day of month (Day = 1 is the first day of the month) + :key hour (list(float)): number indicating the hour of day (Hour = 0 is the first hour of the day) + :key minute (list(float)): number indicating minute of hour (Minute = 0 is the first minute of the hour) + :key dn (list(float)): Beam normal irradiance (W/m2) + :key df (list(float)): Diffuse horizontal irradiance (W/m2) + :key gh (list(float)): Global horizontal irradiance (W/m2) + :key wspd (list(float)): Wind speed at 10 meters above the ground (m/s) + :key tdry (list(float)): Ambient dry bulb temperature (°C) + :key pres (list(float)): Atmospheric pressure (millibar) + :key tdew (list(float)): Dew point temperature (°C) + """ + if "dn" not in data_dict.keys(): + dic = { + 'tz' : self.time_zone, + 'elev' : self.elevation, + 'lat' : self.nsrdb_latitude, + 'lon' : self.nsrdb_longitude, + 'year' : self.year_arr, + 'month' : self.month_arr, + 'day' : self.day_arr, + 'hour' : self.hour_arr, + 'minute' : self.minute_arr, + 'dn' : self.dni_arr, + 'df' : self.dhi_arr, + 'gh' : self.ghi_arr, + 'wspd' : self.wspd_arr, + 'tdry' : self.tdry_arr, + 'pres' : self.pres_arr, + 'tdew' : self.tdew_arr + } + self._data = dic + else: + self._data = data_dict \ No newline at end of file diff --git a/hopp/simulation/technologies/resource/wind_toolkit_data.py b/hopp/simulation/technologies/resource/wind_toolkit_data.py new file mode 100644 index 000000000..5a4da7f40 --- /dev/null +++ b/hopp/simulation/technologies/resource/wind_toolkit_data.py @@ -0,0 +1,206 @@ +from rex import WindX +from rex.sam_resource import SAMResource +import numpy as np +from typing import Optional, Union +from pathlib import Path +import os +from hopp.simulation.technologies.resource.resource import Resource + +WTK_V10_BASE = "/datasets/WIND/conus/v1.0.0/wtk_conus_" +WTK_V11_BASE = "/datasets/WIND/conus/v1.1.0/wtk_conus_" + + +class HPCWindData(Resource): + """ + Class to manage Wind Resource data from Wind Toolkit Datasets + + Attributes: + wtk_file: (str) path of file that resource data is pulled from + site_gid: (int) id for Wind Toolkit location that resource data was pulled from + wtk_latitude: (float) latitude of Wind Toolkit location corresponding to site_gid + wtk_longitude: (float) longitude of Wind Toolkit location corresponding to site_gid + """ + + + def __init__( + self, + lat: float, + lon: float, + year: int, + wind_turbine_hub_ht: float, + wtk_source_path: Union[str,Path] = "", + filepath: str = "", + ): + """Class to pull wind resource data from WIND Toolkit datasets hosted on the HPC + + Args: + lat (float): latitude corresponding to location for wind resource data + lon (float): longitude corresponding to location for wind resource data + year (int): year for resource data. must be between 2007 and 2014 + wind_turbine_hub_ht (float): turbine hub height (m) + wtk_source_path (Union[str,Path], optional): directory where Wind Toolkit data is hosted on HPC. Defaults to "". + filepath (str, optional): filepath to Wind Toolkit h5 file on HPC. Defaults to "". + - should be formatted as: /path/to/file/name_of_file.h5 + Raises: + ValueError: if year is not between 2007 and 2014 (inclusive) + FileNotFoundError: if wtk_file is not valid filepath + """ + super().__init__(lat, lon, year) + + self.hub_height_meters = wind_turbine_hub_ht + self.allowed_hub_heights_meters = [10, 40, 60, 80, 100, 120, 140, 160, 200] + self.data_hub_heights = self.calculate_heights_to_download() + + # Check for valid year + if self.year < 2007 or self.year > 2014: + raise ValueError(f"Resource year for WIND Toolkit Data must be between 2007 and 2014 but {self.year} was provided") + + if filepath == "" and wtk_source_path=="": + # use default filepaths based on resource year + if self.year < 2014 and self.year>=2007: + self.wtk_file = WTK_V10_BASE + f"{self.year}.h5" + elif self.year == 2014: + self.wtk_file = WTK_V11_BASE + f"{self.year}.h5" + elif filepath != "" and wtk_source_path == "": + # filepath (full h5 filepath) is provided by user + if ".h5" not in filepath: + filepath = filepath + ".h5" + self.wtk_file = str(filepath) + elif filepath == "" and wtk_source_path != "": + # directory of h5 files (wtk_source_path) is provided by user + self.wtk_file = os.path.join(str(wtk_source_path),f"wtk_conus_{self.year}.h5") + else: + # use default filepaths + if self.year < 2014 and self.year>=2007: + self.wtk_file = WTK_V10_BASE + f"{self.year}.h5" + elif self.year == 2014: + self.wtk_file = WTK_V11_BASE + f"{self.year}.h5" + + # Check for valid filepath for Wind Toolkit file + if not os.path.isfile(self.wtk_file): + raise FileNotFoundError(f"Cannot find Wind Toolkit .h5 file, filepath {self.wtk_file} does not exist") + + # Pull data from HPC Wind Toolkit dataset + self.download_resource() + + # Set wind resource data into SAM/PySAM digestible format + self.format_data() + + + def calculate_heights_to_download(self): + """ + Given the system hub height, and the available hub heights from WindToolkit, + determine which heights to download to bracket the hub height + """ + hub_height_meters = self.hub_height_meters + + # evaluate hub height, determine what heights to download + heights = [hub_height_meters] + if hub_height_meters not in self.allowed_hub_heights_meters: + height_low = self.allowed_hub_heights_meters[0] + height_high = self.allowed_hub_heights_meters[-1] + for h in self.allowed_hub_heights_meters: + if h < hub_height_meters: + height_low = h + elif h > hub_height_meters: + height_high = h + break + heights[0] = height_low + heights.append(height_high) + + return heights + + def download_resource(self): + """load WTK h5 file using rex and get wind resource data for location + specified by (self.lat, self.lon) + """ + # NOTE: Current setup of files on HPC WINDToolkit v1.0.0 = 2007-2013, v1.1.0 = 2014 + + # Open file with rex WindX object + with WindX(self.wtk_file, hsds=False) as f: + # get gid of location closest to given lat/lon coordinates and timezone offset + site_gid = f.lat_lon_gid((self.latitude, self.longitude)) + time_zone = f.meta['timezone'].iloc[site_gid] + + # instantiate temp dictionary to hold each attributes dataset + self.wind_dict = {} + # loop through hub heights to download, capture datasets + # NOTE: datasets are not auto shifted by timezone offset + # -> wrap extraction in SAMResource.roll_timeseries(input_array, timezone, #steps in an hour=1) to roll timezones + # NOTE: pressure datasets unit = Pa, convert to atm via division by 101325 + for h in self.data_hub_heights: + self.wind_dict['temperature_{height}m_arr'.format(height=h)] = SAMResource.roll_timeseries((f['temperature_{height}m'.format(height=h), :, site_gid]), time_zone, 1) + self.wind_dict['pressure_{height}m_arr'.format(height=h)] = SAMResource.roll_timeseries((f['pressure_{height}m'.format(height=h), :, site_gid]/101325), time_zone, 1) + self.wind_dict['windspeed_{height}m_arr'.format(height=h)] = SAMResource.roll_timeseries((f['windspeed_{height}m'.format(height=h), :, site_gid]), time_zone, 1) + self.wind_dict['winddirection_{height}m_arr'.format(height=h)] = SAMResource.roll_timeseries((f['winddirection_{height}m'.format(height=h), :, site_gid]), time_zone, 1) + + self.site_gid = site_gid + def format_data(self): + # Remove data from feb29 on leap years + if (self.year % 4) == 0: + feb29 = np.arange(1416,1440) + for key, value in self.wind_dict.items(): + self.wind_dict[key] = np.delete(value, feb29) + + # round to desired precision and concatenate data into format needed for data dictionary + if len(self.data_hub_heights) == 2: + # NOTE: Unsure if SAM/PySAM is sensitive to data types ie: floats with long precision vs to 2 or 3 decimals. + # If not sensitive, can remove following 8 lines of code to increase computational efficiency + self.wind_dict['temperature_{h}m_arr'.format(h=self.data_hub_heights[0])] = np.round((self.wind_dict['temperature_{h}m_arr'.format(h=self.data_hub_heights[0])]), decimals=1) + self.wind_dict['pressure_{h}m_arr'.format(h=self.data_hub_heights[0])] = np.round((self.wind_dict['pressure_{h}m_arr'.format(h=self.data_hub_heights[0])]), decimals=2) + self.wind_dict['windspeed_{h}m_arr'.format(h=self.data_hub_heights[0])] = np.round((self.wind_dict['windspeed_{h}m_arr'.format(h=self.data_hub_heights[0])]), decimals=3) + self.wind_dict['winddirection_{h}m_arr'.format(h=self.data_hub_heights[0])] = np.round((self.wind_dict['winddirection_{h}m_arr'.format(h=self.data_hub_heights[0])]), decimals=1) + self.wind_dict['temperature_{h}m_arr'.format(h=self.data_hub_heights[1])] = np.round((self.wind_dict['temperature_{h}m_arr'.format(h=self.data_hub_heights[1])]), decimals=1) + self.wind_dict['pressure_{h}m_arr'.format(h=self.data_hub_heights[1])] = np.round((self.wind_dict['pressure_{h}m_arr'.format(h=self.data_hub_heights[1])]), decimals=2) + self.wind_dict['windspeed_{h}m_arr'.format(h=self.data_hub_heights[1])] = np.round((self.wind_dict['windspeed_{h}m_arr'.format(h=self.data_hub_heights[1])]), decimals=3) + self.wind_dict['winddirection_{h}m_arr'.format(h=self.data_hub_heights[1])] = np.round((self.wind_dict['winddirection_{h}m_arr'.format(h=self.data_hub_heights[1])]), decimals=1) + # combine all data into one 2D list + self.combined_data = [list(a) for a in zip(self.wind_dict['temperature_{h}m_arr'.format(h=self.data_hub_heights[0])], + self.wind_dict['pressure_{h}m_arr'.format(h=self.data_hub_heights[0])], + self.wind_dict['windspeed_{h}m_arr'.format(h=self.data_hub_heights[0])], + self.wind_dict['winddirection_{h}m_arr'.format(h=self.data_hub_heights[0])], + self.wind_dict['temperature_{h}m_arr'.format(h=self.data_hub_heights[1])], + self.wind_dict['pressure_{h}m_arr'.format(h=self.data_hub_heights[1])], + self.wind_dict['windspeed_{h}m_arr'.format(h=self.data_hub_heights[1])], + self.wind_dict['winddirection_{h}m_arr'.format(h=self.data_hub_heights[1])])] + + elif len(self.data_hub_heights) == 1: + # NOTE: Unsure if SAM/PySAM is sensitive to data types ie: floats with long precision vs to 2 or 3 decimals. + # If not sensitive, can remove following 4 lines of code to increase computational efficiency + self.wind_dict['temperature_{h}m_arr'.format(h=self.data_hub_heights[0])] = np.round((self.wind_dict['temperature_{h}m_arr'.format(h=self.data_hub_heights[0])]), decimals=1) + self.wind_dict['pressure_{h}m_arr'.format(h=self.data_hub_heights[0])] = np.round((self.wind_dict['pressure_{h}m_arr'.format(h=self.data_hub_heights[0])]), decimals=2) + self.wind_dict['windspeed_{h}m_arr'.format(h=self.data_hub_heights[0])] = np.round((self.wind_dict['windspeed_{h}m_arr'.format(h=self.data_hub_heights[0])]), decimals=3) + self.wind_dict['winddirection_{h}m_arr'.format(h=self.data_hub_heights[0])] = np.round((self.wind_dict['winddirection_{h}m_arr'.format(h=self.data_hub_heights[0])]), decimals=1) + # combine all data into one 2D list + self.combined_data = [list(a) for a in zip(self.wind_dict['temperature_{h}m_arr'.format(h=self.data_hub_heights[0])], + self.wind_dict['pressure_{h}m_arr'.format(h=self.data_hub_heights[0])], + self.wind_dict['windspeed_{h}m_arr'.format(h=self.data_hub_heights[0])], + self.wind_dict['winddirection_{h}m_arr'.format(h=self.data_hub_heights[0])])] + self.data = self.combined_data + + @Resource.data.setter + def data(self, combined_data): + """Sets data property with wind resource data formatted for SAM + + data (dict): + :key heights (list(float)): floats corresponding to hub-height for 'data' entry. + ex: [100, 100, 100, 100, 120, 120, 120, 120] + :key fields (list(int)): integers corresponding to data type for 'data' entry + ex: [1, 2, 3, 4, 1, 2, 3, 4] + for each field (int) the corresponding data is: + - 1: Ambient temperature in degrees Celsius + - 2: Atmospheric pressure in in atmospheres. + - 3: Wind speed in meters per second (m/s) + - 4: Wind direction in degrees east of north (degrees). + :key data (list(list(floats)): 8760 list with data of corresponding field and hub-height + ex. data[timestep] is [-23.5, 0.65, 7.6, 261.2, -23.7, 0.65, 7.58, 261.1] + - -23.5 is temperature at 100m at timestep + - 7.6 is wind speed at 100m at timestep + - 7.58 is wind speed at 120m at timestep + """ + dic = { + 'heights': [float(h) for h in self.data_hub_heights for i in range(4)], + 'fields': [1, 2, 3, 4] * len(self.data_hub_heights), + 'data': combined_data + } + self._data = dic \ No newline at end of file diff --git a/hopp/simulation/technologies/sites/site_info.py b/hopp/simulation/technologies/sites/site_info.py index b1c40ed4d..fed80d108 100644 --- a/hopp/simulation/technologies/sites/site_info.py +++ b/hopp/simulation/technologies/sites/site_info.py @@ -17,7 +17,9 @@ SolarResource, WindResource, WaveResource, - ElectricityPrices + ElectricityPrices, + HPCWindData, + HPCSolarData, ) from hopp.tools.layout.plot_tools import plot_shape from hopp.utilities.log import hybrid_logger as logger @@ -29,6 +31,7 @@ from hopp.simulation.base import BaseClass from hopp.utilities.validators import contains +from hopp import ROOT_DIR def plot_site(verts, plt_style, labels): for i in range(len(verts)): if i == 0: @@ -49,6 +52,14 @@ class SiteInfo(BaseClass): solar_resource_file: Path to solar resource file. Defaults to "". wind_resource_file: Path to wind resource file. Defaults to "". grid_resource_file: Path to grid pricing data file. Defaults to "". + path_resource: Path to folder to save resource files. + Defaults to ROOT/simulation/resource_files + wtk_source_path (Optional): directory of Wind Toolkit h5 files hosted on HPC. + Only used if renewable_resource_origin != "API" + nsrdb_source_path (Optional): directory of NSRDB h5 files hosted on HPC. + Only used if renewable_resource_origin != "API" + renewable_resource_origin (str): whether to download resource data from API or load directly from datasets files. + Options are "API" or "HPC". Defaults to "API" hub_height: Turbine hub height for resource download in meters. Defaults to 97.0. capacity_hours: Boolean list indicating hours for capacity payments. Defaults to []. desired_schedule: Absolute desired load profile in MWe. Defaults to []. @@ -65,6 +76,11 @@ class SiteInfo(BaseClass): wind_resource_file: Union[Path, str] = field(default="", converter=resource_file_converter) wave_resource_file: Union[Path, str] = field(default="", converter=resource_file_converter) grid_resource_file: Union[Path, str] = field(default="", converter=resource_file_converter) + + path_resource: Optional[Union[Path, str]] = field(default=ROOT_DIR / "simulation" / "resource_files") + wtk_source_path: Optional[Union[Path,str]] = field(default = "") + nsrdb_source_path: Optional[Union[Path,str]] = field(default = "") + hub_height: hopp_float_type = field(default=97., converter=hopp_float_type) capacity_hours: NDArray = field(default=[], converter=converter(bool)) desired_schedule: NDArrayFloat = field(default=[], converter=converter()) @@ -73,6 +89,7 @@ class SiteInfo(BaseClass): solar: bool = field(default=True) wind: bool = field(default=True) wave: bool = field(default=False) + renewable_resource_origin: str = field(default="API", validator=contains(["API", "HPC"])) wind_resource_origin: str = field(default="WTK", validator=contains(["WTK", "TAP"])) # Set in post init hook @@ -81,8 +98,8 @@ class SiteInfo(BaseClass): lon: hopp_float_type = field(init=False) year: int = field(init=False, default=2012) tz: Optional[int] = field(init=False, default=None) - solar_resource: Optional[SolarResource] = field(init=False, default=None) - wind_resource: Optional[WindResource] = field(init=False, default=None) + solar_resource: Optional[Union[SolarResource,HPCSolarData]] = field(default=None) + wind_resource: Optional[Union[WindResource,HPCWindData]] = field(default=None) wave_resoure: Optional[WaveResource] = field(init=False, default=None) elec_prices: Optional[ElectricityPrices] = field(init=False, default=None) n_periods_per_day: int = field(init=False) @@ -113,7 +130,8 @@ def __attrs_post_init__(self): urdb_label (str): Link to `Utility Rate DataBase `_ label for REopt runs. follow_desired_schedule (bool): Indicates if a desired schedule was provided. Defaults to False. """ - set_nrel_key_dot_env() + if self.renewable_resource_origin=="API": + set_nrel_key_dot_env() data = self.data if 'site_boundaries' in data: @@ -130,12 +148,19 @@ def __attrs_post_init__(self): self.lon = data['lon'] if 'year' not in data: - data['year'] = 2012 + data['year'] = self.year + + self.year = data["year"] + if 'tz' in data: self.tz = data['tz'] if self.solar: - self.solar_resource = SolarResource(data['lat'], data['lon'], data['year'], filepath=self.solar_resource_file) + if self.solar_resource is None: + if self.renewable_resource_origin=="API": + self.solar_resource = SolarResource(data['lat'], data['lon'], data['year'], path_resource=self.path_resource, filepath=self.solar_resource_file) + else: + self.solar_resource = HPCSolarData(data['lat'], data['lon'], data['year'],nsrdb_source_path = self.nsrdb_source_path, filepath=self.solar_resource_file) self.n_timesteps = len(self.solar_resource.data['gh']) // 8760 * 8760 if self.wave: self.wave_resource = WaveResource(data['lat'], data['lon'], data['year'], filepath = self.wave_resource_file) @@ -143,8 +168,13 @@ def __attrs_post_init__(self): if self.wind: # TODO: allow hub height to be used as an optimization variable - self.wind_resource = WindResource(data['lat'], data['lon'], data['year'], wind_turbine_hub_ht=self.hub_height, - filepath=self.wind_resource_file, source=self.wind_resource_origin) + if self.wind_resource is None: + if self.renewable_resource_origin=="API": + self.wind_resource = WindResource(data['lat'], data['lon'], data['year'], wind_turbine_hub_ht=self.hub_height, + path_resource=self.path_resource, filepath=self.wind_resource_file, source=self.wind_resource_origin) + else: + self.wind_resource = HPCWindData(data['lat'], data['lon'], data['year'], wind_turbine_hub_ht=self.hub_height, + wtk_source_path=self.wtk_source_path, filepath=self.wind_resource_file) n_timesteps = len(self.wind_resource.data['data']) // 8760 * 8760 if self.n_timesteps is None: self.n_timesteps = n_timesteps diff --git a/pyproject.toml b/pyproject.toml index 76156a499..94a822091 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,7 +48,8 @@ dependencies = [ "attrs", "utm", "pyyaml-include", - "profast" + "profast", + "NREL-rex" ] keywords = [ "python3", diff --git a/tests/hopp/test_resource_download.py b/tests/hopp/test_resource_download.py index b2a026578..553e5f8a4 100644 --- a/tests/hopp/test_resource_download.py +++ b/tests/hopp/test_resource_download.py @@ -5,12 +5,14 @@ from hopp import ROOT_DIR from hopp.simulation.technologies.resource.solar_resource import BASE_URL as SOLAR_URL from hopp.simulation.technologies.resource.wind_resource import WTK_BASE_URL, TAP_BASE_URL -from hopp.simulation.technologies.resource import SolarResource, WindResource, Resource +from hopp.simulation.technologies.resource import SolarResource, WindResource, Resource, HPCWindData, HPCSolarData from hopp.utilities.utils_for_tests import DEFAULT_WIND_RESOURCE_FILE import PySAM.Windpower as wp import PySAM.Pvwattsv8 as pv +import pytest + dir_path = os.path.dirname(os.path.realpath(__file__)) year = 2012 @@ -154,3 +156,47 @@ def test_from_file(): filepath=str(solar_file) ) assert(len(solar_resource.data['gh']) > 0) + +def test_wtk_resource_filenotfound_wtk_source_path(): + wtk_fake_dir = str(ROOT_DIR) + resource_year = 2012 + wtk_fake_fpath = os.path.join(str(ROOT_DIR),f"wtk_conus_{resource_year}.h5") + with pytest.raises(FileNotFoundError) as err: + HPCWindData(lat = 35.201, lon = -101.945, year = resource_year, wind_turbine_hub_ht = 110, wtk_source_path=wtk_fake_dir) + assert str(err.value) == f"Cannot find Wind Toolkit .h5 file, filepath {wtk_fake_fpath} does not exist" + +def test_wtk_resource_filenotfound_filepath(): + resource_year = 2012 + wtk_fake_fpath = os.path.join(str(ROOT_DIR),f"wtk_conus_{resource_year}.h5") + with pytest.raises(FileNotFoundError) as err: + HPCWindData(lat = 35.201, lon = -101.945, year = resource_year, wind_turbine_hub_ht = 110, filepath = wtk_fake_fpath) + assert str(err.value) == f"Cannot find Wind Toolkit .h5 file, filepath {wtk_fake_fpath} does not exist" + +def test_wtk_resource_invalid_year(): + wtk_fake_dir = str(ROOT_DIR) + resource_year = 2006 + with pytest.raises(ValueError) as err: + HPCWindData(lat = 35.201, lon = -101.945, year = resource_year, wind_turbine_hub_ht = 110, wtk_source_path=wtk_fake_dir) + assert str(err.value) == f"Resource year for WIND Toolkit Data must be between 2007 and 2014 but {resource_year} was provided" + +def test_nsrdb_resource_filenotfound_nsrdb_source_path(): + nsrdb_fake_dir = str(ROOT_DIR) + resource_year = 2012 + nsrdb_fake_fpath = os.path.join(str(ROOT_DIR),f"nsrdb_{resource_year}.h5") + with pytest.raises(FileNotFoundError) as err: + HPCSolarData(lat = 35.201, lon = -101.945, year = resource_year, nsrdb_source_path=nsrdb_fake_dir) + assert str(err.value) == f"Cannot find NSRDB .h5 file, filepath {nsrdb_fake_fpath} does not exist" + +def test_nsrdb_resource_filenotfound_filepath(): + resource_year = 2012 + nsrdb_fake_fpath = os.path.join(str(ROOT_DIR),f"nsrdb_{resource_year}.h5") + with pytest.raises(FileNotFoundError) as err: + HPCSolarData(lat = 35.201, lon = -101.945, year = resource_year, filepath = nsrdb_fake_fpath) + assert str(err.value) == f"Cannot find NSRDB .h5 file, filepath {nsrdb_fake_fpath} does not exist" + +def test_nsrdb_resource_invalid_year(): + nsrdb_fake_dir = str(ROOT_DIR) + resource_year = 2023 + with pytest.raises(ValueError) as err: + HPCSolarData(lat = 35.201, lon = -101.945, year = resource_year, nsrdb_source_path=nsrdb_fake_dir) + assert str(err.value) == f"Resource year for NSRDB Data must be between 1998 and 2022 but {resource_year} was provided" \ No newline at end of file From 8499a7ddd8a665490d4d2e6e35fd9252dae0428e Mon Sep 17 00:00:00 2001 From: genevievestarke <103534902+genevievestarke@users.noreply.github.com> Date: Fri, 24 Jan 2025 14:31:33 -0500 Subject: [PATCH 03/48] Bugfix: Load following heuristic method for variable load signals only using the beginning of the load signal (#421) * Fixing bug in heuristic method that affects variable load following * Updating release notes * Updated test for varied heuristics --------- Co-authored-by: John Jasa --- RELEASE.md | 1 + .../dispatch/hybrid_dispatch_builder_solver.py | 2 +- tests/hopp/test_dispatch.py | 10 ++++++---- 3 files changed, 8 insertions(+), 5 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 965578a60..b400bdcc9 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -4,6 +4,7 @@ * Added option and functionality to load wind and solar resource data from NSRDB and Wind Toolkit data files if user-specified. * Fixed a bug in site_info that set resource year to 2012 even if otherwise specified. + minor clean up to floris.py - removed unnecessary data exportation and fixed bug in value() ++ bug fix in load following heuristic method: only using beginning of variable load signals ## Version 3.1.1, Dec. 18, 2024 diff --git a/hopp/simulation/technologies/dispatch/hybrid_dispatch_builder_solver.py b/hopp/simulation/technologies/dispatch/hybrid_dispatch_builder_solver.py index eabac4fc3..725297547 100644 --- a/hopp/simulation/technologies/dispatch/hybrid_dispatch_builder_solver.py +++ b/hopp/simulation/technologies/dispatch/hybrid_dispatch_builder_solver.py @@ -696,7 +696,7 @@ def battery_heuristic(self): required_keys = ["desired_load"] if self.site.follow_desired_schedule: # Get difference between baseload demand and power generation and control scenario variables - load_value = self.site.desired_schedule + load_value = grid_limit load_difference = [ (load_value[x] - tot_gen[x]) for x in range(len(tot_gen)) ] diff --git a/tests/hopp/test_dispatch.py b/tests/hopp/test_dispatch.py index 8ffbaa85c..0ca53fec6 100644 --- a/tests/hopp/test_dispatch.py +++ b/tests/hopp/test_dispatch.py @@ -973,7 +973,9 @@ def create_test_objective_rule(m): def test_hybrid_dispatch_baseload_heuristic_and_analysis(site): - desired_schedule = 8760*[20] + desired_schedule = 8760 * [20] + # Using a non-uniform schedule to test the baseload heuristic bugfix + desired_schedule[:2000] = [10.] * 2000 desired_schedule_site = SiteInfo(flatirons_site, desired_schedule=desired_schedule) @@ -998,9 +1000,9 @@ def test_hybrid_dispatch_baseload_heuristic_and_analysis(site): hybrid_plant = hi.system - assert hybrid_plant.grid.time_load_met == pytest.approx(92.87, 1e-2) - assert hybrid_plant.grid.capacity_factor_load == pytest.approx(94.45, 1e-2) - assert hybrid_plant.grid.total_number_hours == pytest.approx(3844, 1e-2) + assert hybrid_plant.grid.time_load_met == pytest.approx(94.429, 1e-2) + assert hybrid_plant.grid.capacity_factor_load == pytest.approx(95.659, 1e-2) + assert hybrid_plant.grid.total_number_hours == pytest.approx(4270, 1e-2) def test_dispatch_load_following_heuristic_with_wave(site, subtests): dispatch_options = {'battery_dispatch': 'load_following_heuristic', 'grid_charging': False} From ef569fcb1e314a402c411407f2b494105809160b Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Tue, 4 Feb 2025 14:31:10 -0700 Subject: [PATCH 04/48] feature: option to initialize site_info with resource data (#415) * added ability to grab wind and solar resource data off the HPC and updated upstream functions * updated doc strings and comments for wtk_data and nsrdb_data scripts * added comments to site_info for added options, fixed bug of resource year being set to 2012 * updated formatting and docstrings for previous commit * updated RELEASE.md with recent changes * changed input variable name in HPCWindData to align with WindResource and updated doc strings * added high level test for hpc resource data * added ability to supply preloaded and formatted resource data to site_info and updated doc strings * added tests for using preloaded resource data and updated RELEASE.md * tried to improve the site_info documentation but it aint much better * added elevation as site_info attribute and updated with solar resource elevation if called * added sphinx config path in readthedocs to fix build * updated doc strings and minor updates to hpc resource functions * split out tests for initializing site_info with resource data * updated docstrings and added valid year check and tests for hpc resource data * added sphinx config path in readthedocs to fix build * updated doc strings and minor updates to hpc resource functions * updated docstrings and added valid year check and tests for hpc resource data * changed site_info api doc file to markdown * added doc files for wind and solar resource * minor updates to doc strings in hpc resource classes * resolved merge conflict in doc string of wind_resource.py * updated some formatting in resource files for readthedocs build * updated string formatting * added tests for wtk and nsrdb resource if filepath is provided * split long comments into multiple lines * minor clean ups to wind and solar resource functions * now passing tests --------- Co-authored-by: John Jasa --- RELEASE.md | 5 +- .../technologies/resource/resource.py | 20 ++-- .../technologies/resource/solar_resource.py | 110 ++++++++++-------- .../technologies/resource/wind_resource.py | 78 ++++++++----- .../technologies/sites/site_info.py | 27 ++++- tests/hopp/test_site_info.py | 74 +++++++++++- 6 files changed, 226 insertions(+), 88 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index b400bdcc9..9a971ec18 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -3,8 +3,9 @@ ## Unreleased, TBD * Added option and functionality to load wind and solar resource data from NSRDB and Wind Toolkit data files if user-specified. * Fixed a bug in site_info that set resource year to 2012 even if otherwise specified. -+ minor clean up to floris.py - removed unnecessary data exportation and fixed bug in value() -+ bug fix in load following heuristic method: only using beginning of variable load signals +* Minor clean up to floris.py - removed unnecessary data exportation and fixed bug in value() +* Added ability and option to initialize site_info with preloaded and formatted wind and solar resource data ++ Bug fix in load following heuristic method: only using beginning of variable load signals ## Version 3.1.1, Dec. 18, 2024 diff --git a/hopp/simulation/technologies/resource/resource.py b/hopp/simulation/technologies/resource/resource.py index 80806cd8c..c3f760d5e 100644 --- a/hopp/simulation/technologies/resource/resource.py +++ b/hopp/simulation/technologies/resource/resource.py @@ -3,6 +3,7 @@ import json import requests import time +from pathlib import Path from hopp import ROOT_DIR class Resource(metaclass=ABCMeta): @@ -45,22 +46,27 @@ def __init__(self, lat, lon, year, **kwargs): # update any passed in self.__dict__.update(kwargs) - self.filename = None + self.filename = None #: filepath of resource data file, defaults to None self._data = dict() def check_download_dir(self): + """Creates directory for the resource file if it does not exist. + """ + if not isinstance(self.filename,str): + self.filename = str(self.filename) if not os.path.isdir(os.path.dirname(self.filename)): os.makedirs(os.path.dirname(self.filename)) @staticmethod def call_api(url, filename): """ - Parameters - --------- - url: string - The API endpoint to return data from - filename: string - The filename where data should be written + Args: + url (str): The API endpoint to return data from + filename (str): The filename where data should be written + + Returns: + True if downloaded file successfully, False if encountered error in downloading + """ n_tries = 0 diff --git a/hopp/simulation/technologies/resource/solar_resource.py b/hopp/simulation/technologies/resource/solar_resource.py index 4262117f1..ac16a3c36 100644 --- a/hopp/simulation/technologies/resource/solar_resource.py +++ b/hopp/simulation/technologies/resource/solar_resource.py @@ -1,7 +1,7 @@ import os from collections import defaultdict from pathlib import Path -from typing import Union +from typing import Union, Optional import numpy as np import csv from PySAM.ResourceTools import SAM_CSV_to_solar_data @@ -17,18 +17,11 @@ class SolarResource(Resource): """ - Class to manage Solar Resource data. - - Args: - lat: latitude - lon: longitude - year: year - path_resource: directory where to save downloaded files - filepath: file path of resource file to load - use_api: Make an API call even if there's an existing file. Defaults to False - kwargs: extra kwargs - + Class to manage Solar Resource data from API calls or preloaded data. """ + + #: attributes of solar resource data to download from API call + solar_attributes: str = 'ghi,dhi,dni,wind_speed,air_temperature,solar_zenith_angle,surface_pressure,dew_point' def __init__( self, @@ -38,25 +31,41 @@ def __init__( path_resource: Union[str, Path] = ROOT_DIR / "simulation" / "resource_files", filepath: Union[str, Path] ="", use_api: bool = False, + resource_data: Optional[dict] = None, **kwargs ): + """Resource class to download solar resource data using API call or set with preloaded dictionary + + Args: + lat (float): latitude corresponding to location for solar resource data + lon (float): longitude corresponding to location for solar resource data + year (int): year for resource data. must be between 1998 and 2022 + path_resource (Union[str, Path], optional): filepath to resource_files directory. Defaults to ROOT_DIR/"simulation"/"resource_files". + filepath (Union[str, Path], optional): full filepath to solar resource data file. Defaults to "". + use_api (bool, optional): Make an API call even if there's an existing file. Defaults to False. + resource_data (Optional[dict], optional): dictionary of preloaded and formatted solar resource data. Defaults to None. + kwargs: extra kwargs + """ super().__init__(lat, lon, year) + # if resource_data is input as a dictionary then set_data + if isinstance(resource_data,dict): + self.data = resource_data + return + # if resource_data is not provided, download or load resource data + if isinstance(path_resource,str): + path_resource = Path(path_resource).resolve() if os.path.isdir(path_resource): self.path_resource = path_resource - - self.solar_attributes = 'ghi,dhi,dni,wind_speed,air_temperature,solar_zenith_angle,surface_pressure,dew_point' - - self.path_resource = os.path.join(self.path_resource, 'solar') + if path_resource.parts[-1]!="solar": + self.path_resource = self.path_resource/ 'solar' # Force override any internal definitions if passed in self.__dict__.update(kwargs) # resource_files files if filepath == "": - filepath = os.path.join(self.path_resource, - str(lat) + "_" + str(lon) + "_psmv3_" + str(self.interval) + "_" + str( - year) + ".csv") + filepath = self.path_resource / f"{self.latitude}_{self.longitude}_psmv3_{self.interval}_{self.year}.csv" self.filename = filepath self.check_download_dir() # FIXME: This breaks if weather file is in the same directory as caller @@ -69,6 +78,11 @@ def __init__( logger.info("SolarResource: {}".format(self.filename)) def download_resource(self): + """Download solar resource file from NSRDB API call + + Returns: + success (bool): whether API download was successful or not + """ url = '{base}?wkt=POINT({lon}+{lat})&names={year}&leap_day={leap}&interval={interval}&utc={utc}&full_name={name}&email={email}&affiliation={affiliation}&mailing_list={mailing_list}&reason={reason}&api_key={api}&attributes={attr}'.format( base=BASE_URL, year=self.year, lat=self.latitude, lon=self.longitude, leap=self.leap_year, interval=self.interval, utc=self.utc, name=self.name, email=get_developer_nrel_gov_email(), @@ -89,7 +103,7 @@ def format_data(self): self.data = self.filename @Resource.data.setter - def data(self, data_dict): + def data(self, data_info): """ Sets the solar resource data @@ -109,32 +123,36 @@ def data(self, data_dict): :key tdew: array, dew point temp [C] :key press: array, atmospheric pressure [mbar] """ - self._data = SAM_CSV_to_solar_data(data_dict) - # TODO: Update ResourceTools.py in pySAM to include pressure and dew point or relative humidity - with open(data_dict) as file_in: - wfd = defaultdict(list) - for i in range(2): - file_in.readline() - reader = csv.DictReader(file_in) - for row in reader: - for col, dat in row.items(): - if len(col) > 0: - wfd[col].append(float(dat)) - - if 'Dew Point' in wfd: - self._data['tdew'] = wfd.pop('Dew Point') - elif 'RH' in wfd: - self._data['rh'] = wfd.pop('RH') - elif 'Pressure' in wfd: - self._data['pres'] = wfd.pop('Pressure') - - - def roll_timezone(self, roll_hours, timezone): - """ - - :param roll_hours: - :param timezone: - :return: + if isinstance(data_info,dict): + self._data = data_info + self.filename = None + else: + self._data = SAM_CSV_to_solar_data(data_info) + # TODO: Update ResourceTools.py in pySAM to include pressure and dew point or relative humidity + with open(data_info) as file_in: + wfd = defaultdict(list) + for i in range(2): + file_in.readline() + reader = csv.DictReader(file_in) + for row in reader: + for col, dat in row.items(): + if len(col) > 0: + wfd[col].append(float(dat)) + + if 'Dew Point' in wfd: + self._data['tdew'] = wfd.pop('Dew Point') + elif 'RH' in wfd: + self._data['rh'] = wfd.pop('RH') + elif 'Pressure' in wfd: + self._data['pres'] = wfd.pop('Pressure') + + + def roll_timezone(self, roll_hours:Union[int,float], timezone:int): + """Roll weather data timezone. This function appears unused. + + Args: + roll_hours (Union[int,float]): number of hours to roll the timezone by + timezone (int): timezone for location """ rollable_keys = ['dn', 'df', 'gh', 'wspd', 'tdry'] for key in rollable_keys: diff --git a/hopp/simulation/technologies/resource/wind_resource.py b/hopp/simulation/technologies/resource/wind_resource.py index 34c60f4b4..9145bf4ee 100644 --- a/hopp/simulation/technologies/resource/wind_resource.py +++ b/hopp/simulation/technologies/resource/wind_resource.py @@ -1,6 +1,6 @@ import csv, os from pathlib import Path -from typing import Union +from typing import Union, Optional, List from PySAM.ResourceTools import SRW_to_wind_data from hopp.utilities.keys import get_developer_nrel_gov_key, get_developer_nrel_gov_email @@ -13,16 +13,19 @@ class WindResource(Resource): - """ Class to manage Wind Resource data - - Attributes: - hub_height_meters - the system height - TODO: if optimizer will modify hub height, need to download a range rather than a single - file_resource_heights - dictionary of heights and filenames to download from Wind Toolkit - filename - the combined resource filename + """ Class to manage Wind Resource data from API calls or preloaded data. """ - allowed_hub_height_meters = [10, 40, 60, 80, 100, 120, 140, 160, 200] + allowed_hub_height_meters: List[int] = [10, 40, 60, 80, 100, 120, 140, 160, 200] + + #: the hub-height for wind resource data (meters) + hub_height_meters: float + # TODO: if optimizer will modify hub height, need to download a range rather than a single + + #: dictionary of heights and filenames to download from Wind Toolkit + file_resource_heights: dict + + def __init__( self, lat: float, @@ -33,28 +36,39 @@ def __init__( filepath: Union[str, Path] ="", source: str ="WTK", use_api: bool = False, + resource_data: Optional[dict] = None, **kwargs ): - """ + """Resource class to download wind resource data using API call or set with preloaded dictionary Args: - lat: latitude - lon: longitude - year: year - wind_turbine_hub_ht: turbine hub height - path_resource: directory where to save downloaded files - filepath: file path of resource file to load - source: Which API to use. Options are TAP and WIND Toolkit (WTK). - use_api: Make an API call even if there's an existing file. Defaults to False + lat (float): latitude corresponding to location for wind resource data + lon (float): longitude corresponding to location for wind resource data + year (int): year for resource data. must be between 2007 and 2014 + wind_turbine_hub_ht (float): turbine hub height (m) + path_resource (Union[str, Path], optional): filepath to resource_files directory. Defaults to ROOT_DIR/"simulation"/"resource_files". + filepath (Union[str, Path], optional): file path of resource file to load + source (str): Which API to use. Options are TAP and WIND Toolkit (WTK). + use_api (bool, optional): Make an API call even if there's an existing file. Defaults to False. + resource_data (Optional[dict], optional): dictionary of preloaded and formatted wind resource data. Defaults to None. kwargs: extra kwargs """ - super().__init__(lat, lon, year) + super().__init__(lat, lon, year) + + # if resource_data is input as a dictionary then set_data + if isinstance(resource_data,dict): + self.data = resource_data + return + # if resource_data is not provided, download or load resource data + if isinstance(path_resource,str): + path_resource = Path(path_resource).resolve() if os.path.isdir(path_resource): self.path_resource = path_resource - - self.path_resource = os.path.join(self.path_resource, 'wind') - + if path_resource.parts[-1]!="wind": + self.path_resource = self.path_resource / 'wind' + + # Force override any internal definitions if passed in self.__dict__.update(kwargs) self.file_resource_heights = None @@ -96,18 +110,18 @@ def calculate_heights_to_download(self): heights[0] = height_low heights.append(height_high) - file_resource_base = os.path.join(self.path_resource, str(self.latitude) + "_" + str(self.longitude) + "_windtoolkit_" + str( - self.year) + "_" + str(self.interval) + "min") - file_resource_full = file_resource_base + filename_base = f"{self.latitude}_{self.longitude}_windtoolkit_{self.year}_{self.interval}min" + file_resource_full = filename_base file_resource_heights = dict() for h in heights: - file_resource_heights[int(h)] = file_resource_base + '_' + str(int(h)) + 'm.srw' - file_resource_full += "_" + str(int(h)) + 'm' + h_int = int(h) + file_resource_heights[h_int] = self.path_resource/(filename_base + f'_{h_int}m.srw') + file_resource_full += f'_{h_int}m' file_resource_full += ".srw" self.file_resource_heights = file_resource_heights - self.filename = file_resource_full + self.filename = self.path_resource / file_resource_full def update_height(self, hub_height_meters): self.hub_height_meters = hub_height_meters @@ -183,9 +197,11 @@ def format_data(self): self.data = self.filename @Resource.data.setter - def data(self, data_file): + def data(self, data_info): """ Sets the wind resource data to a dictionary in SAM Wind format (see Pysam.ResourceTools.SRW_to_wind_data) """ - - self._data = SRW_to_wind_data(data_file) + if isinstance(data_info,dict): + self._data = data_info + else: + self._data = SRW_to_wind_data(data_info) diff --git a/hopp/simulation/technologies/sites/site_info.py b/hopp/simulation/technologies/sites/site_info.py index fed80d108..33b813cf9 100644 --- a/hopp/simulation/technologies/sites/site_info.py +++ b/hopp/simulation/technologies/sites/site_info.py @@ -68,10 +68,26 @@ class SiteInfo(BaseClass): solar: Whether to set solar data for this site. Defaults to True. wind: Whether to set wind data for this site. Defaults to True. wave: Whether to set wave data for this site. Defaults to False. - wind_resource_origin: Which wind resource API to use, defaults to WIND Toolkit + wind_resource_origin: Which wind resource API to use, defaults toto "WTK" for WIND Toolkit. + Options are "WTK" or "TAP". + solar_resource (Optional): dictionary or object containing solar resource data + wind_resource (Optional): dictionary or object containing wind resource data """ # User provided data: dict + """dictionary of site info data with key as: + + - lat (float): site latitude + - lon (float): site longitude + - year (int): year to get resource data for. Default to 2012 + - tz (int, optional): timezone of site + - elev (int, optional): elevation of site (m) + - site_boundaries (dict): + - verts (list(list(float))): vertices of site polygon + - urdb_label (str,optional): string corresponding to data from utility rate databse + """ + + solar_resource_file: Union[Path, str] = field(default="", converter=resource_file_converter) wind_resource_file: Union[Path, str] = field(default="", converter=resource_file_converter) wave_resource_file: Union[Path, str] = field(default="", converter=resource_file_converter) @@ -98,6 +114,7 @@ class SiteInfo(BaseClass): lon: hopp_float_type = field(init=False) year: int = field(init=False, default=2012) tz: Optional[int] = field(init=False, default=None) + elev: Optional[float] = field(init=False, default=None) solar_resource: Optional[Union[SolarResource,HPCSolarData]] = field(default=None) wind_resource: Optional[Union[WindResource,HPCWindData]] = field(default=None) wave_resoure: Optional[WaveResource] = field(init=False, default=None) @@ -155,13 +172,19 @@ def __attrs_post_init__(self): if 'tz' in data: self.tz = data['tz'] + if 'elev' in data: + self.elev = data['elev'] + if self.solar: if self.solar_resource is None: if self.renewable_resource_origin=="API": self.solar_resource = SolarResource(data['lat'], data['lon'], data['year'], path_resource=self.path_resource, filepath=self.solar_resource_file) else: self.solar_resource = HPCSolarData(data['lat'], data['lon'], data['year'],nsrdb_source_path = self.nsrdb_source_path, filepath=self.solar_resource_file) + elif isinstance(self.solar_resource,dict): + self.solar_resource = SolarResource(data['lat'], data['lon'], data['year'],resource_data = self.solar_resource) self.n_timesteps = len(self.solar_resource.data['gh']) // 8760 * 8760 + self.elev = self.solar_resource.data["elev"] if self.wave: self.wave_resource = WaveResource(data['lat'], data['lon'], data['year'], filepath = self.wave_resource_file) self.n_timesteps = 8760 @@ -175,6 +198,8 @@ def __attrs_post_init__(self): else: self.wind_resource = HPCWindData(data['lat'], data['lon'], data['year'], wind_turbine_hub_ht=self.hub_height, wtk_source_path=self.wtk_source_path, filepath=self.wind_resource_file) + elif isinstance(self.wind_resource,dict): + self.wind_resource = WindResource(data['lat'], data['lon'], data['year'],wind_turbine_hub_ht=self.hub_height,resource_data = self.wind_resource) n_timesteps = len(self.wind_resource.data['data']) // 8760 * 8760 if self.n_timesteps is None: self.n_timesteps = n_timesteps diff --git a/tests/hopp/test_site_info.py b/tests/hopp/test_site_info.py index ce3698791..6dbe63349 100644 --- a/tests/hopp/test_site_info.py +++ b/tests/hopp/test_site_info.py @@ -11,6 +11,8 @@ from hopp.simulation.technologies.sites import SiteInfo, flatirons_site from hopp import ROOT_DIR +from PySAM.ResourceTools import SRW_to_wind_data, SAM_CSV_to_solar_data + solar_resource_file = os.path.join( ROOT_DIR, "simulation", "resource_files", "solar", "35.2018863_-101.945027_psmv3_60_2012.csv" @@ -156,4 +158,74 @@ def test_site_kml_file_append(): assert filepath_new.exists() k, valid_region, lat, lon = SiteInfo.kml_read(kml_filepath) assert valid_region.area > 0 - os.remove(filepath_new) \ No newline at end of file + os.remove(filepath_new) + +def test_site_wind_resource_input_filename(): + data = copy.deepcopy(flatirons_site) + wind_resource_data_dict = SRW_to_wind_data(wind_resource_file) + site = SiteInfo( + data, + hub_height = 90, + wind = True, + solar = False, + wind_resource = wind_resource_data_dict + ) + assert site.wind_resource.filename is None + +def test_site_wind_resource_input_data_length(): + data = copy.deepcopy(flatirons_site) + wind_resource_data_dict = SRW_to_wind_data(wind_resource_file) + site = SiteInfo( + data, + hub_height = 90, + wind = True, + solar = False, + wind_resource = wind_resource_data_dict + ) + assert len(site.wind_resource.data['data'])==8760 + +def test_site_wind_resource_input_data_format(): + data = copy.deepcopy(flatirons_site) + wind_resource_data_dict = SRW_to_wind_data(wind_resource_file) + site = SiteInfo( + data, + hub_height = 90, + wind = True, + solar = False, + wind_resource = wind_resource_data_dict + ) + assert int(site.wind_resource.data['heights'][0])==80 + +def test_site_solar_resource_input_filename(): + data = copy.deepcopy(flatirons_site) + solar_resource_data_dict = SAM_CSV_to_solar_data(solar_resource_file) + site = SiteInfo( + data, + wind = False, + solar = True, + solar_resource = solar_resource_data_dict + ) + assert site.solar_resource.filename is None + +def test_site_solar_resource_input_data_length(): + data = copy.deepcopy(flatirons_site) + solar_resource_data_dict = SAM_CSV_to_solar_data(solar_resource_file) + site = SiteInfo( + data, + wind = False, + solar = True, + solar_resource = solar_resource_data_dict + ) + assert len(site.solar_resource.data['dn'])==8760 + +def test_site_solar_resource_input_data_format(): + data = copy.deepcopy(flatirons_site) + solar_resource_data_dict = SAM_CSV_to_solar_data(solar_resource_file) + site = SiteInfo( + data, + wind = False, + solar = True, + solar_resource = solar_resource_data_dict + ) + assert site.solar_resource.data['tz']==-6 + From 4f188e4e4d15cd1726912a341137b4f6df3092dd Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Wed, 12 Feb 2025 11:21:29 -0700 Subject: [PATCH 05/48] Feature add: alternative to define site boundaries (#426) - Added different ways to define site boundaries; square, circle, etc - Expanded docs and tests accordingly --------- Co-authored-by: bayc --- RELEASE.md | 3 +- docs/_toc.yml | 3 +- docs/api/tools/site_shape_tools.md | 50 +++ .../technologies/sites/site_info.py | 243 +++++++++++--- .../technologies/sites/site_shape_tools.py | 217 +++++++++++++ tests/hopp/test_site_info.py | 302 +++++++++++++++++- tests/hopp/test_site_shape_tools.py | 154 +++++++++ 7 files changed, 923 insertions(+), 49 deletions(-) create mode 100644 docs/api/tools/site_shape_tools.md create mode 100644 hopp/simulation/technologies/sites/site_shape_tools.py create mode 100644 tests/hopp/test_site_shape_tools.py diff --git a/RELEASE.md b/RELEASE.md index 9a971ec18..3181316af 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -5,7 +5,8 @@ * Fixed a bug in site_info that set resource year to 2012 even if otherwise specified. * Minor clean up to floris.py - removed unnecessary data exportation and fixed bug in value() * Added ability and option to initialize site_info with preloaded and formatted wind and solar resource data -+ Bug fix in load following heuristic method: only using beginning of variable load signals +* Bug fix in load following heuristic method: only using beginning of variable load signals +* Feature add: added alternative method to defining site boundary. ## Version 3.1.1, Dec. 18, 2024 diff --git a/docs/_toc.yml b/docs/_toc.yml index eff23f32e..61c6878d0 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -49,4 +49,5 @@ parts: - caption: Additional Tools chapters: - file: api/technology/flicker - - file: api/cost_calculator \ No newline at end of file + - file: api/cost_calculator + - file: api/tools/site_shape_tools \ No newline at end of file diff --git a/docs/api/tools/site_shape_tools.md b/docs/api/tools/site_shape_tools.md new file mode 100644 index 000000000..60087f322 --- /dev/null +++ b/docs/api/tools/site_shape_tools.md @@ -0,0 +1,50 @@ +(tools:site_shape)= +# Site Shape Tools + +If the site is defined as user-provided vertices, the vertices are [checked for validity](tools:check_verts). + +The site shape can be defined as a variety of default shapes: +- [Square](tools:square_site) +- [Circle](tools:circle_site) +- [Rectangle](tools:rectangle_site) +- [Hexagon](tools:hexagon_site) + + +(tools:square_site)= +## Square Site Boundary + +```{eval-rst} +.. autofunction:: hopp.simulation.technologies.sites.site_shape_tools.make_square +``` + +(tools:circle_site)= +## Circle Site Boundary + +```{eval-rst} +.. autofunction:: hopp.simulation.technologies.sites.site_shape_tools.make_circle +``` + +(tools:rectangle_site)= +## Rectangle Site Boundary + +```{eval-rst} +.. autofunction:: hopp.simulation.technologies.sites.site_shape_tools.make_rectangle +``` + +(tools:hexagon_site)= +## Hexagon Site Boundary + +```{eval-rst} +.. autofunction:: hopp.simulation.technologies.sites.site_shape_tools.make_hexagon +``` + + +(tools:check_verts)= +## Check Site Vertices + +```{eval-rst} +.. autofunction:: hopp.simulation.technologies.sites.site_shape_tools.check_site_verts +``` diff --git a/hopp/simulation/technologies/sites/site_info.py b/hopp/simulation/technologies/sites/site_info.py index 33b813cf9..0f17a895c 100644 --- a/hopp/simulation/technologies/sites/site_info.py +++ b/hopp/simulation/technologies/sites/site_info.py @@ -8,7 +8,7 @@ from shapely.geometry import Polygon, MultiPolygon, Point, shape from shapely.geometry.base import BaseGeometry from shapely.ops import transform -from shapely import make_valid +from shapely.validation import make_valid from fastkml import kml, KML import pyproj import utm @@ -30,7 +30,7 @@ ) from hopp.simulation.base import BaseClass from hopp.utilities.validators import contains - +import hopp.simulation.technologies.sites.site_shape_tools as shape_tools from hopp import ROOT_DIR def plot_site(verts, plt_style, labels): for i in range(len(verts)): @@ -51,15 +51,14 @@ class SiteInfo(BaseClass): data: Dictionary containing site-specific information. solar_resource_file: Path to solar resource file. Defaults to "". wind_resource_file: Path to wind resource file. Defaults to "". + wave_resource_file: Path to wave resource file. Defaults to "". grid_resource_file: Path to grid pricing data file. Defaults to "". path_resource: Path to folder to save resource files. - Defaults to ROOT/simulation/resource_files + Defaults to ROOT/simulation/resource_files. wtk_source_path (Optional): directory of Wind Toolkit h5 files hosted on HPC. - Only used if renewable_resource_origin != "API" + Only used if renewable_resource_origin != "API". nsrdb_source_path (Optional): directory of NSRDB h5 files hosted on HPC. - Only used if renewable_resource_origin != "API" - renewable_resource_origin (str): whether to download resource data from API or load directly from datasets files. - Options are "API" or "HPC". Defaults to "API" + Only used if renewable_resource_origin != "API". hub_height: Turbine hub height for resource download in meters. Defaults to 97.0. capacity_hours: Boolean list indicating hours for capacity payments. Defaults to []. desired_schedule: Absolute desired load profile in MWe. Defaults to []. @@ -68,23 +67,43 @@ class SiteInfo(BaseClass): solar: Whether to set solar data for this site. Defaults to True. wind: Whether to set wind data for this site. Defaults to True. wave: Whether to set wave data for this site. Defaults to False. + renewable_resource_origin (str): whether to download resource data from API or load directly from datasets files. + Options are "API" or "HPC". Defaults to "API". wind_resource_origin: Which wind resource API to use, defaults toto "WTK" for WIND Toolkit. Options are "WTK" or "TAP". - solar_resource (Optional): dictionary or object containing solar resource data - wind_resource (Optional): dictionary or object containing wind resource data + site_buffer (Optional): value to buffer site polygon. Defaults to 1e-8. + solar_resource (Optional): dictionary or object containing solar resource data. + wind_resource (Optional): dictionary or object containing wind resource data. """ # User provided data: dict """dictionary of site info data with key as: - - lat (float): site latitude - - lon (float): site longitude - - year (int): year to get resource data for. Default to 2012 - - tz (int, optional): timezone of site - - elev (int, optional): elevation of site (m) - - site_boundaries (dict): - - verts (list(list(float))): vertices of site polygon - - urdb_label (str,optional): string corresponding to data from utility rate databse + - **lat** (*float*): site latitude. + - **lon** (*float*): site longitude. + - **elev** (*int, Optional*): elevation of site (m). + - **year** (*int*): year to get resource data for. Defaults to 2012. + - **tz** (*int, Optional*): timezone of site. + - **site_boundaries** (*dict,Optional*): + - **verts** (*list[list[float]]*): vertices of site polygon. list of [x,y] coordinates in meters. + - **verts_simple** (*list[list[float]]*): TODO + - **site_details** (*dict, Optional*): + - **site_area_m2** (*float*): area of site in square meters. + - **site_area_km2** (*float*): area of site in square kilometers. required if ``site_area_m2`` is not provided. + - **site_shape** (*str, Optional*): shape of site area. Options are "circle", "rectangle", "square" or "hexagon". Defaults to "square". + - **x0** (*float, Optional*): left-most x coordinate of the site in meters. Defaults to 0.0. + - **y0** (*float, Optional*): bottom-most x coordinate of the site in meters. Defaults to 0.0. + - **aspect_ratio** (*float, Optional*): aspect ratio (width/height) + Only used if ``site_shape`` is set as "rectangle". Defaults to 1.5. + - **degrees_between_points** (*float | int, Optional*): difference in degrees for generating circular boundary. + Only used if ``site_shape`` is set as "circle". Defaults to 10. + - **solar_lat** (*float, Optional*): latitude to get solar resource data if solar plant is in a different location than lat/lon. Defaults to **lat** value above. + - **solar_lon** (*float, Optional*): longitude to get solar resource data if solar plant is in a different location than lat/lon. Defaults to **lon** value above. + - **solar_year** (*int, Optional*): resource year for solar data if wanting a different resource year than ``data["year"]``. Defaults to **year** value above. + - **wind_lat** (*float, Optional*): latitude to get wind resource data if wind plant is in a different location than lat/lon. Defaults to **lat** value above. + - **wind_lon** (*float, Optional*): longitude to get wind resource data if wind plant is in a different location than lat/lon. Defaults to **lon** value above. + - **wind_year** (*int, Optional*): resource year for wind data if wanting different resource than ``data["year"]``. Defaults to **year** value above. + - **urdb_label** (*str, Optional*): string corresponding to data from utility rate database. Defaults to None. """ @@ -108,22 +127,25 @@ class SiteInfo(BaseClass): renewable_resource_origin: str = field(default="API", validator=contains(["API", "HPC"])) wind_resource_origin: str = field(default="WTK", validator=contains(["WTK", "TAP"])) + site_buffer: Optional[float] = field(default = 1e-8) + # Set in post init hook - n_timesteps: int = field(init=False, default=None) lat: hopp_float_type = field(init=False) lon: hopp_float_type = field(init=False) + elev: Optional[float] = field(init=False, default=None) year: int = field(init=False, default=2012) tz: Optional[int] = field(init=False, default=None) - elev: Optional[float] = field(init=False, default=None) + vertices: NDArrayFloat = field(init=False) + polygon: Union[Polygon, BaseGeometry] = field(init=False) solar_resource: Optional[Union[SolarResource,HPCSolarData]] = field(default=None) wind_resource: Optional[Union[WindResource,HPCWindData]] = field(default=None) wave_resoure: Optional[WaveResource] = field(init=False, default=None) elec_prices: Optional[ElectricityPrices] = field(init=False, default=None) + n_timesteps: int = field(init=False, default=None) n_periods_per_day: int = field(init=False) interval: int = field(init=False) + urdb_label: str = field(init=False) follow_desired_schedule: bool = field(init=False) - polygon: Union[Polygon, BaseGeometry] = field(init=False) - vertices: NDArrayFloat = field(init=False) kml_data: Optional[KML] = field(init=False, default=None) # .. TODO: Can we get rid of verts_simple and simplify site_boundaries @@ -133,10 +155,11 @@ def __attrs_post_init__(self): The following are set in this post init hook: lat (numpy.float64): Site latitude in decimal degrees. lon (numpy.float64): Site longitude in decimal degrees. - tz (int, optional): Timezone code for metadata purposes only. Defaults to None. + elev (float, Optional): Elevation of the site in meters. Defaults to None. + year(int): Resource data year. + tz (int, Optional): Timezone code for metadata purposes only. Defaults to None. vertices (:obj:`NDArray`): Site boundary vertices in meters. polygon (:obj:`shapely.geometry.polygon.Polygon`): Site polygon. - valid_region (:obj:`shapely.geometry.polygon.Polygon`): Tidy site polygon. solar_resource (:obj:`hopp.simulation.technologies.resource.SolarResource`): Class containing solar resource data. wind_resource (:obj:`hopp.simulation.technologies.resource.WindResource`): Class containing wind resource data. wave_resoure (:obj:`hopp.simulation.technologies.resource.WaveResource`): Class containing wave resource data. @@ -146,18 +169,17 @@ def __attrs_post_init__(self): interval (int): Number of minutes per time interval. urdb_label (str): Link to `Utility Rate DataBase `_ label for REopt runs. follow_desired_schedule (bool): Indicates if a desired schedule was provided. Defaults to False. + kml_data (KML, Optional): KML data to be used when definining site boundaries. """ if self.renewable_resource_origin=="API": set_nrel_key_dot_env() data = self.data - if 'site_boundaries' in data: - self.vertices = np.array([np.array(v) for v in data['site_boundaries']['verts']]) - self.polygon = Polygon(self.vertices) - self.polygon = self.polygon.buffer(1e-8) + self.polygon,self.vertices = self.create_site_polygon(data) + if 'kml_file' in data: self.kml_data, self.polygon, data['lat'], data['lon'] = self.kml_read(data['kml_file']) - self.polygon = self.polygon.buffer(1e-8) + self.polygon = self.polygon.buffer(self.site_buffer) if 'lat' not in data or 'lon' not in data: raise ValueError("SiteInfo requires lat and lon") @@ -176,30 +198,21 @@ def __attrs_post_init__(self): self.elev = data['elev'] if self.solar: - if self.solar_resource is None: - if self.renewable_resource_origin=="API": - self.solar_resource = SolarResource(data['lat'], data['lon'], data['year'], path_resource=self.path_resource, filepath=self.solar_resource_file) - else: - self.solar_resource = HPCSolarData(data['lat'], data['lon'], data['year'],nsrdb_source_path = self.nsrdb_source_path, filepath=self.solar_resource_file) - elif isinstance(self.solar_resource,dict): - self.solar_resource = SolarResource(data['lat'], data['lon'], data['year'],resource_data = self.solar_resource) + self.solar_resource = self.initialize_solar_resource(data) self.n_timesteps = len(self.solar_resource.data['gh']) // 8760 * 8760 - self.elev = self.solar_resource.data["elev"] + data.setdefault("elev", self.solar_resource.data["elev"]) + data.setdefault("tz", self.solar_resource.data["tz"]) + if self.tz is None: + self.tz = data['tz'] + if self.elev is None: + self.elev = data['elev'] if self.wave: self.wave_resource = WaveResource(data['lat'], data['lon'], data['year'], filepath = self.wave_resource_file) self.n_timesteps = 8760 if self.wind: # TODO: allow hub height to be used as an optimization variable - if self.wind_resource is None: - if self.renewable_resource_origin=="API": - self.wind_resource = WindResource(data['lat'], data['lon'], data['year'], wind_turbine_hub_ht=self.hub_height, - path_resource=self.path_resource, filepath=self.wind_resource_file, source=self.wind_resource_origin) - else: - self.wind_resource = HPCWindData(data['lat'], data['lon'], data['year'], wind_turbine_hub_ht=self.hub_height, - wtk_source_path=self.wtk_source_path, filepath=self.wind_resource_file) - elif isinstance(self.wind_resource,dict): - self.wind_resource = WindResource(data['lat'], data['lon'], data['year'],wind_turbine_hub_ht=self.hub_height,resource_data = self.wind_resource) + self.wind_resource = self.initialize_wind_resource(data) n_timesteps = len(self.wind_resource.data['data']) // 8760 * 8760 if self.n_timesteps is None: self.n_timesteps = n_timesteps @@ -226,9 +239,147 @@ def __attrs_post_init__(self): logger.info("Set up SiteInfo with solar resource files: {}".format(self.solar_resource.filename)) if self.wave: logger.info("Set up SiteInfo with wave resource files: {}".format(self.wave_resource.filename)) + + def create_site_polygon(self,data:dict): + """function to create site polygon. - # TODO: determine if the below functions are obsolete + Args: + data (dict): dictionary of site info data + + Returns: + 2-element tuple containing + + - **poly** (:obj:`shapely.geometry.Polygon`): site boundary polygon + - **vertices** (2D :obj:`numpy.ndarray`): vertices of site polygon. list of [x,y] coordinates in meters. + """ + polygon = None + vertices = None + if 'site_boundaries' in data: + if 'verts' in data['site_boundaries']: + vertices = np.array(data["site_boundaries"]["verts"]) + polygon = Polygon(vertices) + polygon = polygon.buffer(self.site_buffer) #why is this needed? + elif 'site_details' in data: + if 'site_area_m2' in data["site_details"] or 'site_area_km2' in data["site_details"]: + if 'site_area_km2' in data["site_details"]: + data["site_details"].update({"site_area_m2": data["site_details"]["site_area_km2"]*1e6}) + data["site_details"].setdefault("site_shape", "square") + data["site_details"].setdefault("x0", 0.0) + data["site_details"].setdefault("y0", 0.0) + polygon, vertices = self.make_site_polygon_from_shape(data["site_details"]) + polygon = polygon.buffer(self.site_buffer) + return polygon, vertices + + def make_site_polygon_from_shape(self,site_details:dict): + """create site polygon and vertices if "site_details" provided in ``data``. + + Args: + site_details (dict): sub-dictionary of ``data``, equivalent to ``data["site_details"]`` + + Raises: + ValueError: if ``site_details["site_shape"]`` is not one of the following: "circle", "square", "rectangle", or "hexagon" + + Returns: + 2-element tuple containing + + - **poly** (:obj:`shapely.geometry.Polygon`): site boundary polygon + - **vertices** (2D :obj:`numpy.ndarray`): vertices of site polygon. list of [x,y] coordinates in meters. + """ + if (shape := site_details.get("site_shape", None)) is None: + return None, None + + shape = shape.lower() + if shape == "circle": + site_details.setdefault("degrees_between_points", 10.0) + polygon, vertices = shape_tools.make_circle( + area_m2 = site_details['site_area_m2'], + deg_diff = site_details["degrees_between_points"], + x0 = site_details["x0"], + y0 = site_details["y0"] + ) + return polygon, vertices + if shape == "square": + polygon, vertices = shape_tools.make_square( + area_m2 = site_details['site_area_m2'], + x0 = site_details["x0"], + y0 = site_details["y0"] + ) + return polygon, vertices + if shape == "rectangle": + site_details.setdefault("aspect_ratio", 1.5) + polygon, vertices = shape_tools.make_rectangle( + area_m2 = site_details['site_area_m2'], + aspect_ratio = site_details["aspect_ratio"], + x0 = site_details["x0"], + y0 = site_details["y0"] + ) + return polygon, vertices + if shape == "hexagon": + polygon, vertices = shape_tools.make_hexagon( + area_m2 = site_details['site_area_m2'], + x0 = site_details["x0"], + y0 = site_details["y0"] + ) + return polygon, vertices + + raise ValueError("invalid entry for `site_shape`, site_shape must be either 'circle', 'rectangle', 'square' or 'hexagon'") + + + + def initialize_solar_resource(self,data:dict): + """Download/load solar resource data + + Args: + data (dict): Dictionary containing site-specific information. + Returns: + :obj:`hopp.simulation.technologies.resource.SolarResource` or :obj:`hopp.simulation.technologies.resource.HPCSolarData`: solar resource data class + """ + + solar_lat = data.setdefault("solar_lat", data["lat"]) + solar_lon = data.setdefault("solar_lon", data["lon"]) + solar_year = data.setdefault("solar_year", data["year"]) + + if self.solar_resource is None: + if self.renewable_resource_origin == "API": + solar_resource = SolarResource(solar_lat, solar_lon, solar_year, path_resource=self.path_resource, filepath=self.solar_resource_file) + else: + solar_resource = HPCSolarData(solar_lat, solar_lon, solar_year,nsrdb_source_path = self.nsrdb_source_path, filepath=self.solar_resource_file) + return solar_resource + if isinstance(self.solar_resource,dict): + solar_resource = SolarResource(solar_lat, solar_lon, solar_year,resource_data = self.solar_resource) + return solar_resource + + return self.solar_resource + + def initialize_wind_resource(self,data:dict): + """Download/load wind resource data + + Args: + data (dict): Dictionary containing site-specific information. + + Returns: + :obj:`hopp.simulation.technologies.resource.WindResource` or :obj:`hopp.simulation.technologies.resource.HPCWindData`: wind resource data class + """ + wind_lat = data.setdefault("wind_lat", data["lat"]) + wind_lon = data.setdefault("wind_lon", data["lon"]) + wind_year = data.setdefault("wind_year", data["year"]) + + if self.wind_resource is None: + if self.renewable_resource_origin == "API": + wind_resource = WindResource(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height, + path_resource=self.path_resource, filepath=self.wind_resource_file, source=self.wind_resource_origin) + else: + wind_resource = HPCWindData(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height, + wtk_source_path=self.wtk_source_path, filepath=self.wind_resource_file) + return wind_resource + if isinstance(self.wind_resource,dict): + wind_resource = WindResource(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height,resource_data = self.wind_resource) + return wind_resource + + return self.wind_resource + + # TODO: determine if the below functions are obsolete @property def boundary(self) -> BaseGeometry: # TODO: remove boundaries of interior holes diff --git a/hopp/simulation/technologies/sites/site_shape_tools.py b/hopp/simulation/technologies/sites/site_shape_tools.py new file mode 100644 index 000000000..5262bdc4a --- /dev/null +++ b/hopp/simulation/technologies/sites/site_shape_tools.py @@ -0,0 +1,217 @@ +from shapely.geometry import Polygon, MultiPolygon, Point, shape, box +import numpy as np +import pandas as pd + +def calc_dist_between_two_points_cartesian(x1,y1,x2,y2): + """Calculate the distance between two points. + + Args: + x1 (np.ndarray | float): x coordinate of first point. + y1 (np.ndarray | float): y coordinate of first point. + x2 (np.ndarray | float): x coordinate of second point. + y2 (np.ndarray | float): y coordinate of second point. + + Returns: + np.ndarray | float: distance between two points + """ + dx = np.abs(x2-x1) + dy = np.abs(y2-y1) + return np.sqrt((dx**2) + (dy**2)) + +def calc_angle_between_two_points_cartesian(x0, y0, x1, y1): + """Calculate angle between two points. + + Args: + x0 (np.ndarray | float): x coordinate of first point. + y0 (np.ndarray | float): y coordinate of first point. + x1 (np.ndarray | float): x coordinate of second point. + y1 (np.ndarray | float): y coordinate of second point. + + Returns: + np.ndarray | float: angle between two points (degrees) + """ + dx = x1 - x0 + dy = y1 - y0 + angle_deg = np.rad2deg(np.arctan2(dx,dy)) + if isinstance(angle_deg, float): + return angle_deg if angle_deg >= 0 else angle_deg + 360 + return np.where(angle_deg < 0, angle_deg + 360, angle_deg) + + +def check_site_verts(verts): + """Check that vertices are valid and re-sort as needed. + + Args: + verts (2D :obj:`numpy.ndarray`): vertices of site polygon. list of [x,y] coordinates in meters. + + Returns: + numpy.ndarray: vertices ordered so that no linear rings may cross each other. + """ + x_points, y_points = verts.T + x0 = x_points.min() + y0 = y_points.min() + + dx = 0 - x0 #dx is positive if x0 is negative + dy = 0 - y0 #dy is positive if y0 is negative + + x_pos = x_points + dx + y_pos = y_points + dy + + x_center = (max(x_pos) - min(x_pos))/2 + y_center = (max(y_pos) - min(y_pos))/2 + + distances = calc_dist_between_two_points_cartesian(x_center,y_center,x_pos,y_pos) + angles = calc_angle_between_two_points_cartesian(x_center,y_center,x_pos,y_pos) + + df = ( + pd.DataFrame({"x_pos": x_pos, "y_pos": y_pos, "distances": distances, "angles": angles}) + .sort_values(["angles", "distances"]) + ) + df.x_pos += dx + df.y_pos += dy + organized_verts = df[["x_pos", "y_pos"]].values + return organized_verts + +def make_square(area_m2, x0=0.0, y0=0.0): + """Generate square polygon shape of specified area. + + Args: + area_m2 (float): area of shape in square meters. + x0 (float, Optional): left-most x coordinate of the shape. Defaults to 0.0. + y0 (float, Optional): bottom-most x coordinate of the shape. Defaults to 0.0. + + Returns: + 2-element tuple containing + + - **poly** (:obj:`shapely.geometry.Polygon`): site boundary polygon + - **vertices** (2D :obj:`numpy.ndarray`): vertices of site polygon. list of [x,y] coordinates in meters. + """ + site_length = np.sqrt(area_m2) + y1 = y0 + site_length + x1 = x0 + site_length + poly = box(x0, y0, x1, y1) + vertices = np.array([[x0,x0], [x1,y0], [x1,y1], [x0,y1]]) + return poly, vertices + +def make_rectangle(area_m2, aspect_ratio=1.5, x0=0.0, y0=0.0): + """Generate rectangle polygon shape of specified area. + + Args: + area_m2 (float): area of shape in square meters. + aspect_ratio (float, Optional): ratio of width/height. Defaults to 1.5. + (width corresponds to x coordinates, height corresponds to y coordinates) + x0 (float, Optional): left-most x coordinate of the shape. Defaults to 0.0. + y0 (float, Optional): bottom-most x coordinate of the shape. Defaults to 0.0. + + Returns: + 2-element tuple containing + + - **poly** (:obj:`shapely.geometry.Polygon`): site boundary polygon + - **vertices** (2D :obj:`numpy.ndarray`): vertices of site polygon. list of [x,y] coordinates in meters. + """ + height = np.sqrt(area_m2/aspect_ratio) + width = area_m2/height + x1 = x0 + width + y1 = y0 + height + poly = box(x0, y0, x1, y1) + vertices = np.array([[x0,x0], [x1,y0], [x1,y1], [x0,y1]]) + return poly,vertices + +def make_circle(area_m2, deg_diff = 5.0, x0=0.0, y0=0.0): + """Generate circle polygon shape of specified area. + + Args: + area_m2 (float): area of shape in square meters. + deg_diff (float | int): difference in degrees for generating boundary. default to 10. + number of points generated is equal to ``360/deg_diff`` + x0 (float, Optional): left-most x coordinate of the shape. Defaults to 0.0. + y0 (float, Optional): bottom-most x coordinate of the shape. Defaults to 0.0. + + Returns: + 2-element tuple containing + + - **poly** (:obj:`shapely.geometry.Polygon`): site boundary polygon + - **vertices** (2D :obj:`numpy.ndarray`): vertices of site polygon. list of [x,y] coordinates in meters. + """ + r = np.sqrt(area_m2/np.pi) + dx = np.deg2rad(deg_diff) + rads = np.arange(0, 2*np.pi, dx) + x_coords = r*np.cos(rads) + y_coords = r*np.sin(rads) + + x_points = x_coords + if any(x_coords < x0): + x_diff = x0 - x_coords + x_points += x_diff.max() + + y_points = y_coords + if any(y_coords < y0): + y_diff = y0 - y_coords + y_points += y_diff.max() + + vertices = np.vstack((x_points, y_points)).T + poly = Polygon(vertices) + + return poly, vertices + +def make_hexagon(area_m2, x0=0.0, y0=0.0): + """Generate hexagon polygon shape of specified area. + + Args: + area_m2 (float): area of shape in square meters. + x0 (float, Optional): left-most x coordinate of the shape. Defaults to 0.0. + y0 (float, Optional): bottom-most x coordinate of the shape. Defaults to 0.0. + + Returns: + 2-element tuple containing + + - **poly** (:obj:`shapely.geometry.Polygon`): site boundary polygon + - **vertices** (2D :obj:`numpy.ndarray`): vertices of site polygon. list of [x,y] coordinates in meters. + """ + s = np.sqrt(area_m2*(2/(3*np.sqrt(3)))) + rads = np.arange(0,2*np.pi,np.deg2rad(60)) + x_coords = s*np.cos(rads) + y_coords = s*np.sin(rads) + + x_points = x_coords + if any(x_coords < x0): + x_diff = x0 - x_coords + x_points += x_diff.max() + + y_points = y_coords + if any(y_coords < y0): + y_diff = y0 - y_coords + y_points += y_diff.max() + + vertices = np.vstack((x_points, y_points)).T + poly = Polygon(vertices) + + return poly, vertices + +def rotate_shape(site_polygon, rotation_angle_deg): + # in degrees where 0 is north, increasing clockwise + # 90 degrees is east, 180 degrees is south, 270 degrees is west + # get center points + xc = site_polygon.centroid.x + yc = site_polygon.centroid.y + + vertices = np.array(site_polygon.exterior.coords) + + # translate coordinates to have origin at polygon center + xc_points, yc_points = (vertices - [xc, yc]).T + + theta = np.deg2rad(rotation_angle_deg) + + # rotate clockwise about the origin + cos_theta = np.cos(theta) + sin_theta = np.sin(theta) + xr_points = (xc_points * cos_theta) + (yc_points * sin_theta) + yr_points = (-1 * xc_points * sin_theta) + (yc_points * cos_theta) + + # translate points back to original coordinate reference system + rotated_vertices = np.vstack((xr_points, yr_points)).T + [xc, yc] + rotated_polygon = Polygon(rotated_vertices) + + return rotated_polygon, rotated_vertices + + diff --git a/tests/hopp/test_site_info.py b/tests/hopp/test_site_info.py index 6dbe63349..bcea0c45a 100644 --- a/tests/hopp/test_site_info.py +++ b/tests/hopp/test_site_info.py @@ -3,7 +3,7 @@ from pathlib import Path import pytest -from pytest import fixture +from pytest import fixture, approx from shapely.geometry import Polygon import numpy as np from numpy.testing import assert_array_equal @@ -229,3 +229,303 @@ def test_site_solar_resource_input_data_format(): ) assert site.solar_resource.data['tz']==-6 +def test_site_polygon_valid_verts(): + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_boundaries": + { + "verts": + [[0.0,0.0],[500.0,0.0],[500.0,500.0],[0.0,500.0]] + } + } + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + + assert site.polygon.area == approx(250000,rel = 1e-3) + assert site.vertices[0][0] == 0.0 + assert site.vertices[0][1] == 0.0 + assert site.vertices[1][0] == 500.0 + assert site.vertices[1][1] == 0.0 + + +def test_site_polygon_invalid_verts(): + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_boundaries": + { + "verts": + [[0.0,0.0],[500.0,500.0],[500.0,0.0],[0.0,500.0]] + } + } + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + assert site.polygon.area != approx(250000,rel = 1e-3) + +def test_site_polygon_square_defaults(): + site_area_km2 = 2.5 + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_details": + { + "site_area_km2": site_area_km2, + "site_shape":"square", + } + } + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + assert site.polygon.area == approx(site_area_km2*1e6,rel=1e-3) + + +def test_site_polygon_square_offset(): + site_area_km2 = 2.5 + x0 = 25.0 + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_details": + { + "site_area_km2": site_area_km2, + "site_shape":"Square", + "x0": x0, + } + } + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + x_verts,y_verts = site.polygon.exterior.coords.xy + assert site.polygon.area == approx(site_area_km2*1e6,rel=1e-3) + assert min(x_verts) == approx(x0,rel=1e-3) + assert min(y_verts) == approx(0.0,abs=1e-8) + + +def test_site_polygon_rectangle_default(): + site_area_km2 = 2.5 + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_details": + { + "site_area_km2": site_area_km2, + "site_shape":"rectangle", + } + } + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + x_verts,y_verts = site.polygon.exterior.coords.xy + dx = max(x_verts) - min(x_verts) + dy = max(y_verts) - min(y_verts) + width_to_height = dx/dy + + assert site.polygon.area == approx(site_area_km2*1e6,rel=1e-3) + assert width_to_height == approx(1.5,rel=1e-3) + + +def test_site_polygon_rectangle_aspect_ratio(): + site_area_km2 = 2.5 + aspect_ratio = 2.0 + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_details": + { + "site_area_km2": site_area_km2, + "site_shape":"rectangle", + "aspect_ratio": aspect_ratio, + } + } + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + x_verts,y_verts = site.polygon.exterior.coords.xy + dx = max(x_verts) - min(x_verts) + dy = max(y_verts) - min(y_verts) + width_to_height = dx/dy + + assert site.polygon.area == approx(site_area_km2*1e6,rel=1e-3) + assert width_to_height == approx(aspect_ratio,rel=1e-3) + + +def test_site_polygon_circle_default(): + site_area_km2 = 2.5 + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_details": + { + "site_area_km2": site_area_km2, + "site_shape":"circle", + } + } + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + + assert site.polygon.area == approx(site_area_km2*1e6,rel=1e-2) + assert len(site.vertices) == 36 + + +def test_site_polygon_circle_detail(): + site_area_km2 = 2.5 + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_details": + { + "site_area_km2": site_area_km2, + "site_shape":"circle", + "degrees_between_points":1.0 + } + } + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + + assert site.polygon.area == approx(site_area_km2*1e6,rel=1e-3) + assert len(site.vertices) == 360 + + +def test_site_polygon_hexagon_default(): + site_area_km2 = 2.5 + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_details": + { + "site_area_km2": site_area_km2, + "site_shape":"hexagon", + } + } + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + + assert site.polygon.area == approx(site_area_km2*1e6,rel=1e-3) + + +def test_site_polygon_hexagon_m2(): + site_area_km2 = 2.5 + site_area_m2 = site_area_km2*1e6 + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_details": + { + "site_area_m2": site_area_m2, + "site_shape":"hexagon", + } + } + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + + assert site.polygon.area == approx(site_area_km2*1e6,rel=1e-3) + + +def test_site_invalid_shape(): + site_area_km2 = 2.5 + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_details": + { + "site_area_km2": site_area_km2, + "site_shape":"triangle", + } + } + with pytest.raises(ValueError) as err: + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + assert str(err.value) == "invalid entry for `site_shape`, site_shape must be either 'circle', 'rectangle', 'square' or 'hexagon'" + + +def test_site_none_shape(): + site_area_km2 = 2.5 + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2012, + "tz": -6, + "site_details": {} + } + site = SiteInfo( + site_data, + solar_resource_file=solar_resource_file, + wind_resource_file=wind_resource_file, + grid_resource_file=grid_resource_file + ) + assert site.polygon is None \ No newline at end of file diff --git a/tests/hopp/test_site_shape_tools.py b/tests/hopp/test_site_shape_tools.py new file mode 100644 index 000000000..80ce616bc --- /dev/null +++ b/tests/hopp/test_site_shape_tools.py @@ -0,0 +1,154 @@ +from pytest import approx +from shapely.geometry import Polygon +import hopp.simulation.technologies.sites.site_shape_tools as shape_tools +import numpy as np + +def test_circle_area(): + area_m2 = 1e3 + polygon, vertices = shape_tools.make_circle(area_m2, deg_diff = 1.0) + assert polygon.area == approx(area_m2,rel = 1e-3) + +def test_square_area(): + area_m2 = 1e3 + polygon, vertices = shape_tools.make_square(area_m2) + assert polygon.area == approx(area_m2,rel = 1e-3) + +def test_rectangle_area(): + area_m2 = 1e3 + polygon, vertices = shape_tools.make_rectangle(area_m2) + assert polygon.area == approx(area_m2,rel = 1e-3) + +def test_hexagon_area(): + area_m2 = 1e3 + polygon, vertices = shape_tools.make_hexagon(area_m2) + assert polygon.area == approx(area_m2,rel = 1e-3) + +def test_circle_vertices_default(): + area_m2 = 1e3 + polygon, vertices = shape_tools.make_circle(area_m2) + x_verts = [v[0] for v in vertices] + y_verts = [v[1] for v in vertices] + assert min(x_verts) == 0.0 + assert min(y_verts) == 0.0 + +def test_square_vertices_default(): + area_m2 = 1e3 + polygon, vertices = shape_tools.make_square(area_m2) + x_verts = [v[0] for v in vertices] + y_verts = [v[1] for v in vertices] + assert len(vertices)==4 + assert min(x_verts) == 0.0 + assert min(y_verts) == 0.0 + +def test_rectangle_vertices_default(): + area_m2 = 1e3 + polygon, vertices = shape_tools.make_rectangle(area_m2) + x_verts = [v[0] for v in vertices] + y_verts = [v[1] for v in vertices] + assert len(vertices)==4 + assert min(x_verts) == 0.0 + assert min(y_verts) == 0.0 + +def test_hexagon_vertices_default(): + area_m2 = 1e3 + polygon, vertices = shape_tools.make_hexagon(area_m2) + x_verts = [v[0] for v in vertices] + y_verts = [v[1] for v in vertices] + assert len(vertices)==6 + assert min(x_verts) == 0.0 + assert min(y_verts) == 0.0 + + +def test_circle_vertices_offset(): + area_m2 = 1e3 + x0 = 5.0 + y0 = -4.0 + polygon, vertices = shape_tools.make_circle(area_m2, x0 = x0, y0 = y0) + x_verts = [v[0] for v in vertices] + y_verts = [v[1] for v in vertices] + assert min(x_verts) == x0 + assert min(y_verts) == y0 + +def test_square_vertices_offset(): + area_m2 = 1e3 + x0 = 5.0 + y0 = -4.0 + polygon, vertices = shape_tools.make_square(area_m2, x0 = x0, y0 = y0) + x_verts = [v[0] for v in vertices] + y_verts = [v[1] for v in vertices] + assert len(vertices) == 4 + assert min(x_verts) == x0 + assert min(y_verts) == y0 + +def test_rectangle_vertices_offset(): + area_m2 = 1e3 + x0 = 5.0 + y0 = -4.0 + aspect_ratio = 1.5 + polygon, vertices = shape_tools.make_rectangle(area_m2, aspect_ratio = aspect_ratio, x0 = x0, y0 = y0) + x_verts = [v[0] for v in vertices] + y_verts = [v[1] for v in vertices] + assert len(vertices) == 4 + assert min(x_verts) == x0 + assert min(y_verts) == y0 + +def test_hexagon_vertices_offset(): + area_m2 = 1e3 + x0 = 5.0 + y0 = -4.0 + polygon, vertices = shape_tools.make_hexagon(area_m2, x0 = x0, y0 = y0) + x_verts = [v[0] for v in vertices] + y_verts = [v[1] for v in vertices] + assert len(vertices) == 6 + assert min(x_verts) == x0 + assert min(y_verts) == y0 + +def test_distance_between_points(): + x0 = 0.0 + y0 = 0.0 + dy = 5.0 + dx = 0.0 + x1 = x0 + dx + y2 = y0 + dy + distance = shape_tools.calc_dist_between_two_points_cartesian(x0,y0,x1,y2) + assert distance == approx(dy,1e-3) + +def test_angle_between_points(): + x0 = 0.0 + y0 = 0.0 + x1 = 1.0 + y1 = 1.0 + angle = shape_tools.calc_angle_between_two_points_cartesian(x0,y0,x1,y1) + assert angle == approx(45.0,1e-3) + +def test_sort_site_verts(): + invalid_x_points = [0.0,5.0,0.0,5.0] + invalid_y_points = [0.0,0.0,5.0,5.0] + invalid_verts = [[x,y] for x,y in zip(invalid_x_points,invalid_y_points)] + invalid_verts = np.array(invalid_verts) + valid_verts = shape_tools.check_site_verts(invalid_verts) + valid_shape = Polygon(valid_verts) + assert valid_shape.area == approx(25.0,1e-3) + +def test_rotate_site_center_area(): + area_m2 = 1e3 + rotation_angle = 45 + polygon_original, vertices_original = shape_tools.make_hexagon(area_m2) + rotated_polygon, rotated_vertices = shape_tools.rotate_shape( + site_polygon = polygon_original, + rotation_angle_deg = rotation_angle + ) + assert rotated_polygon.centroid.x == approx(polygon_original.centroid.x,abs = 1e-3) + assert rotated_polygon.centroid.y == approx(polygon_original.centroid.y,abs = 1e-3) + assert rotated_polygon.area == approx(polygon_original.area,abs = 1e-3) + +def test_rotate_site_vertices(): + area_m2 = 1e3 + rotation_angle = 45 + polygon_original, vertices_original = shape_tools.make_square(area_m2) + rotated_polygon, rotated_vertices = shape_tools.rotate_shape( + site_polygon = polygon_original, + rotation_angle_deg = rotation_angle + ) + assert rotated_polygon.exterior.xy[0][0] == approx(polygon_original.centroid.x,abs = 1e-3) + assert rotated_polygon.exterior.xy[1][1] == approx(polygon_original.centroid.y,abs = 1e-3) \ No newline at end of file From 35a962c6e76589764f4ae3134c94b958f28ddf51 Mon Sep 17 00:00:00 2001 From: kbrunik <102193481+kbrunik@users.noreply.github.com> Date: Fri, 14 Feb 2025 15:52:49 -0600 Subject: [PATCH 06/48] Remove Wave Resource Deprecated Methods (#430) * update deprecated methods * H to h * update RELEASE.md * update wave resource docstring * fix docstring spacing * wave resource doc * fix doc build --- RELEASE.md | 1 + docs/_toc.yml | 1 + docs/api/resource/index.md | 1 + docs/api/resource/wave_data.md | 11 +++ .../technologies/resource/wave_resource.py | 91 +++++++++++++------ 5 files changed, 76 insertions(+), 29 deletions(-) create mode 100644 docs/api/resource/wave_data.md diff --git a/RELEASE.md b/RELEASE.md index 3181316af..70038b7a4 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -7,6 +7,7 @@ * Added ability and option to initialize site_info with preloaded and formatted wind and solar resource data * Bug fix in load following heuristic method: only using beginning of variable load signals * Feature add: added alternative method to defining site boundary. +* Update deprecated methods in wave_resource.py ## Version 3.1.1, Dec. 18, 2024 diff --git a/docs/_toc.yml b/docs/_toc.yml index 61c6878d0..5e9af9fa5 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -21,6 +21,7 @@ parts: - file: api/resource/wind_api - file: api/resource/solar_hpc - file: api/resource/wind_hpc + - file: api/resource/wave_data - file: api/hybrid_simulation - file: api/technology/index sections: diff --git a/docs/api/resource/index.md b/docs/api/resource/index.md index 1728c3e58..7b72a5058 100644 --- a/docs/api/resource/index.md +++ b/docs/api/resource/index.md @@ -6,6 +6,7 @@ These are the primary methods for accessing wind and solar resource data. - [Wind Resource (API)](resource:wind-resource) - [Solar Resource (NSRDB Dataset on NREL HPC)](resource:nsrdb-data) - [Wind Resource (Wind Toolkit Dataset on NREL HPC)](resource:wtk-data) +- [Wave Resource (Data)](resource:wave-resource) ## NREL API Keys diff --git a/docs/api/resource/wave_data.md b/docs/api/resource/wave_data.md new file mode 100644 index 000000000..47eb7c5c0 --- /dev/null +++ b/docs/api/resource/wave_data.md @@ -0,0 +1,11 @@ +(resource:wave-resource)= +# Wave Resource + +**NOTE: Downloading wave resource data is not yet enabled** but can still be loaded from existing data files. + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.resource.wave_resource.WaveResource + :members: + :undoc-members: + :exclude-members: _abc_impl, check_download_dir, call_api +``` diff --git a/hopp/simulation/technologies/resource/wave_resource.py b/hopp/simulation/technologies/resource/wave_resource.py index 370df958c..0d6bc255f 100644 --- a/hopp/simulation/technologies/resource/wave_resource.py +++ b/hopp/simulation/technologies/resource/wave_resource.py @@ -7,7 +7,11 @@ class WaveResource(Resource): """ - Class to manage Wave Resource data + Class to manage Wave Resource data. + + This class loads, processes, and formats wave energy resource data, + either from a file or a provided dataset, for compatibility with + PySAM's wave energy models. """ def __init__( self, @@ -19,20 +23,27 @@ def __init__( **kwargs ): """ - lat (float): latitude - lon (float): longitude - year (int): year - path_resource (str): directory where to save downloaded files - filepath (str): file path of resource file to load - - see 'hopp/simulation/resource_files/wave/Wave_resource_timeseries.csv' for example wave resource file - file format for time series for wave energy resource data - rows 1 and 2: header rows containing info about location - row 3: headings for time series wave data - (month, day, hour, minute, wave height, wave period) - row 4 and higher: contains data itself - (significant) wave height in meters - wave (energy) period in seconds + Initializes the WaveResource object. + + Args: + lat (float): Latitude of the resource location. + lon (float): Longitude of the resource location. + year (int): Year of the resource data. + path_resource (str, optional): Directory where downloaded files are saved. Defaults to "". + filepath (str, optional): File path of the resource file to load. Defaults to "". + **kwargs: Additional keyword arguments. + + Notes: + The wave resource data should be in the format: + - Rows 1 and 2: Header rows with location info. + - Row 3: Column headings for time-series data + - (`Year`, `Month`, `Day`, `Hour`, `Minute`, `wave height`, `wave period`). + - Rows 4+: Data values: + - `wave height` (significant wave height) in meters. + - `wave period` (energy period) in seconds. + + Example file: + `hopp/simulation/resource_files/wave/Wave_resource_timeseries.csv` """ super().__init__(lat, lon, year) @@ -50,13 +61,24 @@ def __init__( logger.info("WaveResource: {}".format(self.filename)) def download_resource(self): - #TODO: Add ability to use MHKit for resource downloads - # https://mhkit-software.github.io/MHKiT/ + """ + Placeholder for downloading wave resource data. + + Raises: + NotImplementedError: Currently, downloading functionality is not implemented. + + TODO: + Implement resource downloads using MHKit: + https://mhkit-software.github.io/MHKiT/ + """ raise NotImplementedError def format_data(self): """ - Format as 'wave_resource_data' dictionary for use in PySAM. + Formats wave resource data as a dictionary for PySAM. + + Raises: + FileNotFoundError: If the specified resource file does not exist. """ if not os.path.isfile(self.filename): raise FileNotFoundError(self.filename + " does not exist.") @@ -66,14 +88,25 @@ def format_data(self): @Resource.data.setter def data(self, data_file): """ - Sets the wave resource data to a dictionary in the SAM Wave format: - - significant_wave_height: wave height time series data [m] - - energy period: wave period time series data [s] - - year - - month - - day - - hour - - minute + Sets the wave resource data in PySAM's wave energy format. + + Args: + data_file (str): File path to the wave resource data. + + Raises: + ValueError: If the resource time series contains sub-hourly data. + + The output dictionary includes: + - `significant_wave_height` (list[float]): Wave height time series data [m]. + - `energy_period` (list[float]): Wave period time series data [s]. + - `year` (list[int]): Year timestamps. + - `month` (list[int]): Month timestamps. + - `day` (list[int]): Day timestamps. + - `hour` (list[int]): Hour timestamps. + - `minute` (list[int]): Minute timestamps. + + If the time series is incomplete (less than 8760 hours), the function + linearly interpolates missing values to create a complete hourly dataset. """ wavefile_model = wavefile.new() #Load resource file @@ -99,7 +132,7 @@ def data(self, data_file): df['energy_period'] = wavefile_model.Outputs.energy_period # Resample data and linearly interpolate to hourly data - data_df = df.resample("H").mean() + data_df = df.resample("h").mean() data_df = data_df.interpolate(method='linear') # If data cannot interpolate last hours @@ -107,10 +140,10 @@ def data(self, data_file): last_hour = data_df.index.max() missing_hours = 8760 - len(data_df['energy_period']) - missing_time = pd.date_range(last_hour + pd.Timedelta(hours=1),periods=missing_hours, freq='H') + missing_time = pd.date_range(last_hour + pd.Timedelta(hours=1),periods=missing_hours, freq='h') missing_rows = pd.DataFrame(index=missing_time, columns=df.columns) data_df = pd.concat([data_df, missing_rows]).sort_index() - data_df = data_df.fillna(method='ffill') # forward fill + data_df = data_df.ffill() # forward fill data_df = data_df.reset_index() dic = dict() From 2e7157b3a208da4210bf3dae2f4ca1b0137c0b56 Mon Sep 17 00:00:00 2001 From: kbrunik <102193481+kbrunik@users.noreply.github.com> Date: Tue, 18 Feb 2025 14:49:48 -0600 Subject: [PATCH 07/48] PySAM 6.0.1 (#425) * update pysam to 6.0.0 * update grid default json to CustomGenerationProfile json * update wave plant loading resource file to handle 1hr timesteps by default * Updated for pysam 6.0.0 * wave cost model updates. update test values add additional test for costs. * Update regression test values based on updates to wind and solar pysam default jsons and updates to singleowner model. * Reopt test: update default json and financial value for wind * update test values in test_capacity_credit. changes due to json defaults and impact on battery optimization * CSP update. Update lcoe value because of changes to SingleOwner financial model * WIP; updating test values for detailed PV * Bringing tests back for detailed PV * update regression test results * update RELEASE.md * update example default fin config * remove outdated comments * update docstrings to google format * update RELEASE.md with PR number. Force update for NREL-PySAM dependency * remove commented code * update detailed pv attribute for pysam 6.0.1 * update release notes --------- Co-authored-by: John Jasa --- RELEASE.md | 5 + examples/inputs/default_fin_config.yaml | 66 +++-- .../Detailed_PV_Layout/detailed_pv_layout.py | 3 - hopp/simulation/technologies/grid.py | 2 +- .../technologies/layout/pv_layout.py | 2 +- .../technologies/layout/pv_module.py | 59 ++-- .../technologies/pv/detailed_pv_plant.py | 28 +- .../technologies/wave/mhk_wave_plant.py | 10 +- pyproject.toml | 2 +- tests/hopp/inputs/pysam_simulation_input.yaml | 2 - tests/hopp/pvsamv1_basic_params.json | 133 +++++---- tests/hopp/test_csp.py | 8 +- tests/hopp/test_custom_financial.py | 58 ++-- tests/hopp/test_detailed_pv_plant.py | 13 - tests/hopp/test_dispatch.py | 10 +- tests/hopp/test_hybrid.py | 252 ++++++++++-------- tests/hopp/test_layout.py | 80 +++--- tests/hopp/test_reopt.py | 4 +- 18 files changed, 375 insertions(+), 362 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 70038b7a4..d7deb290f 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -7,6 +7,11 @@ * Added ability and option to initialize site_info with preloaded and formatted wind and solar resource data * Bug fix in load following heuristic method: only using beginning of variable load signals * Feature add: added alternative method to defining site boundary. +* Updated PySAM version from 4.2.0 to 6.0.1. Main changes noted in [PR #425](https://github.com/NREL/HOPP/pull/425) +* PySAM generation plant defaults have been updated. Current defaults can be found [here](https://github.com/NREL/SAM/tree/develop/api/api_autogen/library/defaults) +* PySAM SingleOwner financial model update investment-tax credit and depreciation basis calculations to remove financing fees and reserve account funding from basis. +* PySAM MHKWave update marine energy device cost curves. +* PySAM Detailed PV update module and inverter libraries, snow module, tracking, losses. * Update deprecated methods in wave_resource.py ## Version 3.1.1, Dec. 18, 2024 diff --git a/examples/inputs/default_fin_config.yaml b/examples/inputs/default_fin_config.yaml index eacb350a0..b11a61841 100644 --- a/examples/inputs/default_fin_config.yaml +++ b/examples/inputs/default_fin_config.yaml @@ -1,29 +1,39 @@ -batt_replacement_schedule_percent: - - 0 -batt_bank_replacement: - - 0 -batt_replacement_option: 0 -batt_computed_bank_capacity: 0 -batt_meter_position: 0 -om_fixed: - - 1 -om_production: - - 2 -om_capacity: - - 0 -om_batt_fixed_cost: 0 -om_batt_variable_cost: - - 0 -om_batt_capacity_cost: 0 -om_batt_replacement_cost: 0 -om_replacement_cost_escal: 0 +battery_system: + batt_replacement_schedule_percent: [0] + batt_bank_replacement: [0] + batt_replacement_option: 0 + batt_computed_bank_capacity: 0 + batt_meter_position: 0 +system_costs: + om_fixed: [1] + om_production: [2] + om_capacity: [0] + om_batt_fixed_cost: 0 + om_batt_variable_cost: [0.75] + om_batt_capacity_cost: 0 + om_batt_replacement_cost: 0 + om_replacement_cost_escal: 0 +revenue': + ppa_price_input: [25] # cents/kWh + ppa_escalation: 2.5 # % system_use_lifetime_output: 0 -inflation_rate: 2.5 -real_discount_rate: 6.4 -cp_capacity_credit_percent: - - 0 -degradation: - - 0 -ppa_price_input: - - 0.01 -ppa_escalation: 1 +financial_parameters: + inflation_rate: 2.5 + real_discount_rate: 6.4 + federal_tax_rate: 21.0 + state_tax_rate: 4.0 + property_tax_rate: 1.0 + insurance_rate: 0.5 + debt_percent: 68.5 + term_int_rate: 6.0 + months_working_reserve: 1 + analysis_start_year: 2025 + installation_months: 12 + sales_tax_rate_state: 4.5 + admin_expense_percent_of_sales: 1.0 + capital_gains_tax_rate: 15.0 + debt_type: "Revolving debt" + depreciation_method: "MACRS" + depreciation_period: 5 +cp_capacity_credit_percent: [0] +degradation: [0] \ No newline at end of file diff --git a/examples/legacy/Detailed_PV_Layout/detailed_pv_layout.py b/examples/legacy/Detailed_PV_Layout/detailed_pv_layout.py index 3b64620ca..51269053c 100644 --- a/examples/legacy/Detailed_PV_Layout/detailed_pv_layout.py +++ b/examples/legacy/Detailed_PV_Layout/detailed_pv_layout.py @@ -152,9 +152,6 @@ def _set_system_layout(self): elif self._system_model.value('subarray1_track_mode') == 1: self._system_model.value('subarray1_rotlim', self.parameters.tilt_tracker_angle) self._system_model.value('constant', self.flicker_loss * 100) # percent - self._system_model.value('subarray2_enable', 0) - self._system_model.value('subarray3_enable', 0) - self._system_model.value('subarray4_enable', 0) else: # PVWatts self._system_model.value('system_capacity', self.calculated_system_capacity) diff --git a/hopp/simulation/technologies/grid.py b/hopp/simulation/technologies/grid.py index d654f0dab..95458bfec 100644 --- a/hopp/simulation/technologies/grid.py +++ b/hopp/simulation/technologies/grid.py @@ -50,7 +50,7 @@ class Grid(PowerSource): schedule_curtailed: NDArrayFloat = field(init=False) schedule_curtailed_percentage: float = field(init=False, default=0.) total_gen_max_feasible_year1: NDArrayFloat = field(init=False) - config_name: Optional[str] = field(default="GenericSystemSingleOwner") + config_name: Optional[str] = field(default="CustomGenerationProfileSingleOwner") def __attrs_post_init__(self): """ diff --git a/hopp/simulation/technologies/layout/pv_layout.py b/hopp/simulation/technologies/layout/pv_layout.py index d491740e9..df375581a 100644 --- a/hopp/simulation/technologies/layout/pv_layout.py +++ b/hopp/simulation/technologies/layout/pv_layout.py @@ -96,7 +96,7 @@ def _set_system_layout(self): self._system_model.SystemDesign.inverter_count = n_inverters logger.info(f"Solar Layout set for {self.module_power * self.num_modules} kw") - self._system_model.AdjustmentFactors.constant = self.flicker_loss * 100 # percent + self._system_model.AdjustmentFactors.adjust_constant = self.flicker_loss * 100 # percent def compute_pv_layout(self, solar_kw: float, diff --git a/hopp/simulation/technologies/layout/pv_module.py b/hopp/simulation/technologies/layout/pv_module.py index 0b4035fab..d2fdd7064 100644 --- a/hopp/simulation/technologies/layout/pv_module.py +++ b/hopp/simulation/technologies/layout/pv_module.py @@ -38,18 +38,33 @@ def get_module_attribs(model: Union[pv_simple.Pvwattsv8, pv_detailed.Pvsamv1, di Returns the module attributes for either the PVsamv1 or PVWattsv8 models, see: https://nrel-pysam.readthedocs.io/en/main/modules/Pvsamv1.html#module-group - :param model: PVsamv1 or PVWattsv8 model or parameter dictionary - :param only_ref_vals: if True, only return the reference values (e.g., I_sc_ref) - :return: dict, with keys (if only_ref_values is True, otherwise will include all model-specific parameters): - area [m2] - aspect_ratio [-] - length [m] - I_mp_ref [A] - I_sc_ref [A] - P_mp_ref [kW] - V_mp_ref [V] - V_oc_ref [V] - width [m] + This function extracts module attributes from a given PV model or parameter dictionary. + If `only_ref_vals` is set to True, only the reference values (e.g., `I_sc_ref`, `V_mp_ref`) + are returned; otherwise, all model-specific parameters are included. + + Args: + model (Union[pv_simple.Pvwattsv8, pv_detailed.Pvsamv1, dict]): + The PV model (PVsamv1 or PVWattsv8) or a dictionary of parameters. + only_ref_vals (bool, optional): + If True, only returns the reference values. If False, includes all model-specific parameters. + Defaults to True. + + Returns: + dict: A dictionary containing module attributes. If `only_ref_vals` is True, the dictionary includes: + - `area` (float): Module area [m²]. + - `aspect_ratio` (float): Module aspect ratio [-]. + - `length` (float): Module length [m]. + - `width` (float): Module width [m]. + - `I_mp_ref` (float): Reference current at maximum power point [A]. + - `I_sc_ref` (float): Reference short-circuit current [A]. + - `P_mp_ref` (float): Reference power at maximum power point [kW]. + - `V_mp_ref` (float): Reference voltage at maximum power point [V]. + - `V_oc_ref` (float): Reference open-circuit voltage [V]. + + If `only_ref_vals` is False, additional model-specific attributes are included. + + Raises: + Exception: If the module model number is not recognized. """ MODEL_PREFIX = ['spe', 'cec', '6par', 'snl', 'sd11par', 'mlm'] @@ -153,10 +168,21 @@ def get_module_attribs(model: Union[pv_simple.Pvwattsv8, pv_detailed.Pvsamv1, di def set_module_attribs(model: Union[pv_simple.Pvwattsv8, pv_detailed.Pvsamv1], params: dict): """ Sets the module model parameters for either the PVsamv1 or PVWattsv8 models. - Will raise exception if not all required parameters are provided. - - :param model: PVWattsv8 or PVsamv1 model - :param params: dictionary of parameters + + This function assigns the required parameters to the given model. It verifies that all + necessary parameters are provided based on the selected module type and raises an + exception if any required parameters are missing. + + Args: + model (Union[pv_simple.Pvwattsv8, pv_detailed.Pvsamv1]): + The PVWattsv8 or PVsamv1 model instance. + params (dict): + Dictionary containing parameter key-value pairs required for the respective + module model. + + Raises: + Exception: If not all required parameters are provided or if the module model + number is unrecognized. """ if isinstance(model, pv_simple.Pvwattsv8): @@ -193,7 +219,6 @@ def set_module_attribs(model: Union[pv_simple.Pvwattsv8, pv_detailed.Pvsamv1], p 'cec_adjust', 'cec_alpha_sc', 'cec_beta_oc', - 'cec_gamma_r', 'cec_i_l_ref', 'cec_i_mp_ref', 'cec_i_o_ref', diff --git a/hopp/simulation/technologies/pv/detailed_pv_plant.py b/hopp/simulation/technologies/pv/detailed_pv_plant.py index d59c2e167..91f45253d 100644 --- a/hopp/simulation/technologies/pv/detailed_pv_plant.py +++ b/hopp/simulation/technologies/pv/detailed_pv_plant.py @@ -133,11 +133,6 @@ def processed_assign(self): if self.config.tech_config is not None: config = self.config.tech_config - if 'subarray2_enable' in config.keys() and config['subarray2_enable'] == 1 \ - or 'subarray3_enable' in config.keys() and config['subarray3_enable'] == 1 \ - or 'subarray4_enable' in config.keys() and config['subarray4_enable'] == 1: - raise Exception('Detailed PV plant currently only supports one subarray.') - # Get PV module attributes system_params = flatten_dict(self._system_model.export()) system_params.update(config) @@ -289,12 +284,6 @@ def system_capacity_kw(self, system_capacity_kw_: float): ) self._system_model.value('system_capacity', system_capacity) self._system_model.value('subarray1_nstrings', n_strings) - self._system_model.value('subarray2_nstrings', 0) - self._system_model.value('subarray3_nstrings', 0) - self._system_model.value('subarray4_nstrings', 0) - self._system_model.value('subarray2_enable', 0) - self._system_model.value('subarray3_enable', 0) - self._system_model.value('subarray4_enable', 0) self._system_model.value('inverter_count', n_inverters) @property @@ -325,12 +314,6 @@ def dc_ac_ratio(self, target_dc_ac_ratio: float): ) self._system_model.value('system_capacity', system_capacity) self._system_model.value('subarray1_nstrings', n_strings) - self._system_model.value('subarray2_nstrings', 0) - self._system_model.value('subarray3_nstrings', 0) - self._system_model.value('subarray4_nstrings', 0) - self._system_model.value('subarray2_enable', 0) - self._system_model.value('subarray3_enable', 0) - self._system_model.value('subarray4_enable', 0) self._system_model.value('inverter_count', n_inverters) @property @@ -370,9 +353,6 @@ def modules_per_string(self) -> float: def modules_per_string(self, _modules_per_string: float): """Sets the modules per string and updates the system capacity.""" self._system_model.SystemDesign.subarray1_modules_per_string = _modules_per_string - self._system_model.SystemDesign.subarray2_modules_per_string = 0 - self._system_model.SystemDesign.subarray3_modules_per_string = 0 - self._system_model.SystemDesign.subarray4_modules_per_string = 0 # update system capacity directly to not recalculate the number of inverters, consistent with the SAM UI self._system_model.value('system_capacity', self.module_power * _modules_per_string * self.n_strings) @@ -392,18 +372,12 @@ def subarray1_modules_per_string(self, subarray1_modules_per_string_: float): @property def n_strings(self) -> float: """Total number of strings.""" - return self._system_model.SystemDesign.subarray1_nstrings \ - + self._system_model.SystemDesign.subarray2_nstrings \ - + self._system_model.SystemDesign.subarray3_nstrings \ - + self._system_model.SystemDesign.subarray4_nstrings + return self._system_model.SystemDesign.subarray1_nstrings @n_strings.setter def n_strings(self, _n_strings: float): """Sets the total number of strings and updates the system capacity.""" self._system_model.SystemDesign.subarray1_nstrings = _n_strings - self._system_model.SystemDesign.subarray2_nstrings = 0 - self._system_model.SystemDesign.subarray3_nstrings = 0 - self._system_model.SystemDesign.subarray4_nstrings = 0 # update system capacity directly to not recalculate the number of inverters, consistent with the SAM UI self._system_model.value('system_capacity', self.module_power * self.modules_per_string * _n_strings) diff --git a/hopp/simulation/technologies/wave/mhk_wave_plant.py b/hopp/simulation/technologies/wave/mhk_wave_plant.py index 0af69c911..7b5d6b2dd 100644 --- a/hopp/simulation/technologies/wave/mhk_wave_plant.py +++ b/hopp/simulation/technologies/wave/mhk_wave_plant.py @@ -217,27 +217,23 @@ def system_capacity_kw(self, size_kw: float): """ self.system_capacity_by_num_devices(size_kw) - #### These are also in Power Source but overwritten here because MhkWave - #### Expects 3-hr timeseries data so values are inflated by 3x - #### TODO: If additional system models are added will need to revise these properties so correct values are assigned @property def annual_energy_kwh(self) -> float: if self.system_capacity_kw > 0: - return self._system_model.value("annual_energy") / 3 + return self._system_model.value("annual_energy") else: return 0 @property def capacity_factor(self) -> float: if self.system_capacity_kw > 0: - return self._system_model.value("capacity_factor") / 3 + return self._system_model.value("capacity_factor") else: return 0 - ### Not in Power Source but affected by hourly data @property def numberHours(self) -> float: if self.system_capacity_kw > 0: - return self._system_model.value("numberHours") / 3 + return self._system_model.value("numberHours") else: return 0 \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 94a822091..7fd416aca 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ requires-python = ">=3.10, <3.12" license = {file = "LICENSE"} dependencies = [ "Cython", - "NREL-PySAM==4.2.0", + "NREL-PySAM>=6.0.0", "Pillow", "Pyomo>=6.1.2", "fastkml<1", diff --git a/tests/hopp/inputs/pysam_simulation_input.yaml b/tests/hopp/inputs/pysam_simulation_input.yaml index afdfc805a..f5710b895 100644 --- a/tests/hopp/inputs/pysam_simulation_input.yaml +++ b/tests/hopp/inputs/pysam_simulation_input.yaml @@ -490,6 +490,4 @@ Losses: wake_int_loss: 0.0 Uncertainty: total_uncert: 12.085 -AdjustmentFactors: - constant: 0.0 Outputs: {} diff --git a/tests/hopp/pvsamv1_basic_params.json b/tests/hopp/pvsamv1_basic_params.json index 3e7710b44..f545deaf9 100644 --- a/tests/hopp/pvsamv1_basic_params.json +++ b/tests/hopp/pvsamv1_basic_params.json @@ -1,76 +1,69 @@ { - "system_capacity" : 50002.22178, - "albedo" : [ 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2 ], - "inverter_count" : 99, - "subarray1_nstrings" : 13435, - "subarray1_modules_per_string" : 12, - "subarray1_tilt" : 0, - "subarray1_azimuth" : 180, - "subarray1_track_mode" : 1, - "subarray1_rotlim" : 45, - "subarray1_shade_mode" : 2, - "subarray1_gcr" : 0.3, - "subarray1_slope_tilt" : 0, - "subarray1_slope_azm" : 0, - "subarray1_soiling" : [ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 ], - "subarray1_mismatch_loss" : 2, + "system_capacity" : 4993.277184, + "albedo" : [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2], + "inverter_count" : 1.0, + "subarray1_nstrings" : 336.0, + "subarray1_modules_per_string" : 28.0, + "subarray1_tilt" : 0.0, + "subarray1_azimuth" : 180.0, + "subarray1_track_mode" : 1.0, + "subarray1_rotlim" : 45.0, + "subarray1_shade_mode" : 0.0, + "subarray1_gcr" : 0.5, + "subarray1_slope_tilt" : 0.0, + "subarray1_slope_azm" : 0.0, + "subarray1_soiling" : [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0], + "subarray1_mismatch_loss" : 2.0, "subarray1_diodeconn_loss" : 0.5, - "subarray1_dcwiring_loss" : 2, - "subarray1_tracking_loss" : 0, - "subarray1_nameplate_loss" : 0, - "dcoptimizer_loss" : 0, - "acwiring_loss" : 1, - "transmission_loss" : 0, - "subarray1_mod_orient" : 0, - "subarray1_nmodx" : 30, - "subarray1_nmody" : 2, - "subarray1_backtrack" : 0, - "subarray2_enable" : 0, - "subarray2_track_mode" : 1, - "subarray3_enable" : 0, - "subarray4_enable" : 0, - "module_model" : 1, - "module_aspect_ratio" : 1.7, - "cec_area" : 1.631, - "cec_a_ref" : 2.57764, - "cec_adjust" : 22.9092, - "cec_alpha_sc" : 0.00373527, - "cec_beta_oc" : -0.175619, - "cec_gamma_r" : -0.386, - "cec_i_l_ref" : 6.05373, - "cec_i_mp_ref" : 5.67, - "cec_i_o_ref" : 8.36043e-11, - "cec_i_sc_ref" : 6.05, - "cec_n_s" : 96, - "cec_r_s" : 0.30812, - "cec_r_sh_ref" : 500.069, - "cec_t_noct" : 46, - "cec_v_mp_ref" : 54.7, - "cec_v_oc_ref" : 64.4, - "cec_temp_corr_mode" : 0, - "cec_is_bifacial" : 0, + "subarray1_dcwiring_loss" : 2.0, + "subarray1_tracking_loss" : 0.0, + "subarray1_nameplate_loss" : 0.0, + "dcoptimizer_loss" : 0.0, + "acwiring_loss" : 1.0, + "transmission_loss" : 0.0, + "subarray1_mod_orient" : 0.0, + "subarray1_nmodx" : 42.0, + "subarray1_nmody" : 2.0, + "subarray1_backtrack" : 0.0, + "module_model" : 1.0, + "module_aspect_ratio" : 2.01, + "cec_area" : 2.58, + "cec_a_ref" : 1.82452, + "cec_adjust" : 0.0529963, + "cec_alpha_sc" : 0.005484, + "cec_beta_oc" : -0.140712, + "cec_i_l_ref" : 13.7267, + "cec_i_mp_ref" : 12.82, + "cec_i_o_ref" : 2.59771e-11, + "cec_i_sc_ref" : 13.71, + "cec_n_s" : 72.0, + "cec_r_s" : 0.16229, + "cec_r_sh_ref" : 133.611, + "cec_t_noct" : 43.1, + "cec_v_mp_ref" : 41.4, + "cec_v_oc_ref" : 49.2, + "cec_temp_corr_mode" : 0.0, + "cec_is_bifacial" : 1.0, "cec_bifacial_transmission_factor" : 0.013, "cec_bifaciality" : 0.7, - "cec_bifacial_ground_clearance_height" : 1, - "cec_standoff" : 6, - "cec_height" : 0, + "cec_bifacial_ground_clearance_height" : 1.7, + "cec_standoff" : 6.0, + "cec_height" : 0.0, "cec_transient_thermal_model_unit_mass" : 11.0919, - "inverter_model" : 0, - "mppt_low_inverter" : 545, - "mppt_hi_inverter" : 820, - "inv_num_mppt" : 1, - "inv_snl_c0" : -1.40704e-08, - "inv_snl_c1" : 6.34566e-06, - "inv_snl_c2" : 0.00155414, - "inv_snl_c3" : -0.000271668, - "inv_snl_paco" : 753200, - "inv_snl_pdco" : 777216, - "inv_snl_pnt" : 122.55, - "inv_snl_pso" : 3714.14, - "inv_snl_vdco" : 615, - "inv_snl_vdcmax" : 820, - "inv_tdc_cec_db" : [ [ 1300, 50, -0.02, 53, -0.47 ] ], - "en_batt" : 0, - "adjust:constant" : 0, - "dc_adjust:constant" : 0 + "inverter_model" : 0.0, + "mppt_low_inverter" : 800.0, + "mppt_hi_inverter" : 1500.0, + "inv_num_mppt" : 1.0, + "inv_snl_c0" : -7.05627e-09, + "inv_snl_c1" : 5.56504e-06, + "inv_snl_c2" : 0.000106673, + "inv_snl_c3" : -0.000143943, + "inv_snl_paco" : 2507190.0, + "inv_snl_pdco" : 2579160.0, + "inv_snl_pnt" : 62.8, + "inv_snl_pso" : 8485.63, + "inv_snl_vdco" : 975.0, + "inv_snl_vdcmax" : 1500.0, + "inv_tdc_cec_db" : [[1500.0, 50.0, -0.02, 53.0, -0.47]], + "en_batt" : 0.0 } diff --git a/tests/hopp/test_csp.py b/tests/hopp/test_csp.py index ce180e4ea..b11bf7db5 100644 --- a/tests/hopp/test_csp.py +++ b/tests/hopp/test_csp.py @@ -348,8 +348,8 @@ def test_trough_annual_financial(site): # Expected values from SAM UI (develop) built 9/24/2021 (default parameters except those in trough_config, weather file, and ppa_soln_mode = 1) # Note results should be close, but won't match exactly because daotk-develop ssc branch is used for performance simulations expected_energy = 180106837 - expected_lcoe_nom = 17.0971 - expected_ppa_nom = 12.347 + expected_lcoe_nom = 17.71 + expected_ppa_nom = 14.513 config = TroughConfig.from_dict(trough_config) csp = TroughPlant(site, config=config) @@ -373,8 +373,8 @@ def test_tower_annual_financial(site): # Note results should be close, but won't match exactly because daotk-develop ssc branch is used for performance simulations expected_Nhel = 6172 expected_energy = 371737920 - expected_lcoe_nom = 12.952 - expected_ppa_nom = 9.0977 + expected_lcoe_nom = 13.28 + expected_ppa_nom = 10.289 config = TowerConfig.from_dict(tower_config) csp = TowerPlant(site, config=config) diff --git a/tests/hopp/test_custom_financial.py b/tests/hopp/test_custom_financial.py index 391e0785a..8be0367f6 100644 --- a/tests/hopp/test_custom_financial.py +++ b/tests/hopp/test_custom_financial.py @@ -46,8 +46,8 @@ def test_custom_financial(): def test_detailed_pv(site, subtests): # Run detailed PV model (pvsamv1) using a custom financial model - annual_energy_expected = 108833068 - npv_expected = -39094449 + annual_energy_expected = 8884369 + npv_expected = -4066396 with open(pvsamv1_defaults_file, 'r') as f: tech_config = json.load(f) @@ -193,23 +193,18 @@ def test_hybrid_simple_pv_with_wind(site, subtests): def test_hybrid_detailed_pv_with_wind(site, subtests): # Test wind + detailed PV (pvsamv1) hybrid plant with custom financial model - annual_energy_expected_pv = 21541876 - annual_energy_expected_wind = 32296230 - annual_energy_expected_hybrid = 53838106 - npv_expected_pv = -7844643 - npv_expected_wind = -11896652 - npv_expected_hybrid = -19733945 + annual_energy_expected_pv = 8863135 + annual_energy_expected_wind = 31453286 + annual_energy_expected_hybrid = 40316422 + npv_expected_pv = -4068134 + npv_expected_wind = -11965644 + npv_expected_hybrid = -16033778 interconnect_kw = 150e6 wind_kw = 10000 with open(pvsamv1_defaults_file, 'r') as f: tech_config = json.load(f) - - # NOTE: PV array shrunk to avoid problem associated with flicker calculation - tech_config['system_capacity'] = 10000 - tech_config['inverter_count'] = 10 - tech_config['subarray1_nstrings'] = 2687 layout_params = { "x_position": 0.5, @@ -263,7 +258,7 @@ def test_hybrid_detailed_pv_with_wind(site, subtests): npvs = hybrid_plant.net_present_values with subtests.test("with minimal params"): - assert sizes.pv == approx(10000, 1e-3) + assert sizes.pv == approx(4993, 1e-3) assert sizes.wind == approx(wind_kw, 1e-3) assert aeps.pv == approx(annual_energy_expected_pv, 1e-3) assert aeps.wind == approx(annual_energy_expected_wind, 1e-3) @@ -279,22 +274,22 @@ def test_hybrid_simple_pv_with_wind_wave_storage_dispatch(subtests): annual_energy_expected_pv = 10761987 annual_energy_expected_wind = 31951719 annual_energy_expected_wave = 12132526 - annual_energy_expected_battery = -98292 + annual_energy_expected_battery = -103752 annual_energy_expected_hybrid = 54747904 npv_expected_pv = -1640023 npv_expected_wind = -5159400 - npv_expected_wave = -50006845 + npv_expected_wave = -62903172 npv_expected_battery = -8183543 - npv_expected_hybrid = -64990137 + npv_expected_hybrid = -77887529 lcoe_expected_pv = 3.104064331441355 lcoe_expected_wind = 3.162940789633178 - lcoe_expected_wave = 28.83013114281512 - lcoe_expected_battery = 13.29435118093791 - lcoe_expected_hybrid = 9.810109326608142 + lcoe_expected_wave = 35.719370712383856 + lcoe_expected_battery = 13.333128855903514 + lcoe_expected_hybrid = 11.337551789830751 - total_installed_cost_expected = 81063378.16191691 + total_installed_cost_expected = 93959704.39847898 interconnect_kw = 20000 pv_kw = 5000 @@ -433,14 +428,14 @@ def test_hybrid_simple_pv_with_wind_wave_storage_dispatch(subtests): def test_hybrid_detailed_pv_with_wind_storage_dispatch(site, subtests): # Test wind + detailed PV (pvsamv1) + storage with dispatch hybrid plant with custom financial model - annual_energy_expected_pv = 20416252 - annual_energy_expected_wind = 32321927 - annual_energy_expected_battery = -91312 - annual_energy_expected_hybrid = 52645082 - npv_expected_pv = -3606490 - npv_expected_wind = -5050712 + annual_energy_expected_pv = 8851251 + annual_energy_expected_wind = 31559803 + annual_energy_expected_battery = -102220 + annual_energy_expected_hybrid = 40308834 + npv_expected_pv = -2194945 + npv_expected_wind = -5274461 npv_expected_battery = -8181700 - npv_expected_hybrid = -16839535 + npv_expected_hybrid = -15654417 interconnect_kw = 15000 wind_kw = 10000 @@ -448,11 +443,6 @@ def test_hybrid_detailed_pv_with_wind_storage_dispatch(site, subtests): with open(pvsamv1_defaults_file, 'r') as f: tech_config = json.load(f) - - # NOTE: PV array shrunk to avoid problem associated with flicker calculation - tech_config['system_capacity'] = 10000 - tech_config['inverter_count'] = 10 - tech_config['subarray1_nstrings'] = 2687 power_sources = { 'pv': { @@ -509,7 +499,7 @@ def test_hybrid_detailed_pv_with_wind_storage_dispatch(site, subtests): aeps = hybrid_plant.annual_energies npvs = hybrid_plant.net_present_values with subtests.test("with minimal params"): - assert sizes.pv == approx(10000, 1e-3) + assert sizes.pv == approx(4993, 1e-3) assert sizes.wind == approx(wind_kw, 1e-3) assert sizes.battery == approx(batt_kw, 1e-3) assert aeps.pv == approx(annual_energy_expected_pv, 1e-3) diff --git a/tests/hopp/test_detailed_pv_plant.py b/tests/hopp/test_detailed_pv_plant.py index 7841b7940..961ce83ec 100644 --- a/tests/hopp/test_detailed_pv_plant.py +++ b/tests/hopp/test_detailed_pv_plant.py @@ -51,19 +51,6 @@ def test_detailed_pv_plant_initialization(site, subtests): assert pv_plant.config is not None -def test_single_subarray_limitation(site): - """Ensure only one subarray is allowed.""" - config_with_multiple_subarrays = { - "system_capacity_kw": 100, - "tech_config": {"subarray2_enable": 1}, - } - config = DetailedPVConfig.from_dict(config_with_multiple_subarrays) - with pytest.raises( - Exception, match=r"Detailed PV plant currently only supports one subarray." - ): - DetailedPVPlant(site=site, config=config) - - def test_processed_assign(site, subtests): """Test more detailed instantiation with `tech_config`.""" pvsamv1_defaults_file = ( diff --git a/tests/hopp/test_dispatch.py b/tests/hopp/test_dispatch.py index 0ca53fec6..69d7eb2e6 100644 --- a/tests/hopp/test_dispatch.py +++ b/tests/hopp/test_dispatch.py @@ -67,7 +67,7 @@ def site(): } def test_solar_dispatch(site): - expected_objective = 34300.55 + expected_objective = 34021.43 dispatch_n_look_ahead = 48 @@ -110,7 +110,7 @@ def create_test_objective_rule(m): # results = HybridDispatchBuilderSolver.xpress_solve_call(model) assert results.solver.termination_condition == TerminationCondition.optimal - assert model.pv[0].cost_per_generation.value == pytest.approx(round(15/8760*1000,6), 1e-3) + assert model.pv[0].cost_per_generation.value == pytest.approx(round(19/8760*1000,6), 1e-3) gen = sum([model.pv[t].generation.value for t in model.forecast_horizon]) assert gen == pytest.approx(588.46, 1e-3) assert pyomo.value(model.test_objective) == pytest.approx(expected_objective, 1e-3) @@ -413,7 +413,7 @@ def create_test_objective_rule(m): assert dispatch_generation[t] * 1e3 == pytest.approx(available_resource[t], 1e-3) def test_wind_dispatch(site): - expected_objective = 19947.1769 + expected_objective = 20071.18 dispatch_n_look_ahead = 48 @@ -660,7 +660,7 @@ def create_test_objective_rule(m): def test_pv_wind_battery_hybrid_dispatch(site): - expected_objective = 49012 + expected_objective = 48837.60 wind_solar_battery = {key: technologies[key] for key in ('pv', 'wind', 'battery', 'grid')} hopp_config = { @@ -761,7 +761,7 @@ def test_hybrid_dispatch_one_cycle_heuristic(site): def test_hybrid_solar_battery_dispatch(site): - expected_objective = 28445 + expected_objective = 28134.87 solar_battery_technologies = {k: technologies[k] for k in ('pv', 'battery', 'grid')} hopp_config = { diff --git a/tests/hopp/test_hybrid.py b/tests/hopp/test_hybrid.py index 50d28d597..f4b9617ec 100644 --- a/tests/hopp/test_hybrid.py +++ b/tests/hopp/test_hybrid.py @@ -329,9 +329,11 @@ def test_hybrid_wave_only(hybrid_config, subtests): assert cf.wave == approx(48.42, 1e-2) with subtests.test("hybrid wave only cf"): assert cf.hybrid == approx(cf.wave) + with subtests.test("wave cost"): + assert hybrid_plant.wave.total_installed_cost == approx(66465112.398478985, 1e-2) with subtests.test("wave npv"): # TODO check/verify this test value somehow, not sure how to do it right now - assert npvs.wave == approx(-53714525.2968821, 5.e-2) + assert npvs.wave == approx(-66610851.533444166, 5.e-2) with subtests.test("hybrid wave only npv"): assert npvs.hybrid == approx(npvs.wave) @@ -405,9 +407,9 @@ def test_hybrid_wind_only(hybrid_config, subtests): with subtests.test("hybrid aep"): assert aeps.hybrid == approx(31977778, 1e-3) with subtests.test("wind npv"): - assert npvs.wind == approx(-7256658, 1e-3) + assert npvs.wind == approx(-6068047, 1e-3) with subtests.test("hybrid npv"): - assert npvs.hybrid == approx(-7256658, 1e-3) + assert npvs.hybrid == approx(-6068047, 1e-3) def test_hybrid_wind_only_floris(hybrid_config, subtests): @@ -436,9 +438,9 @@ def test_hybrid_wind_only_floris(hybrid_config, subtests): with subtests.test("hybrid aep"): assert aeps.hybrid == approx(68271657, 1e-3) with subtests.test("wind npv"): - assert npvs.wind == approx(3592293, 1e-3) + assert npvs.wind == approx(5999302, 1e-3) with subtests.test("hybrid npv"): - assert npvs.hybrid == approx(2108687, 1e-3) + assert npvs.hybrid == approx(4345865, 1e-3) def test_hybrid_pv_only(hybrid_config, subtests): technologies = hybrid_config["technologies"] @@ -631,8 +633,8 @@ def test_detailed_pv_system_capacity(hybrid_config, subtests): with subtests.test( "Detailed PV model (pvsamv1) using defaults except the top level system_capacity_kw parameter" ): - annual_energy_expected = 11128604 - npv_expected = -2436229 + annual_energy_expected = 8873966 + npv_expected = -1818194 technologies = hybrid_config["technologies"] solar_only = deepcopy( {key: technologies[key] for key in ("pv", "grid")} @@ -644,7 +646,7 @@ def test_detailed_pv_system_capacity(hybrid_config, subtests): hybrid_config["technologies"] = solar_only hi = HoppInterface(hybrid_config) hybrid_plant = hi.system - assert hybrid_plant.pv.value("subarray1_nstrings") == 1343 + assert hybrid_plant.pv.value("subarray1_nstrings") == 336 hybrid_plant.layout.plot() hi.simulate() @@ -671,16 +673,10 @@ def test_detailed_pv_system_capacity(hybrid_config, subtests): solar_only["pv"]["tech_config"] = tech_config # specify parameters solar_only["grid"]["interconnect_kw"] = 150e3 hybrid_config["technologies"] = solar_only - with raises(Exception) as context: - hi = HoppInterface(hybrid_config) - assert ( - "The specified system capacity of 5000 kW is more than 5% from the value calculated" - in str(context.value) - ) # Run detailed PV model (pvsamv1) using file parameters, minus the number of strings, and the top level system_capacity_kw parameter - annual_energy_expected = 8955045 - npv_expected = -2622684 + annual_energy_expected = 8873966 + npv_expected = -1818194 pvsamv1_defaults_file = ( Path(__file__).absolute().parent / "pvsamv1_basic_params.json" ) @@ -696,7 +692,7 @@ def test_detailed_pv_system_capacity(hybrid_config, subtests): hybrid_config["technologies"] = solar_only hi = HoppInterface(hybrid_config) hybrid_plant = hi.system - assert hybrid_plant.pv.value("subarray1_nstrings") == 1343 + assert hybrid_plant.pv.value("subarray1_nstrings") == 336 hybrid_plant.layout.plot() hi.simulate() @@ -710,7 +706,7 @@ def test_detailed_pv_system_capacity(hybrid_config, subtests): def test_hybrid_detailed_pv_only(site, hybrid_config, subtests): with subtests.test("standalone detailed PV model (pvsamv1) using defaults"): - annual_energy_expected = 11128604 + annual_energy_expected = 8873966 config = DetailedPVConfig.from_dict(detailed_pv) pv_plant = DetailedPVPlant(site=site, config=config) assert pv_plant.system_capacity_kw == approx(pv_kw, 1e-2) @@ -719,11 +715,11 @@ def test_hybrid_detailed_pv_only(site, hybrid_config, subtests): assert pv_plant._system_model.Outputs.annual_energy == approx( annual_energy_expected, 1e-2 ) - assert pv_plant._system_model.Outputs.capacity_factor == approx(25.66, 1e-2) + assert pv_plant._system_model.Outputs.capacity_factor == approx(20.29, 1e-2) with subtests.test("detailed PV model (pvsamv1) using defaults"): technologies = hybrid_config["technologies"] - npv_expected = -2436229 + npv_expected = -1818194 solar_only = {"pv": detailed_pv, "grid": technologies["grid"]} solar_only["pv"][ "use_pvwatts" @@ -744,8 +740,8 @@ def test_hybrid_detailed_pv_only(site, hybrid_config, subtests): assert npvs.hybrid == approx(npv_expected, 1e-3) with subtests.test("Detailed PV model (pvsamv1) using parameters from file"): - annual_energy_expected = 102997528 - npv_expected = -25049424 + annual_energy_expected = 8873966 + npv_expected = -1818194 pvsamv1_defaults_file = ( Path(__file__).absolute().parent / "pvsamv1_basic_params.json" ) @@ -755,7 +751,7 @@ def test_hybrid_detailed_pv_only(site, hybrid_config, subtests): solar_only["pv"]["use_pvwatts"] = False # specify detailed PV model solar_only["pv"]["tech_config"] = tech_config # specify parameters solar_only["grid"]["interconnect_kw"] = 150e3 - solar_only["pv"]["system_capacity_kw"] = 50000 # use another system capacity + solar_only["pv"]["system_capacity_kw"] = 4993 # use another system capacity hybrid_config["technologies"] = solar_only hi = HoppInterface(hybrid_config) hybrid_plant = hi.system @@ -793,8 +789,8 @@ def test_hybrid_detailed_pv_only(site, hybrid_config, subtests): with subtests.test( "Detailed PV model using parameters from file and autosizing electrical parameters" ): - annual_energy_expected = 102319358 - npv_expected = -25110524 + annual_energy_expected = 8873966 + npv_expected = -1818194 pvsamv1_defaults_file = ( Path(__file__).absolute().parent / "pvsamv1_basic_params.json" ) @@ -826,10 +822,10 @@ def test_hybrid_detailed_pv_only(site, hybrid_config, subtests): n_inputs_combiner=32, ) ) - assert n_strings == 13435 - assert n_combiners == 420 - assert n_inverters == 50 - assert calculated_system_capacity == approx(50002.2, 1e-3) + assert n_strings == 336 + assert n_combiners == 11 + assert n_inverters == 1 + assert calculated_system_capacity == approx(4993.2, 1e-3) solar_only["pv"]["tech_config"]["subarray1_nstrings"] = n_strings solar_only["pv"]["tech_config"]["inverter_count"] = n_inverters solar_only["pv"]["tech_config"]["system_capacity"] = calculated_system_capacity @@ -843,7 +839,7 @@ def test_hybrid_detailed_pv_only(site, hybrid_config, subtests): aeps = hybrid_plant.annual_energies npvs = hybrid_plant.net_present_values - assert hybrid_plant.pv.system_capacity_kw == approx(50002.2, 1e-2) + assert hybrid_plant.pv.system_capacity_kw == approx(4993.2, 1e-2) assert aeps.pv == approx(annual_energy_expected, 1e-3) assert aeps.hybrid == approx(annual_energy_expected, 1e-3) assert npvs.pv == approx(npv_expected, 1e-3) @@ -852,8 +848,8 @@ def test_hybrid_detailed_pv_only(site, hybrid_config, subtests): def test_hybrid_user_instantiated(site, subtests): # Run detailed PV model (pvsamv1) using defaults and user-instantiated financial models - annual_energy_expected = 11128604 - npv_expected = -2436229 + annual_energy_expected = 8873966 + npv_expected = -1818194 system_capacity_kw = 5000 system_capacity_kw_expected = 4998 interconnect_kw = 150e3 @@ -903,7 +899,7 @@ def test_hybrid_user_instantiated(site, subtests): }, "grid": { "interconnect_kw": interconnect_kw, - "fin_model": "GenericSystemSingleOwner", + "fin_model": "CustomGenerationProfileSingleOwner", "ppa_price": 0.01, }, } @@ -990,22 +986,22 @@ def test_wind_pv_with_storage_dispatch(hybrid_config,subtests): assert aeps.hybrid == approx(43489117, rel=0.05) with subtests.test("pv npv"): - assert npvs.pv == approx(-507296, rel=5e-2) + assert npvs.pv == approx(546682.31, rel=5e-2) with subtests.test("wind npv"): - assert npvs.wind == approx(-2573090, rel=5e-2) + assert npvs.wind == approx(-1385231.71, rel=5e-2) with subtests.test("battery npv"): assert npvs.battery == approx(-4871034, rel=5e-2) with subtests.test("hybrid npv"): - assert npvs.hybrid == approx(-8254104, rel=5e-2) + assert npvs.hybrid == approx(-5664495.73, rel=5e-2) with subtests.test("pv taxes"): - assert taxes.pv[1] == approx(86124, rel=5e-2) + assert taxes.pv[1] == approx(115320.51, rel=5e-2) with subtests.test("wind taxes"): - assert taxes.wind[1] == approx(413068, rel=5e-2) + assert taxes.wind[1] == approx(419276.09, rel=5e-2) with subtests.test("battery taxes"): assert taxes.battery[1] == approx(248373, rel=5e-2) with subtests.test("hybrid taxes"): - assert taxes.hybrid[1] == approx(760211, rel=5e-2) + assert taxes.hybrid[1] == approx(783576.67, rel=5e-2) with subtests.test("pv apv"): assert apv.pv[1] == approx(0, rel=5e-2) @@ -1035,13 +1031,13 @@ def test_wind_pv_with_storage_dispatch(hybrid_config,subtests): assert esv.hybrid[1] == approx(42058135, rel=5e-2) with subtests.test("pv depr"): - assert depr.pv[1] == approx(745532, rel=5e-2) + assert depr.pv[1] == approx(875121.61, rel=5e-2) with subtests.test("wind depr"): - assert depr.wind[1] == approx(2651114, rel=5e-2) + assert depr.wind[1] == approx(2651114.55, rel=5e-2) with subtests.test("battery depr"): assert depr.battery[1] == approx(1266736, rel=5e-2) with subtests.test("hybrid depr"): - assert depr.hybrid[1] == approx(4663383, rel=5e-2) + assert depr.hybrid[1] == approx(4792972.69, rel=5e-2) with subtests.test("pv insr"): assert insr.pv[0] == approx(0, rel=5e-2) @@ -1053,9 +1049,9 @@ def test_wind_pv_with_storage_dispatch(hybrid_config,subtests): assert insr.hybrid[0] == approx(0, rel=5e-2) with subtests.test("pv om"): - assert om.pv[1] == approx(74993, rel=5e-2) + assert om.pv[1] == approx(94991.92, rel=5e-2) with subtests.test("wind om"): - assert om.wind[1] == approx(430000, rel=5e-2) + assert om.wind[1] == approx(400000.0, rel=5e-2) with subtests.test("battery om"): assert om.battery[1] == approx(75000, rel=5e-2) with subtests.test("hybrid om"): @@ -1071,13 +1067,13 @@ def test_wind_pv_with_storage_dispatch(hybrid_config,subtests): assert rev.hybrid[1] == approx(1334802, rel=5e-2) with subtests.test("pv tc"): - assert tc.pv[1] == approx(1295889, rel=5e-2) + assert tc.pv[1] == approx(322913.40, rel=5e-2) with subtests.test("wind tc"): - assert tc.wind[1] == approx(830744, rel=5e-2) + assert tc.wind[1] == approx(958551.59, rel=5e-2) with subtests.test("battery tc"): assert tc.battery[1] == approx(2201850, rel=5e-2) with subtests.test("hybrid tc"): - assert tc.hybrid[1] == approx(4338902, rel=5e-2) + assert tc.hybrid[1] == approx(3491000.32, rel=5e-2) def test_tower_pv_hybrid(hybrid_config): @@ -1344,7 +1340,7 @@ def test_hybrid_tax_incentives(hybrid_config): ) -def test_capacity_credit(hybrid_config): +def test_capacity_credit(hybrid_config,subtests): technologies = hybrid_config["technologies"] site = create_default_site_info(capacity_hours=capacity_credit_hours) wind_pv_battery = {key: technologies[key] for key in ("pv", "wind", "battery")} @@ -1440,8 +1436,8 @@ def reinstate_orig_values(): capcred = hybrid_plant.capacity_credit_percent assert capcred["pv"][0] == approx(6.85, rel=0.05) assert capcred["wind"][0] == approx(33.25, rel=0.10) - assert capcred["battery"][0] == approx(58.95, rel=0.05) - assert capcred["hybrid"][0] == approx(43.88, rel=0.05) + assert capcred["battery"][0] == approx(55.77, rel=0.05) + assert capcred["hybrid"][0] == approx(40.80, rel=0.05) cp_pay = hybrid_plant.capacity_payments np_cap = ( @@ -1482,60 +1478,104 @@ def reinstate_orig_values(): print("REV", [rev.pv[1], rev.wind[1], rev.battery[1], rev.hybrid[1]]) print("TC", [tc.pv[1], tc.wind[1], tc.battery[1], tc.hybrid[1]]) - assert aeps.pv == approx(10761987, rel=0.05) - assert aeps.wind == approx(31951719, rel=0.05) - assert aeps.battery == approx(-97166, rel=0.05) - assert aeps.hybrid == approx(43489117, rel=0.05) - - assert npvs.pv == approx(-253177, rel=5e-2) - assert npvs.wind == approx(-369348, rel=5e-2) - assert npvs.battery == approx(-2700460, rel=5e-2) - assert npvs.hybrid == approx(-1982008.05, rel=5e-2) - - assert taxes.pv[1] == approx(79229.26, rel=5e-2) - assert taxes.wind[1] == approx(365206, rel=5e-2) - assert taxes.battery[1] == approx(189346, rel=5e-2) - assert taxes.hybrid[1] == approx(598426, rel=5e-2) - - assert apv.pv[1] == approx(0, rel=5e-2) - assert apv.wind[1] == approx(0, rel=5e-2) - assert apv.battery[1] == approx(-4070354, rel=5e-2) - assert apv.hybrid[1] == approx(-348443, rel=5e-2) - - assert debt.pv[1] == approx(0, rel=5e-2) - assert debt.wind[1] == approx(0, rel=5e-2) - assert debt.battery[1] == approx(0, rel=5e-2) - assert debt.hybrid[1] == approx(0, rel=5e-2) - - assert esv.pv[1] == approx(10761986, rel=5e-2) - assert esv.wind[1] == approx(31951719, rel=5e-2) - assert esv.battery[1] == approx(3973442, rel=5e-2) - assert esv.hybrid[1] == approx(42058135, rel=5e-2) - - assert depr.pv[1] == approx(745532, rel=5e-2) - assert depr.wind[1] == approx(2651114, rel=5e-2) - assert depr.battery[1] == approx(1266736, rel=5e-2) - assert depr.hybrid[1] == approx(4663383, rel=5e-2) - - assert insr.pv[0] == approx(0, rel=5e-2) - assert insr.wind[0] == approx(0, rel=5e-2) - assert insr.battery[0] == approx(0, rel=5e-2) - assert insr.hybrid[0] == approx(0, rel=5e-2) - - assert om.pv[1] == approx(74993, rel=5e-2) - assert om.wind[1] == approx(430000, rel=5e-2) - assert om.battery[1] == approx(75000, rel=5e-2) - assert om.hybrid[1] == approx(579993, rel=5e-2) - - assert rev.pv[1] == approx(413803, rel=5e-2) - assert rev.wind[1] == approx(1211138, rel=5e-2) - assert rev.battery[1] == approx(470175, rel=5e-2) - assert rev.hybrid[1] == approx(2187556, rel=5e-2) - - assert tc.pv[1] == approx(1295889, rel=5e-2) - assert tc.wind[1] == approx(830744, rel=5e-2) - assert tc.battery[1] == approx(2201850, rel=5e-2) - assert tc.hybrid[1] == approx(4338902, rel=5e-2) + with subtests.test("pv aeps"): + assert aeps.pv == approx(10761987, rel=0.05) + with subtests.test("wind aeps"): + assert aeps.wind == approx(31951719, rel=0.05) + with subtests.test("battery aeps"): + assert aeps.battery == approx(-97166, rel=0.05) + with subtests.test("hybrid aeps"): + assert aeps.hybrid == approx(43489117, rel=0.05) + + with subtests.test("pv npvs"): + assert npvs.pv == approx(792671, rel=5e-2) + with subtests.test("wind npvs"): + assert npvs.wind == approx(818510, rel=5e-2) + with subtests.test("battery npvs"): + assert npvs.battery == approx(-2895459, rel=5e-2) + with subtests.test("hybrid npvs"): + assert npvs.hybrid == approx(210321, rel=5e-2) + + with subtests.test("pv taxes"): + assert taxes.pv[1] == approx(108631, rel=5e-2) + with subtests.test("wind taxes"): + assert taxes.wind[1] == approx(365206, rel=5e-2) + with subtests.test("battery taxes"): + assert taxes.battery[1] == approx(189346, rel=5e-2) + with subtests.test("hybrid taxes"): + assert taxes.hybrid[1] == approx(598426, rel=5e-2) + + with subtests.test("pv apv"): + assert apv.pv[1] == approx(0, rel=5e-2) + with subtests.test("wind apv"): + assert apv.wind[1] == approx(0, rel=5e-2) + with subtests.test("battery apv"): + assert apv.battery[1] == approx(-4070354, rel=5e-2) + with subtests.test("hybrid apv"): + assert apv.hybrid[1] == approx(-348443, rel=5e-2) + + with subtests.test("pv debt"): + assert debt.pv[1] == approx(0, rel=5e-2) + with subtests.test("wind debt"): + assert debt.wind[1] == approx(0, rel=5e-2) + with subtests.test("battery debt"): + assert debt.battery[1] == approx(0, rel=5e-2) + with subtests.test("hybrid debt"): + assert debt.hybrid[1] == approx(0, rel=5e-2) + + with subtests.test("pv esv"): + assert esv.pv[1] == approx(10761986, rel=5e-2) + with subtests.test("wind esv"): + assert esv.wind[1] == approx(31951719, rel=5e-2) + with subtests.test("battery esv"): + assert esv.battery[1] == approx(3973442, rel=5e-2) + with subtests.test("hybrid esv"): + assert esv.hybrid[1] == approx(42058135, rel=5e-2) + + with subtests.test("pv depr"): + assert depr.pv[1] == approx(875122, rel=5e-2) + with subtests.test("wind depr"): + assert depr.wind[1] == approx(2651114, rel=5e-2) + with subtests.test("battery depr"): + assert depr.battery[1] == approx(1266736, rel=5e-2) + with subtests.test("hybrid depr"): + assert depr.hybrid[1] == approx(4663383, rel=5e-2) + + with subtests.test("pv insr"): + assert insr.pv[0] == approx(0, rel=5e-2) + with subtests.test("wind insr"): + assert insr.wind[0] == approx(0, rel=5e-2) + with subtests.test("battery insr"): + assert insr.battery[0] == approx(0, rel=5e-2) + with subtests.test("hybrid insr"): + assert insr.hybrid[0] == approx(0, rel=5e-2) + + with subtests.test("pv om"): + assert om.pv[1] == approx(94992, rel=5e-2) + with subtests.test("wind om"): + assert om.wind[1] == approx(400000, rel=5e-2) + with subtests.test("battery om"): + assert om.battery[1] == approx(75000, rel=5e-2) + with subtests.test("hybrid om"): + assert om.hybrid[1] == approx(579993, rel=5e-2) + + with subtests.test("pv rev"): + assert rev.pv[1] == approx(413803, rel=5e-2) + with subtests.test("wind rev"): + assert rev.wind[1] == approx(1211138, rel=5e-2) + with subtests.test("battery rev"): + assert rev.battery[1] == approx(446518, rel=5e-2) + with subtests.test("hybrid rev"): + assert rev.hybrid[1] == approx(2187556, rel=5e-2) + + with subtests.test("pv tc"): + assert tc.pv[1] == approx(322913, rel=5e-2) + with subtests.test("wind tc"): + assert tc.wind[1] == approx(958551, rel=5e-2) + with subtests.test("battery tc"): + assert tc.battery[1] == approx(2201850, rel=5e-2) + with subtests.test("hybrid tc"): + assert tc.hybrid[1] == approx(3491000, rel=5e-2) def test_hybrid_financials(hybrid_config, subtests): """ @@ -1554,7 +1594,7 @@ def test_hybrid_financials(hybrid_config, subtests): with subtests.test("pv om_production"): assert hi.system.pv._financial_model.SystemCosts.om_production == hi.system.pv.om_production with subtests.test("pv om total"): - assert hi.system.om_total_expenses['pv'][1] == approx(248536, rel=5e-2) + assert hi.system.om_total_expenses['pv'][1] == approx(257625.05, rel=5e-2) with subtests.test("wind om total"): - assert hi.system.om_total_expenses['wind'][1] == approx(493903.4397049556, rel=5e-2) + assert hi.system.om_total_expenses['wind'][1] == approx(463903.43, rel=5e-2) diff --git a/tests/hopp/test_layout.py b/tests/hopp/test_layout.py index b0d531928..64163a23c 100644 --- a/tests/hopp/test_layout.py +++ b/tests/hopp/test_layout.py @@ -260,14 +260,14 @@ def test_system_electrical_sizing(site): def test_detailed_pv_properties(site): - SYSTEM_CAPACITY_DEFAULT = 50002.22178 - SUBARRAY1_NSTRINGS_DEFAULT = 13435 - SUBARRAY1_MODULES_PER_STRING_DEFAULT = 12 - INVERTER_COUNT_DEFAULT = 99 - CEC_V_MP_REF_DEFAULT = 54.7 - CEC_I_MP_REF_DEFAULT = 5.67 - INV_SNL_PACO_DEFAULT = 753200 - DC_AC_RATIO_DEFAULT = 0.67057 + SYSTEM_CAPACITY_DEFAULT = 4993.277184 + SUBARRAY1_NSTRINGS_DEFAULT = 336.0 + SUBARRAY1_MODULES_PER_STRING_DEFAULT = 28.0 + INVERTER_COUNT_DEFAULT = 1.0 + CEC_V_MP_REF_DEFAULT = 41.4 + CEC_I_MP_REF_DEFAULT = 12.82 + INV_SNL_PACO_DEFAULT = 2507190.0 + DC_AC_RATIO_DEFAULT = 1.9915 pvsamv1_defaults_file = Path(__file__).absolute().parent.parent / "hopp/pvsamv1_basic_params.json" with open(pvsamv1_defaults_file, 'r') as f: @@ -303,30 +303,30 @@ def verify_defaults(): # Modify system capacity and check that values update correctly detailed_pvplant.value('system_capacity', 20000) - assert detailed_pvplant.value('system_capacity') == approx(20000.889, 1e-6) - assert detailed_pvplant.value('subarray1_nstrings') == 5374 + assert detailed_pvplant.value('system_capacity') == approx(20002.8306, 1e-6) + assert detailed_pvplant.value('subarray1_nstrings') == 1346.0 assert detailed_pvplant.value('subarray1_modules_per_string') == SUBARRAY1_MODULES_PER_STRING_DEFAULT - assert detailed_pvplant.value('inverter_count') == 40 + assert detailed_pvplant.value('inverter_count') == 4.0 assert detailed_pvplant.value('cec_v_mp_ref') == approx(CEC_V_MP_REF_DEFAULT, 1e-3) assert detailed_pvplant.value('cec_i_mp_ref') == approx(CEC_I_MP_REF_DEFAULT, 1e-3) assert detailed_pvplant.value('inv_snl_paco') == approx(INV_SNL_PACO_DEFAULT, 1e-3) # The dc_ac_ratio changes because the inverter_count is a function of the system capacity, and it is rounded to an integer. # Changes to the inverter count do not influence the system capacity, therefore the dc_ac_ratio does not adjust back to the original value - assert detailed_pvplant.dc_ac_ratio == approx(0.6639, 1e-3) + assert detailed_pvplant.dc_ac_ratio == approx(1.994, 1e-3) # Reset system capacity back to the default value to verify values update correctly detailed_pvplant.value('system_capacity', SYSTEM_CAPACITY_DEFAULT) # The dc_ac_ratio is not noticeably affected because the inverter_count, calculated from the prior dc_ac_ratio, barely changed when rounded - assert detailed_pvplant.dc_ac_ratio == approx(0.6639, 1e-3) + assert detailed_pvplant.dc_ac_ratio == approx(1.991, 1e-3) assert detailed_pvplant.value('system_capacity') == approx(SYSTEM_CAPACITY_DEFAULT, 1e-3) assert detailed_pvplant.value('subarray1_nstrings') == SUBARRAY1_NSTRINGS_DEFAULT assert detailed_pvplant.value('subarray1_modules_per_string') == SUBARRAY1_MODULES_PER_STRING_DEFAULT # The inverter count did not change back to the default value because the dc_ac_ratio did not change back to the default value, # and unlike the UI, there is no 'desired' dc_ac_ratio that is used to calculate the inverter count, only the prior dc_ac_ratio - assert detailed_pvplant.value('inverter_count') == INVERTER_COUNT_DEFAULT + 1 + assert detailed_pvplant.value('inverter_count') == INVERTER_COUNT_DEFAULT assert detailed_pvplant.value('cec_v_mp_ref') == approx(CEC_V_MP_REF_DEFAULT, 1e-3) assert detailed_pvplant.value('cec_i_mp_ref') == approx(CEC_I_MP_REF_DEFAULT, 1e-3) assert detailed_pvplant.value('inv_snl_paco') == approx(INV_SNL_PACO_DEFAULT, 1e-3) - assert detailed_pvplant.dc_ac_ratio == approx(0.664, 1e-3) + assert detailed_pvplant.dc_ac_ratio == approx(1.991, 1e-3) # Reinstantiate (reset) the detailed PV plant detailed_pvplant = DetailedPVPlant( @@ -336,14 +336,14 @@ def verify_defaults(): # Modify the number of strings and verify that values update correctly detailed_pvplant.value('subarray1_nstrings', 10000) - assert detailed_pvplant.value('system_capacity') == approx(37217.88, 1e-3) + assert detailed_pvplant.value('system_capacity') == approx(148609, 1e-3) assert detailed_pvplant.value('subarray1_nstrings') == 10000 assert detailed_pvplant.value('subarray1_modules_per_string') == SUBARRAY1_MODULES_PER_STRING_DEFAULT assert detailed_pvplant.value('inverter_count') == INVERTER_COUNT_DEFAULT assert detailed_pvplant.value('cec_v_mp_ref') == approx(CEC_V_MP_REF_DEFAULT, 1e-3) assert detailed_pvplant.value('cec_i_mp_ref') == approx(CEC_I_MP_REF_DEFAULT, 1e-3) assert detailed_pvplant.value('inv_snl_paco') == approx(INV_SNL_PACO_DEFAULT, 1e-3) - assert detailed_pvplant.dc_ac_ratio == approx(0.499, 1e-3) + assert detailed_pvplant.dc_ac_ratio == approx(59.27, 1e-3) # Reset the number of strings back to the default value to verify other values reset back to their defaults detailed_pvplant.value('subarray1_nstrings', SUBARRAY1_NSTRINGS_DEFAULT) verify_defaults() @@ -356,14 +356,14 @@ def verify_defaults(): # Modify the modules per string and verify that values update correctly detailed_pvplant.value('subarray1_modules_per_string', 10) - assert detailed_pvplant.value('system_capacity') == approx(41668.52, 1e-3) + assert detailed_pvplant.value('system_capacity') == approx(1783.31, 1e-3) assert detailed_pvplant.value('subarray1_nstrings') == SUBARRAY1_NSTRINGS_DEFAULT assert detailed_pvplant.value('subarray1_modules_per_string') == 10 assert detailed_pvplant.value('inverter_count') == INVERTER_COUNT_DEFAULT assert detailed_pvplant.value('cec_v_mp_ref') == approx(CEC_V_MP_REF_DEFAULT, 1e-3) assert detailed_pvplant.value('cec_i_mp_ref') == approx(CEC_I_MP_REF_DEFAULT, 1e-3) assert detailed_pvplant.value('inv_snl_paco') == approx(INV_SNL_PACO_DEFAULT, 1e-3) - assert detailed_pvplant.dc_ac_ratio == approx(0.559, 1e-3) + assert detailed_pvplant.dc_ac_ratio == approx(0.7112, 1e-3) # Reset the modules per string back to the default value to verify other values reset back to their defaults detailed_pvplant.value('subarray1_modules_per_string', SUBARRAY1_MODULES_PER_STRING_DEFAULT) verify_defaults() @@ -385,7 +385,6 @@ def verify_defaults(): 'cec_adjust': 13.0949, 'cec_alpha_sc': 0.0020822, 'cec_beta_oc': -0.134854, - 'cec_gamma_r': -0.3904, 'cec_i_l_ref': 5.81, 'cec_i_mp_ref': 5.4, 'cec_i_o_ref': 3.698e-11, @@ -406,14 +405,14 @@ def verify_defaults(): 'cec_transient_thermal_model_unit_mass': 0, } detailed_pvplant.set_pv_module(module_params) - assert detailed_pvplant.value('system_capacity') == approx(34649.402, 1e-3) + assert detailed_pvplant.value('system_capacity') == approx(2021.96, 1e-3) assert detailed_pvplant.value('subarray1_nstrings') == SUBARRAY1_NSTRINGS_DEFAULT assert detailed_pvplant.value('subarray1_modules_per_string') == SUBARRAY1_MODULES_PER_STRING_DEFAULT assert detailed_pvplant.value('inverter_count') == INVERTER_COUNT_DEFAULT assert detailed_pvplant.value('cec_v_mp_ref') == approx(module_params['cec_v_mp_ref'], 1e-3) assert detailed_pvplant.value('cec_i_mp_ref') == approx(module_params['cec_i_mp_ref'], 1e-3) assert detailed_pvplant.value('inv_snl_paco') == approx(INV_SNL_PACO_DEFAULT, 1e-3) - assert detailed_pvplant.dc_ac_ratio == approx(0.465, 1e-3) + assert detailed_pvplant.dc_ac_ratio == approx(0.806, 1e-3) # Reset the PV module back to the default module to verify other values reset back to their defaults detailed_pvplant.set_pv_module(default_pv_module) verify_defaults() @@ -452,7 +451,7 @@ def verify_defaults(): assert detailed_pvplant.value('cec_v_mp_ref') == approx(CEC_V_MP_REF_DEFAULT, 1e-3) assert detailed_pvplant.value('cec_i_mp_ref') == approx(CEC_I_MP_REF_DEFAULT, 1e-3) assert detailed_pvplant.value('inv_snl_paco') == approx(507000, 1e-3) - assert detailed_pvplant.dc_ac_ratio == approx(0.996, 1e-3) + assert detailed_pvplant.dc_ac_ratio == approx(9.84, 1e-3) # Reset the inverter back to the default inverter to verify other values reset back to their defaults detailed_pvplant.set_inverter(default_inverter) verify_defaults() @@ -465,7 +464,7 @@ def test_detailed_pv_plant_custom_design(site): # Modify the inputs for a custom design target_solar_kw = 3e5 - target_dc_ac_ratio = 1.34 + target_dc_ac_ratio = 1.3444 modules_per_string = 12 module_power = tech_config['cec_v_mp_ref'] * tech_config['cec_i_mp_ref'] * 1e-3 # [kW] inverter_power = tech_config['inv_snl_paco'] * 1e-3 # [kW] @@ -495,13 +494,12 @@ def test_detailed_pv_plant_custom_design(site): ) assert detailed_pvplant.system_capacity == pytest.approx(calculated_system_capacity, 1e-3) - assert detailed_pvplant.dc_ac_ratio == pytest.approx(1.341, 1e-3) + assert detailed_pvplant.dc_ac_ratio == pytest.approx(1.3444, 1e-3) detailed_pvplant.simulate(target_solar_kw) - assert detailed_pvplant._system_model.Outputs.annual_ac_inv_clip_loss_percent < 1.3 + assert detailed_pvplant._system_model.Outputs.annual_ac_inv_clip_loss_percent < 1.31 assert detailed_pvplant._system_model.Outputs.annual_ac_inv_eff_loss_percent < 3 - assert detailed_pvplant._system_model.Outputs.annual_ac_gross / detailed_pvplant._system_model.Outputs.annual_dc_gross > 0.91 def test_detailed_pv_plant_modify_after_init(site): @@ -517,27 +515,27 @@ def test_detailed_pv_plant_modify_after_init(site): ) assert detailed_pvplant.system_capacity == pytest.approx(tech_config['system_capacity'], 1e-3) - assert detailed_pvplant.dc_ac_ratio == pytest.approx(0.671, 1e-3) + assert detailed_pvplant.dc_ac_ratio == pytest.approx(1.99, 1e-3) detailed_pvplant.simulate(5e5) - assert detailed_pvplant._system_model.Outputs.annual_ac_inv_clip_loss_percent < 1.2 - assert detailed_pvplant._system_model.Outputs.annual_ac_inv_eff_loss_percent < 3 - assert detailed_pvplant._system_model.Outputs.annual_ac_gross / detailed_pvplant._system_model.Outputs.annual_dc_gross > 0.91 - assert detailed_pvplant.annual_energy_kwh * 1e-6 == pytest.approx(108.239, abs=10) + assert detailed_pvplant._system_model.Outputs.annual_ac_inv_clip_loss_percent < 23.4 + assert detailed_pvplant._system_model.Outputs.annual_ac_inv_eff_loss_percent < 4.49 + assert detailed_pvplant._system_model.Outputs.annual_ac_gross / detailed_pvplant._system_model.Outputs.annual_dc_gross > 0.7 + assert detailed_pvplant.annual_energy_kwh * 1e-6 == pytest.approx(8.87, abs=10) # modify dc ac ratio - detailed_pvplant.dc_ac_ratio = 1.341 + detailed_pvplant.dc_ac_ratio = 1.3444 detailed_pvplant.simulate(5e5) - assert detailed_pvplant._system_model.Outputs.annual_ac_inv_clip_loss_percent < 1.2 - assert detailed_pvplant._system_model.Outputs.annual_ac_inv_eff_loss_percent < 3 - assert detailed_pvplant._system_model.Outputs.annual_ac_gross / detailed_pvplant._system_model.Outputs.annual_dc_gross > 0.91 - assert detailed_pvplant.annual_energy_kwh * 1e-6 == pytest.approx(107.502, abs=10) + assert detailed_pvplant._system_model.Outputs.annual_ac_inv_clip_loss_percent < 23.4 + assert detailed_pvplant._system_model.Outputs.annual_ac_inv_eff_loss_percent < 4.49 + assert detailed_pvplant._system_model.Outputs.annual_ac_gross / detailed_pvplant._system_model.Outputs.annual_dc_gross > 0.7 + assert detailed_pvplant.annual_energy_kwh * 1e-6 == pytest.approx(8.87, abs=10) # modify system capacity detailed_pvplant.system_capacity_kw *= 2 detailed_pvplant.simulate(5e5) - assert detailed_pvplant._system_model.Outputs.annual_ac_inv_clip_loss_percent < 1.2 - assert detailed_pvplant._system_model.Outputs.annual_ac_inv_eff_loss_percent < 3 - assert detailed_pvplant._system_model.Outputs.annual_ac_gross / detailed_pvplant._system_model.Outputs.annual_dc_gross > 0.91 - assert detailed_pvplant.annual_energy_kwh * 1e-6 == pytest.approx(215.0, abs=10) + assert detailed_pvplant._system_model.Outputs.annual_ac_inv_clip_loss_percent < 23.4 + assert detailed_pvplant._system_model.Outputs.annual_ac_inv_eff_loss_percent < 4.9 + assert detailed_pvplant._system_model.Outputs.annual_ac_gross / detailed_pvplant._system_model.Outputs.annual_dc_gross > 0.7 + assert detailed_pvplant.annual_energy_kwh * 1e-6 == pytest.approx(17.74, abs=10) diff --git a/tests/hopp/test_reopt.py b/tests/hopp/test_reopt.py index 52b2b8a87..4e2aec5b2 100644 --- a/tests/hopp/test_reopt.py +++ b/tests/hopp/test_reopt.py @@ -38,7 +38,7 @@ def test_ReOPT(): wind_model = WindPlant(site, config=wind_config) wind_model._system_model.Resource.wind_resource_filename = os.path.join( "data", "39.7555_-105.2211_windtoolkit_2012_60min_60m.srw") - fin_model = so.default("GenericSystemSingleOwner") + fin_model = so.default("CustomGenerationProfileSingleOwner") fileout = os.path.join(filepath, "REoptResultsNoExportAboveLoad.json") @@ -57,7 +57,7 @@ def test_ReOPT(): pv = reopt_site['PV'] assert(pv['dc_ac_ratio'] == pytest.approx(1.3, 0.01)) wind = reopt_site['Wind'] - assert(wind['pbi_us_dollars_per_kwh'] == pytest.approx(0.026)) + assert(wind['pbi_us_dollars_per_kwh'] == pytest.approx(0.03)) results = reopt.get_reopt_results(poll_interval=0) assert(isinstance(results, dict)) From 38552ed77604535c200d5ef493064128ed02c33c Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Wed, 19 Feb 2025 10:30:22 -0700 Subject: [PATCH 08/48] Feature add: option to adjust air density for site elevation (#427) * added site shape tools script * updated site_info for new option of site boundary definition * updated doc strings for site info and site shape tool functions * added tests for site shape tools functions * added site shape tools to documentation * added another optional input for site_details if using circle as shape * removed checking vertices so tests pass * added function to rotate site * added site boundary buffer for verts back in * added site polygon buffer as optional input to site info * added wind resource tools script * added test for air density adjustment for elevation calc * integrated adjusting for elevation in wind models * added weighted parser, updated tests, updated wind_resource.py * minor docstring updates to wind resource tools * minor fix to wind_plant and made regression test for elevation adjustment option * removed unnecessary lines and comments * integrated weighted parse resource data method into wind plant --------- Co-authored-by: John Jasa --- RELEASE.md | 2 + .../technologies/resource/wind_resource.py | 4 +- hopp/simulation/technologies/wind/floris.py | 70 ++++---- .../technologies/wind/wind_plant.py | 18 ++- hopp/tools/resource/wind_tools.py | 151 ++++++++++++++++++ tests/hopp/test_hybrid.py | 29 ++++ tests/hopp/test_site_shape_tools.py | 2 +- tests/hopp/test_wind_resource_tools.py | 135 ++++++++++++++++ 8 files changed, 362 insertions(+), 49 deletions(-) create mode 100644 hopp/tools/resource/wind_tools.py create mode 100644 tests/hopp/test_wind_resource_tools.py diff --git a/RELEASE.md b/RELEASE.md index d7deb290f..49f492147 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -7,6 +7,8 @@ * Added ability and option to initialize site_info with preloaded and formatted wind and solar resource data * Bug fix in load following heuristic method: only using beginning of variable load signals * Feature add: added alternative method to defining site boundary. +* Feature add: added function to adjust air density based on site elevation +* Added weighted average wind resource parsing method option when using floris. * Updated PySAM version from 4.2.0 to 6.0.1. Main changes noted in [PR #425](https://github.com/NREL/HOPP/pull/425) * PySAM generation plant defaults have been updated. Current defaults can be found [here](https://github.com/NREL/SAM/tree/develop/api/api_autogen/library/defaults) * PySAM SingleOwner financial model update investment-tax credit and depreciation basis calculations to remove financing fees and reserve account funding from basis. diff --git a/hopp/simulation/technologies/resource/wind_resource.py b/hopp/simulation/technologies/resource/wind_resource.py index 9145bf4ee..2110d27a0 100644 --- a/hopp/simulation/technologies/resource/wind_resource.py +++ b/hopp/simulation/technologies/resource/wind_resource.py @@ -58,6 +58,7 @@ def __init__( # if resource_data is input as a dictionary then set_data if isinstance(resource_data,dict): self.data = resource_data + self.hub_height_meters = wind_turbine_hub_ht return # if resource_data is not provided, download or load resource data @@ -67,9 +68,6 @@ def __init__( self.path_resource = path_resource if path_resource.parts[-1]!="wind": self.path_resource = self.path_resource / 'wind' - - # Force override any internal definitions if passed in - self.__dict__.update(kwargs) self.file_resource_heights = None self.update_height(wind_turbine_hub_ht) diff --git a/hopp/simulation/technologies/wind/floris.py b/hopp/simulation/technologies/wind/floris.py index 50339b311..71f558a5c 100644 --- a/hopp/simulation/technologies/wind/floris.py +++ b/hopp/simulation/technologies/wind/floris.py @@ -10,7 +10,13 @@ from hopp.simulation.base import BaseClass from hopp.simulation.technologies.sites import SiteInfo from hopp.type_dec import resource_file_converter - +from pathlib import Path +from hopp.utilities import load_yaml +from hopp.tools.resource.wind_tools import ( + calculate_air_density_for_elevation, + parse_resource_data, + weighted_parse_resource_data +) # avoid circular dep if TYPE_CHECKING: from hopp.simulation.technologies.wind.wind_plant import WindConfig @@ -28,22 +34,32 @@ class Floris(BaseClass): fi: FlorisModel = field(init=False) def __attrs_post_init__(self): - # floris_input_file = resource_file_converter(self.config["simulation_input_file"]) - floris_input_file = self.config.floris_config # DEBUG!!!!! - - if floris_input_file is None: + # 1) check that floris config is provided + if self.config.floris_config is None: raise ValueError("A floris configuration must be provided") if self.config.timestep is None: raise ValueError("A timestep is required.") - # the above change is a temporary patch to bridge to refactor floris + # 2) load floris config if needed + if isinstance(self.config.floris_config,(str, Path)): + floris_config = load_yaml(self.config.floris_config) + else: + floris_config = self.config.floris_config - self.fi = FlorisModel(floris_input_file) + # 3) modify air density in floris config if needed + if self.config.adjust_air_density_for_elevation and self.site.elev is not None: + rho = calculate_air_density_for_elevation(self.site.elev) + floris_config["flow_field"].update({"air_density":rho}) + + #initialize floris model + self.fi = FlorisModel(floris_config) self._timestep = self.config.timestep self._operational_losses = self.config.operational_losses - self.wind_resource_data = self.site.wind_resource.data - self.speeds, self.wind_dirs = self.parse_resource_data() + if self.config.resource_parse_method == "average": + self.speeds, self.wind_dirs = parse_resource_data(self.site.wind_resource) + elif self.config.resource_parse_method == "weighted_average": + self.speeds, self.wind_dirs = weighted_parse_resource_data(self.site.wind_resource) self.wind_farm_xCoordinates = self.fi.layout_x self.wind_farm_yCoordinates = self.fi.layout_y @@ -54,7 +70,7 @@ def __attrs_post_init__(self): self.system_capacity = self.nTurbs * self.turb_rating # turbine power curve (array of kW power outputs) - self.wind_turbine_powercurve_powerout = [] + self.wind_turbine_powercurve_powerout = [1] * 30 # dummy for now # time to simulate if len(self.config.timestep) > 0: @@ -69,16 +85,6 @@ def __attrs_post_init__(self): self.annual_energy = None self.capacity_factor = None - self.initialize_from_floris() - - def initialize_from_floris(self): - """ - Please populate all the wind farm parameters - """ - self.nTurbs = len(self.fi.layout_x) - self.wind_turbine_powercurve_powerout = [1] * 30 # dummy for now - pass - def value(self, name: str, set_value=None): """ if set_value = None, then retrieve value; otherwise overwrite variable's value @@ -88,28 +94,8 @@ def value(self, name: str, set_value=None): else: return self.__getattribute__(name) - def parse_resource_data(self): - - # extract data for simulation - speeds = np.zeros(len(self.wind_resource_data['data'])) - wind_dirs = np.zeros(len(self.site.wind_resource.data['data'])) - data_rows_total = 4 - if np.shape(self.site.wind_resource.data['data'])[1] > data_rows_total: - height_entries = int(np.round(np.shape(self.site.wind_resource.data['data'])[1]/data_rows_total)) - data_entries = np.empty((height_entries)) - for j in range(height_entries): - data_entries[j] = int(j*data_rows_total) - data_entries = data_entries.astype(int) - for i in range((len(self.site.wind_resource.data['data']))): - data_array = np.array(self.site.wind_resource.data['data'][i]) - speeds[i] = np.mean(data_array[2+data_entries]) - wind_dirs[i] = np.mean(data_array[3+data_entries]) - else: - for i in range((len(self.site.wind_resource.data['data']))): - speeds[i] = self.site.wind_resource.data['data'][i][2] - wind_dirs[i] = self.site.wind_resource.data['data'][i][3] - - return speeds, wind_dirs + def set_floris_value(self,name,value): + self.fi.set(**{name:value}) def execute(self, project_life): diff --git a/hopp/simulation/technologies/wind/wind_plant.py b/hopp/simulation/technologies/wind/wind_plant.py index 6b512439b..7dab48926 100644 --- a/hopp/simulation/technologies/wind/wind_plant.py +++ b/hopp/simulation/technologies/wind/wind_plant.py @@ -15,6 +15,7 @@ from hopp.simulation.technologies.layout.wind_layout import WindLayout, WindBoundaryGridParameters from hopp.simulation.technologies.financial import CustomFinancialModel, FinancialModelType from hopp.utilities.log import hybrid_logger as logger +from hopp.tools.resource.wind_tools import calculate_elevation_air_density_losses @define @@ -35,8 +36,12 @@ class WindConfig(BaseClass): layout_params: layout configuration rating_range_kw: allowable kw range of turbines, default is 1000 - 3000 kW floris_config: Floris configuration, only used if `model_name` == 'floris' + adjust_air_density_for_elevation (bool): whether to adjust air density for elevation. Defaults to False. + Only used if True and ``site.elev`` is not None. + resource_parse_method (str): method to parse wind resource data if using floris and downloaded resource data for 2 heights. + Can either be "weighted_average" or "average". Defaults to "average". operational_losses: total percentage losses in addition to wake losses, defaults based on PySAM (only used for Floris model) - timestep: Timestep (required for floris runs, otherwise optional) + timestep: Timestep (required for floris runs, otherwise optional). Defaults to (0,8760) fin_model: Optional financial model. Can be any of the following: - a string representing an argument to `Singleowner.default` @@ -55,8 +60,10 @@ class WindConfig(BaseClass): model_input_file: Optional[str] = field(default=None) rating_range_kw: Tuple[int, int] = field(default=(1000, 3000)) floris_config: Optional[Union[dict, str, Path]] = field(default=None) + adjust_air_density_for_elevation: Optional[bool] = field(default = False) + resource_parse_method: str = field(default="average", validator=contains(["weighted_average", "average"])) operational_losses: float = field(default = 12.83, validator=range_val(0, 100)) - timestep: Optional[Tuple[int, int]] = field(default=None) + timestep: Optional[Tuple[int, int]] = field(default=(0,8760)) fin_model: Optional[Union[dict, FinancialModelType]] = field(default=None) name: str = field(default="WindPlant") @@ -146,7 +153,12 @@ def __attrs_post_init__(self): self._system_model.Turbine.wind_turbine_hub_ht = self.config.hub_height if self.config.rotor_diameter is not None: self.rotor_diameter = self.config.rotor_diameter - + + if self.config.model_name == "pysam": + if self.config.adjust_air_density_for_elevation and self.site.elev is not None: + air_dens_losses = calculate_elevation_air_density_losses(self.site.elev) + self._system_model.Losses.assign({"turb_specific_loss":air_dens_losses}) + @property def wake_model(self) -> str: try: diff --git a/hopp/tools/resource/wind_tools.py b/hopp/tools/resource/wind_tools.py new file mode 100644 index 000000000..147f43acc --- /dev/null +++ b/hopp/tools/resource/wind_tools.py @@ -0,0 +1,151 @@ +from scipy.constants import R, g, convert_temperature +import numpy as np + +def calculate_air_density_for_elevation(elevation_m: float) -> float: + """ + Calculate air density based on site elevation using the Barometric formula. + + This function is based on Equation 1 from: https://en.wikipedia.org/wiki/Barometric_formula#Density_equations + + Args: + elevation_m (float): Elevation of site in meters + + Returns: + float: Air density in kg/m^3 at elevation of site + """ + rho0 = 1.225 # Air density at sea level (kg/m3) + t_ref = 20 # Standard air temperature (Celsius) + elevation_sea_level = 0.0 # Reference elevation at sea level (m) + l = 0.0065 # Temperature lapse rate (K/m) for 0-11000m above sea level + molar_mass_air = 28.96 # Molar mass of air (g/mol) + + # Convert temperature to Kelvin + T_ref = convert_temperature([t_ref], "C", "K")[0] + + # Exponent value used in equation below + e = g * (molar_mass_air / 1e3) / (R * l) + # g: acceleration due to gravity (m/s2) + # R: universal gas constant (J/mol-K) + + # Calculate air density at site elevation + rho = rho0 * ((T_ref - ((elevation_m - elevation_sea_level) * l)) / T_ref) ** (e - 1) + return rho + +def calculate_elevation_air_density_losses(elevation_m: float) -> float: + """Calculate loss (%) from air density drop at site elevation. + + Args: + elevation_m (float): site elevation in meters + + Returns: + float: percentage loss associated with air density decrease at elevation. + """ + if elevation_m <= 0.0: + loss_percent = 0.0 + else: + rho0 = 1.225 + air_density = calculate_air_density_for_elevation(elevation_m) + loss_ratio = 1 - (air_density / rho0) + loss_percent = loss_ratio * 100 + + return loss_percent + +def parse_resource_data(wind_resource): + """Parse wind resource data into floris-friendly format. + Average wind speed and wind direction if there's data for + 2 resource heights. This method assumes that the turbine hub-height + is in-between two resource heights. + + In ``wind_resource.data['fields']``, values correspond to: + - 3: Wind speed in meters per second (m/s) + - 4: Wind direction in degrees east of north (degrees). + + Args: + wind_resource (HPCWindData | WindResource): wind resource data object + + Returns: + 2-element tuple containing + + - **speeds** (:obj:`numpy.ndarray`): wind speed in m/s + - **wind_dirs** (:obj:`numpy.ndarray`): wind direction in deg from North (clockwise) + """ + data = np.array(wind_resource.data['data']) + + # Get indices of wind speed data + idx_ws = [ii for ii, field in enumerate(wind_resource.data['fields']) if field == 3] + + # Get indices of wind direction data + idx_wd = [ii for ii, field in enumerate(wind_resource.data['fields']) if field == 4] + + # If there's multiple hub-heights - average the data + if len(idx_ws) > 1: + speeds = data[:, idx_ws].mean(axis=1) + wind_dirs = data[:, idx_wd].mean(axis=1) + else: + # If there's only one hub-height, grab speed and direction data + speeds = data[:, idx_ws[0]] + wind_dirs = data[:, idx_wd[0]] + + return speeds, wind_dirs + +def weighted_parse_resource_data(wind_resource): + """Parse wind resource data into floris-friendly format. + Weighted average wind speed and wind direction if there's data for + 2 resource heights. Weight wind resource data based on resource-height + relative to turbine hub-height. + + In ``wind_resource.data['fields']``, values correspond to: + - 3: Wind speed in meters per second (m/s) + - 4: Wind direction in degrees east of north (degrees). + + Args: + wind_resource (HPCWindData | WindResource): wind resource data object + + Returns: + 2-element tuple containing + + - **speeds** (:obj:`numpy.ndarray`): wind speed in m/s + - **wind_dirs** (:obj:`numpy.ndarray`): wind direction in deg from North (clockwise) + """ + data = np.array(wind_resource.data['data']) + + # Get indices of wind speed data + idx_ws = [ii for ii, field in enumerate(wind_resource.data['fields']) if field == 3] + + # Get indices of wind direction data + idx_wd = [ii for ii, field in enumerate(wind_resource.data['fields']) if field == 4] + + # If there's multiple hub-heights - average the data + if len(idx_ws) > 1: + # Weights corresponding to difference of resource height and hub-height + hh1, hh2 = np.unique(wind_resource.data['heights']) + weight1 = np.abs(hh1 - wind_resource.hub_height_meters) + weight2 = np.abs(hh2 - wind_resource.hub_height_meters) + + # Wind speed data indices for each resource height + idx_ws1 = [i for i in idx_ws if wind_resource.data['heights'][i] == hh1][0] + idx_ws2 = [i for i in idx_ws if wind_resource.data['heights'][i] == hh2][0] + + # Wind speeds at the two resource heights + ws1 = data[:, idx_ws1] + ws2 = data[:, idx_ws2] + + # Weight wind speed data based on height relative to turbine hub-height + speeds = np.round(((weight1 * ws1) + (weight2 * ws2)) / (weight1 + weight2), 3) + + # Wind direction data indices for each resource height + idx_wd1 = [i for i in idx_wd if wind_resource.data['heights'][i] == hh1][0] + idx_wd2 = [i for i in idx_wd if wind_resource.data['heights'][i] == hh2][0] + + # Wind directions at the two resource heights + wd1 = data[:, idx_wd1] + wd2 = data[:, idx_wd2] + + # Weight wind direction data based on height relative to turbine hub-height + wind_dirs = np.round(((weight1 * wd1) + (weight2 * wd2)) / (weight1 + weight2), 3) + else: + # If there's only one hub-height, grab speed and direction data + speeds = data[:, idx_ws[0]] + wind_dirs = data[:, idx_wd[0]] + + return speeds, wind_dirs diff --git a/tests/hopp/test_hybrid.py b/tests/hopp/test_hybrid.py index f4b9617ec..5cdaf7971 100644 --- a/tests/hopp/test_hybrid.py +++ b/tests/hopp/test_hybrid.py @@ -1598,3 +1598,32 @@ def test_hybrid_financials(hybrid_config, subtests): with subtests.test("wind om total"): assert hi.system.om_total_expenses['wind'][1] == approx(463903.43, rel=5e-2) +def test_hybrid_wind_only_floris_elevation_adjusted(hybrid_config, subtests): + + floris_config_path = ( + ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" + ) + technologies = hybrid_config["technologies"] + wind_only = {key: technologies[key] for key in ("wind", "grid")} + + wind_only["wind"]["model_name"] = "floris" + wind_only["wind"]["floris_config"] = floris_config_path + wind_only["wind"]["timestep"] = [0, 8760] + wind_only["wind"]["num_turbines"] = 4 + + hybrid_config["technologies"] = wind_only + hi = HoppInterface(hybrid_config) + hi.simulate(25) + hybrid_plant = hi.system + aeps_default = hybrid_plant.annual_energies + + wind_only["wind"].update({"adjust_air_density_for_elevation": True}) + hybrid_config["technologies"] = wind_only + hi = HoppInterface(hybrid_config) + hi.simulate(25) + hybrid_plant = hi.system + aeps_adjusted = hybrid_plant.annual_energies + + + with subtests.test("wind aep"): + assert aeps_adjusted.wind < aeps_default.wind \ No newline at end of file diff --git a/tests/hopp/test_site_shape_tools.py b/tests/hopp/test_site_shape_tools.py index 80ce616bc..e186ce83f 100644 --- a/tests/hopp/test_site_shape_tools.py +++ b/tests/hopp/test_site_shape_tools.py @@ -151,4 +151,4 @@ def test_rotate_site_vertices(): rotation_angle_deg = rotation_angle ) assert rotated_polygon.exterior.xy[0][0] == approx(polygon_original.centroid.x,abs = 1e-3) - assert rotated_polygon.exterior.xy[1][1] == approx(polygon_original.centroid.y,abs = 1e-3) \ No newline at end of file + assert rotated_polygon.exterior.xy[1][1] == approx(polygon_original.centroid.y,abs = 1e-3) diff --git a/tests/hopp/test_wind_resource_tools.py b/tests/hopp/test_wind_resource_tools.py new file mode 100644 index 000000000..e72c0a428 --- /dev/null +++ b/tests/hopp/test_wind_resource_tools.py @@ -0,0 +1,135 @@ +import os +from PySAM.ResourceTools import SRW_to_wind_data +from hopp.tools.resource.wind_tools import ( + calculate_air_density_for_elevation, + calculate_elevation_air_density_losses, + parse_resource_data, + weighted_parse_resource_data +) +from hopp.simulation.technologies.resource import WindResource +from hopp import ROOT_DIR +from pytest import fixture, approx +from numpy.testing import assert_array_almost_equal +import numpy as np + +wind_resource_file_multi_heights = os.path.join( + ROOT_DIR, "simulation", "resource_files", "wind", + "35.2018863_-101.945027_windtoolkit_2012_60min_80m_100m.srw" +) + +wind_resource_file_single_height = os.path.join( + ROOT_DIR, "simulation", "resource_files", "wind", + "35.2018863_-101.945027_windtoolkit_2012_60min_100m.srw" +) + +@fixture +def wind_resource_data_90m(): + wind_resource_data_dict = SRW_to_wind_data(wind_resource_file_multi_heights) + return WindResource( + lat = 35.2018863, + lon = -101.945027, + year = 2012, + wind_turbine_hub_ht = 90, + resource_data = wind_resource_data_dict + ) + +@fixture +def wind_resource_data_85m(): + wind_resource_data_dict = SRW_to_wind_data(wind_resource_file_multi_heights) + return WindResource( + lat = 35.2018863, + lon = -101.945027, + year = 2012, + wind_turbine_hub_ht = 85, + resource_data = wind_resource_data_dict + ) + +@fixture +def wind_resource_data_100m(): + wind_resource_data_dict = SRW_to_wind_data(wind_resource_file_single_height) + return WindResource( + lat = 35.2018863, + lon = -101.945027, + year = 2012, + wind_turbine_hub_ht = 100, + resource_data = wind_resource_data_dict + ) + +def test_sea_level_air_density(): + elevation = 0.0 #meters + air_dens = calculate_air_density_for_elevation(elevation) + assert air_dens == approx(1.225, rel = 1e-3) + +def test_mile_high_air_density(): + #test elevation at 1 mile above sea level + elevation = 1609.34 #meters + air_dens = calculate_air_density_for_elevation(elevation) + assert air_dens == approx(1.05, rel = 1e-3) + +def test_sea_level_air_density_losses(): + elevation = 0.0 #meters + loss_percent = calculate_elevation_air_density_losses(elevation) + assert loss_percent == 0.0 + +def test_mile_high_air_density_losses(): + elevation = 1609.34 #meters + loss_percent = calculate_elevation_air_density_losses(elevation) + assert loss_percent == approx(14.325, rel = 1e-3) + +def test_weighted_parsing_100m(wind_resource_data_100m): + wind_speeds, wind_dirs = weighted_parse_resource_data(wind_resource_data_100m) + + assert wind_speeds[0] == approx(wind_resource_data_100m.data['data'][0][2], abs=1e-3) + assert wind_dirs[0] == approx(wind_resource_data_100m.data['data'][0][3], abs=1e-3) + +def test_weighted_parsing_90m(wind_resource_data_90m): + wind_speeds, wind_dirs = weighted_parse_resource_data(wind_resource_data_90m) + t0_wind_speeds = [wind_resource_data_90m.data['data'][0][2],wind_resource_data_90m.data['data'][0][6]] + t0_wind_dirs = [wind_resource_data_90m.data['data'][0][3],wind_resource_data_90m.data['data'][0][7]] + + assert wind_speeds[0] > min(t0_wind_speeds) + assert wind_speeds[0] < max(t0_wind_speeds) + assert wind_dirs[0] > min(t0_wind_dirs) + assert wind_dirs[0] < max(t0_wind_dirs) + +def test_weighted_parsing_85m(wind_resource_data_85m): + wind_speeds, wind_dirs = weighted_parse_resource_data(wind_resource_data_85m) + ws_frac_80m = wind_speeds[0]/wind_resource_data_85m.data['data'][0][2] + ws_frac_100m = wind_speeds[0]/wind_resource_data_85m.data['data'][0][6] + wd_frac_80m = wind_dirs[0]/wind_resource_data_85m.data['data'][0][3] + wd_frac_100m = wind_dirs[0]/wind_resource_data_85m.data['data'][0][7] + assert ws_frac_80m > 1 + assert ws_frac_100m < 1 + assert wd_frac_80m > 1 + assert wd_frac_100m < 1 + +def test_average_parsing_100m(wind_resource_data_100m): + wind_speeds, wind_dirs = parse_resource_data(wind_resource_data_100m) + + assert wind_speeds[0] == approx(wind_resource_data_100m.data['data'][0][2], abs=1e-3) + assert wind_dirs[0] == approx(wind_resource_data_100m.data['data'][0][3], abs=1e-3) + +def test_average_parsing_90m(wind_resource_data_90m): + wind_speeds, wind_dirs = parse_resource_data(wind_resource_data_90m) + t0_wind_speeds = [wind_resource_data_90m.data['data'][0][2],wind_resource_data_90m.data['data'][0][6]] + t0_wind_dirs = [wind_resource_data_90m.data['data'][0][3],wind_resource_data_90m.data['data'][0][7]] + + assert wind_speeds[0] > min(t0_wind_speeds) + assert wind_speeds[0] < max(t0_wind_speeds) + assert wind_dirs[0] > min(t0_wind_dirs) + assert wind_dirs[0] < max(t0_wind_dirs) + +def test_average_parsing_85m(wind_resource_data_85m): + wind_speeds, wind_dirs = parse_resource_data(wind_resource_data_85m) + t0_wind_speeds = [wind_resource_data_85m.data['data'][0][2],wind_resource_data_85m.data['data'][0][6]] + t0_wind_dirs = [wind_resource_data_85m.data['data'][0][3],wind_resource_data_85m.data['data'][0][7]] + + assert wind_speeds[0] == approx(np.mean(t0_wind_speeds), rel = 1e-3) + assert wind_dirs[0] == approx(np.mean(t0_wind_dirs), rel = 1e-3) + +def test_weighted_vs_average_parsing_90m(wind_resource_data_90m): + avg_wind_speeds, avg_wind_dirs = parse_resource_data(wind_resource_data_90m) + wavg_wind_speeds, wavg_wind_dirs = weighted_parse_resource_data(wind_resource_data_90m) + assert_array_almost_equal(avg_wind_speeds,wavg_wind_speeds,decimal=3) + assert_array_almost_equal(avg_wind_dirs,wavg_wind_dirs,decimal=3) + \ No newline at end of file From b37e9b0a48beca8d422f714b2a2631bd8a6ecca8 Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Thu, 20 Feb 2025 13:59:36 -0700 Subject: [PATCH 09/48] Intermediate: update to wind layout and floris functions (1/2) (#429) * added site shape tools script * updated site_info for new option of site boundary definition * updated doc strings for site info and site shape tool functions * added tests for site shape tools functions * added site shape tools to documentation * added another optional input for site_details if using circle as shape * updated RELEASE.md with new feature * removed checking vertices so tests pass * added function to rotate site * added site boundary buffer for verts back in * added site polygon buffer as optional input to site info * added wind resource tools script * added test for air density adjustment for elevation calc * integrated adjusting for elevation in wind models * updated doc strings for recent changes * updated release file * fixed bug in make_grid_lines * fixed input to create_grid to be in degrees instead of radians * added new wind layout tools and fixed doc strings related to bug fix * updated call to wind layout tools and fixed inputs for test_custom_financial * adding in option for basicgrid layout option * renamed test_layout test * added weighted parser, updated tests, updated wind_resource.py * added plot function to site_shape_tools * added new wind layout tools function and cleaned up wind_layout a bit * added more functionality to floris.py and added some optional parameters to windconfig * added layout test for basicgrid layout * added doc strings to wind layout files and wind plant files * added warning if user inputs incorrect turbine rating with floris * updated tests that were failing because of floris update * minor fix to wind_plant and made regression test for elevation adjustment option * added in integrated of weighted average resource data from v3/elevated_wind --------- Co-authored-by: John Jasa --- RELEASE.md | 7 + examples/inputs/05-floris-wake-model.yaml | 2 +- .../technologies/layout/pv_layout_tools.py | 9 +- .../technologies/layout/wind_layout.py | 369 ++++++++++++++---- .../technologies/layout/wind_layout_tools.py | 199 +++++++++- .../technologies/sites/site_shape_tools.py | 41 +- hopp/simulation/technologies/wind/floris.py | 128 ++++-- .../technologies/wind/wind_plant.py | 71 ++-- hopp/tools/resource/wind_tools.py | 124 +++--- tests/hopp/test_custom_financial.py | 11 +- tests/hopp/test_hybrid.py | 15 +- tests/hopp/test_layout.py | 34 +- tests/hopp/test_wind.py | 56 +-- tests/hopp/test_wind_resource_tools.py | 12 +- 14 files changed, 824 insertions(+), 254 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 49f492147..94b6aa809 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -15,6 +15,13 @@ * PySAM MHKWave update marine energy device cost curves. * PySAM Detailed PV update module and inverter libraries, snow module, tracking, losses. * Update deprecated methods in wave_resource.py +* For further details on the following updates, users are referred [here](https://github.com/NREL/HOPP/pull/429#issue-2852391571) + + Feature add: new wind layout method called `basicgrid` that makes the most-square layout that has the option to be site-constrained. + + Updated wind layout methods to classes + + Bug-fix: grid angle converted from degrees to radians in `make_grid_lines()` function in `wind_layout_tools.py` + + Updated floris initialization to set attributes from `floris_config` + + Update: raise errors when using floris if theres a discrepancy between inputs in `WindConfig` and information in `floris_config` (such as `num_turbines` and the `floris_config` layout, and turbine parameters like rotor diameter and turbine rating.) + ## Version 3.1.1, Dec. 18, 2024 diff --git a/examples/inputs/05-floris-wake-model.yaml b/examples/inputs/05-floris-wake-model.yaml index 158513213..eb90493b4 100644 --- a/examples/inputs/05-floris-wake-model.yaml +++ b/examples/inputs/05-floris-wake-model.yaml @@ -34,7 +34,7 @@ technologies: pv: system_capacity_kw: 50000 wind: - num_turbines: 4 + num_turbines: 3 turbine_rating_kw: 5000.0 model_name: floris timestep: [0, 8760] diff --git a/hopp/simulation/technologies/layout/pv_layout_tools.py b/hopp/simulation/technologies/layout/pv_layout_tools.py index 9265cf897..14559ffe1 100644 --- a/hopp/simulation/technologies/layout/pv_layout_tools.py +++ b/hopp/simulation/technologies/layout/pv_layout_tools.py @@ -8,10 +8,15 @@ from shapely.prepared import ( PreparedGeometry, ) +from shapely.geometry.base import BaseGeometry +from shapely.geometry import Point, LineString, Polygon +from shapely.prepared import prep +from shapely.affinity import translate +from typing import Optional from hopp.simulation.technologies.layout.layout_tools import * from hopp.simulation.technologies.sites.site_info import SiteInfo -from hopp.simulation.technologies.layout.wind_layout_tools import * +from hopp.simulation.technologies.layout.wind_layout_tools import make_grid_lines def find_best_gcr( @@ -156,7 +161,7 @@ def place_solar_strands(max_num_modules: int, grid_lines = make_grid_lines( site_shape, translate(center, xoff=raw_phase_offset), - np.pi / 2, # N-S orientation + np.rad2deg(np.pi / 2), # N-S orientation interrow_spacing ) diff --git a/hopp/simulation/technologies/layout/wind_layout.py b/hopp/simulation/technologies/layout/wind_layout.py index b0e046a0a..264865ddf 100644 --- a/hopp/simulation/technologies/layout/wind_layout.py +++ b/hopp/simulation/technologies/layout/wind_layout.py @@ -1,83 +1,206 @@ from __future__ import annotations -from typing import Union, NamedTuple +from typing import Union import numpy as np import matplotlib.pyplot as plt from shapely.geometry import Polygon, Point, MultiPolygon +from shapely.geometry.base import BaseGeometry from shapely.affinity import scale import PySAM.Windpower as windpower - +from attrs import define, field +from typing import Optional from hopp.utilities.log import hybrid_logger as logger from hopp.simulation.technologies.layout.wind_layout_tools import ( get_best_grid, get_evenly_spaced_points_along_border, - subtract_turbine_exclusion_zone + subtract_turbine_exclusion_zone, + make_site_boundary_for_square_grid_layout, + create_grid, + check_turbines_in_site, + adjust_site_for_box_grid_layout ) -from hopp.simulation.technologies.sites.site_info import SiteInfo - - -class WindBoundaryGridParameters(NamedTuple): +from hopp.utilities.validators import contains, range_val +from hopp.simulation.technologies.sites.site_shape_tools import plot_site_polygon +from hopp.simulation.base import BaseClass + +@define +class WindBasicGridParameters(BaseClass): + """Configuration class for 'basicgrid' wind layout. + + Args: + row_D_spacing (float, Optional): rotor diameter multiplier for spacing between rows of turbines (y direction). + Defaults to 5.0. + turbine_D_spacing (float, Optional): rotor diameter multiplier for spacing between turbines in a row (x direction). + Defaults to 5.0. + grid_angle (float, Optional): grid rotation angle in degrees where 0 is North, increasing clockwise. + Defaults to 0.0. + row_phase_offset (float, Optional): offset of turbines along row from one row to the next. + Value must be between 0 and 1. Defaults to 0.0. + site_boundary_constrained (bool, Optional): whether to constrain the layout to the site. Defaults to False. """ - border_spacing: spacing along border = (1 + border_spacing) * min spacing - border_offset: turbine border spacing offset as ratio of border spacing (0, 1) - grid_angle: turbine inner grid rotation (0, pi) [radians] - grid_aspect_power: grid aspect ratio [cols / rows] = 2^grid_aspect_power - row_phase_offset: inner grid phase offset (0,1) (20% suggested) - """ - border_spacing: float - border_offset: float - grid_angle: float - grid_aspect_power: float - row_phase_offset: float - -class WindCustomParameters(NamedTuple): + row_D_spacing: Optional[float] = field(default = 5.0) + turbine_D_spacing: Optional[float] = field(default = 5.0) + grid_angle: Optional[float] = field(default = 0.0) + row_phase_offset: Optional[float] = field(default = 0.0, validator = range_val(0.0, 1.0)) + site_boundary_constrained: Optional[bool] = field(default = False) + +@define +class WindBoundaryGridParameters(BaseClass): + """Configuration class for 'boundarygrid' wind layout. + + Args: + border_spacing (float): border spacing ratio for turbines placed along border. Defaults to 0.0 + spacing along border = (1 + border_spacing) * min spacing + border_offset (float): turbine border spacing offset as ratio of border spacing (0, 1). Defaults to 0.0 + border_spacing_m (float, Optional): spacing along border in meters. Is used to calculate ``border_spacing`` + if ``min_spacing_m`` is also input. + min_spacing_m (float, Optional): minimum spacing between turbines in meters. + grid_angle (float): turbine inner grid rotation (0, 180) [degrees] + grid_aspect_power (float, Optional): used to calculate grid_aspect_ratio. + grid aspect ratio [cols / rows] = 2^grid_aspect_power. ``grid_aspect_ratio = np.exp(grid_aspect_power)`` + grid_aspect_ratio (float, Optional): cols / rows of turbine grid. Defaults to 1.0 if neither ``grid_aspect_ratio`` + or ``grid_aspect_power`` are provided. + row_phase_offset (float): inner grid phase offset (0,1). 20% suggested + Value must be between 0 and 1. Defaults to 0.2 """ - direct user input of the x and y coordinates - """ - - layout_x: list - layout_y: list - -class WindLayout: + #TODO: rename to border_spacing_ratio? + border_spacing: float = field(default = 0.0) + #TODO: rename to border_offset_ratio? + border_offset: float = field(default = 0.0, validator = range_val(0.0, 1.0)) + border_spacing_m: Optional[float] = field(default = None) + min_spacing_m: Optional[float] = field(default = None) + + grid_angle: float = field(default = 0.0, validator = range_val(0.0, 180.0)) + grid_aspect_power: Optional[float] = field(default = None) + grid_aspect_ratio: Optional[float] = field(default = None) + row_phase_offset: float = field(default = 0.2, validator = range_val(0.0, 1.0)) + + def __attrs_post_init__(self): + + if self.grid_aspect_ratio is None: + #NOTE: unsure if this equation is correct given doc strong + self.grid_aspect_ratio = 1 if self.grid_aspect_power is None else np.exp(self.grid_aspect_power) + + if self.min_spacing_m is not None and self.border_spacing_m is not None: + self.border_spacing = (self.border_spacing_m/self.min_spacing_m) - 1 + +@define +class WindCustomParameters(BaseClass): """ + Configuration class for 'custom' wind layout. + Args: + layout_x (list[float]): x-coordinates of turbines + layout_y (list[float]): y-coordinates of turbines """ - def __init__(self, - site_info: SiteInfo, - wind_source: windpower.Windpower, - layout_mode: str, - parameters: Union[WindBoundaryGridParameters, WindCustomParameters, None], - min_spacing: float = 200., - ): - """ + layout_x: list[float] + layout_y: list[float] + + +@define +class WindLayout(BaseClass): + """Class to manage wind farm layout. + + Args: + site_polygon (Polygon | BaseGeometry): site polygon shape. + _system_model (windpower.Windpower | Floris): pysam wind power object. Not currently tested to work with floris. + layout_mode (str): layout choice: "boundarygrid", "grid", "custom", "basicgrid" + parameters (Union[WindBoundaryGridParameters, WindCustomParameters, WindBasicGridParameters, None, dict]): wind + layout parameters for the corresponding `layout_mode` + min_spacing_meters (float, Optional): minimum spacing between turbines in meters. Defaults to 0.0. + max_spacing_meters (float, Optional): maximum spacing between turbines in meters. Defaults to 2e6. + min_rotor_diameter_multiplier (float, Optional): minimum spacing between turbines as multiplier of rotor diameter. Defaults to 2.0 + max_rotor_diameter_multiplier (float, Optional): maximum spacing between turbines as multiplier of rotor diameter. Defaults to 20.0 + turbine_rating_kW (float, Optional): rating of a single turbine in kW. if not provided, turbine power is estimated from the power-curve. + """ + site_polygon: Union[Polygon, BaseGeometry] + _system_model: windpower.Windpower + layout_mode: str = field(validator = contains(['boundarygrid', 'grid', 'custom','basicgrid']), converter=(str.strip, str.lower)) + parameters: Union[WindBoundaryGridParameters, WindCustomParameters, WindBasicGridParameters, None] + # TODO: convert min_spacing and max_spacing to be within the parameter class that uses it. + min_spacing_meters: Optional[float] = field(default = 0.0) + max_spacing_meters: Optional[float] = field(default = 2e6) + + min_rotor_diameter_multiplier: Optional[float] = field(default = 2.0) + max_rotor_diameter_multiplier: Optional[float] = field(default = 20.0) + + turbine_rating_kW: Optional[float] = field(default = None) + + turb_pos_x: list[float] = field(init=False) + turb_pos_y: list[float] = field(init=False) + + min_spacing: float = field(init = False) + max_spacing: float = field(init = False) + + def __attrs_post_init__(self): + """The following are initialized in this post init hook: + + - min_spacing (float): minimum spacing between turbines in meters. + Only used if layout_mode is `grid` or `boundarygrid`. + - max_spacing (float): maximum spacing between turbines in meters. + Only used if layout_mode is `grid` or `boundarygrid`. + - turb_pos_x (list[float]): x-coordinates of turbines + - turb_pos_y (list[float]): x-coordinates of turbines + + Note: these calculations are based on the default values of rotor diamter and turbine layout. + `min_spacing` and `max_spacing` are re-calculated in _get_system_config(). `turb_pos_x` and `turb_pos_y` + are reset in layout-specific functions. """ - self.site: SiteInfo = site_info - self._system_model: windpower.Windpower = wind_source - self.min_spacing = max(min_spacing, self._system_model.value("wind_turbine_rotor_diameter") * 2) - - if layout_mode not in ('boundarygrid', 'grid', 'custom'): - raise ValueError('Options for `layout_mode` are: "boundarygrid", "grid", "custom"') - self._layout_mode = layout_mode - - # layout design parameters - self.parameters = parameters + self.min_spacing = max( + self.min_spacing_meters, + self._system_model.value("wind_turbine_rotor_diameter") * self.min_rotor_diameter_multiplier + ) + self.max_spacing = max( + self.max_spacing_meters, + self._system_model.value("wind_turbine_rotor_diameter") * self.max_rotor_diameter_multiplier + ) # turbine layout values self.turb_pos_x = self._system_model.value("wind_farm_xCoordinates") self.turb_pos_y = self._system_model.value("wind_farm_yCoordinates") + if isinstance(self.parameters, dict): + if self.layout_mode == 'boundarygrid': + self.parameters = WindBoundaryGridParameters.from_dict(self.parameters) + elif self.layout_mode == 'basicgrid': + self.parameters = WindBasicGridParameters.from_dict(self.parameters) + elif self.layout_mode == 'custom': + self.parameters = WindCustomParameters.from_dict(self.parameters) + def _get_system_config(self): - self.min_spacing = max(self.min_spacing, self._system_model.value("wind_turbine_rotor_diameter") * 2) + """The following are re-calculated based on the actual rotor diameter of the wind turbine. + + - min_spacing (float): minimum spacing between turbines in meters. + Only used if layout_mode is `grid` or `boundarygrid`. + - max_spacing (float): maximum spacing between turbines in meters. + Only used if layout_mode is `grid` or `boundarygrid`. + """ + self.min_spacing = max( + self.min_spacing, + self.min_spacing_meters, + self._system_model.value("wind_turbine_rotor_diameter") * self.min_rotor_diameter_multiplier + ) + self.max_spacing = max( + self.max_spacing_meters, + self._system_model.value("wind_turbine_rotor_diameter") * self.max_rotor_diameter_multiplier + ) + def _set_system_layout(self): + """Set the number of turbines. System capacity gets modified as a result. + """ + self._system_model.value("wind_farm_xCoordinates", self.turb_pos_x) self._system_model.value("wind_farm_yCoordinates", self.turb_pos_y) n_turbines = len(self.turb_pos_x) - turb_rating = max(self._system_model.value("wind_turbine_powercurve_powerout")) - self._system_model.value("system_capacity", n_turbines * turb_rating) + if self.turbine_rating_kW is None: + turb_rating = max(self._system_model.value("wind_turbine_powercurve_powerout")) + self._system_model.value("system_capacity", n_turbines * turb_rating) + else: + self._system_model.value("system_capacity", n_turbines * self.turbine_rating_kW) logger.info("Wind Layout set with {} turbines for {} kw system capacity".format(n_turbines, n_turbines * turb_rating)) @@ -87,15 +210,17 @@ def rotor_diameter(self): def reset_boundarygrid(self, n_turbines, - parameters: WindBoundaryGridParameters, exclusions: Polygon = None): - """ + """Create `boundarygrid` layout for input number of turbines. + Args: + n_turbines (int): number of turbines to include in layout. + exclusions (Polygon, Optional): exclusion area shape. Defaults to None. """ self._get_system_config() - wind_shape = Polygon(self.site.polygon.exterior) - if exclusions: + wind_shape = Polygon(self.site_polygon.exterior) + if exclusions is not None: wind_shape = wind_shape.difference(exclusions) # compute valid wind layout shape # place border turbines @@ -103,13 +228,13 @@ def reset_boundarygrid(self, if not isinstance(wind_shape, MultiPolygon): wind_shape = MultiPolygon([wind_shape, ]) - border_spacing = (parameters.border_spacing + 1) * self.min_spacing + border_spacing = (self.parameters.border_spacing + 1) * self.min_spacing for bounding_shape in wind_shape.geoms: turbine_positions.extend( get_evenly_spaced_points_along_border( bounding_shape.exterior, border_spacing, - parameters.border_offset, + self.parameters.border_offset, n_turbines - len(turbine_positions), )) @@ -117,14 +242,13 @@ def reset_boundarygrid(self, # place interior grid turbines max_num_interior_turbines = n_turbines - len(turbine_positions) - grid_aspect = np.exp(parameters.grid_aspect_power) intrarow_spacing, grid_sites = get_best_grid( valid_wind_shape, wind_shape.centroid, - parameters.grid_angle, - grid_aspect, - parameters.row_phase_offset, - self.min_spacing * 10000, + self.parameters.grid_angle, + self.parameters.grid_aspect_ratio, + self.parameters.row_phase_offset, + self.max_spacing, self.min_spacing, max_num_interior_turbines, ) @@ -139,33 +263,33 @@ def reset_boundarygrid(self, def reset_grid(self, n_turbines): - """ - Set the number of turbines. System capacity gets modified as a result. - Wind turbines will be placed in a grid + """Create a `grid` layout for specified number of turbines within the `site_polygon`. + Spacing turbines based on `min_spacing` attribute. Does not use `parameters` attribute. - :param n_turbines: int + Args: + n_turbines (int): number of turbines to include in layout. """ self._get_system_config() xcoords = [] ycoords = [] - if not self.site.polygon: + if not self.site_polygon: raise ValueError("WindPlant set_num_turbines_in_grid requires site polygon") if n_turbines > 0: spacing = np.sqrt( - self.site.polygon.area / n_turbines) * self.site.polygon.envelope.area / self.site.polygon.area - spacing = max(spacing, self._system_model.value("wind_turbine_rotor_diameter") * 3) + self.site_polygon.area / n_turbines) * self.site_polygon.envelope.area / self.site_polygon.area + spacing = max(spacing, self.min_spacing) coords = [] while len(coords) < n_turbines: - envelope = Polygon(self.site.polygon.envelope) + envelope = Polygon(self.site_polygon.envelope) while len(coords) < n_turbines and envelope.area > spacing * spacing: d = 0 sub_boundary = envelope.boundary while d <= sub_boundary.length and len(coords) < n_turbines: coord = sub_boundary.interpolate(d) - if self.site.polygon.buffer(1e3).contains(coord): + if self.site_polygon.buffer(1e3).contains(coord): coords.append(coord) d += spacing if len(coords) < n_turbines: @@ -181,31 +305,115 @@ def reset_grid(self, self.turb_pos_x, self.turb_pos_y = xcoords, ycoords self._set_system_layout() + def reset_basic_grid(self,n_turbines): + """Create a most-square `basicgrid` layout for specified number of turbines. + requires parameters are `WindBasicGridParameters`. + + Args: + n_turbines (int): number of turbines to include in layout. + """ + self._get_system_config() + + interrow_spacing = self.parameters.row_D_spacing*self.rotor_diameter + intrarow_spacing = self.parameters.turbine_D_spacing*self.rotor_diameter + + data = make_site_boundary_for_square_grid_layout( + n_turbines, + self.rotor_diameter, + self.parameters.row_D_spacing, + self.parameters.turbine_D_spacing + ) + vertices = np.array([np.array(v) for v in data['site_boundaries']['verts']]) + square_bounds = Polygon(vertices) + grid_position_square = create_grid(square_bounds, + square_bounds.centroid, + self.parameters.grid_angle, + intrarow_spacing, + interrow_spacing, + self.parameters.row_phase_offset, + int(n_turbines), + ) + + if self.parameters.site_boundary_constrained: + # 1) see if turbines are in the site polygon + xcoords_grid = [point.x for point in grid_position_square] + ycoords_grid = [point.y for point in grid_position_square] + x_ingrid,y_ingrid = check_turbines_in_site(xcoords_grid,ycoords_grid,self.site_polygon) + if len(x_ingrid)==n_turbines: + self.turb_pos_x, self.turb_pos_y = x_ingrid,y_ingrid + self._set_system_layout() + return + x,y = adjust_site_for_box_grid_layout( + self.site_polygon, + n_turbines, + interrow_spacing, + intrarow_spacing, + self.parameters.row_phase_offset, + self.parameters.grid_angle + ) + if len(x)==n_turbines or len(x)>x_ingrid: + self.turb_pos_x, self.turb_pos_y = x_ingrid,y_ingrid + self._set_system_layout() + return + else: + self.reset_grid(n_turbines) + else: + xcoords_grid = [point.x for point in grid_position_square] + ycoords_grid = [point.y for point in grid_position_square] + self.turb_pos_x, self.turb_pos_y = xcoords_grid,ycoords_grid + self._set_system_layout() + def set_layout_params(self, wind_kw, - params: Union[WindBoundaryGridParameters, WindCustomParameters, None], + params: Optional[Union[WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters]], exclusions: Polygon = None): - self.parameters = params + """Set wind farm layout to accommodate input wind capacity. + + Args: + wind_kw (float): wind farm capacity in kW + params (Optional[Union[WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters]]): wind farm parameters. + exclusions (Polygon, optional): exclusions in site. Only used if layout_mode is 'boundarygrid'. Defaults to None. + """ + if params: + self.parameters = params + if isinstance(params,WindBoundaryGridParameters): + self.layout_mode = "boundarygrid" + elif isinstance(params,WindCustomParameters): + self.layout_mode = "custom" + elif isinstance(params,WindBasicGridParameters): + self.layout_mode = "basicgrid" + + # below is not floris-friendly n_turbines = int(np.floor(wind_kw / max(self._system_model.Turbine.wind_turbine_powercurve_powerout))) - if self._layout_mode == 'boundarygrid': - self.reset_boundarygrid(n_turbines, params, exclusions) - elif self._layout_mode == 'grid': + + if self.layout_mode == 'boundarygrid': + self.reset_boundarygrid(n_turbines, exclusions) + elif self.layout_mode == 'grid': self.reset_grid(n_turbines) - elif self._layout_mode == 'custom': + elif self.layout_mode == 'basicgrid': + self.reset_basic_grid(n_turbines) + elif self.layout_mode == 'custom': self.turb_pos_x, self.turb_pos_y = self.parameters.layout_x, self.parameters.layout_y self._set_system_layout() def set_num_turbines(self, n_turbines: int): - """ - Changes number of turbines in the existing layout + """Set number of turbines and wind farm layout. + + Args: + n_turbines (int): number of turbines to include in layout. """ self._get_system_config() - if self._layout_mode == 'boundarygrid': - self.reset_boundarygrid(n_turbines, self.parameters) - elif self._layout_mode == 'grid': + if self.layout_mode == 'boundarygrid': + self.reset_boundarygrid(n_turbines) + elif self.layout_mode == 'grid': self.reset_grid(n_turbines) + elif self.layout_mode == 'basicgrid': + self.reset_basic_grid(n_turbines) + elif self.layout_mode == 'custom': + self.turb_pos_x, self.turb_pos_y = self.parameters.layout_x, self.parameters.layout_y + self._set_system_layout() def plot(self, figure=None, @@ -216,7 +424,7 @@ def plot(self, linewidth=4.0 ): if not figure and not axes: - figure, axes = self.site.plot(figure, axes, site_border_color, site_alpha, linewidth) + figure, axes = plot_site_polygon(self.site_polygon,figure, axes, site_border_color, site_alpha, linewidth) turb_pos_x = self._system_model.value("wind_farm_xCoordinates") turb_pos_y = self._system_model.value("wind_farm_yCoordinates") @@ -225,7 +433,6 @@ def plot(self, circle = plt.Circle( (x, y), radius=self.rotor_diameter/2.0, - # linewidth=linewidth * 10, color=turbine_color, fill=True, linewidth=linewidth, diff --git a/hopp/simulation/technologies/layout/wind_layout_tools.py b/hopp/simulation/technologies/layout/wind_layout_tools.py index a2336ff20..c00d186e3 100644 --- a/hopp/simulation/technologies/layout/wind_layout_tools.py +++ b/hopp/simulation/technologies/layout/wind_layout_tools.py @@ -1,4 +1,5 @@ import numpy as np +import pandas as pd from typing import Optional from shapely.affinity import rotate, translate @@ -6,9 +7,9 @@ from shapely.geometry.base import BaseGeometry from shapely.prepared import prep from shapely.ops import unary_union - +from shapely.geometry import Polygon, MultiPoint from hopp.simulation.technologies.layout.layout_tools import binary_search_float - +from hopp.simulation.technologies.sites.site_shape_tools import calc_dist_between_two_points_cartesian, rotate_shape def get_evenly_spaced_points_along_border(boundary: BaseGeometry, spacing: float, @@ -40,17 +41,28 @@ def make_grid_lines(site_shape: BaseGeometry, grid_angle: float, interrow_spacing: float ) -> list: - """ - Place parallel lines inside a site - :param site_shape: Polygon - :param center: where to center the grid - :param grid_angle: in degrees where 0 is east - :param interrow_spacing: distance between lines - :return: list of lines + """Place parallel lines inside a site. + + Process runs as follows: + + - `bounding_box_line`: line from (xmin,ymin) to (xmax,ymax) + - `base_line`: at y=0, x goes from negative to positive `bounding_box_line.length` + - `line_length`: `2x(bounding_box_line.length) = 2*(sqrt[(xmax-xmin)^2 + (ymax-ymin)^2])` + - shift `base_line` so ymax,ymin = center.y and (xmax - xmin)/2 = center.x + + Args: + site_shape (BaseGeometry): Polygon + center (Point): where to center the grid + grid_angle (float): in degrees where 0 is east + interrow_spacing (float): distance between lines + + Returns: + list[LineString]: grid lines as rows. """ if site_shape.is_empty: return [] + grid_angle = np.deg2rad(grid_angle) grid_angle = (grid_angle + np.pi) % (2 * np.pi) - np.pi # reset grid_angle to (-pi, pi) bounds = site_shape.bounds @@ -264,3 +276,172 @@ def subtract_turbine_exclusion_zone(min_spacing: float, each of the grid variables can change individually, however the discrete values remain fixed. """ + +def find_most_square_layout_dimensions(n_turbs): + """Calculate dimensions of the most-square shaped layout for + a given number of turbines. + + Args: + n_turbs (int): number of wind turbines. + + Returns: + 2-element tuple containing + + - **n_turbs_per_row** (int): number of turbines per row + - **n_rows** (int): number of rows in layout (rows are parallel to x-axis) + """ + n_turbs_per_row = np.floor_divide(n_turbs,np.sqrt(n_turbs)) + n_rows_min = n_turbs//n_turbs_per_row + remainder_turbs = n_turbs%n_turbs_per_row + if remainder_turbs>n_turbs_per_row: + n_extra_rows = np.ceil(remainder_turbs/n_turbs_per_row) + elif remainder_turbs==0: + n_extra_rows = 0 + else: + n_extra_rows = 1 + + n_rows = n_rows_min + n_extra_rows + + return n_turbs_per_row.astype(int),n_rows.astype(int) + +def make_site_boundary_for_square_grid_layout(n_turbs, rotor_diam,row_spacing, turbine_spacing): + """Generate coordinates for shape that would result in the most-square turbine layout. + + Args: + n_turbs (int): number of wind turbines + rotor_diam (float): rotor diameter of turbine in meters + row_spacing (int | float): spacing between rows as multiplier for rotor diameter + turbine_spacing (int | float): spacing between turbines in the same row + as multiplier for rotor diameter. + + Returns: + dict: coordinates for wind layout boundary, formatted as ``site_boundaries`` entry in ``site["data"]`` + """ + + + #distance between turbines in same row + intrarow_spacing = turbine_spacing*rotor_diam + #distance between rows + interrow_spacing = row_spacing*rotor_diam + + n_turbs_per_row,n_rows = find_most_square_layout_dimensions(n_turbs) + + center_x = ((n_turbs_per_row/2)*intrarow_spacing) + center_y = ((n_rows/2)*interrow_spacing) + (interrow_spacing*0.25) + x_dist_m = 2*center_x + y_dist_m = 2*center_y + + p0 = [0.0,0.0] + p1 = [0.0,y_dist_m] + p2 = [x_dist_m,y_dist_m] + p3 = [x_dist_m,0.0] + verts = [p0,p1,p2,p3] + return {"site_boundaries" : {"verts":verts, "verts_simple":verts}} + +def make_bounding_box_for_wind_layout(layout_x,layout_y): + """Get convex hull of wind layout. + + Args: + layout_x (List[float]): x-coordinates of turbines + layout_y (List[float]): y-coordinates of turbines + + Returns: + shapely.MultiPoint: convex hull of wind farm layout. + """ + + coords = [[x,y] for x,y in zip(layout_x,layout_y)] + multip = MultiPoint(coords) + return multip.convex_hull + + +def check_turbines_in_site(layout_x, layout_y, site_boundaries:BaseGeometry, tol=1e-3): + """Check that turbines are within site boundaries for a given tolerance. + + Args: + layout_x (List[float]): x-coordinates of turbines + layout_y (List[float]): y-coordinates of turbines + site_boundaries (BaseGeometry): Site polygon. + tol (float, Optional): distance tolerance in meters. Defaults to 1e-3. + + Returns: + 2-element tuple containing + + - **x_coords** (List[float]): x-coordinates of turbines within site boundaries. + - **y_coords** (List[float]): y-coordinates of turbines within site boundaries. + """ + n_decimals = len(str(int(1/tol)).split("1")[-1]) + x_coords = [] + y_coords = [] + for x,y in zip(layout_x,layout_y): + if site_boundaries.contains(Point(x,y)): + x_coords.append(x) + y_coords.append(y) + else: + if site_boundaries.distance(Point(x,y)) 0: self.start_idx = self.config.timestep[0] @@ -79,15 +97,59 @@ def __attrs_post_init__(self): else: self.start_idx = 0 self.end_idx = 8759 + - # results - self.gen = [] - self.annual_energy = None - self.capacity_factor = None - - def value(self, name: str, set_value=None): + def initialize_from_floris(self,floris_config): """ - if set_value = None, then retrieve value; otherwise overwrite variable's value + Please populate all the wind farm parameters + """ + + if self.config.turbine_name is None: + # NOTE: eventually the turbine name provided in the config will be used + # to load a turbine from the turbine-models library. + if isinstance(floris_config["farm"]["turbine_type"][0],dict): + self.turbine_name = floris_config["farm"]["turbine_type"][0]["turbine_type"] + + # load file from internal floris library + if isinstance(floris_config["farm"]["turbine_type"][0],str): + self.turbine_name = floris_config["farm"]["turbine_type"][0] + turb_dict = load_yaml(INTERNAL_LIBRARY / "{}.yaml".format(floris_config["farm"]["turbine_type"][0])) + floris_config["farm"]["turbine_type"][0] = turb_dict + + # see if rotor diameter was input in config but not set in floris config + if self.config.rotor_diameter is not None: + floris_config["farm"]["turbine_type"][0].setdefault("rotor_diameter",self.config.rotor_diameter) + # see if hub-height was input in config but not set in floris config + # NOTE: hub-height should also be checked against wind resource hub-height + if self.config.hub_height is not None: + floris_config["farm"]["turbine_type"][0].setdefault("hub_height",self.config.hub_height) + + # set attributes: + self.wind_turbine_rotor_diameter = floris_config["farm"]["turbine_type"][0]["rotor_diameter"] + self.wind_turbine_powercurve_powerout = floris_config["farm"]["turbine_type"][0]["power_thrust_table"]["power"] + self.wind_farm_xCoordinates = floris_config["farm"]["layout_x"] + self.wind_farm_yCoordinates = floris_config["farm"]["layout_y"] + self.nTurbs = len(self.wind_farm_xCoordinates) + + self.turb_rating = max(self.wind_turbine_powercurve_powerout) + if self.config.turbine_rating_kw is not None: + if self.config.turbine_rating_kw != self.turb_rating: + raise UserWarning(f"input turbine rating ({self.config.turbine_rating_kw} kW) does not match rating from floris power-curve ({self.turb_rating} kW)") + # check if user-input num_turbines equals number of turbines in layout + if self.config.num_turbines is not None: + # raise warning if discrepancy in number of turbines + if self.nTurbs != self.config.num_turbines: + raise UserWarning(f"num_turbines input ({self.config.num_turbines}) does not equal number of turbines in floris layout ({self.nTurbs})") + return floris_config + + def value(self, name: str, set_value=None): + """Set or retrieve attribute of `hopp.simulation.technologies.wind.floris.Floris`. + if set_value = None, then retrieve value; otherwise overwrite variable's value. + + Args: + name (str): name of attribute to set or retrieve. + set_value (Optional): value to set for variable `name`. + If `None`, then retrieve value. Defaults to None. """ if set_value is not None: self.__setattr__(name, set_value) @@ -95,9 +157,22 @@ def value(self, name: str, set_value=None): return self.__getattribute__(name) def set_floris_value(self,name,value): - self.fi.set(**{name:value}) + if value is not None: + self.fi.set(**{name:value}) + + def set_floris_param(self,param,value): + if value is not None: + self.fi.set_param(param,value) + + def get_floris_param(self,param): + return self.fi.get_param(param) def execute(self, project_life): + """Simulate wind farm performance using floris. + + Args: + project_life (int): unused project life in years + """ if self.verbose: print('Simulating wind farm output in FLORIS...') @@ -136,4 +211,5 @@ def export(self): 'system_capacity': self.system_capacity, 'annual_energy': self.annual_energy, } - return config \ No newline at end of file + return config + \ No newline at end of file diff --git a/hopp/simulation/technologies/wind/wind_plant.py b/hopp/simulation/technologies/wind/wind_plant.py index 7dab48926..935987edf 100644 --- a/hopp/simulation/technologies/wind/wind_plant.py +++ b/hopp/simulation/technologies/wind/wind_plant.py @@ -12,10 +12,13 @@ from hopp.simulation.technologies.wind.floris import Floris from hopp.simulation.technologies.power_source import PowerSource from hopp.simulation.technologies.sites import SiteInfo -from hopp.simulation.technologies.layout.wind_layout import WindLayout, WindBoundaryGridParameters +from hopp.simulation.technologies.layout.wind_layout import ( + WindLayout, + WindBoundaryGridParameters, + WindBasicGridParameters) from hopp.simulation.technologies.financial import CustomFinancialModel, FinancialModelType from hopp.utilities.log import hybrid_logger as logger -from hopp.tools.resource.wind_tools import calculate_elevation_air_density_losses +from hopp.tools.resource.wind_tools import calculate_air_density_losses @define @@ -24,25 +27,28 @@ class WindConfig(BaseClass): Configuration class for WindPlant. Args: - num_turbines: number of turbines in the farm - turbine_rating_kw: turbine rating - rotor_diameter: turbine rotor diameter - hub_height: turbine hub height - layout_mode: - - 'boundarygrid': regular grid with boundary turbines, requires WindBoundaryGridParameters as 'params' - - 'grid': regular grid with dx, dy distance, 0 angle; does not require 'params' - model_name: which model to use. Options are 'floris' and 'pysam' - model_input_file: file specifying a full PySAM input - layout_params: layout configuration - rating_range_kw: allowable kw range of turbines, default is 1000 - 3000 kW - floris_config: Floris configuration, only used if `model_name` == 'floris' + num_turbines (int): number of turbines in the farm + turbine_rating_kw (float): turbine rating in kW + rotor_diameter (float | int, Optional): turbine rotor diameter in meters + hub_height (float, Optional): turbine hub height in meters + turbine_name (str, Optional): unused currently. Defaults to None. + layout_mode (str): + - 'boundarygrid': regular grid with boundary turbines, requires WindBoundaryGridParameters as 'layout_params' + - 'grid': regular grid with dx, dy distance, 0 angle; does not require 'layout_params' + - 'basicgrid': most-square grid layout, requires WindBasicGridParameters as 'layout_params' + - 'custom': use a user-provided layout. + model_name (str): which model to use. Options are 'floris' and 'pysam' + model_input_file (str): file specifying a full PySAM input + layout_params (obj | dict, Optional): layout configuration object corresponding to `layout_mode` or dictionary. + rating_range_kw (Tuple[int]): allowable kw range of turbines, default is 1000 - 3000 kW + floris_config (dict | str | Path): Floris configuration, only used if `model_name` == 'floris' adjust_air_density_for_elevation (bool): whether to adjust air density for elevation. Defaults to False. Only used if True and ``site.elev`` is not None. resource_parse_method (str): method to parse wind resource data if using floris and downloaded resource data for 2 heights. Can either be "weighted_average" or "average". Defaults to "average". - operational_losses: total percentage losses in addition to wake losses, defaults based on PySAM (only used for Floris model) - timestep: Timestep (required for floris runs, otherwise optional). Defaults to (0,8760) - fin_model: Optional financial model. Can be any of the following: + operational_losses (float, Optional): total percentage losses in addition to wake losses, defaults based on PySAM (only used for Floris model) + timestep (Tuple[int]): Timestep (required for floris runs, otherwise optional). Defaults to (0,8760) + fin_model (obj | dict | str): Optional financial model. Can be any of the following: - a string representing an argument to `Singleowner.default` @@ -55,13 +61,14 @@ class WindConfig(BaseClass): rotor_diameter: Optional[float] = field(default=None) layout_params: Optional[Union[dict, WindBoundaryGridParameters]] = field(default=None) hub_height: Optional[float] = field(default=None) - layout_mode: str = field(default="grid", validator=contains(["boundarygrid", "grid"])) - model_name: str = field(default="pysam", validator=contains(["pysam", "floris"])) + turbine_name: Optional[str] = field(default=None) + layout_mode: str = field(default="grid", validator=contains(["boundarygrid", "grid", "basicgrid", "custom"]), converter=(str.strip, str.lower)) + model_name: str = field(default="pysam", validator=contains(["pysam", "floris"]), converter=(str.strip, str.lower)) model_input_file: Optional[str] = field(default=None) rating_range_kw: Tuple[int, int] = field(default=(1000, 3000)) floris_config: Optional[Union[dict, str, Path]] = field(default=None) adjust_air_density_for_elevation: Optional[bool] = field(default = False) - resource_parse_method: str = field(default="average", validator=contains(["weighted_average", "average"])) + resource_parse_method: str = field(default="average", validator=contains(["weighted_average", "average"]), converter=(str.strip, str.lower)) operational_losses: float = field(default = 12.83, validator=range_val(0, 100)) timestep: Optional[Tuple[int, int]] = field(default=(0,8760)) fin_model: Optional[Union[dict, FinancialModelType]] = field(default=None) @@ -133,32 +140,36 @@ def __attrs_post_init__(self): financial_model = Singleowner.from_existing(system_model, self.config_name) else: financial_model = self.import_financial_model(financial_model, system_model, self.config_name) - - if isinstance(self.config.layout_params, dict): + + # below is unnecessary now - this functionality exists in WindLayout. + if isinstance(self.config.layout_params, dict) and self.config.layout_mode=="boundarygrid": layout_params = WindBoundaryGridParameters(**self.config.layout_params) + elif isinstance(self.config.layout_params, dict) and self.config.layout_mode=="basicgrid": + layout_params = WindBasicGridParameters(**self.config.layout_params) else: layout_params = self.config.layout_params super().__init__("WindPlant", self.site, system_model, financial_model) self._system_model.value("wind_resource_data", self.site.wind_resource.data) - self._layout = WindLayout(self.site, system_model, self.config.layout_mode, layout_params) + self._layout = WindLayout(self.site.polygon, system_model, self.config.layout_mode, layout_params) self._dispatch = None self.turb_rating = self.config.turbine_rating_kw self.num_turbines = self.config.num_turbines - - if self.config.hub_height is not None: - self._system_model.Turbine.wind_turbine_hub_ht = self.config.hub_height if self.config.rotor_diameter is not None: self.rotor_diameter = self.config.rotor_diameter - - if self.config.model_name == "pysam": + + if self.config.model_name=="pysam": + if self.config.hub_height is not None: + self._system_model.Turbine.wind_turbine_hub_ht = self.config.hub_height if self.config.adjust_air_density_for_elevation and self.site.elev is not None: - air_dens_losses = calculate_elevation_air_density_losses(self.site.elev) + air_dens_losses = calculate_air_density_losses(self.site.elev) self._system_model.Losses.assign({"turb_specific_loss":air_dens_losses}) - + + + @property def wake_model(self) -> str: try: diff --git a/hopp/tools/resource/wind_tools.py b/hopp/tools/resource/wind_tools.py index 147f43acc..99199390a 100644 --- a/hopp/tools/resource/wind_tools.py +++ b/hopp/tools/resource/wind_tools.py @@ -1,37 +1,42 @@ from scipy.constants import R, g, convert_temperature import numpy as np -def calculate_air_density_for_elevation(elevation_m: float) -> float: +RHO_0 = 1.225 # Air density at sea level (kg/m3) +T_REF = 20 # Standard air temperature (Celsius) +MOLAR_MASS_AIR = 28.96 # Molar mass of air (g/mol) +LAPSE_RATE = 0.0065 # Temperature lapse rate (K/m) for 0-11000m above sea level + +def calculate_air_density(elevation_m: float) -> float: """ Calculate air density based on site elevation using the Barometric formula. This function is based on Equation 1 from: https://en.wikipedia.org/wiki/Barometric_formula#Density_equations + Imported constants are: + - g: acceleration due to gravity (m/s2) + - R: universal gas constant (J/mol-K) + Args: elevation_m (float): Elevation of site in meters Returns: float: Air density in kg/m^3 at elevation of site """ - rho0 = 1.225 # Air density at sea level (kg/m3) - t_ref = 20 # Standard air temperature (Celsius) - elevation_sea_level = 0.0 # Reference elevation at sea level (m) - l = 0.0065 # Temperature lapse rate (K/m) for 0-11000m above sea level - molar_mass_air = 28.96 # Molar mass of air (g/mol) + + # Reference elevation at sea level (m) + elevation_sea_level = 0.0 # Convert temperature to Kelvin - T_ref = convert_temperature([t_ref], "C", "K")[0] + T_ref_K = convert_temperature([T_REF], "C", "K")[0] # Exponent value used in equation below - e = g * (molar_mass_air / 1e3) / (R * l) - # g: acceleration due to gravity (m/s2) - # R: universal gas constant (J/mol-K) + e = g * (MOLAR_MASS_AIR / 1e3) / (R * LAPSE_RATE) # Calculate air density at site elevation - rho = rho0 * ((T_ref - ((elevation_m - elevation_sea_level) * l)) / T_ref) ** (e - 1) + rho = RHO_0 * ((T_ref_K - ((elevation_m - elevation_sea_level) * LAPSE_RATE)) / T_ref_K) ** (e - 1) return rho -def calculate_elevation_air_density_losses(elevation_m: float) -> float: +def calculate_air_density_losses(elevation_m: float) -> float: """Calculate loss (%) from air density drop at site elevation. Args: @@ -40,13 +45,13 @@ def calculate_elevation_air_density_losses(elevation_m: float) -> float: Returns: float: percentage loss associated with air density decrease at elevation. """ + if elevation_m <= 0.0: - loss_percent = 0.0 - else: - rho0 = 1.225 - air_density = calculate_air_density_for_elevation(elevation_m) - loss_ratio = 1 - (air_density / rho0) - loss_percent = loss_ratio * 100 + return 0.0 + + air_density = calculate_air_density(elevation_m) + loss_ratio = 1 - (air_density / RHO_0) + loss_percent = loss_ratio * 100 return loss_percent @@ -71,20 +76,19 @@ def parse_resource_data(wind_resource): """ data = np.array(wind_resource.data['data']) - # Get indices of wind speed data + # Get indices of wind speed data and wind direction data idx_ws = [ii for ii, field in enumerate(wind_resource.data['fields']) if field == 3] - - # Get indices of wind direction data idx_wd = [ii for ii, field in enumerate(wind_resource.data['fields']) if field == 4] - # If there's multiple hub-heights - average the data - if len(idx_ws) > 1: - speeds = data[:, idx_ws].mean(axis=1) - wind_dirs = data[:, idx_wd].mean(axis=1) - else: - # If there's only one hub-height, grab speed and direction data + # If there's only one hub-height, grab speed and direction data + if len(idx_ws)==1: speeds = data[:, idx_ws[0]] wind_dirs = data[:, idx_wd[0]] + return speeds, wind_dirs + + # If there's multiple hub-heights - average the data + speeds = data[:, idx_ws].mean(axis=1) + wind_dirs = data[:, idx_wd].mean(axis=1) return speeds, wind_dirs @@ -109,43 +113,43 @@ def weighted_parse_resource_data(wind_resource): """ data = np.array(wind_resource.data['data']) - # Get indices of wind speed data + # Get indices of wind speed data and wind direction data idx_ws = [ii for ii, field in enumerate(wind_resource.data['fields']) if field == 3] - - # Get indices of wind direction data idx_wd = [ii for ii, field in enumerate(wind_resource.data['fields']) if field == 4] - # If there's multiple hub-heights - average the data - if len(idx_ws) > 1: - # Weights corresponding to difference of resource height and hub-height - hh1, hh2 = np.unique(wind_resource.data['heights']) - weight1 = np.abs(hh1 - wind_resource.hub_height_meters) - weight2 = np.abs(hh2 - wind_resource.hub_height_meters) - - # Wind speed data indices for each resource height - idx_ws1 = [i for i in idx_ws if wind_resource.data['heights'][i] == hh1][0] - idx_ws2 = [i for i in idx_ws if wind_resource.data['heights'][i] == hh2][0] - - # Wind speeds at the two resource heights - ws1 = data[:, idx_ws1] - ws2 = data[:, idx_ws2] - - # Weight wind speed data based on height relative to turbine hub-height - speeds = np.round(((weight1 * ws1) + (weight2 * ws2)) / (weight1 + weight2), 3) - - # Wind direction data indices for each resource height - idx_wd1 = [i for i in idx_wd if wind_resource.data['heights'][i] == hh1][0] - idx_wd2 = [i for i in idx_wd if wind_resource.data['heights'][i] == hh2][0] - - # Wind directions at the two resource heights - wd1 = data[:, idx_wd1] - wd2 = data[:, idx_wd2] - - # Weight wind direction data based on height relative to turbine hub-height - wind_dirs = np.round(((weight1 * wd1) + (weight2 * wd2)) / (weight1 + weight2), 3) - else: - # If there's only one hub-height, grab speed and direction data + # If there's only one hub-height, grab speed and direction data + if len(idx_ws) == 1: speeds = data[:, idx_ws[0]] wind_dirs = data[:, idx_wd[0]] + return speeds, wind_dirs + + # If there's multiple hub-heights - average the data + hh1, hh2 = np.unique(wind_resource.data['heights']) + + # Weights corresponding to difference of resource height and hub-height + weight1 = np.abs(hh1 - wind_resource.hub_height_meters) + weight2 = np.abs(hh2 - wind_resource.hub_height_meters) + + # Wind speed data indices for each resource height + idx_ws1 = [i for i in idx_ws if wind_resource.data['heights'][i] == hh1][0] + idx_ws2 = [i for i in idx_ws if wind_resource.data['heights'][i] == hh2][0] + + # Wind speeds at the two resource heights + ws1 = data[:, idx_ws1] + ws2 = data[:, idx_ws2] + + # Weight wind speed data based on height relative to turbine hub-height + speeds = np.round(((weight1 * ws1) + (weight2 * ws2)) / (weight1 + weight2), 3) + + # Wind direction data indices for each resource height + idx_wd1 = [i for i in idx_wd if wind_resource.data['heights'][i] == hh1][0] + idx_wd2 = [i for i in idx_wd if wind_resource.data['heights'][i] == hh2][0] + + # Wind directions at the two resource heights + wd1 = data[:, idx_wd1] + wd2 = data[:, idx_wd2] + + # Weight wind direction data based on height relative to turbine hub-height + wind_dirs = np.round(((weight1 * wd1) + (weight2 * wd2)) / (weight1 + weight2), 3) return speeds, wind_dirs diff --git a/tests/hopp/test_custom_financial.py b/tests/hopp/test_custom_financial.py index 8be0367f6..de2ebd8ad 100644 --- a/tests/hopp/test_custom_financial.py +++ b/tests/hopp/test_custom_financial.py @@ -7,6 +7,7 @@ from tests.hopp.utils import create_default_site_info, DEFAULT_FIN_CONFIG import copy +import numpy as np DEFAULT_FIN_CONFIG_LOCAL = copy.deepcopy(DEFAULT_FIN_CONFIG) DEFAULT_FIN_CONFIG_LOCAL.pop("revenue") # these tests were written before the revenue section was added to the default financial config @@ -152,7 +153,7 @@ def test_hybrid_simple_pv_with_wind(site, subtests): 'layout_params': { "border_spacing": 2, "border_offset": 0.5, - "grid_angle": 0.5, + "grid_angle": np.rad2deg(0.5), "grid_aspect_power": 0.5, "row_phase_offset": 0.5 }, @@ -230,7 +231,7 @@ def test_hybrid_detailed_pv_with_wind(site, subtests): 'layout_params': { "border_spacing": 2, "border_offset": 0.5, - "grid_angle": 0.5, + "grid_angle": np.rad2deg(0.5), "grid_aspect_power": 0.5, "row_phase_offset": 0.5 }, @@ -260,8 +261,8 @@ def test_hybrid_detailed_pv_with_wind(site, subtests): with subtests.test("with minimal params"): assert sizes.pv == approx(4993, 1e-3) assert sizes.wind == approx(wind_kw, 1e-3) - assert aeps.pv == approx(annual_energy_expected_pv, 1e-3) assert aeps.wind == approx(annual_energy_expected_wind, 1e-3) + assert aeps.pv == approx(annual_energy_expected_pv, 1e-3) assert aeps.hybrid == approx(annual_energy_expected_hybrid, 1e-3) assert npvs.pv == approx(npv_expected_pv, 1e-3) assert npvs.wind == approx(npv_expected_wind, 1e-3) @@ -318,7 +319,7 @@ def test_hybrid_simple_pv_with_wind_wave_storage_dispatch(subtests): 'layout_params': { "border_spacing": 2, "border_offset": 0.5, - "grid_angle": 0.5, + "grid_angle": np.rad2deg(0.5), "grid_aspect_power": 0.5, "row_phase_offset": 0.5 }, @@ -466,7 +467,7 @@ def test_hybrid_detailed_pv_with_wind_storage_dispatch(site, subtests): 'layout_params': { "border_spacing": 2, "border_offset": 0.5, - "grid_angle": 0.5, + "grid_angle": np.rad2deg(0.5), "grid_aspect_power": 0.5, "row_phase_offset": 0.5 }, diff --git a/tests/hopp/test_hybrid.py b/tests/hopp/test_hybrid.py index 5cdaf7971..6774bdf0e 100644 --- a/tests/hopp/test_hybrid.py +++ b/tests/hopp/test_hybrid.py @@ -412,7 +412,6 @@ def test_hybrid_wind_only(hybrid_config, subtests): assert npvs.hybrid == approx(-6068047, 1e-3) def test_hybrid_wind_only_floris(hybrid_config, subtests): - floris_config_path = ( ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" ) @@ -422,7 +421,9 @@ def test_hybrid_wind_only_floris(hybrid_config, subtests): wind_only["wind"]["model_name"] = "floris" wind_only["wind"]["floris_config"] = floris_config_path wind_only["wind"]["timestep"] = [0, 8760] - + wind_only["wind"]["num_turbines"] = 4 + wind_only["wind"]["turbine_rating_kw"] = 5000 + hybrid_config["technologies"] = wind_only hi = HoppInterface(hybrid_config) hybrid_plant = hi.system @@ -432,15 +433,18 @@ def test_hybrid_wind_only_floris(hybrid_config, subtests): aeps = hybrid_plant.annual_energies npvs = hybrid_plant.net_present_values cf = hybrid_plant.capacity_factors - + with subtests.test("floris farm capacity"): + assert hybrid_plant.wind._system_model.system_capacity == 20000.0 + with subtests.test("windplant farm capacity"): + assert hybrid_plant.wind.system_capacity_kw == 20000.0 with subtests.test("wind aep"): assert aeps.wind == approx(74149945, 1e-3) with subtests.test("hybrid aep"): assert aeps.hybrid == approx(68271657, 1e-3) with subtests.test("wind npv"): - assert npvs.wind == approx(5999302, 1e-3) + assert npvs.wind == approx(-9193785, 1e-3) with subtests.test("hybrid npv"): - assert npvs.hybrid == approx(4345865, 1e-3) + assert npvs.hybrid == approx(-10847221, 1e-3) def test_hybrid_pv_only(hybrid_config, subtests): technologies = hybrid_config["technologies"] @@ -1610,6 +1614,7 @@ def test_hybrid_wind_only_floris_elevation_adjusted(hybrid_config, subtests): wind_only["wind"]["floris_config"] = floris_config_path wind_only["wind"]["timestep"] = [0, 8760] wind_only["wind"]["num_turbines"] = 4 + wind_only["wind"]["turbine_rating_kw"] = 5000 hybrid_config["technologies"] = wind_only hi = HoppInterface(hybrid_config) diff --git a/tests/hopp/test_layout.py b/tests/hopp/test_layout.py index 64163a23c..b7d10d6ea 100644 --- a/tests/hopp/test_layout.py +++ b/tests/hopp/test_layout.py @@ -11,7 +11,11 @@ from hopp.simulation.technologies.wind.wind_plant import WindPlant, WindConfig from hopp.simulation.technologies.pv.pv_plant import PVPlant, PVConfig -from hopp.simulation.technologies.layout.hybrid_layout import HybridLayout, WindBoundaryGridParameters, PVGridParameters, get_flicker_loss_multiplier +from hopp.simulation.technologies.layout.wind_layout import ( + WindLayout, + WindBoundaryGridParameters, + WindBasicGridParameters) +from hopp.simulation.technologies.layout.hybrid_layout import HybridLayout, PVGridParameters, get_flicker_loss_multiplier from hopp.simulation.technologies.layout.wind_layout_tools import create_grid from hopp.simulation.technologies.layout.pv_design_utils import size_electrical_parameters, find_modules_per_string from hopp.simulation.technologies.pv.detailed_pv_plant import DetailedPVPlant, DetailedPVConfig @@ -52,7 +56,7 @@ def test_create_grid(site): site.plot() turbine_positions = create_grid(bounding_shape, site.polygon.centroid, - np.pi / 4, + 45.0, 200, 200, .5) @@ -72,7 +76,7 @@ def test_create_grid(site): assert(t.y == pytest.approx(expected_positions[n][1], 1e-1)) -def test_wind_layout(site): +def test_wind_boundary_grid_layout_pysam(site): config = WindConfig.from_dict(technology['wind']) wind_model = WindPlant(site, config=config) xcoords, ycoords = wind_model._layout.turb_pos_x, wind_model._layout.turb_pos_y @@ -84,8 +88,28 @@ def test_wind_layout(site): assert xcoords[i] == pytest.approx(expected_xcoords[i], abs=1) assert ycoords[i] == pytest.approx(expected_ycoords[i], abs=1) - # wind_model.plot() - # plt.show() +def test_wind_basic_grid_layout_pysam_default(site): + wind_technology = { + 'num_turbines': 16, + 'rotor_diameter': 40.0, + 'turbine_rating_kw': 600, + 'layout_mode': 'basicgrid', + 'layout_params': WindBasicGridParameters() + } + config = WindConfig.from_dict(wind_technology) + wind_model = WindPlant(site, config=config) + xcoords, ycoords = wind_model._layout.turb_pos_x, wind_model._layout.turb_pos_y + unique_x_coords = np.unique(xcoords) + unique_y_coords = np.unique(ycoords) + + expected_unique_x_coords = [196, 396, 596, 796] + expected_unique_y_coords = [50, 250, 450, 650] + assert len(xcoords) == wind_technology["num_turbines"] + assert len(unique_x_coords) == len(unique_y_coords) + for i in range(len(unique_x_coords)): + assert unique_x_coords[i] == pytest.approx(expected_unique_x_coords[i], abs=1) + for i in range(len(unique_y_coords)): + assert unique_y_coords[i] == pytest.approx(expected_unique_y_coords[i], abs=1) def test_solar_layout(site): diff --git a/tests/hopp/test_wind.py b/tests/hopp/test_wind.py index 00caa7784..0ebafc918 100644 --- a/tests/hopp/test_wind.py +++ b/tests/hopp/test_wind.py @@ -2,10 +2,10 @@ import math import PySAM.Windpower as windpower - +import pytest from hopp.simulation.technologies.wind.wind_plant import WindPlant, WindConfig from tests.hopp.utils import create_default_site_info - +from hopp.utilities import load_yaml from hopp import ROOT_DIR @fixture @@ -130,45 +130,55 @@ def test_changing_system_capacity_pysam(site): assert model.system_capacity_kw == approx(n) #################### FLORIS tests ################ +def test_floris_num_turbines(site): + floris_config_path = ( + ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" + ) + f_config = load_yaml(floris_config_path) + floris_n_turbines = len(f_config["farm"]["layout_x"]) + config = WindConfig.from_dict({'num_turbines': 20, "turbine_rating_kw": 5000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) + with pytest.raises(UserWarning) as err: + model = WindPlant(site, config=config) + assert str(err.value) == f"num_turbines input ({config.num_turbines}) does not equal number of turbines in floris layout ({floris_n_turbines})" + def test_changing_rotor_diam_recalc_floris(site): floris_config_path = ( ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" ) - config = WindConfig.from_dict({'num_turbines': 20, "turbine_rating_kw": 1000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) + config = WindConfig.from_dict({'num_turbines': 4, "turbine_rating_kw": 5000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) model = WindPlant(site, config=config) - assert model.system_capacity_kw == 20000 + assert model._system_model.system_capacity == 20000 diams = range(50, 70, 140) for d in diams: - model.rotor_diameter = d - assert model.rotor_diameter == d, "rotor diameter should be " + str(d) + model._system_model.wind_turbine_rotor_diameter = d + assert model._system_model.wind_turbine_rotor_diameter == d, "rotor diameter should be " + str(d) def test_changing_turbine_rating_floris(site): floris_config_path = ( ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" ) - config = WindConfig.from_dict({'num_turbines': 20, "turbine_rating_kw": 1000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) - model = WindPlant(site, config=config) - n_turbs = model.num_turbines - for n in range(1000, 3000, 150): - model.turb_rating = n - assert model.system_capacity_kw == model.turb_rating * n_turbs, "system size error when rating is " + str(n) + config = WindConfig.from_dict({'num_turbines': 4, "turbine_rating_kw": 1000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) + with pytest.raises(UserWarning) as err: + model = WindPlant(site, config=config) + assert str(err.value) == "input turbine rating (1000 kW) does not match rating from floris power-curve (5000.0 kW)" + + def test_changing_system_capacity_floris(site): floris_config_path = ( ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" ) - config = WindConfig.from_dict({'num_turbines': 20, "turbine_rating_kw": 1000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) + + config = WindConfig.from_dict({'num_turbines': 4, "turbine_rating_kw": 5000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) model = WindPlant(site, config=config) - rating = model.turb_rating - for n in range(1000, 20000, 1000): - model.system_capacity_by_num_turbines(n) - assert model.turb_rating == rating, str(n) - assert model.system_capacity_kw == rating * round(n/rating) + + rating = model._system_model.turb_rating + + assert model._system_model.nTurbs == 4 + assert model._system_model.turb_rating == rating + assert model._system_model.system_capacity == 20000 + model.system_capacity_by_num_turbines(10000) + assert model._system_model.system_capacity == 10000.0 - # adjust turbine rating first, system capacity will be exact - model = WindPlant(site, config=config) - for n in range(40000, 60000, 1000): - model.system_capacity_by_rating(n) - assert model.system_capacity_kw == approx(n) \ No newline at end of file diff --git a/tests/hopp/test_wind_resource_tools.py b/tests/hopp/test_wind_resource_tools.py index e72c0a428..40cd2ec0a 100644 --- a/tests/hopp/test_wind_resource_tools.py +++ b/tests/hopp/test_wind_resource_tools.py @@ -1,8 +1,8 @@ import os from PySAM.ResourceTools import SRW_to_wind_data from hopp.tools.resource.wind_tools import ( - calculate_air_density_for_elevation, - calculate_elevation_air_density_losses, + calculate_air_density, + calculate_air_density_losses, parse_resource_data, weighted_parse_resource_data ) @@ -57,23 +57,23 @@ def wind_resource_data_100m(): def test_sea_level_air_density(): elevation = 0.0 #meters - air_dens = calculate_air_density_for_elevation(elevation) + air_dens = calculate_air_density(elevation) assert air_dens == approx(1.225, rel = 1e-3) def test_mile_high_air_density(): #test elevation at 1 mile above sea level elevation = 1609.34 #meters - air_dens = calculate_air_density_for_elevation(elevation) + air_dens = calculate_air_density(elevation) assert air_dens == approx(1.05, rel = 1e-3) def test_sea_level_air_density_losses(): elevation = 0.0 #meters - loss_percent = calculate_elevation_air_density_losses(elevation) + loss_percent = calculate_air_density_losses(elevation) assert loss_percent == 0.0 def test_mile_high_air_density_losses(): elevation = 1609.34 #meters - loss_percent = calculate_elevation_air_density_losses(elevation) + loss_percent = calculate_air_density_losses(elevation) assert loss_percent == approx(14.325, rel = 1e-3) def test_weighted_parsing_100m(wind_resource_data_100m): From 319d4264e9b26c41b66ba33aae02fc05a97c9304 Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Fri, 21 Feb 2025 13:03:42 -0700 Subject: [PATCH 10/48] Feature add: integrated wind layout methods when using Floris (2/2) (#431) * added site shape tools script * updated site_info for new option of site boundary definition * updated doc strings for site info and site shape tool functions * added tests for site shape tools functions * added site shape tools to documentation * added another optional input for site_details if using circle as shape * updated RELEASE.md with new feature * removed checking vertices so tests pass * added function to rotate site * added site boundary buffer for verts back in * added site polygon buffer as optional input to site info * added wind resource tools script * added test for air density adjustment for elevation calc * integrated adjusting for elevation in wind models * updated doc strings for recent changes * updated release file * fixed bug in make_grid_lines * fixed input to create_grid to be in degrees instead of radians * added new wind layout tools and fixed doc strings related to bug fix * updated call to wind layout tools and fixed inputs for test_custom_financial * adding in option for basicgrid layout option * renamed test_layout test * added weighted parser, updated tests, updated wind_resource.py * added plot function to site_shape_tools * added new wind layout tools function and cleaned up wind_layout a bit * added more functionality to floris.py and added some optional parameters to windconfig * updated example 05 input file so it wont cause a warning * added layout test for basicgrid layout * added comments to tests that will fail because of floris update * updated tests that were failing because of floris update * minor fix to wind_plant and made regression test for elevation adjustment option * adjusted basic grid to center layout on site center and integrated layout with floris * integrated wind layout with floris and updated wind layout mode in floris test in test_hybrid * updated tests for floris because of recent feature-adds * added in integrated of weighted average resource data from v3/elevated_wind --------- Co-authored-by: John Jasa Co-authored-by: bayc --- RELEASE.md | 2 +- .../technologies/layout/wind_layout.py | 303 +++++++++++------- hopp/simulation/technologies/wind/floris.py | 147 ++++++--- .../technologies/wind/wind_plant.py | 184 +++++++---- tests/hopp/test_hybrid.py | 5 +- tests/hopp/test_layout.py | 74 ++++- tests/hopp/test_wind.py | 150 ++++++--- 7 files changed, 580 insertions(+), 285 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 94b6aa809..4359143e2 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -21,7 +21,7 @@ + Bug-fix: grid angle converted from degrees to radians in `make_grid_lines()` function in `wind_layout_tools.py` + Updated floris initialization to set attributes from `floris_config` + Update: raise errors when using floris if theres a discrepancy between inputs in `WindConfig` and information in `floris_config` (such as `num_turbines` and the `floris_config` layout, and turbine parameters like rotor diameter and turbine rating.) - + + Integrated wind layout functionality when using floris ## Version 3.1.1, Dec. 18, 2024 diff --git a/hopp/simulation/technologies/layout/wind_layout.py b/hopp/simulation/technologies/layout/wind_layout.py index 264865ddf..1b545c11c 100644 --- a/hopp/simulation/technologies/layout/wind_layout.py +++ b/hopp/simulation/technologies/layout/wind_layout.py @@ -1,14 +1,15 @@ from __future__ import annotations -from typing import Union -import numpy as np +from typing import Union, Optional + import matplotlib.pyplot as plt +import numpy as np +from attrs import define, field from shapely.geometry import Polygon, Point, MultiPolygon from shapely.geometry.base import BaseGeometry from shapely.affinity import scale + import PySAM.Windpower as windpower -from attrs import define, field -from typing import Optional -from hopp.utilities.log import hybrid_logger as logger +from hopp.simulation.base import BaseClass from hopp.simulation.technologies.layout.wind_layout_tools import ( get_best_grid, get_evenly_spaced_points_along_border, @@ -18,70 +19,76 @@ check_turbines_in_site, adjust_site_for_box_grid_layout ) -from hopp.utilities.validators import contains, range_val from hopp.simulation.technologies.sites.site_shape_tools import plot_site_polygon -from hopp.simulation.base import BaseClass +from hopp.simulation.technologies.wind.floris import Floris +from hopp.utilities.log import hybrid_logger as logger +from hopp.utilities.validators import contains, range_val + @define class WindBasicGridParameters(BaseClass): """Configuration class for 'basicgrid' wind layout. Args: - row_D_spacing (float, Optional): rotor diameter multiplier for spacing between rows of turbines (y direction). - Defaults to 5.0. - turbine_D_spacing (float, Optional): rotor diameter multiplier for spacing between turbines in a row (x direction). - Defaults to 5.0. - grid_angle (float, Optional): grid rotation angle in degrees where 0 is North, increasing clockwise. - Defaults to 0.0. + row_D_spacing (float, Optional): rotor diameter multiplier for spacing between rows of + turbines (y direction). Defaults to 5.0. + turbine_D_spacing (float, Optional): rotor diameter multiplier for spacing between + turbines in a row (x direction). Defaults to 5.0. + grid_angle (float, Optional): grid rotation angle in degrees where 0 is North, increasing + clockwise. Defaults to 0.0. row_phase_offset (float, Optional): offset of turbines along row from one row to the next. Value must be between 0 and 1. Defaults to 0.0. - site_boundary_constrained (bool, Optional): whether to constrain the layout to the site. Defaults to False. + site_boundary_constrained (bool, Optional): whether to constrain the layout to the site. + Defaults to False. """ - row_D_spacing: Optional[float] = field(default = 5.0) - turbine_D_spacing: Optional[float] = field(default = 5.0) - grid_angle: Optional[float] = field(default = 0.0) - row_phase_offset: Optional[float] = field(default = 0.0, validator = range_val(0.0, 1.0)) - site_boundary_constrained: Optional[bool] = field(default = False) + row_D_spacing: Optional[float] = field(default=5.0) + turbine_D_spacing: Optional[float] = field(default=5.0) + grid_angle: Optional[float] = field(default=0.0) + row_phase_offset: Optional[float] = field(default=0.0, validator=range_val(0.0, 1.0)) + site_boundary_constrained: Optional[bool] = field(default=False) @define class WindBoundaryGridParameters(BaseClass): """Configuration class for 'boundarygrid' wind layout. Args: - border_spacing (float): border spacing ratio for turbines placed along border. Defaults to 0.0 - spacing along border = (1 + border_spacing) * min spacing - border_offset (float): turbine border spacing offset as ratio of border spacing (0, 1). Defaults to 0.0 - border_spacing_m (float, Optional): spacing along border in meters. Is used to calculate ``border_spacing`` - if ``min_spacing_m`` is also input. + border_spacing (float): border spacing ratio for turbines placed along border. + Defaults to 0.0; spacing along border = (1 + border_spacing) * min spacing. + border_offset (float): turbine border spacing offset as ratio of border spacing (0, 1). + Defaults to 0.0 + border_spacing_m (float, Optional): spacing along border in meters. Is used to calculate + ``border_spacing`` if ``min_spacing_m`` is also input. min_spacing_m (float, Optional): minimum spacing between turbines in meters. grid_angle (float): turbine inner grid rotation (0, 180) [degrees] grid_aspect_power (float, Optional): used to calculate grid_aspect_ratio. - grid aspect ratio [cols / rows] = 2^grid_aspect_power. ``grid_aspect_ratio = np.exp(grid_aspect_power)`` - grid_aspect_ratio (float, Optional): cols / rows of turbine grid. Defaults to 1.0 if neither ``grid_aspect_ratio`` - or ``grid_aspect_power`` are provided. - row_phase_offset (float): inner grid phase offset (0,1). 20% suggested + grid aspect ratio [cols / rows] = 2^grid_aspect_power. + ``grid_aspect_ratio = np.exp(grid_aspect_power)``. + grid_aspect_ratio (float, Optional): cols / rows of turbine grid. Defaults to 1.0 + if neither ``grid_aspect_ratio`` or ``grid_aspect_power`` are provided. + row_phase_offset (float): inner grid phase offset (0,1). 20% suggested. Value must be between 0 and 1. Defaults to 0.2 """ #TODO: rename to border_spacing_ratio? - border_spacing: float = field(default = 0.0) + border_spacing: float = field(default=0.0) #TODO: rename to border_offset_ratio? - border_offset: float = field(default = 0.0, validator = range_val(0.0, 1.0)) - border_spacing_m: Optional[float] = field(default = None) - min_spacing_m: Optional[float] = field(default = None) - - grid_angle: float = field(default = 0.0, validator = range_val(0.0, 180.0)) - grid_aspect_power: Optional[float] = field(default = None) - grid_aspect_ratio: Optional[float] = field(default = None) - row_phase_offset: float = field(default = 0.2, validator = range_val(0.0, 1.0)) + border_offset: float = field(default=0.0, validator=range_val(0.0, 1.0)) + border_spacing_m: Optional[float] = field(default=None) + min_spacing_m: Optional[float] = field(default=None) + + grid_angle: float = field(default=0.0, validator=range_val(0.0, 180.0)) + grid_aspect_power: Optional[float] = field(default=None) + grid_aspect_ratio: Optional[float] = field(default=None) + row_phase_offset: float = field(default=0.2, validator=range_val(0.0, 1.0)) def __attrs_post_init__(self): - if self.grid_aspect_ratio is None: #NOTE: unsure if this equation is correct given doc strong - self.grid_aspect_ratio = 1 if self.grid_aspect_power is None else np.exp(self.grid_aspect_power) - + self.grid_aspect_ratio = 1 if self.grid_aspect_power is None else np.exp( + self.grid_aspect_power + ) + if self.min_spacing_m is not None and self.border_spacing_m is not None: self.border_spacing = (self.border_spacing_m/self.min_spacing_m) - 1 @@ -105,34 +112,55 @@ class WindLayout(BaseClass): Args: site_polygon (Polygon | BaseGeometry): site polygon shape. - _system_model (windpower.Windpower | Floris): pysam wind power object. Not currently tested to work with floris. + _system_model (windpower.Windpower | Floris): pysam wind power object. Not currently + tested to work with floris. layout_mode (str): layout choice: "boundarygrid", "grid", "custom", "basicgrid" - parameters (Union[WindBoundaryGridParameters, WindCustomParameters, WindBasicGridParameters, None, dict]): wind - layout parameters for the corresponding `layout_mode` - min_spacing_meters (float, Optional): minimum spacing between turbines in meters. Defaults to 0.0. - max_spacing_meters (float, Optional): maximum spacing between turbines in meters. Defaults to 2e6. - min_rotor_diameter_multiplier (float, Optional): minimum spacing between turbines as multiplier of rotor diameter. Defaults to 2.0 - max_rotor_diameter_multiplier (float, Optional): maximum spacing between turbines as multiplier of rotor diameter. Defaults to 20.0 - turbine_rating_kW (float, Optional): rating of a single turbine in kW. if not provided, turbine power is estimated from the power-curve. + parameters ( + Union[ + WindBoundaryGridParameters, + WindCustomParameters, + WindBasicGridParameters, + None, + dict + ] + ): wind layout parameters for the corresponding `layout_mode` + min_spacing_meters (float, Optional): minimum spacing between turbines in meters. + Defaults to 0.0. + max_spacing_meters (float, Optional): maximum spacing between turbines in meters. + Defaults to 2e6. + min_rotor_diameter_multiplier (float, Optional): minimum spacing between turbines as + multiplier of rotor diameter. Defaults to 2.0. + max_rotor_diameter_multiplier (float, Optional): maximum spacing between turbines as + multiplier of rotor diameter. Defaults to 20.0. + turbine_rating_kW (float, Optional): rating of a single turbine in kW. if not provided, + turbine power is estimated from the power-curve. """ site_polygon: Union[Polygon, BaseGeometry] - _system_model: windpower.Windpower - layout_mode: str = field(validator = contains(['boundarygrid', 'grid', 'custom','basicgrid']), converter=(str.strip, str.lower)) - parameters: Union[WindBoundaryGridParameters, WindCustomParameters, WindBasicGridParameters, None] + _system_model: Union[windpower.Windpower, Floris] + layout_mode: str = field( + validator=contains(['boundarygrid', 'grid', 'custom', 'basicgrid']), + converter=(str.strip, str.lower), + ) + parameters: Union[ + WindBoundaryGridParameters, + WindCustomParameters, + WindBasicGridParameters, + None + ] # TODO: convert min_spacing and max_spacing to be within the parameter class that uses it. - min_spacing_meters: Optional[float] = field(default = 0.0) - max_spacing_meters: Optional[float] = field(default = 2e6) + min_spacing_meters: Optional[float] = field(default=0.0) + max_spacing_meters: Optional[float] = field(default=2e6) - min_rotor_diameter_multiplier: Optional[float] = field(default = 2.0) - max_rotor_diameter_multiplier: Optional[float] = field(default = 20.0) + min_rotor_diameter_multiplier: Optional[float] = field(default=2.0) + max_rotor_diameter_multiplier: Optional[float] = field(default=20.0) - turbine_rating_kW: Optional[float] = field(default = None) + turbine_rating_kW: Optional[float] = field(default=None) turb_pos_x: list[float] = field(init=False) turb_pos_y: list[float] = field(init=False) - min_spacing: float = field(init = False) - max_spacing: float = field(init = False) + min_spacing: float = field(init=False) + max_spacing: float = field(init=False) def __attrs_post_init__(self): """The following are initialized in this post init hook: @@ -144,9 +172,9 @@ def __attrs_post_init__(self): - turb_pos_x (list[float]): x-coordinates of turbines - turb_pos_y (list[float]): x-coordinates of turbines - Note: these calculations are based on the default values of rotor diamter and turbine layout. - `min_spacing` and `max_spacing` are re-calculated in _get_system_config(). `turb_pos_x` and `turb_pos_y` - are reset in layout-specific functions. + Note: these calculations are based on the default values of rotor diamter and turbine + layout. `min_spacing` and `max_spacing` are re-calculated in _get_system_config(). + `turb_pos_x` and `turb_pos_y` are reset in layout-specific functions. """ self.min_spacing = max( self.min_spacing_meters, @@ -158,8 +186,11 @@ def __attrs_post_init__(self): ) # turbine layout values - self.turb_pos_x = self._system_model.value("wind_farm_xCoordinates") - self.turb_pos_y = self._system_model.value("wind_farm_yCoordinates") + if isinstance(self._system_model, Floris): + self.turb_pos_x, self.turb_pos_y = self._system_model.wind_farm_layout + else: + self.turb_pos_x = self._system_model.value("wind_farm_xCoordinates") + self.turb_pos_y = self._system_model.value("wind_farm_yCoordinates") if isinstance(self.parameters, dict): if self.layout_mode == 'boundarygrid': @@ -191,26 +222,35 @@ def _get_system_config(self): def _set_system_layout(self): """Set the number of turbines. System capacity gets modified as a result. """ - - self._system_model.value("wind_farm_xCoordinates", self.turb_pos_x) - self._system_model.value("wind_farm_yCoordinates", self.turb_pos_y) + if isinstance(self._system_model, Floris): + self._system_model.set_wind_farm_layout(self.turb_pos_x, self.turb_pos_y) + else: + self._system_model.value("wind_farm_xCoordinates", self.turb_pos_x) + self._system_model.value("wind_farm_yCoordinates", self.turb_pos_y) n_turbines = len(self.turb_pos_x) if self.turbine_rating_kW is None: turb_rating = max(self._system_model.value("wind_turbine_powercurve_powerout")) self._system_model.value("system_capacity", n_turbines * turb_rating) + logger.info( + "Wind Layout set with {} turbines for {} kw system capacity".format( + n_turbines, n_turbines * turb_rating + ) + ) else: self._system_model.value("system_capacity", n_turbines * self.turbine_rating_kW) - logger.info("Wind Layout set with {} turbines for {} kw system capacity".format(n_turbines, - n_turbines * turb_rating)) + logger.info( + "Wind Layout set with {} turbines for {} kw system capacity".format( + n_turbines, n_turbines * self.turbine_rating_kW + ) + ) + @property def rotor_diameter(self): return self._system_model.value("wind_turbine_rotor_diameter") - def reset_boundarygrid(self, - n_turbines, - exclusions: Polygon = None): + def reset_boundarygrid(self, n_turbines, exclusions: Polygon=None): """Create `boundarygrid` layout for input number of turbines. Args: @@ -236,9 +276,13 @@ def reset_boundarygrid(self, border_spacing, self.parameters.border_offset, n_turbines - len(turbine_positions), - )) + ) + ) - valid_wind_shape = subtract_turbine_exclusion_zone(self.min_spacing, wind_shape, turbine_positions) + valid_wind_shape = subtract_turbine_exclusion_zone( + self.min_spacing, + wind_shape, turbine_positions, + ) # place interior grid turbines max_num_interior_turbines = n_turbines - len(turbine_positions) @@ -261,8 +305,7 @@ def reset_boundarygrid(self, self.turb_pos_x, self.turb_pos_y = xcoords, ycoords self._set_system_layout() - def reset_grid(self, - n_turbines): + def reset_grid(self, n_turbines): """Create a `grid` layout for specified number of turbines within the `site_polygon`. Spacing turbines based on `min_spacing` attribute. Does not use `parameters` attribute. @@ -278,7 +321,8 @@ def reset_grid(self, if n_turbines > 0: spacing = np.sqrt( - self.site_polygon.area / n_turbines) * self.site_polygon.envelope.area / self.site_polygon.area + self.site_polygon.area / n_turbines + ) * self.site_polygon.envelope.area / self.site_polygon.area spacing = max(spacing, self.min_spacing) coords = [] while len(coords) < n_turbines: @@ -293,8 +337,11 @@ def reset_grid(self, coords.append(coord) d += spacing if len(coords) < n_turbines: - envelope = scale(envelope, (envelope.bounds[2] - spacing) / envelope.bounds[2], - (envelope.bounds[3] - spacing) / envelope.bounds[3]) + envelope = scale( + envelope, + (envelope.bounds[2] - spacing) / envelope.bounds[2], + (envelope.bounds[3] - spacing) / envelope.bounds[3] + ) if len(coords) < n_turbines: spacing *= .95 coords = [] @@ -314,15 +361,15 @@ def reset_basic_grid(self,n_turbines): """ self._get_system_config() - interrow_spacing = self.parameters.row_D_spacing*self.rotor_diameter - intrarow_spacing = self.parameters.turbine_D_spacing*self.rotor_diameter + interrow_spacing = self.parameters.row_D_spacing * self.rotor_diameter + intrarow_spacing = self.parameters.turbine_D_spacing * self.rotor_diameter data = make_site_boundary_for_square_grid_layout( n_turbines, self.rotor_diameter, self.parameters.row_D_spacing, - self.parameters.turbine_D_spacing - ) + self.parameters.turbine_D_spacing, + ) vertices = np.array([np.array(v) for v in data['site_boundaries']['verts']]) square_bounds = Polygon(vertices) grid_position_square = create_grid(square_bounds, @@ -333,14 +380,18 @@ def reset_basic_grid(self,n_turbines): self.parameters.row_phase_offset, int(n_turbines), ) - + if self.parameters.site_boundary_constrained: # 1) see if turbines are in the site polygon xcoords_grid = [point.x for point in grid_position_square] ycoords_grid = [point.y for point in grid_position_square] - x_ingrid,y_ingrid = check_turbines_in_site(xcoords_grid,ycoords_grid,self.site_polygon) + x_ingrid, y_ingrid = check_turbines_in_site( + xcoords_grid, + ycoords_grid, + self.site_polygon, + ) if len(x_ingrid)==n_turbines: - self.turb_pos_x, self.turb_pos_y = x_ingrid,y_ingrid + self.turb_pos_x, self.turb_pos_y = x_ingrid, y_ingrid self._set_system_layout() return x,y = adjust_site_for_box_grid_layout( @@ -349,30 +400,50 @@ def reset_basic_grid(self,n_turbines): interrow_spacing, intrarow_spacing, self.parameters.row_phase_offset, - self.parameters.grid_angle + self.parameters.grid_angle, ) if len(x)==n_turbines or len(x)>x_ingrid: - self.turb_pos_x, self.turb_pos_y = x_ingrid,y_ingrid + self.turb_pos_x, self.turb_pos_y = x_ingrid, y_ingrid self._set_system_layout() return else: self.reset_grid(n_turbines) else: + # center on the site xcoords_grid = [point.x for point in grid_position_square] ycoords_grid = [point.y for point in grid_position_square] - self.turb_pos_x, self.turb_pos_y = xcoords_grid,ycoords_grid + x_center = (max(xcoords_grid) - min(xcoords_grid)) / 2 + y_center = (max(ycoords_grid) - min(ycoords_grid)) / 2 + x_shift = self.site_polygon.centroid.x - x_center + y_shift = self.site_polygon.centroid.y - y_center + xcoords = [x + x_shift for x in xcoords_grid] + ycoords = [y + y_shift for y in ycoords_grid] + self.turb_pos_x, self.turb_pos_y = xcoords, ycoords self._set_system_layout() - - def set_layout_params(self, - wind_kw, - params: Optional[Union[WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters]], - exclusions: Polygon = None): + + def set_layout_params( + self, + wind_kw, + params: Optional[ + Union[WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters] + ], + exclusions: Polygon=None, + ): """Set wind farm layout to accommodate input wind capacity. Args: wind_kw (float): wind farm capacity in kW - params (Optional[Union[WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters]]): wind farm parameters. - exclusions (Polygon, optional): exclusions in site. Only used if layout_mode is 'boundarygrid'. Defaults to None. + params ( + Optional[ + Union[ + WindBoundaryGridParameters, + WindBasicGridParameters, + WindCustomParameters + ] + ] + ): wind farm parameters. + exclusions (Polygon, optional): exclusions in site. Only used if layout_mode is + 'boundarygrid'. Defaults to None. """ if params: self.parameters = params @@ -382,9 +453,14 @@ def set_layout_params(self, self.layout_mode = "custom" elif isinstance(params,WindBasicGridParameters): self.layout_mode = "basicgrid" + + # If using FLORIS, set the turbine_rating_kW to the max power curve value + if self.turbine_rating_kW is None: + self.turbine_rating_kW = max( + self._system_model.value("wind_turbine_powercurve_powerout") + ) - # below is not floris-friendly - n_turbines = int(np.floor(wind_kw / max(self._system_model.Turbine.wind_turbine_powercurve_powerout))) + n_turbines = int(np.floor(wind_kw / self.turbine_rating_kW)) if self.layout_mode == 'boundarygrid': self.reset_boundarygrid(n_turbines, exclusions) @@ -396,8 +472,7 @@ def set_layout_params(self, self.turb_pos_x, self.turb_pos_y = self.parameters.layout_x, self.parameters.layout_y self._set_system_layout() - def set_num_turbines(self, - n_turbines: int): + def set_num_turbines(self, n_turbines: int): """Set number of turbines and wind farm layout. Args: @@ -415,16 +490,24 @@ def set_num_turbines(self, self.turb_pos_x, self.turb_pos_y = self.parameters.layout_x, self.parameters.layout_y self._set_system_layout() - def plot(self, - figure=None, - axes=None, - turbine_color='b', - site_border_color='k', - site_alpha=0.95, - linewidth=4.0 - ): + def plot( + self, + figure=None, + axes=None, + turbine_color='b', + site_border_color='k', + site_alpha=0.95, + linewidth=4.0, + ): if not figure and not axes: - figure, axes = plot_site_polygon(self.site_polygon,figure, axes, site_border_color, site_alpha, linewidth) + figure, axes = plot_site_polygon( + self.site_polygon, + figure, + axes, + site_border_color, + site_alpha, + linewidth, + ) turb_pos_x = self._system_model.value("wind_farm_xCoordinates") turb_pos_y = self._system_model.value("wind_farm_yCoordinates") @@ -432,11 +515,11 @@ def plot(self, x, y = turb_pos_x[n], turb_pos_y[n] circle = plt.Circle( (x, y), - radius=self.rotor_diameter/2.0, + radius=self.rotor_diameter / 2.0, color=turbine_color, fill=True, linewidth=linewidth, - ) + ) axes.add_patch(circle) return figure, axes diff --git a/hopp/simulation/technologies/wind/floris.py b/hopp/simulation/technologies/wind/floris.py index 1cc16e870..d36e9c5e0 100644 --- a/hopp/simulation/technologies/wind/floris.py +++ b/hopp/simulation/technologies/wind/floris.py @@ -1,56 +1,58 @@ -# tools to add floris to the hybrid simulation class -from attrs import define, field -from dataclasses import dataclass, asdict -import csv +from pathlib import Path from typing import TYPE_CHECKING, Tuple + +from attrs import define, field import numpy as np from floris import FlorisModel, TimeSeries from floris.turbine_library.turbine_previewer import INTERNAL_LIBRARY from hopp.simulation.base import BaseClass from hopp.simulation.technologies.sites import SiteInfo -from hopp.type_dec import resource_file_converter -from pathlib import Path -from hopp.utilities import load_yaml +# avoid circular dep +if TYPE_CHECKING: + from hopp.simulation.technologies.wind.wind_plant import WindConfig from hopp.tools.resource.wind_tools import ( calculate_air_density, parse_resource_data, weighted_parse_resource_data ) -# avoid circular dep -if TYPE_CHECKING: - from hopp.simulation.technologies.wind.wind_plant import WindConfig +from hopp.utilities import load_yaml +from hopp.utilities.log import hybrid_logger as logger @define class Floris(BaseClass): + site: SiteInfo = field() config: "WindConfig" = field() verbose: bool = field(default = True) _operational_losses: float = field(init=False) _timestep: Tuple[int, int] = field(init=False) - annual_energy_pre_curtailment_ac: float = field(init=False) fi: FlorisModel = field(init=False) - - turbine_name: str = field(init = False) - wind_turbine_rotor_diameter: float = field(init = False) + + # turbine parameters + turbine_name: str = field(init=False) + wind_turbine_rotor_diameter: float = field(init=False) + turb_rating: float = field(init=False) # turbine power curve (array of kW power outputs) - wind_turbine_powercurve_powerout: list[float] = field(init = False) - wind_farm_xCoordinates: list[float] = field(init = False) - wind_farm_yCoordinates: list[float] = field(init = False) - turb_rating: float = field(init = False) - system_capacity: float = field(init = False) - - gen: list[float] = field(init = False) - annual_energy: float = field(init = False) - capacity_factor: float = field(init = False) - annual_energy_pre_curtailment_ac: float = field(init = False) - #TODO: add option to store turbine-powers and velocities or not + wind_turbine_powercurve_powerout: list[float] = field(init=False) + wind_farm_xCoordinates: list[float] = field(init=False) + wind_farm_yCoordinates: list[float] = field(init=False) + system_capacity: float = field(init=False) + #results + gen: list[float] = field(init=False) + annual_energy: float = field(init=False) + capacity_factor: float = field(init=False) + annual_energy_pre_curtailment_ac: float = field(init=False) + + #TODO: add option to store turbine-powers and velocities or not + turb_velocities: np.ndarray = field(init=False) + turb_powers: np.ndarray = field(init=False) def __attrs_post_init__(self): - """_summary_ + """Set-up and initialize floris_config and floris model. This method does the following: 1) check that floris config is provided 2) load floris config if needed @@ -113,35 +115,46 @@ def initialize_from_floris(self,floris_config): # load file from internal floris library if isinstance(floris_config["farm"]["turbine_type"][0],str): self.turbine_name = floris_config["farm"]["turbine_type"][0] - turb_dict = load_yaml(INTERNAL_LIBRARY / "{}.yaml".format(floris_config["farm"]["turbine_type"][0])) + turb_dict = load_yaml( + INTERNAL_LIBRARY / "{}.yaml".format(floris_config["farm"]["turbine_type"][0]) + ) floris_config["farm"]["turbine_type"][0] = turb_dict - + # see if rotor diameter was input in config but not set in floris config if self.config.rotor_diameter is not None: - floris_config["farm"]["turbine_type"][0].setdefault("rotor_diameter",self.config.rotor_diameter) + floris_config["farm"]["turbine_type"][0].setdefault( + "rotor_diameter",self.config.rotor_diameter + ) # see if hub-height was input in config but not set in floris config - # NOTE: hub-height should also be checked against wind resource hub-height if self.config.hub_height is not None: - floris_config["farm"]["turbine_type"][0].setdefault("hub_height",self.config.hub_height) + floris_config["farm"]["turbine_type"][0].setdefault( + "hub_height", self.config.hub_height + ) + # NOTE: hub-height should also be checked against wind resource hub-height # set attributes: self.wind_turbine_rotor_diameter = floris_config["farm"]["turbine_type"][0]["rotor_diameter"] self.wind_turbine_powercurve_powerout = floris_config["farm"]["turbine_type"][0]["power_thrust_table"]["power"] self.wind_farm_xCoordinates = floris_config["farm"]["layout_x"] self.wind_farm_yCoordinates = floris_config["farm"]["layout_y"] - self.nTurbs = len(self.wind_farm_xCoordinates) - + self.nTurbs = len(self.wind_farm_xCoordinates) + self.turb_rating = max(self.wind_turbine_powercurve_powerout) if self.config.turbine_rating_kw is not None: if self.config.turbine_rating_kw != self.turb_rating: - raise UserWarning(f"input turbine rating ({self.config.turbine_rating_kw} kW) does not match rating from floris power-curve ({self.turb_rating} kW)") + raise UserWarning( + f"input turbine rating ({self.config.turbine_rating_kw} kW) does not match " + f"rating from floris power-curve ({self.turb_rating} kW)" + ) + # check if user-input num_turbines equals number of turbines in layout - if self.config.num_turbines is not None: - # raise warning if discrepancy in number of turbines - if self.nTurbs != self.config.num_turbines: - raise UserWarning(f"num_turbines input ({self.config.num_turbines}) does not equal number of turbines in floris layout ({self.nTurbs})") + if self.nTurbs != self.config.num_turbines: + logger.warning( + f"num_turbines in WindConfig ({self.config.num_turbines}) does not equal " + f"number of turbines in floris config layout ({self.nTurbs})" + ) return floris_config - + def value(self, name: str, set_value=None): """Set or retrieve attribute of `hopp.simulation.technologies.wind.floris.Floris`. if set_value = None, then retrieve value; otherwise overwrite variable's value. @@ -156,15 +169,15 @@ def value(self, name: str, set_value=None): else: return self.__getattribute__(name) - def set_floris_value(self,name,value): + def set_floris_value(self, name, value): if value is not None: self.fi.set(**{name:value}) - - def set_floris_param(self,param,value): + + def set_floris_param(self, param, value): if value is not None: - self.fi.set_param(param,value) - - def get_floris_param(self,param): + self.fi.set_param(param, value) + + def get_floris_param(self, param): return self.fi.get_param(param) def execute(self, project_life): @@ -177,6 +190,17 @@ def execute(self, project_life): if self.verbose: print('Simulating wind farm output in FLORIS...') + # check if user-input num_turbines equals number of turbines in layout + if self.nTurbs != self.config.num_turbines: + # Log warning if discrepancy in number of turbines. + # Not raising a warning since wind farm capacity can be modified + # before simulation begins. + logger.warning( + f"num_turbines input in WindConfig ({self.config.num_turbines}) does not equal " + f"number of turbines in floris model ({self.nTurbs})" + ) + logger.info(f"simulating {self.nTurbs} turbines using FLORIS") + # find generation of wind farm power_turbines = np.zeros((self.nTurbs, 8760)) power_farm = np.zeros(8760) @@ -190,8 +214,12 @@ def execute(self, project_life): self.fi.set(wind_data=time_series) self.fi.run() - power_turbines[:, self.start_idx:self.end_idx] = self.fi.get_turbine_powers().reshape((self.nTurbs, self.end_idx - self.start_idx)) - power_farm[self.start_idx:self.end_idx] = self.fi.get_farm_power().reshape((self.end_idx - self.start_idx)) + power_turbines[:, self.start_idx:self.end_idx] = self.fi.get_turbine_powers().reshape( + (self.nTurbs, self.end_idx - self.start_idx) + ) + power_farm[self.start_idx:self.end_idx] = self.fi.get_farm_power().reshape( + (self.end_idx - self.start_idx) + ) operational_efficiency = ((100 - self._operational_losses)/100) # Adding losses from PySAM defaults (excluding turbine and wake losses) @@ -212,4 +240,27 @@ def export(self): 'annual_energy': self.annual_energy, } return config - \ No newline at end of file + + @property + def wind_farm_layout(self): + xcoords, ycoords = self.fi.get_turbine_layout() + return xcoords, ycoords + + def set_wind_farm_layout(self, xcoords, ycoords): + """ + Sets the wind farm layout and updates relevant parameters. + + Args: + xcoords (list[float]): A list of x-coordinates for turbine lcoations. + ycoords (list[float]): A list of y-coordinates for turbine lcoations. + + Raises: + ValueError: If x- and y-coordinates are not the same length, an error is raised. + """ + if len(xcoords) != len(ycoords): + raise ValueError("WindPlant turbine coordinate arrays must have same length") + self.fi.set(layout_x=xcoords, layout_y=ycoords) + self.nTurbs = len(xcoords) + self.system_capacity = len(xcoords) * self.turb_rating + self.value("wind_farm_xCoordinates", xcoords) + self.value("wind_farm_yCoordinates", ycoords) diff --git a/hopp/simulation/technologies/wind/wind_plant.py b/hopp/simulation/technologies/wind/wind_plant.py index 935987edf..b6e4dc214 100644 --- a/hopp/simulation/technologies/wind/wind_plant.py +++ b/hopp/simulation/technologies/wind/wind_plant.py @@ -1,24 +1,26 @@ from pathlib import Path from typing import Optional, Tuple, Union, Sequence -import PySAM.Windpower as Windpower -import PySAM.Singleowner as Singleowner from attrs import define, field +import PySAM.Singleowner as Singleowner +import PySAM.Windpower as Windpower from hopp.simulation.base import BaseClass -from hopp.type_dec import resource_file_converter -from hopp.utilities import load_yaml -from hopp.utilities.validators import gt_zero, contains, range_val -from hopp.simulation.technologies.wind.floris import Floris -from hopp.simulation.technologies.power_source import PowerSource -from hopp.simulation.technologies.sites import SiteInfo +from hopp.simulation.technologies.financial import CustomFinancialModel, FinancialModelType from hopp.simulation.technologies.layout.wind_layout import ( WindLayout, WindBoundaryGridParameters, - WindBasicGridParameters) -from hopp.simulation.technologies.financial import CustomFinancialModel, FinancialModelType -from hopp.utilities.log import hybrid_logger as logger + WindBasicGridParameters, + WindCustomParameters, +) +from hopp.simulation.technologies.power_source import PowerSource +from hopp.simulation.technologies.sites import SiteInfo +from hopp.simulation.technologies.wind.floris import Floris from hopp.tools.resource.wind_tools import calculate_air_density_losses +from hopp.type_dec import resource_file_converter +from hopp.utilities import load_yaml +from hopp.utilities.log import hybrid_logger as logger +from hopp.utilities.validators import gt_zero, contains, range_val @define @@ -33,21 +35,29 @@ class WindConfig(BaseClass): hub_height (float, Optional): turbine hub height in meters turbine_name (str, Optional): unused currently. Defaults to None. layout_mode (str): - - 'boundarygrid': regular grid with boundary turbines, requires WindBoundaryGridParameters as 'layout_params' + - 'boundarygrid': regular grid with boundary turbines, requires + WindBoundaryGridParameters as 'layout_params' - 'grid': regular grid with dx, dy distance, 0 angle; does not require 'layout_params' - - 'basicgrid': most-square grid layout, requires WindBasicGridParameters as 'layout_params' - - 'custom': use a user-provided layout. + - 'basicgrid': most-square grid layout, requires WindBasicGridParameters + as 'layout_params' + - 'custom': use a user-provided layout + - 'floris_layout': use layout provided in `floris_config`. model_name (str): which model to use. Options are 'floris' and 'pysam' model_input_file (str): file specifying a full PySAM input - layout_params (obj | dict, Optional): layout configuration object corresponding to `layout_mode` or dictionary. + layout_params (obj | dict, Optional): layout configuration object corresponding to + `layout_mode` or dictionary. rating_range_kw (Tuple[int]): allowable kw range of turbines, default is 1000 - 3000 kW - floris_config (dict | str | Path): Floris configuration, only used if `model_name` == 'floris' - adjust_air_density_for_elevation (bool): whether to adjust air density for elevation. Defaults to False. - Only used if True and ``site.elev`` is not None. - resource_parse_method (str): method to parse wind resource data if using floris and downloaded resource data for 2 heights. - Can either be "weighted_average" or "average". Defaults to "average". - operational_losses (float, Optional): total percentage losses in addition to wake losses, defaults based on PySAM (only used for Floris model) - timestep (Tuple[int]): Timestep (required for floris runs, otherwise optional). Defaults to (0,8760) + floris_config (dict | str | Path): Floris configuration, only used if + `model_name` == 'floris' + adjust_air_density_for_elevation (bool): whether to adjust air density for elevation. + Defaults to False. Only used if True and ``site.elev`` is not None. + resource_parse_method (str): method to parse wind resource data if using floris and + downloaded resource data for 2 heights. Can either be "weighted_average" or "average". + Defaults to "average". + operational_losses (float, Optional): total percentage losses in addition to wake losses, + defaults based on PySAM (only used for Floris model) + timestep (Tuple[int]): Timestep (required for floris runs, otherwise optional). + Defaults to (0,8760) fin_model (obj | dict | str): Optional financial model. Can be any of the following: - a string representing an argument to `Singleowner.default` @@ -59,17 +69,33 @@ class WindConfig(BaseClass): num_turbines: int = field(validator=gt_zero) turbine_rating_kw: float = field(validator=gt_zero) rotor_diameter: Optional[float] = field(default=None) - layout_params: Optional[Union[dict, WindBoundaryGridParameters]] = field(default=None) + layout_params: Optional[ + Union[ + dict, WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters + ] + ] = field(default=None) hub_height: Optional[float] = field(default=None) turbine_name: Optional[str] = field(default=None) - layout_mode: str = field(default="grid", validator=contains(["boundarygrid", "grid", "basicgrid", "custom"]), converter=(str.strip, str.lower)) - model_name: str = field(default="pysam", validator=contains(["pysam", "floris"]), converter=(str.strip, str.lower)) + layout_mode: str = field( + default="grid", + validator=contains(["boundarygrid", "grid", "basicgrid", "custom", "floris_layout"]), + converter=(str.strip, str.lower) + ) + model_name: str = field( + default="pysam", + validator=contains(["pysam", "floris"]), + converter=(str.strip, str.lower) + ) model_input_file: Optional[str] = field(default=None) rating_range_kw: Tuple[int, int] = field(default=(1000, 3000)) floris_config: Optional[Union[dict, str, Path]] = field(default=None) - adjust_air_density_for_elevation: Optional[bool] = field(default = False) - resource_parse_method: str = field(default="average", validator=contains(["weighted_average", "average"]), converter=(str.strip, str.lower)) - operational_losses: float = field(default = 12.83, validator=range_val(0, 100)) + adjust_air_density_for_elevation: Optional[bool] = field(default=False) + resource_parse_method: str = field( + default="average", + validator=contains(["weighted_average", "average"]), + converter=(str.strip, str.lower) + ) + operational_losses: float = field(default=12.83, validator=range_val(0, 100)) timestep: Optional[Tuple[int, int]] = field(default=(0,8760)) fin_model: Optional[Union[dict, FinancialModelType]] = field(default=None) name: str = field(default="WindPlant") @@ -79,7 +105,9 @@ def __attrs_post_init__(self): raise ValueError("Timestep (Tuple[int, int]) required for floris") if self.layout_mode == 'boundarygrid' and self.layout_params is None: - raise ValueError("Parameters of WindBoundaryGridParameters required for boundarygrid layout mode") + raise ValueError( + "Parameters of WindBoundaryGridParameters required for boundarygrid layout mode" + ) @define @@ -99,7 +127,8 @@ def __attrs_post_init__(self): config: Wind plant configuration """ self._rating_range_kw = self.config.rating_range_kw - + layout_params = self.config.layout_params + layout_mode = self.config.layout_mode # Parse input for a financial model if isinstance(self.config.fin_model, str): financial_model = Singleowner.default(self.config_name) @@ -111,12 +140,23 @@ def __attrs_post_init__(self): if self.config.model_name == 'floris': print('FLORIS is the system model...') system_model = Floris(self.site, self.config) + if ( + self.config.num_turbines == len(system_model.wind_farm_xCoordinates) + and self.config.layout_mode == "floris_layout" + ): + # use layout in floris config by using "floris_layout" layout params + x_coords,y_coords = system_model.wind_farm_layout + layout_params = WindCustomParameters(layout_x=x_coords, layout_y=y_coords) + # modify to custom for WindLayout + layout_mode = "custom" if financial_model is None: # default financial_model = Singleowner.default(self.config_name) else: - financial_model = self.import_financial_model(financial_model, system_model, self.config_name) + financial_model = self.import_financial_model( + financial_model, system_model, self.config_name + ) else: if self.config.model_input_file is None: system_model = Windpower.default(self.config_name) @@ -139,20 +179,14 @@ def __attrs_post_init__(self): # default financial_model = Singleowner.from_existing(system_model, self.config_name) else: - financial_model = self.import_financial_model(financial_model, system_model, self.config_name) - - # below is unnecessary now - this functionality exists in WindLayout. - if isinstance(self.config.layout_params, dict) and self.config.layout_mode=="boundarygrid": - layout_params = WindBoundaryGridParameters(**self.config.layout_params) - elif isinstance(self.config.layout_params, dict) and self.config.layout_mode=="basicgrid": - layout_params = WindBasicGridParameters(**self.config.layout_params) - else: - layout_params = self.config.layout_params + financial_model = self.import_financial_model( + financial_model, system_model, self.config_name + ) super().__init__("WindPlant", self.site, system_model, financial_model) self._system_model.value("wind_resource_data", self.site.wind_resource.data) - self._layout = WindLayout(self.site.polygon, system_model, self.config.layout_mode, layout_params) + self._layout = WindLayout(self.site.polygon, system_model, layout_mode, layout_params) self._dispatch = None @@ -160,16 +194,14 @@ def __attrs_post_init__(self): self.num_turbines = self.config.num_turbines if self.config.rotor_diameter is not None: self.rotor_diameter = self.config.rotor_diameter - + if self.config.model_name=="pysam": if self.config.hub_height is not None: self._system_model.Turbine.wind_turbine_hub_ht = self.config.hub_height if self.config.adjust_air_density_for_elevation and self.site.elev is not None: air_dens_losses = calculate_air_density_losses(self.site.elev) self._system_model.Losses.assign({"turb_specific_loss":air_dens_losses}) - - - + @property def wake_model(self) -> str: try: @@ -201,6 +233,16 @@ def num_turbines(self): @num_turbines.setter def num_turbines(self, n_turbines: int): + + if ( + self._layout.layout_mode == "custom" + and n_turbines != len(self._system_model.value("wind_farm_xCoordinates")) + ): + n_turbs_layout = len(self._system_model.value("wind_farm_xCoordinates")) + raise UserWarning( + f"Using custom layout and input number of turbines ({n_turbines}) does not equal " + f"length of layout ({n_turbs_layout})." + ) self._layout.set_num_turbines(n_turbines) @property @@ -230,9 +272,14 @@ def turb_rating(self, rating_kw): :param rating_kw: float """ scaling = rating_kw / self.turb_rating - self._system_model.value("wind_turbine_powercurve_powerout", - [i * scaling for i in self._system_model.value("wind_turbine_powercurve_powerout")]) - self._system_model.value("system_capacity", self.turb_rating * len(self._system_model.value("wind_farm_xCoordinates"))) + self._system_model.value( + "wind_turbine_powercurve_powerout", + [i * scaling for i in self._system_model.value("wind_turbine_powercurve_powerout")], + ) + self._system_model.value( + "system_capacity", + self.turb_rating * len(self._system_model.value("wind_farm_xCoordinates")), + ) def modify_powercurve(self, rotor_diam, rating_kw): """ @@ -252,20 +299,24 @@ def modify_powercurve(self, rotor_diam, rating_kw): wind_default_drive_train = 0 try: # could fail if current rotor diameter is too big or small for rating - self._system_model.Turbine.calculate_powercurve(rating_kw, - int(self._system_model.value("wind_turbine_rotor_diameter")), - elevation, - wind_default_max_cp, - wind_default_max_tip_speed, - wind_default_max_tip_speed_ratio, - wind_default_cut_in_speed, - wind_default_cut_out_speed, - wind_default_drive_train) + self._system_model.Turbine.calculate_powercurve( + rating_kw, + int(self._system_model.value("wind_turbine_rotor_diameter")), + elevation, + wind_default_max_cp, + wind_default_max_tip_speed, + wind_default_max_tip_speed_ratio, + wind_default_cut_in_speed, + wind_default_cut_out_speed, + wind_default_drive_train, + ) logger.info("WindPlant recalculated powercurve") except: - raise RuntimeError("WindPlant.turb_rating could not calculate turbine powercurve with diameter={}" - ", rating={}. Check diameter or turn off 'recalculate_powercurve'". - format(rotor_diam, rating_kw)) + raise RuntimeError( + "WindPlant.turb_rating could not calculate turbine powercurve with diameter={}" + ", rating={}. Check diameter or turn off 'recalculate_powercurve'". + format(rotor_diam, rating_kw) + ) self._system_model.value("wind_turbine_rotor_diameter", rotor_diam) self._system_model.value("system_capacity", rating_kw * self.num_turbines) logger.info("WindPlant set system_capacity to {} kW".format(self.system_capacity_kw)) @@ -276,9 +327,13 @@ def modify_coordinates(self, xcoords: Sequence, ycoords: Sequence): """ if len(xcoords) != len(ycoords): raise ValueError("WindPlant turbine coordinate arrays must have same length") - self._system_model.value("wind_farm_xCoordinates", xcoords) - self._system_model.value("wind_farm_yCoordinates", ycoords) - self._system_model.value("system_capacity", self.turb_rating * len(xcoords)) + if self.config.model_name=="floris": + self._system_model.wind_farm_layout(xcoords, ycoords) + else: + self._system_model.value("wind_farm_xCoordinates", xcoords) + self._system_model.value("wind_farm_yCoordinates", ycoords) + self._system_model.value("system_capacity", self.turb_rating * len(xcoords)) + logger.debug("WindPlant set xcoords to {}".format(xcoords)) logger.debug("WindPlant set ycoords to {}".format(ycoords)) logger.info("WindPlant set system_capacity to {} kW".format(self.system_capacity_kw)) @@ -289,7 +344,8 @@ def system_capacity_kw(self): def system_capacity_by_rating(self, wind_size_kw: float): """ - Sets the system capacity by adjusting the rating of the turbines within the provided boundaries + Sets the system capacity by adjusting the rating of the turbines within the + provided boundaries. :param wind_size_kw: desired system capacity in kW """ diff --git a/tests/hopp/test_hybrid.py b/tests/hopp/test_hybrid.py index 6774bdf0e..2ad798bc4 100644 --- a/tests/hopp/test_hybrid.py +++ b/tests/hopp/test_hybrid.py @@ -423,7 +423,9 @@ def test_hybrid_wind_only_floris(hybrid_config, subtests): wind_only["wind"]["timestep"] = [0, 8760] wind_only["wind"]["num_turbines"] = 4 wind_only["wind"]["turbine_rating_kw"] = 5000 - + wind_only["wind"]["layout_mode"] = "floris_layout" + + hybrid_config["technologies"] = wind_only hi = HoppInterface(hybrid_config) hybrid_plant = hi.system @@ -1615,6 +1617,7 @@ def test_hybrid_wind_only_floris_elevation_adjusted(hybrid_config, subtests): wind_only["wind"]["timestep"] = [0, 8760] wind_only["wind"]["num_turbines"] = 4 wind_only["wind"]["turbine_rating_kw"] = 5000 + wind_only["wind"]["layout_mode"] = "floris_layout" hybrid_config["technologies"] = wind_only hi = HoppInterface(hybrid_config) diff --git a/tests/hopp/test_layout.py b/tests/hopp/test_layout.py index b7d10d6ea..ffa1931bb 100644 --- a/tests/hopp/test_layout.py +++ b/tests/hopp/test_layout.py @@ -21,7 +21,7 @@ from hopp.simulation.technologies.pv.detailed_pv_plant import DetailedPVPlant, DetailedPVConfig from hopp.utilities.utils_for_tests import create_default_site_info - +from hopp import ROOT_DIR @pytest.fixture def site(): @@ -88,7 +88,7 @@ def test_wind_boundary_grid_layout_pysam(site): assert xcoords[i] == pytest.approx(expected_xcoords[i], abs=1) assert ycoords[i] == pytest.approx(expected_ycoords[i], abs=1) -def test_wind_basic_grid_layout_pysam_default(site): +def test_wind_basic_grid_layout_pysam_default(site, subtests): wind_technology = { 'num_turbines': 16, 'rotor_diameter': 40.0, @@ -102,14 +102,72 @@ def test_wind_basic_grid_layout_pysam_default(site): unique_x_coords = np.unique(xcoords) unique_y_coords = np.unique(ycoords) - expected_unique_x_coords = [196, 396, 596, 796] - expected_unique_y_coords = [50, 250, 450, 650] - assert len(xcoords) == wind_technology["num_turbines"] - assert len(unique_x_coords) == len(unique_y_coords) + x_spacing_meters = unique_x_coords[-1] - unique_x_coords[-2] + y_spacing_meters = unique_y_coords[-1] - unique_y_coords[-2] + + expected_spacing_D = 5.0 + + expected_unique_x_coords = [554, 754, 954, 1154] + expected_unique_y_coords = [397, 597, 797, 997] + + with subtests.test("number of turbines in layout"): + assert len(xcoords) == wind_technology["num_turbines"] + with subtests.test("x spacing"): + assert x_spacing_meters/wind_technology["rotor_diameter"] == pytest.approx(expected_spacing_D,abs=1e-3) + with subtests.test("y spacing"): + assert y_spacing_meters/wind_technology["rotor_diameter"] == pytest.approx(expected_spacing_D,abs=1e-3) + with subtests.test("number of coordinates"): + assert len(unique_x_coords) == len(unique_y_coords) + for i in range(len(unique_x_coords)): + with subtests.test(f"unique x coordinate #{i}"): + assert unique_x_coords[i] == pytest.approx(expected_unique_x_coords[i], abs=1) + for i in range(len(unique_y_coords)): + with subtests.test(f"unique y coordinate #{i}"): + assert unique_y_coords[i] == pytest.approx(expected_unique_y_coords[i], abs=1) + +def test_wind_basic_grid_layout_floris_default(site, subtests): + floris_config_path = ( + ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" + ) + wind_technology = { + 'model_name': "floris", + 'floris_config': floris_config_path, + 'num_turbines': 16, + 'rotor_diameter': 125.88, + 'turbine_rating_kw': 5000, + 'layout_mode': 'basicgrid', + 'layout_params': WindBasicGridParameters() + } + config = WindConfig.from_dict(wind_technology) + wind_model = WindPlant(site, config=config) + xcoords, ycoords = wind_model._system_model.wind_farm_layout + unique_x_coords = np.unique(xcoords) + unique_y_coords = np.unique(ycoords) + + x_spacing_meters = unique_x_coords[-1] - unique_x_coords[-2] + y_spacing_meters = unique_y_coords[-1] - unique_y_coords[-2] + + expected_spacing_D = 5.0 + + expected_unique_x_coords = [330, 959, 1589, 2218] + expected_unique_y_coords = [-139, 489, 1119, 1748] + + with subtests.test("number of turbines"): + assert wind_model._system_model.nTurbs == wind_model.num_turbines + with subtests.test("x spacing"): + assert x_spacing_meters/wind_technology["rotor_diameter"] == pytest.approx(expected_spacing_D,abs=1e-3) + with subtests.test("y spacing"): + assert y_spacing_meters/wind_technology["rotor_diameter"] == pytest.approx(expected_spacing_D,abs=1e-3) + with subtests.test("number of turbines in layout"): + assert len(xcoords) == wind_technology["num_turbines"] + with subtests.test("number of coordinates"): + assert len(unique_x_coords) == len(unique_y_coords) for i in range(len(unique_x_coords)): - assert unique_x_coords[i] == pytest.approx(expected_unique_x_coords[i], abs=1) + with subtests.test(f"unique x coordinate #{i}"): + assert unique_x_coords[i] == pytest.approx(expected_unique_x_coords[i], abs=1) for i in range(len(unique_y_coords)): - assert unique_y_coords[i] == pytest.approx(expected_unique_y_coords[i], abs=1) + with subtests.test(f"unique y coordinate #{i}"): + assert unique_y_coords[i] == pytest.approx(expected_unique_y_coords[i], abs=1) def test_solar_layout(site): diff --git a/tests/hopp/test_wind.py b/tests/hopp/test_wind.py index 0ebafc918..1c33fa172 100644 --- a/tests/hopp/test_wind.py +++ b/tests/hopp/test_wind.py @@ -1,12 +1,14 @@ -from pytest import fixture, approx import math -import PySAM.Windpower as windpower import pytest +from pytest import fixture, approx + +import PySAM.Windpower as windpower +from hopp import ROOT_DIR from hopp.simulation.technologies.wind.wind_plant import WindPlant, WindConfig -from tests.hopp.utils import create_default_site_info from hopp.utilities import load_yaml -from hopp import ROOT_DIR +from tests.hopp.utils import create_default_site_info + @fixture def site(): @@ -21,25 +23,30 @@ def site(): wind_default_cut_out_speed = 25 wind_default_drive_train = 0 -powercurveKW = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 56.9014, 72.8929, 90.7638, 110.618, 132.561, 156.696, - 183.129, 211.962, 243.302, 277.251, 313.915, 353.398, 395.805, 441.239, 489.805, 541.608, 596.752, - 655.341, 717.481, 783.274, 852.826, 926.241, 1003.62, 1088.85, 1174.66, 1260.47, 1346.28, 1432.09, - 1517.9, 1603.71, 1689.53, 1775.34, 1861.15, 1946.96, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, - 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, - 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, - 2000, 2000, 2000, 2000, 2000, 2000, 2000, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0) - -powercurveWS = (0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3, 3.25, 3.5, 3.75, 4, 4.25, 4.5, 4.75, 5, - 5.25, 5.5, 5.75, 6, 6.25, 6.5, 6.75, 7, 7.25, 7.5, 7.75, 8, 8.25, 8.5, 8.75, 9, 9.25, 9.5, 9.75, 10, - 10.25, 10.5, 10.75, 11, 11.25, 11.5, 11.75, 12, 12.25, 12.5, 12.75, 13, 13.25, 13.5, 13.75, 14, 14.25, - 14.5, 14.75, 15, 15.25, 15.5, 15.75, 16, 16.25, 16.5, 16.75, 17, 17.25, 17.5, 17.75, 18, 18.25, 18.5, - 18.75, 19, 19.25, 19.5, 19.75, 20, 20.25, 20.5, 20.75, 21, 21.25, 21.5, 21.75, 22, 22.25, 22.5, 22.75, - 23, 23.25, 23.5, 23.75, 24, 24.25, 24.5, 24.75, 25, 25.25, 25.5, 25.75, 26, 26.25, 26.5, 26.75, 27, - 27.25, 27.5, 27.75, 28, 28.25, 28.5, 28.75, 29, 29.25, 29.5, 29.75, 30, 30.25, 30.5, 30.75, 31, 31.25, - 31.5, 31.75, 32, 32.25, 32.5, 32.75, 33, 33.25, 33.5, 33.75, 34, 34.25, 34.5, 34.75, 35, 35.25, 35.5, - 35.75, 36, 36.25, 36.5, 36.75, 37, 37.25, 37.5, 37.75, 38, 38.25, 38.5, 38.75, 39, 39.25, 39.5, 39.75, 40) +powercurveKW = ( + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 56.9014, 72.8929, 90.7638, 110.618, 132.561, + 156.696, 183.129, 211.962, 243.302, 277.251, 313.915, 353.398, 395.805, 441.239, 489.805, + 541.608, 596.752, 655.341, 717.481, 783.274, 852.826, 926.241, 1003.62, 1088.85, 1174.66, + 1260.47, 1346.28, 1432.09, 1517.9, 1603.71, 1689.53, 1775.34, 1861.15, 1946.96, 2000, 2000, + 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, + 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, + 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 +) + +powercurveWS = ( + 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3, 3.25, 3.5, 3.75, 4, 4.25, 4.5, + 4.75, 5, 5.25, 5.5, 5.75, 6, 6.25, 6.5, 6.75, 7, 7.25, 7.5, 7.75, 8, 8.25, 8.5, 8.75, 9, 9.25, + 9.5, 9.75, 10, 10.25, 10.5, 10.75, 11, 11.25, 11.5, 11.75, 12, 12.25, 12.5, 12.75, 13, 13.25, + 13.5, 13.75, 14, 14.25, 14.5, 14.75, 15, 15.25, 15.5, 15.75, 16, 16.25, 16.5, 16.75, 17, 17.25, + 17.5, 17.75, 18, 18.25, 18.5, 18.75, 19, 19.25, 19.5, 19.75, 20, 20.25, 20.5, 20.75, 21, 21.25, + 21.5, 21.75, 22, 22.25, 22.5, 22.75, 23, 23.25, 23.5, 23.75, 24, 24.25, 24.5, 24.75, 25, 25.25, + 25.5, 25.75, 26, 26.25, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 28, 28.25, 28.5, 28.75, 29, 29.25, + 29.5, 29.75, 30, 30.25, 30.5, 30.75, 31, 31.25, 31.5, 31.75, 32, 32.25, 32.5, 32.75, 33, 33.25, + 33.5, 33.75, 34, 34.25, 34.5, 34.75, 35, 35.25, 35.5, 35.75, 36, 36.25, 36.5, 36.75, 37, 37.25, + 37.5, 37.75, 38, 38.25, 38.5, 38.75, 39, 39.25, 39.5, 39.75, 40 +) def test_wind_powercurve_pysam(): @@ -47,15 +54,17 @@ def test_wind_powercurve_pysam(): model.Turbine.wind_turbine_rotor_diameter = 75 # calculate system capacity. To evaluate other turbines, update the defaults dictionary - model.Turbine.calculate_powercurve(wind_default_rated_output, - int(model.Turbine.wind_turbine_rotor_diameter), - wind_default_elevation, - wind_default_max_cp, - wind_default_max_tip_speed, - wind_default_max_tip_speed_ratio, - wind_default_cut_in_speed, - wind_default_cut_out_speed, - wind_default_drive_train) + model.Turbine.calculate_powercurve( + wind_default_rated_output, + int(model.Turbine.wind_turbine_rotor_diameter), + wind_default_elevation, + wind_default_max_cp, + wind_default_max_tip_speed, + wind_default_max_tip_speed_ratio, + wind_default_cut_in_speed, + wind_default_cut_out_speed, + wind_default_drive_train + ) windspeeds_truth = [round(x, 2) for x in powercurveWS] windspeeds_calc = [round(x, 2) for x in model.Turbine.wind_turbine_powercurve_windspeeds] @@ -129,25 +138,44 @@ def test_changing_system_capacity_pysam(site): model.system_capacity_by_rating(n) assert model.system_capacity_kw == approx(n) + #################### FLORIS tests ################ def test_floris_num_turbines(site): - floris_config_path = ( - ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" - ) + floris_config_path = (ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml") f_config = load_yaml(floris_config_path) floris_n_turbines = len(f_config["farm"]["layout_x"]) - config = WindConfig.from_dict({'num_turbines': 20, "turbine_rating_kw": 5000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) - with pytest.raises(UserWarning) as err: - model = WindPlant(site, config=config) - assert str(err.value) == f"num_turbines input ({config.num_turbines}) does not equal number of turbines in floris layout ({floris_n_turbines})" - + config = WindConfig.from_dict( + { + 'num_turbines': 16, + "turbine_rating_kw": 5000, + "model_name": "floris", + "timestep": [1, 8760], + "floris_config": floris_config_path + } + ) + model = WindPlant(site, config=config) + xcoords, ycoords = model._system_model.wind_farm_layout + assert len(xcoords) == config.num_turbines + assert len(ycoords) == model.num_turbines + assert model._system_model.nTurbs == model.num_turbines + # with pytest.raises(UserWarning) as err: + # assert str(err.value) == f"num_turbines input ({config.num_turbines}) does not equal number of turbines in floris layout ({floris_n_turbines})" + def test_changing_rotor_diam_recalc_floris(site): floris_config_path = ( ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" ) - config = WindConfig.from_dict({'num_turbines': 4, "turbine_rating_kw": 5000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) + config = WindConfig.from_dict( + { + 'num_turbines': 4, + "turbine_rating_kw": 5000, + "model_name": "floris", + "timestep": [1, 8760], + "floris_config": floris_config_path + } + ) model = WindPlant(site, config=config) assert model._system_model.system_capacity == 20000 diams = range(50, 70, 140) @@ -155,30 +183,46 @@ def test_changing_rotor_diam_recalc_floris(site): model._system_model.wind_turbine_rotor_diameter = d assert model._system_model.wind_turbine_rotor_diameter == d, "rotor diameter should be " + str(d) + def test_changing_turbine_rating_floris(site): - floris_config_path = ( - ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" + floris_config_path = (ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml") + config = WindConfig.from_dict( + { + 'num_turbines': 4, + "turbine_rating_kw": 1000, + "model_name": "floris", + "timestep": [1, 8760], + "floris_config": floris_config_path + } ) - config = WindConfig.from_dict({'num_turbines': 4, "turbine_rating_kw": 1000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) with pytest.raises(UserWarning) as err: model = WindPlant(site, config=config) assert str(err.value) == "input turbine rating (1000 kW) does not match rating from floris power-curve (5000.0 kW)" - + def test_changing_system_capacity_floris(site): - floris_config_path = ( - ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" - ) + floris_config_path = (ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml") - config = WindConfig.from_dict({'num_turbines': 4, "turbine_rating_kw": 5000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) + config = WindConfig.from_dict( + { + 'num_turbines': 4, + "turbine_rating_kw": 5000, + "model_name": "floris", + "timestep": [1, 8760], + "floris_config": floris_config_path + } + ) model = WindPlant(site, config=config) - + + new_num_turbs = 16 + new_capacity_kW = new_num_turbs*config.turbine_rating_kw rating = model._system_model.turb_rating - + assert model._system_model.nTurbs == 4 assert model._system_model.turb_rating == rating assert model._system_model.system_capacity == 20000 - model.system_capacity_by_num_turbines(10000) - assert model._system_model.system_capacity == 10000.0 + model.system_capacity_by_num_turbines(new_capacity_kW) + assert model._system_model.nTurbs == new_num_turbs + assert model._system_model.system_capacity == new_capacity_kW From d6dcb57c127e66c30aab00107729ff572a1f681c Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Sat, 22 Feb 2025 14:39:59 -0700 Subject: [PATCH 11/48] Wind Layout Clean-up (#433) * added site shape tools script * updated site_info for new option of site boundary definition * updated doc strings for site info and site shape tool functions * added tests for site shape tools functions * added site shape tools to documentation * added another optional input for site_details if using circle as shape * updated RELEASE.md with new feature * removed checking vertices so tests pass * added function to rotate site * added site boundary buffer for verts back in * added site polygon buffer as optional input to site info * added wind resource tools script * added test for air density adjustment for elevation calc * integrated adjusting for elevation in wind models * updated doc strings for recent changes * updated release file * fixed bug in make_grid_lines * fixed input to create_grid to be in degrees instead of radians * added new wind layout tools and fixed doc strings related to bug fix * updated call to wind layout tools and fixed inputs for test_custom_financial * adding in option for basicgrid layout option * renamed test_layout test * added weighted parser, updated tests, updated wind_resource.py * added plot function to site_shape_tools * added new wind layout tools function and cleaned up wind_layout a bit * added more functionality to floris.py and added some optional parameters to windconfig * updated example 05 input file so it wont cause a warning * added layout test for basicgrid layout * added test that shows how floris breaks - other floris tests will fail * added comments to tests that will fail because of floris update * added doc strings to wind layout files and wind plant files * added minor comment to floris.py * updated README with recent changes * added warning if user inputs incorrect turbine rating with floris * updated tests that were failing because of floris update * added some extra asserts to floris test in test_hybrid * minor docstring updates to wind resource tools * minor fix to wind_plant and made regression test for elevation adjustment option * removed unnecessary lines and comments * added some property and setters to floris * bug fix in test_hybrid for floris run * fixed failing test in test_hybrid * adjusted basic grid to center layout on site center and integrated layout with floris * fixed typo in floris.py * integrated wind layout with floris and updated wind layout mode in floris test in test_hybrid * updated tests for floris because of recent feature-adds * updated RELEASE.md with feature-add description * minor clean ups to scripts * cleaned up wind layout script * added function to modify layout params in wind_plant * minor update to wind_layout.py set_layout_params function * bug fix for failing tests * added in integrated of weighted average resource data from v3/elevated_wind * updated RELEASE.md * updated release.md * made changes based on review feedback * updated release file and removed unnecessary comments in some scripts * further workflow and comment clean-up from feedback * updated doc string and comments in make_grid_lines() * updated some doc strings in floris.py * updated handling if layout_params is None for wind * Minor docstring update * Fixed merge bug * Modifications based on Rob's PR * Fixed validators call * Updated spacing calls --------- Co-authored-by: John Jasa --- RELEASE.md | 1 + .../technologies/layout/wind_layout.py | 181 +++++++++++++----- hopp/simulation/technologies/wind/floris.py | 39 ++-- .../technologies/wind/wind_plant.py | 22 ++- tests/hopp/test_wind.py | 3 +- 5 files changed, 179 insertions(+), 67 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 4359143e2..d5dadafb5 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -22,6 +22,7 @@ + Updated floris initialization to set attributes from `floris_config` + Update: raise errors when using floris if theres a discrepancy between inputs in `WindConfig` and information in `floris_config` (such as `num_turbines` and the `floris_config` layout, and turbine parameters like rotor diameter and turbine rating.) + Integrated wind layout functionality when using floris + + Updated wind layout parameters. ## Version 3.1.1, Dec. 18, 2024 diff --git a/hopp/simulation/technologies/layout/wind_layout.py b/hopp/simulation/technologies/layout/wind_layout.py index 1b545c11c..3fedb75a5 100644 --- a/hopp/simulation/technologies/layout/wind_layout.py +++ b/hopp/simulation/technologies/layout/wind_layout.py @@ -3,7 +3,7 @@ import matplotlib.pyplot as plt import numpy as np -from attrs import define, field +from attrs import define, field, validators from shapely.geometry import Polygon, Point, MultiPolygon from shapely.geometry.base import BaseGeometry from shapely.affinity import scale @@ -59,7 +59,14 @@ class WindBoundaryGridParameters(BaseClass): Defaults to 0.0 border_spacing_m (float, Optional): spacing along border in meters. Is used to calculate ``border_spacing`` if ``min_spacing_m`` is also input. - min_spacing_m (float, Optional): minimum spacing between turbines in meters. + min_spacing_m (float, Optional): minimum spacing between turbines in meters. + Defaults to 0.0. + min_spacing_D (float, Optional): minimum spacing between turbines as a multiplier of rotor diameter. + Defaults to 2.0. + max_spacing_m (float, Optional): maximum spacing between turbines in meters. + Defaults to 2e6. + max_spacing_D (float, Optional): maximum spacing between turbines as a multiplier of rotor diameter. + Defaults to 20.0. grid_angle (float): turbine inner grid rotation (0, 180) [degrees] grid_aspect_power (float, Optional): used to calculate grid_aspect_ratio. grid aspect ratio [cols / rows] = 2^grid_aspect_power. @@ -73,16 +80,36 @@ class WindBoundaryGridParameters(BaseClass): #TODO: rename to border_spacing_ratio? border_spacing: float = field(default=0.0) #TODO: rename to border_offset_ratio? - border_offset: float = field(default=0.0, validator=range_val(0.0, 1.0)) - border_spacing_m: Optional[float] = field(default=None) - min_spacing_m: Optional[float] = field(default=None) + border_offset: float = field(default = 0.0, validator = range_val(0.0, 1.0)) + border_spacing_m: Optional[float] = field(default = None) + + min_spacing_m: Optional[float] = field(default = 0.0, validator=validators.instance_of((float, type(None)))) + min_spacing_D: Optional[float] = field(default = 2.0, validator=validators.instance_of((float, type(None)))) + max_spacing_m: Optional[float] = field(default = 2e6, validator=validators.instance_of((float, type(None)))) + max_spacing_D: Optional[float] = field(default = 20.0, validator=validators.instance_of((float, type(None)))) grid_angle: float = field(default=0.0, validator=range_val(0.0, 180.0)) grid_aspect_power: Optional[float] = field(default=None) grid_aspect_ratio: Optional[float] = field(default=None) row_phase_offset: float = field(default=0.2, validator=range_val(0.0, 1.0)) + min_spacing: float = field(init = False) #min spacing in meters + max_spacing: float = field(init = False) #max spacing in meters def __attrs_post_init__(self): + """ + Post-initialization hook for setting up additional attributes. + This method initializes the following attributes: + - grid_aspect_ratio (float): The aspect ratio of the turbine grid (cols / rows). + If `grid_aspect_ratio` is None, it is set to 1 if `grid_aspect_power` is None, + otherwise it is set to the exponential of `grid_aspect_power`. + - border_spacing (float): The turbine border spacing offset as a ratio of border spacing (0, 1). + Defaults to 0.0. Calculated as (border_spacing_m / min_spacing_m) - 1. + - min_spacing (float): The minimum spacing between turbines in meters. It takes the maximum of + `min_spacing_m` and `min_spacing_D * rotor_diameter`. + - max_spacing (float): The maximum spacing between turbines in meters. It takes the maximum of + `max_spacing_m` and `max_spacing_D * rotor_diameter`. + """ + if self.grid_aspect_ratio is None: #NOTE: unsure if this equation is correct given doc strong self.grid_aspect_ratio = 1 if self.grid_aspect_power is None else np.exp( @@ -92,6 +119,64 @@ def __attrs_post_init__(self): if self.min_spacing_m is not None and self.border_spacing_m is not None: self.border_spacing = (self.border_spacing_m/self.min_spacing_m) - 1 + def value(self, name: str, set_value=None): + """Set or retrieve an attribute of the class instance. + + If `set_value` is provided, the method sets the attribute `name` to `set_value`. + If `set_value` is not provided, the method retrieves the value of the attribute `name`. + + Args: + name (str): The name of the attribute to set or retrieve. + set_value (Optional): The value to set for the attribute `name`. + If `None`, the method retrieves the value of the attribute. Defaults to None. + + Returns: + The value of the attribute `name` if `set_value` is not provided. + """ + if set_value is not None: + self.__setattr__(name, set_value) + else: + return self.__getattribute__(name) + + def update_min_spacing_with_rotor_diameter(self,rotor_diameter: float): + """Update min_spacing based on rotor diameter. Sets min_spacing as the maximum + of min_spacing_m and rotor_diameter*min_spacing_D. + + Args: + rotor_diameter (float): rotor diameter in meters. + """ + min_spacing = max( + self.min_spacing_m, + rotor_diameter * self.min_spacing_D + ) + self.value("min_spacing", min_spacing) + +@define +class WindGridParameters(BaseClass): + """Configuration class for 'grid' wind layout. + + Args: + min_spacing_m (float, Optional): minimum spacing between turbines in meters. + Defaults to 0.0. + min_spacing_D (float, Optional): minimum spacing between turbines as a multiplier of rotor diameter. + Defaults to 2.0 + """ + min_spacing_m: Optional[float] = field(default = 0.0) + min_spacing_D: Optional[float] = field(default = 2.0) + min_spacing: float = field(init = False) #min spacing in meters + + def update_min_spacing_with_rotor_diameter(self,rotor_diameter: float): + """update min_spacing based on rotor diameter. Sets min_spacing as the maximum + of min_spacing_m and rotor_diameter*min_spacing_D. + + Args: + rotor_diameter (float): rotor diameter in meters. + """ + self.min_spacing = max( + self.min_spacing_m, + rotor_diameter * self.min_spacing_D + ) + @define class WindCustomParameters(BaseClass): """ @@ -145,7 +230,8 @@ class WindLayout(BaseClass): WindBoundaryGridParameters, WindCustomParameters, WindBasicGridParameters, - None + WindGridParameters, + dict, ] # TODO: convert min_spacing and max_spacing to be within the parameter class that uses it. min_spacing_meters: Optional[float] = field(default=0.0) @@ -165,26 +251,17 @@ class WindLayout(BaseClass): def __attrs_post_init__(self): """The following are initialized in this post init hook: - - min_spacing (float): minimum spacing between turbines in meters. - Only used if layout_mode is `grid` or `boundarygrid`. - - max_spacing (float): maximum spacing between turbines in meters. - Only used if layout_mode is `grid` or `boundarygrid`. - turb_pos_x (list[float]): x-coordinates of turbines - turb_pos_y (list[float]): x-coordinates of turbines + - parameters.min_spacing (float): minimum spacing between turbines in meters. + Only used if layout_mode is `grid` or `boundarygrid`. + - parameters.max_spacing (float): maximum spacing between turbines in meters. + Only used if layout_mode is `boundarygrid`. - Note: these calculations are based on the default values of rotor diamter and turbine + Note: these calculations are based on the default values of rotor diameter and turbine layout. `min_spacing` and `max_spacing` are re-calculated in _get_system_config(). `turb_pos_x` and `turb_pos_y` are reset in layout-specific functions. """ - self.min_spacing = max( - self.min_spacing_meters, - self._system_model.value("wind_turbine_rotor_diameter") * self.min_rotor_diameter_multiplier - ) - self.max_spacing = max( - self.max_spacing_meters, - self._system_model.value("wind_turbine_rotor_diameter") * self.max_rotor_diameter_multiplier - ) - # turbine layout values if isinstance(self._system_model, Floris): self.turb_pos_x, self.turb_pos_y = self._system_model.wind_farm_layout @@ -199,24 +276,27 @@ def __attrs_post_init__(self): self.parameters = WindBasicGridParameters.from_dict(self.parameters) elif self.layout_mode == 'custom': self.parameters = WindCustomParameters.from_dict(self.parameters) + elif self.layout_mode == 'grid': + self.parameters = WindGridParameters.from_dict(self.parameters) + elif self.parameters is None: + self.parameters = WindGridParameters() + + self._get_system_config() def _get_system_config(self): - """The following are re-calculated based on the actual rotor diameter of the wind turbine. - - - min_spacing (float): minimum spacing between turbines in meters. - Only used if layout_mode is `grid` or `boundarygrid`. - - max_spacing (float): maximum spacing between turbines in meters. - Only used if layout_mode is `grid` or `boundarygrid`. + """Update min and max spacing constraints in `parameters` based on actual rotor diameter of the wind turbine. + Only required if layout_mode is `grid` or `boundarygrid`. """ - self.min_spacing = max( - self.min_spacing, - self.min_spacing_meters, - self._system_model.value("wind_turbine_rotor_diameter") * self.min_rotor_diameter_multiplier - ) - self.max_spacing = max( - self.max_spacing_meters, - self._system_model.value("wind_turbine_rotor_diameter") * self.max_rotor_diameter_multiplier - ) + + if self.layout_mode == "boundarygrid" or self.layout_mode == "grid": + rotor_diameter = self._system_model.value("wind_turbine_rotor_diameter") + self.parameters.update_min_spacing_with_rotor_diameter(rotor_diameter) + if self.layout_mode == "boundarygrid": + max_spacing = max( + self.parameters.max_spacing_m, + rotor_diameter * self.parameters.max_spacing_D + ) + self.parameters.value("max_spacing", max_spacing) def _set_system_layout(self): @@ -268,7 +348,7 @@ def reset_boundarygrid(self, n_turbines, exclusions: Polygon=None): if not isinstance(wind_shape, MultiPolygon): wind_shape = MultiPolygon([wind_shape, ]) - border_spacing = (self.parameters.border_spacing + 1) * self.min_spacing + border_spacing = (self.parameters.border_spacing + 1) * self.parameters.min_spacing for bounding_shape in wind_shape.geoms: turbine_positions.extend( get_evenly_spaced_points_along_border( @@ -280,7 +360,7 @@ def reset_boundarygrid(self, n_turbines, exclusions: Polygon=None): ) valid_wind_shape = subtract_turbine_exclusion_zone( - self.min_spacing, + self.parameters.min_spacing, wind_shape, turbine_positions, ) @@ -292,8 +372,8 @@ def reset_boundarygrid(self, n_turbines, exclusions: Polygon=None): self.parameters.grid_angle, self.parameters.grid_aspect_ratio, self.parameters.row_phase_offset, - self.max_spacing, - self.min_spacing, + self.parameters.max_spacing, + self.parameters.min_spacing, max_num_interior_turbines, ) turbine_positions.extend(grid_sites) @@ -323,7 +403,7 @@ def reset_grid(self, n_turbines): spacing = np.sqrt( self.site_polygon.area / n_turbines ) * self.site_polygon.envelope.area / self.site_polygon.area - spacing = max(spacing, self.min_spacing) + spacing = max(spacing, self.parameters.min_spacing) coords = [] while len(coords) < n_turbines: @@ -402,12 +482,22 @@ def reset_basic_grid(self,n_turbines): self.parameters.row_phase_offset, self.parameters.grid_angle, ) - if len(x)==n_turbines or len(x)>x_ingrid: + if len(x) == n_turbines or len(x) > x_ingrid: self.turb_pos_x, self.turb_pos_y = x_ingrid, y_ingrid self._set_system_layout() return else: + # Use the largest min spacing to set the parameters + largest_min_spacing_D = max(self.parameters.row_D_spacing, self.parameters.turbine_D_spacing) + largest_min_spacing_m = max(interrow_spacing, intrarow_spacing) + original_parameters = self.parameters._get_model_dict() + self.parameters = WindGridParameters(min_spacing_D = largest_min_spacing_D, min_spacing_m=largest_min_spacing_m) + self.layout_mode = "grid" + self.parameters.update_min_spacing_with_rotor_diameter(self.rotor_diameter) self.reset_grid(n_turbines) + self.layout_mode = "basicgrid" + self.parameters = original_parameters + else: # center on the site xcoords_grid = [point.x for point in grid_position_square] @@ -425,7 +515,7 @@ def set_layout_params( self, wind_kw, params: Optional[ - Union[WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters] + Union[WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters, WindGridParameters] ], exclusions: Polygon=None, ): @@ -438,7 +528,8 @@ def set_layout_params( Union[ WindBoundaryGridParameters, WindBasicGridParameters, - WindCustomParameters + WindCustomParameters, + WindGridParameters, ] ] ): wind farm parameters. @@ -453,7 +544,9 @@ def set_layout_params( self.layout_mode = "custom" elif isinstance(params,WindBasicGridParameters): self.layout_mode = "basicgrid" - + elif isinstance(params,WindGridParameters): + self.layout_mode = "grid" + # If using FLORIS, set the turbine_rating_kW to the max power curve value if self.turbine_rating_kW is None: self.turbine_rating_kW = max( diff --git a/hopp/simulation/technologies/wind/floris.py b/hopp/simulation/technologies/wind/floris.py index d36e9c5e0..6c4dcae5f 100644 --- a/hopp/simulation/technologies/wind/floris.py +++ b/hopp/simulation/technologies/wind/floris.py @@ -22,7 +22,6 @@ @define class Floris(BaseClass): - site: SiteInfo = field() config: "WindConfig" = field() verbose: bool = field(default = True) @@ -30,26 +29,26 @@ class Floris(BaseClass): _operational_losses: float = field(init=False) _timestep: Tuple[int, int] = field(init=False) fi: FlorisModel = field(init=False) - + # turbine parameters - turbine_name: str = field(init=False) - wind_turbine_rotor_diameter: float = field(init=False) - turb_rating: float = field(init=False) + turbine_name: str = field(init = False) + wind_turbine_rotor_diameter: float = field(init = False) + turb_rating: float = field(init = False) # turbine power curve (array of kW power outputs) - wind_turbine_powercurve_powerout: list[float] = field(init=False) - wind_farm_xCoordinates: list[float] = field(init=False) - wind_farm_yCoordinates: list[float] = field(init=False) - system_capacity: float = field(init=False) - + wind_turbine_powercurve_powerout: list[float] = field(init = False) + wind_farm_xCoordinates: list[float] = field(init = False) + wind_farm_yCoordinates: list[float] = field(init = False) + system_capacity: float = field(init = False) + #results - gen: list[float] = field(init=False) - annual_energy: float = field(init=False) - capacity_factor: float = field(init=False) - annual_energy_pre_curtailment_ac: float = field(init=False) - + gen: list[float] = field(init = False) + annual_energy: float = field(init = False) + capacity_factor: float = field(init = False) + annual_energy_pre_curtailment_ac: float = field(init = False) + #TODO: add option to store turbine-powers and velocities or not - turb_velocities: np.ndarray = field(init=False) - turb_powers: np.ndarray = field(init=False) + turb_velocities: np.ndarray = field(init = False) + turb_powers: np.ndarray = field(init = False) def __attrs_post_init__(self): """Set-up and initialize floris_config and floris model. This method does the following: @@ -143,7 +142,7 @@ def initialize_from_floris(self,floris_config): if self.config.turbine_rating_kw is not None: if self.config.turbine_rating_kw != self.turb_rating: raise UserWarning( - f"input turbine rating ({self.config.turbine_rating_kw} kW) does not match " + f"Input turbine rating ({self.config.turbine_rating_kw} kW) does not match " f"rating from floris power-curve ({self.turb_rating} kW)" ) @@ -251,8 +250,8 @@ def set_wind_farm_layout(self, xcoords, ycoords): Sets the wind farm layout and updates relevant parameters. Args: - xcoords (list[float]): A list of x-coordinates for turbine lcoations. - ycoords (list[float]): A list of y-coordinates for turbine lcoations. + xcoords (list[float]): A list of x-coordinates for turbine locations. + ycoords (list[float]): A list of y-coordinates for turbine locations. Raises: ValueError: If x- and y-coordinates are not the same length, an error is raised. diff --git a/hopp/simulation/technologies/wind/wind_plant.py b/hopp/simulation/technologies/wind/wind_plant.py index b6e4dc214..1c44cea18 100644 --- a/hopp/simulation/technologies/wind/wind_plant.py +++ b/hopp/simulation/technologies/wind/wind_plant.py @@ -12,6 +12,7 @@ WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters, + WindGridParameters, ) from hopp.simulation.technologies.power_source import PowerSource from hopp.simulation.technologies.sites import SiteInfo @@ -333,7 +334,6 @@ def modify_coordinates(self, xcoords: Sequence, ycoords: Sequence): self._system_model.value("wind_farm_xCoordinates", xcoords) self._system_model.value("wind_farm_yCoordinates", ycoords) self._system_model.value("system_capacity", self.turb_rating * len(xcoords)) - logger.debug("WindPlant set xcoords to {}".format(xcoords)) logger.debug("WindPlant set ycoords to {}".format(ycoords)) logger.info("WindPlant set system_capacity to {} kW".format(self.system_capacity_kw)) @@ -374,3 +374,23 @@ def system_capacity_kw(self, size_kw: float): :return: """ self.system_capacity_by_num_turbines(size_kw) + + def modify_layout_params( + self, + wind_capacity_kW: float, + layout_params: Union[dict, WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters, WindGridParameters], + layout_mode: Optional[str] = None): + + if isinstance(layout_params, dict): + if layout_mode == "custom": + layout_params = WindCustomParameters(**layout_params) + elif layout_mode == "grid": + layout_params = WindGridParameters(**layout_params) + elif layout_mode == "basicgrid": + layout_params = WindBasicGridParameters(**layout_params) + elif layout_mode == "boundarygrid": + layout_params = WindBoundaryGridParameters(**layout_params) + elif layout_mode is None: + raise UserWarning("if providing layout_params as dictionary, please specify layout mode") + + self._layout.set_layout_params(wind_capacity_kW, params = layout_params) \ No newline at end of file diff --git a/tests/hopp/test_wind.py b/tests/hopp/test_wind.py index 1c33fa172..a615c0c2a 100644 --- a/tests/hopp/test_wind.py +++ b/tests/hopp/test_wind.py @@ -198,7 +198,7 @@ def test_changing_turbine_rating_floris(site): ) with pytest.raises(UserWarning) as err: model = WindPlant(site, config=config) - assert str(err.value) == "input turbine rating (1000 kW) does not match rating from floris power-curve (5000.0 kW)" + assert str(err.value) == "Input turbine rating (1000 kW) does not match rating from floris power-curve (5000.0 kW)" def test_changing_system_capacity_floris(site): @@ -214,7 +214,6 @@ def test_changing_system_capacity_floris(site): } ) model = WindPlant(site, config=config) - new_num_turbs = 16 new_capacity_kW = new_num_turbs*config.turbine_rating_kw rating = model._system_model.turb_rating From ea461bead1603b73db80a5f30aeef13444bc7f0b Mon Sep 17 00:00:00 2001 From: Jared Thomas Date: Thu, 6 Mar 2025 16:19:41 -0700 Subject: [PATCH 12/48] Missed load (#432) * update print/save statements to not multiply missed load by 100 since it is already a percentage and not a decimal * correct setting and printing of 'schedule_curtailed_percentage' with removing extra 100 multiples at print and including brackets for clarification * add clarifying parentheses * update RELEASE.md --- RELEASE.md | 1 + examples/legacy/CSP_PV_Battery_Analysis/print_output.py | 4 ++-- hopp/simulation/hybrid_simulation.py | 4 ++-- hopp/simulation/technologies/grid.py | 4 ++-- hopp/tools/dispatch/csp_pv_battery_plot.py | 4 ++-- 5 files changed, 9 insertions(+), 8 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index d5dadafb5..2c201a759 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -23,6 +23,7 @@ + Update: raise errors when using floris if theres a discrepancy between inputs in `WindConfig` and information in `floris_config` (such as `num_turbines` and the `floris_config` layout, and turbine parameters like rotor diameter and turbine rating.) + Integrated wind layout functionality when using floris + Updated wind layout parameters. +* Remove erroneous 100 multiples for percentages and add clarifying parentheses for correct 100 multiples for percentages ## Version 3.1.1, Dec. 18, 2024 diff --git a/examples/legacy/CSP_PV_Battery_Analysis/print_output.py b/examples/legacy/CSP_PV_Battery_Analysis/print_output.py index a5974122d..5133f2d02 100644 --- a/examples/legacy/CSP_PV_Battery_Analysis/print_output.py +++ b/examples/legacy/CSP_PV_Battery_Analysis/print_output.py @@ -82,6 +82,6 @@ def print_hybrid_output(hybrid: HybridSimulation): if hybrid.site.follow_desired_schedule: print("\tMissed load [MWh]: {:.2f}".format(sum(hybrid.grid.missed_load[0:8760])/1.e3)) - print("\tMissed load percentage [%]: {:.2f}".format(hybrid.grid.missed_load_percentage*100.0)) + print("\tMissed load percentage [%]: {:.2f}".format(hybrid.grid.missed_load_percentage)) print("\tSchedule curtailed [MWh]: {:.2f}".format(sum(hybrid.grid.schedule_curtailed[0:8760])/1.e3)) - print("\tSchedule curtailed percentage [%]: {:.2f}".format(hybrid.grid.schedule_curtailed_percentage*100.0)) \ No newline at end of file + print("\tSchedule curtailed percentage [%]: {:.2f}".format(hybrid.grid.schedule_curtailed_percentage)) \ No newline at end of file diff --git a/hopp/simulation/hybrid_simulation.py b/hopp/simulation/hybrid_simulation.py index 4a59dabd3..6aee2f2d3 100644 --- a/hopp/simulation/hybrid_simulation.py +++ b/hopp/simulation/hybrid_simulation.py @@ -1085,9 +1085,9 @@ def hybrid_simulation_outputs(self, filename: str = "") -> dict: outputs['Grid Capacity Factor at Interconnect (%)'] = self.grid.capacity_factor_at_interconnect if self.site.follow_desired_schedule: outputs['Missed Load year 1 (MWh)'] = sum(self.grid.missed_load[0:8760])/1.e3 - outputs['Missed Scheduled Load (%)'] = self.grid.missed_load_percentage * 100 + outputs['Missed Scheduled Load (%)'] = self.grid.missed_load_percentage outputs['Schedule Curtailment year 1 (MWh)'] = sum(self.grid.schedule_curtailed[0:8760])/1.e3 - outputs['Schedule Curtailment (%)'] = self.grid.schedule_curtailed_percentage * 100 + outputs['Schedule Curtailment (%)'] = self.grid.schedule_curtailed_percentage attr_map = {'annual_energies': {'name': 'AEP (GWh)', 'scale': 1/1e6}, 'capacity_factors': {'name': 'Capacity Factor (-)'}, diff --git a/hopp/simulation/technologies/grid.py b/hopp/simulation/technologies/grid.py index 95458bfec..d54d489be 100644 --- a/hopp/simulation/technologies/grid.py +++ b/hopp/simulation/technologies/grid.py @@ -145,12 +145,12 @@ def simulate_grid_connection( max(schedule - gen, 0) for schedule, gen in zip(desired_schedule, self.generation_profile) ]) - self.missed_load_percentage = sum(self.missed_load)/sum(desired_schedule) * 100 + self.missed_load_percentage = (sum(self.missed_load)/sum(desired_schedule)) * 100 # Calculate curtailed schedule and curtailed schedule percentage self.schedule_curtailed = np.array([gen - schedule if gen > schedule else 0. for (gen, schedule) in zip(total_gen, lifetime_schedule)]) - self.schedule_curtailed_percentage = sum(self.schedule_curtailed)/sum(lifetime_schedule) * 100 + self.schedule_curtailed_percentage = (sum(self.schedule_curtailed)/sum(lifetime_schedule)) * 100 # NOTE: This is currently only happening for load following, would be good to make it more general # i.e. so that this analysis can be used when load following isn't being used (without storage) diff --git a/hopp/tools/dispatch/csp_pv_battery_plot.py b/hopp/tools/dispatch/csp_pv_battery_plot.py index 00551c471..77aefff69 100644 --- a/hopp/tools/dispatch/csp_pv_battery_plot.py +++ b/hopp/tools/dispatch/csp_pv_battery_plot.py @@ -245,9 +245,9 @@ def init_hybrid_plant(): print("\tCurtailment percentage: {:.2f}".format(hybrid_plant.grid.curtailment_percent)) if hybrid_plant.site.follow_desired_schedule: print("\tMissed load [MWh]: {:.2f}".format(sum(hybrid_plant.grid.missed_load[0:8760]) / 1.e3)) - print("\tMissed load percentage: {:.2f}".format(hybrid_plant.grid.missed_load_percentage * 100.0)) + print("\tMissed load percentage: {:.2f}".format(hybrid_plant.grid.missed_load_percentage)) print("\tSchedule curtailed [MWh]: {:.2f}".format(sum(hybrid_plant.grid.schedule_curtailed[0:8760]) / 1.e3)) - print("\tSchedule curtailed percentage: {:.2f}".format(hybrid_plant.grid.schedule_curtailed_percentage * 100.0)) + print("\tSchedule curtailed percentage: {:.2f}".format(hybrid_plant.grid.schedule_curtailed_percentage)) # BCR Breakdown print("\n ======= Benefit Cost Ratio Breakdown ======= \n") From a9543a355f3b651f3022e633da7fcab18a5b65ff Mon Sep 17 00:00:00 2001 From: kbrunik <102193481+kbrunik@users.noreply.github.com> Date: Fri, 7 Mar 2025 11:12:55 -0600 Subject: [PATCH 13/48] MHK Tidal Plant (#444) * update pysam to 6.0.0 * update grid default json to CustomGenerationProfile json * update wave plant loading resource file to handle 1hr timesteps by default * Updated for pysam 6.0.0 * wave cost model updates. update test values add additional test for costs. * Update regression test values based on updates to wind and solar pysam default jsons and updates to singleowner model. * Reopt test: update default json and financial value for wind * update test values in test_capacity_credit. changes due to json defaults and impact on battery optimization * CSP update. Update lcoe value because of changes to SingleOwner financial model * WIP; updating test values for detailed PV * Bringing tests back for detailed PV * update regression test results * update RELEASE.md * update example default fin config * remove outdated comments * update docstrings to google format * update deprecated methods * H to h * update RELEASE.md * initial tidal model * update RELEASE.md with PR number. Force update for NREL-PySAM dependency * update tidal model with tests * add ability to interpolate resource data * add validators back * update documentation * Converted tabs to spaces * change resource import * update docstrings and documentation * update description of tidal resource * reduce attrs logic Co-authored-by: John Jasa * reduce logic Co-authored-by: John Jasa * check identity with is not None * update path handling * fixing docs * fix description * clean up readthedoc warnings * fix typo --------- Co-authored-by: John Jasa --- RELEASE.md | 2 + docs/_toc.yml | 5 +- docs/api/resource/index.md | 1 + docs/api/resource/tidal_data.md | 11 + docs/api/technology/index.md | 1 + docs/api/technology/mhk_tidal_plant.md | 24 + docs/api/technology/mhk_wave_plant.md | 13 +- docs/api/tools/mhk_cost.md | 19 + .../tidal/Tidal_resource_timeseries.csv | 8763 +++++++++++++++++ .../technologies/csp/pySSC_daotk/ssc_wrap.py | 2 +- .../technologies/financial/mhk_cost_model.py | 43 +- .../technologies/pySSC_daotk/ssc_wrap.py | 2 +- .../technologies/resource/__init__.py | 1 + .../technologies/resource/tidal_resource.py | 161 + .../technologies/sites/site_info.py | 24 +- .../technologies/tidal/mhk_tidal_plant.py | 231 + .../technologies/wave/mhk_wave_plant.py | 29 +- tests/hopp/inputs/tidal/tidal_device.yaml | 87 + tests/hopp/test_dispatch.py | 26 +- tests/hopp/test_tidal.py | 111 + tests/hopp/test_wave.py | 254 +- 21 files changed, 9623 insertions(+), 187 deletions(-) create mode 100644 docs/api/resource/tidal_data.md create mode 100644 docs/api/technology/mhk_tidal_plant.md create mode 100644 docs/api/tools/mhk_cost.md create mode 100644 hopp/simulation/resource_files/tidal/Tidal_resource_timeseries.csv create mode 100644 hopp/simulation/technologies/resource/tidal_resource.py create mode 100644 hopp/simulation/technologies/tidal/mhk_tidal_plant.py create mode 100644 tests/hopp/inputs/tidal/tidal_device.yaml create mode 100644 tests/hopp/test_tidal.py diff --git a/RELEASE.md b/RELEASE.md index 2c201a759..51e1d8f93 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -23,6 +23,8 @@ + Update: raise errors when using floris if theres a discrepancy between inputs in `WindConfig` and information in `floris_config` (such as `num_turbines` and the `floris_config` layout, and turbine parameters like rotor diameter and turbine rating.) + Integrated wind layout functionality when using floris + Updated wind layout parameters. +* Added TidalResource to load tidal resource data for simulating tidal energy. +* Added MHKTidalPlant to simulate tidal energy. * Remove erroneous 100 multiples for percentages and add clarifying parentheses for correct 100 multiples for percentages ## Version 3.1.1, Dec. 18, 2024 diff --git a/docs/_toc.yml b/docs/_toc.yml index 5e9af9fa5..f73c7f00c 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -22,6 +22,7 @@ parts: - file: api/resource/solar_hpc - file: api/resource/wind_hpc - file: api/resource/wave_data + - file: api/resource/tidal_data - file: api/hybrid_simulation - file: api/technology/index sections: @@ -31,6 +32,7 @@ parts: - file: api/technology/battery - file: api/technology/grid - file: api/technology/mhk_wave_plant + - file: api/technology/mhk_tidal_plant - file: api/dispatch/index sections: - file: api/dispatch/storage/index @@ -51,4 +53,5 @@ parts: chapters: - file: api/technology/flicker - file: api/cost_calculator - - file: api/tools/site_shape_tools \ No newline at end of file + - file: api/tools/site_shape_tools + - file: api/tools/mhk_cost \ No newline at end of file diff --git a/docs/api/resource/index.md b/docs/api/resource/index.md index 7b72a5058..789fdc819 100644 --- a/docs/api/resource/index.md +++ b/docs/api/resource/index.md @@ -7,6 +7,7 @@ These are the primary methods for accessing wind and solar resource data. - [Solar Resource (NSRDB Dataset on NREL HPC)](resource:nsrdb-data) - [Wind Resource (Wind Toolkit Dataset on NREL HPC)](resource:wtk-data) - [Wave Resource (Data)](resource:wave-resource) +- [Tidal Resource (Data)](resource:tidal-resource) ## NREL API Keys diff --git a/docs/api/resource/tidal_data.md b/docs/api/resource/tidal_data.md new file mode 100644 index 000000000..6be03c39d --- /dev/null +++ b/docs/api/resource/tidal_data.md @@ -0,0 +1,11 @@ +(resource:tidal-resource)= +# Tidal Resource + +**NOTE: Downloading tidal resource data is not yet enabled** but can still be loaded from existing data files. + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.resource.tidal_resource.TidalResource + :members: + :undoc-members: + :exclude-members: _abc_impl, check_download_dir, call_api +``` diff --git a/docs/api/technology/index.md b/docs/api/technology/index.md index eb34adde0..53027cac3 100644 --- a/docs/api/technology/index.md +++ b/docs/api/technology/index.md @@ -13,6 +13,7 @@ These are the primary technologies that may be configured for a standard HOPP si - [Stateless Battery](tech:battery-stateless) - [Grid](tech:grid) - [Wave Plant](tech:wave) +- [Tidal Plant](tech:tidal) (tech:power-source)= ## Power Source Base Class diff --git a/docs/api/technology/mhk_tidal_plant.md b/docs/api/technology/mhk_tidal_plant.md new file mode 100644 index 000000000..0aa598b76 --- /dev/null +++ b/docs/api/technology/mhk_tidal_plant.md @@ -0,0 +1,24 @@ +(tech:tidal)= +# MHK Tidal Plant + +MHK Tidal Generator class + +## Tidal Plant Model + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.tidal.mhk_tidal_plant.MHKTidalPlant + :members: + :undoc-members: +``` + +## Tidal Plant Configuration + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.tidal.mhk_tidal_plant.MHKTidalConfig + :members: + :undoc-members: +``` + +## Tidal Plant Cost Model + +For details on the cost model used in MHK Tidal Plants, refer to the [MHK Cost Model](cost_model.md). diff --git a/docs/api/technology/mhk_wave_plant.md b/docs/api/technology/mhk_wave_plant.md index 9320ae9bb..210d38621 100644 --- a/docs/api/technology/mhk_wave_plant.md +++ b/docs/api/technology/mhk_wave_plant.md @@ -21,16 +21,5 @@ MHK Wave Generator class ## Wave Plant Cost Model -```{eval-rst} -.. autoclass:: hopp.simulation.technologies.financial.mhk_cost_model.MHKCosts - :members: - :undoc-members: -``` - -## Wave Plant Cost Model Inputs +For details on the cost model used in MHK Wave Plants, refer to the [MHK Cost Model](cost_model.md). -```{eval-rst} -.. autoclass:: hopp.simulation.technologies.financial.mhk_cost_model.MHKCostModelInputs - :members: - :undoc-members: -``` diff --git a/docs/api/tools/mhk_cost.md b/docs/api/tools/mhk_cost.md new file mode 100644 index 000000000..bb2640706 --- /dev/null +++ b/docs/api/tools/mhk_cost.md @@ -0,0 +1,19 @@ +(tools:mhk_costs)= +# MHK Cost Model + +This section documents the cost model for MHK technologies. + +## MHK Plant Cost Model +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.financial.mhk_cost_model.MHKCosts + :members: + :undoc-members: +``` + +## MHK Plant Cost Model Inputs + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.financial.mhk_cost_model.MHKCostModelInputs + :members: + :undoc-members: +``` \ No newline at end of file diff --git a/hopp/simulation/resource_files/tidal/Tidal_resource_timeseries.csv b/hopp/simulation/resource_files/tidal/Tidal_resource_timeseries.csv new file mode 100644 index 000000000..144b27fd2 --- /dev/null +++ b/hopp/simulation/resource_files/tidal/Tidal_resource_timeseries.csv @@ -0,0 +1,8763 @@ +Source,Location ID,Jurisdiction,Latitude,Longitude,Time Zone,Local Time Zone,Distance to Shore,Directionality Coefficient,Energy Period,Maximum Energy Direction,Mean Absolute Period,Mean Wave Direction,Mean Zero-Crossing Period,Omni-Directional Wave Power,Peak Period,Significant Wave Height,Spectral Width,Water Depth,Version +CMIST,PUG1527,The Narrows 0.3 miles North of Bridge,47.27432,-122.54532,0,-10,0,-,s,deg,s,deg,s,W/m,s,m,-,42.9,- +Year,Month,Day,Hour,Minute,Speed,,,,,,,,,,,,,, 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@@ -63,7 +63,7 @@ def __init__(self, tech_name, financial_name, defaults=None): self.params = {} self.params['tech_model'] = self.tech_name self.params['financial_model'] = self.financial_name - + def set(self, param_dict): if 'is_elec_heat_dur_off' in param_dict and type(param_dict['is_elec_heat_dur_off']) == list: param_dict['is_elec_heat_dur_off'] = param_dict['is_elec_heat_dur_off'][0] diff --git a/hopp/simulation/technologies/financial/mhk_cost_model.py b/hopp/simulation/technologies/financial/mhk_cost_model.py index 719b78588..57c7ffe0d 100644 --- a/hopp/simulation/technologies/financial/mhk_cost_model.py +++ b/hopp/simulation/technologies/financial/mhk_cost_model.py @@ -16,8 +16,9 @@ class MHKCostModelInputs(BaseClass): Configuration class for MHK Cost Model. Args: - reference_model_num: Reference model number from Sandia - Project (3, 5, or 6). + reference_model_num: Reference model number from the + Department of Energy Reference Model Project + (1, 3, 5, or 6). water_depth: Water depth in meters distance_to_shore: Distance to shore in meters number_rows: Number of rows in the device layout @@ -25,6 +26,19 @@ class MHKCostModelInputs(BaseClass): (default 'device_spacing') cable_system_overbuild: Cable system overbuild percentage (default 10%) + Note: + More information about the reference models and their + associated costs can be found in the + [Reference Model Project](https://energy.sandia.gov/programs/renewable-energy/water-power/projects/reference-model-project-rmp/) + + The supported reference models in this cost model are: + - Reference Model 1: Tidal Current Turbine + - Reference Model 3: Wave Point Absorber + - Reference Model 5: Oscillating Surge Flap + - Reference Model 6: Oscillating Water Column + + Additional MHK cost model information can be found + through the [System Advisor Model](https://sam.nrel.gov/) """ reference_model_num: int water_depth: float = field(validator=gt_zero) @@ -41,7 +55,7 @@ class MHKCosts(BaseClass): A class for calculating the costs associated with Marine Hydrokinetic (MHK) energy systems. This class initializes and configures cost calculations for MHK systems based on provided input parameters. - It uses the PySAM library for cost modeling which is based on the [Sandia Reference Model Project](https://energy.sandia.gov/programs/renewable-energy/water-power/projects/reference-model-project-rmp/). + It uses the PySAM library for cost modeling which is based on the [Department of Energy Reference Model Project](https://energy.sandia.gov/programs/renewable-energy/water-power/projects/reference-model-project-rmp/). Args: mhk_config: MHK system configuration parameters. @@ -75,7 +89,11 @@ def __attrs_post_init__(self): self._device_spacing = self.cost_model_inputs.device_spacing self._cable_sys_overbuild = self.cost_model_inputs.cable_system_overbuild - self._ref_model_num = "RM"+str(self.cost_model_inputs.reference_model_num) + ref_model_numbers = {1,3,5,6} + if self.cost_model_inputs.reference_model_num in ref_model_numbers: + self._ref_model_num = f"RM{self.cost_model_inputs.reference_model_num}" + else: + raise ValueError("reference_model_num can be 1, 3, 5 or 6") if self.cost_model_inputs.row_spacing is None: self._row_spacing = self.cost_model_inputs.device_spacing @@ -126,7 +144,12 @@ def initialize(self): else: raise Exception("Layout must be square or rectangular. Modify 'number_rows' or 'num_devices'.") self._cost_model.value("lib_wave_device", self._ref_model_num) - self._cost_model.value("marine_energy_tech", 0) + if self._ref_model_num == "RM3" or self._ref_model_num == "RM5" or self._ref_model_num == "RM6": + self._cost_model.value("marine_energy_tech", 0) # Wave + elif self._ref_model_num == "RM1": + self._cost_model.value('marine_energy_tech',1) # Tidal + else: + self._cost_model.value("marine_energy_tech", 0) # Generic self._cost_model.value("library_or_input_wec", 0) # Inter-array cable length, m # The total length of cable used within the array of devices @@ -199,11 +222,12 @@ def ref_model_num(self): @ref_model_num.setter def ref_model_num(self, ref_model_number: int): - if ref_model_number == 3 or ref_model_number == 5 or ref_model_number == 6: - self._ref_model_num = "RM"+ str(ref_model_number) + model_numbers = {1,3,5,6} + if ref_model_number in model_numbers: + self._ref_model_num = f"RM{ref_model_number}" self.initialize() else: - raise NotImplementedError + raise ValueError(f"Reference model number {ref_model_number} is not supported. Choose from {model_numbers}.") @property def library_or_input_wec(self): @@ -211,7 +235,8 @@ def library_or_input_wec(self): @library_or_input_wec.setter def library_or_input_wec(self): - if self.ref_model_num == 3 or self.ref_model_num == 5 or self.ref_model_num == 6: + model_numbers = {1,3,5,6} + if self.ref_model_num in model_numbers: self._cost_model.value("library_or_input_wec", 0) else: raise NotImplementedError diff --git a/hopp/simulation/technologies/pySSC_daotk/ssc_wrap.py b/hopp/simulation/technologies/pySSC_daotk/ssc_wrap.py index 6c931d28f..6d68ba11f 100644 --- a/hopp/simulation/technologies/pySSC_daotk/ssc_wrap.py +++ b/hopp/simulation/technologies/pySSC_daotk/ssc_wrap.py @@ -63,7 +63,7 @@ def __init__(self, tech_name, financial_name, defaults=None): self.params = {} self.params['tech_model'] = self.tech_name self.params['financial_model'] = self.financial_name - + def set(self, param_dict): if 'is_elec_heat_dur_off' in param_dict and type(param_dict['is_elec_heat_dur_off']) == list: param_dict['is_elec_heat_dur_off'] = param_dict['is_elec_heat_dur_off'][0] diff --git a/hopp/simulation/technologies/resource/__init__.py b/hopp/simulation/technologies/resource/__init__.py index 0d3601457..a26621e34 100644 --- a/hopp/simulation/technologies/resource/__init__.py +++ b/hopp/simulation/technologies/resource/__init__.py @@ -1,6 +1,7 @@ from hopp.simulation.technologies.resource.solar_resource import SolarResource from hopp.simulation.technologies.resource.wind_resource import WindResource from hopp.simulation.technologies.resource.wave_resource import WaveResource +from hopp.simulation.technologies.resource.tidal_resource import TidalResource from hopp.simulation.technologies.resource.elec_prices import ElectricityPrices from hopp.simulation.technologies.resource.resource import Resource from hopp.simulation.technologies.resource.greet_data import GREETData diff --git a/hopp/simulation/technologies/resource/tidal_resource.py b/hopp/simulation/technologies/resource/tidal_resource.py new file mode 100644 index 000000000..0bde4ff19 --- /dev/null +++ b/hopp/simulation/technologies/resource/tidal_resource.py @@ -0,0 +1,161 @@ +import os +import pandas as pd +import PySAM.TidalFileReader as tidalfile + +from hopp.utilities.log import hybrid_logger as logger +from hopp.simulation.technologies.resource.resource import Resource + +class TidalResource(Resource): + """ + Class to manage Tidal Resource data. + + This class loads, processes, and formats tidal energy resource data, + either from a file or a provided dataset, for compatibility with + PySAM's tidal energy models. + """ + def __init__( + self, + lat: float, + lon: float, + year: int, + path_resource: str = "", + filepath: str = "", + **kwargs + ): + """ + Initializes the TidalResource object. + + Args: + lat (float): Latitude of the resource location. + lon (float): Longitude of the resource location. + year (int): Year of the resource data. + path_resource (str, optional): Directory where downloaded files are saved. Defaults to "". + filepath (str, optional): File path of the resource file to load. Defaults to "". + **kwargs: Additional keyword arguments. + + Notes: + The tidal resource data should be in the format: + - Rows 1 and 2: Header rows with location info. + - Row 3: Column headings for time-series data + (`Year`, `Month`, `Day`, `Hour`, `Minute`, `Speed`). + - Rows 4+: Data values: + - `Speed` (current speed) in meters/second. + + Example file: + `hopp/simulation/resource_files/tidal/Tidal_resource_timeseries.csv` + """ + super().__init__(lat, lon, year) + + if os.path.isdir(path_resource): + self.path_resource = path_resource + + self.path_resource = os.path.join(self.path_resource, 'wave') + + self.__dict__.update(kwargs) + + # resource_files files + self.filename = filepath + self.format_data() + + logger.info("WaveResource: {}".format(self.filename)) + + def download_resource(self): + """ + Placeholder for downloading tidal resource data. + + Raises: + NotImplementedError: Currently, downloading functionality is not implemented. + """ + raise NotImplementedError + + def format_data(self): + """ + Formats tidal resource data as a dictionary for PySAM. + + Raises: + FileNotFoundError: If the specified resource file does not exist. + """ + if not os.path.isfile(self.filename): + raise FileNotFoundError(self.filename + " does not exist.") + + self.data = self.filename + + @Resource.data.setter + def data(self, data_file): + """ + Sets the tidal resource data in PySAM's tidal energy format. + + Args: + data_file (str): File path to the tidal resource data. + + Raises: + ValueError: If the resource time series contains sub-hourly data. + + The output dictionary includes: + - `speed` (list[float]): Current speed data [m/s]. + - `year` (list[int]): Year timestamps. + - `month` (list[int]): Month timestamps. + - `day` (list[int]): Day timestamps. + - `hour` (list[int]): Hour timestamps. + - `minute` (list[int]): Minute timestamps. + + If the time series is incomplete (less than 8760 hours), the function + linearly interpolates missing values to create a complete hourly dataset. + """ + tidalfile_model = tidalfile.new() + #Load resource file + tidalfile_model.WeatherReader.tidal_resource_filename = str(self.filename) + tidalfile_model.WeatherReader.tidal_resource_model_choice = 1 #Time-series=1 JPD=0 + + #Read in resource file, output time series arrays to pass to wave performance module + tidalfile_model.execute() + hours = tidalfile_model.Outputs.hour + + if len(hours) < 8760: + # Set up dataframe for data manipulation + df = pd.DataFrame() + df['year'] = tidalfile_model.Outputs.year + df['month'] = tidalfile_model.Outputs.month + df['day'] = tidalfile_model.Outputs.day + df['hour'] = tidalfile_model.Outputs.hour + df['minute'] = tidalfile_model.Outputs.minute + df['date_time'] = pd.to_datetime(dict(year=df.year, month=df.month, day=df.day, hour=df.hour, minute=df.minute)) + df = df.drop(['year','month','day','hour','minute'], axis=1) + df = df.set_index(['date_time']) + df['tidal_velocity'] = tidalfile_model.Outputs.tidal_velocity + + # Resample data and linearly interpolate to hourly data + data_df = df.resample("h").mean() + data_df = data_df.interpolate(method='linear') + + + # If data cannot interpolate last hours + if len(data_df['tidal_velocity']) < 8760: + last_hour = data_df.index.max() + missing_hours = 8760 - len(data_df['tidal_velocity']) + + missing_time = pd.date_range(last_hour + pd.Timedelta(hours=1),periods=missing_hours, freq='h') + missing_rows = pd.DataFrame(index=missing_time, columns=df.columns) + data_df = pd.concat([data_df, missing_rows]).sort_index() + data_df = data_df.ffill() # forward fill + + data_df = data_df.reset_index() + dic = dict() + + # Extract outputs + dic['tidal_velocity'] = data_df['tidal_velocity'] + print(data_df.head()) + dic['year'] = data_df['index'].dt.year + dic['month'] = data_df['index'].dt.month + dic['day'] = data_df['index'].dt.day + dic['hour'] = data_df['index'].dt.hour + dic['minute'] = data_df['index'].dt.minute + + elif len(hours) == 8760: + dic = dict() + # Extract outputs + dic['tidal_velocity'] = tidalfile_model.Outputs.tidal_velocity + else: + raise ValueError("Resource time-series cannot be subhourly.") + + self._data = dic \ No newline at end of file diff --git a/hopp/simulation/technologies/sites/site_info.py b/hopp/simulation/technologies/sites/site_info.py index 0f17a895c..9ae4a5cf2 100644 --- a/hopp/simulation/technologies/sites/site_info.py +++ b/hopp/simulation/technologies/sites/site_info.py @@ -17,6 +17,7 @@ SolarResource, WindResource, WaveResource, + TidalResource, ElectricityPrices, HPCWindData, HPCSolarData, @@ -52,6 +53,7 @@ class SiteInfo(BaseClass): solar_resource_file: Path to solar resource file. Defaults to "". wind_resource_file: Path to wind resource file. Defaults to "". wave_resource_file: Path to wave resource file. Defaults to "". + tidal_resource_file: Path to tidal resource file. Defaults to "". grid_resource_file: Path to grid pricing data file. Defaults to "". path_resource: Path to folder to save resource files. Defaults to ROOT/simulation/resource_files. @@ -67,6 +69,7 @@ class SiteInfo(BaseClass): solar: Whether to set solar data for this site. Defaults to True. wind: Whether to set wind data for this site. Defaults to True. wave: Whether to set wave data for this site. Defaults to False. + tidal: Whether to set tidal data for this site. Defaults to False. renewable_resource_origin (str): whether to download resource data from API or load directly from datasets files. Options are "API" or "HPC". Defaults to "API". wind_resource_origin: Which wind resource API to use, defaults toto "WTK" for WIND Toolkit. @@ -110,6 +113,7 @@ class SiteInfo(BaseClass): solar_resource_file: Union[Path, str] = field(default="", converter=resource_file_converter) wind_resource_file: Union[Path, str] = field(default="", converter=resource_file_converter) wave_resource_file: Union[Path, str] = field(default="", converter=resource_file_converter) + tidal_resource_file: Union[Path, str] = field(default="", converter=resource_file_converter) grid_resource_file: Union[Path, str] = field(default="", converter=resource_file_converter) path_resource: Optional[Union[Path, str]] = field(default=ROOT_DIR / "simulation" / "resource_files") @@ -124,6 +128,7 @@ class SiteInfo(BaseClass): solar: bool = field(default=True) wind: bool = field(default=True) wave: bool = field(default=False) + tidal: bool = field(default=False) renewable_resource_origin: str = field(default="API", validator=contains(["API", "HPC"])) wind_resource_origin: str = field(default="WTK", validator=contains(["WTK", "TAP"])) @@ -139,7 +144,8 @@ class SiteInfo(BaseClass): polygon: Union[Polygon, BaseGeometry] = field(init=False) solar_resource: Optional[Union[SolarResource,HPCSolarData]] = field(default=None) wind_resource: Optional[Union[WindResource,HPCWindData]] = field(default=None) - wave_resoure: Optional[WaveResource] = field(init=False, default=None) + wave_resource: Optional[WaveResource] = field(init=False, default=None) + tidal_resource: Optional[TidalResource] = field(init=False, default=None) elec_prices: Optional[ElectricityPrices] = field(init=False, default=None) n_timesteps: int = field(init=False, default=None) n_periods_per_day: int = field(init=False) @@ -162,7 +168,8 @@ def __attrs_post_init__(self): polygon (:obj:`shapely.geometry.polygon.Polygon`): Site polygon. solar_resource (:obj:`hopp.simulation.technologies.resource.SolarResource`): Class containing solar resource data. wind_resource (:obj:`hopp.simulation.technologies.resource.WindResource`): Class containing wind resource data. - wave_resoure (:obj:`hopp.simulation.technologies.resource.WaveResource`): Class containing wave resource data. + wave_resource (:obj:`hopp.simulation.technologies.resource.WaveResource`): Class containing wave resource data. + tidal_resource (:obj:`hopp.simulation.technologies.resource.TidalResource`): Class containing tidal resource data. elec_prices (:obj:`hopp.simulation.technologies.resource.ElectricityPrices`): Class containing electricity prices. n_timesteps (int): Number of timesteps in resource data. n_periods_per_day (int): Number of time periods per day. @@ -209,7 +216,9 @@ def __attrs_post_init__(self): if self.wave: self.wave_resource = WaveResource(data['lat'], data['lon'], data['year'], filepath = self.wave_resource_file) self.n_timesteps = 8760 - + if self.tidal: + self.tidal_resource = TidalResource(data['lat'], data['lon'], data['year'], filepath = self.tidal_resource_file) + self.n_timesteps = 8760 if self.wind: # TODO: allow hub height to be used as an optimization variable self.wind_resource = self.initialize_wind_resource(data) @@ -234,12 +243,13 @@ def __attrs_post_init__(self): # FIXME: this a hack if self.wind: - logger.info("Set up SiteInfo with wind resource files: {}".format(self.wind_resource.filename)) + logger.info("Set up SiteInfo with wind resource file: {}".format(self.wind_resource.filename)) if self.solar: - logger.info("Set up SiteInfo with solar resource files: {}".format(self.solar_resource.filename)) + logger.info("Set up SiteInfo with solar resource file: {}".format(self.solar_resource.filename)) if self.wave: - logger.info("Set up SiteInfo with wave resource files: {}".format(self.wave_resource.filename)) - + logger.info("Set up SiteInfo with wave resource file: {}".format(self.wave_resource.filename)) + if self.tidal: + logger.info("Set up SiteInfo with tidal resource file: {}".format(self.tidal_resource.filename)) def create_site_polygon(self,data:dict): """function to create site polygon. diff --git a/hopp/simulation/technologies/tidal/mhk_tidal_plant.py b/hopp/simulation/technologies/tidal/mhk_tidal_plant.py new file mode 100644 index 000000000..ae6d8fd3a --- /dev/null +++ b/hopp/simulation/technologies/tidal/mhk_tidal_plant.py @@ -0,0 +1,231 @@ +from typing import Optional, List, Union +import PySAM.MhkTidal as MhkTidal + +from attrs import define, field +from hopp.simulation.base import BaseClass + +from hopp.simulation.technologies.power_source import PowerSource, SiteInfo, Sequence, logger +from hopp.simulation.technologies.financial.custom_financial_model import CustomFinancialModel +from hopp.simulation.technologies.financial.mhk_cost_model import MHKCosts, MHKCostModelInputs +from hopp.utilities.validators import gt_zero, range_val + + +@define +class MHKTidalConfig(BaseClass): + """ + Configuration class for MHKTidalPlant. + + Args: + device_rating_kw (float): Rated power of the MHK device [kW] + num_devices (int): Number of MHK tidal devices in the system + tidal_power_curve (List[List[float]]): Power curve of tidal energy device as function of stream speeds [kW] + tidal_resource (List[List[float]]): Required by the PySAM MhkTidal module for initialization. Although this parameter + is not actively used in HOPP's timeseries simulation mode, it must still be provided to fully + instantiate the PySAM MhkTidal model. + Frequency distribution of resource as a function of stream speeds. + fin_model (obj | dict): Optional financial model. Can be any of the following: + - a dict representing a `CustomFinancialModel` + - an object representing a `CustomFinancialModel` instance + loss_array_spacing (float): Array spacing loss in % (default: 0) + loss_resource_overprediction (float): Resource overprediction loss + in % (default: 0) + loss_transmission (float): Transmission loss in % (default: 0) + loss_downtime (float): Array/WEC downtime loss in % (default: 0) + loss_additional (float): Additional losses in % (default: 0) + """ + device_rating_kw: float = field(validator=gt_zero) + num_devices: int = field(validator=gt_zero) + tidal_power_curve: List[List[float]] + tidal_resource: List[List[float]] + fin_model: Union[dict, CustomFinancialModel] + loss_array_spacing: float = field(default=0., validator=range_val(0, 100)) + loss_resource_overprediction: float = field(default=0., validator=range_val(0, 100)) + loss_transmission: float = field(default=0., validator=range_val(0, 100)) + loss_downtime: float = field(default=0., validator=range_val(0, 100)) + loss_additional: float = field(default=0., validator=range_val(0, 100)) + name: str = field(default="MHKTidalPlant") + + +@define +class MHKTidalPlant(PowerSource): + """ + Marine Hydrokinetic (MHK) Tidal Plant. + + Args: + site: Site information + config: MHK system configuration parameters + cost_model_inputs (dict, Optional): An optional dictionary containing input parameters for + cost modeling. + """ + site: SiteInfo + config: MHKTidalConfig + cost_model_inputs: Optional[MHKCostModelInputs] = field(default=None) + config_name: str = field(default="MhkWave") + + mhk_costs: Optional[MHKCosts] = field(init=False) + + def __attrs_post_init__(self): + system_model = MhkTidal.new() + + if isinstance(self.config.fin_model, dict): + financial_model = CustomFinancialModel(self.config.fin_model, name=self.config.name) + else: + financial_model = self.config.fin_model + + financial_model = self.import_financial_model(financial_model, system_model, self.config_name) + + if self.cost_model_inputs is not None: + self.mhk_costs = MHKCosts(self.config, self.cost_model_inputs) + else: + self.mhk_costs = None + + super().__init__("MHKTidalPlant", self.site, system_model, financial_model) + + # Set tidal resource model choice + system_model.MHKTidal.tidal_resource_model_choice = 1 # Time-series data=1 JPD=0 (Joint-probability distribution) + + # Copy values from self.site.tidal_resource.data to system_model.MHKTidal + attributes_to_copy = ['tidal_velocity'] + for attribute in attributes_to_copy: + setattr(system_model.MHKTidal, attribute, self.site.tidal_resource.data[attribute]) + + # System parameter inputs + self._system_model.device_rated_power = self.config.device_rating_kw + self._system_model.value("number_devices", self.config.num_devices) + self._system_model.value("tidal_power_curve", self.config.tidal_power_curve) + self._system_model.value("tidal_resource", self.config.tidal_resource) + + # Losses + loss_attributes = [ + 'loss_array_spacing', + 'loss_downtime', + 'loss_resource_overprediction', + 'loss_transmission', + 'loss_additional' + ] + + for attribute in loss_attributes: + attr = getattr(self.config, attribute, 0) + setattr(self._system_model.MHKTidal, attribute, attr) + + def create_mhk_cost_calculator(self, cost_model_inputs: Union[dict, MHKCostModelInputs]): + """ + Instantiates MHKCosts, cost calculator for MHKTidalPlant. + + Args: + cost_model_inputs: Input parameters for cost modeling. + """ + if isinstance(cost_model_inputs, dict): + cost_model = MHKCostModelInputs.from_dict(cost_model_inputs) + else: + cost_model = cost_model_inputs + + self.mhk_costs = MHKCosts(self.config, cost_model) + + def calculate_total_installed_cost(self) -> float: + if self.mhk_costs is None: + raise AttributeError("mhk_costs must be set before calling this method.") + + self.mhk_costs.simulate_costs() + cost_dict = self.mhk_costs.cost_outputs + + capex = cost_dict['structural_assembly_cost_modeled']+\ + cost_dict['power_takeoff_system_cost_modeled']+\ + cost_dict['mooring_found_substruc_cost_modeled'] + bos = cost_dict['development_cost_modeled']+\ + cost_dict['eng_and_mgmt_cost_modeled']+\ + cost_dict['plant_commissioning_cost_modeled']+\ + cost_dict['site_access_port_staging_cost_modeled']+\ + cost_dict['assembly_and_install_cost_modeled']+\ + cost_dict['other_infrastructure_cost_modeled'] + elec_infrastruc_costs = cost_dict['array_cable_system_cost_modeled']+\ + cost_dict['export_cable_system_cost_modeled']+\ + cost_dict['onshore_substation_cost_modeled']+\ + cost_dict['offshore_substation_cost_modeled']+\ + cost_dict['other_elec_infra_cost_modeled'] + financial = cost_dict['project_contingency']+\ + cost_dict['insurance_during_construction']+\ + cost_dict['reserve_accounts'] + + total_installed_cost = capex+bos+elec_infrastruc_costs+financial + + return self._financial_model.value("total_installed_cost", total_installed_cost) + + def system_capacity_by_num_devices(self, tidal_size_kw: float): + """ + Sets the system capacity by adjusting the number of devices + """ + new_num_devices = round(tidal_size_kw / self.device_rated_power) + self.number_devices = new_num_devices + + def simulate(self, interconnect_kw: float, project_life: int = 25, lifetime_sim=False): + """ + Run the system and financial model + + Args: + interconnect_kw: grid interconnect + project_life: Number of years in the analysis period (expected + project lifetime) + lifetime_sim: + For simulation modules which support simulating each year of the + project_life, whether or not to do so; otherwise the first year + data is repeated + """ + + self.calculate_total_installed_cost() + super().simulate(interconnect_kw, project_life) + + @property + def device_rated_power(self) -> float: + return self._system_model.device_rated_power + + @device_rated_power.setter + def device_rated_power(self, device_rate_power: float): + self._system_model.device_rated_power = device_rate_power + if self.mhk_costs is not None: + self.mhk_costs.device_rated_power = device_rate_power + + @property + def number_devices(self) -> int: + return self._system_model.MHKTidal.number_devices + + @number_devices.setter + def number_devices(self, number_devices: int): + self._system_model.MHKTidal.number_devices = number_devices + if self.mhk_costs is not None: + self.mhk_costs.number_devices = number_devices + + @property + def tidal_power_curve(self) -> List[List[float]]: + return self._system_model.MHKTidal.tidal_power_curve + + @tidal_power_curve.setter + def tidal_power_curve(self, tidal_power_curve: Sequence): + self._system_model.MHKTidal.tidal_tidal_power_curve =tidal_power_curve + + @property + def system_capacity_kw(self) -> float: + self._system_model.value("system_capacity", self._system_model.device_rated_power * self._system_model.MHKTidal.number_devices) + return self._system_model.value("system_capacity") + + @system_capacity_kw.setter + def system_capacity_kw(self, size_kw: float): + """ + Sets the system capacity by updates the number of tidal devices using device rating + """ + self.system_capacity_by_num_devices(size_kw) + + @property + def annual_energy_kwh(self) -> float: + if self.system_capacity_kw > 0: + return self._system_model.value("annual_energy") + else: + return 0 + + @property + def capacity_factor(self) -> float: + if self.system_capacity_kw > 0: + return self._system_model.value("capacity_factor") + else: + return 0 + \ No newline at end of file diff --git a/hopp/simulation/technologies/wave/mhk_wave_plant.py b/hopp/simulation/technologies/wave/mhk_wave_plant.py index 7b5d6b2dd..a6f7f4219 100644 --- a/hopp/simulation/technologies/wave/mhk_wave_plant.py +++ b/hopp/simulation/technologies/wave/mhk_wave_plant.py @@ -18,22 +18,20 @@ class MHKConfig(BaseClass): Configuration class for MHKWavePlant. Args: - device_rating_kw: Rated power of the MHK device in kilowatts - num_devices: Number of MHK devices in the system - wave_power_matrix: Wave power matrix - fin_model: Optional financial model. Can be any of the following: + device_rating_kw (float): Rated power of the MHK device in kilowatts + num_devices (int): Number of MHK devices in the system + wave_power_matrix (List[List[float]]): Wave power matrix + fin_model (dict | obj): Optional financial model. Can be any of the following: - a dict representing a `CustomFinancialModel` - an object representing a `CustomFinancialModel` instance - - layout_mode: TODO - loss_array_spacing: Array spacing loss in % (default: 0) - loss_resource_overprediction: Resource overprediction loss + loss_array_spacing (float): Array spacing loss in % (default: 0) + loss_resource_overprediction (float): Resource overprediction loss in % (default: 0) - loss_transmission: Transmission loss in % (default: 0) - loss_downtime: Array/WEC downtime loss in % (default: 0) - loss_additional: Additional losses in % (default: 0) + loss_transmission (float): Transmission loss in % (default: 0) + loss_downtime (float): Array/WEC downtime loss in % (default: 0) + loss_additional (float): Additional losses in % (default: 0) """ device_rating_kw: float = field(validator=gt_zero) num_devices: int = field(validator=gt_zero) @@ -55,7 +53,7 @@ class MHKWavePlant(PowerSource): Args: site: Site information config: MHK system configuration parameters - cost_model_inputs: An optional dictionary containing input parameters for + cost_model_inputs (dict): An optional dictionary containing input parameters for cost modeling. """ @@ -154,8 +152,7 @@ def system_capacity_by_num_devices(self, wave_size_kw: float): Sets the system capacity by adjusting the number of devices """ new_num_devices = round(wave_size_kw / self.device_rated_power) - if self.number_devices != new_num_devices: - self.number_devices = new_num_devices + self.number_devices = new_num_devices def simulate(self, interconnect_kw: float, project_life: int = 25, lifetime_sim=False): """ @@ -181,7 +178,7 @@ def device_rated_power(self) -> float: @device_rated_power.setter def device_rated_power(self, device_rate_power: float): self._system_model.MHKWave.device_rated_power = device_rate_power - if self.mhk_costs != None: + if self.mhk_costs is not None: self.mhk_costs.device_rated_power = device_rate_power @property @@ -191,7 +188,7 @@ def number_devices(self) -> int: @number_devices.setter def number_devices(self, number_devices: int): self._system_model.MHKWave.number_devices = number_devices - if self.mhk_costs != None: + if self.mhk_costs is not None: self.mhk_costs.number_devices = number_devices @property diff --git a/tests/hopp/inputs/tidal/tidal_device.yaml b/tests/hopp/inputs/tidal/tidal_device.yaml new file mode 100644 index 000000000..4c2d24a6a --- /dev/null +++ b/tests/hopp/inputs/tidal/tidal_device.yaml @@ -0,0 +1,87 @@ +# RM1 Horizontal Axis Turbine + +# General device info +# wec_reference_model: "RM1" +# technology_type: "Horizontal Axis Turbine" +# pto_type: "Permanent Magnet Synchronous" +# tec_architype: "Axial-flow Turbine" +# unballasted_structural_mass: 265.9 +# foundation_type: "bottom fixed: pile foundation" + +device_rating_kw: 1115 #[kW] +tidal_power_curve: +- [0.000000, 0.000000] +- [0.100000, 0.000000] +- [0.200000, 0.000000] +- [0.300000, 0.000000] +- [0.400000, 0.000000] +- [0.500000, 0.000000] +- [0.600000, 10.421100] +- [0.700000, 20.842300] +- [0.800000, 39.968900] +- [0.900000, 59.095600] +- [1.000000, 89.201600] +- [1.100000, 119.308000] +- [1.200000, 160.886000] +- [1.300000, 202.464000] +- [1.400000, 259.292000] +- [1.500000, 316.120000] +- [1.600000, 392.673000] +- [1.700000, 469.226000] +- [1.800000, 570.306000] +- [1.900000, 671.386000] +- [2.000000, 802.908000] +- [2.100000, 934.430000] +- [2.200000, 1024.710000] +- [2.300000, 1115.000000] +- [2.400000, 1115.000000] +- [2.500000, 1115.000000] +- [2.600000, 1115.000000] +- [2.700000, 1115.000000] +- [2.800000, 1115.000000] +- [2.900000, 1115.000000] +- [3.000000, 1115.000000] +- [3.100000, 1115.000000] +- [3.200000, 1085.370000] +- [3.300000, 1055.730000] + +num_devices: 20 +# Tidal resource is required in PySAM prechecks +# this is a dummy resource profile and does not +# impact simulation when using timeseries data +# TODO: Remove once PySAM Pypi updates +tidal_resource: +- [0.000000, 0.009000] +- [0.100000, 0.031000] +- [0.200000, 0.042000] +- [0.300000, 0.044000] +- [0.400000, 0.048000] +- [0.500000, 0.049000] +- [0.600000, 0.053000] +- [0.700000, 0.051000] +- [0.800000, 0.052000] +- [0.900000, 0.056000] +- [1.000000, 0.050000] +- [1.100000, 0.052000] +- [1.200000, 0.050000] +- [1.300000, 0.048000] +- [1.400000, 0.047000] +- [1.500000, 0.043000] +- [1.600000, 0.042000] +- [1.700000, 0.040000] +- [1.800000, 0.034000] +- [1.900000, 0.031000] +- [2.000000, 0.026000] +- [2.100000, 0.023000] +- [2.200000, 0.020000] +- [2.300000, 0.016000] +- [2.400000, 0.013000] +- [2.500000, 0.011000] +- [2.600000, 0.007000] +- [2.700000, 0.005000] +- [2.800000, 0.004000] +- [2.900000, 0.002000] +- [3.000000, 0.001000] +- [3.100000, 0.000000] +- [3.200000, 0.000000] +- [3.300000, 0.000000] \ No newline at end of file diff --git a/tests/hopp/test_dispatch.py b/tests/hopp/test_dispatch.py index 69d7eb2e6..ed819d8b3 100644 --- a/tests/hopp/test_dispatch.py +++ b/tests/hopp/test_dispatch.py @@ -345,11 +345,11 @@ def test_wave_dispatch(): dispatch_n_look_ahead = 48 data = { - "lat": 44.6899, - "lon": 124.1346, - "year": 2010, - "tz": -7, - } + "lat": 44.6899, + "lon": 124.1346, + "year": 2010, + "tz": -7, + } wave_resource_file = ROOT_DIR / "simulation" / "resource_files" / "wave" / "Wave_resource_timeseries.csv" site = SiteInfo(data, solar=False, wind=False, wave=True, wave_resource_file=wave_resource_file) @@ -362,14 +362,14 @@ def test_wave_dispatch(): config = MHKConfig.from_dict(mhk_config) cost_model_input = MHKCostModelInputs.from_dict({ - 'reference_model_num':3, - 'water_depth': 100, - 'distance_to_shore': 80, - 'number_rows': 10, - 'device_spacing':600, - 'row_spacing': 600, - 'cable_system_overbuild': 20 - }) + 'reference_model_num':3, + 'water_depth': 100, + 'distance_to_shore': 80, + 'number_rows': 10, + 'device_spacing':600, + 'row_spacing': 600, + 'cable_system_overbuild': 20 + }) wave = MHKWavePlant(site, config, cost_model_input) diff --git a/tests/hopp/test_tidal.py b/tests/hopp/test_tidal.py new file mode 100644 index 000000000..c6ee8b207 --- /dev/null +++ b/tests/hopp/test_tidal.py @@ -0,0 +1,111 @@ +import pytest +from pytest import fixture +from pathlib import Path + +from hopp.simulation.technologies.sites import SiteInfo +from hopp.simulation.technologies.financial.mhk_cost_model import MHKCostModelInputs +from hopp.simulation.technologies.financial.custom_financial_model import CustomFinancialModel +from hopp.utilities import load_yaml +from hopp import ROOT_DIR +from tests.hopp.utils import DEFAULT_FIN_CONFIG +from hopp.simulation.technologies.tidal.mhk_tidal_plant import MHKTidalPlant, MHKTidalConfig + +@fixture +def site(): + data = { + "lat": 44.6899, + "lon": 124.1346, + "year": 2010, + "tz": -7, + } + tidal_resource_file = Path.joinpath(ROOT_DIR / "simulation" / "resource_files" / "tidal" / "Tidal_resource_timeseries.csv") + site = SiteInfo(data, solar=False, wind=False, tidal=True, tidal_resource_file=tidal_resource_file) + + return site + +@fixture +def mhk_config(): + mhk_yaml_path = Path(__file__).absolute().parent.parent.parent / "tests" / "hopp" / "inputs" / "tidal" / "tidal_device.yaml" + mhk_config = load_yaml(mhk_yaml_path) + + return mhk_config + +@fixture +def tidalplant(mhk_config, site): + financial_model = {'fin_model': DEFAULT_FIN_CONFIG} + mhk_config.update(financial_model) + config = MHKTidalConfig.from_dict(mhk_config) + + cost_model_input = MHKCostModelInputs.from_dict({ + 'reference_model_num':1, + 'water_depth': 100, + 'distance_to_shore': 80, + 'number_rows': 2, + 'device_spacing':600, + 'row_spacing': 600, + 'cable_system_overbuild': 20 + }) + return MHKTidalPlant(site, config, cost_model_input) + +def test_mhk_config(mhk_config, subtests): + with subtests.test("with basic params"): + financial_model = {'fin_model': DEFAULT_FIN_CONFIG} + mhk_config.update(financial_model) + + config = MHKTidalConfig.from_dict(mhk_config) + + assert config.device_rating_kw == 1115. + assert config.num_devices == 20 + assert config.fin_model is not None + # defaults + assert config.loss_array_spacing == 0. + assert config.loss_resource_overprediction == 0. + assert config.loss_transmission == 0. + assert config.loss_downtime == 0. + assert config.loss_additional == 0. + +def test_system_outputs(tidalplant,subtests): + tidalplant.simulate(25) + + with subtests.test("annual energy kwh"): + assert tidalplant.annual_energy_kwh == pytest.approx(60625516, 1e-3) + + with subtests.test("capacity factor"): + assert tidalplant.capacity_factor == pytest.approx(31.03, 1e-3) + +def test_cost_outputs(tidalplant,subtests): + tidalplant.simulate(25) + with subtests.test("structural assembly cost"): + assert tidalplant.mhk_costs.cost_outputs['structural_assembly_cost_modeled'] == pytest.approx(10371672, 1e-3) + with subtests.test("power_takeoff_system_cost"): + assert tidalplant.mhk_costs.cost_outputs['power_takeoff_system_cost_modeled']== pytest.approx(41212670, 1e-3) + +def test_changing_n_devices(tidalplant, subtests): + with subtests.test("less devices than rows"): + with pytest.raises(Exception): + tidalplant.number_devices = 9 + + with subtests.test("not grid shape"): + with pytest.raises(Exception): + tidalplant.number_devices = 11 + + tidalplant.number_devices = 50 + with subtests.test("change system capacity"): + assert tidalplant.system_capacity_kw == pytest.approx(1115*50,0) + + with subtests.test("update cost model - number_devices"): + assert tidalplant.mhk_costs.number_devices == tidalplant.number_devices + + with subtests.test("update cost model - system_capacity"): + assert tidalplant.mhk_costs.system_capacity_kw == tidalplant.system_capacity_kw + +def test_changing_device_rating(tidalplant, subtests): + tidalplant.device_rated_power = 150 + with subtests.test("change system capacity"): + assert tidalplant.system_capacity_kw == tidalplant.device_rated_power * tidalplant.number_devices + + with subtests.test("update cost model - device rated power"): + assert tidalplant.mhk_costs.device_rated_power == tidalplant.device_rated_power + + with subtests.test("update cost model - system capacity"): + assert tidalplant.mhk_costs.system_capacity_kw == tidalplant.system_capacity_kw \ No newline at end of file diff --git a/tests/hopp/test_wave.py b/tests/hopp/test_wave.py index feeedce0a..627ff6de4 100644 --- a/tests/hopp/test_wave.py +++ b/tests/hopp/test_wave.py @@ -13,169 +13,169 @@ @fixture def site(): - data = { - "lat": 44.6899, - "lon": 124.1346, - "year": 2010, - "tz": -7, - } - wave_resource_file = ROOT_DIR / "simulation" / "resource_files" / "wave" / "Wave_resource_timeseries.csv" - site = SiteInfo(data, solar=False, wind=False, wave=True, wave_resource_file=wave_resource_file) + data = { + "lat": 44.6899, + "lon": 124.1346, + "year": 2010, + "tz": -7, + } + wave_resource_file = ROOT_DIR / "simulation" / "resource_files" / "wave" / "Wave_resource_timeseries.csv" + site = SiteInfo(data, solar=False, wind=False, wave=True, wave_resource_file=wave_resource_file) - return site + return site @fixture def mhk_config(): - mhk_yaml_path = Path(__file__).absolute().parent.parent.parent / "tests" / "hopp" / "inputs" / "wave" / "wave_device.yaml" - mhk_config = load_yaml(mhk_yaml_path) + mhk_yaml_path = Path(__file__).absolute().parent.parent.parent / "tests" / "hopp" / "inputs" / "wave" / "wave_device.yaml" + mhk_config = load_yaml(mhk_yaml_path) - return mhk_config + return mhk_config @fixture def waveplant(mhk_config, site): - financial_model = {'fin_model': DEFAULT_FIN_CONFIG} - mhk_config.update(financial_model) - config = MHKConfig.from_dict(mhk_config) - - cost_model_input = MHKCostModelInputs.from_dict({ - 'reference_model_num':3, - 'water_depth': 100, - 'distance_to_shore': 80, - 'number_rows': 10, - 'device_spacing':600, - 'row_spacing': 600, - 'cable_system_overbuild': 20 - }) - return MHKWavePlant(site, config, cost_model_input) + financial_model = {'fin_model': DEFAULT_FIN_CONFIG} + mhk_config.update(financial_model) + config = MHKConfig.from_dict(mhk_config) + + cost_model_input = MHKCostModelInputs.from_dict({ + 'reference_model_num':3, + 'water_depth': 100, + 'distance_to_shore': 80, + 'number_rows': 10, + 'device_spacing':600, + 'row_spacing': 600, + 'cable_system_overbuild': 20 + }) + return MHKWavePlant(site, config, cost_model_input) def test_mhk_config(mhk_config, subtests): - with subtests.test("with basic params"): - financial_model = {'fin_model': DEFAULT_FIN_CONFIG} - mhk_config.update(financial_model) + with subtests.test("with basic params"): + financial_model = {'fin_model': DEFAULT_FIN_CONFIG} + mhk_config.update(financial_model) - config = MHKConfig.from_dict(mhk_config) + config = MHKConfig.from_dict(mhk_config) - assert config.device_rating_kw == 286. - assert config.num_devices == 100 - assert config.wave_power_matrix == mhk_config["wave_power_matrix"] - assert config.fin_model is not None - - # defaults - assert config.loss_array_spacing == 0. - assert config.loss_resource_overprediction == 0. - assert config.loss_transmission == 0. - assert config.loss_downtime == 0. - assert config.loss_additional == 0. + assert config.device_rating_kw == 286. + assert config.num_devices == 100 + assert config.wave_power_matrix == mhk_config["wave_power_matrix"] + assert config.fin_model is not None + + # defaults + assert config.loss_array_spacing == 0. + assert config.loss_resource_overprediction == 0. + assert config.loss_transmission == 0. + assert config.loss_downtime == 0. + assert config.loss_additional == 0. def test_system_outputs(waveplant,subtests): - # Test to see if there have been changes to PySAM MhkWave model and it is able to handle 1-hr - # Timeseries data. Right now have to divide hourly data outputs by 3 to get the same values - waveplant.simulate(25) + # Test to see if there have been changes to PySAM MhkWave model and it is able to handle 1-hr + # Timeseries data. Right now have to divide hourly data outputs by 3 to get the same values + waveplant.simulate(25) - with subtests.test("annual energy kwh"): - assert waveplant.annual_energy_kwh == pytest.approx(121325260.0,0) + with subtests.test("annual energy kwh"): + assert waveplant.annual_energy_kwh == pytest.approx(121325260.0,0) - with subtests.test("capacity factor"): - assert waveplant.capacity_factor == pytest.approx(48.42,1) + with subtests.test("capacity factor"): + assert waveplant.capacity_factor == pytest.approx(48.42,1) - with subtests.test("number of hours"): - assert waveplant.numberHours == pytest.approx(8760) + with subtests.test("number of hours"): + assert waveplant.numberHours == pytest.approx(8760) def test_cost_outputs(waveplant): - waveplant.simulate(25) + waveplant.simulate(25) - assert waveplant.mhk_costs.cost_outputs['array_cable_system_cost_modeled'] == pytest.approx(13371634.8, abs=1e-6) + assert waveplant.mhk_costs.cost_outputs['array_cable_system_cost_modeled'] == pytest.approx(13371634.8, abs=1e-6) def test_changing_n_devices(waveplant, subtests): - with subtests.test("less devices than rows"): - with pytest.raises(Exception): - waveplant.number_devices = 9 + with subtests.test("less devices than rows"): + with pytest.raises(Exception): + waveplant.number_devices = 9 - with subtests.test("not grid shape"): - with pytest.raises(Exception): - waveplant.number_devices = 11 + with subtests.test("not grid shape"): + with pytest.raises(Exception): + waveplant.number_devices = 11 - with subtests.test("change system capacity"): - waveplant.number_devices = 50 - assert waveplant.system_capacity_kw == pytest.approx(286*50,0) + with subtests.test("change system capacity"): + waveplant.number_devices = 50 + assert waveplant.system_capacity_kw == pytest.approx(286*50,0) - with subtests.test("update cost model - number_devices"): - waveplant.number_devices = 50 - assert waveplant.mhk_costs.number_devices == waveplant.number_devices + with subtests.test("update cost model - number_devices"): + waveplant.number_devices = 50 + assert waveplant.mhk_costs.number_devices == waveplant.number_devices - with subtests.test("update cost model - system_capacity"): - waveplant.number_devices = 50 - assert waveplant.mhk_costs.system_capacity_kw == waveplant.system_capacity_kw + with subtests.test("update cost model - system_capacity"): + waveplant.number_devices = 50 + assert waveplant.mhk_costs.system_capacity_kw == waveplant.system_capacity_kw def test_changing_device_rating(waveplant,subtests): - with subtests.test("change system capacity"): - waveplant.device_rated_power = 150 - assert waveplant.system_capacity_kw == waveplant.device_rated_power * waveplant.number_devices - - with subtests.test("update cost model - device rated power"): - waveplant.device_rated_power = 150 - assert waveplant.mhk_costs.device_rated_power == waveplant.device_rated_power - - with subtests.test("update cost model - system capacity"): - waveplant.device_rated_power = 150 - assert waveplant.mhk_costs.system_capacity_kw == waveplant.system_capacity_kw + with subtests.test("change system capacity"): + waveplant.device_rated_power = 150 + assert waveplant.system_capacity_kw == waveplant.device_rated_power * waveplant.number_devices + + with subtests.test("update cost model - device rated power"): + waveplant.device_rated_power = 150 + assert waveplant.mhk_costs.device_rated_power == waveplant.device_rated_power + + with subtests.test("update cost model - system capacity"): + waveplant.device_rated_power = 150 + assert waveplant.mhk_costs.system_capacity_kw == waveplant.system_capacity_kw def test_changing_wave_power_matrix(waveplant): - waveplant.number_devices = 100 - waveplant.device_rated_power = 360 - waveplant.wave_power_matrix = [ - [0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5], - [0.25, 0, 0, 0, 0, 4.8, 6.7, 7.9, 9.3, 10.2, 10.1, 9.7, 9, 8.8, 7.6, 7.3, 6.4, 5.6, 5, 4.5, 4, 0], - [0.75, 0, 0, 0, 0, 12.3, 16.5, 18.8, 21.2, 22.9, 22.2, 20.9, 19.4, 18.7, 16.5, 16, 14.2, 12.8, 11.5, 10.4, 9.4, 0], - [1.25, 0, 0, 0, 0, 31.8, 40.7, 44.6, 48.5, 51.7, 48.8, 45.1, 41.8, 40.1, 36.2, 35.1, 31.9, 29.2, 26.5, 24.3, 22, 0], - [1.75, 0, 0, 0, 0, 58.3, 72.3, 77.1, 81.7, 86.5, 80.8, 74, 69.7, 66.7, 59.7, 57.6, 52.7, 48.7, 44.5, 41.1, 37.6, 0], - [2.25, 0, 0, 0, 0, 91.3, 110.4, 115.7, 119.3, 126.5, 117.3, 107.9, 102, 97.1, 86.4, 82.6, 75.6, 70.5, 64.7, 60.3, 55.3, 0], - [2.75, 0, 0, 0, 0, 130.5, 154.9, 160, 162.7, 171.7, 158.5, 145.4, 137.5, 130.4, 115.6, 109.7, 101.4, 94.6, 86.6, 80.8, 74, 0], - [3.25, 0, 0, 0, 0, 174.9, 204.4, 208.9, 210.4, 220.5, 202.7, 185.4, 175.4, 165.9, 148, 140.3, 129.7, 120.5, 110.1, 102.2, 93.4, 0], - [3.75, 0, 0, 0, 0, 223.9, 258.5, 261.9, 261.6, 272.4, 249.5, 227.7, 215.3, 204.5, 183.2, 173, 159.8, 147.9, 134.8, 124.8, 113.7, 0], - [4.25, 0, 0, 0, 0, 277.2, 316.8, 318.5, 316, 327, 298.4, 271.6, 257.2, 245.5, 220.2, 207.3, 191.5, 177.1, 161.8, 149.7, 136.8, 0], - [4.75, 0, 0, 0, 0, 334.5, 360, 360, 360, 360, 349.4, 317.2, 302.2, 288.2, 258.7, 243.1, 225.4, 208.6, 190.3, 176.1, 160.7, 0], - [5.25, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 348.9, 332.4, 298.6, 280.1, 261.3, 241.4, 220, 203.3, 185.2, 0], - [5.75, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 339.7, 319.1, 298.4, 275.5, 250.8, 231.5, 210.7, 0], - [6.25, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 340.8, 314.3, 285.8, 263.5, 239.7, 0], - [6.75, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 358.6, 325.8, 300.1, 272.6, 0], - [7.25, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 341.6, 310.1, 0], - [7.75, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 352.8, 0], - [8.25, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 0], - [8.75, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 0], - [9.25, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 0], - [9.75, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 0] - ] - waveplant.simulate(25) - assert waveplant.annual_energy_kwh == pytest.approx(160673260.0,0) + waveplant.number_devices = 100 + waveplant.device_rated_power = 360 + waveplant.wave_power_matrix = [ + [0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5], + [0.25, 0, 0, 0, 0, 4.8, 6.7, 7.9, 9.3, 10.2, 10.1, 9.7, 9, 8.8, 7.6, 7.3, 6.4, 5.6, 5, 4.5, 4, 0], + [0.75, 0, 0, 0, 0, 12.3, 16.5, 18.8, 21.2, 22.9, 22.2, 20.9, 19.4, 18.7, 16.5, 16, 14.2, 12.8, 11.5, 10.4, 9.4, 0], + [1.25, 0, 0, 0, 0, 31.8, 40.7, 44.6, 48.5, 51.7, 48.8, 45.1, 41.8, 40.1, 36.2, 35.1, 31.9, 29.2, 26.5, 24.3, 22, 0], + [1.75, 0, 0, 0, 0, 58.3, 72.3, 77.1, 81.7, 86.5, 80.8, 74, 69.7, 66.7, 59.7, 57.6, 52.7, 48.7, 44.5, 41.1, 37.6, 0], + [2.25, 0, 0, 0, 0, 91.3, 110.4, 115.7, 119.3, 126.5, 117.3, 107.9, 102, 97.1, 86.4, 82.6, 75.6, 70.5, 64.7, 60.3, 55.3, 0], + [2.75, 0, 0, 0, 0, 130.5, 154.9, 160, 162.7, 171.7, 158.5, 145.4, 137.5, 130.4, 115.6, 109.7, 101.4, 94.6, 86.6, 80.8, 74, 0], + [3.25, 0, 0, 0, 0, 174.9, 204.4, 208.9, 210.4, 220.5, 202.7, 185.4, 175.4, 165.9, 148, 140.3, 129.7, 120.5, 110.1, 102.2, 93.4, 0], + [3.75, 0, 0, 0, 0, 223.9, 258.5, 261.9, 261.6, 272.4, 249.5, 227.7, 215.3, 204.5, 183.2, 173, 159.8, 147.9, 134.8, 124.8, 113.7, 0], + [4.25, 0, 0, 0, 0, 277.2, 316.8, 318.5, 316, 327, 298.4, 271.6, 257.2, 245.5, 220.2, 207.3, 191.5, 177.1, 161.8, 149.7, 136.8, 0], + [4.75, 0, 0, 0, 0, 334.5, 360, 360, 360, 360, 349.4, 317.2, 302.2, 288.2, 258.7, 243.1, 225.4, 208.6, 190.3, 176.1, 160.7, 0], + [5.25, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 348.9, 332.4, 298.6, 280.1, 261.3, 241.4, 220, 203.3, 185.2, 0], + [5.75, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 339.7, 319.1, 298.4, 275.5, 250.8, 231.5, 210.7, 0], + [6.25, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 340.8, 314.3, 285.8, 263.5, 239.7, 0], + [6.75, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 358.6, 325.8, 300.1, 272.6, 0], + [7.25, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 341.6, 310.1, 0], + [7.75, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 352.8, 0], + [8.25, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 0], + [8.75, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 0], + [9.25, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 0], + [9.75, 0, 0, 0, 0, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 0] + ] + waveplant.simulate(25) + assert waveplant.annual_energy_kwh == pytest.approx(160673260.0,0) def test_changing_system_capacity(waveplant,subtests): - system_size_kw = 20000 + system_size_kw = 20000 - with subtests.test("system capacity"): - waveplant.system_capacity_by_num_devices(system_size_kw) - assert waveplant.system_capacity_kw == waveplant.device_rated_power * round(system_size_kw/waveplant.device_rated_power) - - with subtests.test("cost model - system capacity"): - waveplant.system_capacity_by_num_devices(system_size_kw) - assert waveplant.mhk_costs.system_capacity_kw == waveplant.device_rated_power * round(system_size_kw/waveplant.device_rated_power) + with subtests.test("system capacity"): + waveplant.system_capacity_by_num_devices(system_size_kw) + assert waveplant.system_capacity_kw == waveplant.device_rated_power * round(system_size_kw/waveplant.device_rated_power) + + with subtests.test("cost model - system capacity"): + waveplant.system_capacity_by_num_devices(system_size_kw) + assert waveplant.mhk_costs.system_capacity_kw == waveplant.device_rated_power * round(system_size_kw/waveplant.device_rated_power) def test_changing_ref_model(waveplant,subtests): - waveplant.device_rated_power = 360 - waveplant.number_devices = 100 - waveplant.mhk_costs.ref_model_num = 5 - waveplant.mhk_costs.simulate_costs() - - with subtests.test("model number"): - assert waveplant.mhk_costs._cost_model.value("lib_wave_device") == "RM5" - - with subtests.test("cost model"): - assert waveplant.mhk_costs.cost_outputs['array_cable_system_cost_modeled'] == pytest.approx(13570902.0, 0) - - with subtests.test("ref model number wrong"): - with pytest.raises(Exception): - waveplant.mhk_costs.ref_model_num = 11 \ No newline at end of file + waveplant.device_rated_power = 360 + waveplant.number_devices = 100 + waveplant.mhk_costs.ref_model_num = 5 + waveplant.mhk_costs.simulate_costs() + + with subtests.test("model number"): + assert waveplant.mhk_costs._cost_model.value("lib_wave_device") == "RM5" + + with subtests.test("cost model"): + assert waveplant.mhk_costs.cost_outputs['array_cable_system_cost_modeled'] == pytest.approx(13570902.0, 0) + + with subtests.test("ref model number wrong"): + with pytest.raises(Exception): + waveplant.mhk_costs.ref_model_num = 11 \ No newline at end of file From 400911b57f2fa8162b74a456428481bee12ae3e5 Mon Sep 17 00:00:00 2001 From: kbrunik <102193481+kbrunik@users.noreply.github.com> Date: Wed, 12 Mar 2025 08:03:50 -0500 Subject: [PATCH 14/48] Integrate Tidal into Hybrid Simulation (#446) * integrate mhk_tidal into HybridSimulation * update tidal and wave docs * add tidal test to test_hybrid.py * add robust_approx method Co-authored-by: John Jasa --- RELEASE.md | 1 + docs/api/technology/mhk_tidal_plant.md | 2 +- docs/api/technology/mhk_wave_plant.md | 2 +- hopp/simulation/hybrid_simulation.py | 44 +++- .../technologies/resource/tidal_resource.py | 12 +- .../technologies/resource/wave_resource.py | 6 +- tests/hopp/test_hybrid.py | 236 +++++++++++++++--- 7 files changed, 258 insertions(+), 45 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 51e1d8f93..83ff73bc9 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -25,6 +25,7 @@ + Updated wind layout parameters. * Added TidalResource to load tidal resource data for simulating tidal energy. * Added MHKTidalPlant to simulate tidal energy. +* Add tidal energy to HybridSimulation. * Remove erroneous 100 multiples for percentages and add clarifying parentheses for correct 100 multiples for percentages ## Version 3.1.1, Dec. 18, 2024 diff --git a/docs/api/technology/mhk_tidal_plant.md b/docs/api/technology/mhk_tidal_plant.md index 0aa598b76..643f07fc8 100644 --- a/docs/api/technology/mhk_tidal_plant.md +++ b/docs/api/technology/mhk_tidal_plant.md @@ -21,4 +21,4 @@ MHK Tidal Generator class ## Tidal Plant Cost Model -For details on the cost model used in MHK Tidal Plants, refer to the [MHK Cost Model](cost_model.md). +For details on the cost model used in MHK Tidal Plants, refer to the [MHK Cost Model](tools:mhk_costs). diff --git a/docs/api/technology/mhk_wave_plant.md b/docs/api/technology/mhk_wave_plant.md index 210d38621..09990a44f 100644 --- a/docs/api/technology/mhk_wave_plant.md +++ b/docs/api/technology/mhk_wave_plant.md @@ -21,5 +21,5 @@ MHK Wave Generator class ## Wave Plant Cost Model -For details on the cost model used in MHK Wave Plants, refer to the [MHK Cost Model](cost_model.md). +For details on the cost model used in MHK Wave Plants, refer to the [MHK Cost Model](tools:mhk_costs). diff --git a/hopp/simulation/hybrid_simulation.py b/hopp/simulation/hybrid_simulation.py index 6aee2f2d3..eef290a5b 100644 --- a/hopp/simulation/hybrid_simulation.py +++ b/hopp/simulation/hybrid_simulation.py @@ -15,6 +15,7 @@ from hopp.simulation.technologies.csp.tower_plant import TowerConfig, TowerPlant from hopp.simulation.technologies.csp.trough_plant import TroughConfig, TroughPlant from hopp.simulation.technologies.wave.mhk_wave_plant import MHKWavePlant, MHKConfig +from hopp.simulation.technologies.tidal.mhk_tidal_plant import MHKTidalPlant, MHKTidalConfig from hopp.simulation.technologies.battery import Battery, BatteryConfig, BatteryStateless, BatteryStatelessConfig from hopp.simulation.technologies.grid import Grid, GridConfig from hopp.simulation.technologies.reopt import REopt @@ -38,7 +39,7 @@ class HybridSimulationOutput: """Class for creating :class:`HybridSimulation` output structure""" - _keys = ("pv", "wind", "wave", "battery", "tower", "trough", "hybrid") + _keys = ("pv", "wind", "wave", "tidal", "battery", "tower", "trough", "hybrid") def __init__(self, power_sources): """ @@ -97,6 +98,7 @@ class TechnologiesConfig(BaseClass): defaults to `PVConfig` wind: Wind config wave: Wave config + tidal: Tidal config tower: CSP tower config trough: CSP trough config battery: Battery config. If `tracking` is False, uses `BatteryStatelessConfig`. @@ -107,6 +109,7 @@ class TechnologiesConfig(BaseClass): pv: Optional[Union[PVConfig, DetailedPVConfig]] = field(default=None) wind: Optional[WindConfig] = field(default=None) wave: Optional[MHKConfig] = field(default=None) + tidal: Optional[MHKTidalConfig] = field(default=None) tower: Optional[TowerConfig] = field(default=None) trough: Optional[TroughConfig] = field(default=None) battery: Optional[Union[BatteryConfig, BatteryStatelessConfig]] = field(default=None) @@ -134,6 +137,9 @@ def from_dict(cls, data: dict): if "wave" in data: config["wave"] = MHKConfig.from_dict(data["wave"]) + if "tidal" in data: + config["tidal"] = MHKTidalConfig.from_dict(data["tidal"]) + if "tower" in data: config["tower"] = TowerConfig.from_dict(data["tower"]) @@ -184,6 +190,7 @@ class HybridSimulation(BaseClass): pv: Optional[Union[PVPlant, DetailedPVPlant]] = field(init=False, default=None) wind: Optional[WindPlant] = field(init=False, default=None) wave: Optional[MHKWavePlant] = field(init=False, default=None) + tidal: Optional[MHKTidalPlant] = field(init=False, default=None) tower: Optional[TowerPlant] = field(init=False, default=None) trough: Optional[TroughPlant] = field(init=False, default=None) battery: Optional[Union[Battery, BatteryStateless]] = field(init=False, default=None) @@ -226,6 +233,14 @@ def __attrs_post_init__(self): logger.info("Created HybridSystem.wave with system size {} mW".format(wave_config)) + tidal_config = self.tech_config.tidal + + if tidal_config is not None: + self.tidal = MHKTidalPlant(self.site, config=tidal_config) + self.technologies["tidal"] = self.tidal + + logger.info("Created HybridSystem.tidal with system size {} mW".format(tidal_config)) + tower_config = self.tech_config.tower if tower_config is not None: @@ -326,18 +341,20 @@ def setup_cost_calculator(self, cost_calculator: object): def set_om_costs(self, pv_om_per_kw=None, wind_om_per_kw=None, tower_om_per_kw=None, trough_om_per_kw=None, - wave_om_per_kw=None, battery_om_per_kw=None, + wave_om_per_kw=None, tidal_om_per_kw=None, + battery_om_per_kw=None, hybrid_om_per_kw=None, pv_om_per_mwh=None,wind_om_per_mwh=None, tower_om_per_mwh=None,trough_om_per_mwh=None, - wave_om_per_mwh=None,battery_om_per_mwh=None, + wave_om_per_mwh=None,tidal_om_per_mwh=None, + battery_om_per_mwh=None, hybrid_om_per_mwh=None,): """ Sets Capacity-based O&M amount for each technology [$/kWcap]. Sets Production-based O&M amount for each technology [$/MWh]. """ - # om_vals = [pv_om_per_kw, wind_om_per_kw, tower_om_per_kw, trough_om_per_kw, wave_om_per_kw, hybrid_om_per_kw] - # techs = ["pv", "wind", "tower", "trough", "wave", "hybrid"] + # om_vals = [pv_om_per_kw, wind_om_per_kw, tower_om_per_kw, trough_om_per_kw, wave_om_per_kw, tidal_om_per_kw, hybrid_om_per_kw] + # techs = ["pv", "wind", "tower", "trough", "wave", "tidal", "hybrid"] # om_lengths = {tech + "_om_per_kw" : om_val for om_val, tech in zip(om_vals, techs)} # if len(set(om_lengths.values())) != 1 and len(set(om_lengths.values())) is not None: # raise ValueError(f"Length of yearly om cost per kw arrays must be equal. Some lengths of om_per_kw values are different from others: {om_lengths}") @@ -371,6 +388,12 @@ def set_om_costs(self, pv_om_per_kw=None, wind_om_per_kw=None, if wave_om_per_mwh: self.wave.om_production = wave_om_per_mwh + if self.tidal: + if tidal_om_per_kw: + self.tidal.om_capacity = tidal_om_per_kw + if tidal_om_per_mwh: + self.tidal.om_production = tidal_om_per_mwh + if self.battery: if battery_om_per_kw: self.battery.om_capacity = battery_om_per_kw @@ -432,6 +455,9 @@ def calculate_installed_cost(self): if self.wave: self.wave.total_installed_cost = self.wave.calculate_total_installed_cost() total_cost += self.wave.total_installed_cost + if self.tidal: + self.tidal.total_installed_cost = self.tidal.calculate_total_installed_cost() + total_cost += self.tidal.total_installed_cost if self.tower: self.tower.total_installed_cost = self.tower.calculate_total_installed_cost() total_cost += self.tower.total_installed_cost @@ -662,7 +688,7 @@ def simulate_power(self, project_life: int = 25, lifetime_sim=False): """ self.setup_performance_models() # simulate non-dispatchable systems - non_dispatchable_systems = ['pv', 'wind','wave'] + non_dispatchable_systems = ['pv', 'wind','wave','tidal'] for system in non_dispatchable_systems: model = getattr(self, system) if model: @@ -880,6 +906,10 @@ def capacity_factors(self) -> HybridSimulationOutput: cf.wave = self.wave.capacity_factor hybrid_generation += self.wave.annual_energy_kwh hybrid_capacity += self.wave.system_capacity_kw + if self.tidal: + cf.tidal = self.tidal.capacity_factor + hybrid_generation += self.tidal.annual_energy_kwh + hybrid_capacity += self.tidal.system_capacity_kw if self.tower: cf.tower = self.tower.capacity_factor hybrid_generation += self.tower.annual_energy_kwh @@ -1066,6 +1096,8 @@ def hybrid_simulation_outputs(self, filename: str = "") -> dict: outputs['Wind (MW)'] = self.wind.system_capacity_kw / 1000 if self.wave: outputs['Wave (MW)'] = self.wave.system_capacity_kw / 1000 + if self.tidal: + outputs['Tidal (MW)'] = self.tidal.system_capacity_kw / 1000 if self.tower: outputs['Tower (MW)'] = self.tower.system_capacity_kw / 1000 outputs['Tower Hours of Storage (hr)'] = self.tower.tes_hours diff --git a/hopp/simulation/technologies/resource/tidal_resource.py b/hopp/simulation/technologies/resource/tidal_resource.py index 0bde4ff19..13d87cefa 100644 --- a/hopp/simulation/technologies/resource/tidal_resource.py +++ b/hopp/simulation/technologies/resource/tidal_resource.py @@ -35,14 +35,14 @@ def __init__( Notes: The tidal resource data should be in the format: - - Rows 1 and 2: Header rows with location info. - - Row 3: Column headings for time-series data - (`Year`, `Month`, `Day`, `Hour`, `Minute`, `Speed`). - - Rows 4+: Data values: - - `Speed` (current speed) in meters/second. + - Rows 1 and 2: Header rows with location info. + - Row 3: Column headings for time-series data + - (`Year`, `Month`, `Day`, `Hour`, `Minute`, `Speed`). + - Rows 4+: Data values: + - `Speed` (current speed) in meters/second. Example file: - `hopp/simulation/resource_files/tidal/Tidal_resource_timeseries.csv` + `hopp/simulation/resource_files/tidal/Tidal_resource_timeseries.csv` """ super().__init__(lat, lon, year) diff --git a/hopp/simulation/technologies/resource/wave_resource.py b/hopp/simulation/technologies/resource/wave_resource.py index 0d6bc255f..a71f8afbf 100644 --- a/hopp/simulation/technologies/resource/wave_resource.py +++ b/hopp/simulation/technologies/resource/wave_resource.py @@ -67,9 +67,9 @@ def download_resource(self): Raises: NotImplementedError: Currently, downloading functionality is not implemented. - TODO: - Implement resource downloads using MHKit: - https://mhkit-software.github.io/MHKiT/ + Notes: + Future task: implement resource downloads using + [MHKit](https://mhkit-software.github.io/MHKiT/). """ raise NotImplementedError diff --git a/tests/hopp/test_hybrid.py b/tests/hopp/test_hybrid.py index 2ad798bc4..53763c254 100644 --- a/tests/hopp/test_hybrid.py +++ b/tests/hopp/test_hybrid.py @@ -23,6 +23,27 @@ from hopp.utilities import load_yaml +def robust_approx(o1, o2): + assert type(o1) == type(o2) + + o1_keys = [v for v in dir(o1) if not v.startswith('__')] + o2_keys = [v for v in dir(o2) if not v.startswith('__')] + + assert sorted(o1_keys) == sorted(o2_keys) + + for k in o1_keys: + v1 = getattr(o1, k) + v2 = getattr(o2, k) + if isinstance(v1, int) or isinstance(v1, float): + assert v1 == approx(v2) + continue + + if isinstance(v1, bool) or isinstance(v1, str): + assert v1 == v2 + continue + + approx(v1, v2) + @fixture def hybrid_config(): """Loads the config YAML and updates site info to use resource files.""" @@ -33,29 +54,48 @@ def hybrid_config(): return hybrid_config - @fixture def site(): return create_default_site_info() - -wave_resource_file = ( +@fixture +def wavesite(): + data = {"lat": 44.6899, "lon": 124.1346, "year": 2010, "tz": -7} + wave_resource_file = ( ROOT_DIR / "simulation" / "resource_files" / "wave" / "Wave_resource_timeseries.csv" -) + ) + wavesite = SiteInfo( + data, wave_resource_file=wave_resource_file, solar=False, wind=False, wave=True + ) + return wavesite +@fixture +def tidalsite(): + data = { + "lat": 44.6899, + "lon": 124.1346, + "year": 2010, + "tz": -7, + } + tidal_resource_file = Path.joinpath(ROOT_DIR / "simulation" / "resource_files" / "tidal" / "Tidal_resource_timeseries.csv") + tidalsite = SiteInfo(data, solar=False, wind=False, tidal=True, tidal_resource_file=tidal_resource_file) + + return tidalsite @fixture -def wavesite(): # TODO this should be used, but there were problems getting it working so tests duplicate the work each time right now - data = {"lat": 44.6899, "lon": 124.1346, "year": 2010, "tz": -7} - return SiteInfo( - data, wave_resource_file=wave_resource_file, solar=False, wind=False, wave=True +def mhk_config(): + mhk_yaml_path = ( + ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "wave" / "wave_device.yaml" ) + mhk_config = load_yaml(mhk_yaml_path) + return mhk_config +@fixture +def mhk_tidal_config(): + mhk_yaml_path = Path(__file__).absolute().parent.parent.parent / "tests" / "hopp" / "inputs" / "tidal" / "tidal_device.yaml" + mhk_config = load_yaml(mhk_yaml_path) -mhk_yaml_path = ( - ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "wave" / "wave_device.yaml" -) -mhk_config = load_yaml(mhk_yaml_path) + return mhk_config interconnection_size_kw = 15000 pv_kw = 5000 @@ -182,9 +222,8 @@ def wavesite(): # TODO this should be used, but there were problems getting it w ] -def test_hybrid_wave_only(hybrid_config, subtests): - hybrid_config["site"]["wave"] = True - hybrid_config["site"]["wave_resource_file"] = wave_resource_file +def test_hybrid_wave_only(hybrid_config, mhk_config, wavesite, subtests): + hybrid_config["site"]=wavesite wave_only_technologies = { "wave": { "device_rating_kw": mhk_config["device_rating_kw"], @@ -200,10 +239,8 @@ def test_hybrid_wave_only(hybrid_config, subtests): hybrid_config["technologies"] = wave_only_technologies - # TODO once the financial model is implemented, romove the line immediately following this comment and un-indent the rest of the test hi = HoppInterface(hybrid_config) hybrid_plant = hi.system - # hybrid_plant = HybridSimulation(wave_only_technologies, wavesite) cost_model_inputs = MHKCostModelInputs.from_dict( { "reference_model_num": 3, @@ -253,9 +290,6 @@ def test_hybrid_wave_only(hybrid_config, subtests): hybrid_plant.grid._financial_model.SystemCosts ) - # with subtests.test("SystemOutput.__dict__"): - # skip(reason="this test will not be consistent until the code is more type stable. Outputs may be tuple or list") - # assert hybrid_plant.wave._financial_model.SystemOutput.__dict__ == hybrid_plant.grid._financial_model.SystemOutput.__dict__ with subtests.test("SystemOutput.gen"): assert hybrid_plant.wave._financial_model.SystemOutput.gen == approx( hybrid_plant.grid._financial_model.SystemOutput.gen @@ -285,9 +319,8 @@ def test_hybrid_wave_only(hybrid_config, subtests): ) with subtests.test("Outputs"): - assert hybrid_plant.wave._financial_model.Outputs == approx( - hybrid_plant.grid._financial_model.Outputs - ) + robust_approx(hybrid_plant.wave._financial_model.Outputs, hybrid_plant.grid._financial_model.Outputs) + with subtests.test("net cash flow"): wave_period = hybrid_plant.wave._financial_model.value("analysis_period") grid_period = hybrid_plant.grid._financial_model.value("analysis_period") @@ -338,9 +371,8 @@ def test_hybrid_wave_only(hybrid_config, subtests): assert npvs.hybrid == approx(npvs.wave) -def test_hybrid_wave_battery(hybrid_config, subtests): - hybrid_config["site"]["wave"] = True - hybrid_config["site"]["wave_resource_file"] = wave_resource_file +def test_hybrid_wave_battery(hybrid_config, mhk_config, wavesite, subtests): + hybrid_config["site"] = wavesite wave_only_technologies = { "wave": { "device_rating_kw": mhk_config["device_rating_kw"], @@ -361,10 +393,9 @@ def test_hybrid_wave_battery(hybrid_config, subtests): hybrid_config["technologies"] = wave_only_technologies - # TODO once the financial model is implemented, romove the line immediately following this comment and un-indent the rest of the test hi = HoppInterface(hybrid_config) hybrid_plant = hi.system - # hybrid_plant = HybridSimulation(wave_only_technologies, wavesite) + cost_model_inputs = MHKCostModelInputs.from_dict( { "reference_model_num": 3, @@ -411,6 +442,155 @@ def test_hybrid_wind_only(hybrid_config, subtests): with subtests.test("hybrid npv"): assert npvs.hybrid == approx(-6068047, 1e-3) +def test_hybrid_tidal_only(hybrid_config, mhk_tidal_config, tidalsite, subtests): + hybrid_config["site"]= tidalsite + tidal_only_technologies = { + "tidal": { + "device_rating_kw": mhk_tidal_config["device_rating_kw"], + "num_devices": 2, + "tidal_power_curve": mhk_tidal_config["tidal_power_curve"], + "tidal_resource": mhk_tidal_config["tidal_resource"], + "fin_model": DEFAULT_FIN_CONFIG, + }, + "grid": { + "interconnect_kw": interconnection_size_kw, + "fin_model": DEFAULT_FIN_CONFIG, + }, + } + + hybrid_config["technologies"] = tidal_only_technologies + + hi = HoppInterface(hybrid_config) + hybrid_plant = hi.system + cost_model_inputs = MHKCostModelInputs.from_dict( + { + "reference_model_num": 1, + "water_depth": 100, + "distance_to_shore": 80, + "number_rows": 2, + "device_spacing": 600, + "row_spacing": 600, + "cable_system_overbuild": 20, + } + ) + assert hybrid_plant.tidal is not None + hybrid_plant.tidal.create_mhk_cost_calculator(cost_model_inputs) + + hi.simulate() + aeps = hybrid_plant.annual_energies + npvs = hybrid_plant.net_present_values + cf = hybrid_plant.capacity_factors + + # check that tidal and grid match when only tidal is in the hybrid system + with subtests.test("financial parameters"): + assert hybrid_plant.tidal._financial_model.FinancialParameters == approx( + hybrid_plant.grid._financial_model.FinancialParameters + ) + with subtests.test("Revenue: ppa price input"): + assert hybrid_plant.tidal._financial_model.Revenue.ppa_price_input == approx( + hybrid_plant.grid._financial_model.Revenue.ppa_price_input + ) + with subtests.test("Revenue: ppa escalation"): + assert hybrid_plant.tidal._financial_model.Revenue.ppa_escalation == approx( + hybrid_plant.grid._financial_model.Revenue.ppa_escalation + ) + with subtests.test("Revenue: ppa multiplier model"): + assert ( + hybrid_plant.tidal._financial_model.Revenue.ppa_multiplier_model + == approx(hybrid_plant.grid._financial_model.Revenue.ppa_multiplier_model) + ) + with subtests.test("Revenue: ppa price input"): + assert ( + hybrid_plant.tidal._financial_model.Revenue.dispatch_factors_ts.all() + == approx( + hybrid_plant.grid._financial_model.Revenue.dispatch_factors_ts.all() + ) + ) + with subtests.test("SystemCosts"): + assert hybrid_plant.tidal._financial_model.SystemCosts == approx( + hybrid_plant.grid._financial_model.SystemCosts + ) + + with subtests.test("SystemOutput.gen"): + assert hybrid_plant.tidal._financial_model.SystemOutput.gen == approx( + hybrid_plant.grid._financial_model.SystemOutput.gen + ) + with subtests.test("SystemOutput.system_capacity"): + assert ( + hybrid_plant.tidal._financial_model.SystemOutput.system_capacity + == approx(hybrid_plant.grid._financial_model.SystemOutput.system_capacity) + ) + with subtests.test("SystemOutput.degradation"): + assert hybrid_plant.tidal._financial_model.SystemOutput.degradation == approx( + hybrid_plant.grid._financial_model.SystemOutput.degradation + ) + with subtests.test("SystemOutput.system_pre_curtailment_kwac"): + assert ( + hybrid_plant.tidal._financial_model.SystemOutput.system_pre_curtailment_kwac + == approx( + hybrid_plant.grid._financial_model.SystemOutput.system_pre_curtailment_kwac + ) + ) + with subtests.test("SystemOutput.annual_energy_pre_curtailment_ac"): + assert ( + hybrid_plant.tidal._financial_model.SystemOutput.annual_energy_pre_curtailment_ac + == approx( + hybrid_plant.grid._financial_model.SystemOutput.annual_energy_pre_curtailment_ac + ) + ) + with subtests.test("Outputs"): + robust_approx(hybrid_plant.tidal._financial_model.Outputs, hybrid_plant.grid._financial_model.Outputs) + + with subtests.test("net cash flow"): + tidal_period = hybrid_plant.tidal._financial_model.value("analysis_period") + grid_period = hybrid_plant.grid._financial_model.value("analysis_period") + assert hybrid_plant.tidal._financial_model.net_cash_flow(tidal_period) == approx( + hybrid_plant.grid._financial_model.net_cash_flow(grid_period) + ) + + with subtests.test("degradation"): + assert hybrid_plant.tidal._financial_model.value("degradation") == approx( + hybrid_plant.grid._financial_model.value("degradation") + ) + with subtests.test("total_installed_cost"): + assert hybrid_plant.tidal._financial_model.value( + "total_installed_cost" + ) == approx(hybrid_plant.grid._financial_model.value("total_installed_cost")) + with subtests.test("inflation_rate"): + assert hybrid_plant.tidal._financial_model.value("inflation_rate") == approx( + hybrid_plant.grid._financial_model.value("inflation_rate") + ) + with subtests.test("annual_energy_kwh"): + assert hybrid_plant.tidal.value("annual_energy_kwh") == approx( + hybrid_plant.grid.value("annual_energy_kwh") + ) + with subtests.test("ppa_price_input"): + assert hybrid_plant.tidal._financial_model.value("ppa_price_input") == approx( + hybrid_plant.grid._financial_model.value("ppa_price_input") + ) + with subtests.test("ppa_escalation"): + assert hybrid_plant.tidal._financial_model.value("ppa_escalation") == approx( + hybrid_plant.grid._financial_model.value("ppa_escalation") + ) + + # test hybrid outputs + with subtests.test("tidal aep"): + assert aeps.tidal == approx(6062551.5, 1e-2) + with subtests.test("hybrid tidal only aep"): + assert aeps.hybrid == approx(aeps.tidal) + with subtests.test("tidal cf"): + assert cf.tidal == approx(31.03, 1e-2) + with subtests.test("hybrid tidal only cf"): + assert cf.hybrid == approx(cf.tidal) + with subtests.test("tidal cost"): + # It seems that there is a difference between PySAM cost curves and SAM gui + assert hybrid_plant.tidal.total_installed_cost == approx(29015651.4, 1e-2) + with subtests.test("tidal npv"): + # TODO check/verify this test value somehow, not sure how to do it right now + assert npvs.tidal == approx(-29088482.4, 5.e-2) + with subtests.test("hybrid tidal only npv"): + assert npvs.hybrid == approx(npvs.tidal) + def test_hybrid_wind_only_floris(hybrid_config, subtests): floris_config_path = ( ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" From 74f879276052fc4271d91d1d84f9f513c7941641 Mon Sep 17 00:00:00 2001 From: kbrunik <102193481+kbrunik@users.noreply.github.com> Date: Wed, 12 Mar 2025 14:08:31 -0500 Subject: [PATCH 15/48] Add Tidal Dispatch (#448) * add tidal dispatch * add tidal battery example --------- Co-authored-by: John Jasa --- RELEASE.md | 3 +- docs/_toc.yml | 1 + docs/api/dispatch/sources/index.md | 1 + docs/api/dispatch/sources/tidal_dispatch.md | 7 + examples/10-tidal-battery.ipynb | 166 ++++++++++++++++++ examples/inputs/10-tidal-battery.yaml | 120 +++++++++++++ .../technologies/dispatch/__init__.py | 3 + .../technologies/dispatch/hybrid_dispatch.py | 4 + .../dispatch/power_sources/__init__.py | 3 + .../dispatch/power_sources/tidal_dispatch.py | 110 ++++++++++++ .../technologies/wave/mhk_wave_plant.py | 2 - tests/hopp/test_hybrid.py | 51 +++++- 12 files changed, 467 insertions(+), 4 deletions(-) create mode 100644 docs/api/dispatch/sources/tidal_dispatch.md create mode 100644 examples/10-tidal-battery.ipynb create mode 100644 examples/inputs/10-tidal-battery.yaml create mode 100644 hopp/simulation/technologies/dispatch/power_sources/tidal_dispatch.py diff --git a/RELEASE.md b/RELEASE.md index 83ff73bc9..eb5fabe1a 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -26,7 +26,8 @@ * Added TidalResource to load tidal resource data for simulating tidal energy. * Added MHKTidalPlant to simulate tidal energy. * Add tidal energy to HybridSimulation. -* Remove erroneous 100 multiples for percentages and add clarifying parentheses for correct 100 multiples for percentages +* Remove erroneous 100 multiples for percentages and add clarifying parentheses for correct 100 multiples for percentages. +* Add tidal energy to dispatch. ## Version 3.1.1, Dec. 18, 2024 diff --git a/docs/_toc.yml b/docs/_toc.yml index f73c7f00c..5742ceefd 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -48,6 +48,7 @@ parts: - file: api/dispatch/sources/pv_dispatch - file: api/dispatch/sources/wind_dispatch - file: api/dispatch/sources/wave_dispatch + - file: api/dispatch/sources/tidal_dispatch - file: api/dispatch/sources/csp_dispatch - caption: Additional Tools chapters: diff --git a/docs/api/dispatch/sources/index.md b/docs/api/dispatch/sources/index.md index a39a0b075..4139d8104 100644 --- a/docs/api/dispatch/sources/index.md +++ b/docs/api/dispatch/sources/index.md @@ -9,6 +9,7 @@ HOPP includes a variety of power source (technology) dispatch options based on t - [Molten Salt Tower Power Plant](dispatch:csp-molten-tower) - [Parabolic Trough Plant](dispatch:csp-parabolic-trough) - [Wave Plant](dispatch:wave) +- [Wave Plant](dispatch:tidal) (dispatch:power-source-dispatch)= ## Power Source Dispatch diff --git a/docs/api/dispatch/sources/tidal_dispatch.md b/docs/api/dispatch/sources/tidal_dispatch.md new file mode 100644 index 000000000..33be4d778 --- /dev/null +++ b/docs/api/dispatch/sources/tidal_dispatch.md @@ -0,0 +1,7 @@ +(dispatch:tidal)= +# Tidal Dispatch + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.dispatch.power_sources.tidal_dispatch.TidalDispatch + :members: +``` diff --git a/examples/10-tidal-battery.ipynb b/examples/10-tidal-battery.ipynb new file mode 100644 index 000000000..10f6c4afd --- /dev/null +++ b/examples/10-tidal-battery.ipynb @@ -0,0 +1,166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple Tidal and Battery Hybrid Plant Example\n", + "---\n", + "In this example, we will walk through the process of simulating a hybrid renewable energy system including both tidal energy and battery energy storage." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Required Modules\n", + "We start by importing the necessary modules and setting up our working environment." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from hopp.simulation import HoppInterface" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the HOPP Model\n", + "To generate the HOPP Model, instantiate the `HoppInterface` class and supply the required YAML configuration with the technology configuration and site information.\n", + "\n", + "For the site information, the tidal resource data **must be pre-loaded** in the format given in the `Tidal_resource_timeseries.csv`.\n", + "\n", + "The tidal technology configuration requires the device rating (kw), power curve of tidal energy device as function of stream speeds (kW), and number of devices. Additionally there's a variable called `tidal_resource`, which is required for model instantiation but doesn't impact a timeseries simulation.\n", + "\n", + "Note that the tidal model doesn't come with a default financial model. To address this, you must establish the `CustomFinancialModel` from HOPP.\n", + "\n", + "The `default_fin_config` contains all of the necessary parameters for the financial calculations.\n", + "\n", + "To maintain consistency across technologies, apply the `default_fin_config` to each technology configuration. This ensures that all technologies use the same financial model throughout the HOPP Model." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "hi = HoppInterface(\"./inputs/10-tidal-battery.yaml\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add Tidal Cost Model Inputs\n", + "Add the inputs to run the tidal technology cost model and instantiate `MHKCosts`.\n", + "\n", + "Tidal costs are available for the Reference Model 1: Tidal Current Turbine. More information about the reference models and their associated costs can be found in the [Reference Model Project](https://energy.sandia.gov/programs/renewable-energy/water-power/projects/reference-model-project-rmp/)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "cost_model_inputs = {\n", + "\t'reference_model_num':1,\n", + "\t'water_depth': 100,\n", + "\t'distance_to_shore': 80,\n", + "\t'number_rows': 2,\n", + "\t'device_spacing':600,\n", + "\t'row_spacing': 600,\n", + "\t'cable_system_overbuild': 20\n", + "}\n", + "hi.system.tidal.create_mhk_cost_calculator(cost_model_inputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the Simulation\n", + "Simulate the hybrid renewable energy system for a specified number of years (in this case, 25 years)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "hi.simulate(25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Retrieve and Display Results\n", + "Access the simulation results, including annual energies, capacity factors and net present values (NPVs), and print them to the console.\n", + "\n", + "You can see that the Annual Energy Production for \"tidal\" is different than \"hybrid\". Part of that difference is due to the battery but also the \"hybrid\" system is limited by the grid interconnection limit, which in this case is less than the rated capacity of the tidal farm (interconnection limit: 22000kW and tidal farm: 22300kW)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annual Energy Production\n", + "{\"tidal\": 60625515.491999194, \"battery\": 62256.91807749245, \"hybrid\": 60446486.28573774}\n", + "Net Present Value\n", + "{\"tidal\": -124990852.96205442, \"battery\": -32620759.796329703, \"hybrid\": -157608702.59276733}\n", + "Capacity Factors\n", + "{\"tidal\": 31.034623078812785, \"battery\": 0, \"hybrid\": 16.377843736864506}\n" + ] + } + ], + "source": [ + "hybrid_plant = hi.system\n", + "\n", + "aeps = hybrid_plant.annual_energies\n", + "npvs = hybrid_plant.net_present_values\n", + "cf = hybrid_plant.capacity_factors\n", + "\n", + "print(\"Annual Energy Production\")\n", + "print(aeps)\n", + "print(\"Net Present Value\")\n", + "print(npvs)\n", + "print(\"Capacity Factors\")\n", + "print(cf)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pysam6", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/inputs/10-tidal-battery.yaml b/examples/inputs/10-tidal-battery.yaml new file mode 100644 index 000000000..60149d306 --- /dev/null +++ b/examples/inputs/10-tidal-battery.yaml @@ -0,0 +1,120 @@ +name: "Example 10" + +# SiteInfo +site: + data: + lat: 47.2690 + lon: -122.55 + year: 2010 + site_boundaries: + verts: + - [3.06, 288.87] + - [0.0, 1084.03] + - [1784.05, 1084.24] + - [1794.09, 999.64] + - [1494.34, 950.97] + - [712.64, 262.8] + - [1216.98, 272.36] + - [1217.76, 151.62] + - [708.14, 0.0] + urdb_label: "5ca4d1175457a39b23b3d45e" + hub_height: 97.0 + solar: false + wind: false + wave: false + tidal: true + wind_resource_file: "" + wave_resource_file: "" + tidal_resource_file: "../../hopp/simulation/resource_files/tidal/Tidal_resource_timeseries.csv" + grid_resource_file: "" + +# Technologies +technologies: + tidal: + device_rating_kw: 1115 #[kW] + num_devices: 20 + tidal_power_curve: + - [0.000000, 0.000000] + - [0.100000, 0.000000] + - [0.200000, 0.000000] + - [0.300000, 0.000000] + - [0.400000, 0.000000] + - [0.500000, 0.000000] + - [0.600000, 10.421100] + - [0.700000, 20.842300] + - [0.800000, 39.968900] + - [0.900000, 59.095600] + - [1.000000, 89.201600] + - [1.100000, 119.308000] + - [1.200000, 160.886000] + - [1.300000, 202.464000] + - [1.400000, 259.292000] + - [1.500000, 316.120000] + - [1.600000, 392.673000] + - [1.700000, 469.226000] + - [1.800000, 570.306000] + - [1.900000, 671.386000] + - [2.000000, 802.908000] + - [2.100000, 934.430000] + - [2.200000, 1024.710000] + - [2.300000, 1115.000000] + - [2.400000, 1115.000000] + - [2.500000, 1115.000000] + - [2.600000, 1115.000000] + - [2.700000, 1115.000000] + - [2.800000, 1115.000000] + - [2.900000, 1115.000000] + - [3.000000, 1115.000000] + - [3.100000, 1115.000000] + - [3.200000, 1085.370000] + - [3.300000, 1055.730000] + # Tidal resource is required in PySAM prechecks + # this is a dummy resource profile and does not + # impact simulation when using timeseries data + # TODO: Remove once PySAM Pypi updates + tidal_resource: + - [0.000000, 0.009000] + - [0.100000, 0.031000] + - [0.200000, 0.042000] + - [0.300000, 0.044000] + - [0.400000, 0.048000] + - [0.500000, 0.049000] + - [0.600000, 0.053000] + - [0.700000, 0.051000] + - [0.800000, 0.052000] + - [0.900000, 0.056000] + - [1.000000, 0.050000] + - [1.100000, 0.052000] + - [1.200000, 0.050000] + - [1.300000, 0.048000] + - [1.400000, 0.047000] + - [1.500000, 0.043000] + - [1.600000, 0.042000] + - [1.700000, 0.040000] + - [1.800000, 0.034000] + - [1.900000, 0.031000] + - [2.000000, 0.026000] + - [2.100000, 0.023000] + - [2.200000, 0.020000] + - [2.300000, 0.016000] + - [2.400000, 0.013000] + - [2.500000, 0.011000] + - [2.600000, 0.007000] + - [2.700000, 0.005000] + - [2.800000, 0.004000] + - [2.900000, 0.002000] + - [3.000000, 0.001000] + - [3.100000, 0.000000] + - [3.200000, 0.000000] + - [3.300000, 0.000000] + fin_model: !include default_fin_config.yaml + battery: + system_capacity_kwh: 80000 + system_capacity_kw: 20000 + minimum_SOC: 20.0 + maximum_SOC: 100.0 + initial_SOC: 90.0 + fin_model: !include default_fin_config.yaml + grid: + interconnect_kw: 22000 + fin_model: !include default_fin_config.yaml \ No newline at end of file diff --git a/hopp/simulation/technologies/dispatch/__init__.py b/hopp/simulation/technologies/dispatch/__init__.py index 1a2b6c417..4670540f9 100644 --- a/hopp/simulation/technologies/dispatch/__init__.py +++ b/hopp/simulation/technologies/dispatch/__init__.py @@ -12,6 +12,9 @@ from hopp.simulation.technologies.dispatch.power_sources.wave_dispatch import ( WaveDispatch, ) +from hopp.simulation.technologies.dispatch.power_sources.tidal_dispatch import ( + TidalDispatch, +) from hopp.simulation.technologies.dispatch.grid_dispatch import GridDispatch from hopp.simulation.technologies.dispatch.hybrid_dispatch_options import ( diff --git a/hopp/simulation/technologies/dispatch/hybrid_dispatch.py b/hopp/simulation/technologies/dispatch/hybrid_dispatch.py index ae9727f01..b73a0d1fc 100644 --- a/hopp/simulation/technologies/dispatch/hybrid_dispatch.py +++ b/hopp/simulation/technologies/dispatch/hybrid_dispatch.py @@ -235,6 +235,10 @@ def wind_generation(self) -> list: @property def wave_generation(self) -> list: return [self.blocks[t].wave_generation.value for t in self.blocks.index_set()] + + @property + def tidal_generation(self) -> list: + return [self.blocks[t].tidal_generation.value for t in self.blocks.index_set()] @property def tower_generation(self) -> list: diff --git a/hopp/simulation/technologies/dispatch/power_sources/__init__.py b/hopp/simulation/technologies/dispatch/power_sources/__init__.py index ea7bd3306..94ddf9dcf 100644 --- a/hopp/simulation/technologies/dispatch/power_sources/__init__.py +++ b/hopp/simulation/technologies/dispatch/power_sources/__init__.py @@ -8,3 +8,6 @@ from hopp.simulation.technologies.dispatch.power_sources.wave_dispatch import ( WaveDispatch, ) +from hopp.simulation.technologies.dispatch.power_sources.tidal_dispatch import ( + TidalDispatch, +) diff --git a/hopp/simulation/technologies/dispatch/power_sources/tidal_dispatch.py b/hopp/simulation/technologies/dispatch/power_sources/tidal_dispatch.py new file mode 100644 index 000000000..a2b682a5e --- /dev/null +++ b/hopp/simulation/technologies/dispatch/power_sources/tidal_dispatch.py @@ -0,0 +1,110 @@ +from typing import Union +from pyomo.environ import ConcreteModel, Expression, NonNegativeReals, Set, units, Var +from pyomo.network import Port + +import PySAM.MhkTidal as MhkTidal + +from hopp.simulation.technologies.financial import FinancialModelType +from hopp.simulation.technologies.dispatch.power_sources.power_source_dispatch import ( + PowerSourceDispatch, +) + + +class TidalDispatch(PowerSourceDispatch): + tidal_obj: Union[Expression, float] + _system_model: MhkTidal.MhkTidal + _financial_model: FinancialModelType + """Dispatch optimization model for mhk tidal power source.""" + + def __init__( + self, + pyomo_model: ConcreteModel, + indexed_set: Set, + system_model: MhkTidal.MhkTidal, + financial_model: FinancialModelType, + block_set_name: str = "tidal", + ): + """Initialize TidalDispatch. + + Args: + pyomo_model (ConcreteModel): Pyomo concrete model. + indexed_set (Set): Indexed set. + system_model (MhkTidal.MhkTidal): System model. + financial_model (FinancialModelType): Financial model. + block_set_name (str): Name of the block set. + + """ + super().__init__( + pyomo_model, + indexed_set, + system_model, + financial_model, + block_set_name=block_set_name, + ) + + def max_gross_profit_objective(self, hybrid_blocks): + """MHK tidal instance of maximum gross profit objective. + + Args: + hybrid_blocks (Pyomo.block): A generalized container for defining hierarchical + models by adding modeling components as attributes. + + """ + self.obj = Expression( + expr=sum( + -(1 / hybrid_blocks[t].time_weighting_factor) + * self.blocks[t].time_duration + * self.blocks[t].cost_per_generation + * hybrid_blocks[t].tidal_generation + for t in hybrid_blocks.index_set() + ) + ) + + def min_operating_cost_objective(self, hybrid_blocks): + """MHK tidal instance of minimum operating cost objective. + + Args: + hybrid_blocks (Pyomo.block): A generalized container for defining hierarchical + models by adding modeling components as attributes. + + """ + self.obj = sum( + hybrid_blocks[t].time_weighting_factor + * self.blocks[t].time_duration + * self.blocks[t].cost_per_generation + * hybrid_blocks[t].tidal_generation + for t in hybrid_blocks.index_set() + ) + + def _create_variables(self, hybrid): + """Create MHK tidal variables to add to hybrid plant instance. + + Args: + hybrid: Hybrid plant instance. + + Returns: + tuple: Tuple containing created variables. + - generation: Generation from given technology. + - load: Load from given technology. + + """ + hybrid.tidal_generation = Var( + doc="Power generation of tidal devices [MW]", + domain=NonNegativeReals, + units=units.MW, + initialize=0.0, + ) + return hybrid.tidal_generation, 0 + + def _create_port(self, hybrid): + """Create mhk tidal port to add to hybrid plant instance. + + Args: + hybrid: Hybrid plant instance. + + Returns: + Port: MHK tidal Port object. + + """ + hybrid.tidal_port = Port(initialize={"generation": hybrid.tidal_generation}) + return hybrid.tidal_port diff --git a/hopp/simulation/technologies/wave/mhk_wave_plant.py b/hopp/simulation/technologies/wave/mhk_wave_plant.py index a6f7f4219..71da8986a 100644 --- a/hopp/simulation/technologies/wave/mhk_wave_plant.py +++ b/hopp/simulation/technologies/wave/mhk_wave_plant.py @@ -8,8 +8,6 @@ from hopp.simulation.technologies.financial.custom_financial_model import CustomFinancialModel from hopp.simulation.technologies.financial.mhk_cost_model import MHKCosts, MHKCostModelInputs from hopp.utilities.validators import gt_zero, range_val -#TODO: Add dispatch for Wave -# hopp.dispatch.power_sources.wave_dispatch import WaveDispatch @define diff --git a/tests/hopp/test_hybrid.py b/tests/hopp/test_hybrid.py index 53763c254..d17e1a96d 100644 --- a/tests/hopp/test_hybrid.py +++ b/tests/hopp/test_hybrid.py @@ -417,7 +417,7 @@ def test_hybrid_wave_battery(hybrid_config, mhk_config, wavesite, subtests): cf = hybrid_plant.capacity_factors with subtests.test("battery aep"): - assert aeps.battery == approx(87.84, 1e3) + assert aeps.battery == approx(87.84, 1e-3) def test_hybrid_wind_only(hybrid_config, subtests): @@ -591,6 +591,55 @@ def test_hybrid_tidal_only(hybrid_config, mhk_tidal_config, tidalsite, subtests) with subtests.test("hybrid tidal only npv"): assert npvs.hybrid == approx(npvs.tidal) +def test_hybrid_tidal_battery(hybrid_config, mhk_tidal_config,tidalsite, subtests): + hybrid_config["site"]=tidalsite + tidal_only_technologies = { + "tidal": { + "device_rating_kw": mhk_tidal_config["device_rating_kw"], + "num_devices": 2, + "tidal_power_curve": mhk_tidal_config["tidal_power_curve"], + "tidal_resource": mhk_tidal_config["tidal_resource"], + "fin_model": DEFAULT_FIN_CONFIG, + }, + "battery": { + "system_capacity_kwh": 20000, + "system_capacity_kw": 80000, + "fin_model": DEFAULT_FIN_CONFIG, + }, + "grid": { + "interconnect_kw": interconnection_size_kw, + "fin_model": DEFAULT_FIN_CONFIG, + }, + } + + hybrid_config["technologies"] = tidal_only_technologies + + hi = HoppInterface(hybrid_config) + hybrid_plant = hi.system + cost_model_inputs = MHKCostModelInputs.from_dict( + { + "reference_model_num": 1, + "water_depth": 100, + "distance_to_shore": 80, + "number_rows": 2, + "device_spacing": 600, + "row_spacing": 600, + "cable_system_overbuild": 20, + } + ) + assert hybrid_plant.tidal is not None + hybrid_plant.tidal.create_mhk_cost_calculator(cost_model_inputs) + hybrid_plant.tidal._financial_model.om_capacity = [50] # $/kWcap + hybrid_plant.battery._financial_model.om_batt_variable_cost = [0.75] + + hi.simulate() + aeps = hybrid_plant.annual_energies + npvs = hybrid_plant.net_present_values + cf = hybrid_plant.capacity_factors + + with subtests.test("battery aep"): + assert aeps.battery == approx(106.537, 1e-3) + def test_hybrid_wind_only_floris(hybrid_config, subtests): floris_config_path = ( ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" From 017d5773ef59f908a74d67dffac8ec7dcef26d46 Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Wed, 19 Mar 2025 09:53:03 -0600 Subject: [PATCH 16/48] Feature add: integration with turbine-models library for wind simulations (#435) * Added initial functionality for integrating turbine-models library * Integrated with turbine-models library and FLORIS and made integration test * Integrated turbine-models library with PySAM simulation and made integration test * Replaced UserWarning about wind turbine hub-height and site info discrepancy with ability to redownload wind resource data * Added initialize_pysam_turbine() function * Made turbine_rating_kw an optional input to WindConfig * Updated README.md, RELEASE.md, and pyproject.toml * Updated wind resource parsing methods to work if more than 2 resource heights are input * Changed UserWarnings to ValueErrors in wind_plant.py and floris.py * Added turbine_group as optional input to check_turbine_library_for_turbine function in turbine_library_tools.py * Updated Ct function in power_curve_tools.py to not fail if multiple roots --------- Co-authored-by: Gen Starke and John Jasa --- README.md | 2 +- RELEASE.md | 4 + .../technologies/layout/wind_layout.py | 24 +- .../technologies/layout/wind_layout_tools.py | 6 +- hopp/simulation/technologies/wind/floris.py | 222 ++++++++++----- .../technologies/wind/wind_plant.py | 136 ++++++++-- hopp/tools/design/__init__.py | 0 hopp/tools/design/wind/__init__.py | 0 hopp/tools/design/wind/floris_helper_tools.py | 147 ++++++++++ hopp/tools/design/wind/power_curve_tools.py | 163 +++++++++++ .../wind/turbine_library_interface_tools.py | 253 ++++++++++++++++++ .../design/wind/turbine_library_tools.py | 53 ++++ hopp/tools/resource/wind_tools.py | 60 ++++- hopp/utilities/utilities.py | 15 +- pyproject.toml | 3 +- tests/hopp/inputs/floris_v4_empty_layout.yaml | 95 +++++++ tests/hopp/test_hybrid.py | 4 +- tests/hopp/test_layout.py | 1 + tests/hopp/test_turbine_models_interface.py | 176 ++++++++++++ tests/hopp/test_wind.py | 72 ++--- tests/hopp/test_wind_design_tools.py | 141 ++++++++++ 21 files changed, 1410 insertions(+), 167 deletions(-) create mode 100644 hopp/tools/design/__init__.py create mode 100644 hopp/tools/design/wind/__init__.py create mode 100644 hopp/tools/design/wind/floris_helper_tools.py create mode 100644 hopp/tools/design/wind/power_curve_tools.py create mode 100644 hopp/tools/design/wind/turbine_library_interface_tools.py create mode 100644 hopp/tools/design/wind/turbine_library_tools.py create mode 100644 tests/hopp/inputs/floris_v4_empty_layout.yaml create mode 100644 tests/hopp/test_turbine_models_interface.py create mode 100644 tests/hopp/test_wind_design_tools.py diff --git a/README.md b/README.md index 7a027675b..5ea66a31e 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) As part of NREL's [Hybrid Energy Systems Research](https://www.nrel.gov/wind/hybrid-energy-systems-research.html), this -software assesses optimal designs for the deployment of utility-scale hybrid energy plants, particularly considering wind, +software assesses optimal designs for the deployment of distributed, commercial, and utility-scale hybrid energy plants, particularly considering wind, solar and storage. ## Software requirements diff --git a/RELEASE.md b/RELEASE.md index eb5fabe1a..08a715b84 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -29,6 +29,10 @@ * Remove erroneous 100 multiples for percentages and add clarifying parentheses for correct 100 multiples for percentages. * Add tidal energy to dispatch. +* Integrated [turbine-models library](https://github.com/NREL/turbine-models/tree/master). For further details on the following updates, users are referred [here](https://github.com/NREL/HOPP/pull/435) + + Wind turbines from the turbine-models library can now be simulated by specifying the turbine name. This feature is compatible with FLORIS and PySAM WindPower simulations. + + Added wind turbine power-curve tools to estimate thrust coefficient, power coefficient, and power-curve. + ## Version 3.1.1, Dec. 18, 2024 * Enhanced PV plant functionality: added tilting solar panel support, improved system design handling, and refined tilt angle calculations. diff --git a/hopp/simulation/technologies/layout/wind_layout.py b/hopp/simulation/technologies/layout/wind_layout.py index 3fedb75a5..54436aec9 100644 --- a/hopp/simulation/technologies/layout/wind_layout.py +++ b/hopp/simulation/technologies/layout/wind_layout.py @@ -205,18 +205,11 @@ class WindLayout(BaseClass): WindBoundaryGridParameters, WindCustomParameters, WindBasicGridParameters, + WindGridParameters, None, dict ] ): wind layout parameters for the corresponding `layout_mode` - min_spacing_meters (float, Optional): minimum spacing between turbines in meters. - Defaults to 0.0. - max_spacing_meters (float, Optional): maximum spacing between turbines in meters. - Defaults to 2e6. - min_rotor_diameter_multiplier (float, Optional): minimum spacing between turbines as - multiplier of rotor diameter. Defaults to 2.0. - max_rotor_diameter_multiplier (float, Optional): maximum spacing between turbines as - multiplier of rotor diameter. Defaults to 20.0. turbine_rating_kW (float, Optional): rating of a single turbine in kW. if not provided, turbine power is estimated from the power-curve. """ @@ -233,30 +226,17 @@ class WindLayout(BaseClass): WindGridParameters, dict, ] - # TODO: convert min_spacing and max_spacing to be within the parameter class that uses it. - min_spacing_meters: Optional[float] = field(default=0.0) - max_spacing_meters: Optional[float] = field(default=2e6) - - min_rotor_diameter_multiplier: Optional[float] = field(default=2.0) - max_rotor_diameter_multiplier: Optional[float] = field(default=20.0) turbine_rating_kW: Optional[float] = field(default=None) turb_pos_x: list[float] = field(init=False) turb_pos_y: list[float] = field(init=False) - - min_spacing: float = field(init=False) - max_spacing: float = field(init=False) def __attrs_post_init__(self): """The following are initialized in this post init hook: - turb_pos_x (list[float]): x-coordinates of turbines - turb_pos_y (list[float]): x-coordinates of turbines - - parameters.min_spacing (float): minimum spacing between turbines in meters. - Only used if layout_mode is `grid` or `boundarygrid`. - - parameters.max_spacing (float): maximum spacing between turbines in meters. - Only used if layout_mode is `boundarygrid`. Note: these calculations are based on the default values of rotor diameter and turbine layout. `min_spacing` and `max_spacing` are re-calculated in _get_system_config(). @@ -435,10 +415,10 @@ def reset_grid(self, n_turbines): def reset_basic_grid(self,n_turbines): """Create a most-square `basicgrid` layout for specified number of turbines. requires parameters are `WindBasicGridParameters`. - Args: n_turbines (int): number of turbines to include in layout. """ + self._get_system_config() interrow_spacing = self.parameters.row_D_spacing * self.rotor_diameter diff --git a/hopp/simulation/technologies/layout/wind_layout_tools.py b/hopp/simulation/technologies/layout/wind_layout_tools.py index c00d186e3..a35204df6 100644 --- a/hopp/simulation/technologies/layout/wind_layout_tools.py +++ b/hopp/simulation/technologies/layout/wind_layout_tools.py @@ -64,13 +64,17 @@ def make_grid_lines(site_shape: BaseGeometry, grid_angle = np.deg2rad(grid_angle) grid_angle = (grid_angle + np.pi) % (2 * np.pi) - np.pi # reset grid_angle to (-pi, pi) - bounds = site_shape.bounds + bounds = site_shape.bounds #(xmin,ymin,xmax,ymax) + #line from (xmin,ymin) to (xmax,ymax) bounding_box_line = LineString([(bounds[0], bounds[1]), (bounds[2], bounds[3])]) + #at y=0, x goes from negative to positive bounding_box_line.length base_line = LineString([(-bounding_box_line.length, 0), (bounding_box_line.length, 0)]) + # line_length = 2x(bounding_box_line.length) = 2*(sqrt[(xmax-xmin)^2 + (ymax-ymin)^2]) line_length = base_line.length base_line = rotate(base_line, -grid_angle, use_radians=True) + #shift baseline so ymax,ymin = center.y and (xmax - xmin)/2 = center.x base_line = translate(base_line, center.x, center.y) row_offset = Point( diff --git a/hopp/simulation/technologies/wind/floris.py b/hopp/simulation/technologies/wind/floris.py index 6c4dcae5f..a8fe10b6b 100644 --- a/hopp/simulation/technologies/wind/floris.py +++ b/hopp/simulation/technologies/wind/floris.py @@ -5,7 +5,6 @@ import numpy as np from floris import FlorisModel, TimeSeries -from floris.turbine_library.turbine_previewer import INTERNAL_LIBRARY from hopp.simulation.base import BaseClass from hopp.simulation.technologies.sites import SiteInfo # avoid circular dep @@ -18,13 +17,13 @@ ) from hopp.utilities import load_yaml from hopp.utilities.log import hybrid_logger as logger - +import hopp.tools.design.wind.floris_helper_tools as floris_tools @define class Floris(BaseClass): + site: SiteInfo = field() config: "WindConfig" = field() - verbose: bool = field(default = True) _operational_losses: float = field(init=False) _timestep: Tuple[int, int] = field(init=False) @@ -46,7 +45,6 @@ class Floris(BaseClass): capacity_factor: float = field(init = False) annual_energy_pre_curtailment_ac: float = field(init = False) - #TODO: add option to store turbine-powers and velocities or not turb_velocities: np.ndarray = field(init = False) turb_powers: np.ndarray = field(init = False) @@ -63,7 +61,7 @@ def __attrs_post_init__(self): ValueError: "A floris configuration must be provided" ValueError: "A timestep is required." """ - + if self.config.floris_config is None: raise ValueError("A floris configuration must be provided") if self.config.timestep is None: @@ -88,7 +86,6 @@ def __attrs_post_init__(self): self.speeds, self.wind_dirs = parse_resource_data(self.site.wind_resource) elif self.config.resource_parse_method == "weighted_average": self.speeds, self.wind_dirs = weighted_parse_resource_data(self.site.wind_resource) - self.system_capacity = self.nTurbs * self.turb_rating # time to simulate @@ -100,60 +97,89 @@ def __attrs_post_init__(self): self.end_idx = 8759 - def initialize_from_floris(self,floris_config): - """ - Please populate all the wind farm parameters - """ - - if self.config.turbine_name is None: - # NOTE: eventually the turbine name provided in the config will be used - # to load a turbine from the turbine-models library. - if isinstance(floris_config["farm"]["turbine_type"][0],dict): - self.turbine_name = floris_config["farm"]["turbine_type"][0]["turbine_type"] + def initialize_from_floris(self, floris_config): + """Initialize wind turbine parmeters and set in floris_config. - # load file from internal floris library - if isinstance(floris_config["farm"]["turbine_type"][0],str): - self.turbine_name = floris_config["farm"]["turbine_type"][0] - turb_dict = load_yaml( - INTERNAL_LIBRARY / "{}.yaml".format(floris_config["farm"]["turbine_type"][0]) - ) - floris_config["farm"]["turbine_type"][0] = turb_dict + Args: + floris_config (dict): floris input dictionary + Raises: + ValueError: if rotor_diameter in WindConfig doesnt match rotor diameter in floris_config + ValueError: if turbine_rating_kw in WindConfig doesnt match turbine rating from power-curve + ValueError: if hub_height in WindConfig doesnt match hub-height in floris_config + + + Returns: + dict: updated floris_config + """ + floris_config = self.initialize_wind_turbine(floris_config) + # see if rotor diameter was input in config but not set in floris config if self.config.rotor_diameter is not None: - floris_config["farm"]["turbine_type"][0].setdefault( - "rotor_diameter",self.config.rotor_diameter - ) + floris_config["farm"]["turbine_type"][0].setdefault("rotor_diameter",self.config.rotor_diameter) # see if hub-height was input in config but not set in floris config if self.config.hub_height is not None: - floris_config["farm"]["turbine_type"][0].setdefault( - "hub_height", self.config.hub_height - ) - # NOTE: hub-height should also be checked against wind resource hub-height - + floris_config["farm"]["turbine_type"][0].setdefault("hub_height",self.config.hub_height) + # set attributes: + hub_height = floris_config["farm"]["turbine_type"][0]["hub_height"] self.wind_turbine_rotor_diameter = floris_config["farm"]["turbine_type"][0]["rotor_diameter"] self.wind_turbine_powercurve_powerout = floris_config["farm"]["turbine_type"][0]["power_thrust_table"]["power"] self.wind_farm_xCoordinates = floris_config["farm"]["layout_x"] self.wind_farm_yCoordinates = floris_config["farm"]["layout_y"] self.nTurbs = len(self.wind_farm_xCoordinates) - + self.turb_rating = max(self.wind_turbine_powercurve_powerout) + if self.config.turbine_rating_kw is not None: if self.config.turbine_rating_kw != self.turb_rating: - raise UserWarning( + msg = ( f"Input turbine rating ({self.config.turbine_rating_kw} kW) does not match " - f"rating from floris power-curve ({self.turb_rating} kW)" + f"rating from floris power-curve ({self.turb_rating} kW). " + "Please either remove turbine_rating_kw from the hopp config input " + f"or correct the value to {self.turb_rating}." ) + raise ValueError(msg) + if self.config.rotor_diameter is not None: + if self.config.rotor_diameter != self.wind_turbine_rotor_diameter: + msg = ( + f"Input rotor diameter ({self.config.rotor_diameter}) does not match " + f"rotor diameter from floris config ({self.wind_turbine_rotor_diameter}). " + "Please either remove rotor_diameter from the hopp config input " + f"or correct the value to {self.wind_turbine_rotor_diameter}." + ) + raise ValueError(msg) + if self.config.hub_height is not None: + if self.config.hub_height != hub_height: + msg = ( + f"Input hub-height ({self.config.hub_height}) does not match " + f"hub-height from floris config ({hub_height}). " + "Please either remove hub_height from the hopp config input " + "(under hopp_config['technologies']['wind'])" + f"or correct the value to {hub_height}." + ) + raise ValueError(msg) + if hub_height != self.site.wind_resource.hub_height_meters: + valid_min_height = hub_height >= min(self.site.wind_resource.data["heights"]) + valid_max_height = hub_height <= max(self.site.wind_resource.data["heights"]) + if valid_min_height and valid_max_height: + self.site.wind_resource.hub_height_meters = float(hub_height) + self.site.hub_height = float(hub_height) + logger.info(f"Updating wind resource hub-height to {hub_height}m") + else: + logger.warning(f"Updating wind resource hub-height to {hub_height}m and redownloading wind resource data") + self.site.hub_height = hub_height + data = { + "lat": self.site.wind_resource.latitude, + "lon": self.site.wind_resource.longitude, + "year": self.site.wind_resource.year, + } + wind_resource = self.site.initialize_wind_resource(data) + self.site.wind_resource = wind_resource - # check if user-input num_turbines equals number of turbines in layout - if self.nTurbs != self.config.num_turbines: - logger.warning( - f"num_turbines in WindConfig ({self.config.num_turbines}) does not equal " - f"number of turbines in floris config layout ({self.nTurbs})" - ) + return floris_config - + def value(self, name: str, set_value=None): """Set or retrieve attribute of `hopp.simulation.technologies.wind.floris.Floris`. if set_value = None, then retrieve value; otherwise overwrite variable's value. @@ -169,14 +195,34 @@ def value(self, name: str, set_value=None): return self.__getattribute__(name) def set_floris_value(self, name, value): + """Set value of FlorisModel object using the `set` function. + + Args: + name (str): name of parameter to update. + value (any): value to assign to specified `parameter`. + """ if value is not None: self.fi.set(**{name:value}) - + def set_floris_param(self, param, value): - if value is not None: - self.fi.set_param(param, value) + """Set parameter of FlorisModel object using the `set_param` function. + Args: + param (list[str]): list of parameter keys in FlorisModel to update. + value (any): values to assign to the specified `param`. + """ + if value is not None: + self.fi.set_param(param,value) + def get_floris_param(self, param): + """Get parameter of FlorisModel object using the `get_param` function. + + Args: + param (list[str]): list of parameter keys in FlorisModel to retrieve. + + Returns: + any: value of FlorisModel parameter + """ return self.fi.get_param(param) def execute(self, project_life): @@ -186,20 +232,16 @@ def execute(self, project_life): project_life (int): unused project life in years """ - if self.verbose: + if self.config.verbose: print('Simulating wind farm output in FLORIS...') # check if user-input num_turbines equals number of turbines in layout if self.nTurbs != self.config.num_turbines: - # Log warning if discrepancy in number of turbines. - # Not raising a warning since wind farm capacity can be modified - # before simulation begins. - logger.warning( - f"num_turbines input in WindConfig ({self.config.num_turbines}) does not equal " - f"number of turbines in floris model ({self.nTurbs})" - ) + # log warning if discrepancy in number of turbines + # not raising a warning since wind farm capacity can be modified before simulation begins + logger.warning(f"num_turbines input in WindConfig ({self.config.num_turbines}) does not equal number of turbines in floris model ({self.nTurbs})") logger.info(f"simulating {self.nTurbs} turbines using FLORIS") - + # find generation of wind farm power_turbines = np.zeros((self.nTurbs, 8760)) power_farm = np.zeros(8760) @@ -225,10 +267,12 @@ def execute(self, project_life): self.gen = power_farm * operational_efficiency / 1000 # kW self.annual_energy = np.sum(self.gen) # kWh - self.capacity_factor = np.sum(self.gen) / (8760 * self.system_capacity) * 100 - self.turb_powers = power_turbines * operational_efficiency / 1000 # kW - self.turb_velocities = self.fi.turbine_average_velocities + self.capacity_factor = np.sum(self.gen) / (len(power_farm) * self.system_capacity) * 100 self.annual_energy_pre_curtailment_ac = np.sum(self.gen) # kWh + if self.config.store_turbine_performance_results: + self.turb_powers = power_turbines * operational_efficiency / 1000 # kW + self.turb_velocities = self.fi.turbine_average_velocities + def export(self): """ @@ -239,27 +283,75 @@ def export(self): 'annual_energy': self.annual_energy, } return config - + @property def wind_farm_layout(self): xcoords, ycoords = self.fi.get_turbine_layout() return xcoords, ycoords - + def set_wind_farm_layout(self, xcoords, ycoords): - """ - Sets the wind farm layout and updates relevant parameters. + """Set wind farm layout coordinates and update system capacity and number + of turbines. Args: - xcoords (list[float]): A list of x-coordinates for turbine locations. - ycoords (list[float]): A list of y-coordinates for turbine locations. + xcoords (list[float]): x-coordinates of wind turbines in meters. + ycoords (list[float]): y-coordinates of wind turbines in meters. Raises: - ValueError: If x- and y-coordinates are not the same length, an error is raised. + ValueError: "WindPlant turbine coordinate arrays must have same length" """ if len(xcoords) != len(ycoords): raise ValueError("WindPlant turbine coordinate arrays must have same length") - self.fi.set(layout_x=xcoords, layout_y=ycoords) + self.fi.set( + layout_x = xcoords, + layout_y = ycoords + ) self.nTurbs = len(xcoords) - self.system_capacity = len(xcoords) * self.turb_rating + self.system_capacity = len(xcoords)*self.turb_rating self.value("wind_farm_xCoordinates", xcoords) self.value("wind_farm_yCoordinates", ycoords) + + def initialize_wind_turbine(self, floris_config): + """Update `floris_config` with turbine parameters. Checks the turbine library + and floris internal library for a turbine with name matching either + `config.turbine_name` or `floris_config["farm"]["turbine_type"][0]["turbine_type"]`. + If no matching turbine is found, find the turbine with the closest + matching name and raise a warning. + + Args: + floris_config (dict): floris (version 4) input dictionary. + + Returns: + dict: floris config with turbine model parameters updated in `floris_config["farm"]["turbine_type"][0]` + """ + if self.config.turbine_name is None: + + # turbine data is included in floris_config + if isinstance(floris_config["farm"]["turbine_type"][0],dict): + self.turbine_name = floris_config["farm"]["turbine_type"][0]["turbine_type"] + return floris_config + + # load file from internal floris library + if isinstance(floris_config["farm"]["turbine_type"][0],str): + turbine_lib_res = floris_tools.check_libraries_for_turbine_name_floris(floris_config["farm"]["turbine_type"][0], self) + floris_config["farm"]["turbine_type"][0] = turbine_lib_res + return floris_config + + turbine_lib_res = floris_tools.check_libraries_for_turbine_name_floris(self.config.turbine_name, self) + floris_config["farm"]["turbine_type"][0] = turbine_lib_res + return floris_config + + def update_wind_turbine(self, turbine_name): + """Update `FlorisModel` (`self.fi`) with with turbine parameters corresponding + to `turbine_name`. Used to update turbine parameters after Floris has been initialized. + Updates system capacity, rotor diameter, power-curve, and turb_rating. + + Args: + turbine_name (str): name of turbine in either floris internal library or turbine-models library. + """ + turbine_lib_res = floris_tools.check_libraries_for_turbine_name_floris(turbine_name, self) + self.fi.set(turbine_type=[turbine_lib_res]) + self.value("wind_turbine_rotor_diameter", turbine_lib_res["rotor_diameter"]) + self.value("wind_turbine_powercurve_powerout", turbine_lib_res["power_thrust_table"]["power"]) + self.turb_rating = np.round(max(turbine_lib_res["power_thrust_table"]["power"]), decimals = 1) + self.system_capacity = self.nTurbs*self.turb_rating diff --git a/hopp/simulation/technologies/wind/wind_plant.py b/hopp/simulation/technologies/wind/wind_plant.py index 1c44cea18..4616a9fdd 100644 --- a/hopp/simulation/technologies/wind/wind_plant.py +++ b/hopp/simulation/technologies/wind/wind_plant.py @@ -2,6 +2,7 @@ from typing import Optional, Tuple, Union, Sequence from attrs import define, field +import numpy as np import PySAM.Singleowner as Singleowner import PySAM.Windpower as Windpower @@ -14,6 +15,8 @@ WindCustomParameters, WindGridParameters, ) +import hopp.tools.design.wind.turbine_library_interface_tools as turb_lib_interface +from hopp.tools.design.wind.turbine_library_tools import check_turbine_library_for_turbine, print_turbine_name_list from hopp.simulation.technologies.power_source import PowerSource from hopp.simulation.technologies.sites import SiteInfo from hopp.simulation.technologies.wind.floris import Floris @@ -66,17 +69,26 @@ class WindConfig(BaseClass): - a dict representing a `CustomFinancialModel` - an object representing a `CustomFinancialModel` or `Singleowner.Singleowner` instance + verbose (bool): if True, print simulation progress statements. Defaults to True. + store_turbine_performance_results (bool): If running FLORIS, whether to save speed and power timeseries + for each turbine in the farm. Defaults to False. """ + # TODO: put `resource_parse_method`, `store_turbine_performance_results`, and `verbose` in "floris_kwargs" dictionary num_turbines: int = field(validator=gt_zero) - turbine_rating_kw: float = field(validator=gt_zero) + turbine_rating_kw: Optional[float] = field(default = None) rotor_diameter: Optional[float] = field(default=None) layout_params: Optional[ Union[ - dict, WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters + dict, WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters, WindGridParameters ] ] = field(default=None) hub_height: Optional[float] = field(default=None) turbine_name: Optional[str] = field(default=None) + turbine_group: str = field( + default="none", + validator=contains(["offshore", "onshore", "distributed", "none"]), + converter=(str.strip, str.lower) + ) layout_mode: str = field( default="grid", validator=contains(["boundarygrid", "grid", "basicgrid", "custom", "floris_layout"]), @@ -100,15 +112,16 @@ class WindConfig(BaseClass): timestep: Optional[Tuple[int, int]] = field(default=(0,8760)) fin_model: Optional[Union[dict, FinancialModelType]] = field(default=None) name: str = field(default="WindPlant") + verbose: bool = field(default = True) + store_turbine_performance_results: bool = field(default = False) def __attrs_post_init__(self): if self.model_name == 'floris' and self.timestep is None: raise ValueError("Timestep (Tuple[int, int]) required for floris") - if self.layout_mode == 'boundarygrid' and self.layout_params is None: - raise ValueError( - "Parameters of WindBoundaryGridParameters required for boundarygrid layout mode" - ) + if self.turbine_rating_kw is None and self.turbine_name is None: + if self.model_name == "pysam" and self.model_input_file is None: + raise ValueError("Parameters of turbine_rating_kw or turbine_name are required") @define @@ -139,7 +152,8 @@ def __attrs_post_init__(self): financial_model = self.config.fin_model if self.config.model_name == 'floris': - print('FLORIS is the system model...') + if self.config.verbose: + print('FLORIS is the system model...') system_model = Floris(self.site, self.config) if ( self.config.num_turbines == len(system_model.wind_farm_xCoordinates) @@ -191,17 +205,84 @@ def __attrs_post_init__(self): self._dispatch = None - self.turb_rating = self.config.turbine_rating_kw + if self.config.turbine_rating_kw is not None: + self.turb_rating = self.config.turbine_rating_kw self.num_turbines = self.config.num_turbines + + if self.config.model_name=="pysam": + self.initialize_pysam_wind_turbine() + + + def initialize_pysam_wind_turbine(self): + """Initialize wind turbine parameters for PySAM simulation. + + Raises: + ValueError: if invalid turbine name is provided. Print list of valid turbine names before error is raised. + ValueError: discrepancy in rotor_diameter value + ValueError: discrepancy in hub-height value + """ + if self.config.rotor_diameter is not None: self.rotor_diameter = self.config.rotor_diameter + + if self.config.turbine_name is not None: + valid_name = check_turbine_library_for_turbine(self.config.turbine_name,turbine_group=self.config.turbine_group) + if not valid_name: + print_turbine_name_list() + msg = ( + f"Turbine name {self.config.turbine_name} was not found the turbine-models library. " + "Please try an available name." + ) + ValueError(msg) + else: + turbine_name = self.config.turbine_name + turbine_dict = turb_lib_interface.get_pysam_turbine_specs(turbine_name,self) + self._system_model.Turbine.assign(turbine_dict) + self.rotor_diameter = turbine_dict["wind_turbine_rotor_diameter"] + self.turb_rating = np.round(max(turbine_dict["wind_turbine_powercurve_powerout"]), decimals = 3) + + if self.config.rotor_diameter is not None: + if self.config.rotor_diameter != self._system_model.Turbine.wind_turbine_rotor_diameter: + msg = ( + f"Input rotor diameter ({self.config.rotor_diameter}) does not match does not match rotor diameter " + f"for turbine ({self._system_model.Turbine.wind_turbine_rotor_diameter})." + f"Please correct the value for rotor_diameter in the hopp config input " + f"to {self._system_model.Turbine.wind_turbine_rotor_diameter}." + ) + raise ValueError(msg) + + if self.config.hub_height is not None: + if self.config.hub_height != self._system_model.Turbine.wind_turbine_hub_ht: + msg = ( + f"Input hub-height ({self.config.hub_height}) does not match hub-height " + f"for turbine ({self._system_model.Turbine.wind_turbine_hub_ht}). " + f"Please correct the value for hub_height in the hopp config input " + f"to {self._system_model.Turbine.wind_turbine_hub_ht}." + ) - if self.config.model_name=="pysam": - if self.config.hub_height is not None: - self._system_model.Turbine.wind_turbine_hub_ht = self.config.hub_height - if self.config.adjust_air_density_for_elevation and self.site.elev is not None: - air_dens_losses = calculate_air_density_losses(self.site.elev) - self._system_model.Losses.assign({"turb_specific_loss":air_dens_losses}) + raise ValueError(msg) + + hub_height = self._system_model.Turbine.wind_turbine_hub_ht + if hub_height != self.site.wind_resource.hub_height_meters: + if hub_height >= min(self.site.wind_resource.data["heights"]) and hub_height<=max(self.site.wind_resource.data["heights"]): + self.site.wind_resource.hub_height_meters = float(hub_height) + self.site.hub_height = float(hub_height) + logger.info(f"updating wind resource hub-height to {hub_height}m") + else: + logger.warning(f"updating wind resource hub-height to {hub_height}m and redownloading wind resource data") + self.site.hub_height = hub_height + data = { + "lat": self.site.wind_resource.latitude, + "lon": self.site.wind_resource.longitude, + "year": self.site.wind_resource.year, + } + wind_resource = self.site.initialize_wind_resource(data) + self.site.wind_resource = wind_resource + self._system_model.value("wind_resource_data", self.site.wind_resource.data) + + if self.config.adjust_air_density_for_elevation and self.site.elev is not None: + air_dens_losses = calculate_air_density_losses(self.site.elev) + self._system_model.Losses.assign({"turb_specific_loss":air_dens_losses}) @property def wake_model(self) -> str: @@ -235,15 +316,19 @@ def num_turbines(self): @num_turbines.setter def num_turbines(self, n_turbines: int): - if ( - self._layout.layout_mode == "custom" - and n_turbines != len(self._system_model.value("wind_farm_xCoordinates")) - ): - n_turbs_layout = len(self._system_model.value("wind_farm_xCoordinates")) - raise UserWarning( - f"Using custom layout and input number of turbines ({n_turbines}) does not equal " - f"length of layout ({n_turbs_layout})." - ) + if self._layout.layout_mode == "custom": + if n_turbines == len(self._layout.parameters.layout_x): + self._layout.set_num_turbines(n_turbines) + else: + if n_turbines != len(self._system_model.value("wind_farm_xCoordinates")): + n_turbs_layout = len(self._system_model.value("wind_farm_xCoordinates")) + msg = ( + f"Using custom wind farm layout and input number of turbines ({n_turbines}) " + f"does not equal length of layout ({n_turbs_layout}). " + f"Please either update num_turbines in the hopp config to {n_turbs_layout} " + f"Or change the layout to include {n_turbines} unique turbine positions." + ) + raise ValueError(msg) self._layout.set_num_turbines(n_turbines) @property @@ -391,6 +476,9 @@ def modify_layout_params( elif layout_mode == "boundarygrid": layout_params = WindBoundaryGridParameters(**layout_params) elif layout_mode is None: - raise UserWarning("if providing layout_params as dictionary, please specify layout mode") + msg = ( + "If providing layout_params as a dictionary, please specify layout_mode." + ) + raise ValueError(msg) self._layout.set_layout_params(wind_capacity_kW, params = layout_params) \ No newline at end of file diff --git a/hopp/tools/design/__init__.py b/hopp/tools/design/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/hopp/tools/design/wind/__init__.py b/hopp/tools/design/wind/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/hopp/tools/design/wind/floris_helper_tools.py b/hopp/tools/design/wind/floris_helper_tools.py new file mode 100644 index 000000000..e2807c4c0 --- /dev/null +++ b/hopp/tools/design/wind/floris_helper_tools.py @@ -0,0 +1,147 @@ +import os +import numpy as np +from hopp.utilities.utilities import write_yaml +from floris.turbine_library.turbine_previewer import INTERNAL_LIBRARY +from hopp.utilities import load_yaml +from hopp.tools.design.wind.turbine_library_interface_tools import get_floris_turbine_specs +from hopp.tools.design.wind.turbine_library_tools import ( + print_turbine_name_list, + check_turbine_library_for_turbine +) + +def check_output_formatting(orig_dict): + """Recursive method to convert arrays to lists and numerical entries to floats. + This is primarily used before writing a dictionary to a YAML file to ensure + proper output formatting. + + Args: + orig_dict (dict): input dictionary + + Returns: + dict: input dictionary with reformatted values. + """ + for key, val in orig_dict.items(): + if isinstance(val, dict): + tmp = check_output_formatting(orig_dict.get(key, { })) + orig_dict[key] = tmp + else: + if isinstance(key, list): + for i,k in enumerate(key): + if isinstance(orig_dict[k], (str,bool,int)): + orig_dict[k] = (orig_dict.get(k, []) + val[i]) + elif isinstance(orig_dict[k], (list, np.ndarray)): + orig_dict[k] = np.array(val, dtype=float).tolist() + else: + orig_dict[k] = float(val[i]) + elif isinstance(key,str): + if isinstance(orig_dict[key], (str, bool, int)): + continue + if isinstance(orig_dict[key], (list, np.ndarray)): + if any(isinstance(v,dict) for v in val): + for vii,v in enumerate(val): + if isinstance(v,dict): + new_val = check_output_formatting(v) + else: + new_val = v if isinstance(v,(str,bool,int)) else float(v) + orig_dict[key][vii] = new_val + else: + new_val = [v if isinstance(v,(str,bool,int)) else float(v) for v in val ] + orig_dict[key] = new_val + else: + orig_dict[key] = float(val) + return orig_dict + +def write_floris_layout_to_file(layout_x,layout_y,output_dir,turbine_desc): + """Export wind farm layout to floris-friendly YAML file. + + Args: + layout_x (list[float]): x-coordinates of turbines + layout_y (list[float]): y-coordinates of turbines + output_dir (str): output folder to write layout file to. + turbine_desc (str): turbine name or description. + """ + + layout_x = [float(x) for x in layout_x] + layout_y = [float(y) for y in layout_y] + + layout = {"layout_x":layout_x,"layout_y":layout_y} + n_turbs = len(layout_x) + output_fpath = os.path.join(output_dir,f"layout_{turbine_desc}_{n_turbs}turbs.yaml") + write_yaml(output_fpath,layout) + +def write_turbine_to_floris_file(turbine_dict,output_dir): + """Export turbine model to floris-friendly YAML file. + + Args: + turbine_dict (dict): turbine entry of floris_config file + output_dir (str): output folder to write turbine model file to. + """ + turb_name = turbine_dict["turbine_type"] + output_fpath = os.path.join(output_dir,f"floris_turbine_{turb_name}.yaml") + new_dict = check_output_formatting(turbine_dict) + write_yaml(output_fpath,new_dict) + +def check_floris_library_for_turbine(turbine_name): + """Check if a turbine exists in the floris internal library. + + Args: + turbine_name (str): name of turbine + + Returns: + bool: whether turbine exists in floris internal library or not. + """ + floris_library_fpath = INTERNAL_LIBRARY / f"{turbine_name}.yaml" + return floris_library_fpath.is_file() + +def load_turbine_from_floris_library(turbine_name): + """Load turbine model file from floris internal library. + + Args: + turbine_name (str): name of turbine + + Raises: + FileNotFoundError: if file does not exist in floris internal library. + + Returns: + dict: floris turbine model dictionary + """ + floris_library_fpath = INTERNAL_LIBRARY / f"{turbine_name}.yaml" + if not os.path.isfile(floris_library_fpath): + raise FileNotFoundError(f"Floris library file for turbine {turbine_name} does not exist.") + turb_dict = load_yaml(floris_library_fpath) + turb_dict.pop("power_thrust_data_file") + return turb_dict + +def check_libraries_for_turbine_name_floris(turbine_name,floris_model): + """Check the FLORIS internal turbine library and the turbine-models library for + a turbine of ``turbine_name``. Return a FLORIS-compatible dictionary of the turbine + parameters if the ``turbine_name`` if valid. If the ``turbine_name`` is invalid, + return a warning message string. + + Args: + turbine_name (str): name of turbine + floris_model (FlorisModel): FlorisModel object. + + Raises: + ValueError: if invalid turbine name is provided. + Will print list of valid turbine names before error is raised. + + Returns: + dict | str: FLORIS-compatible dict of the turbine parameters for a valid ``turbine_name``. + If the ``turbine_name`` is invalid, return a warning message string. + """ + + is_floris_lib_turbine = check_floris_library_for_turbine(turbine_name) + is_turb_lib_turbine = check_turbine_library_for_turbine(turbine_name, turbine_group=floris_model.config.turbine_name) + + if is_floris_lib_turbine: + turb_dict = load_turbine_from_floris_library(turbine_name) + return turb_dict + if is_turb_lib_turbine: + floris_model.value("turbine_name",turbine_name) + turb_dict = get_floris_turbine_specs(turbine_name,floris_model) + return turb_dict + + print_turbine_name_list() + raise ValueError(f"turbine name {turbine_name} not found in floris or turbine-models library. Please try an available name.") + \ No newline at end of file diff --git a/hopp/tools/design/wind/power_curve_tools.py b/hopp/tools/design/wind/power_curve_tools.py new file mode 100644 index 000000000..20886c747 --- /dev/null +++ b/hopp/tools/design/wind/power_curve_tools.py @@ -0,0 +1,163 @@ +import numpy as np +import matplotlib.pyplot as plt + +def plot_power_curve(wind_speed, cp_curve, ct_curve): + """Plot Cp and Ct curve per wind speed. + + Args: + wind_speed (list | np.ndarray): list of wind speeds in m/s + cp_curve (list | np.ndarray): power curve coefficients (Cp) at each wind speed in ``wind_speed`` + ct_curve (list | np.ndarray): thrust curve coefficients (Ct) at each wind speed in ``wind_speed`` + """ + fig1 = plt.figure() + plt.plot(wind_speed, ct_curve, label="Coeff of Thrust") + plt.plot(wind_speed, cp_curve, label="Coeff of Power") + plt.legend() + plt.xlabel("Wind Speed [m/s]") + plt.show() + + +def pad_power_curve(wind_speed, curve, ws_min = 0.0, ws_max = 50.0): + """Pad curve data with zeroes from wind speeds of ``ws_min`` to ``ws_max``. + + Args: + wind_speed (list | np.ndarray): list of wind speeds in m/s + curve (list | np.ndarray): curve data at each wind speed in ``wind_speed``. Can be + either Cp, power, or Ct. + ws_min (float, Optional): wind speed to start curve data at. Defaults to 0.0. + ws_max (float, Optional): wind speed to end curve data at. Defaults to 50.0. + + Returns: + 2-element tuple containing: + + - **padded_wind_speed** (list): padded wind speed in m/s starting at ``ws_min`` and ending at ``ws_max`` + - **padded_curve** (list): padded curve data for wind speeds starting at ``ws_min`` and ending at ``ws_max`` + """ + + if isinstance(wind_speed,list): + wind_speed = np.array(wind_speed) + if isinstance(curve,list): + curve = np.array(curve) + + if min(wind_speed) > ws_min: + wind_speed_pad = np.arange(ws_min,min(wind_speed),1) + wind_speed = np.concatenate((wind_speed_pad,wind_speed)) + curve = np.concatenate((np.zeros(len(wind_speed_pad)),curve)) + + if max(wind_speed) < ws_max: + wind_speed_pad = np.arange(max(wind_speed)+1,ws_max,1) + wind_speed = np.concatenate((wind_speed,wind_speed_pad)) + curve = np.concatenate((curve,np.zeros(len(wind_speed_pad)))) + return wind_speed.tolist(), curve.tolist() + +def calculate_cp_from_power(wind_speed, power_curve_kw, rotor_diameter, air_density = 1.225): + """Calculate power coefficient curve (Cp) from power curve. + + Args: + wind_speed (list | np.ndarray): list of wind speeds in m/s + power_curve_kw (list | np.ndarray): turbine power (in kW) at each wind speed in ``wind_speed`` + rotor_diameter (float): rotor diameter of the turbine in meters. + air_density (float, Optional): Air density assumed for power-curve calculations in kg/m3. + Defaults to 1.225. + + Raises: + ValueError: if ``wind_speed`` and ``power_curve_kw`` are different lengths. + + Returns: + list: power curve coefficients (Cp) at each wind speed in ``wind_speed`` + """ + + if len(wind_speed) != len(power_curve_kw): + raise ValueError("The length of the wind speed and power vectors must be the same") + rotor_area = np.pi*((rotor_diameter/2)**2) + if isinstance(wind_speed, list): + wind_speed = np.array(wind_speed) + if isinstance(power_curve_kw, list): + power_curve_kw = np.array(power_curve_kw) + + # power available in the wind (kW) + p_wind = 0.5*air_density*rotor_area*(wind_speed**3)/1e3 + cp = power_curve_kw/p_wind + cp = np.where(cp < 0, 0, cp) + return cp.tolist() + +def calculate_power_from_cp(wind_speed, cp_curve, rotor_diameter, rated_power_kW, air_density = 1.225): + """Calculate power curve from power coefficient curve (Cp). + + Args: + wind_speed (list | np.ndarray): list of wind speeds in m/s + cp_curve (list | np.ndarray): power curve coefficients (Cp) at each wind speed in ``wind_speed`` + rotor_diameter (float): rotor diameter of the turbine in meters. + air_density (float, Optional): Air density assumed for power-curve calculations in kg/m3. + Defaults to 1.225. + + Raises: + ValueError: if ``wind_speed`` and ``cp_curve`` are different lengths. + + Returns: + list: turbine power (in kW) at each wind speed in ``wind_speed`` + """ + + if len(wind_speed) != len(cp_curve): + raise ValueError("The length of the wind speed and coefficient of power vectors must be the same") + + rotor_area = np.pi*((rotor_diameter/2)**2) + if isinstance(wind_speed, list): + wind_speed = np.array(wind_speed) + if isinstance(cp_curve, list): + cp_curve = np.array(cp_curve) + + # power available in the wind (kW) + p_wind = 0.5*air_density*rotor_area*(wind_speed**3)/1e3 + power_kW = cp_curve*p_wind + power_kW = np.where(power_kW > rated_power_kW, rated_power_kW, power_kW) + power_kW = np.where(power_kW < 0, 0, power_kW) + + return power_kW.tolist() + +def estimate_thrust_coefficient(wind_speed, cp_curve, plot=False, print_output=False): + """Calculate thrust coefficient curve (Ct) from power coefficient curve (Cp). + + Args: + wind_speed (list | np.ndarray): list of wind speeds in m/s + cp_curve (list | np.ndarray): power curve coefficients (Cp) at each wind speed in ``wind_speed`` + plot (bool, Optional): whether to plot Cp and Ct curve. Defaults to False. + print_output (bool, Optional): Whether to print Cp and Ct curves. Defaults to False. + + Raises: + ValueError: if ``wind_speed`` and ``cp_curve`` are different lengths. + + Returns: + list: thrust curve coefficients (Ct) at each wind speed in ``wind_speed`` + """ + + # Check that the wind speed and the coefficient of power are the same length + if len(wind_speed) != len(cp_curve): + raise ValueError("The length of the wind speed and coefficient of power vectors must be the same") + N_wind = len(wind_speed) + ct_curve = np.zeros(N_wind) + + for i in range(N_wind): + # calculate induction factor a + # solve C_P = 4 * a * (1-a)**a -> 4 * a**3 - 8 * a**2 + 4 * a - C_P = 0 + roots = np.roots([4, -8, 4, -cp_curve[i]]) + + # Take root that is in range of a -> [0, 0.5] + a = roots[np.where(np.logical_and(roots>= 0, roots<= 0.5))] + + # Calculate C_T = 4 * a * (1-a) + ct = np.round(4 * a * (1-a), 4) + + ct_curve[i] = ct if isinstance(ct,(float,int)) else ct[0] + + ct_flat = ct_curve.flatten().tolist() + + if plot: + plot_power_curve(wind_speed,cp_curve,ct_flat) + + if print_output: + print("Wind Speed (m/s) | Coefficient of Thrust (Ct) | Coefficient of Power (Cp)") + for ws, ct, cp in zip(wind_speed, ct_flat, cp_curve): + print(f"{ws:7.4f} | {ct:7.4f} | {cp:7.4f}") + + return ct_flat \ No newline at end of file diff --git a/hopp/tools/design/wind/turbine_library_interface_tools.py b/hopp/tools/design/wind/turbine_library_interface_tools.py new file mode 100644 index 000000000..73913ccc8 --- /dev/null +++ b/hopp/tools/design/wind/turbine_library_interface_tools.py @@ -0,0 +1,253 @@ +import numpy as np + +import PySAM.Windpower as windpower +from turbine_models.parser import Turbines +import hopp.tools.design.wind.power_curve_tools as curve_tools +from hopp.utilities.log import hybrid_logger as logger +import hopp.simulation.technologies.wind.floris as floris_wrapper + +def extract_power_curve(turbine_specs: dict, model_name: str): + """Creates power-curve for turbine based on available data and formats it for the corresponding simulation model. + + Args: + turbine_specs (dict): turbine specs loaded from turbine-models library. + model_name (str): wind simulation model, either "pysam" or "floris". + + Raises: + ValueError: if turbine data doesn't have the minimum required power-curve information. + ValueError: if model name is not either 'pysam' or 'floris' + + Returns: + dict: power-curve dictionary formatted for the corresponding ``model_name``. + """ + + if model_name not in ("floris", "pysam"): + raise ValueError(f"model_name {model_name} is invalid, options are either 'floris' or 'pysam'.") + turbine_specs["power_curve"] = turbine_specs["power_curve"].dropna() + wind_speeds = np.nan_to_num(turbine_specs["power_curve"]["wind_speed_ms"].to_list()) + turbine_curve_cols = turbine_specs["power_curve"].columns.to_list() + + has_cp_curve = "cp" in turbine_curve_cols + has_power_curve = "power_kw" in turbine_curve_cols + has_ct_curve = "ct" in turbine_curve_cols + + if not has_cp_curve and not has_power_curve: + turbine_name = turbine_specs["name"] + msg = ( + f"Turbine {turbine_name} does not have the minimum required power curve data. " + "Either power_kw or cp are required." + ) + raise ValueError(msg) + + if has_cp_curve: + cp_curve = np.array(turbine_specs["power_curve"]["cp"].to_list()) + cp_curve = np.nan_to_num(cp_curve) + cp_curve = np.where(cp_curve<0,0,cp_curve).tolist() + + if has_power_curve: + power_curve_kw = np.array(turbine_specs["power_curve"]["power_kw"].to_list()) + power_curve_kw = np.nan_to_num(power_curve_kw) + power_curve_kw = np.where(power_curve_kw<0,0,power_curve_kw) + power_curve_kw = np.where(power_curve_kw>turbine_specs["rated_power"],turbine_specs["rated_power"],power_curve_kw).tolist() + + if has_cp_curve and not has_power_curve: + power_curve_kw = curve_tools.calculate_power_from_cp(wind_speeds,cp_curve,turbine_specs["rotor_diameter"],turbine_specs["rated_power"]) + + if has_power_curve and not has_cp_curve: + cp_curve = curve_tools.calculate_cp_from_power(wind_speeds,power_curve_kw) + + if has_ct_curve: + ct = turbine_specs["power_curve"]["ct"].to_list() + else: + ct = curve_tools.estimate_thrust_coefficient(wind_speeds,cp_curve) + + _, cp_curve = curve_tools.pad_power_curve(wind_speeds,cp_curve) + _, ct = curve_tools.pad_power_curve(wind_speeds,ct) + wind_speeds, power_curve_kw = curve_tools.pad_power_curve(wind_speeds,power_curve_kw) + + if model_name == "floris": + power_thrust_table = { + "wind_speed":wind_speeds, + "power":power_curve_kw, + "thrust_coefficient":ct, + } + return power_thrust_table + + # if model_name is "pysam" + power_thrust_table = { + "wind_turbine_max_cp": max(cp_curve), + "wind_turbine_ct_curve":ct, + "wind_turbine_powercurve_windspeeds":wind_speeds, + "wind_turbine_powercurve_powerout":power_curve_kw, + } + return power_thrust_table + + +def check_hub_height(turbine_specs, wind_plant): + """Check the hub-height from the turbine-library specs against the other possible hub-height entries. + If multiple hub-height options are available from the turbine_specs, this method will choose + one based on other user-input parameters within wind_plant. The other variables checked are: + + 1) wind_plant.config.hub_height + 2) wind_plant.site.hub_height + 3) wind_plant._system_model.Turbine.wind_turbine_hub_ht (for PySAM simulations only) + 4) if none of the heights from 1-3 match a possible hub-height option, the hub-height is chosen + as the median hub-height from the list of options from turbine-library. + + Args: + turbine_specs (dict): turbine specs loaded from turbine-models library. + wind_plant (:obj:`hopp.simulation.technologies.wind.floris.Floris` | :obj:`hopp.simulation.technologies.wind.wind_plant.WindPlant`): wind + plant object for either PySAM or FLORIS wind simulation model. + + Returns: + float: hub-height to use in meters. + """ + turbine_name = turbine_specs["name"] + # if multiple hub height options are available + if isinstance(turbine_specs["hub_height"],list): + # check for hub height in wind_plant + is_pysam = isinstance(wind_plant,windpower.Windpower) + + # check if hub_height was put in WindConfig + if (hub_height := wind_plant.config.hub_height) is not None: + if any(float(k) == float(hub_height) for k in turbine_specs["hub_height"]): + msg = ( + f"Multiple hub height options available for {turbine_name} turbine. " + f"Setting hub height to WindConfig hub_height: {hub_height}" + ) + logger.info(msg) + return hub_height + + # check the hub_height used for wind resource + if (hub_height := wind_plant.site.hub_height) is not None: + if any(float(k) == float(hub_height) for k in turbine_specs["hub_height"]): + msg = ( + f"Multiple hub height options available for {turbine_name} turbine. " + f"Setting hub height to WindConfig hub_height: {hub_height}" + ) + logger.info(msg) + return hub_height + + # check the hub-height of PySAM wind turbine object + if is_pysam: + if any(float(k) == float(wind_plant._system_model.Turbine.wind_turbine_hub_ht) for k in turbine_specs["hub_height"]): + hub_height = wind_plant._system_model.Turbine.wind_turbine_hub_ht + msg = ( + f"Multiple hub height options available for {turbine_name} turbine. " + f"Setting hub height to WindPower.WindPower.Turbine.wind_turbine_hub_ht: {hub_height}" + ) + logger.info(msg) + return hub_height + + # set hub height as median from options + else: + hub_height = np.median(turbine_specs["hub_height"]) + msg = ( + f"Multiple hub height options available for {turbine_name} turbine. " + f"Setting hub height to median available height: {hub_height}" + ) + logger.info(msg) + return hub_height + + else: + hub_height = turbine_specs["hub_height"] + if wind_plant.config.hub_height is not None: + if hub_height != wind_plant.config.hub_height: + msg = ( + f"Turbine hub height ({hub_height}) does not equal " + f"wind_plant.config.hub_height ({wind_plant.config.hub_height})" + ) + logger.warning(msg) + if hub_height != wind_plant.site.hub_height: + msg = ( + f"Turbine hub height ({hub_height}) does not equal " + f"site_info.hub_height ({wind_plant.site.hub_height})" + ) + logger.warning(msg) + + return hub_height + + +def get_pysam_turbine_specs(turbine_name, wind_plant): + """Load turbine data from turbine-models library to use with PySAM wind simulation. + + Args: + turbine_name (str): name of turbine in turbine-models library + wind_plant (:obj:`hopp.simulation.technologies.wind.wind_plant.WindPlant`): wind plant object. + + Raises: + ValueError: if turbine is missing data. + + Returns: + dict: turbine model dictionary formatted for PySAM. + """ + t_lib = Turbines() + turbine_specs = t_lib.specs(turbine_name) + if isinstance(turbine_specs,dict): + turbine_dict = extract_power_curve(turbine_specs, model_name = "pysam") + + hub_height = check_hub_height(turbine_specs,wind_plant) + + turbine_dict.update({ + "wind_turbine_rotor_diameter":turbine_specs["rotor_diameter"], + "wind_turbine_hub_ht":hub_height, + }) + return turbine_dict + + raise ValueError(f"Turbine {turbine_name} is missing some data, please try another turbine.") + + +def get_floris_turbine_specs(turbine_name, wind_plant): + """Load turbine data from turbine-models library to use with FLORIS wind simulation. + + Sets turbine's rated tip speed ratio (TSR) to 8.0 if not included in turbine data. + Sets default values in the power thrust table as: + + - ``ref_air_density``: 1.225 + - ``ref_tilt``: 5.0 + - ``cosine_loss_exponent_yaw``: 1.88 + - ``cosine_loss_exponent_tilt``: 1.88 + + Args: + turbine_name (str): name of turbine in turbine-models library + wind_plant (:obj:`hopp.simulation.technologies.wind.floris.Floris`): FLORIS wrapper object. + + Raises: + ValueError: if turbine is missing data. + + Returns: + dict: turbine model dictionary formatted for FLORIS. + """ + t_lib = Turbines() + turb_group = t_lib.find_group_for_turbine(turbine_name) + turbine_specs = t_lib.specs(turbine_name,group = turb_group) + if isinstance(turbine_specs,dict): + + hub_height = check_hub_height(turbine_specs,wind_plant) + power_thrust_table = extract_power_curve(turbine_specs, model_name = "floris") + + turbine_specs.setdefault("rated_tsr", 8.0) + if turbine_specs["rated_tsr"] is None: + turbine_specs["rated_tsr"] = 8.0 + + power_thrust_table.update({ + "ref_air_density": 1.225, + "ref_tilt": turbine_specs.setdefault("rotor_tilt_angle", 5.0), + "cosine_loss_exponent_yaw": 1.88, + "cosine_loss_exponent_tilt": 1.88, + }) + turbine_dict = { + "turbine_type":turbine_name, + "hub_height":hub_height, + "TSR": turbine_specs["rated_tsr"], + "rotor_diameter":turbine_specs["rotor_diameter"], + "power_thrust_table": power_thrust_table, + } + return turbine_dict + + msg = ( + f"Turbine {turbine_name} is missing some data, " + "please try another turbine." + ) + raise ValueError(msg) + diff --git a/hopp/tools/design/wind/turbine_library_tools.py b/hopp/tools/design/wind/turbine_library_tools.py new file mode 100644 index 000000000..fa6037991 --- /dev/null +++ b/hopp/tools/design/wind/turbine_library_tools.py @@ -0,0 +1,53 @@ +from turbine_models.parser import Turbines + +def check_turbine_library_for_turbine(turbine_name:str, turbine_group = "none"): + """Check turbine-models library for turbine named ``turbine_name``. + + Args: + turbine_name (str): name of turbine in turbine-models library + turbine_group (str, Optional): group of turbine in turbine-models library. + Options include "offshore", "onfshore", or "distributed". + + Returns: + bool: whether the input turbine name matches a turbine available in the turbine-models library. + """ + + t_lib = Turbines() + valid_name = False + if turbine_group not in t_lib.groups: + for turb_group in t_lib.groups: + turbines_in_group = t_lib.turbines(group = turb_group) + if any(turb.lower()==turbine_name.lower() for turb in turbines_in_group.values()): + valid_name = True + else: + turbines_in_group = t_lib.turbines(group = turbine_group) + if any(turb.lower()==turbine_name.lower() for turb in turbines_in_group.values()): + valid_name = True + return valid_name + +def print_turbine_name_list(): + """Print the turbine names for each group of turbines in turbine-models library. + """ + + t_lib = Turbines() + osw_turbines = list(t_lib.turbines(group="offshore").values()) + + print("-".join("" for i in range(25))) + print("Offshore Turbine Names:") + print("-".join("" for i in range(25))) + osw_msg = "\n " + "\n ".join(t for t in osw_turbines) + print(osw_msg) + + lbw_turbines = list(t_lib.turbines(group="onshore").values()) + print("-".join("" for i in range(25))) + print("Onshore Turbine Names:") + print("-".join("" for i in range(25))) + lbw_msg = "\n " + "\n ".join(t for t in lbw_turbines) + print(lbw_msg) + + distributed_turbines = list(t_lib.turbines(group="distributed").values()) + print("-".join("" for i in range(25))) + print("Distributed Turbine Names:") + print("-".join("" for i in range(25))) + dw_msg = "\n " + "\n ".join(t for t in distributed_turbines) + print(dw_msg) \ No newline at end of file diff --git a/hopp/tools/resource/wind_tools.py b/hopp/tools/resource/wind_tools.py index 99199390a..1bd12a10a 100644 --- a/hopp/tools/resource/wind_tools.py +++ b/hopp/tools/resource/wind_tools.py @@ -81,17 +81,50 @@ def parse_resource_data(wind_resource): idx_wd = [ii for ii, field in enumerate(wind_resource.data['fields']) if field == 4] # If there's only one hub-height, grab speed and direction data - if len(idx_ws)==1: + if len(idx_ws) == 1: speeds = data[:, idx_ws[0]] wind_dirs = data[:, idx_wd[0]] return speeds, wind_dirs - - # If there's multiple hub-heights - average the data - speeds = data[:, idx_ws].mean(axis=1) - wind_dirs = data[:, idx_wd].mean(axis=1) + # If there's multiple hub-heights - average the data + if len(idx_ws) > 2: + # find resource-heights closest to hub-height + heights_with_data = [wind_resource.data['heights'][i] for i in idx_ws] + if any(h==wind_resource.hub_height_meters for h in heights_with_data): + hh1 = wind_resource.hub_height_meters + hh2 = wind_resource.hub_height_meters + else: + height_ub = [h for h in heights_with_data if (wind_resource.hub_height_meters - h)<=0] + height_lb = [h for h in heights_with_data if (wind_resource.hub_height_meters - h)>=0] + min_diff_ub = min([np.abs(h-wind_resource.hub_height_meters) for h in height_ub]) + min_diff_lb = min([np.abs(h-wind_resource.hub_height_meters) for h in height_lb]) + hh1 = [h for h in height_ub if np.abs(h-wind_resource.hub_height_meters)==min_diff_ub][0] + hh2 = [h for h in height_lb if np.abs(h-wind_resource.hub_height_meters)==min_diff_lb][0] + + else: + hh1, hh2 = np.unique(wind_resource.data['heights']) + + if hh1 == wind_resource.hub_height_meters: + idx_ws1 = [i for i in idx_ws if wind_resource.data['heights'][i] == hh1][0] + idx_wd1 = [i for i in idx_wd if wind_resource.data['heights'][i] == hh1][0] + speeds = data[:, idx_ws1] + wind_dirs = data[:, idx_wd1] + + elif hh2 == wind_resource.hub_height_meters: + idx_ws2 = [i for i in idx_ws if wind_resource.data['heights'][i] == hh2][0] + idx_wd2 = [i for i in idx_wd if wind_resource.data['heights'][i] == hh2][0] + speeds = data[:, idx_ws2] + wind_dirs = data[:, idx_wd2] + + else: + # If there's multiple hub-heights - average the data + speeds = data[:, idx_ws].mean(axis=1) + wind_dirs = data[:, idx_wd].mean(axis=1) + return speeds, wind_dirs + + def weighted_parse_resource_data(wind_resource): """Parse wind resource data into floris-friendly format. Weighted average wind speed and wind direction if there's data for @@ -124,7 +157,22 @@ def weighted_parse_resource_data(wind_resource): return speeds, wind_dirs # If there's multiple hub-heights - average the data - hh1, hh2 = np.unique(wind_resource.data['heights']) + if len(idx_ws) > 2: + # find resource-heights closest to hub-height + heights_with_data = [wind_resource.data['heights'][i] for i in idx_ws] + if any(h==wind_resource.hub_height_meters for h in heights_with_data): + hh1 = wind_resource.hub_height_meters + hh2 = wind_resource.hub_height_meters + else: + height_ub = [h for h in heights_with_data if (wind_resource.hub_height_meters - h)<=0] + height_lb = [h for h in heights_with_data if (wind_resource.hub_height_meters - h)>=0] + min_diff_ub = min([np.abs(h-wind_resource.hub_height_meters) for h in height_ub]) + min_diff_lb = min([np.abs(h-wind_resource.hub_height_meters) for h in height_lb]) + hh1 = [h for h in height_ub if np.abs(h-wind_resource.hub_height_meters)==min_diff_ub][0] + hh2 = [h for h in height_lb if np.abs(h-wind_resource.hub_height_meters)==min_diff_lb][0] + + else: + hh1, hh2 = np.unique(wind_resource.data['heights']) # Weights corresponding to difference of resource height and hub-height weight1 = np.abs(hh1 - wind_resource.hub_height_meters) diff --git a/hopp/utilities/utilities.py b/hopp/utilities/utilities.py index 07c934243..116e77ecc 100644 --- a/hopp/utilities/utilities.py +++ b/hopp/utilities/utilities.py @@ -1,7 +1,6 @@ import os import yaml - class Loader(yaml.SafeLoader): def __init__(self, stream): @@ -25,3 +24,17 @@ def load_yaml(filename, loader=Loader) -> dict: return filename # filename already yaml dict with open(filename) as fid: return yaml.load(fid, loader) + +def check_create_folder(filepath): + already_exists = True + if not os.path.isdir(filepath): + os.makedirs(filepath,exist_ok=True) + already_exists = False + return already_exists + +def write_yaml(filename,data): + if not '.yaml' in filename: + filename = filename +'.yaml' + + with open(filename, 'w+') as file: + yaml.dump(data, file,sort_keys=False,encoding = None,default_flow_style=False) diff --git a/pyproject.toml b/pyproject.toml index 7fd416aca..0c95f2caf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,7 +49,8 @@ dependencies = [ "utm", "pyyaml-include", "profast", - "NREL-rex" + "NREL-rex", + "turbine-models>=0.1.0", ] keywords = [ "python3", diff --git a/tests/hopp/inputs/floris_v4_empty_layout.yaml b/tests/hopp/inputs/floris_v4_empty_layout.yaml new file mode 100644 index 000000000..aa89fd7a8 --- /dev/null +++ b/tests/hopp/inputs/floris_v4_empty_layout.yaml @@ -0,0 +1,95 @@ + +name: Gauss +description: Onshore template +floris_version: v4.0.0 +logging: + console: + enable: false + level: WARNING + file: + enable: false + level: WARNING +solver: + type: turbine_grid + turbine_grid_points: 1 +flow_field: + air_density: 1.225 + reference_wind_height: -1 + wind_directions: + - 270.0 + wind_shear: 0.33 + wind_speeds: + - 8.0 + wind_veer: 0.0 + turbulence_intensities: + - 0.06 +wake: + model_strings: + combination_model: sosfs + deflection_model: gauss + turbulence_model: crespo_hernandez + velocity_model: gauss + enable_secondary_steering: false + enable_yaw_added_recovery: false + enable_transverse_velocities: false + wake_deflection_parameters: + gauss: + ad: 0.0 + alpha: 0.58 + bd: 0.0 + beta: 0.077 + dm: 1.0 + ka: 0.38 + kb: 0.004 + jimenez: + ad: 0.0 + bd: 0.0 + kd: 0.05 + wake_velocity_parameters: + cc: + a_s: 0.179367259 + b_s: 0.0118889215 + c_s1: 0.0563691592 + c_s2: 0.13290157 + a_f: 3.11 + b_f: -0.68 + c_f: 2.41 + alpha_mod: 1.0 + gauss: + alpha: 0.58 + beta: 0.077 + ka: 0.38 + kb: 0.004 + jensen: + we: 0.05 + wake_turbulence_parameters: + crespo_hernandez: + initial: 0.1 + constant: 0.5 + ai: 0.8 + downstream: -0.32 + enable_active_wake_mixing: false + + wake_velocity_parameters: + cc: + a_f: 3.11 + a_s: 0.179367259 + alpha_mod: 1.0 + b_f: -0.68 + b_s: 0.0118889215 + c_f: 2.41 + c_s1: 0.0563691592 + c_s2: 0.13290157 + gauss: + alpha: 0.58 + beta: 0.077 + ka: 0.38 + kb: 0.004 + jensen: + we: 0.05 +farm: + layout_x: [0.0] + layout_y: [0.0] + turbine_type: + - operation_model: cosine-loss + diff --git a/tests/hopp/test_hybrid.py b/tests/hopp/test_hybrid.py index d17e1a96d..24816801f 100644 --- a/tests/hopp/test_hybrid.py +++ b/tests/hopp/test_hybrid.py @@ -646,13 +646,14 @@ def test_hybrid_wind_only_floris(hybrid_config, subtests): ) technologies = hybrid_config["technologies"] wind_only = {key: technologies[key] for key in ("wind", "grid")} - + hybrid_config["site"].update({"hub_height":90.0}) wind_only["wind"]["model_name"] = "floris" wind_only["wind"]["floris_config"] = floris_config_path wind_only["wind"]["timestep"] = [0, 8760] wind_only["wind"]["num_turbines"] = 4 wind_only["wind"]["turbine_rating_kw"] = 5000 wind_only["wind"]["layout_mode"] = "floris_layout" + hybrid_config["site"]["hub_height"] = 90.0 hybrid_config["technologies"] = wind_only @@ -1847,6 +1848,7 @@ def test_hybrid_wind_only_floris_elevation_adjusted(hybrid_config, subtests): wind_only["wind"]["num_turbines"] = 4 wind_only["wind"]["turbine_rating_kw"] = 5000 wind_only["wind"]["layout_mode"] = "floris_layout" + hybrid_config["site"]["hub_height"] = 90.0 hybrid_config["technologies"] = wind_only hi = HoppInterface(hybrid_config) diff --git a/tests/hopp/test_layout.py b/tests/hopp/test_layout.py index ffa1931bb..52bb1e9e2 100644 --- a/tests/hopp/test_layout.py +++ b/tests/hopp/test_layout.py @@ -138,6 +138,7 @@ def test_wind_basic_grid_layout_floris_default(site, subtests): 'layout_mode': 'basicgrid', 'layout_params': WindBasicGridParameters() } + site.wind_resource.hub_height_meters = 90.0 config = WindConfig.from_dict(wind_technology) wind_model = WindPlant(site, config=config) xcoords, ycoords = wind_model._system_model.wind_farm_layout diff --git a/tests/hopp/test_turbine_models_interface.py b/tests/hopp/test_turbine_models_interface.py new file mode 100644 index 000000000..e0933da01 --- /dev/null +++ b/tests/hopp/test_turbine_models_interface.py @@ -0,0 +1,176 @@ +from pytest import fixture, approx +import pytest +import numpy as np +import json +from hopp.simulation.technologies.wind.floris import Floris +from hopp.utilities import load_yaml +from hopp.simulation.technologies.wind.wind_plant import WindPlant, WindConfig +from hopp.tools.design.wind.turbine_library_tools import check_turbine_library_for_turbine +from hopp.simulation.technologies.sites.site_info import SiteInfo +from hopp import ROOT_DIR +from hopp.simulation import HoppInterface + +FLORIS_V4_TEMPLATE_PATH = ROOT_DIR.parent / "tests"/"hopp"/"inputs"/"floris_v4_empty_layout.yaml" +DEFAULT_WIND_RESOURCE_FILE = ROOT_DIR / "simulation" / "resource_files" / "wind" / "35.2018863_-101.945027_windtoolkit_2012_60min_80m_100m.srw" +DEFAULT_SOLAR_RESOURCE_FILE = ROOT_DIR / "simulation" / "resource_files" / "solar" / "35.2018863_-101.945027_psmv3_60_2012.csv" + +@fixture +def site_input(): + site_dict = { + "data": { + "lat": 35.2018863, + "lon": -101.945027, + "year": 2012, + "site_details": { + "site_shape": "square", + "site_area_km2": 2.0, + }, + }, + "solar_resource_file": DEFAULT_SOLAR_RESOURCE_FILE, + "wind_resource_file": DEFAULT_WIND_RESOURCE_FILE, + "solar": True, + "wind": True, + "hub_height": 80.0 + } + return site_dict + +def test_turbine_library_tools_for_valid_turbine_name(subtests): + valid_turbine_name = "BergeyExcel15_15.6kW_9.6" + is_valid = check_turbine_library_for_turbine(valid_turbine_name) + + with subtests.test("valid name (bool)"): + assert is_valid is True + + +def test_turbine_library_tools_for_invalid_turbine_name(subtests): + + invalid_turbine_name_close_match = "BergeyExcel15" + is_valid = check_turbine_library_for_turbine(invalid_turbine_name_close_match) + + with subtests.test("invalid name (bool)"): + assert is_valid is False + +def test_floris_nrel_5mw(site_input,subtests): + floris_template = load_yaml(str(FLORIS_V4_TEMPLATE_PATH)) + floris_library_turbine_name = "nrel_5MW" + n_turbs = 4 + turbine_rating_kw = 5000.0 + layout_x = [0.0,1841.0,3682.0,5523.0] + layout_y = [0.0]*n_turbs + floris_template["farm"].update({"layout_x":layout_x,"layout_y":layout_y}) + wind_config_dict = { + "num_turbines": n_turbs, + "turbine_rating_kw": turbine_rating_kw, + "turbine_name": floris_library_turbine_name, + "model_name": "floris", + "floris_config": floris_template, + "layout_mode": "floris_layout" + } + project_life = 25 + site_input.update({"hub_height":90.0}) + site = SiteInfo.from_dict(site_input) + wind_config = WindConfig.from_dict(wind_config_dict) + wind_plant = WindPlant.from_dict({"site":site,"config":wind_config}) + wind_plant._system_model.execute(project_life) + with subtests.test("capacity factor"): + assert wind_plant.capacity_factor > 0.0 + with subtests.test("capacity factor"): + assert wind_plant.capacity_factor < 100.0 + with subtests.test("aep"): + wind_plant._system_model.annual_energy == approx(74149945, 1e-3) + with subtests.test("wind capacity factor value"): + assert wind_plant.capacity_factor == approx(42.0, abs = 1.0) + +def test_floris_nrel_5mw_hopp(site_input,subtests): + floris_template = load_yaml(str(FLORIS_V4_TEMPLATE_PATH)) + floris_library_turbine_name = "nrel_5MW" + n_turbs = 4 + turbine_rating_kw = 5000.0 + layout_x = [0.0,1841.0,3682.0,5523.0] + layout_y = [0.0]*n_turbs + floris_template["farm"].update({"layout_x":layout_x,"layout_y":layout_y}) + wind_config_dict = { + "num_turbines": n_turbs, + "turbine_rating_kw": turbine_rating_kw, + "turbine_name": floris_library_turbine_name, + "model_name": "floris", + "floris_config": floris_template, + "layout_mode": "floris_layout" + } + site_input.update({"hub_height":90.0}) + system_capacity_kw = turbine_rating_kw*n_turbs + technologies = {"wind":wind_config_dict,"grid":{"interconnect_kw":system_capacity_kw}} + hybrid_config = {"site":site_input,"technologies":technologies} + hi = HoppInterface(hybrid_config) + hybrid_plant = hi.system + + hi.simulate(25) + + aeps = hybrid_plant.annual_energies + with subtests.test("wind aep"): + assert aeps.wind == approx(74149945, 1e-3) + with subtests.test("wind capacity factor"): + assert hybrid_plant.capacity_factors["wind"] == approx(42.0, abs = 1.0) + +def test_floris_NREL_5MW_RWT_corrected_hopp(site_input,subtests): + floris_template = load_yaml(str(FLORIS_V4_TEMPLATE_PATH)) + turbine_library_turbine_name = "NREL_Reference_5MW_126" + n_turbs = 4 + turbine_rating_kw = 5000.0 + layout_x = [0.0,1841.0,3682.0,5523.0] + layout_y = [0.0]*n_turbs + floris_template["farm"].update({"layout_x":layout_x,"layout_y":layout_y}) + wind_config_dict = { + "num_turbines": n_turbs, + "turbine_rating_kw": turbine_rating_kw, + "turbine_name": turbine_library_turbine_name, + "model_name": "floris", + "floris_config": floris_template, + "layout_mode": "floris_layout" + } + site_input.update({"hub_height":90.0}) + system_capacity_kw = turbine_rating_kw*n_turbs + technologies = {"wind":wind_config_dict,"grid":{"interconnect_kw":system_capacity_kw}} + hybrid_config = {"site":site_input,"technologies":technologies} + hi = HoppInterface(hybrid_config) + hybrid_plant = hi.system + + hi.simulate(25) + + aeps = hybrid_plant.annual_energies + with subtests.test("wind aep"): + assert aeps.wind == approx(74149945, 1e-3) + with subtests.test("wind capacity factor"): + assert hybrid_plant.capacity_factors["wind"] == approx(42.0, abs = 1.0) + + + +def test_pysam_NREL_5MW_RWT_corrected_hopp(site_input,subtests): + turbine_library_turbine_name = "NREL_Reference_5MW_126" + n_turbs = 4 + turbine_rating_kw = 5000.0 + layout_x = [0.0,1841.0,3682.0,5523.0] + layout_y = [0.0]*n_turbs + layout_params = {"layout_x":layout_x,"layout_y":layout_y} + wind_config_dict = { + "num_turbines": n_turbs, + "turbine_rating_kw": turbine_rating_kw, + "turbine_name": turbine_library_turbine_name, + "model_name": "pysam", + "layout_mode": "custom", + "layout_params": layout_params + } + site_input.update({"hub_height":90.0}) + system_capacity_kw = turbine_rating_kw*n_turbs + technologies = {"wind":wind_config_dict,"grid":{"interconnect_kw":system_capacity_kw}} + hybrid_config = {"site":site_input,"technologies":technologies} + hi = HoppInterface(hybrid_config) + hybrid_plant = hi.system + + hi.simulate(25) + + aeps = hybrid_plant.annual_energies + with subtests.test("wind aep"): + assert aeps.wind == approx(66040330, 1e-3) + with subtests.test("wind capacity factor"): + assert hybrid_plant.capacity_factors["wind"] == approx(37.7, abs = 1.0) \ No newline at end of file diff --git a/tests/hopp/test_wind.py b/tests/hopp/test_wind.py index a615c0c2a..d76ee0181 100644 --- a/tests/hopp/test_wind.py +++ b/tests/hopp/test_wind.py @@ -1,14 +1,12 @@ import math -import pytest -from pytest import fixture, approx - import PySAM.Windpower as windpower -from hopp import ROOT_DIR +import pytest +from pytest import fixture,approx from hopp.simulation.technologies.wind.wind_plant import WindPlant, WindConfig from hopp.utilities import load_yaml from tests.hopp.utils import create_default_site_info - +from hopp import ROOT_DIR @fixture def site(): @@ -141,41 +139,25 @@ def test_changing_system_capacity_pysam(site): #################### FLORIS tests ################ def test_floris_num_turbines(site): - floris_config_path = (ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml") - f_config = load_yaml(floris_config_path) - floris_n_turbines = len(f_config["farm"]["layout_x"]) - config = WindConfig.from_dict( - { - 'num_turbines': 16, - "turbine_rating_kw": 5000, - "model_name": "floris", - "timestep": [1, 8760], - "floris_config": floris_config_path - } + floris_config_path = ( + ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" ) + + site.wind_resource.hub_height_meters = 90.0 + config = WindConfig.from_dict({'num_turbines': 16, "turbine_rating_kw": 5000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) model = WindPlant(site, config=config) xcoords, ycoords = model._system_model.wind_farm_layout assert len(xcoords) == config.num_turbines assert len(ycoords) == model.num_turbines assert model._system_model.nTurbs == model.num_turbines - # with pytest.raises(UserWarning) as err: - # assert str(err.value) == f"num_turbines input ({config.num_turbines}) does not equal number of turbines in floris layout ({floris_n_turbines})" - + def test_changing_rotor_diam_recalc_floris(site): floris_config_path = ( ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" ) - - config = WindConfig.from_dict( - { - 'num_turbines': 4, - "turbine_rating_kw": 5000, - "model_name": "floris", - "timestep": [1, 8760], - "floris_config": floris_config_path - } - ) + site.wind_resource.hub_height_meters = 90.0 + config = WindConfig.from_dict({'num_turbines': 4, "turbine_rating_kw": 5000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) model = WindPlant(site, config=config) assert model._system_model.system_capacity == 20000 diams = range(50, 70, 140) @@ -183,7 +165,6 @@ def test_changing_rotor_diam_recalc_floris(site): model._system_model.wind_turbine_rotor_diameter = d assert model._system_model.wind_turbine_rotor_diameter == d, "rotor diameter should be " + str(d) - def test_changing_turbine_rating_floris(site): floris_config_path = (ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml") @@ -196,32 +177,33 @@ def test_changing_turbine_rating_floris(site): "floris_config": floris_config_path } ) - with pytest.raises(UserWarning) as err: + site.wind_resource.hub_height_meters = 90.0 + config = WindConfig.from_dict({'num_turbines': 4, "turbine_rating_kw": 1000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) + with pytest.raises(ValueError) as err: model = WindPlant(site, config=config) - assert str(err.value) == "Input turbine rating (1000 kW) does not match rating from floris power-curve (5000.0 kW)" - + + err_str = "Input turbine rating (1000 kW) does not match rating from floris power-curve (5000.0 kW)" + assert err_str in str(err.value) + def test_changing_system_capacity_floris(site): - floris_config_path = (ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml") - - config = WindConfig.from_dict( - { - 'num_turbines': 4, - "turbine_rating_kw": 5000, - "model_name": "floris", - "timestep": [1, 8760], - "floris_config": floris_config_path - } + # this will fail now + floris_config_path = ( + ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" ) + site.wind_resource.hub_height_meters = 90.0 + config = WindConfig.from_dict({'num_turbines': 4, "turbine_rating_kw": 5000, "model_name": "floris", "timestep": [1, 8760], "floris_config": floris_config_path}) model = WindPlant(site, config=config) + new_num_turbs = 16 new_capacity_kW = new_num_turbs*config.turbine_rating_kw rating = model._system_model.turb_rating - + assert model._system_model.nTurbs == 4 assert model._system_model.turb_rating == rating assert model._system_model.system_capacity == 20000 - + model.system_capacity_by_num_turbines(new_capacity_kW) assert model._system_model.nTurbs == new_num_turbs assert model._system_model.system_capacity == new_capacity_kW + diff --git a/tests/hopp/test_wind_design_tools.py b/tests/hopp/test_wind_design_tools.py new file mode 100644 index 000000000..aeb578728 --- /dev/null +++ b/tests/hopp/test_wind_design_tools.py @@ -0,0 +1,141 @@ +from pytest import fixture +import pytest +from hopp.tools.design.wind.floris_helper_tools import ( + check_floris_library_for_turbine, + load_turbine_from_floris_library, + check_libraries_for_turbine_name_floris +) +from hopp.simulation.technologies.wind.wind_plant import WindConfig +from hopp.simulation.technologies.wind.floris import Floris +from hopp.utilities import load_yaml +from hopp.simulation.technologies.sites.site_info import SiteInfo +from hopp import ROOT_DIR +DEFAULT_WIND_RESOURCE_FILE = ROOT_DIR / "simulation" / "resource_files" / "wind" / "35.2018863_-101.945027_windtoolkit_2012_60min_80m_100m.srw" +DEFAULT_SOLAR_RESOURCE_FILE = ROOT_DIR / "simulation" / "resource_files" / "solar" / "35.2018863_-101.945027_psmv3_60_2012.csv" +FLORIS_V4_TEMPLATE_PATH = ROOT_DIR.parent / "tests"/"hopp"/"inputs"/"floris_v4_empty_layout.yaml" + +@fixture +def site_input(): + site_dict = { + "data": { + "lat": 35.2018863, + "lon": -101.945027, + "year": 2012, + "site_details": { + "site_shape": "square", + "site_area_km2": 2.0, + }, + }, + "solar_resource_file": DEFAULT_SOLAR_RESOURCE_FILE, + "wind_resource_file": DEFAULT_WIND_RESOURCE_FILE, + "solar": True, + "wind": True, + "hub_height": 80.0 + } + return site_dict + + +def test_floris_library_tools_for_valid_floris_turbine(subtests): + floris_library_turbine_name = "nrel_5MW" + is_floris_turbine = check_floris_library_for_turbine(floris_library_turbine_name) + floris_turbine_specs = load_turbine_from_floris_library(floris_library_turbine_name) + with subtests.test("valid floris turbine (bool)"): + assert is_floris_turbine is True + with subtests.test("valid floris turbine loader return type"): + assert isinstance(floris_turbine_specs,dict) + with subtests.test("valid floris turbine loader turbine specs"): + assert floris_turbine_specs["turbine_type"] == floris_library_turbine_name + +def test_floris_library_tools_for_invalid_floris_turbine(subtests): + floris_library_invalid_turbine_name = "nrel_10MW" + is_floris_turbine = check_floris_library_for_turbine(floris_library_invalid_turbine_name) + + with subtests.test("invalid floris turbine (bool)"): + assert is_floris_turbine is False + + with pytest.raises(FileNotFoundError) as err: + floris_turbine_specs = load_turbine_from_floris_library(floris_library_invalid_turbine_name) + assert str(err.value) == f"Floris library file for turbine {floris_library_invalid_turbine_name} does not exist." + +def test_floris_turbine_loader_valid_floris_turbine(site_input,subtests): + floris_template = load_yaml(str(FLORIS_V4_TEMPLATE_PATH)) + floris_library_turbine_name = "nrel_5MW" + wind_config_dict = { + "num_turbines": 4, + "layout_mode": "basicgrid", + "turbine_rating_kw": 5000.0, + "layout_params": {}, + "turbine_name": floris_library_turbine_name, + "model_name": "floris", + "floris_config": floris_template + } + site_input.update({"hub_height":90.0}) + site = SiteInfo.from_dict(site_input) + wind_config = WindConfig.from_dict(wind_config_dict) + floris_model = Floris.from_dict({"site":site,"config":wind_config}) + + floris_turb_res = check_libraries_for_turbine_name_floris(floris_library_turbine_name,floris_model) + with subtests.test("return type"): + assert isinstance(floris_turb_res,dict) + with subtests.test("turbine type name"): + assert floris_turb_res["turbine_type"] == floris_library_turbine_name + with subtests.test("hub-height"): + assert floris_turb_res["hub_height"] == 90.0 + with subtests.test("rated power"): + assert max(floris_turb_res["power_thrust_table"]["power"]) == wind_config_dict["turbine_rating_kw"] + +def test_floris_turbine_loader_multi_hub_height_turbine(site_input,subtests): + floris_template = load_yaml(str(FLORIS_V4_TEMPLATE_PATH)) + turbine_library_turbine_name = "VestasV82_1.65MW_82" #has 80m hub-height option + wind_config_dict = { + "num_turbines": 4, + "layout_mode": "basicgrid", + "turbine_rating_kw": 1650.0, + "layout_params": {}, + "turbine_name": turbine_library_turbine_name, + "model_name": "floris", + "floris_config": floris_template + } + site_input.update({"hub_height":80.0}) + site = SiteInfo.from_dict(site_input) + wind_config = WindConfig.from_dict(wind_config_dict) + floris_model = Floris.from_dict({"site":site,"config":wind_config}) + + floris_turb_res = check_libraries_for_turbine_name_floris(turbine_library_turbine_name,floris_model) + + with subtests.test("return type"): + assert isinstance(floris_turb_res,dict) + with subtests.test("turbine type name"): + assert floris_turb_res["turbine_type"] == turbine_library_turbine_name + with subtests.test("hub-height"): + assert floris_turb_res["hub_height"] == 80.0 + with subtests.test("rated power"): + assert max(floris_turb_res["power_thrust_table"]["power"]) == wind_config_dict["turbine_rating_kw"] + + +def test_floris_turbine_loader_single_hub_height_turbine(site_input,subtests): + floris_template = load_yaml(str(FLORIS_V4_TEMPLATE_PATH)) + turbine_library_turbine_name = "DOE_GE_1.5MW_77" #only has 80m as valid hub-height + wind_config_dict = { + "num_turbines": 4, + "layout_mode": "basicgrid", + "turbine_rating_kw": 1500.0, + "layout_params": {}, + "turbine_name": turbine_library_turbine_name, + "model_name": "floris", + "floris_config": floris_template + } + site_input.update({"hub_height":80.0}) + site = SiteInfo.from_dict(site_input) + wind_config = WindConfig.from_dict(wind_config_dict) + floris_model = Floris.from_dict({"site":site,"config":wind_config}) + floris_turb_res = check_libraries_for_turbine_name_floris(turbine_library_turbine_name,floris_model) + + with subtests.test("return type"): + assert isinstance(floris_turb_res,dict) + with subtests.test("turbine type name"): + assert floris_turb_res["turbine_type"] == turbine_library_turbine_name + with subtests.test("hub-height"): + assert floris_turb_res["hub_height"] == 80.0 + with subtests.test("rated power"): + assert max(floris_turb_res["power_thrust_table"]["power"]) == wind_config_dict["turbine_rating_kw"] \ No newline at end of file From 21ccf91cf51f39a667898af0bf80522982992de6 Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Thu, 20 Mar 2025 18:14:53 -0600 Subject: [PATCH 17/48] Distributed wind-hybrid examples (#452) * updated examples for battery is actually used * updated RELEASE.md for new examples * Minor typographical edits * updated example 9 and minor update to plot_generation_profile function * updated tick labelsize in plot_generation_profile for bottom subplot * updated example 9 with feedback from kbrunik --------- Co-authored-by: John Jasa --- RELEASE.md | 3 + .../08-distributed-residential-example.ipynb | 450 + examples/09-distributed-midsize-example.ipynb | 324 + ...08-distributed-wind-solar-residential.yaml | 58 + .../09-distributed-wind-solar-midsize.yaml | 63 + .../inputs/distributed_load_profile_MW.yaml | 8760 +++++++++++++++++ examples/inputs/floris_v4_template.yaml | 101 + .../inputs/residential_load_profile_MW.yaml | 8760 +++++++++++++++++ examples/inputs/residential_pv.yaml | 14 + hopp/tools/dispatch/plot_tools.py | 17 +- 10 files changed, 18543 insertions(+), 7 deletions(-) create mode 100644 examples/08-distributed-residential-example.ipynb create mode 100644 examples/09-distributed-midsize-example.ipynb create mode 100644 examples/inputs/08-distributed-wind-solar-residential.yaml create mode 100644 examples/inputs/09-distributed-wind-solar-midsize.yaml create mode 100644 examples/inputs/distributed_load_profile_MW.yaml create mode 100644 examples/inputs/floris_v4_template.yaml create mode 100644 examples/inputs/residential_load_profile_MW.yaml create mode 100644 examples/inputs/residential_pv.yaml diff --git a/RELEASE.md b/RELEASE.md index 08a715b84..6e65efb64 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -32,6 +32,9 @@ * Integrated [turbine-models library](https://github.com/NREL/turbine-models/tree/master). For further details on the following updates, users are referred [here](https://github.com/NREL/HOPP/pull/435) + Wind turbines from the turbine-models library can now be simulated by specifying the turbine name. This feature is compatible with FLORIS and PySAM WindPower simulations. + Added wind turbine power-curve tools to estimate thrust coefficient, power coefficient, and power-curve. +* Added two distributed wind-hybrid examples that highlight the turbine-models library package and other recent features for wind system modeling and simulations. These examples are: + - `examples/08-distributed-residential-example.ipynb` + - `examples/09-distributed-residential-midsize.ipynb` ## Version 3.1.1, Dec. 18, 2024 diff --git a/examples/08-distributed-residential-example.ipynb b/examples/08-distributed-residential-example.ipynb new file mode 100644 index 000000000..ff0b528ba --- /dev/null +++ b/examples/08-distributed-residential-example.ipynb @@ -0,0 +1,450 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Distributed Hybrid Plant - Residential Example\n", + "\n", + "This example focuses on distributed-scale energy: energy that is connected at the distribution level of the electrical grid or off-grid. Distributed energy can supply on-site electricity demand and improve energy reliability and resilience for residential, commercial, and agricultural end-users. This particular example considers a small mountain town in south-central Colorado.\n", + "\n", + "## Available Resources for Distributed Energy\n", + "\n", + "This example highlights various resources for distributed energy modeling:\n", + "\n", + "- **Wind Turbine Models**: Users can find available turbines for distributed applications in the [turbine-models repository](https://github.com/NREL/turbine-models).\n", + "- **Load Profile Data**: The [ResStock Dataset](https://www2.nrel.gov/buildings/end-use-load-profiles) provides estimated end-use load profiles for residential buildings.\n", + "- **Annual Technology Baseline (ATB)**: This resource offers transparent, normalized technology cost and performance assumptions for various technology classes. Relevant sections include:\n", + " - [Distributed Wind](https://atb.nrel.gov/electricity/2024/distributed_wind)\n", + " - [Residential PV](https://atb.nrel.gov/electricity/2024/residential_pv)\n", + " - [Residential Battery Storage](https://atb.nrel.gov/electricity/2024/residential_battery_storage)\n", + "- **ATB Data Workbooks**: The ATB Excel Workbooks contain detailed data and calculations for each technology. These workbooks can be accessed [here](https://atb.nrel.gov/electricity/2024/data).\n", + "\n", + "This notebook will guide you through setting up a simulation for a distributed residential hybrid energy system using HOPP. The example includes simulates a system that includes residential rooftop solar PV, distributed wind turbines, and a lithium-ion battery storage system." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import required modules\n", + "\n", + "Start by importing the necessary modules and packaged and setting up our working environment." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/egrant/Documents/projects/HOPP/examples/log/hybrid_systems_2025-03-20T11.05.12.098897.log\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "from floris import TimeSeries\n", + "from floris.layout_visualization import plot_turbine_points\n", + "from turbine_models.parser import Turbines\n", + "from hopp import ROOT_DIR\n", + "from hopp.utilities.keys import set_nrel_key_dot_env\n", + "from hopp.simulation import HoppInterface\n", + "from hopp.tools.design.wind.turbine_library_tools import print_turbine_name_list\n", + "from hopp.utilities.utilities import load_yaml\n", + "from hopp.tools.dispatch.plot_tools import (\n", + " plot_battery_output, plot_battery_dispatch_error, plot_generation_profile\n", + ")\n", + "\n", + "set_nrel_key_dot_env()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display Turbine Model Options\n", + "\n", + "The turbine-models repository hosts power curves and key data for commonly used turbine models in industry and the R&D community. \n", + "\n", + "There are several classes of turbine options, including:\n", + "- [Distributed](https://github.com/NREL/turbine-models/tree/master/turbine_models/data/Distributed)\n", + "- [Onshore (utility scale)](https://github.com/NREL/turbine-models/tree/master/turbine_models/data/Onshore)\n", + "- [Offshore](https://github.com/NREL/turbine-models/tree/master/turbine_models/data/Onshore)\n", + "\n", + "In HOPP, you can view all the turbine models available in the turbine-models repository using the `print_turbine_name_list()` function. The below code will only print the distributed turbine models available.\n", + "\n", + "**NOTE**: Most of the turbine model names are formatted as \"TurbineName_RatingkW_RotorDiameter\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------\n", + "Distributed Turbine Names:\n", + "------------------------\n", + "\n", + " CF11_11kW_9.36\n", + " Skystream3.7_2.1kW_3.7\n", + " EntegrityEW50_50kW_15\n", + " EWT_DW61_1MW_60.9\n", + " EWT_DW54X_1MW_54.1\n", + " NPS100B-24C_95kW_24.4\n", + " NPS100C-21_100kW_20.7\n", + " Bestwind30_27.2kW_13.1\n", + " FortisMontana_3.31kW_5.04\n", + " CF15_15kW_11.15\n", + " Jacobs31-20_12kW_9.45\n", + " NPS100C-28_90kW_28\n", + " BergeyExcel10_8.9kW_7\n", + " PikaT701_1.5kW_3\n", + " SWIFT_1kW_2.1\n", + " NPS100B-21_100kW_20.7\n", + " VestasV29_225kW_29\n", + " GhrepowerFD21-50_61.2kW_21.5\n", + " NPS100C-28_90kW_27.6\n", + " EWT_DW54_900kW_54\n", + " 2019COE_DW20_20kW_12.4\n", + " EWT_DW52_900kW_51.5\n", + " VestasV27_225kW_27\n", + " CF10A_10kW_11.15\n", + " EWT_DW58_1MW_58\n", + " NPS100C-24_95kW_24.4\n", + " CF20_20kW_13.1\n", + " NPS100B-24_95kW_23.6\n", + " BergeyExcel15_15.6kW_9.6\n", + " SD6_5.2kW_5.5\n", + " CF11A_11kW_11.15\n", + " NPS100C-27_90kW_27.4\n", + " Kestrele400nb_2.5kW_4\n", + " 2019COE_DW100_100kW_27.6\n", + " NPS60C-24_60kW_24.4\n" + ] + } + ], + "source": [ + "t_lib = Turbines()\n", + "distributed_turbines = list(t_lib.turbines(group=\"distributed\").values())\n", + "print(\"-\".join(\"\" for i in range(25)))\n", + "print(\"Distributed Turbine Names:\")\n", + "print(\"-\".join(\"\" for i in range(25)))\n", + "dw_msg = \"\\n \" + \"\\n \".join(t for t in distributed_turbines)\n", + "print(dw_msg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selecting a Turbine Model\n", + "\n", + "For this example, we selected the [Bergey Excel 15](https://www.bergey.com/products/grid-tied-turbines/excel-15/) from the available models, listed as \"BergeyExcel15_15.6kW_9.6.\" This 15.6 kW turbine is design for low wind speed conditions, making it well-suited for distributed applications. To use this turbine model in your HOPP simulation, you need to specify it in your configuration YAML file. \n", + "\n", + "In this example, the configuration file is `examples/inputs/08-distributed-wind-solar-residential.yaml`. To include the Bergey Excel 15 turbine, add its name under the `turbine_name` field in the YAML file.\n", + "\n", + "`turbine_name: \"BergeyExcel15_15.6kW_9.6\"`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Inputs\n", + "\n", + "Load the configuration YAML file as `hopp_config`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "example_dir = ROOT_DIR.parent / \"examples\"\n", + "input_filepath = os.path.join(str(example_dir),\"inputs\",\"08-distributed-wind-solar-residential.yaml\")\n", + "hopp_config = load_yaml(input_filepath)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Key Distributed Energy Inputs in the Configuration YAML\n", + "\n", + "While many inputs can be included in the configuration YAML file, this example highlights key parameters specific to distributed energy systems.\n", + "\n", + "#### **Wind**\n", + "- `turbine_name`: Specifies the turbine model, allowing HOPP to extract essential turbine data, including the power curve.\n", + "- `resource_parse_method`: Defines how wind resource data is processed. When set to \"weighted_average\" it calculates a weighted average for multiple hub heights. This is particularly important at lower hub heights due to wind shear effects.\n", + "- `adjust_air_density_for_elevation`: When set to `True`, this parameter adjusts air density based on elevation, which can impact turbine power output. At higher elevations, lower air density reduces power generation due to atmospheric conditions.\n", + "\n", + "#### **Solar PV**\n", + "- The YAML file includes a separate configuration file for solar panel system design parameters.\n", + "- `panel_tilt_angle`: Specifies the tilt angle of the solar panels for optimal energy capture.\n", + "\n", + "### Example Configuration File Structure\n", + "\n", + "Below is an example configuration snippet showing how different distributed energy technologies are defined in the YAML file:\n", + "\n", + "```yaml\n", + "technologies:\n", + " pv:\n", + " panel_system_design: !include \"residential_pv.yaml\"\n", + " dc_degradation: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", + " panel_tilt_angle: 20.0\n", + " system_capacity_kw: 1800.0 # System capacity in kWdc\n", + " wind:\n", + " num_turbines: 56\n", + " turbine_name: \"BergeyExcel15_15.6kW_9.6\"\n", + " model_name: floris\n", + " floris_config: !include floris_v4_template.yaml\n", + " resource_parse_method: \"weighted_average\"\n", + " store_turbine_performance_results: False\n", + " adjust_air_density_for_elevation: True\n", + " layout_mode: \"basicgrid\"\n", + " layout_params:\n", + " row_D_spacing: 11.5\n", + " turbine_D_spacing: 11.5\n", + " \n", + " battery:\n", + " system_capacity_kwh: 1750\n", + " system_capacity_kw: 700\n", + " minimum_SOC: 20.0\n", + " maximum_SOC: 100.0\n", + " initial_SOC: 20.0\n", + "```\n", + "\n", + "This configuration file defines the components of a distributed hybrid energy system, including wind, solar PV, and battery storage. Each section outlines some key parameters that influence system performance.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Simulation Model\n", + "Create an instance of the `HoppInterface` class by providing it a dictionary of inputs (`hopp_config`)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FLORIS is the system model...\n" + ] + } + ], + "source": [ + "hi = HoppInterface(hopp_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulate Hybrid Plant\n", + "\n", + "Run the hybrid plant simulation. By setting `project_life` to 1, you simulate a single year of operation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulating wind farm output in FLORIS...\n" + ] + } + ], + "source": [ + "\n", + "hi.simulate(project_life = 1)\n", + "hybrid_plant = hi.system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the Generation Profile\n", + "\n", + "Three subplots are generated, displaying results for a default period of five days with hourly time steps on the x-axis:\n", + "\n", + "1. **Generation Profile**\n", + " - The red line represents PV generation, while the blue line represents wind generation.\n", + " - The y-axis shows power in megawatts (MW).\n", + "\n", + "2. **Battery Power Flow** (Dual-Axis Plot)\n", + " - Left y-axis: Power (MW)\n", + " - Right y-axis: State-of-Charge (SOC) as a percentage\n", + " - The blue dashed line represents the desired load specified in the simulation.\n", + " - The blue and red bars indicate battery discharge and charge, respectively, at each time step.\n", + " - The black line represents the battery's SOC.\n", + " - The blue dotted line indicates the battery dispatch profile.\n", + "\n", + "3. **Net Generation Profile** (Dual-Axis Plot)\n", + " - Left y-axis: Power (MW)\n", + " - Right y-axis: Grid or energy price (if used)\n", + " - The black dashed line represents the original hybrid generation profile (including wind, PV, and battery storage).\n", + " - The green line represents the optimized generation profile after battery dispatch." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ViyA6AAAAAABI1rFjx8y5xhqioqK88hjPPvusV9YLBCLKuQBeNmHCBHPeoUMHl19sTZs2NecLFy70+dgAAAAAAEDgOXr0qDkvUKAATT8BHyCIDni5lMsvv/xiLnfv3t3lMg0bNjTNRrXcy4EDB3w8QgAAAAAAEKiZ6AULFvRqTEPrrWtpWiDUEUQHvGjt2rWyc+dOyZw5s3Ts2NHlMtmzZ5datWqZy4sWLfLxCAEAAAAAQKAG0TUT3Vu05nqpUqWkTZs2cvz4ca89DhAICKIDPirlki1bNrfLUdIFAAAAAAD4UxA9a9asUrJkSXN58+bNXnscIBAQRAe8aPLkyUmWckkYRCcTHQAAAAAAJKd06dJmxnudOnW8+jhVqlQx5wTREeoi7B4AEMw0s1wD6Z06dUpyuSZNmpjzrVu3yvnz55PMWgcAAAAAAKHt7rvvNidv0yD61KlTZdOmTV5/LMCfkYkOeFGePHmkT58+yQbFtRHIkiVL5OTJkwTQAQAAAACAX6hcubI5J4iOUEcQHfATDRs2NPXGAAAAAMAuFy5ckO3bt8vRo0dl+vTp4nA47B4SABeio6N98jiUcwGuI4gOeMG6detMUPyLL76weygAAAAAkGJjxoyRChUqSKFChaR9+/ayd+9eu4cEwIUSJUpI9uzZvR7ctjLRDx48KGfOnPHqYwH+jCA64AUTJkyQZcuWycyZM1N8n8uXL8tTTz0lDRo0MJcBAAAAwJc06/zTTz+Nd93q1attGw8A12JiYuTEiROmp1ru3Lm9+lg5c+aU119/XUaNGiUZMmTw6mMB/owgOuCFHU8Noqvu3bun+H6RkZEyfvx4Wb58uaxYscKLIwQAAACAxObNmycbN26UqKgouf322811q1atsntYABLQfmqxsbHmcr58+bz+eK+88orce++99HBDSCOIDnjYhg0bZNu2bSYo3rlz5xTfLywsTJo0aWIuL1y40IsjBAAAAIDEPvvsM3Peq1cvadWqlblMJjrgf7RngcqbN69ERETYPRwgJBBEBzzMykJv27at5MiRI1X3bdq0qTkniA4AAADAl/bt2ye//fabudy/f3+pW7euMxOd5qKAfzl27Jg5L1iwoE8e79y5czJnzhyZMmWKTx4P8EccrgL8oJRLwiD6kiVLTI0z6o0BAAAA8IUvv/zSlIdo2bKlVK1aVS5dumR+jxw/flwOHDggxYsXt3uIABIE0QsUKOCTx1uzZo3cdNNNUqpUqVTNuAeCCZnogAdp/cCtW7dKpkyZ0vTFUqNGDdNd++zZs6YsDAAAAAD4Ihlo4sSJ5vLjjz9uzrNkySJVqlQxlynpAoR2EN3aFuzZs0cuXLjgk8cE/A1BdMCDNHOja9eu5qQdrFNLMz0aNWpkLlPSBQAAAIAvaH+mtWvXysiRI+MlAz3//PPmunr16tk6PgDxFStWTDp16iT169f3yeNp89L8+fOby1u2bPHJYwL+hnIugAdpJvmvv/6arpqBWtJFM9qpOwgAAADAV7JmzSq9e/eOd13Pnj1tGw8A97p162ZOvqTZ6PPnz5fNmzc7eyYAoYRMdMBLmRxp9dxzz5mmPk888YRHxwQAAAAACWkpSRJ4ACSncuXK5nzTpk12DwWwBUF0wEMWL14sO3fuTPd6MmbMmK4gPAAAAACk1COPPGIaic6ZM8fl7UuWLJFPPvlETpw44fOxAXAtOjra549p1UUniI5QRRAd8BDNHC9XrpxMmjTJY/XVz50755F1AQAAAEBCR44ckQkTJpjyDLly5XK5TL9+/eTJJ5+U5cuX+3x8ANwHtLNlyyYrVqzw6WMq3V4AoYggOuABhw8fdnasb9KkSbrXN2rUKNO04+mnn/bA6AAAAAAgsa+//lquXr0qjRo1kjp16rhcxrp+1apVPh4dAHeOHTsmFy5ccHvwyxtq164t33zzjWk2DIQiguiAB0ybNs2ca9f6AgUKpHt9efPmlZMnT8rChQs9MDoAAAAAiO/KlSvy1Vdfmcv9+/d3u5zVQNBKGgJgr4sXL8r58+fNZU/EH1IqT548ZmZKw4YNffaYgD8hiA54wNSpU815x44dPbK+xo0bm7ro27Ztk6NHj3pknQAAAABg0TKUWs6lUKFCctttt7ldzspEJ4gO+E8WusqUKZPkyJHD7uEAIYMgOuCBDI6ZM2eayx06dPDIOnPnzi3VqlVzNiwFAAAAAE/67LPPzPnDDz9sgnFJlXBQ+/fvl+PHj/tsfACSDqIXLFjQJN/5kib6jRgxQqZPn+7TxwX8AUF0IJ00yK0NQHUalTXV0ROs2uqUdAEAAADgSevXrze/YyIiIuTBBx9Mctns2bNLhQoVzGWy0QH/CaL7spSLZcqUKWab8d133/n8sQG7EUQHPFTKpX379hIe7rmPVNOmTc05QXQAAAAAnlS9enWZNWuWvPvuu1K4cOFkl7eShWguCoR2EL1KlSrmfPPmzT5/bMBuEXYPAAh0r776qskaL1q0qEfXawXR16xZYzLdNQMEAAAAANJLS0DcfPPN5pQSzzzzjDz22GNSs2ZNr48NQNK0j0Hnzp2lXr16tgXRt27dKteuXTOzWYBQwbsdSCcNbt9yyy0eX2+xYsWke/fuUr58eVN3HQAAAADSy+FwpLqOstVcFID9tBebp/qxpVbx4sUlW7Zscv78eZONrrNagFBBORfAj/3888/y1ltvSd68ee0eCgAAAIAAp5mjWprl2WefldOnT9s9HAABRkvY3nDDDeby8uXL7R4O4FME0YF0GDBggLzyyiumUz0AAAAA+LM//vjDlIv8/vvvJTIyMlX3/e233+SJJ56QlStXem18AJJn90z1hg0bmvNly5bZOg7A1wiiA2l08eJF+fLLL+XNN9+UM2fOeO1xNENEm5fa/UUJAAAAILB9+umn5rxfv36SJUuWVN133Lhx5v5z5szx0ugApET9+vUlKipK5s2bZ8vjN2jQwJwTREeoIYgOpJF+YV2+fFlKlCghVatWTdV9Nx3fJM/OfFa2nNiSbL3CihUrSseOHWX16tXpHDEAAACAULVp0yYTANdyDI888kiq769lYBS/SwB7HTt2zCT15cyZ05bHb968ucyYMUMWLVpky+MDdiGIDqTRn3/+ac61oUdqG/P0m9JP3l/6vtT6qpa8v+R9iYmNcbmcrtc6yrtkyRIPjBoAAABAKP9+ad++vUkESi2rueiqVas8PjYAKRMbGyvHjx83lwsUKGDLGDR436ZNG8mVK5ctjw/YhSA6kAaaIa4lVlRqu2LvP7Nfluy/HhCPjomWZ2c9K02/bypbT2x1O1VLsbMKAAAAIK2sWuZNmzZN0/2tIPqOHTu8Ws4SQNLlXrVBsMqfP7/dwwFCCkF0IA02b94se/bsMc14WrVqlar7/rzxZ3PetERTGdF5hGTPlF2WHlgqtb6uJR8u/TBRVrrV+ZoGPgAAAADSyvo9Yf2+SK28efNKyZIlzWVtTgrA944ePWrOc+fOLZkyZbJtHDt37pTnnnvOnIBQEZRB9DFjxshDDz1kdg40yKklMUaOHJnqetd6P3en1K4PwcXKQm/RooVp6JEa4zeON+d3VbtL+tbpK/88+o+0LtNaLl+7LANnDpTmI5vLtn+3Jao9uG3bNnPUGQAAAABSIyYmRtq2bWt+I1sZ5WlBXXTA/nrodpZysWhs4r333pMRI0aYEjNAKIiQIPTyyy/L3r17JV++fFK4cGFzOT0NEzRQmlCtWrXSOUoEsqtXr0qePHlMw8/U2HVql6w4tELCw8Lltsq3metK5CwhM+6ZISNWjzBB9MX7F0vNr2rK263elidufMK8j0uVKmUy33VnNbWZ7wAAAABCW4YMGeSLL75I93o0AD9p0iRT0gVA6AbRa9SoIVmyZDHBdE34q1Spkq3jAXwhKIPo3377rZQvX95MNXvnnXfkhRdeSPO6NIA+ZMgQj44PgU/fU88++6yzFllqS7m0LNVSCmYr6LxeZzc8WPdBaVu2rTzw+wMye/dsGTBzgEzaMkm+v+V7kzGiQXSdgkkQHQAAAIAdHnzwQXnggQekUKFCdg8FCElaVqlz585SrVo1W8eRMWNGE6dYuHChLFu2jCA6QkJQlnO5+eabnbXaAG+JiIiQzJkzp6mUy51V73R5e8lcJWVWr1nyZccvJVumbLJo3yKp8WUNydcxn3zzzTdy223Xs9cBAAAAIKW2b98uly9fTvd6tJEhAXTAPppU9/vvv8vbb79t91CkQYMG5nzp0qV2DwXwiaAMont6Z+Ojjz6SoUOHyujRo+XgwYN2Dwk2O3TokDgcjlTfT+ucrz2yViLCI6Rb5W5ul9Os9IdveFg2PLJBWpVuJZeuXZKv9n4luRvnlrJly6Zz9AAAAABCzU033STZs2eXFStW2D0UAEGiYcOG5lwz0YFQEJTlXDxp3Lhx5hQ3+/jxxx83DRS0rlxSoqOjzcly9uxZZz1tPSHwaMMMrQOoXbC1uWjFihVTfN9x66+/j24qdZPkyJgj2fdA0aiiMvWuqdJ/Wn/5du238svGX+SW8rek+znA/1jvBbYLABJi+wDAFbYNSI2jR4/K/v37TbKOJuWk933z448/yk8//STdunWT3r17e2ycSD+2DcFPY0waj9DPs92sJsX//POPnDx50hyog/9iu5B+BNGTmKam9dQ7depkmjpeuHDBTFEZNGiQDB8+3GywPvjggyTXodnrr732WqLr586dK1mzZvXi6OEt2kBHd0K1jMuWLVtk586dKb7v91u+N+cVrlYwAfiUKnO+jDn/Y/Mf8uhfj0q1KtXMexLBZ9asWXYPAYCfYvsAwBW2DUgJ7aukihYtauoXp9e0adPMScvD6O9m+B+2DcHdn03jEM8995ypSW433QZoYP+HH36Q0qVL2z0cJOHixYt2DyHgEUR3o2rVquZkiYqKkltuuUVuvPFG04X4k08+keeffz7Jjsi6cRswYEC8TPTixYtLy5YtTTMIBJ4333zTnLdt29a8H1Jq4/GNsm/tPsmUIZO80v0VyZU5V4rv2ya2jQwbPkzORJ+Rb//8Vl4v9rp06NAhTeOH/x4R1h3d1q1bmwYtAGBh+wDAFbYNSI1Vq1aZ8+bNm3vkd4SWttRMdE0u4neJf2HbENy0vPC///4rV65cMXGlpk2b2j0kWbt2reTLl88vMuORNKs6BtKOIHoqaRMVDZ5+++23snz5ctMV2Z3IyEhzSki/zPhCC0wzZsww5/p3T83fcNLWSea8bdm2kj976rI1MkpGubnMzTJx80SRciJr1qzh/ROk2DYAcIftAwBX2DYgJfT3g6pfv75H3i+6HqUzczVoyyxr/8O2IfjMmTNHevToIceOHTNB67p16/rF37hIkSJ2DwEp5A/vl0BHY9E00A2W0hIvCB3Hjx+Xv//+21xu3759qjI1xm8cby7fWfXOND12u3Ltrl8o97/pmAAAAACQ3G8R6/eDp0o/FC5cWAoWLGj6Ra1bt84j6wTgmn7O3n77bTO7QAPoWhlhyZIlkjNnTvHH7Q0QzAiip4FmoCvqUoeW6dOnmy+F2rVrp+po67qj62Tbv9skc0Rm6VKxS5oeWzPYjaIi+0/sN1MnAQQOrRN45MgR03BHD8DS1AUAAPjCoUOHzD5IhgwZpFatWh5Zp5Zt0CxYtXr1ao+sE4Brmn3+0ksvmWB6nz59ZNmyZVK+fHnxFxoj6dWrlxQrVixVPeOAQBTyQfQTJ06YaWh67qpuXEIff/yxaQyqG6169er5aJTwB3/++ac5T23dv/H/XM9C71C+g2SPTFu36uI5i0vV/FWvf2LLkI0OBBL9vGo/DM3a0n4Y2bJlM6XB4urWrZs5OKcHZwu3Kyy9h/aWmNgY28YMAACCg5YX/eCDD0yvLk+WXalTp06Sv5sBeEbHjh3N51hLCn/33XeSJUsW8Sd6UE2D51qvfenSpXYPB/CqoKyJrhuXRYsWmcsbNmxwXjdv3jxzuUmTJtK3b19z+bPPPpPXXntNBg8eLEOGDHGu47bbbjP1gnTKmx5R08xBPeKn9eRy5colY8aMMUfzEToeeeQRE/jSYJcvS7nELemiDUqtki76ZQrAv+nOZJcuXUw5qLgyZcoU7/86NfPw4cMiOsGpocgPV36QqW9PlcFtBkufWn0kKlOUj0cOAAA8TX8b+Lr5npYi1QC6p2kmevbs2SU8POTz8gCPbyf0t4GWTFL33nuvtGjRQkqUKCH+qkGDBiaArjEzzUoHglVQfuNpAH3UqFHmZE0vW7x4sfM6K8CeXMBUMwcXLFggn376qYwcOVIuXbokTz31lAnMW81UEDq0m/1HH33kzLpIiZWHVsru07sla8as0rF8+oLeceui/73iem12AP7r4sWLJoCuwfGqVavKmTNnTBkXPSirM6DiGj16tOlsf+/r9zqvOxFzQh6f9rgUH15cXpr9khw+d9iGZwEAADxh9+7dJiGnZcuWpp5xoOvUqZOcPn3aJKsB8Nzvh969e5tkzrhJOP4cQFcNGzY052SiI9gFZRBdA9569M7dSW+3aPa5Xhc3C109//zzpvuxZhFevnzZbMw2b94sw4cPN5npQEpYWeidK3ROdyZpkxJNJEtEFpHsIi989IKHRgjAW2JiYkwJF80AmzJliuTIkUMiIiLMVOqEjYBKly4tNWvWlI2XNpr/1z1UV+RPkQxnMsipy6fk7UVvS6mPS8kDkx+QjceuLwMAAALHrFmzTHapzo5u3LixCUJ7uymn/s796aefZOvWraaesifpPg1Z6IDnbNu2TW688UaTXKO9DLSMcKCwgujr1683CUNAsOJbD0iBd999V2bPnp2qZoCxjlj5eePPHinlorQxaavSrczlxUcWp3t9ALxLpzhPnjzZZGRokDw5Jy+dlNWHr8+eGv/6eGkY0VC+r/O9TLpjkjQq3kiuxFyR79Z+J9W+rCYdxnaQ2btmmx/HAADA/23atMmca/8TLQuq/Za00efTTz/ttcfcu3evaUpYvXp1rzY193SAHgg1uj3Q7PN//vnHlHHR2MMdd9whgUITTYsWLWqSiOiTgGBGEB1Ixv79+83MhNatW8vZs2dTfL9lB5bJ/rP7JXum7NK+fHuPjKV9uevrmb5zukfWB8A7P5Kt4Lb+SC5XrlyK7jd391xxiEOq5K8iZQuWNaXHet3TS26tfKssvn+xLLl/idxW+TYJDwuXaTumyc2jb5Y639SR5QeWe/kZAQCA9LrnnnvMrGbNMtV9hTvvvJ5kU6FCBa89pvZRUjVq1DCNCT1Ns9x1/FoKFUDaaIyhZ8+ecu7cOWnatKnpw6c10AMNJV0QCmxrLKrlUbQWnNYqP3DggJw4ccJMcc+fP785Uq71p1MaeAC8adq0ac5mGXnz5k3x/cb/c72Uyy2VbjFZ5J5g1UVfsHuBfP7t5/JY38c8sl4AnqHfa1rrVHeEv/76a9OgOqX+2vWXOb+59M3mPO4U6SNHjsiuBbvkl56/yM6TO+WjZR+ZrPS1R9ZKz0k9ZccTO7zwbAAAgKdolqme4gagX3jhBalcubLzup9//ln+/vtvGTRokCkHl14rVqxwPrY3aEmX7du3m5J1ANJGfzNo76RKlSqZDPTU/H7wJ82aNZN9+/Z5ZNsF+CufB9H1qNRXX30lv/zyi6k17m4qunYt1x2Khx9+2HQj5osZdk6tUh07prwxaExsjEzYNMFjpVwsZfOUlfwZ8svxmOPyf7P/jyA64Ef27NkjXbt2lStXrphGW5qFnhp/7f4viF7mehDdogeZ9cev1kbUWupaQ/XTDp/Ky81elmLDi8nOUztlz+k9UipXKY8+HwAA4F3aD8Wi5VY0qL5r1y755ptvZODAgfLMM89IVFRUujPRvRVEr1OnjjnfsGGD2f/JlCmTVx4HCGZa/lHp7PdADaCrxx9/3JyAYOazci4bN26UDh06SJMmTeTHH380zVRefvlls8FYtmyZaaKgjVW0eYIG2bUj8fnz5+WJJ56QsmXLyieffCLXrl3z1XABIzo6Wv7663pgS9+/KbVo3yI5fP6w5IzMKa3LtPbomFoWa2nON139X8kIAPZPw+zcubMcP35cateubaZqp6bZlgbBd5zcIRnCMkjzUs3j3abZHF26dDGf97vvvts0uVYFsxWUekXqOUvBAAAA/6TZmePGjTO/iZPK6v7888/NfoSWdRgyZIj06dMnXXXKrdrE9epd31/wNO35kitXLhNAT+q5AXBPmw3rLBTdzwfg38J9eZRdu4J//PHHJptu5syZ8tprr5mgQ/369U3pFquMy4MPPijfffedyerToHrbtm3Nkfhhw4b5ariAMX/+fFN6qHDhwqbxT0qN33i9lIvWMo6M8Gz9wR71epjz6GLR5jMCwF7aQEd3erURUKFCheT3339PddaYNglVNxa7UXJEJp55pd+dOkVSf1RrQP3UqVPmeqvZ8Jw9czzyXAAAgOfNmTPHlHp78skn3S6jM7HbtWtnssfHjBljrps0aZI5QJ8WO3fuNCUiMmfOLFWqVBFv0DFb2eg0EwTSRg+gde/ePWhmcly4cMHMpAWCUbgv6zxpEL1///6pqpGkQXXdidDmK40aNfLqGIG4dCaEZoBYWei6k5gS12KvyS+bfvF4KRdL6/KtJSwmTCS3yO9Lfvf4+gGkznPPPWfKPumPVA2ga3f61HKWcvmvHnpCOrVTy6CVLFlSduzYIXfddZeZndWyVMv/NSVlZgoAAH5Jf8uquPXP3dGZbBpwr1u3rjlQP2HC9RKRaa2HrolA3iwRoeNUq1ev9tpjAMFIE+J0Fkcweffdd035yddff93uoQCBHUR/4IEHzBG2tCpfvrxp1gb4imaSFi1a1Jw/+uijKb7fvD3z5PjF45I3S165qfRNnh9Xpigpcq2IuTxl0xSPrx9Aym3ZssVkiatRo0alabp0rCPWmYl+Uxn32wxtvK0l0LQJt87m0qZjjYo3kkwZMsnBcwdNORgAAOB/rFJsqckIt0o7aBmYtNDZ3L/99pu89NJL4k1WJjpBdCDlNPlFs8/LlCkjS5YskWBRokQJc/BPSzYDwchnQXSrvjTgzzSzUzPQlWaef/vtt2ZqorVzmBI/b/zZnHer3E0yZvBO1kejAtdnZaw9v9Yr6weQMpUqVZJp06bJe++9J3fccUea1vHPsX/MgbesGbNKg2INki2NNnLkSHN56tSpEhMd47zP3D3URQcAwJ8z0VMTRL/zzjtNw/Knn346TY+ZN29eueWWW0xDcm/SpqW6f2JlpANIWYknLd108uRJkzAaLBo2bGjO16xZI5cuXbJ7OEBgB9G16UirVq3M1I6FCxeaDuSAv9i/f7+0aNHCNPCxyiLoVKSKFSumeB1XY67KxM0TvVbKxXJn3evrPpnjpFy8ctFrjwMgea1bt5Znnnkmzff/a9f1Ui7NSzY3WeXJ0ayVsWPHmgyPbNmy/a+kC0F0AAD8jgaSdu/eneJyLhadEfvrr7/KbbfdJv5Me5utXbvWNEUFkDJWvz+t2KCzTYMpE117RGlyIrNTEIx8GkTPkiWL6TysdaY1WKlBdQ0+vP3227J06VIz7QOww5QpU0y9wMWLF5syCbt27UrTembvni0nL52UAlEFpHmp5uItXRp0kcJRhcUR4ZAF+xZ47XEAuK8zmtbthLsg+s1lXNdDdzfFO0eO6w1IqYsOAID/0r5g+v2smeG+CpZprWVNXPvrr+v7GAD8h850nzVrlmTIkEEGDhwowURn81vZ6BrjA4KNT4Po//77rzka9eGHH5ppZZGRkTJ79mx5+eWXpUmTJpI7d27TwFGnxevUFoIB8DZt5KFfXF26dDFTqXQ6ok49Klu2bJrWN37jeHN+e+XbJSI87T0AkqPNgTpVvD41c/qO6V57HACuPf7442Y78eOPP6ZrPVdirsj8vfNTHUS3aPmpLbO3SGR4pBy9cFQ2n7hecxVA8Dl37pzs3LnT7mEASEcpFw0wpZY2FH/jjTdMH5aUWrBggQwePNinzf30d9WhQ4d89nhAIDffVHfddZeUKlVKgk2DBtdLTRJERzDyaRBddxo02/epp54yzdE0qK5H4T744APp2LGjORI3ffp0ef755+XGG2+UPHnymDpugDfotEo9eKMHdZS+LxctWmSae6RF9LVo+XXzr+byndW8V8rF0q5cO3NOEB3wLf0xu3z5cvOdpSXK0mPZgWVy8epFM3ulWoFqqb7/gAED5OG+D0ueC3mc2egAgtM999xjyiboTBgAgUNnXuus17Q2+NSSca+++qqMHj06xffRhDSlCUK+oM8ve/bsZjsFwL3t27fLL7/8Yi5r3CsYxQ2ikxiLYOPTILqroHrt2rVNs5Tff//dZALrF/77778vVatWlTNnzsgff/xh5xARpGJjY6Vz587mh6jOgNCDOsOHDzezI9Jq5s6Zcib6jBTJXkSalGgi3lbwYkEJc4TJ1n+3yu5T1+ssAvA+rUeubr75ZilYsKBHSrncVPomCQ9L/Vdy7969zfmJFSfMOXXRgeDdb9F9ZWUd/AcQGLSEi87Cbtu2bZruryXc1Lhx41IckLKC6PXq1RNf0P0hzURPTbY8EIo0vqXf6ZpEWr16dQlGevCuR48eMmjQIEo2I+h4r95EKh04cEDmzp3rPO3du9dcr03TAE8LDw+XL7/80mSEjBkzxjTASK/R669nh3Sv0j1NwbDUypU5lzj2OURKikzbPk0erf+o1x8TCHX641W3GcoT2VbaR8EKoqdFo0aNTPPjrdu3ijQTmbdnnsQ6Yn2yDQLgO0ePHnVe1pmcAEKHBuD1N7HWOdem4la9YXeuXr1qylP6MhNd90XU4cOH5ezZs86+LQDi0wTSZs2aSaZMmSRYZc2a1Rz0A4KRbb+yjxw5YmrJPvjgg1K+fHkpWbKkyaibNGmSyULXbsW6k6DZ6YA3NG3aVObPn++RAPre03tl0uZJ5vL9te8XX6hUqZJk3JvRXJ64bqJPHhMIdTp7Rcu56M5h165d07Wus9FnZfmB5Wmuh27N6Orbt6/IQZHwa+Hy76V/5Z9j/6RrXAD8T+HChZ2Zpbod0iw2AP5Ps7O1nvnEiRPTnJEZd58jJYEprcF++fJlyZkzZ5r7PKWWPpZup6xGqgDcq1u3btBmoQPBzqdB9J9//lkeffRRqVy5shQtWlR69uxp6kFpkxVtJqo/CjRorlNcnn32Walfv76pOQt4ih6Y0Wa21lTItDT3ceXTvz+VGEeMySatUbCG+IJ+NqpGVjWXlxxeYhoUAvAuKwtdf8ymd6bU/D3zzXajXJ5yUjJXyTSv59577zWNjGP3XA+qURcdCE41atQwwbTTp09TMgEIEHrgXeuZ9+nTx8yETSurpMv48ePl2rVrSS5rHXDTQF16HjOt2ehsnwDXzcE1kTRU6MH+zZs3O0vRAcHCp0F07T78zTffmCPiWn9ap5nplFStR63N0Xz9RY/Q88orr5g6xlZHbE84F31ORqweYS4PaDhAfKlFpRYi50UuOy7Lkv1LfPrYQKjRg29Tp071WCkXqx76zaXTloVuKVCggHTp0kXkv9YIc/bMSffYAKTP8ePHTfDMkzJmzGgSTNS6des8um4A3qFZ4UqTyNKTvKO/X/Lly2e2LZoQlBRfl3KJO0tWEUQHEvv888+lVKlS8s4770go2L17t0mW7d69u0RHR9s9HMBjwu04IjVv3jxzREozzpcsWWLqtgHepjt0f/31l9mBvfPOOz223u/WfGfKMlTMW1HalWsnvlTvhnoiO69fnr5juk8fGwg1uu1Yv369/PTTT+bHbHr9tfuvdJVyicuUdNkTJ8M9liY+gB1031ZLFerBrSeeeMJj69VgmNZF1mQADaJpwy4A/k8zMZUGk9J7EO2OO+6Q7Nmzy759+5Jc9uOPP5Z//vlHHnnkEfElguiAa1pe6aOPPjLB5CJFikgoKFOmjGmqrCWtrAN7QDDwaRBds8615vkDDzxgfgDo1DZtqpA7d24TkNB6cQsXLiSoDq/44osvzHnnzp3NUWBP0EDVx8s/NpefbvC0z5v5mQyT/xLdtLkoAO/SUgp6EE5/zKbHoXOHZNPxTRImYdKydMt0j6tNmzayY+EOyRGZQ85En5G1R9ame50AUp8o8vjjj8uIESOcJRWs8nHpodO/V61aZWbC3HjjjSYbFUDgZaKn12uvvWaaDPfr1y/5ko9Vq3rs905K6UwZnXneunVrnz4u4O9GjRplPrvFixcPmYPgmnzUoEEDc3np0qV2DwfwmAjxIQ2Wax1ZqzGKBtU1K33u3LnmfPDgwebDljlzZvOBa9GihTlpA0ggvTXIRo4caS4/9thjHlvv71t/l92nd0ueLHmkV81e4mvlypWTAucKyDHHMVl/bL0JzBXJHhpHtwFfB8f0+8lTfRTm7L5ecqVO4Tpm+5Fe+oO5bOmy0qxkM/lj2x8yd89cqVukrgdGCiCldH92z549EhkZabLNNGFk//796W5gvnbt9YNiFSpUkKioKA+NFoAvg+jpzURX/n4ArWHDhuYE4H+0obD2/1MDBw5MdyJOINGY3pQpU0xfOiBY2FqAPG/evHLbbbfJZ599Zqac6dE5nSZ/3333mWlqQ4YMkZYt05+hB2gzQA2k6w9QT5RhsHy47ENz/nDdhyVrxqzia9pD4PDOw1K/2PUaqTN2zPD5GIBQMHbsWKlWrZr83//9n0fW56yH7oFSLnG1LHX9O3PGVrYFgK9Z2wdtIFinTp14Df48EUSvXbu2c2ad7svMmjUr3esG4N3g2datWz2WiW7RGS5ab9iVCRMmmIbj2nMMgP0mTpwoO3fulDx58lwvvxhCrINqZKIjmPhNF0/tMr59+3bZtm2bOR06dMjsIHhiGixCm76H9ECNevTRRz3WvHbFwRWyaN8iyRieUR6r77ns9tTS59Ou7PVa7NN3Uhcd8FYQXbPJDhw44JFtkreC6FumX69DumDvArkaQ2k0wFdOnTplShYqLVtoNfRbsWJFutdt1RK1guha2kUbC+osTgD+SwPdOitFZ1l7qrTKyZMnTVa71h8/ffp0otunT58uo0eP9si2J60HDvR5Hzt2zJbHB/yJ7vMPGzbMXNY+KaE2m6xevXomVqGz8g4ePGj3cIDADqLrF6xO69DuxG3btjWlXrRsizZM0h8GWne2W7du8sknn9g1RAQJneGgpRj0S6t3794eW+/wZcPN+V3V7rK9hIrV0HTWzllyLfaarWMBgo3WI7YyPnv27Jnu9W39d6scPHdQIjNESuPijcWTWlVpJXJJ5ErYFfn7wN8eXTcA98aNG2eCZTVq1JC6des6g+ieyES3gui1atUy540aNTLnS5YsSfe6AXiPBs71APzvv/9uyq55gmaz6rq0WZ914C4ua5tjbYN8TbPgtaHgDz/8YMvjA/5Ek0N1G6Cxrf79+0uoyZYtm1SvXt1cpqQLgoVPa6L//fffzhroixcvlgsXLjgzzXPlymUaPmr5Fj3pjxDAEwoVKmS+vPRLTN9nnnDg7AGZsGmCs6GonbS3QN8OfUU6ipySUyZDvmFx6hECnjJ+/HhzIE7r+mkfgvSystCblGgiWTJmEU+6teutkunPTHKl7BUZMWuENO7r2SA9gKRLuWgWuvZOiBtE133dtPZT0FJ0O3bscBlE1/3qq1evhlR9VSCQREREmDIunizlou6++2556aWXzMG7+++/33n9xYsXZePGjbYG0cuXL2/Ot2y5PjMOCGUVK1aUvXv3mpkhWso4FGlDZD3w16RJE7uHAgReEF0DEPojQn9M5MiRQzp06OAMmusPA081bAMS0veWfol5ymd/f2YyvluUaiG1C1+fXm0XzUg5cuiIyE4RqSYyfcd0guiAh3sqeCoLXXmrlIvShoaNCjeSeTJPpm2Z5vH1A0jsxIkTpsRCpkyZnNsJ7aGgjcw1K11LFqY10K3r1qC5nhcoUMBcp/szOoNTS8isX7/ePAaA0NGjRw8TRJ8zZ44cPnxYChcubK5ft26dme2tCURFixa1ZWzW7y2rFjwQ6vS7u2PHjhKqbrnlFruHAARuOZc2bdrI0KFDZfny5ebHhnbqHTBggKnxSAAd3rB69WqTleFJ56+cl69Xfe0XWejKmfF2PVGNuuiAB+mPQM0k1QyKO++8M93r04Nvc/fM9VoQXT3R5QlzfizymOw/tN8rjwHgf/Llyye7du0y2wor00yD5tqPRZuMpidTvHTp0rJo0SLZvHmz8zqtL2o166KkC+C/hgwZYuoha6Dbk3S7oNsATUz7+eefXZZyseu3tdZqV2SiA/6h/9T+0nxkczlx8YSt4/j888/NjN6aNWua5IDWrVubALvOrOnXr59pvgoEAp8G0bXRyXPPPedsMAB4k9YK1NkOxYoVM5lanjJq7Sg5ffm0lMtTTjpV6CT+wATR//ve0XIudn9JAsHUUFS1a9dO8ufPn+71rTq0Ss5Gn5VcmXNJ7ULemcXStVFXiYiOEMkkMvSHoV55DADx6X6tVffTGxIGxKiLDvg3DXC///77MmjQIJcNQNNLA09KS7r4Sz10VaFCBXOus2f0BIQqDRprzz9t9GuXBXsXyOcrPjfnPSf1lJjYGFt7TGmgXOMyS5culb/++sv0i/jxxx/l22+/NbPrLDqjRk+APyKSjaA1ceJE01Q0S5YsHqtFGOuIlY+Wf2QuP3XjUxIe5h8fIbOzfE4k85nM4hCHzNw50+4hAUHh5ptvNuUZ4tYc9UQpl1alW0mGcM80GXMVbKuZq6a5/OvaX73yGACuO3jwoDlo74o2GtVGWro/klaXL192eb0G0bXPizYrA+B/9u/fb/p/aV10T/RTSah79+5mlpz2RrD6Jpw9e9Yc0NOENbtERUVJiRIlzGVKuiCUabklnUnm6ZkoqTmQ98rcV5z/1/jAGwveELs89NBD5vWYMWOGaYqs5TK//vpr+fDDD+WNN96Q4sWLm+W09I1uR0gSgL/yaU30tAQhNBhgNWsCUkOnUVsbbE813fpj2x+y4+QOk0Xau1Zv8RdWxkn0xmiRRtfrot9d/XqGCoC0a9asmTl5yl+7/6uHXto7pVwsPRv1lFWzV0m51p7/4Q7gf3r37m2yqvTHoJYtjEsbmmvJhWzZssmtt96a6lmY2jRU+55oQGrhwoXxZsPodkkbizOzE/BPmzZtcjba9Ebz34IFC5oyqTVq1JBSpUqZ63799Vdn4N5OWhd93759JojeuDENzhF6NIBtBYGt8mu+Nnv3bJOBHpkhUoa0GCIvzH5BXp//ujQo1kDalWvn8/FodQA9peS10yQE3YZqJj/gb3z6DTty5EhnY9GUIoiOtFizZo354tKdSK2x5SnDlw035w/VfUiyZcom/kK/kLRpybFtx5xBdM2a95dMeQAiF65ckCX7l3i1HrqlQ6UOMmD2AFlxeIVcvnZZMkdk9urjAaFoz549Mnv2bLfNy3UWnGaKnz9/3gTUrVrBKaV10C9dumSy2Kxa6xbNQAXgv6w+BlWqVPHaYzz77LOJrtMMTrvdddddUr9+fVP7GAjV/QMtX6KxCDvKK2m87eU5L5vLD9/wsAxqMkj2nt4rX636ypR1Wf3gaimZq6T4I913mjZtWrxeMIA/8flhat2QaJ1qzdyxOokD3qhBpm6//XaPvc/WHF4j8/bMk4jwCOlfv7/4E/0Br8/1zPkz8mvEr3L84nEz3rpF6to9NCAgaXkGnVqo06U1y8sTFu1bJFdirkiJnCVMTwVvqpC3ghTOVlgOnz8sS/cvleYlm5OxCnjY999/b85vuukmKVmypMt93tq1a8vixYtlxYoVqQ6ir1271pzXqlUryc+vNlCnrAvgn5no3gyi+ytPlcADApXW/FZ16tQxpWV97c/tf8ryg8sla8asJoCuPmr3kaw8vFJWHlop3Sd0l4V9FkpkRKT4G2ubaW1DAX8T7uvApgYjJk+eLHfccYe89dZb5gidHp278cYb3Z6A1Dh58qSzyc5jjz3m8Sz07lW6S7EcyU9F8jX9fI0ZNUbalLs+nXzajml2DwkIWFqv780335S2bdt6rLGNVQ9dS7kkbBLoabr+lqVbmst3v3S3fPPNN159PCDU6HbBCqI/8MADbpezMtCshn+pnVVnBdFd0Rl3ZcqUkRYtWqR63QC8ywoAeaovkzvbt283Gen6va+9EmbOpC8SYDerlIvVBNyXdDa6VQu9f73+UihbIXNZA+YTuk+Q3Jlzy4pDK2TAjAHijwiiw9/5NIj+yCOPmEycdevWyaOPPmqaLXXr1k2KFi1qvvz5oMATdGq1Tn/WKYSeqsN36Nwh+fGfH83lAQ398wvH0q7s9RpnWtIFQNqMHTvWnPfo0cNjZRO0NqG6qcxN4gstS10Poh/JcsR0vQfgOX/99ZdpHJg7d27p2rWrV4LoVia6ZrO7UqRIEdm9e7cJtut+DwD/oSWcfJGJ/s8//8j7778fL/vVblpKQrePup1013gZCGZ2BtF/3fyrrD2yVrJnyi7PNX4u3m2lcpWSMd3GmMtfrPxCxq6//nvHn1gHHrVxuzZLBvyNLXO7q1evLh999JH5YPz000/mx8Hw4cPN9dp4QX/s69RUIC20/ILuuH755Zcey/b8/O/P5VrsNWlSooncUMT3dc1S6tq1a1Imtoy5vPTAUjl16ZTdQwICju6w6Ywpdc8993hknScunpA1R65nld5U2rdBdCkqsmr9KmdADkD6fffdd85tRObMmZMNomugW7+jUxOESi6IriVktGSdrjctQXoA3qONNfUz7O1M9Pbt28f7f926/lHKUQ8etG7dWnbu3Gn3UACfio2NNc1+8+XL5/OmojGxMfLqvFfN5acbPC15s8bvp6I6lO8grzS7nqn+4B8PysZjG8Wf5MqVyyQJKOqiwx/ZWiBVO5VrwFMbB+iOhtaf1fOHHnrIHLkG0qp8+fIe+9K6ePWiacKhBjTw7yx03WFtc2MbKRVVykzlsspHAEi5SZMmyeXLl039YnfBq9Sas3uOOa9eoLoUzFZQfKFM7jJSPEdxEU2kL/H/7J0FeFRXE4Y/4i6EQHAnOAQJ7u5aKBQo0kKhRYq1tNBSqhRK0UKhLV5ciru7kwDBgwcCxN34n5n77xYoEtnduzJvnzznkGzunaTZe8+dM/N9EJNuQdARERERWL9+fbq0f0uUKAFXV1euFM/IwyCZktF5aK38uiScRr7h+ao3QRCMA9JBpq5YOzs7vZ6HNvGoEE3DyybEakDXJo0HxOXLl9UORxAMCnmY0LNEaGgo8uUzrATs8gvLcenxJZZs+bTGp6993df1vkaTIk04z9FpZSdEJ0bDmGjVqhX7ven7+ikImcEoXMaozevgwYPYv38/Hj169w2PtAAA+y5JREFUxNU3b6rqEYQ36aHrmkXnFyEsPowTUm1928KYKVOmDI/5EpQbtuiiC0LmpVyowlRX3SxaPfQijWEoKPaGhRsq/ygELFmyRCQfBEFHVVJU+T1x4sTX6pU//zA9b948XuMWK1YsQ5Vs77//Pjp16vTGh0hJoguCsGDBAk7af/HFFzAWJIkuWDr69j96GeqaH79/PM9H1RwFDweP177W2soaSzsuZZ+3K0+voN+GfpyDMxbIy2nVqlU6K2YSBLNJolNr6+DBg7ldg3RnAwICMGLECK7UadpUMUcUhPRC7YI+Pj5sWpuRluk3QdXcU49N5fkQ/yF8wzFm6taty2PSxSStLrox3RAFwdh58OAB+yoQ3bt319lx1UiiPy/pYudrx1WtVBkjCELWocrP0aNf1Bp9HV27duX7MyW50kvRokU5MbZsmeLHkp4kutzvBcE4INnS/v3749ChQwY5X6VKlRAdHY3vv/8exoKvry+PkkQXLI0nT56ocj+mwr/rYdfh7eSNwdUGv/X13s7eWNl5JWysbLDq0ipMOz7NIHEKgqljpUal8PTp03lXiXQif//9d9SuXZvbYu/du8dVPZqbriBkBNJAT05O5kWkjY2NTo659dpW3p11s3dDX783t2wbA/Xq1eMxaHsQnGydEBITgoBHAWqHJQgmA23iUis0mRIXLlxYJ8ekBW1wRDAvUusWVDa6DEWDwkoSPTlnMmD/r46zIAjmAa2n7e3t+aH9+vXraocjCAKADRs2cAeKIfXAdWWCrutK9CtXrqgdiiAYDOoiI1nZ/Pnzs/G3oUhMScQ3+7/h+ee1P4eLnUu6vq9G/hqY0nQKz0ftHIXDdw7DWKCNCPJQFASLTqJThXDevHnx6aefIjExET/99BM7d1MCvW3btkZ38xdMBzKi1ej9fvzxxzo5ZnxyPN9MiA8rfQhXe1cYO6S96O7ujujwaFT2rKytRhcEIX00atSIq9HfVv2ZEZYELOGxfqH66V7U6ooC7gVYiuoZnqH2e7UxcOBAg55fEMyN4cOHs9RTYGBgur+HuuNITonWvyRhmB4uXbrEhQFvgxLo7733HgYNGiTraEEwEuj9q/EqslSer0SXLhnBUqC/d+r8DA8PN6ge+p9n/8SdyDvI7ZIbA6tkbK3/if8neLfsuywH02V1F4TGhkJt6Hfo5ubGmxGxsbFqhyMIL6Cbct10snr1ajZIatOmDbef0g114cKFb/2+9LbLCpbL8uXL+WJLlaMvu9Rnls93fY6gJ0HwcfHBmNpjYArQAzR1dmzevBk5InNoddE/q/2Z2qEJgslA9ylatOlKEmrBuQU871OxD9SAJF1uht9Eta7V0LlpZ1ViEARz2rCPiop6q6Hoy/fmIUOG8EN1z549WXrhTVBVOXmckD/Q06dP4eTk9MbXi2mwIBgP1HVNHl/PV2NbIlSNS5rQkZGR/PsgyU1BMHc0/iT+/v78PGEIqPDvuwPf8Xxs3bFwtE2/dBxB79N5bebh3MNzuPzkMrqt6YadPXfCKpuVqt4ztAaKiYnhbpa3rZsEwWyT6ARV1VCL28aNG9O1K01vakmiC29j2zal2rpPnz46qcTaeWMnpp+YzvP57ebDy0l9p/uMSLpQEj3qdBRQFjh89zCiEqNYkkYQhNdDUlAuLi46NQLaG7wXtyNvw93eHR1KdoBaSXSqUNl7a68q5xcEc2HNmjWcQKcN+/r166f7++iaQhKGO3fuZEPStz0Mnjt3jkeqYntbAl0QBOOThSNoM97V1fi7WPUFJcBIo52S5xnxgxAEc0iia/xKDMGcU3NYwpW6T/v59cvUMahTdk2XNfCf5489wXuw8cpGtCvZDmpCnTwHDhzgzh5JogsWm0SfP3++IU8nWBBkUktUr149y8cKiw9D73968/zjqh+jebHmMCVatmzJu7ZkznvnzB1cC7uG3Td3o0MpdRJ4gmAqkCQCXUvmzJmDVq1a6eSYf51TNMi7le2W4coQXeuinw05i2Pnj2HXxl345JNPuMpDEIT0o6n4pg17K6uMVWhpkugnT55kw8H0rGkqVqyY7uMnJCTgzJkzKF++PG8GCoKgDiLl8i9jxphGJ68gmGoSPSYpBj8e+pHnX9X9CvY29pk+Vmnv0hjsPxg/Hf4Jk45MUj2JXqpUKU6iazYmBcEik+jvv/++IU8nWAjUJqgx0yKDraxA3REDNw/Eg+gHKOFVAj83+RmmBrWAf/ONYizSIqoFrp24xrrokkQXhDd3Se3bt4+r0XXVchyREIG1QWt5rqYxcR7XPPD18mWT5C4ju+DurrsoVKgQ6zoLgpA+aJ2xf/9+rirv3VvZaM9oEp2gSvS3oalEz8iapnLlypy8o868Zs2aZTg+QRB0gybhI0l0QbAsSIpNY6Sri8K+9DDj+Aw8jnuMYtmLoVeFXlk+3pBqQzDl2BTuZD969ygbj6qF5hqq2ZgUBGNBPaEjQdAR9EA7ffp0DB06FDlyKDrgmWXZhWVYeXElrLNZY0mHJXCyNe02ak0VPemii6mPILye48ePcwLdy8sry5txGpZfWI6ElASU8S6DKnmUBJpakKQLkbtmbq0shSAIGe+mpAR1ZjwTqlatyuOFCxcQHx+v80p0TZJeUwUnCII6hIaGaqsoLR1aV1GBAslMCoK5c+zYMa0XAj1P6JvIhEiuGCfG1xsPW+usa7Dnds2NHuWUIhvNsdVCkugCLD2JrgtXXXHmFV4FOTcPHjwYU6dOzdJxyNF60OZBPP+q3leomld54DVFSLN13bp1uHPgDhxsHHA36i4uPZYbkCC8DpJZIBo3bpxhmYbX8dfZv7RV6LrUWc+KpMtjt8c8UrWq3FMFIX3QJvSyZcu0Ui6ZgfTNc+bMiZSUFJw/f/6N5qWaSraMbOhpWscliS4I6rJkyRJeh3fr1g2WDm0INmjQgCXkBMHcKVCgABf1kYG4Ifj12K8ITwhnGZZ3y76rs+OOrDmSx/WX1+Pq06tQO4l+48YNJCYmqhaHIKiWRCcTpkmTJmXqof3o0aNo3rw5pkyZopfYBCHtWRp6r++NyMRIVMtbDV/U+QKmDFW6dezYEePGjEO9gvX4cyTpIgjCm5PoTZo00cnxLoRewMkHJ2FjZYMe5dWXTWlatClsrWwRHBOMvBXysn7y1q1b1Q5LEExG7omS59WqVcu0X4LGXJQICAh47esCAwORlpaGXLlyIXdupXMkI0l0qoRLTU3NVIyCIOgGMhQVbwKlIpe4ffv2WztwBMHUIU8SKur74gv95xGexj3FlKNKbuyb+t/A2spaZ8cu5V0KrUu0xjM8055DDWgN1KVLF4waNUqS6IJlJtG7d++OsWPHstYsaaNv3LgRjx8rFXEvQ1U6p0+fxk8//YRy5cqhdu3auH//Plq0aGGocAUTgiqu6YGU/m4yy7Rj07D31l6Wb1ncYTEnvkwZelB3dHTk95ifq59W0kUQhFf7Kpw4cUKnSfT5ZxXphzYl2iCnc06ojYeDBxoVacTzwi0L87h2raLXLgjCm7Gzs8O4ceM4Qe3s7Jzp41AxCK1n32QsSuvkb7/9NsOVm1SxRZ15ZCxOG+mCIAhq4+3tzSbm1M1z7do1tcMRBLOBpFaik6JR0aciOpbqqPPjj6o5iscF5xYgNFaRqDI0VHywYsUK/Pjjj7y+EQSLS6LTrhwt6jt06ICVK1eiffv2/KBAbS+kE0mJizp16rApIr1J/P39eRePbrrz5s3j1ldNBY8gaKCqhnfeeQcVKlTAw4cPM10xOma34h4/pekUFPcqDnN44NdUpdnfUVy6D945yA7egiC8yN69e7lys0SJEnxPyipJqUlYHLCY530qZk76QR90KtWJx8feygb2pk2bpLJDEAyIr68v8uTJ88bXFCxYkItO6CMjWFtba43MRNJFENSB/EYaNmyIGTNmqB2KUUBJME01+uXLl9UORxD0Bm2Qk/6/IaQSSQt91slZPJ9QfwKssuk+pVenQB345/VHYmoiZp6YqfPjC4IpY1Bj0eLFi2PRokV8kZk2bRratm3LD/BUdb57924cPnwY169fZyMW0pPav38/J9779u2rM41awbygtmdKflGlQ968eTP8/YkpieixtgffIFoVb4X+lV9fHWZq1K1bl8egw0Eo7FGYE3t7g/eqHZYgGB3FihXDp59+yvcaXbD56mY8jnsMHxcftChuPB1U7Xzb8UL7StQV5PRVquMvXryodliCYNSQDAEVf1CFt7FTo0YNHiWJLgjqcPLkSd6Yl4Txv2iS6BqvB0EwR6i7k/T/u3btqvdzkecSFcaV8S7Dsiv62gDTVKNTwj42SR0fJSqoDQkJkQ47wahQJTOdPXt2blMlGY5Hjx5xIv3BgwcIDw/XJtWp5ZUq0wXhbYY1GvOtzBj3fb3va5x/dB45nHLgj7Z/qG7+p0vq1VO00A/sP4DmxZrzXHTRBeG/lC1blu85n332mU6ON/+cIuXSq3wvo5KG8nb2Rt2CyuZarx97ITQ0FJUqVVI7LEEwepNAeigmWUJdMH36dLRu3VorIfU8VBRAa+Nbt27xg2NGadeuHSZPnsybgoIgGJ6goKAXDPGEf5PosrEgmDOazWvNZra+SE1LxYwTSqfLkGpD9Jq76FCyA4p6FkVYfJj22cbQbN++nTv43n1Xd8apgpBVjKK829bWlqVd3N3d1Q5FMDHOnDnDY2YSQQduH8DPh3/m+dzWc7lq1JwgAzR7e3uWuSnvVF6ri56ZB3NBENJHSHQItlzbwvM+fsYj5fKypMuR8CNwcHBQOxxBMAl5Bk2CWhdQ5+XmzZtfWS1OlZpkCk4be5m5V1NBwYgRI2RzTBBU4tKlSzxKEv1fJIkuWAKae7pGTlVfbLq6CcERwcjumB09yvfQ67nIrHR4jeE8J4PRlLTM+89l9fpx9erVLPnfCYLZJdEFQReV6BkhKjEKvdb1Ytdp0izuUKoDzA1KkFEinch2KxvsrO34pnstTIx9BEEDVYNSUishIUEnxyMt9NRnqaiRrwZK5lAWfsYEVZUQR+4ewYPoB5yo09XPLgjmRnBwMK8zSFJQV0l08gEiTp069Z+vnTt3jkfyeREZQ0EwLeheevPmTZ5LEv1f6Fnk999/F514wWy5d+8e7ty5w94kmnu8vph2fBqPH1b6EE62TtA3vSv25o59yiGsDVoLQ0NeVU5OTkhOTsaNGzcMfn5BeBWyQhdMFrqYBgQE8DyjVVdDtw3F7cjbrBU+rblyMzJHyNCXbjj9e/dngxBi67WtaoclCEYDSR80btwYEydOzPKxKCGtaXfs66cbfXVdk9ctL6rnU8wHxy8fz0aHw4crVSaCIPxX41Qjj5YjRw6dHLNKlSpa7eTXFQZUrFgx08en7jOSoNHELgiCYaBKybS0NHh6eiJnTsV3RAB3m/fv31/vFbqCoBZHjx7VboC7uLjo7TwBjwKw99ZeWGezxsdVP4YhoES95lzUwW/ojnYqKCC/xOc7fQRBbSSJLpgs1BZIGvqurq4oUqRIur9vXdA6LDi3ANmQDYs6LIKrvSvMFarQp98N6aW1KKYYHG67IbrogqDRH6YqdKJJkyZZPt6xe8dw+cllONo4okuZLjBWNJIuJ2JO4Nq1a6zBTA/+giC8WsqlUyflPaMLKleurE24RUZG6qS77nl27NiBnj174pdffslipIIgZFbKxZw8lgRBMA4pl+nHp/PYsVRH5HfPD0NBSXQHGwecDjmNfbf2wdBokugazwlBUBtJogsmS+HChbFlyxZuD0xv2/OTuCf4aPNHPB9dazRqF6gNS0FjLko3v/jkeLXDEQTVoYRVWFgY3Nzc4O/vn+Xj/XX2Lx7fKfMO3OzdYKzQ4pu4EH0BLjlduHJVU0UjCILC/fv3te+LDh10J/nm7e2NggULvuDrQlB1l0bOJStJdM1DPMnFUKGBIAiGIS4uDrly5RIpl9dsMPz55584fPiw2qEIgkkm0SmHsTRwKc+HVR8GQ+Lt7M3yt8SkI5NgaDTXVKlEF4wFSaILJgu1S7Vo0QLvv/9+ur9n2LZhCI0NRWnv0vim/jewBFavXs0JgIv7LiK/W34kpCRg/+39aoclCKqzc+dOHhs0aAAbG5ssHSs2KRYrLq7ged+KxinloqGIZxFU9KnI2u1lOpV5oeJWEASFAwcOaB+K8+TJo9Njv0oXnTRVnz59ypqqZcoo78vMULRoUU7UJyUlvZCkFwRBv/Tt25c3pWfNmqV2KEbHokWL8MEHH2D58uVqhyIIOmf27NmYNm0a6tevr7dzzD09l5/hq+Spwr5LhoYMRqmLf+v1rbgQekGVJLpUogvGgiTRBYth45WNvINrlc0K89vNh72NPSyB06dPY/369di6dau2Gl100QXh3yR606ZNs3ysNUFrEJ0UzQnqugXrwtjRSLokF0vmkfSTDa1zKAjGTLdu3dhYdPp0pX1al5AuOhllRUdHaz+nqUKnh0UyBs8sJCOhqYbTVMcJgmA4bG1t1Q7B6CD/FY0UpyCYG+TNNmTIEOTOnVsvx09OTcask8rm3NBqQ1WRiyqWvZi2k3XykckG//1++umnGDbMsBX4gmB0SXTSaf74Y8MYIgjmB+n3jh8/nhM/VG31NiISIrQyLiNqjIB/3qxLN5gKZIhG7N+/X3TRBeG5tmtNW7Eu9NA1Ui7U7mgKWqiaJHpgbCAcPRxx+/ZtqVoVhJcoVKiQVsNclwwePBhRUVGYMGGC9nPVqlXjKs2xY8e+9vtooyvt2dv9CySJLgiCMVGyZEkeJYkuCBln9aXVeBD9AD4uPqp6Lo2qOYrHvwP/xv2o+wY7b/78+TFlyhT2exEEi06iP3nyhHVoBSEz3LhxA9988w3ee++9dOmhj9g+gm8+JbxKWIyMi4ZatWpxezhV1Pna+cLGygZXn17FzfCbaocmCKpx6NAh3oAjbeJixYpl6Vg3wm6wRBK1Ob5fIf3yUmpSyrsUSuYoieS0ZJTtVJY/J5IugqCg764MqkKn+/Lz5MyZE127dkWXLq9/QB60eRC8fvbCbyd/e2OMzyfRpcNEEPQPJYdpPfHOO++oHYpRV6KTbNXzHTiCYOrMnDkT8+fPx+PHj/V2jmnHp/E4sMpA2FnbQS2q5auGOgXq8LODJiZBsERUS6KXL18eV69eVev0ghkYAhLlypV7q5bx9uvb8de5vzjB9Vfbv+Bo6whLwtXVldugiDNHz6BW/lo8F0kXwZJp3Lgxyyf8/vvvWa4cX3BuAY9NizZFfvf8MBU01eg2ZW1Yq5Q8JgRBADp16oTWrVvj/Pnzej9XepPcx+8dx5zTc7iz7uMtH6PNsjZ4FPPola+l6nmSlCB95jt37ug4YkEQXoYM7+i9Rl1dwn/Jnj07bxQS8vwvmAt0/6bOePJDuHlTP8VpdO8/fv84J88HVB4AtdFUo/9++ndEJUYZ7LwRERFcAGWIdZkgGG0S/bPPPsPGjRuxd+9etUIQTBiN7IAmOfw66OL+4cYPeT7YfzBqFVASyJbG85IuGl10kXQRLBnqYKlQoQKaNWuWpeOkpqViwXklia5xrje1JPq52HOYOmsq6tSpo3ZIgqA69KC2adMmbN68Gfb29no1IitVqhQmTpyIyMhITJo0Cbt3737tg/qY3WN4Xil3Jdhb22Pztc0oP6c8tlzb8p/XOzo6sg/KgwcPuDqW2LVrFzZs2IDkZMUHQRAE3SbRCXpPC2+uRr9y5YraoQiCTrh27RobgtNawc/PTy/n0FR8dyvbDblcckFtWpVoxZ2slGMhs1NDQf409JwydepUg51TEIwuiR4eHs5mbvRBrW8///wzFi5cyO7dL38Iwusq0d+WRP9s52e4G3UXhT0K44dGP8BSeZUu+p7gPezyLQhC5tkdvBv3ou7B08ET7Uq2gylR0aciCnkUQnxKPLZdl001QSAogU6JZjL41Oj46oP4+HiWgDh+/DivaUaPHs0dIa9i181d2HtrL1eireu6Dic/PImyOcsiNDYUrf5uhU+2fIL45PgXvqdRo0YvmJxRtVy7du2QN29eNueiYgSRehEE3RAUFMQjXTeEVyO66IK5ofEdqVq1KuzsdC+zQrrjqy6t0hqKGgNW2awwssZInk89NhVJqW/3ptMFmg1KzbVWECwyid67d2+ukklNTWUd1s8//xx9+vR54YNeQ6MgPA899GmS6G/a9d0bvJdbn4k/2/4JZztnWCq1a9eGg4MDt1IWdyuO3C65EZcch0N3DqkdmiAYnHXr1rE5Dd2DdGUo+l659+Bg4wBTgmRsNNXoa4LWcDLvl19+UTssQVAVMiwnOnbsqNfzVKlShcdTp05p1zQVK1b8z+vISFRThT6oyiAUcC+AcrnKcSJd81A96+QsVJlXBecfvrrNmdbaZFyaK1cu1m2dNm0aS76QtOLkyZMREhKix59UEMwfqUR/O4MGDeJuGzJWFgRzSqLXqFFDL8effWo2UtJSULdgXfjl1k+le2boUb4Hm5zej76P5ReWG+Scmg1KutZKAYCgNm8Wk9YjZMAgCJnh/v37/BBIplykif4qYpNi8cFGpaLro8ofoUHhBrBkPDw8uEVd05pOki7zz81nXfTGRRqrHZ4gGDxJtmTJEuTLly9LOuBh8WFYd3kdz/v4meaGLyXRfzn6CzZd3YQV3VcgLSkN7du3R9GiRdUOTRAMTmxsLLZt26bVRdcnVARAG1lktKc556sKA9ZcWoPTIafhYueCL+p8of08bdpNbT6Vu8t6/9Mblx5fgv8f/vix0Y8YVn0YV4tpoPUSbZCRdMz27du5y/Off/7BhQsXMGrUKJZWJPkaQRAyDm1SaaqrpRL99bxqk1AQzCGJrjHz1iXULU6648ZUha7B3sYeQ/yH4Is9X2DSkUnoWb5nlv2l3kbx4sV5LUPGxJQLomc4QbC4JPr777+v1qkFE0dTsUULVaqufhVf7vkSN8NvIr9bfkxsMtHAERonz2u7apLopIv+C6TyVLAcqHqBtIGJJk2aZOlYywKXcRtjhVwV4OdjPBUiGaFavmrI45oHD6IfoELbCji/+jxvMlBiTRAsDepOIZmVIkWKsGeCvk2/Sd6AWpN37NjxyiQTVaCN3TuW59Q+7e3s/Z/jNCvWDAEfBXDhwIYrGzBixwhsvb4VC9sv5Pf285ARe6tWrfiDZBVXrlzJCfVevXrp9WcVBHOGDAUTEhJ4nV24cGG1wxEEwQBQcdrFixf1Von+d+DfeBL3BAXdC6Kdr/HJRX5U5SP8cOgHXAi9gH239um9YJHkciiRThuWtG6SJLpgkXIugpBZ6OGPTGnmzZv3yq8fvnMY049P5/m8NvPgZu9m4AiNG9rBbVKkCVepUeXancg7aockCAaDKi8fPnzIxnu1amXNaPivc4qUS1+/vnqvwNAXdB3oULIDz52qOPFIEmuCYIlo/vZJysUQ72mNpIuGlyvRF5xbgKtPryKHUw4MrzH8tceh5Pr6rusxp9UcONo4soZ6udnlsC5I6ZR5FZ6enhgwYAAOHz6MLl266OCnEQTLJE+ePOzvVaZMGa6UFF7PsmXL8MUXX4iElGDynDt3jkfq3CSpNF0X/JDeOPGJ/yewtjK+64qnoyd6lOvBc418riElXQTBopPopE1Li3fSZSxWrJj287TLRGaj1K4hCM9jZWWFEiVKsL7ny5CxVt8NffEMz9CnYh+u0BIUkpKS+IGdpF2So5NRI5+ya/6mh2xBMDd27typNdt9vjsjowQ+CsSZkDOwtbJF93LdYcpodNEvP7vMqwLSRieJCUGwNMiMs27duujcubNBzkdmZBq8vLxeqKyi9cz4feN5/mWdL+Fq7/rGY1HSf0CVATgz4Awq5a7EclMdV3bE7pu73xoHfS8ltagqPS0tLUs/kyBYGs7OzpwcXrhwodqhGD0//PADfvzxR20CUhBMlfr167O87OrVq3V+bKrsDgwNhJOtE/r59YOxQtXoxNqgtXgY81Dv59N4TkgSXbDYJDot0rt27coPKlT5Q61wwcHBL1TIfPnll7ygF4T08vW+r7lqi4wzf2kqMiUvt0FRIp3eewcOHEDXMl3580sDl6odmiAYPImeVSkXzfumVYlWXCVqytQpWAdejl4ITwxHmVZltBvcgmBpfPDBB9i/f/8rN+n1gb+/v3ZOUi7PV7//dvI3Nu0iI1HNg2p6KJmjJI72O8pmx8SonaPYnPRNpKSksLQMSS2ePn06Uz+LIFga5COg2XSiCvSyZcuqHZLRQ9cZQqMhLwimTI4cOfSi9T/t+DQe36/wPld8GysVfCqger7qLD3311mlO1eftGvXDrNmzeIuOkGwyCT6r7/+ilWrVvGbgHQZR44c+cLXqS2mTp06YnQkvMDTp0/RrVs3TJ48+T/OzCfun2CDPGJO6zlGfdNRC6qwIyhJ0LVsV1hns8bJBydx5ckVtUMTBL1DmqX0t5/VJDolpJZdWMZzTaLKlLGxskH7ku157lHdg0eRdBEE/UPJetJgP3XqFFdoaohMiGStUWJ8vfFsIpoR7Kzt8GuzX+Fq54qzD89i+YXlb3w9aaU3bqyYjG/ZsiVTP4sgWBL0HNK6dWv06dPnP88jwuuRJLogvBnydCN/E2JItSEwdgZWGcjj3NNzkZqWqvfuvUGDBqFSpUp6PY8gGG0SfcGCBfxG+O233+Dm5vZK7UmSd3m+Ol0Qzpw5g+XLl2Pu3Lkv/M0kpiSizz99OLlF0gptfduqGqexQhIWBCUSczrn1MrdSDW6YAk8ePCApaBy586dpYqxI3ePsJcAJahaFW8Fc0Aj6XLN5hqQDbh69Son9wTBEkhOTsZff/3FrdmGhgzSK1eu/EJV+uQjk1mOpVSOUuhZoWemjks66Z/X/lxrtk7rpDfRsmVLrbmqIAiv548//tCab5NGr6l6oqiBr68vj+RtJQim7K9E8m8TJ07U+bFnnpjJsrTNijbjzjJj553S78DTwRO3I29j+43taocjCOadRL9+/TpXmr8J0oekyuOMsmTJEq5wJ/1n0rylxQ0l7TMKtejNmDED5cqVYxM6b29vroIm6RlBHc6ePftK861fj/3KJpmUGJ7eXDEVFV5fiR4YGIiwsDCtIciSgCVSSSOYPUWKFGEdzmvXrmXpoffvwL957FiqIxxtHWEONCzckE2YQ+ND8ce2P3D37l2+7wmCJUAby/369UOFChVU1wR/FPOI1zTE9w2/506RzDKs+jDkcc2DWxG3MPvU7De+tnnz5jyeOHFClc0EQTAFqIu6f//+PP/ss8/4Q0g/UokumAMHDx7Enj17+EOXRCdG48+zf/J8aLWhMAXoOah3xd48f9s6QxcEBASw/wTlEgXB4pLo9HAeGRn5xtfcvn2bTRAzytixY7lSmb6fKg4zCyXihwwZwslFGukBY+3atVxBT0kYQZ1KdOL5Np7k1GTMODGD5z83/hleTl6qxWfskEySZgFLC4B2JdvBxc4FwRHBOHrvqNrhCYLBTMAyC11vVl5caTZSLhrsbezRpkQbnl96don1XQXBUtDIF5E8A5mXq8n3B79HbHIs/PP6a2WWMguZkn1T/xuef3vgW0QkRLz2tXnz5uVNBFrz7tixI0vnFQRzZPv27Xjvvff4PUKJdDLIFDJXif7o0SOWcxUEU+TIkSM81qxZU6fHXXh+IaISo+Dr5avtFjcFBlRWNMo3X92M2xG39Xou8kzs3bu3rFMEVVHtSYEqiWkxQhq1r4KqZLdt24bq1atnqs3u1q1bXEnz0UfpN2N6nr179/JxqHKXErfUrrN48WKsX7+eY/vkk08ydVxB95Xo6y+vx4PoB8jlnAvdynVTMTrTk3ShB2yNjMPi84tVjkwQ9Afda+Li4rJ8nJ03d+Jp/FO+3jQo3ADmhOZasCZojXSmCBZDamqq1ki3Y8eOqsYSHB6MOafm8PzHRj/qRCaCKsRIFobkYSYeenPreYsWLXgUXXRB+G/SjK4PJP3UtWtXliMVGZeM4+rqyht2hEi6CKYK+ZgQujQhj02KZSk3jRa6VTZ1N/Qzgm8OX+5oJRmaP878oddzkYQWcenSJb2eRxDehGrvTqrsvnfvHjp16sTj89y4cQMdOnTgSnV6XUYhc6SCBQtmKb558+bx+O2338LOzu6FB4z69evz7tedO3eydA4hY0RFRbFO78tJ9JknZ2p3QclMS3gzVGnXvXt3rbRLj/KKpMuKiyuQlJqkcnSCoB82bNgAT0/PLDu6a/wDupbpmiWZBWOEql5oY410DVt92Ir14+U+J5g7R48e5apId3d3NGzYUNVYvt73NZLTktGkSBN+INUFdJ36qfFPPJ96fCruRb245n6VLvrOnTtVl7URBGOCrhGUQKeu5EWLFkm3VhagTUsqdnveB0IQTAW6N2qkfTXd3bq6/9P6u4B7Abxf4X2YGh9VVgpX/zj7B3ft6otSpUrxGBQUpLdzCMLbUC0D0K5dO9aRowpvSnhr2utz5szJOuhUBTdu3DjVHmj27dvHMdWqVes/X2vWrBl/nSp5e/Z8veFTYmIifzyfBCZoEUYfQsY4ffo0j/ny5WOZH/odBoQG4MDtA/yQ2LdCX/m9pgP6+6UPgn5ftfPWRm6X3AiJCcHGyxvRtoSYshoazd+t/P3qD+p8SkpKYimxzP6eqUqEOl+ILqW6mN3/L1vYolmRZlh3ZR1OxZ7C42uPcfLkySzJoglZR64P+tc41mwwU2WpWr/nC6EX2J+EmFBvgk7jaF64OWrlq4XD9w5j3J5xmNtq7itfR15CpDXapEkTrtCnD8F4kWuD4aDrA20uVaxYUdXrhDlAv0NCrjH6Q64N+oM8g+h5wsbGhmVSdfE7PhNyRuuFMrP5TNhlszO5/3cti7bkLt2HMQ+x5uIabXerrqECH00luqn9jowF+b1lHVXL6EhLjpLkM2fOxPHjx7ndnnb3aJefKtA1iT5DExsbi5CQEJQtW/aVlQbFixfn8W266PTzffONokX5slSMk5OTDiO2DGjjgv5/5MmTR9tq/Nvd33is5lYN5w6eA/0nZBx/R3/8E/MPpuycApvr5lVda0rQA5qge2hTduPGjTx3c3PLtFTBgfADiEuOQy67XHh87jG2nDc/yYPCCYV5jC0Qq00w0oOCoD5yfdDPtWHZsmU8z58/v6oyJj/c/IFboWu618Sjs4+w5axuY2nj2AaHcRiLzi9CpYRKKOBY4JWvo4p8MhcVTAe5NuiHJ0+e8DXC29v7hWcRQTAV5NqgewIDA3mk64IudLlTnqVg1NVRSHuWhrqedZF2JQ1brpjm80Vdl7pYFbsKP+78EY7Bjno5R3x8PI8PHz7EypUr4eLiopfzmDO6kDe1dFR/MqZqF/owJjSGp/Qg8SooCfP8617HmDFjMHz48Bcq0ekhrUGDBvDyEvPLjEJtxhMmTEBERAR8fHwQHh+O7jO789e+bf0taheorXaIJgNtVlEbVHR0NPsO5HmUB//8+Q9Ox5xGzYY14eGQcUNfIWs7wrTQpWuhra2t2uGYHbThSR4ZJM1F1+TMGovOXalUb/at2het6rWCOVI7sTZmTp2JOKc4wBuIiYnRSjwI6iDXB/1x+fJl7n6kwgZas1GnihqQsfeJcydgnc0av7/7O5uK6ZqWaIlja45h/ZX12Ja6DetbKl01guki1wb98eDBAy7momTD1q1btdWPgm6kcX7//Xd+Lp48WdGAFnSLXBv0+7sleUgqttTF+njS0UkIjg9Gdsfs+LvX38jpnBOmSpnIMlg9azUCYgJQrFoxlPDSz3WT8mnUEUBjjRo19HIOc0ajjiGYcBLdnLG3t+ePl6GbmdzQMgf93siUhlhyaglXhZbPVR71i9QXg58M8Pfff+O9995jPULqAqmStwrKeJfBxccXseHaBvSr1E/tEC0SuTboB03lWM2aNVkKKjM8jXuKHTeVipOeFXqa7f8nL1sv1mPefG0zUAo4d+6c2f6spoZcH3RPuXLluJrp/Pnz2gIJQ8PyhfvH8bxPxT4o61NWb+cibfSNVzdiy/UtOPrgKOoWrPvKeH7++WeuyqcqL2pXF4wbuTbolosXL7IHFiVpSEKSKh3l96s76Brz3XffcZcbJdHld6s/5Nqgezp37swfpKCQ1d/t9bDr+Pbgtzz/tdmvyOuhmO6aKsVyFEPL4i35GWJ+wHxMbjpZb+aidH0mrzyNx5uQfuSaYMLGokWLFsWHH36IpUuX4v79+zAmNBXor6s01+zevK5SXdA/1PI06+Qsnn9S9RNJoGeQ2rVra3XmqRqdfn8ag9HFAYtVjk4Q9NPOmpWup9WXViMlLQV+Pn4o5a2Y2pgrWh3DcuD7c2hoqNohCYLeoJZsMqRXi+03trO3i721Pb6u/7Vez+Wbwxf9K/fn+eidozmZ9TK0HqDk+YEDB9hLQhAsCZLcJD8sStD4+vqy/xV5dwm6I2/evNz9k5KSguDgYLXDEYRM4eDgkKXvp/tv/439kZCSwMUrPcu/3mfPlPioimIwOv/cfMQnK9IruoY6B7dt24b27dvr5fiCYLRJdDIS+fPPP9GrVy8UKFCA2+QGDBiA5cuXc1WQmlCrPxmp0Y39VYYnGi10jTa6oH/Onj2LqlWrshktse36NtwMv8myI93LKZIuQvqh91yhQoX47/vo0aP8uffKvcfj/tv7cSfyjsoRCoJuoIe0PXv2ZDmJvjRwKY+WcL3pVLoTnG2dWc4FhZTrryAIeqpC36tUoX/i/wnyueXT+zm/rvc1v7+P3z+ONUFrXvkaTYs6yVgIgiV1aZKECxVRUbHJ4cOHUaRIEbXDMjusrKx4g0IjqSUIlgglmffe2gsnWyf83vp3sykIbFGsBQq4F0BYfBgXIOmDevXq8bU6R44cejm+IBhtEv3WrVu4efMm/vjjD3Tv3p1NAubNm8dz2qEuVaoUBg0axNUwakBvTjIYpQXUy2gqc6R9xHCcPHkSp06d0iZzZp6YyWPfin3hbJc5fWNLh6QtNNXoRH73/KhfqD7P/w78W9XYBEFXULvlF198wRu2lSpVytQxaFPp4J2DyIZseLfsuzB33OzdtBUx7k3ceSNCEMyNhQsXskcNJc7UYt+tfTj14BQcbRzxWS2lSEDf5HLJhZE1R/J8zO4xSE5N/s9rSMpCs96V979gCVARF8kckt7xO++8wx1s5uxfdSviFlLT/lsoZihKlizJoyTRBVODNLhbtWrFvgmZ5WHMQ4zYMYLnE+pPQGHPwjAXrK2s0b+S0vE25/QctcMRBPNKohNUCdunTx8sXrxYq2s0d+5cvPvuu+yITqYjlFTXJ3QeuoHT+Dz9+ytv/nHjxiEpKUn7earKIX3dpk2bSnufAdEkzykJRvphW69v5YTWoKqD1A7NZKlYsSKPpHmsoUe5fyVdXtXmLQimBmmZUgcLJcysra0zdYzlF5bzSPrBhqgUNQYGVh3IY0y+GPjV9VM7HEHQORs3buT13PXr11WL4ecjP/PY168vvJ2p9cMwjKgxgs3LaD0197RimPw81apVQ/bs2REeHo4TJ04YLC5BUAvaOCKjwBEjRnBCPatSDcbMt/u/ReFphVFnfh1EJUapmkS/dOmSKucXhMxAXSrHjh1jzxCNR1tmGLJ1CCISIlA5d2UMrT4U5gZ5q9lY2eDI3SMIeBSg8+NTjmLZsmX46quvEBMTo/PjC4JRJ9Gfh6q+6UGGpFKuXLnCC3d6g5BmWkah6vbevXvzx6pVq/7zOZprmDlzJle90/g8VJ30wQcfsCYkJW4pCUOVjKS9RA8WM2bM0MFPLaSXM2fO8Ojn54ffTv7GczKuKJq9qMqRmS4VKlT4TxKdZBxIl/XS40s4/+i8itEJgvGg6czQSB5ZAmTYXLtAbaQ+S8W80/PUDkcQdApVm2q8Epo3b65KDPRgSdJ0VtmsMLzGcIOe29XelWVdiG/2f4PoxOgXvk4bjlQsQlCyQBDMkcTERG3BCPlckbwhGV2S3Ii5MunwJHy17yueH713FM2WNFMlka55BtE83wmCKUAqChovlcwm0Tdc2YBVl1bBOps1/mj7ByebzQ0fFx90KNmB53NO6b4anaRvPv30U3z77bcICgrS+fEF4W1Yqdliv3v3bowdO5ZlJSgxTRqMlMz29PTEhAkTWEolLCwsw8c+dOgQVx3Sh+bmTMfSfI6+nh6oEn7atGk8p5EeJDp06MBVOaThLhgGaiUOCFB2MUuWL4m/zv6l1Q8Vsr6ApY0r2sQiSGO+jW8bni8JWKJqfIKgC0gPnTZo09LSMvX9F0Mv8oaSrZUtbzJZEoOqKJ0+VKkan6gfcyBBUAOqJCOTeJJrqFy5sioxTDoyicd3Sr+DIp6G113+sNKHKJ69OB7HPcbkI5P/83XRRRfMGfLfIgPRqVOnvtC5Zs7MOD4Do3eN1t7fPR08cezeMTRf0tzgiXTyuSJu3779Qse3IJhCEr1o0cwV8dH7bNBmZW1NsmoVfZSucHNEYzBK3e0vb9TrgtKlS/Mo3SyCRSXRKVFOVS6//PIL7OzsWLOW2mqpAp2qg+jfpDllY5Px3bkFCxZwZcHrPujrGsaPH8+fo/FlqBJhyJAhuHDhAif9SfKFWvwye+EUMgfJ7dDvn3Z8j8YeRWRiJIplL4amRZUqKSFz5MqVi99/mzZteuF9ppF0oepbNfUSBSGrUOK8Xbt2bAKd2UoFTRV6i+ItkN0xOyyJjqU6wi7ZDg9iHmDypv8m2QTBVNm2bRuPtA7NrMxTVrgdcRvLApfxfFTNUVADW2tb/NDoB57/cvQX1mh9HjLtoqRivnz5uHJfEMzpuYKeMckT6KeffkJERATMHdoMH7JtCM/H1R2HWa1mYVevXZxIp4r0FktbGDSRnidPHly8eJGL5SgPIAimwI0bN3jMrOHwmF1jcD/6Pop6FtV2g5krDQo1QAmvEohJisGyC8p6R5dIEl2wyCS6poWOdBep2oUMGurUqSM3UuG1eugVKlbQSrl8XPVjboEWssbw4cP5/Wdvb6/9nCZZGBITgj3Be1SNTxCyAlWgk1YeaZv6+vpm+PvpHvX3BSWJ3r2sfv05jBF7G3sUfKp4fyy+vFjtcARBZ2iqqzUGmobm12O/slRSo8KNUDmPOpXwRKdSnVAtbzXEJsfim33fvPC1nDlzcoLrn3/+ga2trWoxCoIuOXjwIHdA37p1C8WKFeNOZQ8PD5gzi84vwkebPtJu2n1TX3mvV8pdSZtIJ+1iSqTro2L0TUkwNTYxBUGNSvTDdw7jt1NKHmNem3lwtHWEOUOSKwMqD9BKuujaa02TRBc5F0ENVMtCrlu3jqu8qfJ8zJgxWhMjqhok6ZTAwEC1QhOMUM6Fdnx9/H0QGBoIJ1sn9K7YW+2wzBY7azt0LdOV50sCRdJFMP0NuPLly2eqq4nanG9F3IKzrbNW5sjSaOXTCkgDrqVcQ9BjWagK5iHjoLk2aHS/DcnTuKeYd0bxGRhdS5FWUPMh9+cmirkpxXQjTKmy0yDJc8GcuH//Pr/n6dmzevXqOHLkCCfSzZkVF1agzz998AzPMNh/MCY2nsjvew2aRDrJOVIivfnS5gZNpAuCJVSiJ6Yk4sONH/K8n18/NCjcAJYA5WvIa+3sw7M4cV+3JuXkaUhIJbpgUUl0SpaTDt358+cRGhqKFStWoHv37nxxIqOAihUrchVM165KMk+wXPr06aPctPyVf/cs35MXe0LWIU3YtWvXsv7/8/Qor0i6rA1ai9gkRS9dEEwNTaKMDImzIuXSoVQH3ryzRBr4NQCuQm/mQIJgaCIjI9G6dWvufiRZM0Mz+9RsxCXHsRZqkyJNoDZ1C9ZleTyqjNd4zrxMcHCwSLoIJg95YpE8JFUwki8XmQOaM+uC1uG9te8h7VkaeyBMbT71hQT6C4n0nv8m0g1VkU566D179uTrsSCYAs7OzmxCnNFK9B8P/YigJ0HI5ZwLk5oofiiWAHW2dy2r5PLmnJ6jl0p06g6IjxffJsGwGIUeBhk7de7cGbNmzcKOHTswadIk5MiRgzXIV69erXZ4ghFwP+o+1l1ep5VyEXRXkdepUyfeuEpN/Vf/vEa+GijsUZh1zMhFXBAsLYmekpaCFRdX8Py9cu/BUuHf3UllvuDcAtlUE0weknbauHEj9u/fb/BzxyfHY/rx6TwfXXP0KxNaavCB3wc8Ljy/8D9eKLTZQFV3R48eVSk6QdANV65c4ZGq0J2czHtjfMu1Lei6uitvjlHx0ZzWc94og0myUppE+uG7h9Hy75Z6T6ST1N6SJUuwZcsWLuoRBGNn/fr13MlSu3btdH/PxdCL+OGg4j8yo8UMeDp6wpL4qLIiJbX8wnKEx4fr7LhUbEsqFiQTc/Xq/6t9BMFSkuiPHz/GypUrMXDgQJQsWRL58+fH6NGj+fO5c+dGt27d1A5RUBFK7NLF8ffTv/NCsF7BeiiXq5zaYZkNtJNOu+q0g3vt2jXt5+nBXlONLpIugilC1w1NEp06mzLK7pu78TjuMbydvFm32FIhU0GvSC8gDIhKitJW5wuCqaNGApuS1HRdKeheEO+UeQfGQlvftlwxRoZnO2/ufOFrBQoUeEFHXhBMeVO4d+/eaNTIvO/ptH7puKIjktOS0aVMF/zV7q90+UhRIn1nz52cSD9055DeE+nUCUTP/c+v1wTBFNYO6V0/UOFJr/W9+L1I99nOpTvD0qierzoq5KqAhJQE9mfQFfT/gDxbKH9RtmxZnR1XEIw6iU566OXKlYOPjw8nyklOgtzR33nnHfz222/snE7adbRDLVguJDXi6e2Jyfsm878/8f9E7ZDMCjL0ofchce7cuRe+pqm+3X59Ox7FPFIlPkHILCEhIbwZa2Vlpf0bzwhLA5fySA+gttaWqwtMi9RKfpW01ehkiqRrcyBBMGT3FUkIqAFVeE8+oqxlRtQYARurjPs06NNEWHPPn39u/gtfI/NxgqpFBcGUadOmDebPn8/yoebKwdsH0WZZGySmJqKdbzss6bAkQ9eaKnmq/CeRTl2p+qJq1ao8njp1Sm/nEAS17vnd1nTDmZAz8HL0wqyWs4ym+8yQ0M/8UZWPtJIuJC+lK6gjgHwtxKBYsJgk+syZM/lhpkOHDpg+fTouXLjA/16+fDkGDBiAEiVKqBWaYERQZUJknkjEW8Ujr2teXhAKukVTpftyEt03hy+q5qnKHQAaWQtBMBXc3NywatUqTJ48OcNt26RXrJGP6l7OfB+200uzZs3QpkAb2GWzw7mH59hwVRBMkTlz5qBQoUIYOnSowc9NHiM3wm9wxXdfv74wNjQxrb+8HmHxYS+8/+khOCAggItbBEEwTujeTEnv+JR4NC/WHCs6r8hUEYAmke5u764k0pfqL5FepUoVHk+e/P9OvSAYKQsWLGAd7m+//fatr6VikyFbh2Dj1Y1wsHHAxm4bkc8tHywV2qR3tXPF5SeXsSRACmQF00e1JDol7KhKkDTPP/74Y605gCA8z5kzZ7SGorSLackVoYZOohNaSRe54QkmhouLC3ttkN5/Rtl0dRM/MBbyKMT+AJbOiBEjsGH5BnSv0F1bjS4Ipsi2bdt4rFChgkHPSw/UEw9P5PknVT+Bs50zjA0yOqWPpNSkF2SbyKPI319ZiImki2CqxMbGIigoCElJSTBHroddR/MlzXnt0rBwQ6ztspY7TDLL84n0g3cOosrcKpxQ11cSXSrRBWPn0qVLfA15+vTpW1/7y9FfeK2cDdm4G6RGfst+lnC1d8W4uuN4PnrnaEQmROrkuKGhofjuu+/4OUUQLCKJXr58ebVOLZgI9NB5/O5xID9gk82GneUFwybR3y37LqyzWePkg5O48kQxZBIEc0eTQOpetrtFtl6+jkFVBvG48uJKPI59rHY4gpAh6MH3xIkT2upqQ7Lv1j6cDjkNRxtHo5al61tRqUb/6+xfr5R0kSS6YKocOXKEC7Yy45FiCnx/8HtEJkay/vCGdzfA0dYxy8esmrcqJ9Jzu+TGladXUGd+HXy8+WNEJerOBLRy5co83rhxgw0bBcFYuXnzJo9ktP0maI08auconk9pNgWdSncySHzGztDqQ+Hr5YtHsY8wft94nRwzMTER48aNY1ULc90gFYwT1Y1Fb926he+//5610OmhhsYffviBPy9YNg8ePEBE8Qiedy7VGblccqkdkllCetGkG/3o0SOWVHqenM450axYsxc0ogXBFCDJMEr4JCQkZOj7yDl+yzVF+1ekXF7c1PRK9IJfTj+uVH05ySYIxs6OHTv475jueXnz5jXouX8+8rNWMsXb2RvGCl3z7KztcPbhWZZu0tCiRQsed+7cKQ+qgkly5YpSCFK8eHGYG3cj72o7Rqc1n6bTThdKpF/6+BI+8PuA/03VtWV/K6tdJ2WV7Nmz8/8TMgYkLxtBMFZoo4coWrToa19D3Rq91vXi+RD/IRhWfZjB4jN2aG0xvcV0ns84MQMXQi9k+Zj58uWDs7MzUlJSEBwcrIMoBcEEkujTpk2Dr68vvvrqK6xZs4YX5zSOHTuWP09fFyyX/Sf3A//3Axxaw/D6pZYC6UWTuzWZ+ebMmfM/X+9R7l9JFzEUFEwBMqkePHgwV0/GxcVl6HvXBK1BcloyyucqjzI5y+gtRlODkmj04FD5WWWtORCZJgmCqUm5aBLChiLgUQC2Xd8Gq2xWGF5jOIwZLycvrffM/LPzX6gWpWsqacIKgikn0UuWLAlz49djvyIlLQX1C9WHf97/a2DqEDIZndd2Hnb32o0inkVwN+ouWv3dCj3W9sCTuCdZPv7FixcRGBgo0q6C0ULPv2+rRKeO7XbL27Gpb/uS7bkKXXiRpkWbomOpjuy3Nnjr4CznFahbWLMxeu3aNR1FKQhGnETftGkTa9W6u7uzlhG12dEO0tGjR7kSnT4/fPhwbN68Wa0QBZVZdH4RYANkT8iOanmrqR2OWdO6dWveuKKK9JdpV7IdXOxcEBwRjB03dqgSnyBkBI00UcGCBbnKKSNoOi5IykX4F43Zt+N1R3g6eOJWxC1ODAqCKZCWlobt27fzvHnz5gY998+HlSr0d0q/wwkoY6dPxT7aa2FiSiLPaW1A7dIdO3aEnZ2dyhEKQsahQhGC1rrmBJkAzz09l+ef1fpMr+cirfXAgYEYWWMkbwrSNaLUrFJYFrgsS8kwW1vxuxKMXw4uKkqRMSpcuPB/vv4o5hFaLG3B70fKWSztuBTWVtYqRGr8TGk6haXtSOaOpG909Xxy9epVHUQnCEaeRJ8yZQonN8g4csyYMahevTonPKpVq4bPP/8cp0+fhqenJ79OsEyeOinGHY1yNhJdYhVxsnXS6qR+tPkjRCdGqx2SIKQrie7n55eh76PWwv239mv9AIR/0fwuA88GsiQFIQajgqlw/vx5liyjtt9atWoZ7Ly3I25j+YXlPB9VU9FINYVKsbyuefE0/ik2Xt2odjiCoBPMtRL9t5O/ITY5FhVyVUCzos0M8kwwqekkHOt3DOVyluNK9O5ru6PNsjYsK5MVSJJBEIxZyoWk4BwcHF74WlxyHNoub8vFZrRRvqHbBn6fCK+moEdBfFHnC56P2DGCzZCzgiTRBYtKolPyvGvXrqxl9Cry58+PLl26cDJdsEwiHBQ99AHtBqgdikXssE+aNOm17tbfNfwOhTwKcfXpyB0jDR6fIGSEs2fP8pgRAzHS+e65riee4RnLGdAiT/iXSpUqaX+3Ayor1+St17biZrjS3ioIxkyxYsWwatUq/PjjjwatpCaZBWpbblS4ESrnUaSQjB2qnutVQdF0nX/uX0kX4uTJkxg/fjzu37+vUnSCkHFiY2Nx9+5ds6tEp+Td9OPTtVXohiw4Iq30U/1P4dsG37LW8eZrm1HmtzKYfXI20p6lZehYycnJvLnp5uaGx4/FtFwwPuhvlJ4pXn6uIFnD7mu648T9E8jumB1b39vKfmLCmxlZcyRvONyPvo/vDnyXpWNJEl2wqCQ6GRNRRdCbcHFxEQMjC4V2JW+EKbu+pE0s6Beq/hg9ejR+/fVXfth4GVd7Vyxop2ihzj0zV2QcBJNIomekEp2c4slIz8vRC3Naz9FjdKYJaZVS8jEyMhLWkdZc8UYbDr+f+l3t0AThrbi6uqJz586s620onsY9xbwz8wwis6AvSRe619+P+jdhPmzYMHzzzTcitSiYFJrkSo4cOeDl5QVzgXwLHsc9RmGPwninzDsGPz8lz8fWHYtzA86hZv6aiE6KxqAtg9B4UeMMVaWTnAsV88THx0vxnGCU1K5dm58tSI5YA0kYDds2DP9c+Qf21vbY8O4GlPBSErrCm3GwcWATZGLK0SmsJ5/VJPr169d1Fp8gGG0Snf7gN27c+NrWLfo8Xag0bwzBsjgRfIITNLldcsPb2VvtcMyeXLlywcfHhxcEFy682i27XqF6GFpNMXjtt6EfwuPDDRylILydhIQEXLp0KUNJ9MN3DmPi4Yk8n9tmLnxcfPQaoylCD7nlypXTdpINqjqI53+e/RMJKQkqRycIxsfsU7O5UrSiT0U0LtIYpkRxr+KoXaA2V5QuDlis/TyZNRNbt25VMTpByBiUPP/2228Nuommb8hIdPLRyTwfUWMEbKxsVIullHcpHOxzEDNazICzrTP23tqLcrPL4e/Av9N9jCpVqmi7XQTBFKBOs5knZ/J8cYfFqFXAcFJx5kDrEq3RqngrJKclY8i2IZn2VahQoQKCgoIkiS5YRhK9V69erE/XrFmz/+w6nzp1Ci1atOCvv//++2qFKKjI/K1KC/GzR1lzbRYydhN6Xk/6VfzQ6AfeZX8Q/QBDtykJdUEwJmgTKDU1lavNXicX9jyk8d9rfS9OFr1f4X12jRdejWZTgqpxaOFbwL0A6yavurhK7dAE4bXs27ePq6cDAgIMds745HitzMLomqNN0tdF44Xy19m/tA+3tDYndu3aJZ2igslAEqFjx47FV199BXOB7rsksZjDKQf6+CmdI2pCRqOf+H+Ccx+dY2PFyMRIvLf2PXRb0y1dRTeaJDrlAATB2Hg5wbs2aC3reROTm0xWpRPEHJjafCp3tOy4sYMr+jMDadST14WYngsWkUQfOnQo2rZti71798Lf359bbYsWLcojmYvu3r2bv06vEyyP8w/P85jfNr/aoVgMGp23NyXRyShlYfuFvFim6rR1QesMGKEgpG8ziP6Gly5dmq7E1fDtw1nXmxLCmtZC4dW0b9+ejb+bN2/OuskabfRZJ2epHZogvBa6FpCO9/z5L+p765MF5xawzEJB94Im+3BNcVNV6bWwazhy94h2nUCdazExMTh69KjaIQqCxSb0NN1zQ/yHGJWJYbHsxXCo7yGMrzce1tms2Vi5/Jzy2BO8543fJ0l0wZihJG3ZsmW10lBf7FaMMT+u+jGG1xiucnSmC10vNKbrJI1DBQiCYAqolkS3trbG+vXrsWDBAtSvX593j+7cucNjgwYNsHDhQqxbtw5WVqqFKKjI7YTbPFbOZxpGXJaSRCeq56vOlXXEgE0D8DhWTIAE45IdoUQ6dTm9jY1XNuKPs38gG7JhUftFcHdwN0iMpkqrVq3YmLFOnTr8735+/WBrZYvj94/j9APRMRWMM9m0bZvi4UGbP4aANuW+2POFUcgsZAUXOxd0KdNFW41O0Jq8bt26PD98+LCq8QlCRrpRKPlFXWrmAFVtnn90nje5Pvb/GMYGXfO+rv81Dvc9jOLZi+Ne1D00WtQII7aPeK38Gz2D0PXlwYMH/CEIxiQTee3aNVy8eBHZs2dHWHwYrjxVNLwnNJhgkp1mxsQXdb7gQqbbkbfx06GfMnUMkoDu3bs3Fi1apPP4BOFVqJ6hJlkXqjonQxFyPqaR2kR79uypdmiCSqSlpSHKMYrnjcualo6oOSTRqeX9bQ8a4+uPR9mcZbnSbuDmgZnWMRMEtaDNnw82fsBzqiIhzX8hY+RyyYXOpTvzfPoJRbpCEIwJeui9d+8et/tqkr/6hDTQO67oiIiECJY06F+5P0wZjcHoyksr2fBdY7BGHDp0SNXYBCG9zxSk5e/r64sbN27AHPjpsJJooutLdsfsMFaq5auGswPOarvWphybAv95/gh49F9pLRcXF5QqVYrnYi4qGBO3bt3i51xSSyCpyBP3T/DnaYPImN9/pgJ10kxpOoXn1GFDhQgZJTAwkAtwKacoCGaZRKf2z4YNG/KFyM3NDU2aNMGJE8rFSBCI45ePAw4AUoFmVd5eTSrohuLFi8PR0RGJiYncFfIm7G3suXKXqk3WBK3hdk1BUBva/BkwYABmzpzJlSOvgxbD/Tf1R2hsKG8GfdfwO4PGaco8efIEO3bs0Br4DKk2hMfF5xdrHywEwVjQVKFThyPd3/QJXVc+2vQRV4jmdM6J1V1W873SlCFzUWq3pgT66kur+XO1atXSbrjLBrpg7Ny9exfx8fHcpVa4cGGYOnSf3XdrH6+/P63+KYwdZztnzGk9Bxve3cDXxcDQQFSdVxWTj0xmL5rnoc2Odu3acX5AEIwFzeYbyQ5T1fnxe8e1m0SCbiA/KjJgT0xNxKfbM35dK1GiBI8auR1BMKskOu0SNWrUiNvqYmNjWVORdozo4YaqhQSB2HJ6C48O0Q5wcXRROxyLgSSWzpw5w+/L9Dxo+OX2w7i643j+8ZaPERIdYoAoBeH1ULvl3Llz8dlnn/ED85v0itdfXs9SJEs6LIGDDe3aCelh2LBhLJWzfPlyrbxTz/I98QxKAjE1zTza5QXzYOvWrQaTciFvAPIKIR3gFZ1XIJ/b242NjR1KGGiq0eefUzTlSS6LdIupOk/a2AVj58qVK9oE2JvWBaaCRgv9vXLvIb+76fhGtfFtg8CBgWhTog2SUpMwaucolni5G3lX+5qff/6ZpV7r1ZPOQMF4uHlTqYwuUqQIjyRhSFC3maAbaC0xvfl03hzccGUDtlxTckHpRZLoglkn0X/66SeuDvzyyy/x8OFD/hg3bhxXCEycqCwKBOHYrWM85rbKrXYoFmmcQm3v6WVM7TGonLsywhPC8eHGD6UqTVCVs2fP8li+fHneFHoVweHBGLJNqZ7+tsG3qOBTwaAxmjqVKlXikTbcNExqMgkeDh44+/Asfjv5m4rRCcK/0IbwwYMHed6iRQu9nuvwncPa6qmJjSeifqH6MBd6VejFZuIHbh/AtafXYGNjg8qVK/MoCKaSRKf1ralz5ckVrAtax/PRtRRvIlOCKtH/efcfzG09l/XcqaKeTEfXXFqjdmiCkK5KdHrO1XRd+uf1Vzky86KUdykMqzaM50O3DUViSmK6v5f+3xBhYWEsDS0IZpVEp4cZ0lL89ttvkTNnTv745ptv2KRs//79hgxFMGKSPZN5rFG0htqhCG/B1toWC9svhJ21HTZf26ytVBMENZPofn5+r/w6VUm/v/59liYgmYKRNUcaOELTR/O71fyuNdroPzT8gedj946VrhTBKLh8+TJLuFD1WLFixfR2Hvp7f2fVO0hJS2EjTvJYMCeoor5p0abaLh5BMLXrAEGa6KYOSaBQ11db37Yo7V0aplpx+mHlD3Huo3OchCT/iM6rOqP/xv6ITYrlJCVJStImqCAYWyU66XU/jX/Kz70VckkRjq4ZV28ccrvkxvWw65hyVNFJTw9OTk7In1/pzJFqdMHskuiPHj1C9erV//P5atWq8dcEgXiUTflbeL/Z+2qHYnGEhoaif//+GaraK5OzDFf0EsO2DcPtiNt6jFAQMp9E/+XoLzh45yBc7Fx488fa6tXV6sLr0fxuScohPDxc+3kyOKuapyqiEqMwYscIFSMUBIUqVaqwhv/OnTv1JjuSnJqMLqu7ICQmBGW8y+DPtn+apcRJ34p9eVx4fiFvRoaEhKBfv35o2lRJrguCsWIulegPoh9gUcAinn9W6zOYOuS1cKjPIXxe63NkQzbMOzMPVeZVQbV21VCwYEH2XhEEY4CS59ThStcQjZSLn4+fyXueGCNu9m7c3Up8te8rjN0zFgkpr/e4eh6RdBHMNomenJzM7tsv4+zszF8ThPjkeFx9qlz8yucqr3Y4Fgft5P7xxx9sxpaRja0RNUagRr4aiE6KRt8Nff9jFiQI+oaql96URD//8DwvxoipzaaiiKeibShkDA8PD61nwvPV6LQhMbvVbJZ9WHZhGXbf3K1ilIKgQBrIGh1TfTByx0gcunOIH/zWdl3LG3TmCFW+ZnfMjvvR97Hz5k5ety9YsIA3KCihLgjGiiaJbuqV6FOPTWUtceqiq5m/Jsylm/XHxj9iV69dXH16+cllnK50GqgOnDx1Uu3wBIGZOnUqzp8/j/r162tNRUXKRX90L9cdPcr34O6+7w9+j3Kzy2FP8J50JdGpiIHkogXBrJLogvA2Dl45yAnYHI45kMs5l9rhWBy0yVW8eHGe04IhvVACjSp7HW0c+UY3++RsPUYpCP/l3r17rINHWuhly5Z94WtUxdBzXU8kpyVzMqivn1JVKehO0oWonKcyBlUZxPNBWwZlSM9QEHRJSkqK3j06/g78G9NPTOf5ovaLUMJLqYIyR6jijowMCZJtc3Nz48o84vDhwypHJwivhq4BkyZNwtdff43SpU1T/oQgyZM5p+aYTRX6yzQs3BABAwN4fZaWLQ1oDsyLnYfQ2FC1QxOEFzjxQNFDF1NR/UGJcFpTremyBnlc87C0C5kQ917fG0/inrz2+77//nvExcXhs8/M7xopGB8GT6IvWbIELVu2fOFj6dKl/LWXP08frVq1MnSIgoos27OMx9QHqWbZEm0KVKigaLydO3cuQ99X3Ks4G6oRo3eNxt3Iu3qJTxDepHtaqlSp/5jjjtszDoGhgfB28sa8NvPk2qIjc9GXk+jEtw2/5Q1Q6igi/VZBUINZs2ax0dTMmTP1cvyARwH4YMMHPP+yzpdoV7IdzJ0+FfvwuP7yeoTFh6FWrVr870OHDqkcmSC8GrrXv/vuuxg/fjzc3d1hqlACnTo9STKqZfGWMEdyOOXA+q7r8Vm5z4AU4Gn2pyg/uzx23BBZF8E4NuSpE+RsiLLurZZPkuj6vnZ3LNURlwZd4uIcknwiOblSs0ph8fnFryyS8PT0/M/znyCYTRL9+vXrLBXx/Ad9jt4ML39e8yFYDudClMRtESeRWlCLihUrZiqJTnzs/zHvzsclx2Ft0Fo9RCcIr6ZJkyaIjIzEmjVrXvh80OMg1kIn/mj7B3I651QpQvOhXbt2LPv0xRdf/OdrHg4emNJMMQP67uB3bMIkCIaG1o7BwcFITNR9N0R4fDg6rOiA+JR4NCvaDN/U/waWgF9uP1T0qciJBKrC1yTRpRJdEPQHddKRlAsxutZolkwz58TZhNYTYDvfFggFHsU+QrMlzVg2i647gmBoqNDT1dWV/cJIFjIxNZGlzYp6FlU7NIvA3cEds1rNwuG+h1E2Z1muRO+1vheaLmmKG2E31A5PsGAMeiemB5qMfmgckQXL4Fb8LR798r7aGFAw7iQ6Le5p55jYFbxL57EJwpsgiQGNsYwGSqA/wzO0823HrcJC1iG5HDIVfF17fLey3bg9mx7+B28drHdZDUF4nvj4eOzbt4/nzZs31+mxSW6ux7oevDlUyKMQlnZcalEGxZpq9L/O/oXatWtrO1JiY2NVjkwQ/suxY8cy7PFjbCw6v4iTyfnd8vO91dyxs7NTngHnAk09m2rXcTX+rKH1zBIEQ3Hjxg2+v1lZWWlNRUkPXTpaDUuN/DVwpv8Z/NDwBzjYOGDXzV0oO7ssfjz4Ixu8axg4cCDq1q2L+/fvqxqvYP4YNIlObtuZ+RAsg7S0NEQ6RPK8YZmGaocDS0+ikxkTaYtllMZFGvO4/9b+F25sgmBoHsY8xOKAxdoKLsEw0MPFrJazYGtliy3XtrD8gyAYiv379yMhIQH58uXTuQ7yt/u/5b9peogjvU4vJy9YEqSLbmdth7MPz+L2s9vInz8/UlNTceKEohMrCMbElClT0KJFC5YSNUVS01Ix6cgkno+oMYKNOC2BKlWqsKRL+bvlWeKFKn/PhJyB3+9++PPMn7IxLxgMTTEnGZSfuC966GpC178xdcYgcGAgGhVuxIU6X+z5ApXnVsaxe8f4NXv37sXBgwe1htKCoC/MtydMMDnOXD+DZ47PgDSgRZUWaodjseTOnRs5c+ZEgQIFMrWTS+3etOAl/caTD07qJUZBeJ7w8HCuPBg2bBhvxmmYcXwGtwDXzF+TPwTdERQUhNmzZ2srfl+mZI6SGFVzFM+HbhuKmKQYA0coWCoaGUBKnumyWuyfy/9g/P7xPJ/Tag4q5Va8ASwJ2jToVb4Xz6nLpGbtmihZsiRiYuT9LRgfmkSKr68vTJGfDv3Epnq0pv6gkuLBYAm0adMGI0aM4Gs4+U0EfBTA3W0kFfnBxg/QZXUXltUSBENUohPksaKpRJckuroUy14MO3vuZPNRL0cv9ryiTpU+//RBgdIF+DVXr0rXiqBfJIkuGA1bTm/h0T7aHh4uHmqHY7FQ0kEjp1S8ePFMSbrQDjFB7VaCoG9IToAqDzZs2MAtlwQlbWefms3zkTVGqhyh+UGVfYMGDdIag7+KL+t+yZIXd6PuYsL+CQaNT7BcNBs75JOgK+aenotOKzvxfGCVgXi/4vuwVH5o9AN7H5x7eA41h9TkDTVKegmCMUEb6ppECm30mBpknjd271iek4SBs50zLAWS4Zo8eTIaNlS6kvO65eWk2cTGE2FjZYPVl1aj/Jzy3PEqCIaoRM+RL4dWTojkXAT1cxU9K/TE5U8uo3fF3vy5BecWYH/5/UANIOhqkNohCmaOJNEFo+HozaM8+mTzUTsUi8fJySlL369Jou8O3q2jiAThzUl0ws/vXy8F0uwNTwhH8ezFRQtdD1SqpFThnjlz5rWvcbJ1wowWM3j+67FfcSH0gsHiEyyTiIgIBAQE8LxOnTo6kVMgU7sBmwYg9VkqepTvganNFZM/S8Xb2RvfN/ye51/t+wqhsaFqhyQI/+HOnTss62Rra4tChQrBlKAClL4b+vKcOroGVBkAS4cKdEiW72i/o7yuuxd1Dw0WNsCXu78U6UhBL0RHRyM0VLm/PXV4yiMZilqajJsxk8MpB+a3m49j/Y6hap6qSMqWBDQD/rT7E9uuK12JgqAPJIkuGA3Zcitt1/VL1Vc7FCGLaHTRj949KjIOgt7RmOBqkugpaSmctCWG1xhuUcZ/hkLzu75w4QKSkpJe+7rWJVqjfcn2/P9k0OZBomUq6N1U9KOPPkKrVq3g45O1DfnYpFiuPidTO2JC/QncPkya4JbOgMoD4Ofjh8jESHy+63OkpKTw714QjE3KhToqbWxsYCqcf3geHVd05Hvmu2XfxU+Nf4IlEhkZyfrGFy9efOHzVfJUwZkBZ9DPrx+bxv9w6AfUnl8bN8IU2Q1B0HUVupeXFy5GKH+H1fKJlIsxQv9fjn1wDKNKjAJigFjHWLRY2gLtlreTa4OgFySJLhgNdxLu8NilXhe1Q7F4Hj9+jEaNGnH1zvMa0+mliGcRlnFITkvGwdsH9RKjILxcia4xxV1zaQ1uRdziCoX3K1iu7II+KVy4MNzd3TmBfunSpTe+dlrzaVyVfvDOQSw6v8hgMQqW6enx22+/YdOmTVk6zoPoB6i7oC7+ufIP7K3t8XfHvzGu3jidaqybMrQxSebBxPxz8+FSygXz589XOyxBMGk99LuRd9Hy75bsKVSvYD0saLeAK7Atka+++orlXObNm/efr7nYueCPtn9g1TurWFqKDB8r/l6R1xeyUS/oCpKHbNeuHZo1ayZ66CYAXSuH1h0KzACyHc3G0k8brmxA6d9Kc8eKFPUJusQy78yC0ZGYkojLTy7zvHyu8mqHY/F4enriyJEjuH37tnYnPiNQoqFxYaUaXXTRBX1C1Y+XL1/WVkfTA9SkI5P4359U/QSOto4qR2ie0HtcU42u2cR4HQXcC+Drel/zfOTOkQiLDzNIjIKQGUjr23+eP86EnOGNuD3v70G3ct3UDsvoqJG/BvpU7MPzxEaJOHhYNswF40GzLjAVPfSIhAiunKQNvDLeZbD+3fWwt7GHpVKlShUeT5069drXdC7dmU1H6xasywmy99e/j/fWvofIhEgDRiqYK+XKlcP69evZA0iS6KZBnjx54GLrgrwX82JHhx1oUqQJklKTuGPFd6Yv/g78WzbaBJ0gSXTBKNh7YS/rjbrbuSOva161w7F4qPWVFg/PS2VklEZFRBdd0D+BgYFITU2Ft7c3L572396P0yGn4WDjgI/9P1Y7PLMmvUl0Ylj1YSjtXRpP4p6g8aLGWBqwlDdPBUFXJCYm8ubvm+SF3samq5tQ+6/auB99HyVzlMTxD46jZv6aOo3TnCCpCRdrFyA3sP3JdrXDEQQtgwcPxsKFC9G5c2cYO3Qv7LCiAy4+vog8rnmw5b0tXGFtyVStWlW7viC5qNeR3z0/9vTawz4N1tmssezCMlSYU4HlJAVBFwRHBPPa1dbKFhV8KqgdjvCWAh/qpr979y4alG2A7T22Y13XddwdTxuUtMnWbEkzlusThKwgSXTBKFhzeI0yeaRcAAX1qVChQpaS6A0LN+Tx/KPzYjwm6A1aLOXKlYsTunTtmHxkMn+eKiSpilRQ11xUA+lIz2szD442jjj78Cx6rOuB/L/m5xbLO5GKlJcgZAWqWKxVqxZKlCiR4e+lyqRpx6axfmZsciybY5OBHUmTCa8np3NOjK83nufhfuE4f+282iEJAlOqVCn06tVLe58yVtKepaH3P72x79Y+uNq5Ykv3Ldy9ZenQddzFxQVxcXEICgp6q7zUF3W+wOG+h/mafTvyNstx0TVdqk6FzBIVFcV/PyQXRFT0qcgFOoJx4+Dw7/8jei4kX6ZLgy7h2wbf8jPIzps70XlVZ65QF4TMIkl0wSg4e1+pZCzkWEjtUIT/o9GXzmwSnR6uK+RSEvF7gvfoNDZB0EAGgg8fPsS6detw6fElbL62GdmQDZ9W/1Tt0Mwe0onct28fNm/enK7XU0XvzaE32aCROo4exz3mFsvC0wqj/fL22HljJycUBCEzHDp0iMfKlStn6PvIwG/w1sEYtn0Y//2RYd3W97ZafCVoehlWaxgcIxwBR2DY5mFqhyMIJsWYXWOw/MJy1u9d23WtVLo+p0etuZa/SdLlZXPBswPOomuZrnxdp2t619VdEZUYpedoBXOE/v7c3Nzwz+l/+N8i5WK6kLTn2LpjsavXLvZo2nZ9G3qt64XUtFS1QxNMFEmiC0ZBcFwwjxVzK4lbwfST6ETjIoou+u6bIuki6BcnJyf8cuQXnnco1QHFvYqrHZLZQxI69erVY4PR9OLj4sMGjbeG3cKaLmu4Y4USl2Tg2HRJU5SaVQpTj01lfVhByAgHDyqa3LVr107391Bype2ytph1chZvvv3c+GfumLC1ttVjpOYFVYG2ytaK5/si94mMgqA6t27dwqxZs3D48GEYMzNPzMTPR37m+V9t/9KumYX066K/jJu9G5Z1WoYZLWaw/MaqS6tQdV5VBD4K1GOkgrlBEkJ0HYmJicG1uGvaTRrB+Dl27BgaNGiAbt26vbKYZ22XtXxtWHFxBT7e8rF0qwiZQpLogurQxSvcPpznDUo1UDsc4f+UL68YvN6/f58lMzIDtcQT1DolNylBn4REh2BJ4BKej6wxUu1whLdAVXcdS3XE7l67uc1ysP9gfvi9+vQqPt3+KfJOyYv+G/vjbuRdtUMVTIC0tDRtwqxOnTrp+p4bYTdQ488a2Hp9K7f4ru6yGqNqjRJJuUzQuVpn4P/WCJ9s/USquwTVu1I++eQTfPnllzBW1gWtw5CtQ3hOet49K/RUOySzSKITdA3/xP8THOhzAPnd8vO6otof1bD4/GI9RSqYG/fu3eNEup2jHS48vcCf88/rr3ZYQjrf/9Qlq+lOfJlmxZphScclXDjx++nfMXbPWIPHKJg+kkQXVOdC8AU8c3oGpAGt/JVqJkF9XF1dUaNGDTRp0gSRkZlzuq9TsA7v9pI+4c3wmzqPUbBsLl26hLx58+Ldd9/FjBMzWN+uVv5aqJG/htqhWQzHjx/H8OHDMW/evEwfo5R3KUxvMR33h9/H7FazUTZnWcQlx2HemXncji0I6bkWREREwNnZWdtF9SaoO4qqE0kCKrdLbuzvvZ83dYTMQdX/DdMawgEOOBNyBnNPz1U7JMGCuXLlCo++vr4wRqhbo/va7niGZxhQeQDG1B6jdkhGSd26dfHbb79h9uzZmfr+6vmq48yAM2hatCniU+LRa30vfLTpIySkJOg8VsG8uHHjBo95KuZBYmoiPB08UTy7dLiaAsWLF9duhMTGvtpAtEuZLpjTeg7PSVZS46clCOlFkuiC6mw6tYlHuxg75HAXI0Bj4siRI9ixYweKFSuWqe93sXPRJjR33dyl4+gES+fs2bN48OABbj24hdmnlIeskTWlCt2QBAQE4Ndff8WqVauyfCy6XnxU5SMEfBTAyXRCNt+EjEi5VK9eHTY2Nq99HXVETT8+Hc2WNEN4QjhXlp3qfwpV81Y1YLTmB21m7t6wG5NbKA+iX+z5Ao9jM9fBJghZ5fLlyzyWLFkSxgbd09osa8OJ3NYlWmNmy5nS/fIa8uTJg4EDB2bJHJYM5smslQyQNZWntf6qheBwRUZUEF7FzZvK2tPZ15lHWivI+9Q0yJ49O7y8vHh+/fr1176uf+X++KnRTzwftXMU/jzzp8FiFEwfSaILqnP4htKCnfNZTrVDEfSARtJld7Doogu6T6ITttVsWUObqkTa+rZVOyyLoly5ctpKYF1BDypUQaaR6RH0I39y9NJRHs0BTdvum6RcElMS8eHGDzF021CkPktFz/I9uQI9j2seA0Zq3tAmWEWfinw9HrNbqmsFdTDWSvT45Hh0WtkJT+OfomqeqljeSTEUFfTv2/B1/a/ZMNrL0Yu7ZSrNrYSNVzaqHZpg5JXoqT6KNJmYipoWJUqU4PHq1atvfN1ntT/DqJqjeN5/U3+sDVprkPgE00eS6ILqOBZy5LG5X3O1QxFeQ1RUVNbNRYN3s4GgIOg0iW4FBLkH8b9H1BgBq2xyWzMkpUqV0nonZFb26VVoEpuhsaFITk3W2XEF4HjQceQYngM1V9VEr+m9YA6MGDECEydORLt27V759Ucxj9BwUUP8efZPvkZMbjIZC9svhIONg8FjNWcePXyEfj79eE6/62P3jqkdkmBhpKamahMnxpZE/2TLJzj38By8nbyxtutaONspVa7C6yFJBpKLW7JE8bzJCqSFfHbAWd6kp42+tsvbYsyuMSwFKAivSqKHOYXxKHro5plEJyY2noh+fv04R9FtTTfpnBfShWQbBNW5Fqm4Xrf1lwpSYyM8PBwFChTgtqiEhMxpCFK1jaudK8Liw/jhQRB0AckycBK9NPA09Sk/lPaqYB4JQVPC3d0duXPnfqGFXhdQCzZV6JFm7KPYRzo7rqUz+PfBqLGoBsI9FTPv/Xf2wxygdv/Ro0e/Ug+dqg6rzKuCI3ePwN3eHZu7b8aImiOkNVtPHhVjeoxBr/LKtfjjLR+LyahgUO7cuYPExETY2dmhUKFCMBb+OvsX/jr3F2/iLeu0DPnc8qkdkklw4sQJ9O/fH1OmTNHJ8fK75+cOpCH+iqnrT4d/Qv5f8+PzXZ/jetjrpR8Ey6JmzZpo1r4ZQtNC+d+SRDffJDqtBX9v/Ts6l+7MG2rtl7fH8XvHDRClYMpIEl1QFbpYkbEXUT5XebXDEV7Cw8MD8fHx7FB+8eLFTB3D1toW9QvV15q5CYKuHpRpkwe1lH9/4v8JHG2VrhbBsJQuXVrnki6UaPBx8eH5g+gHOjuupRIcEoyCIwpi5sOZeObwDNYx1vz5cHslmW6urLiwArX/qo17Uffg6+WL4x8cR/Ni0vWmD6jq183NDTExMeiVuxdvWNAGBhkEC4Kh0Ei5kLmctbVynVObsyFneUOJmFB/AhoVUWQOhbdTpUoVHgMDAzNdzPMydtZ2mNZiGlZ0XsHG0tTxNvHwRBSfURyNFjXC8gvLWf5LsFw+/fRTjJg8gudFPIvA29lb7ZCEDCbRfXx84OLikm7JpyUdlqBJkSaITY5Fi6UtcDE0c3kPwTKQJLqgKrvP70ZyWjKcrJ1QwL2A2uEIr9idrVChAs/PnTuXZV30XcHSIiXoBq5CpyKz3ICjjSMGVR2kdkiwdEmXoCBFVkfXki6SRM8atHlZaV4l3HG7A6QB9VEfV0YpiaY4hzjEJMXAlFm2bBn+/vtvPH78r5ElteV+uftLvLvmXcSnxKNFsRY49sEx+OYwLnkHc4ISljVqKEbiQaeC8F3D73j+xe4v8CTuicrRCZZC7dq12SNh2rRpMAZINqTzqs5sJNqqeCuMqSNeARkhf/788Pb25mIeMjLXJV3KdMHtYbexrus6vkeQ8eie4D0s6ZB3Sl6M3DESV54o90rB8jh+X6lGFj1006NTp04ICQnBrFmz0v099jb2LLNFck9kPN90SVMxIBZeiyTRBVXZeEIxdbF5YiOt1UaKpj0+K0l0jS76wdsH+UFCELIKtWp7tvbkeZ+KfVj+Q1A3iR4crNvFpiTRswZda4dvH47Gixsj4lkEXJNc8UfNP7D3670omrMoV+CRXE7go0CYMj/88APee+89rbloVGIUt+P+cOgH/vfomqOxsdtGeDh4qBypZSQwicOHD7PJaIVcFfhhdPy+8WqHJlgIVHlYq1YtNGqkfrU3bea9v/593Ay/iUIehbCowyLxbckg9GyoqUY/deqUzo9P3bLtS7bHlve2IHhoML6q+xXyuuZl89dfjv6CkrNKot6CelgSsISNYQXzJzY2Fk+ePNFKeoiUi+mR2ZySi50LS/6VzVmWnz2oIl26UoRXIXdyQVVO3zvNY0GHgmqHIugxiV7auzRLM1BF4NG7R3UYnWCpFKhSAOE5wrly6NMan6odjkXTrVs3PHjwACtXrtTpcfO4KEn0kOgQnR7XElh5YCVyjcuFX4/9yv/+qPJHCPk6BP2aKaaPREWf/1/bTdirIiwsDBcuXOA5Jc405n0br26EvbU9FndYjIlNJnKrrqB/NP8PaEODPA2mNp/K/55zao60RgsWx6TDk7DhygaWD1n9zmpkd8yudkgmiSaJfuyYfo2KC3oUxDcNvsGtYbd447VNiTa86XHg9gH0XNcT+X7Nx3PBvNm0aRN3P2y/uJ3/LZXolgVdp7f32M55iytPr/D6RRBeRpLogqrcjLnJY3kf0UM39iT6+fPnkZaWlukdYU01+u5g0UUXss6Uo4rJVMdSHVEsezG1w7FoPD092VxU191EUomecZKSk9Dqh1bouqsrohyi4PzMGZu6bcLs1rPhbOf8wmsdIhx4/G3tbzBVjhw5otXjzpkzJ8uGrLi4gj+39b2t6FG+h8oRWhb+/v4s63Lv3j32rSA/lA4lOyD1WSqG7xjOhtCCoE/GjRuHuXPnIjo6WtU49gbvxRd7vuD5jBYzUDlPZVXjMWXq1q3L4969ew1yDaENwNYlWmNDtw0s90I69lSdHhYfhqUBS/V+fkFdbty4AXgAyXbJ/Lfgl9tP7ZCETPDZZ5+xufTixYsz9fxB73vi2wPfIjIhUg8RCqaMJNEFVQmzC+OxfknFeFIwPig5YW9vzw8kt27dyrou+k3RRReyxuPox9xaS4ysOVLtcAQ9oU2ix0gSPT0E3AxAzlE5sSV5C2AN+ET44ESfE2hVotUrX5/fLj+PwQmmq/mokXDRyIgsOr+IDcsr566MBoUbqByd5eHs7IxKlSppJV2ISU0mwdbKFjtu7MDW61tVjlAwZyIjI/Hdd99hwIABqm7Y0MYv+TGQnEuvCr3wYaUPVYvFHKhZsyZL+FHH2927dw167nxu+TCu3jh83/B7/ndwhOneL4X0cfPmTSCvMidJMgcbpeBAMC2ioqJw+/Ztrdl0Runj1wclc5RkaadJRybpPD7BtJEkuqAaQXeCkOasVDa39m+tdjjCa7C1tUW7du0wZMgQTqZnNYl+8sFJNloShMyyav8qNiS2T7BnAxhBfX7//Xe+TmzdqrskWW7X3DxKJfrbiUuIQ+3ptRHpGQkkAe97vo/7v9xH6YKlX/s9zSs25zHWOZYr2E2RgwcPapPolDSbd2Ye/1uSVupWfy1dulSrSV00e1EMqz6M56TRn5yarHKEgrmiSZZQZ5Sbm5sqMdDfd9fVXREaG4pyOcthdqvZ4vmURZycnLBr1y48ffoUBQoUUCWGwp6FeSR9e8ECKtHzKXORcjFdSpQowePVq1cz9f3UhfBjox+13c/yLCI8jyTRBdXYcmoLjzZRNvDJ7qN2OMIbWLFiBaZNm4a8ef+/NZ8J8rvnh6+XL1fm7L+1X6fxCZbFiZsneHSOf1GeQlAPMvzasGGDTjVLRc4l/TT8riGiPaOBRGBzu81YMGQBrKzevMRrVLERJ9xhC+w6a3odQvHx8Th58iTP69Spg8N3D+Pyk8twsnVCt3Ld1A7PYunUqRO6d+/O8joavqzzJbydvEVfVNArly9f1nZQqsXnuz7HoTuH4GbvhjVd1vD1SMg6dI338FDPHLqIZxEeb0feRkpaimpxCIatRK+WT5LolppEJ9r5tkPN/DXZ0+2bfd/oMDrB1JEkuqAah64pbdg50/590BLMG40uuki6CFnh0qNLPOZ1yPymjqBbSpUqxeOlS8r/G10m0UnnmiQ6hFez8sxKHLc9zvORxUaipX/LdH2fna0dnOOUjajt5xUDLVOCfDqSk5Ph4+ODIkWKaKvQ3y3zLiewBOPB3cEd3zX8judf7/uatYUFQV+V6Gol0ddcWoMpxxS/lvnt5qO4V3FV4hB0D61HyCCWEuj3ou6pHY6gJxITE3Hn/h1AaYSUSnQzSKJfu3YtS55uExtP5PmfZ//kQg1BICSJLqiGWwnlIbd9zfZqhyKkg6SkJDb1yYoeoVYXPViS6ELmuRun/A1SZ4NgXEn0oKAgnR3Ty9GLtZSJhzEPdXZcc+J+1H18vPtjnte2q41JfTOm21jAVmmNP3FH6e4wJapXr4779+9jzZo1iEyMxKqLq/jzH1YWKRe1OX36NCZOnIgzZ85oP9fPrx/K5yqP8IRwqegS9FqJXrJkSYOf++rTq+jzTx+ej6wxkk3PBd3y448/olatWggMDDT4ua2yWaGwh0i6mDukoQ2q7bMF3O3dZSPMhCFTURsbG8TFxbGfQmapXaA22vq2ZYP0L3YrZtGCIEl0QTUuPL7AY+OySnWyYNx069YNDRs2xN9//53pY9QvVJ8XorSTS8kfQcgMYVZKFWPlQpXVDkX4P6VLl9a2Taak6KbVmSpARNLl9VBFXPe13blSv6JPRewctTPDx6DvI67HXIcpkidPHjadWxqwlNtty+YsK5VjRsDUqVPx+eefY/369drPWVtZY0pTpUp31slZCHqsuw03QVCzEv1O5B10WtkJ0UnRqFOgDn5srOjoCrrlwIEDOHLkCHbv3q2qpIsk0c0X8v6q36M+z/3z+vMzq2C6nm7UpZhVSReCtNHpb2Hd5XU4cveIjiIUTBm5MgiqPfxfDL3Ic6pMEoyfBg0a8Lh9e+bb/j0dPVElTxWe7w5WZxEsmL6BYpKzIu1Rr0w9tcMR/k/+/PnZ/IvkNdiUSUdIEv31tPipBQ7cPgAXOxes6LwCDjYOGT5Gw9INeYxzjYOp8rKhqJj4qQ8ZvRKHDx9+4fONijTSVnSN3DlSpegEcyQ1NZXb9g1ZiR6dGI0vd38J35m+uBB6Abmcc/G1mAzpBN1DhTzEnj17VDm/phI9ODxYlfML+qdgwYIoWLMgz2VD3vSpVq0aF1pkdV1Y2rs0+lbsy/PRO0fzulOwbCSJLqgCmZglpibCPpu91vFcMG6aN2/O46FDhxAdHZ3p4zQuLLroQuY5eOEgYA0gGajqW1XtcIT/QyaWmsSFLiVdcrsqwpSSRH+RyWsmY1eScg39otwXKOGlaD9mlHcbvotsyIY46zg8inkEU+HChQt8T6KK51MPTuH8o/Owt7ZHj/I91A5NAFhygTh+/DhvrD3P5CaTWaZpy7Ut2HZ9m0oRCuZ4DyIpBpIdLFBAkanSF6lpqZh3eh6KzyiOHw79gISUBO603PP+Hu09S9BfEn3//v0663jLVCV6hFSimzMn7ivydmIqavosWrSIN/M1hYBZYXz98XC0cWQT+41XN+okPsF0kSS6oApbTm/h0TbMVlqlTIRixYqhaNGi/EBMDymZhSrRNEl02ckVMsqtmFs8eqZ5wsZaqr2MTRfd2dkZT58+1dkx87hIJfrLXAi+gM9OfAZkA3xjfDGm9ZhMH4uq2DWan5SINhX27dvHXVFbt27VVqG/U+YdZHfMrnZowv/lnTw8PBAbG8sGsM9Df2+D/QfzfPj24dyZKAhZhSoNyWS4fv36sLamnXb9QGtXv9/90H9TfzyKfYTi2Ytjfdf12NNrD1crCvqjYsWKfF2Jiop6wW/BUIici/lz9tJZrXkkybkIgoa8bnkxrPownn++63NZu1g4kr0UVOHU3VM85rfLr3YoQiaq0bdty3z1WM38NVl2ICQmRFyuhQwTbad0QTSp1ETtUISXmDNnDj/c9uvXT2fHFDmXF0lOSUa9afWQ5pQG+0h7HBp7KMvH1OiiH799HKYCdUQR/rX9sezCMq2Ui2A8VcE1atR4paQLMa7eODYODnoShN9P/a5ChIKQMUjDv/XfrdFkcRMEhgbCw8EDvzb7FRcGXUC7ku1ERsoA0OYIbZKoJekiSXTzp+PgjniGZ8hlnws5nclhVDAHXu6Iyyyja43mYg1auyw8t1AnxxRME0miC6qgMTETPXTTTKJT9V9mq8gpgU7GS4RIuggZ5cqT/xuHeRnWOEx4Oy4uLpw80yWaJDptuglAyx9bIswzDEgCVnZZiRzuObJ8zLQHaTz+uflPmAJ07zl48CDPE4olICYphq8HmvuKYNy66AQlIL9t8C3Pv9r3FcLjww0en2BezJ49G5999pnOK5TJuHnwlsEoN7scNl/bzHrnQ/yH4Prg61yVaGdtp9PzCcari66RH6W/iajEKIOfX9D/2uI+7vO8cq7Kaocj6ADqjKUueldXV50k0mntMrbOWO3aJS7ZdP2EhKwhSXRBFcJswnisW7Ku2qEIGYAqQOzs7HDr1q0sOV03Kvx/SZdgSaILGSPgQQCPkkS3DKQS/V9+2/wbdqUo18x+ufuhbfW2Ojlu2RxleXz47CFMAbr/PHjwALa2ttgXvY8/90GlD6QS1Eh10U+fPv3Kr39Y+UOU8S6DsPgwfHtASagLQmZZvnw5fv75Z515cpDO+S9HfkGx6cUw8+RMNsMlU9wLAy9gWotp8HLy0sl5hIzRqFEjeHp6IleuXAY/t5u9G3I4KRvXYi5qfjx69AjJOZVEa/3iSseDYNrQtSIkJASJiYm8dtQFg6oOQkH3gvxcMv34dJ0cUzA9JIkuGJybD24i1SWV562rtlY7HCGDlaZ//vknPxQXL67o6GaGxkUUc9F9t/aJppiQIU4FK1JQVuFy+zLGKp733nsPZcuWxZ07d3RyTEmiK0SlRGHsubG8aisSXQRzB83V2bHbVG3DY6JrIsKilA1uU5ByKVm/JE6FnGKTyvcrvK92WMJLVKtWDUeOHMGlS5de+XWq6J3SbArPZ5yYgatPM78xLwhXrvy/S803axvsSalJmH1yNifPR+4cicjESFTIVQG7eu7CP+/+A98csoGvtvfK48ePsXjxYlXOX9hDqUYPjpAkurlx/fp1IJ8yr1mgptrhCDqAumM1+YqsFP89j72NPb5r+B3Pfzr0E57G6c4HSjAdJAshGJwNJzbwaBNtgwI5C6gdjpBBevTogUqVKmVJtoE0eElTjNohTz1QkqKC8DZuP7rNWtBEndIi3WBsUCXwuXPncPHixdcmzjJKbtfcPFK1KlUGWiJpz9Iw9fZUxGSLQR77PDj8xWGdyuZULFoR2eKz8Ypw4/GNMJUkunVVxTywfcn28Hb2Vjkq4WUcHBxYF93e3v61r2latClaFW/Fm+kjd4w0aHyC+RAeHs5VpFlJoienJuPPM3+ixIwSGLRlEO5H30d+t/z4o80fON3/NBoVUTooBfXXGfo0jn0bootuvpy8ehJwAbKlZUOl3JXUDkfQESVKlNBpEp3oXq47b67SJusPB3/Q2XEF00GS6IJqpqI5UrOu5SqYJtZW1mhYWNE1FF10Ib3sPr+bR6tYK+Tz/n+5iGB0VWKErlrqPR08YW+tJOFCoi1TF/3X47/iTPQZ/j1s7bMVPtl9dHp8Ssh7JnjyfM8lw+vMZqbjwdHNEVcdlAciMRQ1bSY3ncxV6RuvbsTOGzvVDkcw4Sr0PHnysPZtRkhNS8Xi84tRalYpfLDxA9yOvI3cLrkxo8UMXBt8Df0q9eM1q2CE+tX3Ff1qQyJJdPPlyJ0jPGZPyQ5HW0e1wxGMOIlulc0KExtP5DnJfd2K0I1UjGA62KgdgGB5OBZ0BMKAdxu+q3YoQibZtGkTVq9ejb59+6Ju3bqZ1kVffWk1J9HH1lVMOgThTRy/fpxHtyQ3tUMR3pJE11UlOlWdkaQLtU6TpIvG2MtSGLNwDCbenMglD1OaTNGbGXcR5yIIQxjOhpyFsTN37lxU/6g6+m3sx631UiFqvJw9e5YNH3PmzInvvlPan1+mZI6S+Ljqx5h2fBr6/NOHO9WS05K5Op2qg3l87t+aOWkTtyjWAm1KtOGqQdHEt1wyI+VCHT60Bv1639e4/OQyf87byRuf1/4cA6sMlCSaERMWFgY/Pz/2xoiIiICzs7PBzi1JdPPlUuQlwAMo5lBM7VAEI0+iazrpqCBwT/AejNs7Dos7qCMxJaiDJNEFgxMQqhgD1iqqmE4Jpsf69euxcOFCZM+ePdNJdI0u+pG7RxCbFAtnO8MtggXT5MLDC4A1kNtOkfgQjI/SpUvrtBKd0CTRQ2IspxI9OCQYjX9pjJuuNzmB7vPUBx/4faC381XNXxWnHp3CrQTTqKaZf34+j/38+nFFkGCckHbxvHnz+CH2dUl04qt6X2FJwBKW0KCP9EBJrBP3T+Cb/d8gr2tetC7RmhPq9FArCVDL4vJlJQlesmTJdFUw/3PlH06eBzwK0HY8ja41Gp/4fwIXOxe9xytkDXr2oA6qlJQUHDx4EM2bNzfYuSWJbr6k5U4D4oGGvkqntGAe6CuJThv3VI1edV5VLA1YihE1RnARgGAZSBJdMCjUNhn4KJDn+qqoE/QPLVjJYHTbtm2YMkUxBssoRT2LooB7AdyJvINj945JNaHwVm7H3AbcgRLZlQWRYNyV6JSs0EV1qKWZi3699Gt8d/47pLmmAWlA5eTK+LTWp3qttG3h1wKzt81GkmcSV2gaa2I6MTERN6Nu4tCdQ7DOZo0+fn3UDkl4A+XLl9catsXFxcHJyemVryOPlIN9DuLgnYMs7UJmsTxa/3+0sn1hTuOVp1dYAmb79e2ceP/99O/84WTrxJv0lFCnxLqPi27ljwTj4+ZNJaH5ukr0+OR4HL57mCsGN13dhMBQ5TnEzd6NEx/Dqg/juWA6NGzYEH/99Rf27NmjShKdNvaN+V4pZDw/cTflLs/fq/+e2uEIOk6ik9E53R/S0tJ06ilUJU8VdC3TFSsurkDHFR1RPV91XnPQB8mCaeb04eXkJdcLM0KS6IJB2XNuD+JT4mHzzIaTqIJp0rhxYzb2oWrT27dvo2DBghk+BiWEquapykl0eqCRJLrwNp7gCY9+BfzUDkV4DbRIpfc2Gb2FhoYiV65cWT6mpSTRX6g+dwZsI20xs/FM9GnSB1u2bNHruZtXaQ77nfZITE1EcHgwimY3zvtzgwYNcLngZaAk0KpEK+3fhmCc0Ps/R44cePLkCW+sValS5bWvLeVdij/SS438NdC7Ym82HN53ax82XtmIDVc34F7UPWy4soE/CP+8/uhdoTcGVh2ok59JMD6WLVuGyZMnazdpSPaHuhQoab7n1h7ueExKTdK+3tnWGUOrDcWImiN4A0cw7SS6Icnnlo83cOnviXxa8rrlNej5Bf1w9elVxCbH8iYsSYwJ5oOXlxeOHTumt+N/3/B7rL+8njfW6ON10HUjl0suTqhT4p26nwTTxWy3Q06ePImWLVvCw8ODtdKqV6+OlStXpvv7FyxYwImA133s27dPr/GbK9vObuPRPtJejHpMGHpf0XuK2L59e6aPUzZnWR4vhF7QWWyC+VaJpLqn8rxheWm1NFYcHR25pb5cuXJ4+vSpTo5J1RzmnkSn6siys8sqCfQ0oEpSFTwc/xD9W/Y3yPmpyldzPT738ByMEapkPnHmBMILhPO/xVDU+KH1Ml0LiMBApfpX1zjYOKB5seaY1WoW7gy7g7MDzmJC/Qm8SU9QMnXQlkG49vSaXs4vGAHZgMc2jzH/yny0XNoSnhM9UXt+bXy17yveYKGEJ0n+9KrQCwvaLcCtYbfwfaPvJYFuwtCGKnHmzBnetDcU1AVT0EMpHBJJF/Nh05lNPJbPWV7yE0KGoKKTcx+dw9KOS/FL018wquYo9CzfE02KNEG5nOXYa4NIfZbKzzFnQs7g812f43HsY7VDF7KAWVai7927F82aNYODgwPeffdddmpfs2YNunbtirt372LEiBHpPla7du1QseJ/9Y0KFSqk46gtg1N3T/FiN4+NVI+ZOtQ+efjwYZZ06d8/c4keSaIL6YU6FlKQAjtrO9QsXVPtcIQ3cPHiRZ1Kj5hzJXp4fDiGbR+GRecXsd6/Y6wjptafarDk+fNUyFUBp0NOY9/lfehUuhOMjRMnTiC1WCrgBE6IUeJUMA1JF1qXBwQo+tP6hK47pElKH+PqjeNrBiVVzz86j+P3j6O4V3G9xyAYlidxT1BlbhXcjrz9wue9HL1YH1/zUTx7cTGfNSPy5MnDG/akh0+FbR06dDCopAsl0OmjTsE6BjuvoD+W7V8G2ANx1+PUDkXQE8nJyYiNjeVCQF1D3Qtv6mCg7qjQ2FA8jHmI3v/05rzH5mubuZtOME3MLolOJiMffvgh6x0dOHBAmwD/6quv4O/vjy+++AKdO3dOt/xE+/bt0bu3/IHrijvRdwA3oJiHOF+bOrRRNW7cOOzatYtvTLa2thk+RhnvMjxefHxRtAWFN0L6t0Sx7MWkSsTI0XWiwlyT6OP/Ho9JlychzjoO2ZCNtXknNJigmili3E3l4XHVwVWY0XEGjA0ykENlZd7Xry9XBArGj74r0d927ahfqD4n0U/eP4ke5XsYPAZBvwydO5QT6PZW9mhctDEaFW7ESfNyucrJmtICJF0oiU6SLgZNonuIuai5cSP+BifRq+ZTOpgE82LmzJkYNmwYevTowWoThoa6PUn6iT46lerESXSSnJMkuulidqsLupHeuHED3bt3f6GC3N3dnRPoSUlJWLhwoaoxWjJPU5X2/pI+ojdm6lSuXJm1Tqkr4/79+5k6BiVEqbI4JimGK40F4XUcvXqURzEVNR3IWFSXSfSQmBCYA2FRYSg2qhi+ufYNJ9Bz2+XG4b6HManpJNUS6EStYrV4fGKjeA8YG9tPbgeKUDNbNvTz66d2OEIGk+hRUVGqnF8r6/LghCrnF/TLlluKX0QTmybY1H0TPq3xKSr4VJAEugXQsWNHDBkyBJ06GbZzSmMuejNCkujmQEpqCqKclPtTy4ot1Q5H0AM5c+ZEamoqrl69qnYoaOfbjsftN7azp4tgmphdGY9Gq7xp06avrJwl9u/fn+7jnT17lnVdqcKdkoVkqEgGBekhMTGRPzRoHiCoapc+LJFYu1gey+UrZ7G/A3OCKkDc3Nx4ntn/n75evmwseu7BOeR1tkyDHs3vTt4Tr2f1vtVsthhyIUR+T0YObWSTlBq1TZKZYFbxdlD0BCMSIhAZF8nGT6ZM518744bLDeAZUDmxMjYP3ozsrtlf+3dtqOtDy0otMfjMYKS6pOLy7csomsd4zEVpDXYiWUmCVveujjzOeeQ6YEJJdDIWpbWCGv/PKuasqNX6j0uI44owc8HS1w6Xbl9ChHsEz4c3H26xvwdLpW7duvxBGPL/fQG3AjzeCLthtH9zln5tyAhbT24F7OiXBTTxayK/MzOkcOHCPFISXe3/v2W8yiCfaz7ci76HHdd2oEWxFgaPQe3fgTlgdkn0a9cU46Dixf+re+jj4wMXFxfta9LD9OnT/2Oa9vXXX+Ozzz576/f++OOP+Oabb/7zedKG1DjIWxLR8dFIc07jeXxIPLZsUapHBMvGM9mTx7WH1iLbNcvWq9y5c6faIRgtt2NvcxLdNdFVrh1GDiXPz58/z/NVq1axuXdWK9qpVT8xLRHLNi1DbnvFaNRUOfL0COAF1IqshVH1R+HYwWNGc32wjrJGqlsqfl70M9qVV6pljIEr164guYyy6K/lWEuuAUK6Iak4JysnxKXE4fd1v6OIk1JFak5Y6trh1wO/skSk/VN7RN2JwpY7cl0Q9M+DOEVa7vLDy0Z/L7LUa0NGmHNkDnutOEQ6YM+uPWqHI+iB+Ph4Hqkwdvny5doCQLUoZ1+Ok+izds/Cs6u66drNCHFxov2fVcwuiR4ZGamVb3kV9KbRvOZtO1YzZszg6vV8+fIhLCyMpWLGjBmDzz//nJPggwcPfuMx6LXDhw9/oRI9f/787Cie3mp2c2Jv4F6AZI2TgH7d+rFuvWBeNyfaZMoogUcCcWDfAaRkT0HLlpbZRkc7wrTQbdKkSaa05S2B+EPK31jHeh3Rspll/p2YEiNHjkRISAgKFCiAatWqZfl4+e7kw43wG/Ct7IvaBWrDVAm6E4TEc0qH2q8DfkXFov81Llfz+pBzX06EIIQlXYzpenxz100gFnBMdcT4buNZBkwQ0kv1yOrYc2sPHIo6oKWf8fxdZxVLXzv03duXx/re9Y3qeiUYjoSEBBw7doyf7du1M8zGb1h8GEZeHYnwlHDUb1LfKLvjLP3akBEmHJvAYzGnYnIdMWPy5s3L8rOU49PFc0lWsLlpg63LtyIwMRDNWzQ3uPyYWvJ65oTZJdF1Rb169fjj+Tdez549UalSJVSpUgXjx4/HwIEDYWPz+l+hvb09f7wM3cws8YaW6prKY9m8ZV/5exFMk6FDh2Lu3Ln4/fff0atXrwx/P2lXEpeeXLLI98XzWOq14W08DHuINBeli6VppabyOzIBSpcuzUl06vyqXbu2TnTRKYkeGh9q0v//Z++czaNDhAOqlqxqdNcHXw9fTqJffHrRaH7P1ImwOmQ1zwfXGQxnh6x1NgiGZ9u2bfj555/h5+eHX375xeDn98/rz0n0Mw/PGM3ftS6xxLXDlbtXEOYexvNRrUdZ3M8vKFCiuFWrVihSpAg6d+5skHPmtMkJd3t3RCZG4n7sfZT2Lg1jxRKvDZkyFXVQTEXld2W+lChRgpPoN2/e1MlzSVYgE2wXOxf2egp4HICqeQ1raCt/51nH7EqBNRXor6s2p52X11Wpp4cyZcrwG48q04OCgjJ9HEskOCKYxyLZza+V1pIhiSSqBKGH5MxQNmdZHi8/uYyUtBQdRyeYA3vOK+2V2eKzGZVOs/B6SpUqxaOu7pMac9EH0Uobtamy48YOHv1c/GCM1Chcg8cHqcbze151aRWO3D3C1X5Dqw9VOxwhkxJPJGV44MABVc5PSXTi5IOTqpxf0D0/rvuRn2IdIxzRyK+R2uEIKlGnTh0uaKPE2K1btwxyzmzZsv1rLhou5qKmLveVnEORiutcyzCbMIJ6SXTCGMxF7W3s0bxYc55vuLJB7XCETGB2SXSNFvqrdM8fPnyImJiYV+qlZ4QcOXJoHwqE9BMcriTRC3so5g6CedC8uXIT2LFjBztfZ5SCHgXhbOuMxNRENukRhJc5eu0ojy4JLmqHIqicRKeqDVMlNS0Vkd7KBv8nzT+BMdLOX2mHT/ZIRlJqktrhICImAn3/ViQbPqv1mfbvQDA9c1HiwoULmVonZBVNldeF0AuISxYtUHPgQuoFHut41VE7FEFFXF1d4e+vbJLRRp2hkCS6eXA97DriUuPgYOOAphWbqh2OoEdIYaJ79+6oWPHtMoqGoG2JtjxuuCpJdFPE7JLoGgkWSui9zPbt2194TWagxf+pU6d4XrBgwUwfxxJZt3cdj4mPFD1YwTyoXr06ew2QWcfp06cz/P2kA1YmZxntA64gvExgSCCPPrY+aociZDCJfunSJZ0czxwq0U+HnMbT+Kdws3fDO9XfgTHi7+sPDwcPpCIVlx7r5v9dVug2tRti7WJhHWuNoVWlCt1UKVq0KHumUNfajRuG3yzP65oXPi4+SH2WinMPzxn8/IJuCY8Px/kYxbx6+kfT1Q5HUJmGDRvyuHv3boOdU5Lo5sHpB8pza4VcFWBjJSrH5ky3bt2wdOlSg8k+vY2WxVtyDiTgUQBuRRimi0bQHWaXRG/UqBHrov399984d+7fhTLJu/zwww+ws7N7QbeZNFsvX778H/mXVyUDKYFOpqLXr19nc9DcuXPr+acxL0ISlArC7FbZ1Q5F0LGuVuPGjXmeaUkXb0XSRZLowquwyaUsbGuWqKl2KEIGNNFLlizJPiJpaYqevaUn0bde28pjkyJNYGttnHqE1KZOD5PE+YdKkkotLgRfwLY45Z7yQaEP4O6UeSk+QV2sra1ZDpEICAhQ5e+6ah6lGv3E/RMGP7+gW6j9neT/yuUsB98cvmqHIxhJEn3Pnj3soWEINF3VGqlSwTRZeWgljyXdS6odimBheDl5oXYBRZt945WNaocjWHoSnXTR/vjjD35or1u3Lvr3748RI0agQoUKrIFEifRChQppXz9mzBiumFu3TqmS1kDmofQ9ZCZKiXM6DiUFJk+ejHz58vE5hIwRbx/PY6XCldQORdCTpEtmk+jaSvTHkkQX/ktYNsU8rEOdDmqHIqSTXLlysZTLqlWrYGWV9aVGbpfcJp9En7FtBo/VvKrBmCnjpVyPt5zZomocXWZ3AewBpwgnzBwwU9VYBN1JugQGKp1FhkaTRBdddNNn1r5ZPHYubRwVhYK61KhRAw4ODlwYd+XKFYOcUyrRzYN9V/fxmC0km9qhCAaA8oNUPBseHg5joJ2vIqEoki6mh9kl0QmqEj906BBq1aqFFStWYPbs2fxAv3z5ck6opwd6Hemskev3lClTuLKdWlHHjh3LVTRU7S6kn7uhd/HMUakOqFW6ltrhCDqmWbNmPB4/fpxNdzNrLiqV6MLLUFXR1aeKCYxUnFkupl6Jfu3eNTx1eMrz+vnqw5hJvK1Iru0I+K8snqFYc2gNghwVPf2JDSfCxlrarE2d8uXLq1aJ/rwu+sn7kkQ3Ze6E3sHJMOX/YU0P6U4TwAl0euYn9u1TkqKGTKIbqvpd0L1PTYRjBM+bV1CKwQTzz1dQ8eymTZtgDLQp0YbHfbf2ITLhRVUMwbgx26cSMhnZulVpnX4TCxYs4I+XoYpzQXccvawYA2aLzwaf7KJrbG4UKFAA/fr1424NapvObBL92tNrSEhJYIMXQSCC7gchNjkW1tmstQ8tgulAD5dxcXFwdnbWSRI9OikaMUkxcLEzLZPZ6ZunA9kA+wh7VPVVknnGSpNyTfDnvj8R6RjJVTu66CTICHTO/qv7A55A3si8+KSNcZqwChmvRPf29oa7uzqyPFXyVOHxWtg1RCREsPa/YHr8uOZHwBqwi7RD4wqKlKAgfP/995g0aZJ2s07fFPQoiGzIxkbFobGhyOWSyyDnFXTHvvP7uNsNKUCbakoyUzBvypYti127duHEiROsNqE2xb2Ko1SOUgh6EoRt17eha9muaockWHIlumB8nA0+y6NjoqPaoQh6giSOhg8fDk9Pz0xJNXg6eLLp15UnhmnFFEyDzcc382gVaQU7azu1wxEyABn4eHh4oHfv3lk+lqu9qzZxHhKt+GuYEluuKdIoFZwUvXFjpkWVFkAq8MzhGY5fPm7w809cOxFhnmH8YLu412KDn1/Qn25xaGjoKwtXDEEOpxxaHeNTD06pEoOQddZfXc9jNTfjlsUSDEu1atXg5+fH/guGgNaj+d3z81wkXUyTDacUCQ3nGGc4OTipHY5goOuEpnPeWGjr25ZHkXQxLSSJLhiEoBClLTt7NjEVFf4LVa+LpIvwKk7fUkyePdMyvjkjqEv27NkRFRXF2uiWLOmSkpqCYGvFfOzdKu/C2HFzdoNDtNINtOm0YVtek1OTsejhIp7Xta+LBhUbGPT8gv7ITJearhFJF9PmwdMHeOjykOdDGw9VOxzBwhFddNPm6C2lS76gXUG1QxEMqFRBnDt3DgkJCTCmJDoV29AaWDANJIkuGISwZ4pOdl7nvGqHIugRMvWhKrObNzO+oJQkuvAqLj+5zGN+J6XiRzAdSHeQIFPvlJQUi02iL9+3HM+cngFJwIfNP4QpkMda+V0fDVYeMtNLwM0AvDPpHVy8dTFT5517ei4uP73MVcMbRkpVjrlCkj1q4J9HeYAWc1HT5MfVP7IQqW2ULTrUFKNx4UX27t2LPn364K+//jLI+TSdLcERyia5YFpcj7v+gtSXYP4ULlwYOXLkQHJyMs6fPw9joFreavB28maZuYN3DqodjpBOJIkuGAT3QooG5vtt31c7FEGPfPDBB7yAXb16daaT6BcfZy75Ipgn9xLu8Vjau7TaoQiZ8EpwcnLixeqNGzeyfDySfTLFJPqCI4p8hU+cD1wcTUPLvWwO5Xp8JSL98lpRsVGoPqM6VsetRt2ldXHk7pEMnfPek3v4et/XPJ9QfwLcHdTRzhb0K/tG1wWSflO1El2S6CbJ2streazqXNXgXg2C8XP27Fku5FmzZo1BzieV6Ka9kRvhIKailtgRp6lGNxZJF2sra7Qu0ZrnG65I8YipICsQwSAEhyu79GIMaN40b64sRLZt25bh75VKdOFVRForbuVVixi3GaPwXyjJUbJkSZ7rQtLFVCvRb0DZQGhUoBFMhbol6vIYahWa7u9p8H0DxHvE8zwsJQz1FtTD9OPT2Vw2PbT/tT2exj9FAYcC+LCyaVTsCxnDxsYGd+/eRUBAgCrnr5S7EqyyWeFe1D2T9FawZMJjwvHAWbn2f9JQzIaFV/suEAcOHODNe30jSXTThf6fke+LTTYbMRW1UF10Mhc1Ol30KxvSvWYW1EWS6ILeoYuBptWtsKfS+iaYdxL90KFDiImJydD3lvEuwyP9rcQkZex7BfMkLCoMKS6KDEiDcqKNbMqSLrpMoofEmE7yKyw+DHee3eH5hF4TYCp0qKFIJaS4piAqMeqtr/9u+Xc4Y3+G5yMLj0SXMl2QkpaCoduGosQXJfAwTNExfh17zu3BaWvF/+DDgh/CxspGJz+HYFyUK1eOR0qiq/GgSObEpXIo1ySpRjctdt3eBdgCOW1zomvdrmqHIxgh5cuXZy8Wev44eVL/729JopsuZx4q65WKuSuaTIegoBtatmyJL7/8Ev369YOx0KRIE9hb23MORDryTQNJogt65/Ldy4hLjgOeAfldRdfYnClWrBiKFCnCFSD79+/P0Pd6OXlp5RouPb6kpwgFU2JvwF6AvOgSgNIFRc7FlJPoly5dsshK9F03dyHtWRrLERXxMp1OrCK5iyC/m3K/Dnj05qrhC8EX8NXZr3hePr48JvWahOWdluPXZr8iW1o2XHe4joLfFcSm4683Ke25qCdgDXhFeGHsu2N1/NMIxkLp0qW5Q+Xp06d4+PDNGyv6QsxFTZPVQYpMYO+qvUXKRXgl9HfRoIFScLFr1y6DJdGpsyUxJVHv5xN0x+kHyqZ95dyV1Q5FMDBVqlTBd999p71WGAPOds5oXKQxz0XSxTSQVYigd45dPsajVawVHO0c1Q5H0LPWWL169Xh+5EjG9HAJkXQRnifKVqmALehSUB6aTRTSHmzVqpVWg9DSkugrz67ksUWxFjA1KvhU4PHcw3OvfU1KagoaTG/Axqn2kfbYP3a/9l4wrPowzPKfxff+JPcktNnQBp/+8el/jjH9n+l44P4ASAP+eOcPPf5Egto4OjqiePHiPFdL0qVqHtFFNzWoEGfz1c08f6fMO2qHIxh5lSmxaNEivRsYkxmgs60znuEZbkfe1uu5BN2y6vAqHgvZF1I7FEFg2vm241GS6KaBZCUEvXPutvIA7pIs7VKWQM2aNTOdRNdIukgSXSAeJCrJ0rqlFX1mwfRo0qQJNm3ahMGDB+s0iW4KmoGpaalYF7CO577WvjA1ijoX5XHtEcXM71UMWToETzyeAMnAis4r4OHi8cLXB7YaiPMDz8Mj3AOwA6benwq/MX6IiY/RJuHH7BvD89IJpdG+Znu9/kyC8Ui6BAYGqp5EN4XriABM+HsCYpNj4WXtJZWjwhvp2rUr3N3d2cxc39XotFmskSnVeH8Jxg9trtxKvMXz/NbSIW+JhIWFYfPmzdi7dy+MBY256PH7x/EwRp1OPSH9SBJd0DtXHl3hMYdNDrVDEQxAjRo1tIYdKSmKnnV6kUp04XmuPFWuHb5eppeAFHSPRu6JkinRSdEwdlYdWIU05zQgCeha3fQ0fG2eKLrkx28ff+XXqUL9zzt/8rxnrp5oV1OponmZsoXLIuTnEFRPqa58n8M55BmTB2dvnMWgOYMQ5xEHJAIrBypV+4L56xarWYlePld52FrZsl+Bxq9HMG6WByznsVBcIU5cCsLrcHZ2Rq9evXizzhCbZKKLbnocunCITUWRCrStrhg6CpbF0qVL0bp1a0yaNAnGQm7X3PDPq3Ttbrr6evlDwTiQJLqgd25HKS1u+VzyqR2KYCAN5LVr13IViI1NxszhJIkuPM/Ra0e1ci6C6UIPsqGhoQgPD8+yZqCbvZvJSLrMPzSfx1xxueDmrMRtSrTwUyRo4lzikJCU8MLXYpNi0W1NNySlJnEL6sJPFr7xWA52Djj67VGMKTyGPQ6iPaPRbG0zLHm0RDmXcwuUKaR0Ignmr0dKHWsavwRDY29jj4o+FXkuuujGT0RMBG7bK88RH9X9SO1wBBPg559/xvnz59GsWTO9n6uIhyTRTY1/Tv7Do2O0I1ydXNUOR1CBatWqaQv+jKkjrW0JZVNHJF2MH0miC3onNCmUx2I5iqkdimAASLu6Q4cO8PHxyfD3kvkeERITgqdxT/UQnWBK7ZbXI67z3DFOvBRMmd69eyNXrlxYsGCBziRdQqJDYOwce6r4gdTPVx+mSL3y9biKHjbA9lPbX/hasynNcPnJZf7/8UfbP9JdHfpDrx+wq8su+Lr74nHcY8Rbx8PHwQd/D/1bTz+FYGy0aNEChw8fxpgxioyPGoguuukwad0kwB6wjrFG7ya91Q5HMAEcHBwM1rGgrUSPkCS6qXDkliI3WtBWCnQslQoVKsDOzo5Nzm/eNJ73bltfJYm+8+ZO9gIRjBdJogt6J9paabsvl0/RwRSE1+Fq74pCHorJy8XHF9UOR1CRS3cu8YMzngENyhuPg7qQcQoUKMDjpUuXLMZc9Paj24hyU4xxBzUZBFPExtoGrrFKldaOgB3az4+ePxqHEw/ze3N2k9nI4ZQxqbZGfo1w5uMz6OfXDx4OHpjfaf5/tNQFQZ9Uzask0U/cP6F2KMJbWH5ekXIpZ1OOr0mCkF5iYmLw119/ITk5WW/nEDkX0+NazDUeK+WupHYogkrY29ujYkWlI+348VdLFqoBdeRTHiQhJQG7burX00HIGpJEF/QKmYaluCi62P4lFJ0nwTIMOyZMmID3338/w9+rkXS5GCpJdEtmb4Bi9mITY4PsbtnVDkfIAqVLKx0mQUFBFpNEn7F5Bq+w7KLsULe86RrjFrRXKrVO3lUqdo8HHcfkq5N5ThrnbctlTk/UydaJK9jDRoehebHmOoxYMBXi4uIQERGhaiX6mZAzbAAsGCdRsVG4aaskJz+s/aHa4QgmBEk0VK5cGf369cOGDRsMkkQ3JlkI4fVdrmH2YTxvVk7/cj+CaUi6GAvUQaORdPnnsiI7JBgnkkQX9ArJcjyzegYbKxtUK6lcrATzh7TQx48fj0WLFuHhw4w5TJf1Fl10ATh5U0nauae6qx2KkEU02seURM/qQ2YeF9NIom8M2shjGTvT1vn2y+3H443YG0hKTkKz35uxIZdThBN2fZn1KhkxCbRMxo4dC1dXV0yerGzIGJqSOUrC2daZTYqDnmR9c0/QD7/+8yvgAFjFWqF/8/5qhyOYEHRv6dy5M89nz56tt/NoumejEqMQnpA13xdB/5y+cRrPHBVT0fY12qsdjqAi/v7+RleJ/ryky8arG2WT34iRJLqgV4Ijgnks4F4A1lbWaocjGAg3NzeULaskw48eVcwhM2wu+liS6JbMpceK9Ec+BzEkNnV8fX35gZY6VB4/fpxl93riQYzxJtFpoyAih1Jh26tmL5gyjco04jHcPhwtf2yJSM9I1knf0GsDnB2d1Q5PMFHII4EqAgMDA1U5P61HK+epzHMxFzVeTsYq/28q2FYQKRchw/Tv35/XHrt378bVq1f1cg5HW0fkdlHWJSLpYvzcTbnLY+kcpU3S8F3QfSX62bNnkZREBkDGQd2CdeFu786+QSI5Z7xIEl3QK8HhShK9sEdhtUMRDEzNmjV5PHJEMXDJcBI99IK0Rlowd+PuaisGBdPG0dERhQsX1okuuinIuQSGBiI0IRSONo74qMVHMGXaVm8LK1jhmdMz7E7dzZ/7MM+HrGsuCJmlfPnyPAYEBKgWg5iLGjfJqck4EqasH3/98Fe1wxFMkIIFC6JVq1Y8nzNnjt7OI7ropsPpB6d5rFlIeUYVLJdixYphwYIFOHPmDHfQZ5aEhAQEByv5Ll1ga22LFsVb8HzDFf1JUQlZQ5Logl5ZuGEhj9F3FXNRwXKoUaNGpirRfXP4wjqbNcLiw/AwJmNSMIL5EG6ltMVWLqRUCwrmI+miiyR6SHQIjJWt17by2KBwAzjYOMCU8XT15GsyYwUUjCqIOQP1l4wQLINy5RSj+Vu3biE6Wp31oSTRDUtMfAxKjS6FAsMLoMMvHdg0jdZ5r2NP8B6Wx8jpnBO1C9Q2aKyC+TBw4EAeKVkWHx+vl3NIEt10OB2iJNHFVFSgLhXybiPfJiurzKdEN23ahCJFiqBLly46i62dbzseN1yVJLqxIkl0Qa/cibrDo3ua6BpbahL91KlTGWqToqRTsezFeC666JZJUmoS0tzTeN68qpgOmgPt27fHkCFDtMkzXVSiG2unyrx983hsVMA8qrUr+lTU3scPfHYgSw8bgkBkz54defIo7+ULF9S5z/vnVfRQzz88j8SURFVisCR6TOuBy86Xcdf9LtbHrEeTxU3g9bMXCk8rjMKfFUbTb5viu+XfIeiOstE6dcdUHjuW7ChykEKmadasGQoVKoTw8HCsWLFCL+eQJLppQBJiu4OUjrrCDtIhL+iGv//+m0dKpOuK5sWas58gSZteD7uus+MKukOehAS98jhF0b/1zfn/SjbBYihevDi8vLyQmJjIemOZlXQRLA96EEl9lsrGb2XzK38LgmnzwQcfYNq0aahdO2sVhRrt0fiUeEQmRsLYuPf4Hm4k3eB5BecKMAc+rf4p2pRog30D96FAzgJqhyOYCWpLupAhoJejF5LTkhHwSD1ZGUvgxoMb2BCpVNQViS6C6q7VUdSzKP/7VsQt3HK6hZ1pOzHuyjiUnl8aNqNssO3+Nv56qWdKF5MgZAZra2sMGDCAq06zKif3OiSJbhocv3wcKXYpbCpavXB1tcMRjICIiAhMnz4dI0aMyNT3R0ZGYsuWLTzv1q0bez9dv571pLeHgwfqFazH841XNmb5eILukSS6oFdibWN5LF9QeVgSLAdasFI1uouLC+7cUToS0osk0S2bK0+u8FjCqwT/HQnC8yZeng6eRquLPnPLTMAasI2yNRvd8Kp5q2JDtw3ainRB0AWarhS1zEXp3kJ/24SYd+mXd2a8g2cOz+AQ6YCgH4NwdPhRXB9yHeGfhWNd+3VoZdcKBSIL8HWTSHVJBeyAbHHZ0L9Zf7XDF0wcSqLfvHkTP//8s16Or/H9Co7QnS6yoHvWn1jPo0O0AzxcPNQORzACUlJSMHToUEyZMoW7VTLKunXruFiQJGHoGlOgQAEMGjRIJ7G19W3Lo0i6GCeSRBf0qn+Y6pzK8+q+suNricyfP593ed95553MJdEfSxLdEtlyQtnV97H1UTsUQYfQtYA8ErKqS5rbNbfRJtH/ufgPj6VspXpSEN5EgwYN0KtXL9SpU0e1GEQXXf/sPrsbZ22VbsSvqn8FO1u7F6rt2ldoj01jNuH2lNtI+iUJdwfdxXS/6XjH+R0sbrYYDnam7SshqI+npydLuugLTSX67YjbSElL0dt5hKxxOPgwj/lt8qsdimAk5MiRA0WLFtXKz2ZWyoWq0P38/NhkdOfOnTh9WtHezwqtiiumyIfuHEJ8sn78HITMI0l0Qa9tU6Ai0mSgTMEyaocjqHRzolbKjKJJopMWWNozRRtbsBz2Be7jMe5OnNqhCDqEKjVq1qyZZfmG53XRjU1v89qzazzvVKGT2uEIglHTokULLFy4EF27dlUtBkmi65/eS3pzd45XhBfGdBnz1tfn886HwW0HY+XIlXiv4XsGiVGwHG7fvo2oqCidHpM29u2t7VmG8G7kXZ0eW9AdV6KULtdKPmIqKvyLv7/ij3L8+PEMfd+jR4+we/dubRKdNuq6d+/O//7xxx91sjmXyzkXb8ydfZgxWVxB/0gSXdAbJ68rDyV2cXZiRCZkyASQjEXtrO0QkxSDO5EZk4IRTJ+HKQ95LJc7ayaUgnFRqpRSnZ1VXVJNEj0kOgTGxMZjGxUZgmTgk1afqB2OIAhvQSPnEvQ4CNGJ0WqHY3YcvnMY99zuIduzbJjXWTFcFgS1GD58OJv/LViwQKfHtcpmhcKeiqSL6KIbJ1Tk8NTuKc+blG2idjiCEVGtWrVMJdFXrVrFf1eUhNdUs3/22Wc8rl27FpcvX86y5Fz1fIqSw/F7GYtN0D+S2RT0RmhSKI/ett5qhyKoyIQJE1CiRAmsXLky3d9DjtSlcigJN9FFtzyi7ZVkRvXiIgNlbpXoRFBQUJaOk8fFOCvR5+1XkkResV7I7pZd7XAEwST0SGlTLSREnQ0xHxcf5HPLh2d4hjMhZ1SJwZwLJ0bsUMzaPqj8ATrU6qB2SIKFQ4kuSnrNmTMnQ4U96UHMRY2b09dO45nTMyAN6FBDrkXCfyvRT5w4kaHrQp8+fbBs2TKMHTtW+7kyZcqgbdu2fJxJkyZlObZqeZUE/7H7x7J8LEG3SBJd0Bv2uex5bF+vvdqhCCry+PFjXLt2DUeOHMnQ94m5qGVyM+Qmnjkqi5hGFc3DmFF4sRL90KFDWXqA1cq5xBhXEv10hKKBWDd3XbVDEQST4P333+eHzkWLFqkWg0i66IdFpxfh+P3jcLZ1xoQGE9QORxDQs2dPODs780b+/v37dXrsIh6SRDdmDt44qDUVlSIH4XlIy9zGxgahoaEs95Re6Fry7rvvok2bNi98fswYRbZs8eLFuHfvXpZiq5bv/1XyUoludEgSXdAbGpdyjWu5YJmQBjJBhoIZQZLolsnu84q+nFWMFXyyi7GoOdGqVSvY29vztWD16tVmpYlO0lNPXZRW4c86Ku2cgiC8GUqgE1n1ScgK/nmVKjRJouuOiJgIfLD8A54P9hvMFf+CoDZubm7o0aMHz2fPnq3TY2vkXDTPvoJxEeEYwWOXOl3UDkUwMhwcHFChQgWWHs6q3CRRvXp11KtXj4uFDh5UNm+yssmfDdlwO/I2HsYoUqeCcSBJdEFvBIcHv7CwECyTGjVq8Hj27FnEx6ffXVqS6JbJ8evKbrt7srvaoQg6pmDBgtoKjU8//RTR0dFmk0TfE7wHyWnJvGnsX1RJygmC8GbKlVN8LwIDA9WvRL9vuUl0eth/EvcEx24dw/il49Frai8s27cs08frMb0HUlxTYBVrhU+rf6rTWAUhKwwcOFCrWfzwoe6SUiLnYtycDjn9gg+GIDzPihUr2HC4ZcuW6Xp9ly5d8MMPPyA8PPyVX581axZu3rzJhqNZwdXeFWVyKsUGUo1uXEgSXdAbp66f4tE62lrtUASVE2e5c+dm7dNTp5S/ifRQxlu5aQQ9CWJnasEyuPRIqQLIY68kSgXzYvTo0WzsRYtV2ljLDLldc2uT6LrWNc0say+t5bFl8ZZsBiQIwtspX748j2TAlZSUpEoMlfNU1laQPo59DHNl+6ntGDV/FDr+3BH+X/qj0IhC8BjmAa9vvOD4vSO8J3mjxsIa+Ob6N1gcuRjd93ZHl8kZr9q8cvcKNkdv5nmfAn2Q0zOnHn4aQcgcVHFKxT30TPLnn3/q7LiSRDdeSAf/9AMliV45t3K9F4SX/RJIniU9XLhwgU1Fx48f/8Yuu/z58+sktup5FX+wY/dEF92YkCS6oBdCw0OR5pTG84oFK6odjqAilFDSVKNnRNKloEdB1tJMSk3C9bDreoxQMCayl1C0CltVb6V2KIIecHR05IoPSprVrZs57fDcLkoSna4N4QmvrgIxJDHxMVh0UtF0bpqvqdrhCILJUKBAAZZYSE5OxpUrV1SJwcPBAyW8SvD81IP0b/SbAlRd/u32b+H8qTOab26OyXcmY138Opy0O4nbbrcR6RmJMIQhMTWRX5/TOSecIp3gHOHMT4irYlfBb4wfkpLTv8HRZVYXwAFwiHDAbwN+0+NPJwhZq0b/+++/dbYRr5EufRr/FJEJkTo5pqAbzlw/g0exj4BnQLmcSveTIGQWMhMlWrRoAU9Pz7e+njrtYmJisq6Lfl8q0Y0JSaILeuFo0P+TpYlAwVwF1Q5HMBJd9IyYi1pls9K2MImki+UQHK3IQDUo10DtUAQ9UaVKFeTJk/lOA3sbe3g5ehmNpMuU9VPwzP4ZrOKs0LxUc7XDEQST2mQ3KkkXM9BFp029GbtmoMOKDsjzSx58dewrxHnEAang5Hi+yHzwS/BDa7vWGJx7MP6q/ReChwYj4csEPBr5CLFTYhH1SxSaWTfj451zOIf8o/PjYdjDdFW7B9gp+vYTak+Ana2dvn9cQcgw77zzDn7//XccO3ZMZ51jJLvg7eTNc9FFNy7WHV/Ho32UPVzsXdQORzBSvvrqK9YzP3/+/GtfQ5tumiR6eqRaBg8ezB138+bNy3Rc1fNV165PUtNSM30cQbdIEl3QC6dvKm1TjvGOaociGAG1atXi1qaSJUtm6PvKeosu+stQS+Kum7tgjtDiQNN14Ovlq3Y4ggHYsWMHNm7caNK66IvPLOaxTLYykjQShAwiSXTdyBUs3bMUFcdUhNs3bhhyeAjWX17PPg1+Pn4YXHwwLvS9gJhfY3B3yl2c+fEMNo7ZiOn9p6NPoz4o5FGINyc1kMHatrHb8Gm+T4FkINQjFL4TfXEv6t4b4+iztA9gDeSMyIlRnUYZ4CcXhMwZCfbv3x+urq46Pa5Iuhgnh24c4jGfVT61QxGMGNpUO378+Bu75unrwcHBLP3Spk2bdMlHEb/88gsSE5WOr4xSKkcpuNi5ICYpBpceZ934VNANkkQX9MKlEOVN7gEPtUMRjADa2SUNsZ9++ilD36cxF734+KKeIjMtwuPDUX9hfTRZ3AQT9k8wGj1oXXH86nGW6LDNZosC7gXUDkfQM6tXr0azZs3w4YcfIjIy0iST6FT1ecPmBs8/rPmhqrEIginSoUMHfPPNN2jbtq1qMWjM5shc1JTuq5duX8J3e76D+xh39DjYA+cdzuOZo9IV07dkXwR8FIAzA85gevfpKFNI6ezLCFP6TcG82vNgHW+NKKcoVPujGs6GvNrLYsP5DQjxCAHSgD+6/qGDn04Q9A+93zOb3HqZwp6KpEtwuFSiGxOXIy/zWDGXyMsKr6daNUU25cSJE699jaYKvV27dunSUO/Zsyd33t6/fx9Lly7NVFzWVtbwz+vPc9FFNx4kiS7oBc0CIo+jmAMKmUeTRJdKdIXJuyfzTjTx9b6vueosJdV8TFd3ntnJo1WEFS8aBPOGqjhKlCiBR48ecRulKSbRf1n3C545PINVrBUGtBigaiyCYIo0bdqU3/8a7xQ1qOhTEdbZrFk3923V1sbCndA7qPR7JZzKfgqJ7olACpA/Mj/GFx+P2G9j8WfXP1EuV9b1fz9o/gEuj7yM0t6l+Xpbe35tLDy28IXXpD1Lw7cnvuV5c+/maFP97RV6gqA2W7duhZ+fH8aOHauT4xXxkEp0Y+SJ7RMeG5durHYoghHj7++vrTZ/FampqezplF4pF8Le3h7Dhw/n+cSJE/kYmaFaXtFFNzYkiS7ohZD4kBd25QWBSEpKwt27dzOcRL/29BoSUhJgyZCx18/7fua5n4sfjwGOASg+ujjiEuJgDpy9o1S45UAOtUMRDAAtLmfOnMlzGt+kQ/g6c1G1k+iLz/5fysVKpFwEwVRxsnXSJpxNRdLlu03fIc0lDVYxVuju2h23PrmFO1Pu4OvuX8PBzkGn5yqWoxgO9z2MJkWaIC45Dr239UaHiR1YRoZYFriMTVld7VyxsM+LCXZBMFZSUlJ43UF6xVkx/vuPnEuEJNGNhXM3ziHNOY07ZDrW7Kh2OIIJVKIHBQUhKirqP1+njtnGjRuzITpt/qcXko4iA9KrV69i3TpFnz+zuuhSiW48SBJd0AupbspOm19hJdknCHv37oW7u3uGWrZ9XHyQ3TE7Up+l4n/t3QdYFFfXB/A/vTcLiKJiQ41G1GDXWGPvvddYY4xGYzRqsGvUqIlBTey99957x97A3isCSq/7Pffut7y0RZCF3YX/7334Ztyd3Znly15mzpx7jq+fb6qnZrae0RpFfymKgKAAZBVTNk5BtE00DMINsL/3fgx0GigbhT2xfQLXUa7w+6jMtNBn9/zvyWVBazYjzi6+++472eRLBGMGDRoUF5RJbSb662DlDVttCAoNwkMTZSmXftX6ae04iPTds2fPsHPnTjx9+lT7ddFf6n4QXZznnAw9KdebOzTH8h+Xo6BTxv7dtDe3x57Oe1AmqgxgAGwP3w730e6y4ejQ3UPlNqOqj4KjlWOGHgeRpjRp0gTFihWTwbFly5al+/1YE133bD23VS7Ngszg6MCxidRzdHSEq6ur/Pvq7e2d5PkcOXJg9erVePToEUxNU580I3oviAajwrRp076oZJwqE13URP8UkTTAT5mPQXTSODE4hFsos4Zb1myp7cMhHVG8eHGEh4fjxo0bCAoKStVrDAwM0lzSpd6ketgWtg0PrR9ixIoRyCq8vL3ksrxheXkiOH/AfIwvMV42/Xrv8B6FPQvj8Wv9rsP4OlIZEC3llPbaraS/Zs+eLWsLnj17FitXrtSbci4HHxwEzACzSDMMaDxAa8dBpO8GDhwoa4zu2bNH60H0i6/U10PVFceeHIPvB1/ZbKx9ifaZtl8TIxNcnXgVTU2bAgrgluUtuExzgV+UHywiLTCs8rBMOxai9BINdIcNU/43O3fu3C8utZA4iP4k8AliYtP3XqQZz2OUs5+LWBbR9qGQHmWjqyvpIhgZpb3cqAiiW1pa4sWLF2maka/iZO0kG4AroNCLG/3ZAYPopHEB4QFxd8nEF55IEI01xBQokWl66VLq/wCUzp36IHrTqU1xVHE07t/nQ7PGtKd9l/bhg/0HedE6q/2suMfFtO35VebL7PQghyB8NfMrOXVRX30yVY4blYoqT2Ioe3BxcYGnp6dcHzlyZLLTKHUxiL7z0U657Fe9H4yNjLV2HET67uuvlaVUbt68qfXmoqIsiajxrctmn5otl11Ld4WlkWWmBx53jd6Fka4j5U38GGtlsLB7vu6wMLHI1GMhSq/u3bvLUgsiu1TMhkkPF1sXGBsaIzImUuul5kjpg+kHuezXlLMFKXV10fPlyyf/zsV3+/ZtmQT4pY3Hc+XKhYMHD+LJkycyFvIlWBddtzCIThp3//19uXSycpJ1JolUqlatKpfnzp1L9WtKOSqzkm+9TzmI3m5mO+yJUmaxVYhVXgzfCbuDp4Hamx6uKaO3jpZLx4+OqOVeK8FzA5sMxMbGG2Vjw3C7cHy39js89FeWmNAnT98+VdYtFLMJyrL5T3YzdOhQNGzYEAsXLpRTH1NdziXotVYCXhHREdjus12uty+VeZmgRFlRmTJl5FJcpGpLqdyl5DmrSAK58VZ7x/E5l3wvYc9D5blOz9I9tXYcf/T8AytqrYDpR1PkCcyDf/or+1sQ6RMxC27AAOVMsjlz5qTrvYwMjVDQTllW6XGgfs8MzQpELy0xa0eoml95/UmUkp9++klmi//6668JHp84cSLc3d1lc9AvVa1aNVhYfPmNZtZF1y0MopPGbT68WS6j3kdp+1BIx1SpUkUuRdmG1EpNOZfuf3XH5lDlf3fVYqrh4oSLqO1aW/57zc010GevPrzCdQNlw8WhVZR1RxNrW6MtjnQ5ghwGOeAX64dqS6vh+pvUN2nUBQsPL5RLk08mGV7blXSPiYkJ9u3bh9atW8syTqnplyBExUbhQ6gy0ygzee33ksE2ZytnXpwRaSgT/datW1+c6aWJUiV1C9WV63vuaa+szOeMWDdCXr3ZBdihvEt5rR5L93rdETE7Ai//fMnZOKS3Bg8eLM9BTp06hatXlQ3uvxTrouuOVWdWITgyGPls8uGbvN9o+3BIDyRXqkWUoN21a1dcH6f0EjPyRZ+4tJ7rxM9E19Z5Ev0Pg+ikcXff3pVL62hrbR8K6WgQ/fz586luICiyw1Q1BoMiktZSX3l9JVYHrpbrHpEeODle2Wyru3t3uZx/Zn6q96WLxmwcI+sui+DyL21+UbudyFC//fNtuDu5423IW9RcXhP/7f8P+uKhqTJ7vpN7J20fCukAf3//FOuTioBXbsvccl0b06a9jit7FDh+cIShAU+liNLbM8XY2FiWcrpy5YrWjqNJsSZyuee+bgbRQ8NDcTr0tFzvVboXdEXiqe9E+lZucurUqdi7d6/MNk0PBtF1x5+7/5TLfEH5eJ5GaSKC1BEREXJ9x44dCAsLg5ubG8qXT9+Na/E+YuZdnTp1ZJ30tATDyzmXg4mhCd6FvJMxEdIujiikcaryGS5WLto+FNIxZcuWlVOZRIDs3r17qXpNTsuccLZ2jutKHd+GWxvQa0cv2Wijc9HOuDDpQtzFXItiLWAQbYCXES+x8kjqmhXqGvHH9XysctpW3zJ9P5vpJbJzj/c8jqouVfEx4iP6n+6P8WvGQ9eFRYVh7/29cn1w3cHaPhzSslWrVqFYsWJYvHhx6kq6BCsb0maWTyGf8MhUeYHcrxrrbBKll6mpKZo3by7Xe/TogdDQUK0cR+NijeOmS/uF+kHXjFk9RpY9E+XbJnWZpO3DIcoyRowYgUaNGqX7hhCD6LohMioS9wyV15ltSrXR9uGQHvHy8oKTkxPGjRsn/71u3Tq57NSpU6pmyqZExECGDx8u30fsZ8iQIakOpJsbm6NsnrJynXXRtY9BdNK4t5Fv5bJIDnbCpoTEdElRb0xkfNjZ2aW5pMvt97fjHhu7Ziy6bO0i6yF/X+57rOq8KsHJr4OVAwqGKsuCzD6ibMKlb448PgIfPx9Ym1pjWsdpqXqNvbk9drbbCcdAR8AEmOAzAefupL4GvTYcfHgQIVEhsimTR14PbR8OaZm4ySZ+Ro8eDT8/P51rLjpz60w5O8Qw2BD9GjGITqQJCxYskBeuAQEBePxYO/WE89vll7O5xI35fff3Qdcsu7VMLqtbVoe1BWd7EmWE9MxeZRBdNyw+sBgKSwUMwg0wuCmTcyj1LC0t8f79e1y4cEFeg4iGoKoguib06tVLJgmJQPo///wj4yKpDaSzLrruYBCdNO6jwUe5LO2iDHwSxTdt2jQZHHN2VmaXf0ld9AlrJ2CK7xTEKGLQukhrLGy6MNmpen0r9lW+DrcQHBYMffPXub/ksod7D9ia2ab6dTltc+LxtMewDbAFjIH+K/tDl/2yVFmmprJt5XTf5Sf998MPP8gp1SKY5unpqXNB9DXXlX0WyhiXYR1gIg1xdHTE7t27cf36dZQqpSzjpg26WtJlx9kd+OjwEYgFZnWape3DIcpyRKmFsWPHyvJSwcFfds3AILpuWHp2qVy6RrrC0txS24dDeqRSJWXtcW9vb2zYsAHR0dGyjIsYFzSld+/ecbNt582bh2HDhqUqkB6/LjppF4PopPG795GWkXLdoygzSkkz4gfRZ26ZifF3xwNGgMtHF6xpvwZGhkkbgQjDWw2X054VFgpM2TgF+uT0rdPYfW+3XB/4zcA0v16cNE6oNUGu3zS5iWsPr0FXa7w+MHwg16vnqq7twyEdIGoj//mnspblypUrERISojNB9MDgQDw2VWbJDqgxINP2S5QdeHh4IFeuXHH/1kbzrKZuTeVy/4P9iIqJgq74fffvcpk3KC8qFK+g7cMhynLMzMywceNGPHjwAMuWKWd9pFUh+0JyKXoThUZppyxVdidiEdcjr8t1lnKhtBLBchsbG1lWbsaMGRrNQlcXSP/rr78wZsyYVGeiX3l9BRHRyprtpB0MopNG3Xh8Q5aQEJkylUsov+hEiT179kze3RVNxNISRD/z9AxGXh0ps6vzBOaB71RfmJuaq32dmakZvjH5Jq4BqT4Zvn64HKFzBORAKacvy8ob0nwIbAJs5O/r+6XfQxd57fGSNzkMQg0wsHHabxZQ1iSa7hQpUkRmg23dulVngugztylLuRiFGKFPgz6Ztl+i7EbcQGvatGmKDYYzQsV8FZHLMpfsK3L2+VnoAtFU/ZG1MrP1t3q/aftwiLIkURJy6NChcn3u3LlfNPY4WDjIsorC4wDtlKXK7rad2YZom2ggCvillXKmK1FqGRkZoUIF5Y3qX3/9FXv27EHXrl0zZF99+vTBokWLYGtrK893UjPTRZyfRMZE4vpb5Y0i0g4G0UmjVCcMFtEWnD5FatWtWxcdO3bE2bOpu0D9KvdXchkaEypv0uQKzAXfyb6p+m9sVKNRcvnK+hUevFRmPOu6D58+4FLMJbk+4JsB6bogGFl5pFy/jMvwee4DXbPyovLmhlusG0xNTLV9OKQjRFmfnj17ynV1GWGqhsOZGUTf/Vg5O6SMEUu5EGWUt2/fYvDgwdi7dy+mTMncWWRiZlujoo3kumo2mLatvrEawVHBKJ6zOAY1GqTtwyHKskRjYwcHBzx69Ai7du36ovdgSRft8jrmJZdOIU5wdHDU9uGQHpd0uXLlCho3bow8efJk2L6+//57PHz4EFWrVk3VtZGqpAvromsXg+ikUaGmyqlrFYtV1PahkA6rUqWKXKY2iH7h1AXAX7luH2AP3wm+sLVKXY3w1tVbwyLQQpZ/mbYrdc05tW348uFQmCtgHGQMz47qa0Knxm/tf4NloCVgCsy7NA+6JDomGncUd+R65/KdtX04pGO6d+8uTxiPHTuGly9fqs1Efx38OlOOJzw6HI9NlDeKPdul73tJROqJBqOi0agwYcIEnD59OtvWRRelCbwuKYNCgyoMYt8QogxkZWWFAQOUySuzZ89OVxD9rt9djR4bpY5/buUFY6/KvbR9KKSnKlZUxrEuXryYKfuLX8bu2rVrsh+UunJ2rIuuGxhEJ416HKgMMBRyUNaEI0qO6m7ruXPnUh10z38zP4q9KQZfT1/ksM2Rpv2pGozeNr4NXScumDc82SDX6+eon+7sbJGNvqynMpN3le8qBIQFQFcsPbgUsVaxQAQwtLlyCi2RSoECBbBw4ULcuXMH+fLlUx9ED3qNWEVshh/PgQcHEBQZBBdbFzQr1yzD90eUnXXp0kXeSBN/Ezt37iwbDWeWBkUbwMjASAbBtJ1N6rXbC7ff34apgalsMk5EGd/cXPRmOXXqFC5dUs4KTQsPZ2VPsEknJ+Hq66sZcISkzpPAJ7j+7joMDQzxc9OftX04pKcqV1aWJM6bN2+m9mYR5zn16tXDxIkT8csvvyS7b1VddGaiaxeD6KRRPm99EjRWIUopE/3ChQtqaw6KzDPVc5aWlriy8Qp85/t+0dS835r9Ji+IxV1bXz9f6LKFexci3C5c1vL7u+ffGnnPtmXa4mvHr2UAcN5F3clGX3R6kVwWjCiY6pkFlL3069cPJUuWTPY5J2snGMAAMYoYvA95n+HHsvbGWrls91U7eYFGRBnrn3/+QdGiRfH8+XP07ds30y5mRU3j6gWUja733NNuNvrM4zPlsnBwYdiZ22n1WIiyA3HTXpScFObMmZPm1w+tPBS1XGshODIYjdc2loFdyhw7fHbIZY0CNZDbKre2D4f0lCjfcuDAAYwePTpTZ3+JUlKTJ0+W63/++SdGjhyZ5LynQj5lvXZxgz8zrn0oebwKJI3ae2avXL6/zy81qVe6dGlYW1sjKCgIt28nzA4PCwvDjz/+iBo1amDWrFkJpjp96R8yEWxrWLShXF9+dTl02fRj0+XSLdINRfIW0ch7ioDfmBrKrt/Tjk3Dqw+ZV0NaHXFS8NzquVxv/3V7bR8O6YHEJ5LGhsbyu52Wuuh/7/kbxiONUXFsRVlOKLX8P/lj4/WNcv27fN+l6biJ6MvY2Nhg3bp1Mit0y5YtsgFXdirpcu3hNTy3Vv6dHN9kvNaOgyi7+fnnn2VZF1FWIa3MjM2wrcM2mbzyJvgNGq5uiA+hHzLkOCmh6TuU11B18tbR9qGQnqtfvz5q1qyZ6fsV4878+fPluoiDjBs3LslN/pK5lMlFLOmiPQyik0YFGQfJZXGn4to+FNLxzteqph3xS7rcvHlT1iET2WfChw+aO+lsml/Z9XrWoVlpCp5lppdBL/HaTlnfeUozzTZTa1OyDcyCzBBuEI7vF34Pbbvx9gbeRr2FubE5fu/8u7YPh3SYGBc6dOggs9LVlXRJTRD9ScATDD83HDFWMbhkcgmFRxZGUKjyb9bnzNw2U/YVMAo2wnclGUQnyiweHh6YNk3Zz+TVq8y7AdzUTXnOcOzJMZlRqg3D1wyX/VxsAmzQoWYHrRwDUXZUrlw52ZehePEvu54Vga59XfYhv21++H7wRbN1zRAapewbRhnD55kP3pi+kesMopM+GzhwILy8lL1Qpk6dKq+D4qvk8v910V8wiK4tDKKTxoRHhiPaShmcrOSm/HITfa4uumguKjJM582bhwoVKuDWrVuyqdi+ffswY8YMje2vvXt7IBzyv9F/dimD9Lpm0ZVFiFZEy2mIbWu01eh7GxsZo3uh7nJ9/6f98PvoB23aenerXDYo0gDWptZaPRbSbSEhIdi4cSPWrFmDT58+JXjO2do5VUH0iOgINFzaENEm0comxTHAc9vncB3jmqqZGeturJPLsiZl5XeJiDI3K1Q0+Bo/PvOysUvkKiFLE0bGROLIoyPQxjn18aDjcr1biW6Zvn8iSrv79++jR48ecvbs6gWrsb/rfhlQP/fiHDpt6YToWN1M4skKZuyYISNbFoEWqF5aWY6LSF8NGjQI7dq1kzGS3377LcFzlfP9f130l6yLri0MopPGeN/zVv4XFQ2UK1pO24dDOk5klq5fvx5Dhw5F06ZNMWTIEERERKBx48a4ceMGGjZUll/RFNGMtHi0MqNk/hnlNCldEhUbhcVXF8v1wRUHZ8g+5vaZC+MgYygsFej3b9Ks3sy09PxSuWxdsrVWj4N0n5i1UqJECVnqSQTTk20uGqycwaHO8IPD4RvsC4QBvYx6YWqpqUAk4G/vjwZrG+BdyLsUS7k8NX8q1wd8O0Ajn4mI0tYgW9xkVxHNRjOaKB+nykbffW83MpvnWk/EWsfCINQA07opM/GJKHNdu3YNnTp1kk3OU+Lr64tu3brJc5WVK1fKmbSiKeFXub/Czo47YWZkhp2+OzF47+BMbVSYnex7tE8uK9srA4xE+k7URxez90V8RFwDJc5Ev/jyImIVGX8+REkxiE4ac/H+Rbk0CTVhph59VqlSpWQgXfxxOHz4MMzMzPD3339j9+7dcHRMe/PQ1Pjx2x/l8r7pfa1nYicXVH4X+g45TXOiVYlWGbIPS3NLdHBRTgnf4bcDn0ISZvVmloOXD+JF5AuZDVzTOfPrzZF+EcGsXr16yfVly5aluZzLupvr4HVJOS1yoNNATPllCkZ3GI3ltZbD3tQet/xvodrSangc8DjZ1/+x9Y+4Ui696/fW4CcjorR6+PChbE6+detWhIeHIzIyEtHR0TKwrungVPy66Jkd+Fp0TVn/vYppFTbeJtISMVtWJPyI2sQxMTFJnr979y66dOmCr776CqtXr5bjUJMmTWRTwK5du8ptahSsgVlVZ8lG6P9e/hdTTn1ZucbnH59jy50tcnYMJfQu4B3eWClLuQyqM0jbh0OkEW5ubrJ33MGDB2FhYRH3eGnH0rA0scSniE/w8fPR6jFmVwyik8bcfK6s12Qbw5N9Sr0yZcpg6dKluHTpkmwompFdsAc0HiAzsUVAbML6CdAlx0KPyaV7lDtMjEwybD/z+86XwcBYq1gM/G8gtGHu/rlymTMoJwo6FtTKMZB+ERejIhtVXNCKjK/UBtHvvr+Lvrv6ynXRXHf+sPlwdlaWgOnxXQ9c6HcBBe0K4oH/A3gs8MDmU5uTvMf6m+vlsqxpWXkMRKQ9S5YskaVd2rRpIy8qxQ14ExMTeUNefD/jB7s7d+4snxe9Vp48eZLmfdV0rQkrEys50+Xqm6vILOKiOMAhAFAAMzvOzLT9ElFCojSLg4ODvHknknziE2ONODdZu3atDJ43b94c3t7ecjtRgkp1PePv74+pXaei4B3l+e64Y+Ow7GrChICUrD66Gh02dkChvwqh7aa26L61e6bMxtEns7bPAkwgr/FaV+MMV8o6kuvLYGxoDI+8HnKdddG1g1eDpDEPPjyQS0fTjMkipqxLZHF8/fXXGb4fcZFdxaqKXF/vowyM6YKVR1YiPJco2A781e2vDN2XyGhrmbulXN/4ciNCwzO/0dFJv5NyWT9//UzfN+knMS1aVeJpxYoVqQqii2aAbTa2QUhUCGq71saEWklvnLnldMPZPmdR1KYo/KP80W5fO8zbOS9BKZdn5s/k+qCazG4i0jZRF71GjRpqn49/I15kqYsfcZO+Zs2aePBAeZ6aWqLxdb3C9eT6nnt7kFkWXFogly1KtEDVUsr+MUSU+aysrNC/f3+5Pnv2bNngLzg4OG6sGTt2LFq2bIkrV65gx44d+Oabb5K8h3iN6O3yZOMTGJ9TztQWN/f33VeWH0lOVHQUflv5G+yH2aPbqW7YeHcjYhQxMpt9w50NaDtLs32T9N3m28oECHdTdyY7UJb0/v172WRUNSMmri76C9ZF1waOMqQ59srF1/kzPhhK9KV+b/G7XPrZ+uHaw2vQBVMOKad2uoa6onSh0hm+v/8G/AejMCNE20Rj5bWVyEyXfC8hxD5EZtiNbDEyU/dN+k1V0kXUG1WdRKoLoosMsf67++Ou312YhJvg6eynuHL5SrLvK97jUOdDsA2wBcyAIZeGyItX4cCjA3LmimWUJXrW65nBn5CIPsfU1BQnTpyQTYY/fvyIgIAAmenp5+eHd+8S9jYQdYxFAEtkcj179kwG0n18fL64pEtmCIkMwfLry+X6oAq8cUekbYMHD4axsTFOnjwpZ88uWKC8ySW0atUK27ZtQ7ly6nuBiXHn1q1bqFevHqIPRAPXIQPi4ib/pZeXEmz7NuAt2v/ZHlajrDDt8TR8tP8oSx+WUZTB1f5X0dZaGTzfFrItwQ3/7CwqJgqvrJTngL2rsuQeZT2iZJ2HhwfGjBkjZ77Er4t+4SUz0bWBQXTSmFg75dSyNnXaaPtQiNSqV74eHCMc5ei3+2nmNwtLrP2s9nhsq6zFPLru6EzZp2iyOqWJMnA/+9JsxMQmrfOYUWbsnCGXNh9tULZI2UzbL+m/Zs2aoXr16rIZcVRUlHzM2VpZmuVtyNsE/x0v9F6ItTfXwhCGiFobhVf3XsHFxUXte7vmccWjCY/gFOAEGAPTHk5Dj797YMeDHfL5Qd8OYnYTkY4QGaA2NjawtbWFvb29LLeQM2dO5M6dO8F2uXLlQunSpWXQXSxfvXoVF9BKrcbFGsc18EqpAbGm/LTkJ1nntKBNwbgseCLSnnz58qFjx45xY8/jx8n3T0lJ/vz5ceDAAXh5ecHikAXwAAiLDkPdpXVx/8N9PAt8hirjqsB5hjM2BW9ClE0UDMINUCW6Cry7euP6+Osom6cs1g9bD9dPrvIaZuiZobh8/zKyuxNPTyDCMAK5LXOjb0Nl+T6irETcxBs0SHlTfdy4cbLRaGUXZSb6zXc35cxbyly8IiSNUTVlc7V31fahEKVoYuuJcrnp3iatB9A3hSiPodjbYuj1nTLTNjMMqjgIOSxy4L7/fWy8vTHT9nv45WG5rOVUK9P2SVmDqG186tQpjBgxAubm5vIxRytHGBoYyu70qgCXyOwaemCoXM/vmx94BgwYMCCuFro6Oe1y4smMJygWXEyeHa0MWIlNt5Xfz3al2mX45yOijOHk5IRjx46hbNmyMltdNDVPbU3hfLb5UC5POSigSLH8giZEx0Rjzb01ct090l2ObUSkffPmzYsr5zJ//vwveg9xI14Ewm5cvYFKzyoBr4Gg2CBUXVoVReYVwXnj81CYK2DyyQQdrDrg9cjXODvpLL5x+ybBe1wcdxHmgeaItYxFrfm1EByWvQNo2322y2WL4i1gYpxxPaWItEn0jROlLZ8+fYp///1XzqJ1sXWR1z+XX/FmWmbj2RlpRHB4sGy8JBSyL6TtwyFKUftS7WFqZIobb2/g+pvrWjmGfy7+ExdArxBRAX9890em7t/GzAbDKg+T6z9s+EFevGe0J++eINAuUK4Pbzw8w/dHWZ+RoRHyWOeJK+niH+aPdpvaITImElVzVMXTdU9lwH3kyNSVDjI3NYfPHz6oHKXM8IhFLPJa5kWFvBUy9HMQUcYSWelHjx5Fo0aNsGHDhjTNLGnq1lQud9/P2NlrbWa2Qbh9OBAJzOk+J0P3RUSpJ2a8DBs2DKVKlUr3exUtWhRnjp7B5JKTUcC2APxC/RAdG43yDuXxm+tvCPkjBOtHrIeTg1Oyr89tnxu7u+8GwoFg+2BUmaDs9ZQdiRmIm24qr6VallD2eyLKiiwtLeHp6SnXJ0+ejKCgoLhsdNZFz3wMopNGnLp5SrkSCeS0yKntwyFKkYOFAxoVbiTXR6waken7n3dhHn7c96Ncb5unLU55ntJKqYgeJXsAEUCASQDGrh6b4fs7+vKo/KtTyKIQapapmeH7o6xJNOgSddF37tyZoC76i08v0H1bdzz9+BRFHIogerPyxlBqstDjE9/Fc5PPoZV5KyAK8IjxSNCskIj0kyj7snfvXlnaJf54ktq66AcfHpQ36DLChhMbsDNEOaZ1ztUZhfMWzpD9EJH2GRkZYcxPY3Cy10lMrDURl/tdxuUhlzGlx5RUZVPXLVcXnqU9ZX+hWxa30G9+P2RHq4+sxrvwdzCMMkSdQnW0fThEGap3795wc3OTTUbFzJhK+VgXXVsYRCeNuPxQOY3ELMyMdWNJL5SOUV5EH/U7ivDI8Ezbb9/FfTFk/xC5PqraKGzst1Fr35n8ufOjmnE1uf73tb9TPb39S229u1Uue1XKvLI1lPUsWrQIPXr0kJkY8YPo446Nk83/zIzMMKLACFw8eTFNWeiJbf11K8J+D8OOUcq66ESUtYha6YUKFZKlXlJSIV8FWW9X1Co//ey0xo/D76Mfuu/oLvsx5AnMg1U/rdL4PohI9xS0L4hxNcehvHP5NL92fJfxqAllQspK/5W48jr55ulZ2X+n/pPLvKF5YWFioe3DIcrw2uiqa59Zs2ahuFXxuEx0hUKh5aPLXhjt1EH7Lu1DmVFlsGDP/7p/67rbL2/Lpb3CXtuHQpQqo9uOhkGYgawpOGOLstllRms9ozUWv1ws13+p8gum1p2q9QzXJX2XyBkkYfZhmLxB+Yc5I4jgw6FHh+R665KtM2w/lPV16dJFnkheunQJt2/fRl7rvHHNdQSvxl64feT2F2WhJ1fehYiybp1jkdHVuHFj2fRPHVGbXNVgdM+9PRo/jppTayLSLhKGIYY4/vNxJqMQUaocHndYzqyNiIlA6w2t8SH0A7IT72BvuWzu1lzbh0KUKdq2bYsqVarIZCJ3R3cYGRjJkspiNi5lHp6l6RBxB6n7X93ReHtj3LS4iUHnBmHu9rnQBw/9H8plHnNlbVoiXWdlYYUyKCPXl3gvyZQA+rawbXK9SnQVTK83XesBdKF4/uLwgIdcn3FxBsIiwjJkP+NWjpPT4J2MnfBV7q8yZB+UPeTOnRtNmyprFC9btgzONv8Lkvcs2xO9y/XG33//jcOHD+PXX3/V4pESkS5bvXo1mjVrhvDwcDRv3hy7du36bEkXTddFX+O9Bncs78j1qRWmyr/JRESpYWxkjLXt1soSdqKUXZNlTRAZlTElp3Qx6VDcfEQMMLLVl804JNI3InZw6tQp/PPPPyjgXADuedzl46yLnrkYRNcR4s5xm41tsCpwFWAKmSELE2DYpWF6kZH+KuyVXLKpKOmT4d8pm1s+s3iGEzdOZNh+Wv3RKi6AXjW6Kk5POK1TmWZL+iyRtZ9D7ENQeFRhBAYrm39mRCmXQuGFdOLmAem3Xr2UJYFWrVoFNwc3uf6149cyC1389yV+6tatizx5eGOXiJInyj1t3rwZbdq0QWRkJFq3bi3/nZz6RerD2NAY9z7cwwP/BxrZv2iG/NPRn+R6Y7vG+LUdb/oRUdrYm9tja4etMIEJLvhdQO1JtZEd/H3wb7nM+SknCjoV1PbhEGVqTwUV1kXXDt2J4mRj++/tR5mFZbDNZxtMDE3Qzq4d3o1+h5wBOWVAfdCZQVhyIOMzZdMjQBEglyWdS2r7UIhSrUvtLrAMtJQ3rGqvqZ0h37OW01tie/h2uV41pipOTdBOE9GUlClcBuNKjAOigTf2b1B7UW0ERQRp7P1FUP6FhXKa2YBvB2jsfSn7atSoERwdHfHu3TuYPTbDzo47caLnCbx4/AL+/v7aPjwi0hOmpqZYv349OnfujOjoaHTo0AEzZ85MUl/UztwONQrU0FhJl1hFLHps74EPYR9QLk85bPtReaOdiCityjiVQV+nvnL9rNFZjF09FlndaT9lf4r6Bepr+1CItOLq1as4s+GMXGcmeubSrUhONvMp5BMqjKmARusayWyU4jmL4/z357Fx6EbksssFnwk+sA+wB8yA/qf64/IrZfNOXRRmpiwBUb5Q2hujEGmLCGYfH3AcFoEWUFgq0O9MP2y5s0Vj7996ZmvsiFA2JawWUw2nxuteAF1lYteJmF1+NswMzHDt0zV8t+o7+IdpJhj557Y/5Q1Bo2AjdKvbTSPvSdmbiYkJunbtKtdXrViFZsWbyWwskaHu6uqKvXv3avsQiUhPiB4LK1euRP/+/WWD7Vu3biW7XVO3phor6dJ2VlscfnQYFsYWWNtmLUyNTNP9nkSUfXkN8ELZ8LJyfcb9GbjyNOs2Gr1y/wqC7YPl+ohmI7R9OERaMX36dNzYe0OuX359GVExUdo+pGxDN6M52cCeC3uQZ2weeJsqG2I0zN0QV/pfSdCdWwTS7/5+F07hTogxiUH91fVx463yi6JLAsMCobBQZuxUKVlF24dDlCYVilfAw7EPUTCiIGKNYtFuUzvMPJM0Cy0tRO3viScmYluoMrOsemx1nBx/UmcD6CrDWg3DyT4nkcMih5wWVn1xddx4lP4xZ/319XJZ2ri0zv8OSP9KuoSGhsrAl6iBfvbsWURFRaFcuXLaPjwi0rPp0QsWLMC6devw77//Jlt2TFUX/cSTE+marbXu+DpsC1KeH/TN3xclcpVIx5ETESmd8TwD2wBbmYA39dFUDFk8BCtOrMD9D/cREhmCrOKc/zm5LGBYAOWLMYGPsqdJkybBMNAQCAPCo8N1Mk6YVTGaoQX9FvRD011NEWYfJmufjy40GvsG7YOliWWSbfPkyIN74+/JekciK7Tuyrq4/Ey3MtJFIxNBBN7y5cqn7cMhSjPnnM54MPkBfqjwAxRQYOThkWjo1RDhkeFpeh8RyPtzz58oPb80PI97yseGuA/BCc8TehM8rpivoiyL4WTphLv+d+Ex3wOnbymnTH6J0PBQPDRWNh7uWamnBo+UsrvSpUvj+fPn2L9/vwx4jR8/Xj4uskmdnf/XbJSIKDXEONKxY0dZK131N71Pnz44c0Y5XdotpxuK5iiKqNgoHHp06Iv28T7wPXru6gkYAc6BzpjTdY5GPwMRZV+W5pY4NeQUDEMMEe0QjYXvFqLn8Z5w+8cN1tOsYTfNDuY/myPnsJwo+ktRVPu9Gjr82QG/r/4dPs99oC923t8pl4NqD9L2oRBpjZubG77v8z3wUvlvlnTJPPoR1fkCly5dQuPGjWFvbw8rKytUrlwZGzduTNN7REREYOLEiShWrJg8oc6bNy/69esna7Cmx9aQrbIGc46AHLj8/WVM7T41xe1tzWyxv+t+fOP8DfxC/VDRqyL2X9oPXfE48LFcsqko6TPRMGxeo3mY02AODGCAgx8OIv+v+fHivbKW9+dsPrUZuYbnwgjvEbjvfx+OVo5Y0nwJ5raYqzcBdJXSjqWxvsF6GAcZI8omCrVW1sLei19WHsNrj5ecqSJuGA5sMlDjx0rZm4uLi1yqstDF3+pff2VzPiJKPy8vLyxduhS1a9fGokWLZJBdlY3+pXXRa06tiUjbSBnkOj78uN6dHxCRbhN9jjY124Q8b/LALsAOeUzywMrESj73KfITIuwi4G/vj4fWD2X99I3BGzHp4SSUXFoSuT1z44c9P2Dj7Y14G/wWuigwPBBHHx+V661KttL24RBplaenJ4zeKBuNbrmguZK0lDJjZEHHjh1DgwYN5MW0yCixsbHBli1bZLMgkbU2fPjwz76HyD5p0aIFDhw4IAPwbdq0wf3797F48WIcOXIE58+fR+7cub/sAGOAFmYtsPnPzTA2St3/C0St172d9sLV0xVhdmFosrEJDpkcQp2ydaAt7wLeYea2mVj7YK2cNuZq76q1YyHSBHGBPLTyULz1fYvp96fDz94PxaYVw/G+x1GppLL7dWK3Ht9CuwXt4GPhA9hDNuesb1sfm37cJG+A6ataZWrhXL9zqLawGiLtItF0a1OsCl6FLnW6pOl9Ln66KJclDUrCzMQsg46WsjNRemnAAGXDWmahE5Gm9O7dG6dOncKmTZtkEs3169fRZEgT/HXhL+y5v0c2BzU0SH0QfNSKUbhrdRdQANMqToObi1uGHj8RZU/NKjeDkb+RTCgUPWTEeVJQZBB8Xvng+OXjePD2AZ4FPMOr4Ff4EPEBHww+yOC6n6Ef5nvPlz+C6SdTFDUuinpF66F3nd5wL+Ku7Y+GyRsnIzo2Gq5WrnJ2EFF2JpJ8W1Vohc3YjFsByfdzIc0zUKSn8K8Oio6ORokSJfDixQsZ6C5bVtlg4+PHj6hYsSKePHmCe/fuoWDBgim+z7Jly+TJc6dOnbBmzZq42ogLFy7EwIED5cm0qJmYFp8+fYKdnR3+2/Ef+jZXdtBOq7vP7qLc3HLyD51o0ifKLlQrVQ2Z5enbp5i+dTp23NuB11avZUa9ShvLNtj8y+ZMOxaijLTi0Ar0PtwbsZaxMmNsVaNV6Fy7c9zzgcGB6PRXJ+wP2S9vIgkuH12wpucafFvm2zTtS9RwFo0QVSe7usT3uS/Kzy6PUPtQIALwquqFQU1Tnj556d4lrD+1HhGOEVh9ezU+RnzEns570LhY48w6bMpGRo4ciZkzZ8r1V69eZbkgui6PD0RZnbhMmjp1KsaOHSv/XaNWDVz57gpCokJwqe8leOT1SNX7XL5/GRWWVJAzsypEVsDFKcobzOnBsYGINDU2+DzzwWW/y7j49iKOPz2ebH1lk08mKGJUBBXyVkDXul1R66taGd4UWZTW3HhyIzZd2oRL7y7hrcVbed1VNaYqzkxUltoiys4evX6EYouKoWSukvDu5w1zY2VJus/FJEV81NZWfxP+tCnLBdEPHjwos9BFwzExBTO+FStWoGfPnpgwYQJ+//33FN+natWqOHfunAy6xw+4i19X0aJF8fbtW7x//x4WFhapPjbVf7B+fn7ImTMnvpRo9Ofh5YEo2yhZbuFs37OyOWJGeRfyDtt9tmPipol4afpS1nFUEft3N3NH32p90bdhX05LpSzlzO0zqLukrrxphUhgXMlxmNBlAjbd2YTuq7ojwiJCbmcZaImZdWd+NrisrxfCoqRN6Sml8dHhIxAFTC83Hb+2UZbMCA4LxsZTG7Hr6i5cfncZrwxfIcY6JsHrnayc8HToU5gZMxOdMqZ8W7169TBq1CiMHj0aWY2ujw9E2cHOnTvRtWtXBAUFwaKXBcIKhsGzpifG11L2YkiJyFj/esbXuBN+BxaBFng39R2sLazTfUwcG4goo8aGBy8fYMnhJdjvsx++4b5yJjwS9VsWAfQyTmXgYugCm2AbNCzTEC2qtICVudWXH3tMFC6/viwbOM/dPhdvTN/EJSupiBKR+9ruQwOPBl+8H6KsRDQ7tzGzSdW2DKKnX5Yr53L8+HG5rF+/fpLnRHBdOHHiRIrvER4ejgsXLqB48eJJMtZFRvp3330ns9C9vb1Ro0YNaKPW2dn+Z1Hl3yqIto1G1X+rYmKjiciZQxmYf/bsGZ48faL29e5l3OUXRxAZ+48eP0p2u4DQAAQ5B+H0s9Oy2SL+/36B2UczVLSpiB/q/IB2NdoxcE5ZlpjlcW/UPZSbWg7+Dv6YdH8SNs/bjLsBd+X3wTDYEH0L9cU/Y/9JdWkmfeSS2wUPJz5ESc+SeG//HuPujMMj00c46XMSPh99/jcjRfV3OBaw+GSB2sVqo2O1jqhfpD4D6JRhKlSoIE8EiYgySvPmzeUMV1Hq8cntJ0BBYP2t9chtmRtnz5xV+7ocOXIgKmeUDKCbG5pjS6ctGgmgExFlpKL5imJaj2kQ/xMev36MxYcX47DvYdwPvg9FHgUCIwLh/cob4n/CqpOrgKOAZbAlXE1d8VWur+T5f9VqVWFkqMzC8/X1Tba/3Oug1/AJ9UGgbSBCo0KVD6piguGAY7gjKjhWQDuPduhQswPMTVPOtiXKTlIbQCfNyHJRH1G3XBDNQBPLkycPrK2t47ZR5+HDh7ImenLvEf+9xfukFEQXjUnFT/y7Pqq7w+InPdwLueNoj6Oos7IOou2i8dvZ31L92jWn1qR+R8+UCw9nD9R0rImaTjXR0KNh3NMxMTHyhyircnZwxqNJj1BxQkXcs7knA+iWJpb4udLPGFBmABztHaGIVSAq9su/06rxIL3jQkaytbCF7wRfdN/WHbuf7MZ/V/5TPmGizAjJFZELX9t/jQalGqBjjY5wzpGwpIYufzYiXaYP4wNRdiDO/8+cOYND5w6h67Wu8P3gi8H7Bqf8InHq//95LaJxeb1y9TT2XebYQESZNTa45HLB+I7jIf6nmp3/KPARrry+gmUHluHyq8sItAiEwlwBUQLyjvhf6B257Zp9qYw9iIS9KCCHRQ5Uz18drnCFRy4PtK7WGqYmCcvGcNwj+jL87qRflguiq7LRVJnWiYkpC5/LWEvNe8TfTp1p06bJ0jHJNT61tLSEJkwoPAFeN73gWNARZubKTM8PHz7gg98Hta/JXyB/XBmagIAAvH/3PtntDGGISs6VZOfr3KbKJqqx72Ll9DCi7GZ6tek46H8QLyNeokXuFsgVnAveZ5WZF5py6NAh6Lpedr1gk8cG/lH+KGpRFDlDcqJsmbIJZqRcPX8V4n9ElL3GB6LswBrWGOAyANeDrstAku89X/XbWlsjX758KGJZBHle5cHe15o/h+bYQETaGhusYIXBxQcDxSGTEG+9vIWLLy/C96Mv/BR+cpuixYrGXSe8ef0mLrEwPmMYo5h5MTT7uhmK5yj+v6bNocDhQ4cz/HMQZRehof8/04O+WJYLousSUZv1559/jvu3+IORP39+1K5dO1010eNrjMb4Bb9o5L2IKGVN0TTD7giLE11RKkof6po2QzNtHwJRtqFv4wNRdiDOv7WNYwMR6drYkFHXSkSkGcndxKJsHkRXZY+ryxIX/9E4ODik+z3ib6eOmZmZ/ElM/DHjyS4RJcaxgYjU4fhARMnh2EBEyeHYQESJcUxIvyzXETJ+vfLE3rx5g+DgYLW1zlUKFy4spxypq52eUt11IiIiIiIiIiIiIso6slwQvWbNmnJ58ODBJM8dOHAgwTbqiHrhFStWlN2jnz59muA5UftQTI+ysrKCh4eHRo+diIiIiIiIiIiIiHRLlgui161bV2aSr127FteuXYt7XJRmmTp1KkxNTdG9e/e4x1+/fg0fH58kpVv69esXV9dcBM5V/v33Xzx69AhdunSJa85JRERERERERERERFlTlguiGxsbY/HixbI79LfffiuD4cOHD4e7uzvu3bsnA+murq5x24sgecmSJbFt27YE79OjRw80aNAA69atQ9WqVTFq1Ci0bdsWgwYNQqFChTB58mQtfDoiIiIiIiIiIiIiykxZLogu1K5dG6dPn0a1atWwYcMGLFiwAE5OTli/fr0MqKeGqIm+Y8cOjB8/Hu/fv8ecOXNw5swZ9OnTB+fOnUPu3Lkz/HMQERERERERERERkXYZI4sSNc337dv32e2WL18uf5JjZmYGT09P+UNERERERERERERE2U+WzEQnIiIiIiIiIiIiItIEBtGJiIiIiIiIiIiIiNRgEJ2IiIiIiIiIiIiISA0G0YmIiIiIiIiIiIiI1GAQnYiIiIiIiIiIiIhIDQbRiYiIiIiIiIiIiIjUYBCdiIiIiIiIiIiIiEgNBtGJiIiIiIiIiIiIiNRgEJ2IiIiIiIiIiIiISA1jdU+Q5ikUCrkMCgqCiYmJtg+HiHREVFQUQkND8enTJ44NRJQAxwciSg7HBiJKDscGIlJHjAvxY5OUdgyiZ6IPHz7IZaFChbR9KERERERERERERJSNiMReOzs7bR+GXmIQPRPlyJFDLp89e8b/YIkowR3h/Pnz4/nz57C1tdX24RCRDuH4QETJ4dhARMnh2EBE6ogMdBFAz5s3r7YPRW8xiJ6JDA2VJehFAJ1/0IgoMTEucGwgouRwfCCi5HBsIKLkcGwgouQwoTd92FiUiIiIiIiIiIiIiEgNBtGJiIiIiIiIiIiIiNRgED0TmZmZwdPTUy6JiFQ4NhCROhwfiCg5HBuIKDkcG4iIMo6BQlSWJyIiIiIiIiIiIiKiJJiJTkRERERERERERESkBoPoRERERERERERERERqMIhORERERERERERERKQGg+hERERERERERERERGowiJ7Iy5cvMXfuXNSvXx8FChSAqakp8uTJgzZt2uDChQtJtjcwMPjsz/Pnz5O8LjIyErNnz4aHhwdsbGzkT+nSpfHDDz8ke1z37t1D+/btkStXLlhYWMDd3R0LFiwA+8ISZa3xISwsTI4N5cuXh4ODA+zt7eX3fcqUKfj48eNnj3HDhg1x771+/XqNfn4i0szYINy/fx+9evVCsWLF5N/1fPny4bvvvsPOnTvV7mfNmjWoWLEirKys5PjQtGlTXLlyJdltxfnB1q1bUbt2bTg7O8PS0hLFixdH//798ejRI419diLS7vjg6+uLvn37oly5csidOzfMzMzg6uoqx4cjR44k2f7Dhw/477//0Lx5cxQuXFhuL64vGjVqhAMHDmTI74CIMn9sePLkSYrXIOPHj1d7bNevX0fnzp3l+4sxIm/evHKMOHbsmEZ/B0RE+sZAwShsAqNGjcIff/yBIkWKoFatWvJkVPyx2r59u7wgXbt2LTp06BC3vbo/Pg8ePJAXu1999RVu376d4LmAgAA0bNgQFy9eRNWqVVGlShX5+OPHj3HixAn4+fkl2P7OnTtyOxFcE4F08Udsz5498n0HDx6MefPmZcjvgogyd3yIiopCjRo15Ilz2bJl5T4EccIqTmZLlSolxw0RDEvOmzdv5M248PBwhISEYN26dejYsaPGfw9ElL6xQXzHRXBbfOdFIEtcDL97904GvcXNMjF2eHp6JtiHuJE2duxYFCxYUF5gBwUFyRtl4qa8CJRVq1YtwfbDhw+XN+REAL1FixawtbWV48jBgwdhbW2Ns2fPyvGCiPR7fNi8ebO8OSauJ8T4IL7rIji3Y8cOfPr0CZMnT8aYMWPitl+4cCEGDhworyfq1q0rg2QvXrzAli1b5LXGjBkz8Msvv2T674kou8nosUEE0QsVKiSTcVq2bJlk/2KfqmuN+FauXInevXvDzs5O3owTY4SIT3h7e8vzj/jjCRFRtiOC6PQ/W7ZsURw/fjzJ4ydPnlSYmJgoHBwcFOHh4Z99n8GDB4ubE4o///wzyXMtW7ZUGBgYKNasWZPkuaioqCSPffvtt/K99u7dG/dYRESEokaNGvLxs2fPpvLTEZEujw8bNmyQj7dq1SrJa1q0aCGfW7Fihdr3bdasmcLV1VUxfPhwue26detS/dmIKPPGhkaNGsnv6Pbt2xNs/+TJE4WNjY3CwsIiwfb37t1TGBsbK9zc3BSBgYFxj1+9elVhZmamKFmypCImJibu8devXysMDQ0VBQsWTLC9MHv2bLnvXr16aezzE5H2xgexHhsbm+T9X758qXB0dJT7CAgIiHv8yJEjip07dyYYMwQfHx+FnZ2d3F68loj0e2x4/Pix3L5Hjx6pPiZvb295vlG5cmWFv79/qmIVRETZCcu5JNK6dWvUrFkzyeMiO1Tc+RVZ5Ddv3kzxPUQWqMgyFVOyunXrluC58+fPy7vLXbt2lVOkEjM2Nk5SxuXkyZNy32IKlYp470mTJsn1RYsWpflzEpHujQ+qEgvxv+sqTZo0kcv3798n+77Lly/Hrl27sHjxYpllSkS6OzaI77qYSp34uy6ySL/++muZDRocHBz3+LJlyxAdHS2zv0RmmIqYsdKpUyfcvXsXp0+fTpB9FhsbK7PT428viKyylMYSItKv8UGUWhDbJyYyzcUYILJWnz59Gvd4nTp10KxZMxgaJrwMFOWeRNar2F7MVCEi/R4bvoQ4z4iJicGqVatk2bjPxSqIiLIbBtHTwMTEJFV/PMSUKvFHT0yzEtOyEtcrFtq1ayenRS1duhTTpk3D6tWrZY3CxI4fPy6XolZaYtWrV5d1UUUJGCLS//FBVVph3759SV4nSjiJE2dxUp2YqKs+dOhQ9OvXT07NJiLdHhvEd11M1U78XX/27Jm8YBZTr3PmzJmqc4EGDRrIZfxzATHFW9yoO3PmjCznEN/u3bvlkmMFUdYYH9QR1xWi/IMoASdqn3/p8RCRfo8Nr169gpeXF6ZOnYolS5bg4cOHye4zMDBQlnwT/RWKFi0qzytmzpyJOXPm8MYaEdH/4xlSKok/TocPH5a1RcWd3pSIP07C999/n+S5y5cvy6Wodyay0eNf3IrsUZFFGr/2mdhOdUGcmJGRkaxzJmqmiww1nvAS6ff4ILLNRc3Cbdu2yRPY+DXRRc8E0QhMNByNT5xM9+nTR9ZAnTVrlkY/FxFlzNggahSLAHfbtm3lDTU3N7e4uqaiNqrqhnv8cwFxjiAajiWmOj9QnS8I4iJ6+vTpsi56iRIlEtREP3r0KAYNGiR7qhCR/o8P8WevihrKIotUBM1Eo0ERFBM10G1sbD57POKaRNRXNzc3l5mwRJQ1xoZDhw7JHxWRlNOlSxc5NoiEPBXRqFxcV+TPn1/OVlHddFcRDUw3bdqUZIYbEVG2ou16MvogMjIyri75ypUrU9z20aNHst55gQIFktQaFIoXLy7fx8jISNG9e3fFw4cPZZ3C1atXK2xtbWX9s+vXr8dt37dvX7n9oUOHkt1f1apV5fPJ1SwjIv0aHwTx+K+//iq3E++p+hH1DEXNw8Tmz58vn9+/f3/cY56enqyJTqTjY4P4PleoUCHB9zxnzpyKv/76SxEdHZ1gW3FukC9fvmT3I+qli9c2b948yXOiz4Kokxp/H9WrV1ecPn1ag5+UiLQ5Pqjs2rUrwfbW1taKVatWpfqYOnXqJF83ceLEdH02ItKNseHt27eKcePGKS5fviz7o4h4weHDhxWVKlWSr2vdunWC7cV1gypO4eTkJGuvf/z4UXH37l15jiGea9++fYb/DoiIdBmD6J8hAlqdO3eWfzREQPtzxo4dK7cVQazkFCtWTD5ftmzZJE2AVMGwPn36xD3GIDpR9hkfQkJC5EmqOHFdv369ws/PT/6IdfGYaBAmmgSpiJtwVlZWit69eyd4HwbRiXR7bLhw4YLC2dlZUb9+fXlxK7774vs8bNgw+Zp27dqlO4g+YcIE+bpp06Ypnj9/rggKClKcOnVK4eHhIZuG7dixQ8Ofmoi0MT4kFhERofD19VWMGDFC3pD/8ccfP3tMo0aNku/dsGFDtUF6ItLvsUFFvE6V2CfeR2XNmjVxgflt27YleI1oWCqSgMSY8uzZMw18WiIi/cQg+mf+kInsT/GHpGvXrmozR+Nv7+LiojA0NFQ8ffo02W3Exat4P3FXOLEXL17I59zd3eMeEyfA4rHNmzcn+36lS5eWf8zYKZtI/8eHMWPGyPdLLrglskHEc99//33cY7Vq1ZKBNZFdEh+D6ES6OzaILLNChQrJ7664kE2sZcuW8rXxs8Vz5cols0qT4+3tLbfv1q1b3GPixrt4TFxYJ/b69WuFhYWFomjRoun8pESkC+NDSgYNGiS337t372dv8NepU0cRGhr6BZ+KiPRtbJg8ebLc/u+//457bPfu3XGZ6GJ/iYlrkOQC7ERE2Qkbi6oRGxuLXr16YcWKFejUqROWL1+epIt9Yvv378eLFy9kvbACBQoku43ofC/Y29sneU71mOisnVKtUxVR81DUSRZ10VkPnUj/xwdVo6DkmoeqHrt69WrcY2L95cuXcuwQ9Q1VPxMmTJDPi2MT/547d266Pi8RaW5s8PHxkX+7K1WqJBv+pea7Ls4FgoOD8ebNmyTbJ9c7JaWxRNRVF3XSHzx4IN+TiPR7fEiJqhmxqjlxYuPGjZN1lkUPll27dsHCwuILPhkR6dvYkCtXLrkMCQlJEqcQ769qbPq5WAURUXbDyGsKf8hWrlwpm3yuWrVKNvH8nJQaBqrUqVMHa9askc1AE1M95urqGvdYzZo15VJ0yh41alSC7U+fPi3/8Km2ISL9Hh8iIyPl8v3790magInHBDMzs7jHunfvjtDQ0CTvIxoDiZNocUJduHBhlC5dOg2fkIgycmyI/z1PTnLfdfF3/ty5c/JcQHzv4ztw4EDcNmnZh7hAT+4imYj0a3xIiWgwKiT3XVcF0MXYsWfPnmQDc0SUNceGCxcuJIk7iOakItFHNDYViT8uLi6fjVUQEWU72k6F1+WpVKKuWGrLpLx7907WHs2dO7esRaiOaM4hpmWbm5srbty4Efe4eE2jRo3kfhcvXpzgNarmIvGnYorta9SoIR8/c+bMF31WItKt8aF///7yvUXT4fjTOEV90i5dusjnRMmXz2E5FyLdHRtEXVHRSFyUdjpw4ECC50SdUTFOiDJtoqaxilgXdczd3NwSlG+6evWqwszMTFGyZMkEY4aqOVipUqWSlHtasGCBfK5atWoa+vREpM3xQZR0StxnSdWAMH/+/MmWeBBlJcXj4loiODhYI5+TiHRrbLhy5UqyY8OWLVvk+zg4OCQ5R5g6dWpcibj45xXHjx+X7+/q6soyskSUrTETPZGJEyfKqVTW1tZwc3OTGRqJtWzZEmXLlk3wmLh7HBUVhW7dusHU1FTt+9va2mLRokVo27YtKleuLJcODg44fPgwbt++jcaNG6Nnz54JXjN//nxUq1ZN7lfcoXZ2dpYZI2L7wYMHo2rVqhr8DRCRtsaH3377DTt27JDbX758Wc5cEY4cOSKzP0S5huHDh2fAJyOizBobRJbYzJkz0b9/fzRq1AhNmzaV5VVEqZatW7fKEiviey7eR0Wsjx8/HmPHjoW7uzvatGmDoKAgrF+/Xj4vziviT/1u164dFixYgJMnT8rXNm/eXE7DFrNUjh49Kks2zJ49O5N+O0TZW0aPD+LfojyTKPMgskjFWPDw4UNZ1klkr44YMUJeR6iIUhGTJk2SpSArVqwo95eYKO8ifohIf8eGYcOGybGgSpUqMqtclIIV5wFiNrt4PzEW2NnZJdjfzz//jN27d8uMeHHt8e233+L169fYsmWLfM3SpUtZRpaIsjdtR/F1jepucEo/y5YtS/I6kQUmnrtz506q9iMyQho2bKiwt7dXmJqaymyxP/74Q+2dXR8fH0Xbtm0VOXLkkFlnX3/9tcLLyyvZu8tEpL/jw8uXLxWDBw+WTf/E2CC+78WLF1f88ssvCn9//1QdJzPRiXR/bDh48KCiSZMmcnaaaOJlZ2cnZ56tXr1a7X7Ec6JBuWgMKrZv3Lix4vLly2qz1qZNm6YoV66cwtLSUmayi4ZkomFZas9ViEj3x4dNmzbJLNbChQsrrKys5Mw38V1v3bq1Yv/+/WrPEVL6EdsQkX6PDYsWLZLxBjEjRZw3iGsKMU6IBqF3795Ve1yicamYraK6FhHxh1atWimuXbuWIb8HIiJ9YiD+j7YD+UREREREREREREREuihh22ciIiIiIiIiIiIiIorDIDoRERERERERERERkRoMohMRERERERERERERqcEgOhERERERERERERGRGgyiExERERERERERERGpwSA6EREREREREREREZEaDKITEREREREREREREanBIDoRERERERERERERkRoMohMRERERERERERERqcEgOhERERFlebVq1YKBgQH0hUKhwDfffIP69evr9edIraioKBQuXBjt27fX9qEQERERESVhnPQhIiIiIiLdldYgsghI65uVK1fiypUrOHfuHLIDExMTjBkzBt9//z3Onz+PypUra/uQiIiIiIjiGCj08aqCiIiIiLKt8ePHJ3ls7ty5+PjxIzw9PZPd/tmzZwgNDUWJEiWg62JjY1GkSBHkz58fJ0+eTJKJfuLECb28MfA50dHRyJcvH8qUKYNDhw5p+3CIiIiIiOIwiE5EREREes/V1RVPnz7NEsHlPXv2oGnTpli0aJHMzM4uQXThp59+wrx583Dv3j0ULVpU24dDRERERCSxJjoRERERZXnJ1RJfvny5fEwsd+3ahUqVKsHS0lJmQ48bN05mhAsrVqyAu7s7LCwsUKBAAcycOTPZfYjA9tKlS1GtWjXY2trK9/Lw8JCPpcWyZcvkcbVp0ybFGuIiw17cPDAzM4Obmxvmz5+f7LYhISEyQ19k4ZubmyNHjhxo0qQJzpw5k2Tbnj17yn0/efIkyXNif+K548ePxz0m1sVj4rmzZ8/KGu729vYJftfHjh1Do0aNkDdvXnmsTk5OqFGjBv77778k+xA10cXvUfzOiYiIiIh0BYPoRERERJStbdu2TQZvRWPLAQMGwNraGpMnT8bvv/8uA+ZDhgyRQfR+/frJwPrIkSNlzfL4ROC3S5cu6NOnD96/f4/OnTvLLHIRwBaPjRgxIlXHIt5HBJ2LFy8OBwcHtdt16tRJBucbNGgg39/f3x8//PCDzF6PLzw8HHXq1MHEiRNhZWWFoUOHokWLFnIfNWvWxKZNm6AJIoCuulEhfk8dOnSIy6qvW7cuLly4II91+PDhaN68OSIiIrBq1aok7yOaqYr66EeOHNHIcRERERERaQIbixIRERFRtrZv3z6ZlV2hQgX57wkTJshSInPmzJEZ5VevXpUBdkEEw8Vzs2bNQvfu3ePeY/HixVi3bh169eqFf//9VwaChcjISLRt2xZ//vmnDHyLIHFK7t69KwPiInM7JS9evMCtW7fk8anKoJQuXVrup2/fvnHbzZgxAxcvXpQBfhG0VmWIixsDonmnCHg3bNgQNjY2SA9Rw1wE9cXnj088proxIG5ExPfhw4ck7yMy5cXn8Pb2loF2kblORERERKRtzEQnIiIiomyta9eucQF0QQSURU1y0Yh04MCBcQF0QTT7rF69Ou7cuSMbYar8888/MtPby8srLoAumJqaYsqUKXJdBNk/RwTHBVHyJCXTpk2LC6ALInNdlJHx9fVFUFBQ3OOiLIo4nunTpycosVKuXDn06NEDgYGB2L59O9KrfPnySQLo8YlSOInlzJkz2W3FZxflat69e5fu4yIiIiIi0gRmohMRERFRtla2bNkkjzk7O6f4XExMDN6+fSvrp4tg+82bN2XN7z/++CPJ9iIgLPj4+Hz2WFTZ2aKueEqSy2h3cXGRSxEYFzcCPn36hEePHqFkyZJxz8VXu3ZtWf7l2rVr6NatG9Ij/k2I+Dp27IitW7fKrHdR4kaUdhH10HPlyqX2vUTNdsHPz0/etCAiIiIi0jYG0YmIiIgoW4uf0a1ibGz82edUwfGAgABZsuTly5eyFIw6oj7656gytkUt8y89ZhHgF0QQPaWsdtWNAtV26aFuH+3atZOZ7rNnz8bChQtlpr7IiBcBfFF6JrmbFGFhYXIpGrMSEREREekClnMhIiIiIkoHVUBbZIeLYLq6H1EX/HNy584tl6IuuqaOS2TMJ+fNmzcJthMMDZWXB/FL1ah8/PhR7b7il4pJTDQyPXHihLzZIOrPi4arx48fl7XYRdZ8YqrPrvpdEBERERFpG4PoRERERETpIEqniJIpoilockHhtChVqpQMZIva5uklguOinvuDBw9klnxiIpAtxM8Gd3BwkMvkthcNVtP7exKB8//++w89e/aUwf0LFy4k2U58dlEmR1XWhYiIiIhI2xhEJyIiIiJKpyFDhsja6H379k22bMvjx4/x5MmTz76PqIVepkwZeHt7IzY2Nt3HJZqHirIzo0ePltnwKjdu3MDy5cthZ2eHli1bJqltLp6Lb/PmzTKbPK1OnjwZV14mPlXTUHNz8wSPP3v2TGbIf/vtt2neFxERERFRRmFNdCIiIiKidOrfvz/Onz+PFStW4MyZM6hXr55sNCqyrUVDUZFxvXbtWri6un72vVq1agVPT0/5flWrVk3XcY0cORJ79uzBqlWrZKa8aOwpAtgbNmyQJVtEY1GRIR6/9EqRIkVkEP358+coV66cfN3Ro0fRuHFj7N27N803F169eoXq1avLzy7Kvpw+fRoXL16UzUbF4/EdOnRILuMH9omIiIiItI2Z6ERERERE6SSCwyLwLILToiTL7t27ZTNNERQW2dazZs2SgfXUEDXDRZPQ1atXp/u4xL5FAHzcuHGygeicOXOwbds21KxZU5ZzEY0/Ezc2PXz4sAxii0D3ggULZJNTkVGuylJPC5EBL5qIisz3f//9F0uWLEFERAT++OMP+bsxMjJKsL34zI6OjvJGAhERERGRrjBQxJ/XSUREREREWtetWzeZQf706dMEmeJZ2f3791G8eHGMHz8ev//+u7YPh4iIiIgoDjPRiYiIiIh0zOTJkxEWFoZ58+Yhu5g4cSKcnZ0xfPhwbR8KEREREVECDKITEREREemYggULyvrq2SULXTQ/FVnoK1euhJWVlbYPh4iIiIgoAZZzISIiIiIiIiIiIiJSg5noRERERERERERERERqMIhORERERERERERERKQGg+hERERERERERERERGowiE5EREREREREREREpAaD6EREREREREREREREajCITkRERERERERERESkBoPoRERERERERERERERqMIhORERERERERERERKQGg+hEREREREREREREREje/wHl6WKzCFlvkwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_generation_profile(hybrid_plant, start_day=115, n_days=5, plot_price = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize Wind Resource and Wind Farm Layout using FLORIS\n", + "\n", + "[FLORIS](https://github.com/NREL/floris) is a wind farm wake modeling and control software. It includes a set of visualization tools that can help you analyze specific site conditions and better understand your farm layout.\n", + "\n", + "### Wind Rose Plot\n", + "The wind rose plot displays wind direction and speed distributions at a given location, providing insights into the prevailing wind patterns over time. It is a valuable tool for understanding the wind resource at your site. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_series = TimeSeries(\n", + " wind_directions=hybrid_plant.wind._system_model.wind_dirs[0:8760],\n", + " wind_speeds=hybrid_plant.wind._system_model.speeds[0:8760],\n", + " turbulence_intensities=hybrid_plant.wind._system_model.fi.core.flow_field.turbulence_intensities[0]\n", + ")\n", + "\n", + "wind_rose = time_series.to_WindRose(wd_edges=np.arange(0, 360, 3.0), ws_edges=np.arange(2.0, 24.0, 2.0))\n", + "fig, ax = plt.subplots(subplot_kw={\"polar\": True})\n", + "\n", + "hub_ht = int(hybrid_plant.site.wind_resource.hub_height_meters)\n", + "wind_rose.plot(ax=ax,legend_kwargs={\"label\": f\"Wind Speed (m/s) at {hub_ht} m\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wind Farm Layout Plot\n", + "This plot shows where in space your wind turbines are located in relation to each other. The x- and y-axis are distance measured in meters." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1)\n", + "plot_turbine_points(hybrid_plant.wind._system_model.fi,ax=ax)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.11.11 64-bit ('hopp_v4')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "c74bd93e7545c41009c08d5ed7affbf327c670204c2ba512a009929a96f58f07" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/09-distributed-midsize-example.ipynb b/examples/09-distributed-midsize-example.ipynb new file mode 100644 index 000000000..affeee564 --- /dev/null +++ b/examples/09-distributed-midsize-example.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Distributed Hybrid Plant - Midsize Example\n", + "\n", + "This example is an extension of [Example 08](./08-distributed-residential-example.ipynb). In this example, we consider another distributed wind-hybrid system to supply nearby electrical loads. It highlights various site definition features and their applications in a distributed energy setup. This notebook will guide you through setting up a simulation for a distributed hybrid energy system using HOPP and assess how well the plant design meets the load demand for varying wind and solar resource conditions.\n", + "\n", + "## Hybrid Plant Design\n", + "\n", + "The hybrid plant design in this example includes:\n", + "- 5850 kW-DC of rooftop solar-PV **located within the town**\n", + "- 25 Vestas V47 turbines **located outside of the town**\n", + " + We selected the \"VestasV47_660kW_47\" turbine from the turbines available in the [turbine-models library](https://github.com/NREL/turbine-models). The [Vestas V47](https://en.wind-turbine-models.com/turbines/13-vestas-v47) turbine has a rated power of 660 kW and is grouped in the \"onshore\" category of the turbine-models library. This turbine is used as the [representative technology](https://atb.nrel.gov/electricity/2024/distributed_wind#representative_technology) for the \"midsize\" scale distributed wind turbine in NREL's Annual Technology Baseline (ATB). \n", + "- A 1 MW battery with 4-hours of storage capacity\n", + " + This battery aligns with the [Commercial Battery Storage](https://atb.nrel.gov/electricity/2024/commercial_battery_storage) representative technology in NREL's ATB.\n", + "\n", + "This plant design is represented under the `technologies` section of the [HOPP config input file](./inputs/09-distributed-wind-solar-midsize.yaml)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import required modules\n", + "\n", + "Start by importing the necessary modules and packaged and setting up our working environment." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/egrant/Documents/projects/HOPP/examples/log/hybrid_systems_2025-03-20T10.59.37.239340.log\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "from floris import TimeSeries\n", + "from floris.layout_visualization import plot_turbine_points\n", + "from hopp import ROOT_DIR\n", + "from hopp.utilities.keys import set_nrel_key_dot_env\n", + "from hopp.simulation import HoppInterface\n", + "from hopp.utilities.utilities import load_yaml\n", + "from hopp.tools.dispatch.plot_tools import (\n", + " plot_battery_output, plot_battery_dispatch_error, plot_generation_profile\n", + ")\n", + "\n", + "\n", + "set_nrel_key_dot_env()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Inputs\n", + "\n", + "Load the configuration YAML file as `hopp_config`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "example_dir = ROOT_DIR.parent / \"examples\"\n", + "input_filepath = os.path.join(str(example_dir),\"inputs\",\"09-distributed-wind-solar-midsize.yaml\")\n", + "hopp_config = load_yaml(input_filepath)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wind and Solar Resource Data\n", + "\n", + "Wind resource data is downloaded from [WIND Toolkit](https://developer.nrel.gov/docs/wind/wind-toolkit/wtk-download/), a dataset within the [Wind Resource Database (or WRDB)](https://wrdb.nrel.gov/data-viewer). WIND Toolkit provides wind resource data for resource years 2007-2014.\n", + "\n", + "Solar resource data is downloaded from [National Solar Radiation Database (NSRDB)](https://nsrdb.nrel.gov) and is available for resource years 1998-2022.\n", + "\n", + "### Different Locations for wind and solar resource\n", + "\n", + "In this example, the wind system is not co-located with the rooftop PV system. Therefore, we need to specify different locations for the wind and solar resource. In the configuration input file, we can specify unique locations and resource years for wind and solar resource. \n", + "\n", + "Wind resource data will be downloaded at the location (`wind_lat`, `wind_lon`) for the resource year `wind_year`. In this example, the wind system is located at (37.7768, -106.033) which is specified in the configuration input file as:\n", + "\n", + "```yaml\n", + "site:\n", + " data:\n", + " wind_year: 2013\n", + " wind_lat: 37.7768 \n", + " wind_lon: -106.033 \n", + "```\n", + "\n", + "Solar resource data will be downloaded at the location (`solar_lat`, `solar_lon`) for the resource year `solar_year`. In this example, the rooftop PV system is located at (38.087, -106.1423) which is specified in the configuration input file as:\n", + "\n", + "```yaml\n", + "site:\n", + " data:\n", + " solar_year: 2018\n", + " solar_lat: 38.087 \n", + " solar_lon: -106.1423\n", + "```\n", + "\n", + "The `site_shape` can be set to values of \"hexagon\", \"circle\", \"square\", or \"rectangle\". The site area can be defined in either kilometers (with the variable `site_area_km2`) or meters (with the variable `site_area_m2`). The site is defined under the `site` section of the configuration input file as:\n", + "\n", + "```yaml\n", + "site:\n", + " data:\n", + " lat: 37.7768\n", + " lon: -106.033\n", + " solar_year: 2018\n", + " solar_lat: 38.087 #for rooftop - center of town\n", + " solar_lon: -106.1423 #for rooftop - center of town\n", + " wind_year: 2013\n", + " wind_lat: 37.7768 #outside of town\n", + " wind_lon: -106.033 #outside of town\n", + " site_details:\n", + " site_shape: \"rectangle\"\n", + " site_area_km2: 0.80\n", + " aspect_ratio: 3.65\n", + " hub_height: 65 \n", + "```\n", + "\n", + "Note that the `hub_height` entry is set to 65 meters because this is one of the hub-heights available for the Vestas V47 wind turbine." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run Simulation Across Resource Years\n", + "\n", + "Often resource data can be impactful on simulation results. This example shows how to analyze how different resource data may impact meeting the load. We can simulate the hybrid plant performance by looping through wind and solar resource years and saving results for the percent of load that was missed for each simulation.\n", + "\n", + "Wind resource years must be between 2007 and 2014 for WIND toolkit data, we will simulate just a subset of these years for our analysis. We set the variable `wind_years` as `wind_years = np.arange(2010,2015,2)` which results in wind resource years of 2010, 2012, and 2014.\n", + "\n", + "Solar resource years must be between 1998 and 2022 for NSRDB data, we will simulate just a subset of these years for our analysis. We set the variable `solar_years` as `solar_years = np.arange(2000,2022,5)` which results in solar resource years of 2000, 2005, 2010, 2015, and 2020.\n", + "\n", + "**NOTE**: the below code block takes 3-4 minutes to run." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# set years of wind and solar resource years\n", + "wind_years = np.arange(2010,2015,2)\n", + "solar_years = np.arange(2000,2022,5)\n", + "\n", + "# initialize an array of zeros that is the shape (3,5)\n", + "missed_load_per_year = np.zeros((len(wind_years),len(solar_years)))\n", + "\n", + "# loop through wind years\n", + "for wi,wind_year in enumerate(wind_years):\n", + " # update the wind_year in the hopp_config\n", + " hopp_config[\"site\"][\"data\"][\"wind_year\"] = wind_year\n", + "\n", + " # loop through solar years\n", + " for si,solar_year in enumerate(solar_years):\n", + " # update the solar_year in the hopp_config\n", + " hopp_config[\"site\"][\"data\"][\"solar_year\"] = solar_year\n", + "\n", + " # initalize HOPP interface\n", + " hi = HoppInterface(hopp_config)\n", + " # simulate hybrid plant for 1-year\n", + " hi.simulate(project_life = 1)\n", + " #storage the results for the percent of load that was missed\n", + " missed_load_per_year[wi,si] = hi.system.grid.missed_load_percentage\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize the Results\n", + "\n", + "Below is a visualization of the missed load results for each wind and solar resource year combination. In the plot, yellow shading indicates a higher percent of the load is missed and darker green or blue colors indicate that a lower percent of the load is missed. Some findings from these results are:\n", + "\n", + "- A wind resource year of 2014 (the bottom row in the figure below) results in about 5% less missed load than wind resource years of 2010 and 2012. \n", + "- The most missed load (65% missed load) occurs for a wind resource year of 2012 and a solar resource year of 2010. \n", + "- The least missed load (56% missed load) occurs for a wind resource year of 2014 and a solar resource year of 2020. \n", + "\n", + "From this analysis, we find that wind and solar resource years can change the annual missed load by up to 9% and should consider this uncertainity in future interations of this plant design." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1)\n", + "im = ax.imshow(\n", + " missed_load_per_year, \n", + " cmap = 'viridis',\n", + " vmin = np.floor(missed_load_per_year.min()),\n", + " vmax = np.ceil(missed_load_per_year.max()),\n", + ")\n", + "ax.set_xlabel(\"Solar Resource Years\")\n", + "ax.set_ylabel(\"Wind Resource Years\")\n", + "ax.set_xticks(np.arange(0,len(solar_years),1),solar_years)\n", + "ax.set_yticks(np.arange(0,len(wind_years),1),wind_years)\n", + "fig.colorbar(im, ax=ax, label = \"Missed Load (%)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wind Rose Plot Visualization Using FLORIS\n", + "\n", + "In [Example 08](./08-distributed-residential-example.ipynb) we made a wind rose plot using wind resource data for the *in-town site* and a resource year of 2013 and a hub-height of 24 meters.\n", + "\n", + "In this example, the wind rose plot is made using wind resource data for the *out-of-town site* and a resource year of 2014 (because that was the last wind year we simulated) and a hub-height of 65 meters.\n", + "\n", + "Take a moment to compare the wind rose below to the wind rose in [Example 08](./08-distributed-residential-example.ipynb).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hybrid_plant = hi.system\n", + "time_series = TimeSeries(\n", + " wind_directions=hybrid_plant.wind._system_model.wind_dirs[0:8760],\n", + " wind_speeds=hybrid_plant.wind._system_model.speeds[0:8760],\n", + " turbulence_intensities=hybrid_plant.wind._system_model.fi.core.flow_field.turbulence_intensities[0]\n", + ")\n", + "\n", + "wind_rose = time_series.to_WindRose(wd_edges=np.arange(0, 360, 3.0), ws_edges=np.arange(2.0, 24.0, 2.0))\n", + "fig, ax = plt.subplots(subplot_kw={\"polar\": True})\n", + "\n", + "hub_ht = int(hybrid_plant.site.wind_resource.hub_height_meters)\n", + "wind_rose.plot(ax=ax,legend_kwargs={\"label\": f\"Wind Speed (m/s) at {hub_ht} m\"})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.11.11 ('hopp_v4')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "c74bd93e7545c41009c08d5ed7affbf327c670204c2ba512a009929a96f58f07" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/inputs/08-distributed-wind-solar-residential.yaml b/examples/inputs/08-distributed-wind-solar-residential.yaml new file mode 100644 index 000000000..69c6a80b7 --- /dev/null +++ b/examples/inputs/08-distributed-wind-solar-residential.yaml @@ -0,0 +1,58 @@ +name: "Example 08 - Distributed Residential" + +# SiteInfo +site: + data: + lat: 38.087 #center of twon + lon: -106.1423 #center of town + solar_year: 2018 #year to get solar resource data for + wind_year: 2013 #year to get wind resource data for + site_details: + site_shape: "rectangle" + site_area_km2: 0.80 + aspect_ratio: 3.65 + hub_height: 24 #18m or 24m for Bergey + solar: true + wind: true + wave: false + desired_schedule: !include "residential_load_profile_MW.yaml" #Absolute desired load profile in MWe. + curtailment_value_type: "desired_schedule" + +# Technologies +technologies: + pv: + panel_system_design: !include "residential_pv.yaml" + dc_degradation: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + panel_tilt_angle: 20.0 # corresponds to roof-top tilt angle + system_capacity_kw: 1800.0 #system capacity in kWdc + wind: + num_turbines: 56 #number of turbines + turbine_name: "BergeyExcel15_15.6kW_9.6" #turbine model to use + model_name: floris + floris_config: !include floris_v4_template.yaml + resource_parse_method: "weighted_average" #weight wind resource data based on hub-height + store_turbine_performance_results: False + adjust_air_density_for_elevation: True #adjust air density based on site elevation + layout_mode: "basicgrid" + layout_params: + row_D_spacing: 11.5 + turbine_D_spacing: 11.5 + + battery: + system_capacity_kwh: 1750 + system_capacity_kw: 700 + minimum_SOC: 20.0 + maximum_SOC: 100.0 + initial_SOC: 20.0 + grid: + interconnect_kw: 4170.0 + ppa_price: 0.01 + +config: + dispatch_options: + battery_dispatch: load_following_heuristic + solver: cbc + n_look_ahead_periods: 48 + grid_charging: false + pv_charging_only: false + include_lifecycle_count: false \ No newline at end of file diff --git a/examples/inputs/09-distributed-wind-solar-midsize.yaml b/examples/inputs/09-distributed-wind-solar-midsize.yaml new file mode 100644 index 000000000..2f1478111 --- /dev/null +++ b/examples/inputs/09-distributed-wind-solar-midsize.yaml @@ -0,0 +1,63 @@ +name: "Example 08 - Distributed Midsize" + +# SiteInfo +site: + data: + lat: 38.087 #center of town + lon: -106.1423 #center of town + solar_year: 2018 + solar_lat: 38.087 #for rooftop - center of town + solar_lon: -106.1423 #for rooftop - center of town + wind_year: 2013 + wind_lat: 37.7768 #outside of town + wind_lon: -106.033 #outside of town + site_details: + site_shape: "rectangle" + site_area_km2: 0.80 + aspect_ratio: 3.65 + hub_height: 65 + solar: true + wind: true + wave: false + desired_schedule: !include "distributed_load_profile_MW.yaml" #Absolute desired load profile in MWe. + curtailment_value_type: "desired_schedule" + +# Technologies +technologies: + pv: + panel_system_design: !include "residential_pv.yaml" + dc_degradation: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + panel_tilt_angle: 20.0 #corresponds to roof-top tilt angle + system_capacity_kw: 5850.0 #system capacity in kWdc + wind: + num_turbines: 25 #number of turbines + turbine_name: "VestasV47_660kW_47" #turbine model to use + model_name: floris + floris_config: !include floris_v4_template.yaml + resource_parse_method: "weighted_average" #weight wind resource data based on hub-height + store_turbine_performance_results: False + adjust_air_density_for_elevation: True #adjust air density based on site elevation + layout_mode: "basicgrid" + layout_params: + row_D_spacing: 5.0 + turbine_D_spacing: 5.0 + verbose: False + + battery: + system_capacity_kwh: 4000 + system_capacity_kw: 1000 + minimum_SOC: 20.0 + maximum_SOC: 100.0 + initial_SOC: 20.0 + grid: + interconnect_kw: 16674.0 + ppa_price: 0.01 + +config: + dispatch_options: + battery_dispatch: load_following_heuristic + solver: cbc + n_look_ahead_periods: 48 + grid_charging: false + pv_charging_only: false + include_lifecycle_count: false \ No newline at end of file diff --git a/examples/inputs/distributed_load_profile_MW.yaml b/examples/inputs/distributed_load_profile_MW.yaml new file mode 100644 index 000000000..d2c713e41 --- /dev/null +++ b/examples/inputs/distributed_load_profile_MW.yaml @@ -0,0 +1,8760 @@ +- 6.91601073834396 +- 7.652079909694651 +- 8.466046516542907 +- 9.391285124931189 +- 9.9005646041258 +- 10.288427183846979 +- 10.278676599381335 +- 10.755967736834465 +- 11.400962787904815 +- 11.596971314121602 +- 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WARNING + file: + enable: false + level: WARNING +solver: + type: turbine_grid + turbine_grid_points: 1 +flow_field: + air_density: 1.225 + reference_wind_height: -1 + wind_directions: + - 270.0 + wind_shear: 0.33 + wind_speeds: + - 8.0 + wind_veer: 0.0 + turbulence_intensities: + - 0.06 +wake: + model_strings: + combination_model: sosfs + deflection_model: gauss + turbulence_model: crespo_hernandez + velocity_model: gauss + enable_secondary_steering: false + enable_yaw_added_recovery: false + enable_transverse_velocities: false + wake_deflection_parameters: + gauss: + ad: 0.0 + alpha: 0.58 + bd: 0.0 + beta: 0.077 + dm: 1.0 + ka: 0.38 + kb: 0.004 + jimenez: + ad: 0.0 + bd: 0.0 + kd: 0.05 + wake_velocity_parameters: + cc: + a_s: 0.179367259 + b_s: 0.0118889215 + c_s1: 0.0563691592 + c_s2: 0.13290157 + a_f: 3.11 + b_f: -0.68 + c_f: 2.41 + alpha_mod: 1.0 + gauss: + alpha: 0.58 + beta: 0.077 + ka: 0.38 + kb: 0.004 + jensen: + we: 0.05 + wake_turbulence_parameters: + crespo_hernandez: + initial: 0.1 + constant: 0.5 + ai: 0.8 + downstream: -0.32 + enable_active_wake_mixing: false + + wake_velocity_parameters: + cc: + a_f: 3.11 + a_s: 0.179367259 + alpha_mod: 1.0 + b_f: -0.68 + b_s: 0.0118889215 + c_f: 2.41 + c_s1: 0.0563691592 + c_s2: 0.13290157 + gauss: + alpha: 0.58 + beta: 0.077 + ka: 0.38 + kb: 0.004 + jensen: + we: 0.05 +farm: + layout_x: + - 0.0 + layout_y: + - 0.0 + turbine_type: + - operation_model: cosine-loss + # hub_height: 115.0 + # turbine_type: lbw_6MW + # rotor_diameter: 196.0 + # TSR: 9.0 + diff --git a/examples/inputs/residential_load_profile_MW.yaml b/examples/inputs/residential_load_profile_MW.yaml new file mode 100644 index 000000000..0c09215ee --- /dev/null +++ b/examples/inputs/residential_load_profile_MW.yaml @@ -0,0 +1,8760 @@ +- 1.72900268458599 +- 1.9130199774236627 +- 2.1165116291357267 +- 2.347821281232797 +- 2.47514115103145 +- 2.5721067959617447 +- 2.5696691498453337 +- 2.6889919342086164 +- 2.8502406969762037 +- 2.8992428285304004 +- 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2.4290345857644855 +- 2.520312409924681 +- 2.531846305712041 +- 2.614622049335693 +- 2.747168497730112 +- 2.9095320749292752 +- 3.1447103381935424 +- 3.3902591071997534 +- 3.5468526462474648 +- 3.2365405956114905 +- 2.7279486737938132 +- 2.2674563571856914 +- 2.05450632591954 +- 1.8270449171420189 +- 1.6137469168016094 +- 1.3866974405332149 +- 1.3156358328519384 +- 1.4380900042489688 diff --git a/examples/inputs/residential_pv.yaml b/examples/inputs/residential_pv.yaml new file mode 100644 index 000000000..5d9ba7ca1 --- /dev/null +++ b/examples/inputs/residential_pv.yaml @@ -0,0 +1,14 @@ +# PySAM Pvwattsv8 System Design Inputs: https://nrel-pysam.readthedocs.io/en/main/modules/Pvwattsv8.html#systemdesign-group +SystemDesign: + # use fixed roof mount system corresponding to array_type = 1.0 + array_type: 1.0 # 0: fixed open rack 1: fixed roof mount 2: 1-axis tracking 3: 1-axis backtracking 4: 2-axis tracking + bifaciality: 0.0 # monofacial modules have no bifaciality + module_type: 1.0 # 0: standard 1: premium 2: thin film. Premium modules have an efficiency of 21% + losses: 15.0 # DC-losses represented as a percentage + # inverter specifications. Inverters convert DC-power from the solar panels to AC-power + dc_ac_ratio: 1.21 #inverter is (1/dc_ac_ratio) the capacity of the pv system. + inv_eff: 96.0 #inverter efficiency as a percentage + # panel layout and orientation + gcr: 0.3 # groud coverage ratio default value + azimuth: 180.0 # South-facing panels. East is 90, South is 180, West is 270 + rotlim: 0.0 #no rotational limit because using a fixed-tilt panel \ No newline at end of file diff --git a/hopp/tools/dispatch/plot_tools.py b/hopp/tools/dispatch/plot_tools.py index 3fcf119a8..21e0e857c 100644 --- a/hopp/tools/dispatch/plot_tools.py +++ b/hopp/tools/dispatch/plot_tools.py @@ -250,7 +250,8 @@ def plot_generation_profile(hybrid: HybridSimulation, discharge_color='b', charge_color='r', gen_color='g', - price_color='r' + price_color='r', + plot_price = True, ): if not hasattr(hybrid, 'dispatch_builder'): @@ -318,6 +319,7 @@ def plot_generation_profile(hybrid: HybridSimulation, ax2.plot(time, hybrid.battery.outputs.dispatch_SOC[time_slice], '.', label='Dispatch') ax2.set_ylabel('State-of-Charge (-)', fontsize=font_size) ax2.legend(fontsize=font_size-2, loc='upper right') + ax2.tick_params(which='both', labelsize=font_size) plt.title('Battery Power Flow', fontsize=font_size) # Net action @@ -333,12 +335,13 @@ def plot_generation_profile(hybrid: HybridSimulation, ax1.legend(fontsize=font_size-2, loc='upper left') ax1.set_ylabel('Power (MW)', fontsize=font_size) - ax2 = ax1.twinx() - - price = [p * hybrid.ppa_price[0] for p in hybrid.site.elec_prices.data[time_slice]] - ax2.plot(time, price, color=price_color, label='Price') - ax2.set_ylabel('Grid Price ($/kWh)', fontsize=font_size) - ax2.legend(fontsize=font_size-2, loc='upper right') + if plot_price: + ax2 = ax1.twinx() + ax2.tick_params(which='y', labelsize=font_size) + price = [p * hybrid.ppa_price[0] for p in hybrid.site.elec_prices.data[time_slice]] + ax2.plot(time, price, color=price_color, label='Price') + ax2.set_ylabel('Grid Price ($/kWh)', fontsize=font_size) + ax2.legend(fontsize=font_size-2, loc='upper right') plt.xlabel('Time (hours)', fontsize=font_size) plt.title('Net Generation', fontsize=font_size) From 7e9e95f663ab9cc7e1d21385a1b92f3e7f0975c5 Mon Sep 17 00:00:00 2001 From: John Jasa Date: Fri, 21 Mar 2025 10:08:29 -0600 Subject: [PATCH 18/48] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5ea66a31e..d3d3eb2b0 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ solar and storage. ## Software requirements -- Python version 3.10, and 3.11 only (PySAM 4.2 is incompatible with 3.12) +- Python version 3.10, and 3.11 only ## Installing from Package Repositories From 1597faf5041d705443cf2a5ecf36d19f1bf84122 Mon Sep 17 00:00:00 2001 From: John Jasa Date: Fri, 21 Mar 2025 11:39:01 -0500 Subject: [PATCH 19/48] Version bump for 3.2.0 release (#453) * Version bump for release * Update RELEASE.md --- RELEASE.md | 56 +++++++++++++++++++++++++++--------------------- hopp/__init__.py | 2 +- 2 files changed, 33 insertions(+), 25 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 6e65efb64..1dd49ee3a 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,21 +1,15 @@ # Release Notes -## Unreleased, TBD -* Added option and functionality to load wind and solar resource data from NSRDB and Wind Toolkit data files if user-specified. -* Fixed a bug in site_info that set resource year to 2012 even if otherwise specified. -* Minor clean up to floris.py - removed unnecessary data exportation and fixed bug in value() -* Added ability and option to initialize site_info with preloaded and formatted wind and solar resource data -* Bug fix in load following heuristic method: only using beginning of variable load signals -* Feature add: added alternative method to defining site boundary. -* Feature add: added function to adjust air density based on site elevation -* Added weighted average wind resource parsing method option when using floris. -* Updated PySAM version from 4.2.0 to 6.0.1. Main changes noted in [PR #425](https://github.com/NREL/HOPP/pull/425) -* PySAM generation plant defaults have been updated. Current defaults can be found [here](https://github.com/NREL/SAM/tree/develop/api/api_autogen/library/defaults) -* PySAM SingleOwner financial model update investment-tax credit and depreciation basis calculations to remove financing fees and reserve account funding from basis. -* PySAM MHKWave update marine energy device cost curves. -* PySAM Detailed PV update module and inverter libraries, snow module, tracking, losses. -* Update deprecated methods in wave_resource.py -* For further details on the following updates, users are referred [here](https://github.com/NREL/HOPP/pull/429#issue-2852391571) +## Version 3.2.0, March 21, 2025 + +* Updates related to PySAM: + + Updated PySAM version from 4.2.0 to >6.0.0. Main changes noted in [PR #425](https://github.com/NREL/HOPP/pull/425) + + PySAM generation plant defaults have been updated. Current defaults can be found [here](https://github.com/NREL/SAM/tree/develop/api/api_autogen/library/defaults) + + PySAM SingleOwner financial model update investment-tax credit and depreciation basis calculations to remove financing fees and reserve account funding from basis. + + PySAM MHKWave update marine energy device cost curves. + + PySAM Detailed PV update module and inverter libraries, snow module, tracking, losses. + +* Wind-focused usability additions that are detailed [here](https://github.com/NREL/HOPP/pull/429#issue-2852391571) + Feature add: new wind layout method called `basicgrid` that makes the most-square layout that has the option to be site-constrained. + Updated wind layout methods to classes + Bug-fix: grid angle converted from degrees to radians in `make_grid_lines()` function in `wind_layout_tools.py` @@ -23,18 +17,32 @@ + Update: raise errors when using floris if theres a discrepancy between inputs in `WindConfig` and information in `floris_config` (such as `num_turbines` and the `floris_config` layout, and turbine parameters like rotor diameter and turbine rating.) + Integrated wind layout functionality when using floris + Updated wind layout parameters. -* Added TidalResource to load tidal resource data for simulating tidal energy. -* Added MHKTidalPlant to simulate tidal energy. -* Add tidal energy to HybridSimulation. -* Remove erroneous 100 multiples for percentages and add clarifying parentheses for correct 100 multiples for percentages. -* Add tidal energy to dispatch. - -* Integrated [turbine-models library](https://github.com/NREL/turbine-models/tree/master). For further details on the following updates, users are referred [here](https://github.com/NREL/HOPP/pull/435) - + Wind turbines from the turbine-models library can now be simulated by specifying the turbine name. This feature is compatible with FLORIS and PySAM WindPower simulations. + + Minor clean up to floris.py - removed unnecessary data exportation and fixed bug in value() + +* Integrated [turbine-models library](https://github.com/NREL/turbine-models/tree/master). For further details see [here](https://github.com/NREL/HOPP/pull/435) + + Wind turbines from the turbine-models library can now be simulated by specifying the turbine name. This feature is compatible with floris and PySAM WindPower simulations. + Added wind turbine power-curve tools to estimate thrust coefficient, power coefficient, and power-curve. * Added two distributed wind-hybrid examples that highlight the turbine-models library package and other recent features for wind system modeling and simulations. These examples are: - `examples/08-distributed-residential-example.ipynb` - `examples/09-distributed-residential-midsize.ipynb` +* Added tidal models + + Added TidalResource to load tidal resource data for simulating tidal energy. + + Added MHKTidalPlant to simulate tidal energy. + + Add tidal energy to HybridSimulation. + + Add tidal energy to dispatch. + +* Other feature additions: + + Added option and functionality to load wind and solar resource data from NSRDB and Wind Toolkit data files if user-specified. + + Added ability and option to initialize site_info with preloaded and formatted wind and solar resource data + + Feature add: added alternative method to defining site boundary. + + Feature add: added function to adjust air density based on site elevation + + Added weighted average wind resource parsing method option when using floris. + + Update deprecated methods in wave_resource.py + +* Bug fixes: + + Remove erroneous 100 multiples for percentages and add clarifying parentheses for correct 100 multiples for percentages. + + Fixed a bug in site_info that set resource year to 2012 even if otherwise specified. + + Bug fix in load following heuristic method: only using beginning of variable load signals ## Version 3.1.1, Dec. 18, 2024 diff --git a/hopp/__init__.py b/hopp/__init__.py index a2ea68c37..7518ef071 100644 --- a/hopp/__init__.py +++ b/hopp/__init__.py @@ -1,7 +1,7 @@ from pathlib import Path -__version__ = "3.1.1" +__version__ = "3.2.0" ROOT_DIR = Path(__file__).resolve().parent From 2eb44eec645123b7a1a3b206d5bff89b2ca69b4f Mon Sep 17 00:00:00 2001 From: John Jasa Date: Fri, 21 Mar 2025 12:39:17 -0500 Subject: [PATCH 20/48] Update doc book deployment --- .github/workflows/gh_pages.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/gh_pages.yml b/.github/workflows/gh_pages.yml index ca501fe9f..517ab0578 100644 --- a/.github/workflows/gh_pages.yml +++ b/.github/workflows/gh_pages.yml @@ -42,11 +42,11 @@ jobs: # Upload the book's HTML as an artifact - name: Upload artifact - uses: actions/upload-pages-artifact@v2 + uses: actions/upload-pages-artifact@v3 with: path: "docs/_build/html" # Deploy the book's HTML to GitHub Pages - name: Deploy to GitHub Pages id: deployment - uses: actions/deploy-pages@v2 + uses: actions/deploy-pages@v4 From c7fa08adf973a117733826cecebd498a8c4e8809 Mon Sep 17 00:00:00 2001 From: John Jasa Date: Wed, 26 Mar 2025 15:52:38 -0500 Subject: [PATCH 21/48] Update wind tests for floats (#460) * Loosened wind turbine property checks * Loosened wind turbine property checks and fixed wind initialization logic * Added to release.md * Removed unnecessary logic for assigning financial values --- RELEASE.md | 4 ++++ .../financial/custom_financial_model.py | 2 +- hopp/simulation/technologies/power_source.py | 4 ---- hopp/simulation/technologies/wind/floris.py | 16 ++++++++-------- 4 files changed, 13 insertions(+), 13 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 1dd49ee3a..3503daf54 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,5 +1,9 @@ # Release Notes +## Unreleased + +* Loosened strictness of comparison for wind turbine config checking + ## Version 3.2.0, March 21, 2025 * Updates related to PySAM: diff --git a/hopp/simulation/technologies/financial/custom_financial_model.py b/hopp/simulation/technologies/financial/custom_financial_model.py index 2ce59c6c1..65684e9d4 100644 --- a/hopp/simulation/technologies/financial/custom_financial_model.py +++ b/hopp/simulation/technologies/financial/custom_financial_model.py @@ -184,7 +184,7 @@ class CustomFinancialModel(): """ This custom financial model slots into the PowerSource's financial model that is originally a PySAM.Singleowner model. PowerSource and the overlaying classes that call on PowerSource expect - properties and functions from the financial model. The mininum expectations are listed here as + properties and functions from the financial model. The minimum expectations are listed here as the class interface. The financial model is constructed with financial configuration inputs. During simulation, the diff --git a/hopp/simulation/technologies/power_source.py b/hopp/simulation/technologies/power_source.py index 5caf1a227..bf31f91db 100644 --- a/hopp/simulation/technologies/power_source.py +++ b/hopp/simulation/technologies/power_source.py @@ -49,10 +49,6 @@ def __init__(self, name, site: SiteInfo, system_model, financial_model): if isinstance(self._financial_model, Singleowner.Singleowner): self.initialize_financial_values() else: - if inspect.ismethod(getattr(self._system_model, 'export', None)): - self._financial_model.assign(self._system_model.export(), ignore_missing_vals=True) # copy system parameter values having same name - else: - pass self._financial_model.set_financial_inputs(system_model=self._system_model) # for custom financial models self.capacity_factor_mode = "cap_hours" # to calculate via "cap_hours" method or None to use external value diff --git a/hopp/simulation/technologies/wind/floris.py b/hopp/simulation/technologies/wind/floris.py index a8fe10b6b..c42453657 100644 --- a/hopp/simulation/technologies/wind/floris.py +++ b/hopp/simulation/technologies/wind/floris.py @@ -98,15 +98,15 @@ def __attrs_post_init__(self): def initialize_from_floris(self, floris_config): - """Initialize wind turbine parmeters and set in floris_config. + """Initialize wind turbine parameters and set in floris_config. Args: floris_config (dict): floris input dictionary Raises: - ValueError: if rotor_diameter in WindConfig doesnt match rotor diameter in floris_config - ValueError: if turbine_rating_kw in WindConfig doesnt match turbine rating from power-curve - ValueError: if hub_height in WindConfig doesnt match hub-height in floris_config + ValueError: if rotor_diameter in WindConfig doesn't match rotor diameter in floris_config + ValueError: if turbine_rating_kw in WindConfig doesn't match turbine rating from power-curve + ValueError: if hub_height in WindConfig doesn't match hub-height in floris_config Returns: @@ -132,7 +132,7 @@ def initialize_from_floris(self, floris_config): self.turb_rating = max(self.wind_turbine_powercurve_powerout) if self.config.turbine_rating_kw is not None: - if self.config.turbine_rating_kw != self.turb_rating: + if not np.isclose(self.config.turbine_rating_kw, self.turb_rating, atol=1e-3): msg = ( f"Input turbine rating ({self.config.turbine_rating_kw} kW) does not match " f"rating from floris power-curve ({self.turb_rating} kW). " @@ -141,7 +141,7 @@ def initialize_from_floris(self, floris_config): ) raise ValueError(msg) if self.config.rotor_diameter is not None: - if self.config.rotor_diameter != self.wind_turbine_rotor_diameter: + if not np.isclose(self.config.rotor_diameter, self.wind_turbine_rotor_diameter, atol=1e-4): msg = ( f"Input rotor diameter ({self.config.rotor_diameter}) does not match " f"rotor diameter from floris config ({self.wind_turbine_rotor_diameter}). " @@ -150,7 +150,7 @@ def initialize_from_floris(self, floris_config): ) raise ValueError(msg) if self.config.hub_height is not None: - if self.config.hub_height != hub_height: + if not np.isclose(self.config.hub_height, hub_height, atol=1e-4): msg = ( f"Input hub-height ({self.config.hub_height}) does not match " f"hub-height from floris config ({hub_height}). " @@ -159,6 +159,7 @@ def initialize_from_floris(self, floris_config): f"or correct the value to {hub_height}." ) raise ValueError(msg) + if hub_height != self.site.wind_resource.hub_height_meters: valid_min_height = hub_height >= min(self.site.wind_resource.data["heights"]) valid_max_height = hub_height <= max(self.site.wind_resource.data["heights"]) @@ -280,7 +281,6 @@ def export(self): """ config = { 'system_capacity': self.system_capacity, - 'annual_energy': self.annual_energy, } return config From d2e0e5227d4033f56f74888c89c24a9f9ed7fc97 Mon Sep 17 00:00:00 2001 From: John Jasa Date: Thu, 27 Mar 2025 11:03:11 -0500 Subject: [PATCH 22/48] Hotfix for GreenHEART plotting (#462) --- hopp/tools/dispatch/plot_tools.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hopp/tools/dispatch/plot_tools.py b/hopp/tools/dispatch/plot_tools.py index 21e0e857c..87b19118e 100644 --- a/hopp/tools/dispatch/plot_tools.py +++ b/hopp/tools/dispatch/plot_tools.py @@ -337,7 +337,7 @@ def plot_generation_profile(hybrid: HybridSimulation, if plot_price: ax2 = ax1.twinx() - ax2.tick_params(which='y', labelsize=font_size) + ax2.tick_params(axis='y', labelsize=font_size) price = [p * hybrid.ppa_price[0] for p in hybrid.site.elec_prices.data[time_slice]] ax2.plot(time, price, color=price_color, label='Price') ax2.set_ylabel('Grid Price ($/kWh)', fontsize=font_size) From 93722080d5d6df3c72aace8133b72965e42403bf Mon Sep 17 00:00:00 2001 From: John Jasa Date: Thu, 27 Mar 2025 16:21:05 -0500 Subject: [PATCH 23/48] Loosen turbine rating check (#464) * Made turbine rating check +/- 10% * Loosened strictness of wind turbine rating comparison and added tests --- RELEASE.md | 2 +- hopp/simulation/technologies/wind/floris.py | 2 +- tests/hopp/test_turbine_models_interface.py | 57 +++++++++++++++++++++ 3 files changed, 59 insertions(+), 2 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 3503daf54..7754ae3af 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -2,7 +2,7 @@ ## Unreleased -* Loosened strictness of comparison for wind turbine config checking +* Loosened strictness of comparison for wind turbine config checking and added tests ## Version 3.2.0, March 21, 2025 diff --git a/hopp/simulation/technologies/wind/floris.py b/hopp/simulation/technologies/wind/floris.py index c42453657..80ca718fe 100644 --- a/hopp/simulation/technologies/wind/floris.py +++ b/hopp/simulation/technologies/wind/floris.py @@ -132,7 +132,7 @@ def initialize_from_floris(self, floris_config): self.turb_rating = max(self.wind_turbine_powercurve_powerout) if self.config.turbine_rating_kw is not None: - if not np.isclose(self.config.turbine_rating_kw, self.turb_rating, atol=1e-3): + if not np.isclose(self.config.turbine_rating_kw, self.turb_rating, rtol=0.1): msg = ( f"Input turbine rating ({self.config.turbine_rating_kw} kW) does not match " f"rating from floris power-curve ({self.turb_rating} kW). " diff --git a/tests/hopp/test_turbine_models_interface.py b/tests/hopp/test_turbine_models_interface.py index e0933da01..bd4ceb537 100644 --- a/tests/hopp/test_turbine_models_interface.py +++ b/tests/hopp/test_turbine_models_interface.py @@ -143,6 +143,63 @@ def test_floris_NREL_5MW_RWT_corrected_hopp(site_input,subtests): with subtests.test("wind capacity factor"): assert hybrid_plant.capacity_factors["wind"] == approx(42.0, abs = 1.0) +def test_floris_NREL_5MW_RWT_error_hopp(site_input, subtests): + floris_template = load_yaml(str(FLORIS_V4_TEMPLATE_PATH)) + turbine_library_turbine_name = "NREL_Reference_5MW_126" + n_turbs = 4 + turbine_rating_kw = 6000.0 + layout_x = [0.0, 1841.0, 3682.0, 5523.0] + layout_y = [0.0] * n_turbs + floris_template["farm"].update({"layout_x": layout_x, "layout_y": layout_y}) + wind_config_dict = { + "num_turbines": n_turbs, + "turbine_rating_kw": turbine_rating_kw, + "turbine_name": turbine_library_turbine_name, + "model_name": "floris", + "floris_config": floris_template, + "layout_mode": "floris_layout" + } + site_input.update({"hub_height": 90.0}) + system_capacity_kw = turbine_rating_kw * n_turbs + technologies = {"wind": wind_config_dict, "grid": {"interconnect_kw": system_capacity_kw}} + hybrid_config = {"site": site_input, "technologies": technologies} + + with subtests.test("error on invalid turbine rating"): + with pytest.raises(ValueError): + hi = HoppInterface(hybrid_config) + hi.simulate(25) + +def test_floris_NREL_5MW_RWT_no_error_hopp(site_input, subtests): + floris_template = load_yaml(str(FLORIS_V4_TEMPLATE_PATH)) + turbine_library_turbine_name = "NREL_Reference_5MW_126" + n_turbs = 4 + turbine_rating_kw = 4900.0 + layout_x = [0.0, 1841.0, 3682.0, 5523.0] + layout_y = [0.0] * n_turbs + floris_template["farm"].update({"layout_x": layout_x, "layout_y": layout_y}) + wind_config_dict = { + "num_turbines": n_turbs, + "turbine_rating_kw": turbine_rating_kw, + "turbine_name": turbine_library_turbine_name, + "model_name": "floris", + "floris_config": floris_template, + "layout_mode": "floris_layout" + } + site_input.update({"hub_height": 90.0}) + system_capacity_kw = turbine_rating_kw * n_turbs + technologies = {"wind": wind_config_dict, "grid": {"interconnect_kw": system_capacity_kw}} + hybrid_config = {"site": site_input, "technologies": technologies} + + hi = HoppInterface(hybrid_config) + hybrid_plant = hi.system + + hi.simulate(25) + + aeps = hybrid_plant.annual_energies + with subtests.test("wind aep"): + assert aeps.wind == approx(74149945, 1e-3) + with subtests.test("wind capacity factor"): + assert hybrid_plant.capacity_factors["wind"] == approx(43.2, abs = 1.0) def test_pysam_NREL_5MW_RWT_corrected_hopp(site_input,subtests): From 988a25dc637c17b98a0b15f5a090ed1fc7b553da Mon Sep 17 00:00:00 2001 From: Dakota Sky Ramos <85905407+dakotaramos@users.noreply.github.com> Date: Fri, 28 Mar 2025 15:19:57 -0600 Subject: [PATCH 24/48] Added coke_supply_EI to greet_data.py parsing and greet_2023_processed.yaml. Updated test_cost_calculator.py > test_bos_calculate_bos_cost_interpolate assertions failing locally because of floating point precision issues, changed assert X == Y == Z to 3 separate assert X == pytest.approx(Y) statements (#466) --- .../greet/2023/greet_2023_processed.yaml | 1 + .../technologies/resource/greet_data.py | 22 ++++++++++++++----- tests/hopp/test_cost_calculator.py | 4 +++- 3 files changed, 20 insertions(+), 7 deletions(-) diff --git a/hopp/simulation/resource_files/greet/2023/greet_2023_processed.yaml b/hopp/simulation/resource_files/greet/2023/greet_2023_processed.yaml index 1c2574d12..a1924c1d9 100644 --- a/hopp/simulation/resource_files/greet/2023/greet_2023_processed.yaml +++ b/hopp/simulation/resource_files/greet/2023/greet_2023_processed.yaml @@ -18,6 +18,7 @@ atr_electricity_consume: 3.561733081975984 battery_LFP_EI: 20 bio_capex_EI: 0.8023917580032691 coal_capex_EI: 0.7870905519183622 +coke_supply_EI: 0.39783475314651884 desal_H2O_supply_EI: 0.010889 gas_capex_EI: 0.41713301095483096 geothermal_binary_capex_EI: 20.43919040434449 diff --git a/hopp/simulation/technologies/resource/greet_data.py b/hopp/simulation/technologies/resource/greet_data.py index bfcf9b2a2..2de2c2f86 100644 --- a/hopp/simulation/technologies/resource/greet_data.py +++ b/hopp/simulation/technologies/resource/greet_data.py @@ -149,11 +149,11 @@ def preprocess_greet(self): geothermal_flash_capex_EI = (greet1['ElecInfra']['J112'].value / mmbtu_to_kWh) # Geothermal Flash CAPEX emissions (g CO2e/kWh) geothermal_binary_capex_EI = (greet1['ElecInfra']['K112'].value / mmbtu_to_kWh) # Geothermal Binary CAPEX emissions (g CO2e/kWh) # Lime - lime_supply_EI = ((greet1['Chemicals']['BA247'].value + # GHG Emissions Intensity of supplying Lime to processes accounting for limestone mining, lime production, lime processing, and lime transportation assuming 20 miles transport via Diesel engines (kg CO2e/kg lime) - (greet1['Chemicals']['BA237'].value * VOC_to_CO2e) + - (greet1['Chemicals']['BA238'].value * CO_to_CO2e) + - (greet1['Chemicals']['BA245'].value * CH4_gwp_to_CO2e) + - (greet1['Chemicals']['BA246'].value * N2O_gwp_to_CO2e) + lime_supply_EI = ((greet1['Ag_Inputs']['BN121'].value + # GHG Emissions Intensity of supplying Lime to processes accounting for limestone mining, lime production, lime processing, and lime transportation assuming 20 miles transport via Diesel engines (kg CO2e/kg lime) + (greet1['Ag_Inputs']['BN111'].value * VOC_to_CO2e) + + (greet1['Ag_Inputs']['BN112'].value * CO_to_CO2e) + + (greet1['Ag_Inputs']['BN119'].value * CH4_gwp_to_CO2e) + + (greet1['Ag_Inputs']['BN120'].value * N2O_gwp_to_CO2e) ) * g_to_kg * (1/ton_to_kg)) # Natural Gas (NG) NG_combust_EI = ((greet1['EF']['B16'].value + # GHG Emissions Intensity of Natural Gas combustion in a utility / industrial large boiler (g CO2e/MJ Natural Gas combusted) @@ -188,6 +188,14 @@ def preprocess_greet(self): alk_ely_stack_and_BoP_capex_EI = (greet2['Electrolyzers']['R257'].value * g_to_kg) # Alkaline electrolyzer stack + Balance of Plant CAPEX emissions (kg CO2e/kg H2) soec_ely_stack_capex_EI = (greet2['Electrolyzers']['C257'].value * g_to_kg) # SOEC electrolyzer stack CAPEX emissions (kg CO2e/kg H2) soec_ely_stack_and_BoP_capex_EI = (greet2['Electrolyzers']['F257'].value * g_to_kg) # SOEC electrolyzer stack + Balance of Plant CAPEX emissions (kg CO2e/kg H2) + # Carbon Coke + coke_supply_EI = ((greet2['Steel']['B125'].value + + (greet2['Steel']['B115'].value * VOC_to_CO2e) + + (greet2['Steel']['B116'].value * CO_to_CO2e) + + (greet2['Steel']['B123'].value * CH4_gwp_to_CO2e) + + (greet2['Steel']['B124'].value * N2O_gwp_to_CO2e) + ) * (g_to_kg/ton_to_kg)) # GHG Emissions Intensity of supplying Coke to processes accounting for combustion and non-combustion emissions of coke production (kg CO2e/kg Coke) + # Does not account for mining of coal or transportation # Steel steel_H2O_consume = ((greet2['Steel']['AE80'].value + # Total H2O consumption for DRI-EAF Steel production w/ 83% H2 and 0% scrap, accounts for water used in iron ore mining, pelletizing, DRI, and EAF (metric tonne H2O/metric tonne steel production) greet2['Steel']['AG80'].value + # NOTE: Does not include water consumption for H2 production via electrolysis @@ -206,7 +214,7 @@ def preprocess_greet(self): steel_electricity_consume = ((greet2['Steel']['AK65'].value + # Total Electrical Energy consumption for DRI-EAF Steel production accounting for DRI with 83% H2 and EAF + LRF (MWh/metric tonne steel production) greet2['Steel']['AM65'].value ) * (mmbtu_to_MWh/ton_to_MT)) - #Iron + # Iron DRI_iron_ore_mining_EI_per_MT_steel = ((greet2['Steel']['AE92'].value + # GHG Emissions Intensity of Iron ore mining for use in DRI-EAF Steel production (kg CO2e/metric tonne steel production) (greet2['Steel']['AE82'].value * VOC_to_CO2e) + (greet2['Steel']['AE83'].value * CO_to_CO2e) + @@ -273,6 +281,8 @@ def preprocess_greet(self): 'NG_supply_EI':NG_supply_EI, # Lime 'lime_supply_EI':lime_supply_EI, + # Coke + 'coke_supply_EI':coke_supply_EI, # Iron ore 'DRI_iron_ore_mining_EI_per_MT_steel':DRI_iron_ore_mining_EI_per_MT_steel, 'DRI_iron_ore_pelletizing_EI_per_MT_steel':DRI_iron_ore_pelletizing_EI_per_MT_steel, diff --git a/tests/hopp/test_cost_calculator.py b/tests/hopp/test_cost_calculator.py index e83d24a0c..e208a751e 100644 --- a/tests/hopp/test_cost_calculator.py +++ b/tests/hopp/test_cost_calculator.py @@ -100,7 +100,9 @@ def test_bos_calculate_bos_costs_interpolate(self): assert wind_bos_cost > low_wind_bos assert wind_bos_cost < high_wind_bos - assert solar_bos_cost == low_solar_bos_cost == high_solar_bos_cost + assert solar_bos_cost == pytest.approx(low_solar_bos_cost) + assert solar_bos_cost == pytest.approx(high_solar_bos_cost) + assert low_solar_bos_cost == pytest.approx(high_solar_bos_cost) assert total_bos_cost == pytest.approx(75356239) assert min_distance != 0 From 5f902c393f7a14bf8e0f7b72e383c37dafe4145e Mon Sep 17 00:00:00 2001 From: John Jasa Date: Tue, 1 Apr 2025 10:03:24 -0600 Subject: [PATCH 25/48] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 0c95f2caf..12149e555 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,7 +16,7 @@ dependencies = [ "Pillow", "Pyomo>=6.1.2", "fastkml<1", - "floris>=4.0", + "floris==4.3", "future", "global_land_mask", "hybridbosse", From 270d6f15b675f43a9ed15c0f2508f20ffbb2242d Mon Sep 17 00:00:00 2001 From: Jared Thomas Date: Wed, 2 Apr 2025 17:12:27 -0600 Subject: [PATCH 26/48] Update README.md to not specify `coin-or-cbc` version. (#470) * Update README.md to not specify `coin-or-cbc` version. --------- Co-authored-by: John Jasa --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d3d3eb2b0..830ae6b8d 100644 --- a/README.md +++ b/README.md @@ -45,7 +45,7 @@ solar and storage. 4. Install HOPP and its dependencies: ```bash - conda install -y -c conda-forge coin-or-cbc=2.10.8 glpk + conda install -y -c conda-forge coin-or-cbc glpk ``` Note if you are on Windows, you will have to manually install Cbc: https://github.com/coin-or/Cbc. From 5981e1431069bdae96136738fe010b27004753d6 Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Mon, 7 Apr 2025 15:42:27 -0600 Subject: [PATCH 27/48] Bug-fix: WindPlant update for checking inputs (#469) * fixed issues for pysam related to specifying the turbine hub-height * updated pysam wind turbine initialization steps * updated default value in windplant --------- Co-authored-by: John Jasa --- RELEASE.md | 2 + hopp/simulation/technologies/wind/floris.py | 25 ++-- .../technologies/wind/wind_plant.py | 117 +++++++++++------- .../wind/turbine_library_interface_tools.py | 22 ++-- 4 files changed, 100 insertions(+), 66 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 7754ae3af..62f9c43f4 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -3,6 +3,8 @@ ## Unreleased * Loosened strictness of comparison for wind turbine config checking and added tests +* Loosened strictness of comparison for wind turbine hub-height and wind resource hub-height +* Updated workflow for specifying wind turbine parameters without specifying a turbine name with PySAM. ## Version 3.2.0, March 21, 2025 diff --git a/hopp/simulation/technologies/wind/floris.py b/hopp/simulation/technologies/wind/floris.py index 80ca718fe..e2cf575ee 100644 --- a/hopp/simulation/technologies/wind/floris.py +++ b/hopp/simulation/technologies/wind/floris.py @@ -38,6 +38,7 @@ class Floris(BaseClass): wind_farm_xCoordinates: list[float] = field(init = False) wind_farm_yCoordinates: list[float] = field(init = False) system_capacity: float = field(init = False) + floris_config_input: dict = field(init = False) #results gen: list[float] = field(init = False) @@ -77,7 +78,8 @@ def __attrs_post_init__(self): floris_config["flow_field"].update({"air_density":rho}) floris_config = self.initialize_from_floris(floris_config) - + if self.config.store_floris_config_dict: + self.floris_config_input = floris_config self.fi = FlorisModel(floris_config) self._timestep = self.config.timestep self._operational_losses = self.config.operational_losses @@ -149,7 +151,7 @@ def initialize_from_floris(self, floris_config): f"or correct the value to {self.wind_turbine_rotor_diameter}." ) raise ValueError(msg) - if self.config.hub_height is not None: + if self.config.hub_height is not None and not self.config.override_wind_resource_height: if not np.isclose(self.config.hub_height, hub_height, atol=1e-4): msg = ( f"Input hub-height ({self.config.hub_height}) does not match " @@ -168,15 +170,16 @@ def initialize_from_floris(self, floris_config): self.site.hub_height = float(hub_height) logger.info(f"Updating wind resource hub-height to {hub_height}m") else: - logger.warning(f"Updating wind resource hub-height to {hub_height}m and redownloading wind resource data") - self.site.hub_height = hub_height - data = { - "lat": self.site.wind_resource.latitude, - "lon": self.site.wind_resource.longitude, - "year": self.site.wind_resource.year, - } - wind_resource = self.site.initialize_wind_resource(data) - self.site.wind_resource = wind_resource + if not self.config.override_wind_resource_height: + logger.warning(f"Updating wind resource hub-height to {hub_height}m and redownloading wind resource data") + self.site.hub_height = hub_height + data = { + "lat": self.site.wind_resource.latitude, + "lon": self.site.wind_resource.longitude, + "year": self.site.wind_resource.year, + } + wind_resource = self.site.initialize_wind_resource(data) + self.site.wind_resource = wind_resource return floris_config diff --git a/hopp/simulation/technologies/wind/wind_plant.py b/hopp/simulation/technologies/wind/wind_plant.py index 4616a9fdd..883430982 100644 --- a/hopp/simulation/technologies/wind/wind_plant.py +++ b/hopp/simulation/technologies/wind/wind_plant.py @@ -72,6 +72,12 @@ class WindConfig(BaseClass): verbose (bool): if True, print simulation progress statements. Defaults to True. store_turbine_performance_results (bool): If running FLORIS, whether to save speed and power timeseries for each turbine in the farm. Defaults to False. + store_floris_config_dict (bool): If running FLORIS, whether to store the input dictionary as an attribute. + Defaults to True. + override_wind_resource_height (bool): Whether to ignore a possible discrepancy in wind resource height + and the turbine hub-height. Defaults to False. + recalculate_pysam_powercurve (bool): If True, recalculates the turbine power-curve for the rotor diameter and turbine rating. + If False, only scales turbine power-curve for turbine rated power. Defaults to False. Only used if ``model_name = 'pysam'`` """ # TODO: put `resource_parse_method`, `store_turbine_performance_results`, and `verbose` in "floris_kwargs" dictionary num_turbines: int = field(validator=gt_zero) @@ -114,6 +120,9 @@ class WindConfig(BaseClass): name: str = field(default="WindPlant") verbose: bool = field(default = True) store_turbine_performance_results: bool = field(default = False) + store_floris_config_dict: bool = field(default = True) + override_wind_resource_height: bool = field(default = False) + recalculate_pysam_powercurve: bool = field(default = False) def __attrs_post_init__(self): if self.model_name == 'floris' and self.timestep is None: @@ -211,46 +220,40 @@ def __attrs_post_init__(self): if self.config.model_name=="pysam": self.initialize_pysam_wind_turbine() - - - def initialize_pysam_wind_turbine(self): - """Initialize wind turbine parameters for PySAM simulation. + + def initalize_pysam_turbine_from_turbine_library(self, turbine_name): + """Initialize PySAM wind turbine from a turbine available in the turbine-models library. + + Args: + turbine_name (str): name of turbine in turbine-models library. Raises: - ValueError: if invalid turbine name is provided. Print list of valid turbine names before error is raised. - ValueError: discrepancy in rotor_diameter value - ValueError: discrepancy in hub-height value + ValueError: rotor diameter from turbine library specs does not match hub-height in WindConfig. + ValueError: hub-height from turbine library specs does not match hub-height in WindConfig. """ + valid_name = check_turbine_library_for_turbine(turbine_name,turbine_group=self.config.turbine_group) + if not valid_name: + print_turbine_name_list() + msg = ( + f"Turbine name {turbine_name} was not found the turbine-models library. " + "Please try an available name." + ) + ValueError(msg) + + turbine_dict = turb_lib_interface.get_pysam_turbine_specs(turbine_name,self) + self._system_model.Turbine.assign(turbine_dict) + self.rotor_diameter = turbine_dict["wind_turbine_rotor_diameter"] + self.turb_rating = np.round(max(turbine_dict["wind_turbine_powercurve_powerout"]), decimals = 3) if self.config.rotor_diameter is not None: - self.rotor_diameter = self.config.rotor_diameter - - if self.config.turbine_name is not None: - valid_name = check_turbine_library_for_turbine(self.config.turbine_name,turbine_group=self.config.turbine_group) - if not valid_name: - print_turbine_name_list() + if self.config.rotor_diameter != self._system_model.Turbine.wind_turbine_rotor_diameter: msg = ( - f"Turbine name {self.config.turbine_name} was not found the turbine-models library. " - "Please try an available name." + f"Input rotor diameter ({self.config.rotor_diameter}) does not match does not match rotor diameter " + f"for turbine ({self._system_model.Turbine.wind_turbine_rotor_diameter})." + f"Please correct the value for rotor_diameter in the hopp config input " + f"to {self._system_model.Turbine.wind_turbine_rotor_diameter}." ) - ValueError(msg) - else: - turbine_name = self.config.turbine_name - turbine_dict = turb_lib_interface.get_pysam_turbine_specs(turbine_name,self) - self._system_model.Turbine.assign(turbine_dict) - self.rotor_diameter = turbine_dict["wind_turbine_rotor_diameter"] - self.turb_rating = np.round(max(turbine_dict["wind_turbine_powercurve_powerout"]), decimals = 3) - - if self.config.rotor_diameter is not None: - if self.config.rotor_diameter != self._system_model.Turbine.wind_turbine_rotor_diameter: - msg = ( - f"Input rotor diameter ({self.config.rotor_diameter}) does not match does not match rotor diameter " - f"for turbine ({self._system_model.Turbine.wind_turbine_rotor_diameter})." - f"Please correct the value for rotor_diameter in the hopp config input " - f"to {self._system_model.Turbine.wind_turbine_rotor_diameter}." - ) - raise ValueError(msg) - + raise ValueError(msg) if self.config.hub_height is not None: if self.config.hub_height != self._system_model.Turbine.wind_turbine_hub_ht: msg = ( @@ -261,7 +264,31 @@ def initialize_pysam_wind_turbine(self): ) raise ValueError(msg) + + def initialize_pysam_wind_turbine(self): + """Initialize wind turbine parameters for PySAM simulation. + + """ + + if self.config.turbine_name is not None: + self.initalize_pysam_turbine_from_turbine_library(self.config.turbine_name) + else: + if self.config.rotor_diameter is not None: + self.rotor_diameter = self.config.rotor_diameter # this will update the layout + if self.config.hub_height is not None: + self._system_model.value("wind_turbine_hub_ht", self.config.hub_height) + if self.config.turbine_rating_kw is not None: + self.turb_rating = self.config.turbine_rating_kw + if self.config.recalculate_pysam_powercurve: + self.modify_powercurve(self.rotor_diameter, self.turb_rating) + msg = ( + f"updating wind turbine power-curve for rotor diameter {self.rotor_diameter}m " + f"and rating {self.turb_rating} kW" + ) + logger.info(msg) + + # check wind resource height against turbine hub-height hub_height = self._system_model.Turbine.wind_turbine_hub_ht if hub_height != self.site.wind_resource.hub_height_meters: if hub_height >= min(self.site.wind_resource.data["heights"]) and hub_height<=max(self.site.wind_resource.data["heights"]): @@ -269,17 +296,19 @@ def initialize_pysam_wind_turbine(self): self.site.hub_height = float(hub_height) logger.info(f"updating wind resource hub-height to {hub_height}m") else: - logger.warning(f"updating wind resource hub-height to {hub_height}m and redownloading wind resource data") - self.site.hub_height = hub_height - data = { - "lat": self.site.wind_resource.latitude, - "lon": self.site.wind_resource.longitude, - "year": self.site.wind_resource.year, - } - wind_resource = self.site.initialize_wind_resource(data) - self.site.wind_resource = wind_resource - self._system_model.value("wind_resource_data", self.site.wind_resource.data) - + if not self.config.override_wind_resource_height: + logger.warning(f"updating wind resource hub-height to {hub_height}m and redownloading wind resource data") + self.site.hub_height = hub_height + data = { + "lat": self.site.wind_resource.latitude, + "lon": self.site.wind_resource.longitude, + "year": self.site.wind_resource.year, + } + wind_resource = self.site.initialize_wind_resource(data) + self.site.wind_resource = wind_resource + self._system_model.value("wind_resource_data", self.site.wind_resource.data) + + # add losses for air density if specified and site elevation is input if self.config.adjust_air_density_for_elevation and self.site.elev is not None: air_dens_losses = calculate_air_density_losses(self.site.elev) self._system_model.Losses.assign({"turb_specific_loss":air_dens_losses}) diff --git a/hopp/tools/design/wind/turbine_library_interface_tools.py b/hopp/tools/design/wind/turbine_library_interface_tools.py index 73913ccc8..e17b2c611 100644 --- a/hopp/tools/design/wind/turbine_library_interface_tools.py +++ b/hopp/tools/design/wind/turbine_library_interface_tools.py @@ -4,7 +4,6 @@ from turbine_models.parser import Turbines import hopp.tools.design.wind.power_curve_tools as curve_tools from hopp.utilities.log import hybrid_logger as logger -import hopp.simulation.technologies.wind.floris as floris_wrapper def extract_power_curve(turbine_specs: dict, model_name: str): """Creates power-curve for turbine based on available data and formats it for the corresponding simulation model. @@ -96,7 +95,7 @@ def check_hub_height(turbine_specs, wind_plant): Args: turbine_specs (dict): turbine specs loaded from turbine-models library. - wind_plant (:obj:`hopp.simulation.technologies.wind.floris.Floris` | :obj:`hopp.simulation.technologies.wind.wind_plant.WindPlant`): wind + wind_plant (None | :obj:`hopp.simulation.technologies.wind.floris.Floris` | :obj:`hopp.simulation.technologies.wind.wind_plant.WindPlant`): wind plant object for either PySAM or FLORIS wind simulation model. Returns: @@ -151,19 +150,20 @@ def check_hub_height(turbine_specs, wind_plant): else: hub_height = turbine_specs["hub_height"] - if wind_plant.config.hub_height is not None: - if hub_height != wind_plant.config.hub_height: + if wind_plant is not None: + if wind_plant.config.hub_height is not None: + if hub_height != wind_plant.config.hub_height: + msg = ( + f"Turbine hub height ({hub_height}) does not equal " + f"wind_plant.config.hub_height ({wind_plant.config.hub_height})" + ) + logger.warning(msg) + if hub_height != wind_plant.site.hub_height: msg = ( f"Turbine hub height ({hub_height}) does not equal " - f"wind_plant.config.hub_height ({wind_plant.config.hub_height})" + f"site_info.hub_height ({wind_plant.site.hub_height})" ) logger.warning(msg) - if hub_height != wind_plant.site.hub_height: - msg = ( - f"Turbine hub height ({hub_height}) does not equal " - f"site_info.hub_height ({wind_plant.site.hub_height})" - ) - logger.warning(msg) return hub_height From 219437ba116e392b98d40f5cefac74287155a8e2 Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Tue, 15 Apr 2025 09:55:41 -0600 Subject: [PATCH 28/48] Feature add: Download wind resource for Alaska (#461) * added alaska wind resource download tools and class * updated alaska wind filel and resource.py for handling non alphanumeric characters * updated parsing methods in case pressure or temperature data is given for other resource heights * updated pysam wind resource tools to be flexible whether pressure is provided or not * integrated alaska wind into workflow * updated RELEASE.md * added tests for AK wind and updated site_info and alaska_wind file * added new resource file for alaska wind resource data used for new tests * updated doc strings and added documentation for Alaska wind API call * actually added alaska documentation file * fixed latitude for alaska test site * remove whitespace in resource.py Co-authored-by: John Jasa * updated doc string in site_info.py Co-authored-by: John Jasa * updated doc string in site_info.py Co-authored-by: John Jasa * updated pysam_wind_tools.py for docstrings and handling resource year * updated wind_resource.py combine_files function to use function in pysam_wind_tools.py * added missing return value in combine_wind_files --------- Co-authored-by: John Jasa --- RELEASE.md | 4 +- docs/_toc.yml | 1 + docs/api/resource/alaska_wind.md | 17 + docs/api/resource/index.md | 3 +- docs/api/resource/wind_api.md | 6 +- ..._-162.5_WTK_Alaksa_2019_60min_80m_100m.csv | 8762 +++++++++++++++++ .../technologies/resource/__init__.py | 1 + .../technologies/resource/alaska_wind.py | 156 + .../technologies/resource/resource.py | 8 +- .../technologies/resource/wind_resource.py | 26 +- .../technologies/sites/site_info.py | 34 +- hopp/tools/resource/pysam_wind_tools.py | 309 + hopp/tools/resource/wind_tools.py | 6 +- tests/hopp/test_site_info.py | 30 +- tests/hopp/test_wind.py | 72 + tests/hopp/test_wind_resource_tools.py | 17 +- 16 files changed, 9408 insertions(+), 44 deletions(-) create mode 100644 docs/api/resource/alaska_wind.md create mode 100644 hopp/simulation/resource_files/wind/66.68_-162.5_WTK_Alaksa_2019_60min_80m_100m.csv create mode 100644 hopp/simulation/technologies/resource/alaska_wind.py create mode 100644 hopp/tools/resource/pysam_wind_tools.py diff --git a/RELEASE.md b/RELEASE.md index 62f9c43f4..b5feb90bf 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,10 +1,12 @@ # Release Notes -## Unreleased + +## Unreleased, TBD * Loosened strictness of comparison for wind turbine config checking and added tests * Loosened strictness of comparison for wind turbine hub-height and wind resource hub-height * Updated workflow for specifying wind turbine parameters without specifying a turbine name with PySAM. +* Added ability to download wind resource data from WTK-LED for Alaska ## Version 3.2.0, March 21, 2025 diff --git a/docs/_toc.yml b/docs/_toc.yml index 5742ceefd..fc8cd5efa 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -19,6 +19,7 @@ parts: sections: - file: api/resource/solar_api - file: api/resource/wind_api + - file: api/resource/alaska_wind - file: api/resource/solar_hpc - file: api/resource/wind_hpc - file: api/resource/wave_data diff --git a/docs/api/resource/alaska_wind.md b/docs/api/resource/alaska_wind.md new file mode 100644 index 000000000..c59f0589e --- /dev/null +++ b/docs/api/resource/alaska_wind.md @@ -0,0 +1,17 @@ +(resource:ak-wind-resource)= +# Wind Resource for Alaska (API) + +Wind resource data can downloaded for Alaska from the NREL Developer Network hosted Wind Integration National Dataset (WIND) Toolkit dataset [Wind Toolkit Data - Alaska V1.0.0](https://developer.nrel.gov/docs/wind/wind-toolkit/wtk-alaska-v1-0-0-download/). Using this functionality requires an NREL API key. + +Wind resource data for Alaska can only be downloaded for wind resource years 2018-2020 and is only downloaded if the `wind_resource_region` input to [SiteInfo](../site_info.md) is set to "ak". For example: + +```yaml +site: + wind_resource_region: "ak" +``` + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.resource.alaska_wind.AlaskaWindData + :members: + :exclude-members: _abc_impl, check_download_dir +``` \ No newline at end of file diff --git a/docs/api/resource/index.md b/docs/api/resource/index.md index 789fdc819..fa411937f 100644 --- a/docs/api/resource/index.md +++ b/docs/api/resource/index.md @@ -3,7 +3,8 @@ These are the primary methods for accessing wind and solar resource data. - [Solar Resource (API)](resource:solar-resource) -- [Wind Resource (API)](resource:wind-resource) +- [Conus Wind Resource (API)](resource:wind-resource) +- [Alaska Wind Resource (API)](resource:ak-wind-resource) - [Solar Resource (NSRDB Dataset on NREL HPC)](resource:nsrdb-data) - [Wind Resource (Wind Toolkit Dataset on NREL HPC)](resource:wtk-data) - [Wave Resource (Data)](resource:wave-resource) diff --git a/docs/api/resource/wind_api.md b/docs/api/resource/wind_api.md index ee8f2ab98..c02273baf 100644 --- a/docs/api/resource/wind_api.md +++ b/docs/api/resource/wind_api.md @@ -1,7 +1,9 @@ (resource:wind-resource)= -# Wind Resource (API) +# Wind Resource for Continental U.S. (API) -By default, wind resource data is downloaded from the NREL Developer Network hosted Wind Integration National Dataset (WIND) Toolkit dataset [Wind Toolkit Data - SAM format (srw)](https://developer.nrel.gov/docs/wind/wind-toolkit/wtk-srw-download/). Using this functionality requires an NREL API key. +By default, wind resource data is downloaded from the NREL Developer Network hosted Wind Integration National Dataset (WIND) Toolkit dataset [Wind Toolkit Data - SAM format (srw)](https://developer.nrel.gov/docs/wind/wind-toolkit/wtk-srw-download/). + +Wind resource data for the continental U.S. can only be downloaded for wind resource years 2007 - 2014. Using this functionality requires an NREL API key. ```{eval-rst} .. autoclass:: hopp.simulation.technologies.resource.wind_resource.WindResource diff --git a/hopp/simulation/resource_files/wind/66.68_-162.5_WTK_Alaksa_2019_60min_80m_100m.csv b/hopp/simulation/resource_files/wind/66.68_-162.5_WTK_Alaksa_2019_60min_80m_100m.csv new file mode 100644 index 000000000..1b9ddc9c9 --- /dev/null +++ b/hopp/simulation/resource_files/wind/66.68_-162.5_WTK_Alaksa_2019_60min_80m_100m.csv @@ -0,0 +1,8762 @@ +SiteID,525331,Site Timezone,-9,Data Timezone,0,Longitude,-162.50232,Latitude,66.67967 +Year,Month,Day,Hour,Minute,air pressure at 100m (Pa),air temperature at 80m (C),wind speed at 80m (m/s),wind direction at 80m (deg),air temperature at 100m (C),wind speed at 100m (m/s),wind direction at 100m (deg) +2019,1,1,0,0,95660,-4,9.56,114.17,-4.05,10.1,114.12 +2019,1,1,1,0,95680,-3.86,8.05,134.23,-3.66,7.76,139.26 +2019,1,1,2,0,95780,-3.0500000000000003,9.290000000000001,169.75,-2.79,9.08,173.35 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hopp.simulation.technologies.resource.nsrdb_data import HPCSolarData from hopp.simulation.technologies.resource.wind_toolkit_data import HPCWindData +from hopp.simulation.technologies.resource.alaska_wind import AlaskaWindData \ No newline at end of file diff --git a/hopp/simulation/technologies/resource/alaska_wind.py b/hopp/simulation/technologies/resource/alaska_wind.py new file mode 100644 index 000000000..59f995c4a --- /dev/null +++ b/hopp/simulation/technologies/resource/alaska_wind.py @@ -0,0 +1,156 @@ +import csv, os +from pathlib import Path +from typing import Union, Optional, List +import pandas as pd +import urllib.parse + +from attrs import define, field + +from hopp.utilities.keys import get_developer_nrel_gov_key, get_developer_nrel_gov_email +from hopp.utilities.validators import range_val +from hopp.simulation.technologies.resource.resource import Resource +from hopp import ROOT_DIR +from hopp.tools.resource.pysam_wind_tools import combine_wind_files + +AK_BASE_URL = "https://developer.nrel.gov/api/wind-toolkit/v2/wind/wtk-alaska-v1-0-0-download.csv?" + +@define +class AlaskaWindData(Resource): + #: latitude corresponding to location for wind resource data + lat: float = field() + #: longitude corresponding to location for wind resource data + lon: float = field() + #: year for resource data. must be between 2018 and 2020 + year: int = field(validator=range_val(2018, 2020)) + + #: the hub-height for wind resource data (meters) + hub_height_meters: float = field(validator=range_val(10.0, 1000.0)) + + #: filepath to resource_files directory. Defaults to ROOT_DIR/"simulation"/"resource_files". + path_resource: Optional[Union[str, Path]] = field(default = ROOT_DIR / "simulation" / "resource_files") + #: file path of resource file to load or download + filename: Optional[Union[str, Path]] = field(default = None) + #: Make an API call even if there's an existing file. Defaults to False. + use_api: Optional[bool] = field(default = False) + #: dictionary of preloaded and formatted wind resource data. Defaults to None. + resource_data: Optional[dict] = field(default = None) + + #: dictionary of heights and filenames to download from Wind Toolkit + file_resource_heights: dict = field(default = None) + + #: list of heights that wind resource data is available for downloading (meters) + allowed_hub_height_meters: list[int] = [10, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 250, 300, 500, 1000] + + + def __attrs_post_init__(self): + super().__init__(self.lat, self.lon, self.year) + + # if resource_data is input as a dictionary then set_data + if isinstance(self.resource_data,dict): + self.data = self.resource_data + return + + # if resource_data is not provided, download or load resource data + if isinstance(self.path_resource,str): + self.path_resource = Path(self.path_resource).resolve() + if self.path_resource.parts[-1]!="wind": + self.path_resource = self.path_resource / 'wind' + + if self.filename is None: + self.calculate_heights_to_download() + + self.check_download_dir() + + if not os.path.isfile(self.filename) or self.use_api: + self.download_resource() + + self.format_data() + + def calculate_heights_to_download(self): + """ + Given the system hub height, and the available hubheights from WindToolkit, + determine which heights to download to bracket the hub height + """ + hub_height_meters = self.hub_height_meters + + # evaluate hub height, determine what heights to download + heights = [hub_height_meters] + if hub_height_meters not in self.allowed_hub_height_meters: + height_low = self.allowed_hub_height_meters[0] + height_high = self.allowed_hub_height_meters[-1] + for h in self.allowed_hub_height_meters: + if h < hub_height_meters: + height_low = h + elif h > hub_height_meters: + height_high = h + break + heights[0] = height_low + heights.append(height_high) + + filename_base = f"{self.latitude}_{self.longitude}_WTK_Alaksa_{self.year}_{self.interval}min" + file_resource_full = filename_base + file_resource_heights = dict() + + for h in heights: + h_int = int(h) + file_resource_heights[h_int] = self.path_resource/(filename_base + f'_{h_int}m.csv') + file_resource_full += f'_{h_int}m' + file_resource_full += ".csv" + + self.file_resource_heights = file_resource_heights + self.filename = self.path_resource / file_resource_full + + def update_height(self, hub_height_meters): + """Update hub-height and corresponding attributes. + Also updates ``file_resource_heights`` and ``filename``. + + Args: + hub_height_meters (float): hub-height for wind resource data (meters) + """ + self.hub_height_meters = hub_height_meters + self.calculate_heights_to_download() + + def download_resource(self): + success = False + + base_attributs = ["temperature","windspeed","winddirection"] + attributes = ["pressure_100m"] + for height, f in self.file_resource_heights.items(): + attributes += [f"{a}_{height}m" for a in base_attributs] + + attributes_str = ",".join(k for k in attributes) + input_data = { + 'attributes': attributes_str, + 'interval': self.interval, + 'api_key': get_developer_nrel_gov_key(), + 'email': get_developer_nrel_gov_email(), + 'names': [str(self.year)], + 'wkt': f"POINT({self.longitude} {self.latitude})" + } + url = AK_BASE_URL + urllib.parse.urlencode(input_data, True) + success = self.call_api(url, filename=self.filename) + + if not success: + raise ValueError('Unable to download wind data') + + return success + + def format_data(self): + """ + Format as 'wind_resource_data' dictionary for use in PySAM. + """ + if not os.path.isfile(self.filename): + raise FileNotFoundError(f"{self.filename} does not exist. Try `download_resource` first.") + + self.data = self.filename + + @Resource.data.setter + def data(self, data_info): + """ + Sets the wind resource data to a dictionary in SAM Wind format. + """ + if isinstance(data_info,dict): + self._data = data_info + if isinstance(data_info,(str, Path)): + resource_heights = [k for k in self.file_resource_heights.keys()] + self._data = combine_wind_files(str(data_info),resource_heights) diff --git a/hopp/simulation/technologies/resource/resource.py b/hopp/simulation/technologies/resource/resource.py index c3f760d5e..de5880d2a 100644 --- a/hopp/simulation/technologies/resource/resource.py +++ b/hopp/simulation/technologies/resource/resource.py @@ -38,15 +38,14 @@ def __init__(self, lat, lon, year, **kwargs): self.affiliation = 'NREL' self.reason = 'hybrid-analysis' self.mailing_list = 'true' - # paths self.path_current = os.path.dirname(os.path.abspath(__file__)) self.path_resource = os.path.join(ROOT_DIR, 'simulation', 'resource_files') - + self.filename = None #: filepath of resource data file, defaults to None + # update any passed in self.__dict__.update(kwargs) - self.filename = None #: filepath of resource data file, defaults to None self._data = dict() def check_download_dir(self): @@ -77,7 +76,8 @@ def call_api(url, filename): r = requests.get(url) if r: localfile = open(filename, mode='w+') - localfile.write(r.text) + txt = r.text.replace("(°C)","(C)").replace("(°)","(deg)") + localfile.write(txt) localfile.close() if os.path.isfile(filename): success = True diff --git a/hopp/simulation/technologies/resource/wind_resource.py b/hopp/simulation/technologies/resource/wind_resource.py index 2110d27a0..c0b0a5a16 100644 --- a/hopp/simulation/technologies/resource/wind_resource.py +++ b/hopp/simulation/technologies/resource/wind_resource.py @@ -5,6 +5,7 @@ from hopp.utilities.keys import get_developer_nrel_gov_key, get_developer_nrel_gov_email from hopp.simulation.technologies.resource.resource import Resource +from hopp.tools.resource.pysam_wind_tools import combine_and_write_srw_files from hopp import ROOT_DIR @@ -163,27 +164,10 @@ def combine_wind_files(self): file_out: string File path to write combined srw file """ - data = [None] * 2 - for height, f in self.file_resource_heights.items(): - if os.path.isfile(f): - with open(f) as file_in: - csv_reader = csv.reader(file_in, delimiter=',') - line = 0 - for row in csv_reader: - if line < 2: - data[line] = row - else: - if line >= len(data): - data.append(row) - else: - data[line] += row - line += 1 - - with open(self.filename, 'w', newline='') as fo: - writer = csv.writer(fo) - writer.writerows(data) - - return os.path.isfile(self.filename) + + success = combine_and_write_srw_files(self.file_resource_heights,self.filename) + + return success def format_data(self): """ diff --git a/hopp/simulation/technologies/sites/site_info.py b/hopp/simulation/technologies/sites/site_info.py index 9ae4a5cf2..c5f777265 100644 --- a/hopp/simulation/technologies/sites/site_info.py +++ b/hopp/simulation/technologies/sites/site_info.py @@ -21,6 +21,7 @@ ElectricityPrices, HPCWindData, HPCSolarData, + AlaskaWindData, ) from hopp.tools.layout.plot_tools import plot_shape from hopp.utilities.log import hybrid_logger as logger @@ -72,11 +73,15 @@ class SiteInfo(BaseClass): tidal: Whether to set tidal data for this site. Defaults to False. renewable_resource_origin (str): whether to download resource data from API or load directly from datasets files. Options are "API" or "HPC". Defaults to "API". - wind_resource_origin: Which wind resource API to use, defaults toto "WTK" for WIND Toolkit. + wind_resource_origin: Which wind resource API to use, defaults to "WTK" for WIND Toolkit. Options are "WTK" or "TAP". site_buffer (Optional): value to buffer site polygon. Defaults to 1e-8. solar_resource (Optional): dictionary or object containing solar resource data. wind_resource (Optional): dictionary or object containing wind resource data. + wind_resource_region (Optional): which region to use for wind resource data. Defaults to "conus". Options are: + + - "conus": continental United States + - "ak": Alaska """ # User provided data: dict @@ -131,6 +136,7 @@ class SiteInfo(BaseClass): tidal: bool = field(default=False) renewable_resource_origin: str = field(default="API", validator=contains(["API", "HPC"])) wind_resource_origin: str = field(default="WTK", validator=contains(["WTK", "TAP"])) + wind_resource_region: str = field(default="conus", validator=contains(["conus", "ak"]), converter=(str.strip, str.lower)) site_buffer: Optional[float] = field(default = 1e-8) @@ -143,7 +149,7 @@ class SiteInfo(BaseClass): vertices: NDArrayFloat = field(init=False) polygon: Union[Polygon, BaseGeometry] = field(init=False) solar_resource: Optional[Union[SolarResource,HPCSolarData]] = field(default=None) - wind_resource: Optional[Union[WindResource,HPCWindData]] = field(default=None) + wind_resource: Optional[Union[WindResource,HPCWindData,AlaskaWindData]] = field(default=None) wave_resource: Optional[WaveResource] = field(init=False, default=None) tidal_resource: Optional[TidalResource] = field(init=False, default=None) elec_prices: Optional[ElectricityPrices] = field(init=False, default=None) @@ -376,15 +382,23 @@ def initialize_wind_resource(self,data:dict): wind_year = data.setdefault("wind_year", data["year"]) if self.wind_resource is None: - if self.renewable_resource_origin == "API": - wind_resource = WindResource(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height, - path_resource=self.path_resource, filepath=self.wind_resource_file, source=self.wind_resource_origin) - else: - wind_resource = HPCWindData(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height, - wtk_source_path=self.wtk_source_path, filepath=self.wind_resource_file) - return wind_resource + if self.wind_resource_region == "conus": + if self.renewable_resource_origin == "API": + wind_resource = WindResource(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height, + path_resource=self.path_resource, filepath=self.wind_resource_file, source=self.wind_resource_origin) + else: + wind_resource = HPCWindData(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height, + wtk_source_path=self.wtk_source_path, filepath=self.wind_resource_file) + return wind_resource + if self.wind_resource_region == "ak": + wind_resource = AlaskaWindData(lat=wind_lat, lon=wind_lon, year=wind_year, hub_height_meters=self.hub_height, + path_resource=self.path_resource, filename=self.wind_resource_file) + return wind_resource if isinstance(self.wind_resource,dict): - wind_resource = WindResource(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height,resource_data = self.wind_resource) + if self.wind_resource_region == "conus": + wind_resource = WindResource(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height,resource_data = self.wind_resource) + if self.wind_resource_region == "ak": + wind_resource = AlaskaWindData(lat=wind_lat, lon=wind_lon, year=wind_year, hub_height_meters=self.hub_height,resource_data = self.wind_resource) return wind_resource return self.wind_resource diff --git a/hopp/tools/resource/pysam_wind_tools.py b/hopp/tools/resource/pysam_wind_tools.py new file mode 100644 index 000000000..6a34a6b70 --- /dev/null +++ b/hopp/tools/resource/pysam_wind_tools.py @@ -0,0 +1,309 @@ +import pandas as pd +import numpy as np +import csv, os, re, copy +from PySAM.ResourceTools import SRW_to_wind_data + +def csv_to_dataframe(wind_csv_filepath, resource_height, resource_year): + """Converts csv file of wind resource data to dataframe. This function is a slightly modified version of the function in + ``PySAM.ResourceTools.FetchResourceFiles._csv_to_srw``. + + Args: + wind_csv_filepath (str): filepath for wind resource .csv file + resource_height (int): wind resource height in meters. + resource_year (int | str, Optional): year corresponding to the wind resource data. Defaults to None. + + Returns: + dataframe: wind resource data reformatted into dataframe. + """ + if resource_year is not None: + site_year = str(int(resource_year)) + else: + site_year = 'None' + + # --- grab df --- + for_df = copy.deepcopy(wind_csv_filepath) + df = pd.read_csv(for_df, header=1) + + # --- grab header data --- + for_header = copy.deepcopy(wind_csv_filepath) + header = pd.read_csv(for_header, nrows=1, header=None).values + site_id = header[0, 1] + site_tz = header[0, 3] + site_lon = header[0, 7] + site_lat = header[0, 9] + + # --- create header lines --- + h1 = np.array([int(site_id), 'city??', 'state??', 'USA', site_year, + site_lat, site_lon, 'elevation??', site_tz, 8760]) # meta info + h2 = np.array(["WTK .csv converted to .srw for SAM", None, None, + None, None, None, None, None, None, None]) # descriptive text + h3 = np.array(['temperature', 'pressure', 'direction', + 'speed', None, None, None, None, None, None]) # variables + h4 = np.array(['C', 'atm', 'degrees', 'm/s', None, + None, None, None, None, None]) # units + h5 = np.array([resource_height, 100, resource_height, resource_height, None, None, + None, None, None, None]) # hubheight + header = pd.DataFrame(np.vstack([h1, h2, h3, h4, h5])) + assert header.shape == (5, 10) + + # --- resample to 8760 --- + df['datetime'] = pd.to_datetime( + df[['Year', 'Month', 'Day', 'Hour', 'Minute']]) + df.set_index('datetime', inplace=True) + df = df.resample('h').first() + + # --- drop leap days --- + df = df.loc[~((df.index.month == 2) & (df.index.day == 29))] + + # initialize data info: + data_fieldnames = ['temperature', 'direction', 'speed'] + data_fieldnumbers = [0, 2, 3] + + # make sure data fieldnames are lower-case + old_colnames = [c for c in df.columns.to_list() if "(" in c] + new_colnames = [c.split("(")[0].lower() + "(" + c.split("(")[1] for c in old_colnames] + df = df.rename(columns = dict(zip(old_colnames,new_colnames))) + + # --- convert K to celsius --- + df['temperature'] = df['air temperature at {}m (C)'.format(resource_height)] + + # --- convert PA to atm --- + if 'air pressure at 100m (Pa)' in new_colnames: + df['pressure'] = df['air pressure at 100m (Pa)'] / 101325 + data_fieldnames += ['pressure'] + data_fieldnumbers += [1] + # if 'surface air pressure (Pa)' in new_colnames: + # df['pressure'] = df['surface air pressure (Pa)'] / 101325 + + # --- rename --- + rename_dict = {'wind speed at {}m (m/s)'.format(resource_height): 'speed', + 'wind direction at {}m (deg)'.format(resource_height): 'direction'} + df.rename(rename_dict, inplace=True, axis='columns') + + # --- clean up --- + df = df[data_fieldnames] + df.columns = data_fieldnumbers + assert df.shape == (8760, len(data_fieldnumbers)) + + out = pd.concat([header, df], axis='rows') + out.reset_index(drop=True, inplace=True) + return out + +def csv_to_srw(wind_csv_filepath, resource_height, resource_year = None, data_source = "WTK_LED"): + """Write wind resource data to .srw file from input .csv file. + More information can be found here: + https://sam.nrel.gov/images/web_page_files/sam-help-2020-2-29-r2_weather_file_formats.pdf + + Args: + wind_csv_filepath (str): filepath for wind resource .csv file + resource_height (int): wind resource height in meters. + resource_year (int | str, Optional): year corresponding to the wind resource data. + Defaults to None. + + Returns: + str: filename of .srw output filepath + """ + interval = 60 + if resource_year is not None: + site_year = str(int(resource_year)) + else: + site_year = 'None' + # --- grab header data --- + for_header = copy.deepcopy(wind_csv_filepath) + header = pd.read_csv(for_header, nrows=1, header=None).values + site_lon = header[0, 7] + site_lat = header[0, 9] + output_filename = f"{site_lat}_{site_lon}_{data_source}_{site_year}_{interval}min_{resource_height}.srw" + output_filepath = os.path.join(os.path.dirname(wind_csv_filepath),output_filename) + + out = csv_to_dataframe(wind_csv_filepath, resource_height, resource_year) + + txt = "\n".join(k for k in out.to_string(index=False,na_rep='').split('\n')[1:]) + txt = txt.replace("None",'') + text_lines = txt.split("\n") + file_lines = [] + for line in text_lines: + if 'WTK .csv converted to srw for SAM' in line: + new_line = line.strip() + else: + new_line = re.sub(' +', ',',line.strip()) + file_lines.append(new_line) + + file_contents = '\n'.join(l for l in file_lines) + localfile = open(output_filepath, mode='w+') + localfile.write(file_contents) + localfile.close() + return output_filepath + +def CSV_to_wind_data(wind_csv_filepath, resource_height, resource_year = None): + """Converts wind resource data from a .csv file to wind resource dictionary. + This function is the .csv file equivalent of ``PySAM.ResourceTools.SRW_to_wind_data`` + + Args: + wind_csv_filepath (str): filepath for wind resource .csv file + resource_height (int): wind resource height in meters. + resource_year (int | str, Optional): year corresponding to the wind resource data. + Defaults to None. + + Returns: + dict: wind resource data dictionary in PySAM format + """ + data_to_field_number = {'temperature': 1, 'pressure': 2, 'speed': 3, 'direction': 4} + out = csv_to_dataframe(wind_csv_filepath, resource_height, resource_year = resource_year) + heights = [h for h in out.iloc[4].to_list() if h is not None] + field_names = [h for h in out.iloc[2].to_list() if h is not None] + field_numbers = [data_to_field_number[f] for f in field_names] + data = out.loc[5:].dropna(axis=1) + formatted_data = [d.tolist() for d in data.values] + wind_resource_data = { + 'heights':heights, + 'fields':field_numbers, + 'data':formatted_data} + return wind_resource_data + + +def combine_and_write_srw_files(file_resource_heights, output_filepath): + """Combine wind resource data for multiple hub-heights stored in multiple .srw files + and write a combined .srw file that contains resource data for multiple hub-heights. + + Args: + file_resource_heights (dict): Keys are height in meters, values are corresponding filepaths. + example {40: path_to_file, 60: path_to_file2} + output_filepath (str | Path): filepath to write combined .srw file to. + + Returns: + bool: whether the file was successfully writen to the output filepath. + """ + + data = [None] * 2 + for height, f in file_resource_heights.items(): + if os.path.isfile(f): + with open(f) as file_in: + csv_reader = csv.reader(file_in, delimiter=',') + line = 0 + for row in csv_reader: + if line < 2: + data[line] = row + else: + if line >= len(data): + data.append(row) + else: + data[line] += row + line += 1 + + with open(output_filepath, 'w', newline='') as fo: + writer = csv.writer(fo) + writer.writerows(data) + return os.path.isfile(output_filepath) + +def combine_wind_resource_data(wind_resource_data): + """Combines dictionaries of wind resoure data. + + Args: + wind_resource_data (list[dict]): list of wind resource data dictionaries for different resource heights + + Returns: + dict: wind resource data dictionary for all hub-heights + """ + all_heights = [wind_resource_data[i]['heights'] for i in range(len(wind_resource_data))] + all_fields = [wind_resource_data[i]['fields'] for i in range(len(wind_resource_data))] + all_data = [wind_resource_data[i]['data'] for i in range(len(wind_resource_data))] + + + heights = sum(all_heights,[]) + fields = sum(all_fields,[]) + data = np.concatenate(all_data,axis=1) + + height_field = [f"{f}-{h}m" for f,h in zip(fields,heights)] + if any(height_field.count(ff)>1 for ff in height_field): + duplicate_data_entries = [ff for ff in height_field if height_field.count(ff)>1] + duplicate_data_entries = list(set(duplicate_data_entries)) + for drop_data_entry in duplicate_data_entries: + if drop_data_entry in height_field: + i_drop = height_field.index(drop_data_entry) + heights.pop(i_drop) + fields.pop(i_drop) + height_field.pop(i_drop) + data = np.delete(data,i_drop,axis=1) + + combined_resource_data = { + 'heights':heights, + 'fields':fields, + 'data':data.tolist(), + } + return combined_resource_data + +def combine_CSV_to_wind_data(file_resource_heights, resource_year = None): + """Combine wind resource data stored in .csv files for multiple resource heights. + + Args: + file_resource_heights (dict): Keys are height in meters, values are corresponding filepaths. + example {40: path_to_file, 60: path_to_file2} + resource_year (str | int, Optional): resource year for wind resource data. Only needed for formatting purposes + in ``csv_to_dataframe()``. Defaults to None. + + Returns: + dict: wind resource data dictionary of combined resource data + """ + wind_resource_data = [] + for resource_height,wind_csv_filepath in file_resource_heights.items(): + d = CSV_to_wind_data(wind_csv_filepath, resource_height, resource_year = resource_year) + wind_resource_data.append(d) + combined_data = combine_wind_resource_data(wind_resource_data) + return combined_data + +def combine_SRW_to_wind_data(file_resource_heights): + """Combine wind resource data stored in .srw files for multiple resource heights. + + Args: + file_resource_heights (dict): Keys are height in meters, values are corresponding filepaths. + example {40: path_to_file, 60: path_to_file2} + + Returns: + dict: wind resource data dictionary of combined resource data + """ + wind_resource_data = [] + for wind_filepath in file_resource_heights.values(): + d = SRW_to_wind_data(wind_filepath) + wind_resource_data.append(d) + combined_data = combine_wind_resource_data(wind_resource_data) + return combined_data + + +def combine_wind_files(wind_resource_filepath,resource_heights): + """Combine wind resource data from the file or file(s) for wind resource data into a + dictionary that is formatted as needed for WindPlant. + + Args: + wind_resource_filepath (list[str] | str): wind resource filenames + resource_heights (list[int]): list of resource data hub-heights from the allowed_hub_height_meters + variable that are closest to the wind turbine hub-height. + + Returns: + dict: wind resource data dictionary of combined resource data + """ + resource_heights = [int(h) for h in resource_heights] + + if isinstance(wind_resource_filepath,list): + if len(wind_resource_filepath) != len(resource_heights): + msg = ( + "Wind resource filepath must be a list of filenames that is the length as " + f"resource_heights. ``wind_resource_filepath`` has {len(wind_resource_filepath)} " + f"entries but ``resource_heights`` has {len(resource_heights)} entries." + ) + raise ValueError(msg) + file_resource_heights = dict(zip(resource_heights,wind_resource_filepath)) + elif isinstance(wind_resource_filepath, str): + filepaths = [wind_resource_filepath]*len(resource_heights) + file_resource_heights = dict(zip(resource_heights,filepaths)) + + is_srw = any(f.split(".")[-1]=="srw" for f in file_resource_heights.values()) + is_csv = any(f.split(".")[-1]=="csv" for f in file_resource_heights.values()) + + if is_srw: + combined_data = combine_SRW_to_wind_data(file_resource_heights) + return combined_data + if is_csv: + combined_data = combine_CSV_to_wind_data(file_resource_heights) + return combined_data + diff --git a/hopp/tools/resource/wind_tools.py b/hopp/tools/resource/wind_tools.py index 1bd12a10a..bdfe5b892 100644 --- a/hopp/tools/resource/wind_tools.py +++ b/hopp/tools/resource/wind_tools.py @@ -102,7 +102,8 @@ def parse_resource_data(wind_resource): hh2 = [h for h in height_lb if np.abs(h-wind_resource.hub_height_meters)==min_diff_lb][0] else: - hh1, hh2 = np.unique(wind_resource.data['heights']) + speed_heights = [wind_resource.data['heights'][ii] for ii in idx_ws] + hh1, hh2 = np.unique(speed_heights) if hh1 == wind_resource.hub_height_meters: idx_ws1 = [i for i in idx_ws if wind_resource.data['heights'][i] == hh1][0] @@ -172,7 +173,8 @@ def weighted_parse_resource_data(wind_resource): hh2 = [h for h in height_lb if np.abs(h-wind_resource.hub_height_meters)==min_diff_lb][0] else: - hh1, hh2 = np.unique(wind_resource.data['heights']) + speed_heights = [wind_resource.data['heights'][ii] for ii in idx_ws] + hh1, hh2 = np.unique(speed_heights) # Weights corresponding to difference of resource height and hub-height weight1 = np.abs(hh1 - wind_resource.hub_height_meters) diff --git a/tests/hopp/test_site_info.py b/tests/hopp/test_site_info.py index bcea0c45a..a01873ec5 100644 --- a/tests/hopp/test_site_info.py +++ b/tests/hopp/test_site_info.py @@ -26,7 +26,7 @@ "pricing-data-2015-IronMtn-002_factors.csv" ) kml_filepath = Path(__file__).absolute().parent / "layout_example.kml" - +from hopp.simulation.technologies.resource import AlaskaWindData @fixture def site(): @@ -528,4 +528,30 @@ def test_site_none_shape(): wind_resource_file=wind_resource_file, grid_resource_file=grid_resource_file ) - assert site.polygon is None \ No newline at end of file + assert site.polygon is None + +def test_alaska_wind_resource(): + site_data = { + "lat": 66.68, + "lon": -162.5, + "year": 2019, + "site_details": + { + "site_area_km2": 1.0, + "site_shape":"square", + } + } + alaska_wind_resource_file = os.path.join( + ROOT_DIR, "simulation", "resource_files", "wind", + "66.68_-162.5_WTK_Alaksa_2019_60min_80m_100m.csv" + ) + site_info = { + "data": site_data, + "wind_resource_file": alaska_wind_resource_file, + "wind_resource_region": "ak", + "wind": True, + "solar":False, + "hub_height": 90.0, + } + site = SiteInfo.from_dict(site_info) + assert isinstance(site.wind_resource,AlaskaWindData) diff --git a/tests/hopp/test_wind.py b/tests/hopp/test_wind.py index d76ee0181..3366ac0b0 100644 --- a/tests/hopp/test_wind.py +++ b/tests/hopp/test_wind.py @@ -7,6 +7,9 @@ from hopp.utilities import load_yaml from tests.hopp.utils import create_default_site_info from hopp import ROOT_DIR +import os +from hopp.simulation.technologies.sites import SiteInfo + @fixture def site(): @@ -207,3 +210,72 @@ def test_changing_system_capacity_floris(site): assert model._system_model.nTurbs == new_num_turbs assert model._system_model.system_capacity == new_capacity_kW +def test_alaska_wind_pysam(): + site_data = { + "lat": 66.68, + "lon": -162.5, + "year": 2019, + "site_details": + { + "site_area_km2": 1.0, + "site_shape":"square", + } + } + alaska_wind_resource_file = os.path.join( + ROOT_DIR, "simulation", "resource_files", "wind", + "66.68_-162.5_WTK_Alaksa_2019_60min_80m_100m.csv" + ) + site_info = { + "data": site_data, + "wind_resource_file": alaska_wind_resource_file, + "wind_resource_region": "ak", + "wind": True, + "solar": False, + "hub_height": 90.0, + } + site = SiteInfo.from_dict(site_info) + config = WindConfig.from_dict({'num_turbines': 5, "turbine_rating_kw": 2000}) + model = WindPlant(site, config=config) + model._system_model.execute(1) + assert model._system_model.Outputs.capacity_factor == approx(45.85,abs = 0.1) + + +def test_alaska_wind_floris(): + site_data = { + "lat": 66.68, + "lon": -162.5, + "year": 2019, + "site_details": + { + "site_area_km2": 1.0, + "site_shape":"square", + } + } + alaska_wind_resource_file = os.path.join( + ROOT_DIR, "simulation", "resource_files", "wind", + "66.68_-162.5_WTK_Alaksa_2019_60min_80m_100m.csv" + ) + site_info = { + "data": site_data, + "wind_resource_file": alaska_wind_resource_file, + "wind_resource_region": "ak", + "wind": True, + "solar":False, + "hub_height": 90.0, + } + site = SiteInfo.from_dict(site_info) + floris_config_path = ( + ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" + ) + wind_config_input = { + 'num_turbines': 4, + "turbine_rating_kw": 5000, + "model_name": "floris", + "timestep": [1, 8760], + "resource_parse_method":"weighted_average", + "floris_config": floris_config_path + } + config = WindConfig.from_dict(wind_config_input) + model = WindPlant(site, config=config) + model._system_model.execute(1) + assert model._system_model.annual_energy == approx(78514174,rel=1e-6) \ No newline at end of file diff --git a/tests/hopp/test_wind_resource_tools.py b/tests/hopp/test_wind_resource_tools.py index 40cd2ec0a..a54b7641e 100644 --- a/tests/hopp/test_wind_resource_tools.py +++ b/tests/hopp/test_wind_resource_tools.py @@ -11,6 +11,7 @@ from pytest import fixture, approx from numpy.testing import assert_array_almost_equal import numpy as np +from hopp.tools.resource.pysam_wind_tools import combine_wind_files wind_resource_file_multi_heights = os.path.join( ROOT_DIR, "simulation", "resource_files", "wind", @@ -132,4 +133,18 @@ def test_weighted_vs_average_parsing_90m(wind_resource_data_90m): wavg_wind_speeds, wavg_wind_dirs = weighted_parse_resource_data(wind_resource_data_90m) assert_array_almost_equal(avg_wind_speeds,wavg_wind_speeds,decimal=3) assert_array_almost_equal(avg_wind_dirs,wavg_wind_dirs,decimal=3) - \ No newline at end of file + +def test_pysam_combine_wind_files_csv(): + alaska_wind_resource_file = os.path.join( + ROOT_DIR, "simulation", "resource_files", "wind", + "66.68_-162.5_WTK_Alaksa_2019_60min_80m_100m.csv" + ) + resource_heights = [80.0,100.0] + wind_data = combine_wind_files(alaska_wind_resource_file,resource_heights) + assert len(wind_data["heights"]) == 7 + +def test_pysam_combine_wind_files_srw(): + + resource_heights = [80.0,100.0] + wind_data = combine_wind_files(wind_resource_file_multi_heights,resource_heights) + assert len(wind_data["heights"]) == 8 \ No newline at end of file From ca3b85e77a765e8ba1344a72dfc03dfb85b34d4e Mon Sep 17 00:00:00 2001 From: genevievestarke <103534902+genevievestarke@users.noreply.github.com> Date: Thu, 17 Apr 2025 15:06:42 -0600 Subject: [PATCH 29/48] Feature add: Download wind data from Bias-Corrected HRRR Dataset (#474) * added alaska wind resource download tools and class * updated alaska wind filel and resource.py for handling non alphanumeric characters * updated parsing methods in case pressure or temperature data is given for other resource heights * updated pysam wind resource tools to be flexible whether pressure is provided or not * integrated alaska wind into workflow * updated RELEASE.md * added tests for AK wind and updated site_info and alaska_wind file * added new resource file for alaska wind resource data used for new tests * updated doc strings and added documentation for Alaska wind API call * actually added alaska documentation file * fixed latitude for alaska test site * Add BC-HRRR dataset to HOPP * Upate docs * Finish updating docs * Add testing * Make dataset title shorter for docs * BCHRRR file cleanup * Debugging adding precipitation rate * Add precipitation rate back into the data * Update doc strings * Apply suggestions from code review * Approximate surface to 100m pressure * Updated the test_wind BCHRRR test * Updated BCHRRR test * Update after pressure conversion * Enable running pysam again --------- Co-authored-by: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Co-authored-by: John Jasa --- RELEASE.md | 1 + docs/_toc.yml | 1 + docs/api/resource/bc-hrrr_wind.md | 17 + docs/api/resource/index.md | 1 + docs/api/resource/wind_api.md | 4 +- ...101.945027_BC_HRRR_2015_60min_80m_100m.csv | 8762 +++++++++++++++++ .../technologies/resource/__init__.py | 3 +- .../technologies/resource/alaska_wind.py | 4 +- .../technologies/resource/bchrrr_wind.py | 161 + .../technologies/sites/site_info.py | 71 +- .../technologies/wind/wind_plant.py | 2 +- hopp/tools/resource/pysam_wind_tools.py | 36 +- tests/hopp/test_site_info.py | 29 + tests/hopp/test_wind.py | 76 +- tests/hopp/test_wind_resource_tools.py | 12 +- 15 files changed, 9139 insertions(+), 41 deletions(-) create mode 100644 docs/api/resource/bc-hrrr_wind.md create mode 100644 hopp/simulation/resource_files/wind/35.2018863_-101.945027_BC_HRRR_2015_60min_80m_100m.csv create mode 100644 hopp/simulation/technologies/resource/bchrrr_wind.py diff --git a/RELEASE.md b/RELEASE.md index b5feb90bf..740f7eb18 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -7,6 +7,7 @@ * Loosened strictness of comparison for wind turbine hub-height and wind resource hub-height * Updated workflow for specifying wind turbine parameters without specifying a turbine name with PySAM. * Added ability to download wind resource data from WTK-LED for Alaska +* Added ability to download wind resource data from BC-HRRR CONUS 60-minute (NOAA + NREL) for 2015-2023 ## Version 3.2.0, March 21, 2025 diff --git a/docs/_toc.yml b/docs/_toc.yml index fc8cd5efa..c54925b69 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -20,6 +20,7 @@ parts: - file: api/resource/solar_api - file: api/resource/wind_api - file: api/resource/alaska_wind + - file: api/resource/bc-hrrr_wind - file: api/resource/solar_hpc - file: api/resource/wind_hpc - file: api/resource/wave_data diff --git a/docs/api/resource/bc-hrrr_wind.md b/docs/api/resource/bc-hrrr_wind.md new file mode 100644 index 000000000..0603f47c0 --- /dev/null +++ b/docs/api/resource/bc-hrrr_wind.md @@ -0,0 +1,17 @@ +(resource:bc-hrrr-wind-resource)= +# Wind Resource from the BC-HRRR Dataset (API) + +Wind resource data can be downloaded from the NREL-processed bias-corrected High-Resolution Rapid Refresh (HRRR) dataset from the National Oceanic and Atmospheric Administration (NOAA) for the years 2015-2023. This data is available as an hourly operational forecast product. The data is bias-corrected so that it can be used in continuity with the legacy WIND toolkit data (2007-2013). The data is available over CONUS from the NREL Developer Network hosted Wind Integration National Dataset (WIND) Toolkit dataset [Wind Toolkit Data - BC-HRRR CONUS 60-minute (NOAA + NREL)](https://developer.nrel.gov/docs/wind/wind-toolkit/wtk-bchrrr-v1-0-0-download/). Using this functionality requires an NREL API key. + +Wind resource data from the BC-HRRR dataset can only be downloaded for wind resource years 2015-2023 and is only downloaded if the `wind_resource_origin` input to [SiteInfo](../site_info.md) is set to "BC-HRRR". For example: + +```yaml +site: + wind_resource_origin: "BC-HRRR" +``` + +```{eval-rst} +.. autoclass:: hopp.simulation.technologies.resource.alaska_wind.BCHRRRWindData + :members: + :exclude-members: _abc_impl, check_download_dir +``` \ No newline at end of file diff --git a/docs/api/resource/index.md b/docs/api/resource/index.md index fa411937f..a27d8ff87 100644 --- a/docs/api/resource/index.md +++ b/docs/api/resource/index.md @@ -5,6 +5,7 @@ These are the primary methods for accessing wind and solar resource data. - [Solar Resource (API)](resource:solar-resource) - [Conus Wind Resource (API)](resource:wind-resource) - [Alaska Wind Resource (API)](resource:ak-wind-resource) +- [BC-HRRR Dataset Wind Resource (API)](resource:bc-hrrr-wind-resource) - [Solar Resource (NSRDB Dataset on NREL HPC)](resource:nsrdb-data) - [Wind Resource (Wind Toolkit Dataset on NREL HPC)](resource:wtk-data) - [Wave Resource (Data)](resource:wave-resource) diff --git a/docs/api/resource/wind_api.md b/docs/api/resource/wind_api.md index c02273baf..417243be3 100644 --- a/docs/api/resource/wind_api.md +++ b/docs/api/resource/wind_api.md @@ -3,7 +3,9 @@ By default, wind resource data is downloaded from the NREL Developer Network hosted Wind Integration National Dataset (WIND) Toolkit dataset [Wind Toolkit Data - SAM format (srw)](https://developer.nrel.gov/docs/wind/wind-toolkit/wtk-srw-download/). -Wind resource data for the continental U.S. can only be downloaded for wind resource years 2007 - 2014. Using this functionality requires an NREL API key. +Wind resource data for the continental U.S. can be downloaded from Wind Toolkit for wind resource years 2007 - 2014. Using this functionality requires an NREL API key. + +Wind data for the continental U.S. for wind resource years 2015 - 2023 can be downloaded from the BC-HRRR dataset, see [BC-HRRR Dataset Wind Resource (API)](resource:bc-hrrr-wind-resource). ```{eval-rst} .. autoclass:: hopp.simulation.technologies.resource.wind_resource.WindResource diff --git a/hopp/simulation/resource_files/wind/35.2018863_-101.945027_BC_HRRR_2015_60min_80m_100m.csv b/hopp/simulation/resource_files/wind/35.2018863_-101.945027_BC_HRRR_2015_60min_80m_100m.csv new file mode 100644 index 000000000..525d43825 --- /dev/null +++ b/hopp/simulation/resource_files/wind/35.2018863_-101.945027_BC_HRRR_2015_60min_80m_100m.csv @@ -0,0 +1,8762 @@ +SiteID,976301,Site Timezone,-6,Data Timezone,0,Longitude,-101.94092,Latitude,35.207012 +Year,Month,Day,Hour,Minute,Surface Air Pressure (Pa),Precipitation Rate 0m,Air Temperature at 80m (C),Wind Speed at 80m (m/s),Wind Direction at 80m (deg),Air Temperature at 100m (C),Wind Speed at 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import HPCSolarData from hopp.simulation.technologies.resource.wind_toolkit_data import HPCWindData -from hopp.simulation.technologies.resource.alaska_wind import AlaskaWindData \ No newline at end of file +from hopp.simulation.technologies.resource.alaska_wind import AlaskaWindData +from hopp.simulation.technologies.resource.bchrrr_wind import BCHRRRWindData diff --git a/hopp/simulation/technologies/resource/alaska_wind.py b/hopp/simulation/technologies/resource/alaska_wind.py index 59f995c4a..49db5d221 100644 --- a/hopp/simulation/technologies/resource/alaska_wind.py +++ b/hopp/simulation/technologies/resource/alaska_wind.py @@ -113,10 +113,10 @@ def update_height(self, hub_height_meters): def download_resource(self): success = False - base_attributs = ["temperature","windspeed","winddirection"] + base_attributes = ["temperature","windspeed","winddirection"] attributes = ["pressure_100m"] for height, f in self.file_resource_heights.items(): - attributes += [f"{a}_{height}m" for a in base_attributs] + attributes += [f"{a}_{height}m" for a in base_attributes] attributes_str = ",".join(k for k in attributes) input_data = { diff --git a/hopp/simulation/technologies/resource/bchrrr_wind.py b/hopp/simulation/technologies/resource/bchrrr_wind.py new file mode 100644 index 000000000..3d2975b19 --- /dev/null +++ b/hopp/simulation/technologies/resource/bchrrr_wind.py @@ -0,0 +1,161 @@ +import os +from pathlib import Path +from typing import Union, Optional, List +import urllib.parse + +from attrs import define, field + +from hopp.utilities.keys import get_developer_nrel_gov_key, get_developer_nrel_gov_email +from hopp.utilities.validators import range_val +from hopp.simulation.technologies.resource.resource import Resource +from hopp import ROOT_DIR +from hopp.tools.resource.pysam_wind_tools import combine_wind_files + +BCHRRR_BASE_URL = "https://developer.nrel.gov/api/wind-toolkit/v2/wind/wtk-bchrrr-v1-0-0-download.csv?" + +@define +class BCHRRRWindData(Resource): + """ Class to manage Wind Resource data from BC-HRRR dataset using API calls or preloaded data. + + Args: + lat (float): latitude corresponding to location for wind resource data + lon (float): longitude corresponding to location for wind resource data + year (int): year for resource data. must be between 2007 and 2014 + hub_height_meters (float): turbine hub height (m) + path_resource (Union[str, Path], optional): filepath to resource_files directory. Defaults to ROOT_DIR/"simulation"/"resource_files". + filepath (Union[str, Path], optional): file path of resource file to load + use_api (bool, optional): Make an API call even if there's an existing file. Defaults to False. + resource_data (Optional[dict], optional): dictionary of preloaded and formatted wind resource data. Defaults to None. + kwargs: extra kwargs + """ + + lat: float = field() + lon: float = field() + #: year for resource data. Must be between 2015 and 2023 + year: int = field(validator=range_val(2015, 2023)) + + #: the hub-height for wind resource data (meters) + hub_height_meters: float = field(validator=range_val(10.0, 200.0)) + + # OPTIONAL INPUTS + path_resource: Optional[Union[str, Path]] = field(default = ROOT_DIR / "simulation" / "resource_files") + filename: Optional[Union[str, Path]] = field(default = None) + use_api: Optional[bool] = field(default = False) + resource_data: Optional[dict] = field(default = None) + + #: dictionary of heights and filenames to download from Wind Toolkit + file_resource_heights: dict = field(default = None) + + # NOT INPUTS + allowed_hub_height_meters: List[int] = [10, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200] + + + def __attrs_post_init__(self): + super().__init__(self.lat, self.lon, self.year) + + # if resource_data is input as a dictionary then set_data + if isinstance(self.resource_data,dict): + self.data = self.resource_data + return + + # if resource_data is not provided, download or load resource data + if isinstance(self.path_resource,str): + self.path_resource = Path(self.path_resource).resolve() + if self.path_resource.parts[-1]!="wind": + self.path_resource = self.path_resource / 'wind' + + if self.filename is None: + self.calculate_heights_to_download() + + self.check_download_dir() + + if not os.path.isfile(self.filename) or self.use_api: + self.download_resource() + + self.format_data() + + def calculate_heights_to_download(self): + """ + Given the system hub height, and the available hubheights from BC-HRRR Data, + determine which heights to download to bracket the hub height + """ + hub_height_meters = self.hub_height_meters + + # evaluate hub height, determine what heights to download + heights = [hub_height_meters] + if hub_height_meters not in self.allowed_hub_height_meters: + height_low = self.allowed_hub_height_meters[0] + height_high = self.allowed_hub_height_meters[-1] + for h in self.allowed_hub_height_meters: + if h < hub_height_meters: + height_low = h + elif h > hub_height_meters: + height_high = h + break + heights[0] = height_low + heights.append(height_high) + + filename_base = f"{self.latitude}_{self.longitude}_BC_HRRR_{self.year}_{self.interval}min" + file_resource_full = filename_base + file_resource_heights = dict() + + for h in heights: + h_int = int(h) + file_resource_heights[h_int] = self.path_resource/(filename_base + f'_{h_int}m.csv') + file_resource_full += f'_{h_int}m' + file_resource_full += ".csv" + + self.file_resource_heights = file_resource_heights + self.filename = self.path_resource / file_resource_full + + def update_height(self, hub_height_meters): + self.hub_height_meters = hub_height_meters + self.calculate_heights_to_download() + + def download_resource(self): + """ + Downloads the wind data from the BC-HRRR dataset using an API call + """ + success = False + + base_attributes = ["temperature","windspeed","winddirection"] + attributes = ["pressure_0m", "precipitationrate_0m"] + for height, f in self.file_resource_heights.items(): + attributes += [f"{a}_{height}m" for a in base_attributes] + + attributes_str = ",".join(k for k in attributes) + input_data = { + 'attributes': attributes_str, + 'interval': self.interval, + 'api_key': get_developer_nrel_gov_key(), + 'email': get_developer_nrel_gov_email(), + 'names': [str(self.year)], + 'wkt': f"POINT({self.longitude} {self.latitude})" + } + url = BCHRRR_BASE_URL + urllib.parse.urlencode(input_data, True) + success = self.call_api(url, filename=self.filename) + + if not success: + raise ValueError('Unable to download wind data') + + return success + + def format_data(self): + """ + Format as 'wind_resource_data' dictionary for use in PySAM. + """ + if not os.path.isfile(self.filename): + raise FileNotFoundError(f"{self.filename} does not exist. Try `download_resource` first.") + + self.data = self.filename + + @Resource.data.setter + def data(self, data_info): + """ + Sets the wind resource data to a dictionary in SAM Wind format (see Pysam.ResourceTools.SRW_to_wind_data) + """ + if isinstance(data_info,dict): + self._data = data_info + if isinstance(data_info,(str, Path)): + resource_heights = [k for k in self.file_resource_heights.keys()] + self._data = combine_wind_files(str(data_info),resource_heights) \ No newline at end of file diff --git a/hopp/simulation/technologies/sites/site_info.py b/hopp/simulation/technologies/sites/site_info.py index c5f777265..09f3e4bdf 100644 --- a/hopp/simulation/technologies/sites/site_info.py +++ b/hopp/simulation/technologies/sites/site_info.py @@ -22,6 +22,7 @@ HPCWindData, HPCSolarData, AlaskaWindData, + BCHRRRWindData, ) from hopp.tools.layout.plot_tools import plot_shape from hopp.utilities.log import hybrid_logger as logger @@ -74,7 +75,7 @@ class SiteInfo(BaseClass): renewable_resource_origin (str): whether to download resource data from API or load directly from datasets files. Options are "API" or "HPC". Defaults to "API". wind_resource_origin: Which wind resource API to use, defaults to "WTK" for WIND Toolkit. - Options are "WTK" or "TAP". + Options are "WTK", "TAP" or "BC-HRRR". site_buffer (Optional): value to buffer site polygon. Defaults to 1e-8. solar_resource (Optional): dictionary or object containing solar resource data. wind_resource (Optional): dictionary or object containing wind resource data. @@ -135,7 +136,7 @@ class SiteInfo(BaseClass): wave: bool = field(default=False) tidal: bool = field(default=False) renewable_resource_origin: str = field(default="API", validator=contains(["API", "HPC"])) - wind_resource_origin: str = field(default="WTK", validator=contains(["WTK", "TAP"])) + wind_resource_origin: str = field(default="WTK", validator=contains(["WTK", "TAP", "BC-HRRR"])) wind_resource_region: str = field(default="conus", validator=contains(["conus", "ak"]), converter=(str.strip, str.lower)) site_buffer: Optional[float] = field(default = 1e-8) @@ -149,7 +150,7 @@ class SiteInfo(BaseClass): vertices: NDArrayFloat = field(init=False) polygon: Union[Polygon, BaseGeometry] = field(init=False) solar_resource: Optional[Union[SolarResource,HPCSolarData]] = field(default=None) - wind_resource: Optional[Union[WindResource,HPCWindData,AlaskaWindData]] = field(default=None) + wind_resource: Optional[Union[WindResource,HPCWindData,AlaskaWindData,BCHRRRWindData]] = field(default=None) wave_resource: Optional[WaveResource] = field(init=False, default=None) tidal_resource: Optional[TidalResource] = field(init=False, default=None) elec_prices: Optional[ElectricityPrices] = field(init=False, default=None) @@ -368,7 +369,7 @@ def initialize_solar_resource(self,data:dict): return self.solar_resource - def initialize_wind_resource(self,data:dict): + def initialize_wind_resource(self, data: dict): """Download/load wind resource data Args: @@ -377,31 +378,53 @@ def initialize_wind_resource(self,data:dict): Returns: :obj:`hopp.simulation.technologies.resource.WindResource` or :obj:`hopp.simulation.technologies.resource.HPCWindData`: wind resource data class """ + # Extract parameters with defaults from data dictionary wind_lat = data.setdefault("wind_lat", data["lat"]) wind_lon = data.setdefault("wind_lon", data["lon"]) wind_year = data.setdefault("wind_year", data["year"]) - - if self.wind_resource is None: - if self.wind_resource_region == "conus": - if self.renewable_resource_origin == "API": - wind_resource = WindResource(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height, - path_resource=self.path_resource, filepath=self.wind_resource_file, source=self.wind_resource_origin) - else: - wind_resource = HPCWindData(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height, - wtk_source_path=self.wtk_source_path, filepath=self.wind_resource_file) - return wind_resource - if self.wind_resource_region == "ak": - wind_resource = AlaskaWindData(lat=wind_lat, lon=wind_lon, year=wind_year, hub_height_meters=self.hub_height, - path_resource=self.path_resource, filename=self.wind_resource_file) - return wind_resource - if isinstance(self.wind_resource,dict): + + # If wind resource is already provided as an object, return it directly + if self.wind_resource is not None and not isinstance(self.wind_resource, dict): + return self.wind_resource + + # If wind resource is provided as a dictionary, convert to appropriate object + if isinstance(self.wind_resource, dict): if self.wind_resource_region == "conus": - wind_resource = WindResource(wind_lat, wind_lon, wind_year, wind_turbine_hub_ht=self.hub_height,resource_data = self.wind_resource) - if self.wind_resource_region == "ak": - wind_resource = AlaskaWindData(lat=wind_lat, lon=wind_lon, year=wind_year, hub_height_meters=self.hub_height,resource_data = self.wind_resource) - return wind_resource + return WindResource(wind_lat, wind_lon, wind_year, + wind_turbine_hub_ht=self.hub_height, + resource_data=self.wind_resource) + elif self.wind_resource_region == "ak": + return AlaskaWindData(lat=wind_lat, lon=wind_lon, year=wind_year, + hub_height_meters=self.hub_height, + resource_data=self.wind_resource) + + # Create new wind resource based on region and resource origin + if self.wind_resource_region == "ak": + return AlaskaWindData(lat=wind_lat, lon=wind_lon, year=wind_year, + hub_height_meters=self.hub_height, + path_resource=self.path_resource, + filename=self.wind_resource_file) - return self.wind_resource + # Handle Continental US (conus) region + if self.renewable_resource_origin == "API": + if self.wind_resource_origin in ["WTK", "TAP"]: + return WindResource(wind_lat, wind_lon, wind_year, + wind_turbine_hub_ht=self.hub_height, + path_resource=self.path_resource, + filepath=self.wind_resource_file, + source=self.wind_resource_origin) + elif self.wind_resource_origin == "BC-HRRR": + return BCHRRRWindData(wind_lat, wind_lon, wind_year, + hub_height_meters=self.hub_height, + path_resource=self.path_resource, + filename=self.wind_resource_file) + else: + raise ValueError("Invalid entry for `wind_resource_origin`, must be either 'WTK', 'TAP' or 'BC-HRRR'") + elif self.renewable_resource_origin == "HPC": + return HPCWindData(wind_lat, wind_lon, wind_year, + wind_turbine_hub_ht=self.hub_height, + wtk_source_path=self.wtk_source_path, + filepath=self.wind_resource_file) # TODO: determine if the below functions are obsolete @property diff --git a/hopp/simulation/technologies/wind/wind_plant.py b/hopp/simulation/technologies/wind/wind_plant.py index 883430982..57f21f527 100644 --- a/hopp/simulation/technologies/wind/wind_plant.py +++ b/hopp/simulation/technologies/wind/wind_plant.py @@ -238,7 +238,7 @@ def initalize_pysam_turbine_from_turbine_library(self, turbine_name): f"Turbine name {turbine_name} was not found the turbine-models library. " "Please try an available name." ) - ValueError(msg) + raise ValueError(msg) turbine_dict = turb_lib_interface.get_pysam_turbine_specs(turbine_name,self) self._system_model.Turbine.assign(turbine_dict) diff --git a/hopp/tools/resource/pysam_wind_tools.py b/hopp/tools/resource/pysam_wind_tools.py index 6a34a6b70..eee135321 100644 --- a/hopp/tools/resource/pysam_wind_tools.py +++ b/hopp/tools/resource/pysam_wind_tools.py @@ -37,11 +37,11 @@ def csv_to_dataframe(wind_csv_filepath, resource_height, resource_year): site_lat, site_lon, 'elevation??', site_tz, 8760]) # meta info h2 = np.array(["WTK .csv converted to .srw for SAM", None, None, None, None, None, None, None, None, None]) # descriptive text - h3 = np.array(['temperature', 'pressure', 'direction', + h3 = np.array(['temperature', None, 'direction', 'speed', None, None, None, None, None, None]) # variables - h4 = np.array(['C', 'atm', 'degrees', 'm/s', None, + h4 = np.array(['C', None, 'degrees', 'm/s', None, None, None, None, None, None]) # units - h5 = np.array([resource_height, 100, resource_height, resource_height, None, None, + h5 = np.array([resource_height, None, resource_height, resource_height, None, None, None, None, None, None]) # hubheight header = pd.DataFrame(np.vstack([h1, h2, h3, h4, h5])) assert header.shape == (5, 10) @@ -68,13 +68,29 @@ def csv_to_dataframe(wind_csv_filepath, resource_height, resource_year): df['temperature'] = df['air temperature at {}m (C)'.format(resource_height)] # --- convert PA to atm --- + if 'surface air pressure (Pa)' in new_colnames: + # approximately convert from surface pressure to pressure at 100m + df['pressure'] = (df['surface air pressure (Pa)'] - 1.2e3)/ 101325 + data_fieldnames += ['pressure'] + data_fieldnumbers += [1] + header.loc[2,1] = "pressure" + header.loc[3,1] = "atm" + header.loc[4,1] = 100 if 'air pressure at 100m (Pa)' in new_colnames: df['pressure'] = df['air pressure at 100m (Pa)'] / 101325 data_fieldnames += ['pressure'] data_fieldnumbers += [1] - # if 'surface air pressure (Pa)' in new_colnames: - # df['pressure'] = df['surface air pressure (Pa)'] / 101325 - + header.loc[2,1] = "pressure" + header.loc[3,1] = "atm" + header.loc[4,1] = 100 + if 'Precipitation Rate 0m' in df.columns.to_list(): + data_fieldnames += ["precipitation_rate"] + data_fieldnumbers += [4] + df = df.rename(columns = {'Precipitation Rate 0m':"precipitation_rate"}) + header.loc[2,4] = "precipitation_rate" + header.loc[3,4] = "mm/hour" + header.loc[4,4] = 0 + # --- rename --- rename_dict = {'wind speed at {}m (m/s)'.format(resource_height): 'speed', 'wind direction at {}m (deg)'.format(resource_height): 'direction'} @@ -148,8 +164,8 @@ def CSV_to_wind_data(wind_csv_filepath, resource_height, resource_year = None): Returns: dict: wind resource data dictionary in PySAM format """ - data_to_field_number = {'temperature': 1, 'pressure': 2, 'speed': 3, 'direction': 4} - out = csv_to_dataframe(wind_csv_filepath, resource_height, resource_year = resource_year) + data_to_field_number = {'temperature': 1, 'pressure': 2, 'speed': 3, 'direction': 4, 'precipitation_rate': 5} + out = csv_to_dataframe(wind_csv_filepath, resource_height, resource_year) heights = [h for h in out.iloc[4].to_list() if h is not None] field_names = [h for h in out.iloc[2].to_list() if h is not None] field_numbers = [data_to_field_number[f] for f in field_names] @@ -172,7 +188,7 @@ def combine_and_write_srw_files(file_resource_heights, output_filepath): output_filepath (str | Path): filepath to write combined .srw file to. Returns: - bool: whether the file was successfully writen to the output filepath. + bool: whether the file was successfully written to the output filepath. """ data = [None] * 2 @@ -197,7 +213,7 @@ def combine_and_write_srw_files(file_resource_heights, output_filepath): return os.path.isfile(output_filepath) def combine_wind_resource_data(wind_resource_data): - """Combines dictionaries of wind resoure data. + """Combines dictionaries of wind resource data. Args: wind_resource_data (list[dict]): list of wind resource data dictionaries for different resource heights diff --git a/tests/hopp/test_site_info.py b/tests/hopp/test_site_info.py index a01873ec5..7389929b3 100644 --- a/tests/hopp/test_site_info.py +++ b/tests/hopp/test_site_info.py @@ -27,6 +27,7 @@ ) kml_filepath = Path(__file__).absolute().parent / "layout_example.kml" from hopp.simulation.technologies.resource import AlaskaWindData +from hopp.simulation.technologies.resource import BCHRRRWindData @fixture def site(): @@ -555,3 +556,31 @@ def test_alaska_wind_resource(): } site = SiteInfo.from_dict(site_info) assert isinstance(site.wind_resource,AlaskaWindData) + +def test_bchrrr_wind_resource(): + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2015, + "tz": -6, + "site_details": + { + "site_area_km2": 1.0, + "site_shape":"square", + } + } + bchrrr_wind_resource_file = os.path.join( + ROOT_DIR, "simulation", "resource_files", "wind", + "35.2018863_-101.945027_BC_HRRR_2015_60min_80m_100m.csv" + ) + site_info = { + "data": site_data, + "wind_resource_file": bchrrr_wind_resource_file, + "wind_resource_origin": "BC-HRRR", + "wind": True, + "solar":False, + "hub_height": 90.0, + } + site = SiteInfo.from_dict(site_info) + assert isinstance(site.wind_resource,BCHRRRWindData) diff --git a/tests/hopp/test_wind.py b/tests/hopp/test_wind.py index 3366ac0b0..455205c41 100644 --- a/tests/hopp/test_wind.py +++ b/tests/hopp/test_wind.py @@ -278,4 +278,78 @@ def test_alaska_wind_floris(): config = WindConfig.from_dict(wind_config_input) model = WindPlant(site, config=config) model._system_model.execute(1) - assert model._system_model.annual_energy == approx(78514174,rel=1e-6) \ No newline at end of file + assert model._system_model.annual_energy == approx(78514174,rel=1e-6) + +def test_bchrrr_wind_pysam(): + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2015, + "tz": -6, + "site_details": + { + "site_area_km2": 1.0, + "site_shape":"square", + } + } + bchrrr_wind_resource_file = os.path.join( + ROOT_DIR, "simulation", "resource_files", "wind", + "35.2018863_-101.945027_BC_HRRR_2015_60min_80m_100m.csv" + ) + site_info = { + "data": site_data, + "wind_resource_file": bchrrr_wind_resource_file, + "wind_resource_origin": "BC-HRRR", + "wind": True, + "solar": False, + "hub_height": 90.0, + } + site = SiteInfo.from_dict(site_info) + config = WindConfig.from_dict({'num_turbines': 5, "turbine_rating_kw": 2000}) + + model = WindPlant(site, config=config) + model._system_model.execute(1) + assert model._system_model.Outputs.capacity_factor == approx(35.97,abs = 0.1) + +def test_bchrrr_wind_floris(): + site_data = { + "lat": 35.2018863, + "lon": -101.945027, + "elev": 1099, + "year": 2015, + "tz": -6, + "site_details": + { + "site_area_km2": 1.0, + "site_shape":"square", + } + } + bchrrr_wind_resource_file = os.path.join( + ROOT_DIR, "simulation", "resource_files", "wind", + "35.2018863_-101.945027_BC_HRRR_2015_60min_80m_100m.csv" + ) + site_info = { + "data": site_data, + "wind_resource_file": bchrrr_wind_resource_file, + "wind_resource_origin": "BC-HRRR", + "wind": True, + "solar":False, + "hub_height": 90.0, + } + site = SiteInfo.from_dict(site_info) + floris_config_path = ( + ROOT_DIR.parent / "tests" / "hopp" / "inputs" / "floris_config.yaml" + ) + wind_config_input = { + 'num_turbines': 4, + "turbine_rating_kw": 5000, + "model_name": "floris", + "timestep": [1, 8760], + "resource_parse_method":"weighted_average", + "floris_config": floris_config_path + } + config = WindConfig.from_dict(wind_config_input) + model = WindPlant(site, config=config) + model._system_model.execute(1) + assert model._system_model.annual_energy == approx(69526231,rel=1e-6) diff --git a/tests/hopp/test_wind_resource_tools.py b/tests/hopp/test_wind_resource_tools.py index a54b7641e..87f6b9a6e 100644 --- a/tests/hopp/test_wind_resource_tools.py +++ b/tests/hopp/test_wind_resource_tools.py @@ -147,4 +147,14 @@ def test_pysam_combine_wind_files_srw(): resource_heights = [80.0,100.0] wind_data = combine_wind_files(wind_resource_file_multi_heights,resource_heights) - assert len(wind_data["heights"]) == 8 \ No newline at end of file + assert len(wind_data["heights"]) == 8 + +def test_pysam_combine_wind_files_surface_pressure(): + bchrrr_wind_resource_file = os.path.join( + ROOT_DIR, "simulation", "resource_files", "wind", + "35.2018863_-101.945027_BC_HRRR_2015_60min_80m_100m.csv" + ) + resource_heights = [80.0,100.0] + wind_data = combine_wind_files(bchrrr_wind_resource_file,resource_heights) + assert len(wind_data["heights"]) == 8 + assert 2 in wind_data["fields"] From bbe06cbab8a3370c31f3b083b906e568bbe68dc0 Mon Sep 17 00:00:00 2001 From: John Jasa Date: Fri, 18 Apr 2025 15:58:32 -0500 Subject: [PATCH 30/48] Updates for pySAM 7.0.0 (#477) * WIP: updating for pysam 7 * Updates for pysam 7 * Updated release.md * Updated test_hybrid based on tidal changes in pysam --- RELEASE.md | 1 + .../technologies/financial/mhk_cost_model.py | 3 ++- tests/hopp/test_battery_dispatch.py | 4 ++-- tests/hopp/test_custom_financial.py | 10 +++++----- tests/hopp/test_hybrid.py | 8 ++++---- tests/hopp/test_tidal.py | 2 +- 6 files changed, 15 insertions(+), 13 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 740f7eb18..ec2a92cae 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -8,6 +8,7 @@ * Updated workflow for specifying wind turbine parameters without specifying a turbine name with PySAM. * Added ability to download wind resource data from WTK-LED for Alaska * Added ability to download wind resource data from BC-HRRR CONUS 60-minute (NOAA + NREL) for 2015-2023 +* Updated HOPP for pySAM 7.0.0 release ## Version 3.2.0, March 21, 2025 diff --git a/hopp/simulation/technologies/financial/mhk_cost_model.py b/hopp/simulation/technologies/financial/mhk_cost_model.py index 57c7ffe0d..40ae1cccd 100644 --- a/hopp/simulation/technologies/financial/mhk_cost_model.py +++ b/hopp/simulation/technologies/financial/mhk_cost_model.py @@ -148,6 +148,7 @@ def initialize(self): self._cost_model.value("marine_energy_tech", 0) # Wave elif self._ref_model_num == "RM1": self._cost_model.value('marine_energy_tech',1) # Tidal + self._cost_model.value('lib_tidal_device', self._ref_model_num) else: self._cost_model.value("marine_energy_tech", 0) # Generic self._cost_model.value("library_or_input_wec", 0) @@ -165,7 +166,7 @@ def initialize(self): self._cost_model.value("export_cable_length", self._export_cable_length) #Riser cable length, m - # The length of cable from the seabed to the water surfacethat + # The length of cable from the seabed to the water surface that # connects the floating device to the seabed cabling. # Applies only to floating array self.riser_cable_length = 1.5 * self._water_depth * self._number_devices * \ diff --git a/tests/hopp/test_battery_dispatch.py b/tests/hopp/test_battery_dispatch.py index c3cf34dbb..e1e7987f1 100644 --- a/tests/hopp/test_battery_dispatch.py +++ b/tests/hopp/test_battery_dispatch.py @@ -149,9 +149,9 @@ def test_batterystateless_dispatch(): battery_sl_actual = np.array(battery_sl.generation_profile)[0:48] * 1e-3 # convert to MWh assert sum(battery_dispatch - battery_sl_dispatch) == 0 - assert sum(abs(battery_actual - battery_dispatch)) <= 33 + assert sum(abs(battery_actual - battery_dispatch)) <= 33.5 assert sum(abs(battery_sl_actual - battery_sl_dispatch)) == 0 - assert sum(abs(battery_actual - battery_sl_actual)) <= 33 + assert sum(abs(battery_actual - battery_sl_actual)) <= 33.5 assert battery_sl.outputs.lifecycles_per_day[0:2] == pytest.approx([0.75048, 1.50096], rel=1e-3) diff --git a/tests/hopp/test_custom_financial.py b/tests/hopp/test_custom_financial.py index de2ebd8ad..bcf962786 100644 --- a/tests/hopp/test_custom_financial.py +++ b/tests/hopp/test_custom_financial.py @@ -280,17 +280,17 @@ def test_hybrid_simple_pv_with_wind_wave_storage_dispatch(subtests): npv_expected_pv = -1640023 npv_expected_wind = -5159400 - npv_expected_wave = -62903172 + npv_expected_wave = -57994156. npv_expected_battery = -8183543 - npv_expected_hybrid = -77887529 + npv_expected_hybrid = -72978515. lcoe_expected_pv = 3.104064331441355 lcoe_expected_wind = 3.162940789633178 - lcoe_expected_wave = 35.719370712383856 + lcoe_expected_wave = 33.09696662806905 lcoe_expected_battery = 13.333128855903514 - lcoe_expected_hybrid = 11.337551789830751 + lcoe_expected_hybrid = 10.756369801042606 - total_installed_cost_expected = 93959704.39847898 + total_installed_cost_expected = 89050689.65833203 interconnect_kw = 20000 pv_kw = 5000 diff --git a/tests/hopp/test_hybrid.py b/tests/hopp/test_hybrid.py index 24816801f..3c065d4e8 100644 --- a/tests/hopp/test_hybrid.py +++ b/tests/hopp/test_hybrid.py @@ -363,10 +363,10 @@ def test_hybrid_wave_only(hybrid_config, mhk_config, wavesite, subtests): with subtests.test("hybrid wave only cf"): assert cf.hybrid == approx(cf.wave) with subtests.test("wave cost"): - assert hybrid_plant.wave.total_installed_cost == approx(66465112.398478985, 1e-2) + assert hybrid_plant.wave.total_installed_cost == approx(61556097.658332035, 1e-2) with subtests.test("wave npv"): # TODO check/verify this test value somehow, not sure how to do it right now - assert npvs.wave == approx(-66610851.533444166, 5.e-2) + assert npvs.wave == approx(-61701836.79329722, 5.e-2) with subtests.test("hybrid wave only npv"): assert npvs.hybrid == approx(npvs.wave) @@ -584,10 +584,10 @@ def test_hybrid_tidal_only(hybrid_config, mhk_tidal_config, tidalsite, subtests) assert cf.hybrid == approx(cf.tidal) with subtests.test("tidal cost"): # It seems that there is a difference between PySAM cost curves and SAM gui - assert hybrid_plant.tidal.total_installed_cost == approx(29015651.4, 1e-2) + assert hybrid_plant.tidal.total_installed_cost == approx(23212571.95064662, 1e-2) with subtests.test("tidal npv"): # TODO check/verify this test value somehow, not sure how to do it right now - assert npvs.tidal == approx(-29088482.4, 5.e-2) + assert npvs.tidal == approx(-23285402.94450588, 5.e-2) with subtests.test("hybrid tidal only npv"): assert npvs.hybrid == approx(npvs.tidal) diff --git a/tests/hopp/test_tidal.py b/tests/hopp/test_tidal.py index c6ee8b207..5204dc629 100644 --- a/tests/hopp/test_tidal.py +++ b/tests/hopp/test_tidal.py @@ -76,7 +76,7 @@ def test_system_outputs(tidalplant,subtests): def test_cost_outputs(tidalplant,subtests): tidalplant.simulate(25) with subtests.test("structural assembly cost"): - assert tidalplant.mhk_costs.cost_outputs['structural_assembly_cost_modeled'] == pytest.approx(10371672, 1e-3) + assert tidalplant.mhk_costs.cost_outputs['structural_assembly_cost_modeled'] == pytest.approx(8495602, 1e-3) with subtests.test("power_takeoff_system_cost"): assert tidalplant.mhk_costs.cost_outputs['power_takeoff_system_cost_modeled']== pytest.approx(41212670, 1e-3) From 4beff84db8edccc0bf8187da376129c4aebba197 Mon Sep 17 00:00:00 2001 From: Jared Thomas Date: Tue, 22 Apr 2025 10:36:59 -0600 Subject: [PATCH 31/48] Add long duration energy storage (LDES) (#471) * Feature/ldes framework (#443) * rename pysam battery model from BatteryModel to PySAMBatteryModel to make room for LDES * create space for LDES system model integration * switch from LDES to AEF * isolate assert statements in subtests * start tests for LDES and start LDES initialization * work on integrating LDES * ldes initial tests passing * add comments/TODO * Feature/ldes framework working framework, need to fill in execute (#449) * rename pysam battery model from BatteryModel to PySAMBatteryModel to make room for LDES * create space for LDES system model integration * switch from LDES to AEF * isolate assert statements in subtests * start tests for LDES and start LDES initialization * work on integrating LDES * ldes initial tests passing * add comments/TODO * Missed load (#432) * update print/save statements to not multiply missed load by 100 since it is already a percentage and not a decimal * correct setting and printing of 'schedule_curtailed_percentage' with removing extra 100 multiples at print and including brackets for clarification * add clarifying parentheses * update RELEASE.md * MHK Tidal Plant (#444) * update pysam to 6.0.0 * update grid default json to CustomGenerationProfile json * update wave plant loading resource file to handle 1hr timesteps by default * Updated for pysam 6.0.0 * wave cost model updates. update test values add additional test for costs. * Update regression test values based on updates to wind and solar pysam default jsons and updates to singleowner model. * Reopt test: update default json and financial value for wind * update test values in test_capacity_credit. changes due to json defaults and impact on battery optimization * CSP update. Update lcoe value because of changes to SingleOwner financial model * WIP; updating test values for detailed PV * Bringing tests back for detailed PV * update regression test results * update RELEASE.md * update example default fin config * remove outdated comments * update docstrings to google format * update deprecated methods * H to h * update RELEASE.md * initial tidal model * update RELEASE.md with PR number. Force update for NREL-PySAM dependency * update tidal model with tests * add ability to interpolate resource data * add validators back * update documentation * Converted tabs to spaces * change resource import * update docstrings and documentation * update description of tidal resource * reduce attrs logic Co-authored-by: John Jasa * reduce logic Co-authored-by: John Jasa * check identity with is not None * update path handling * fixing docs * fix description * clean up readthedoc warnings * fix typo --------- Co-authored-by: John Jasa * working on fixing setters * save comments from discussion with gen * include tests for battery with ldes * Integrate Tidal into Hybrid Simulation (#446) * integrate mhk_tidal into HybridSimulation * update tidal and wave docs * add tidal test to test_hybrid.py * add robust_approx method Co-authored-by: John Jasa * Add Tidal Dispatch (#448) * add tidal dispatch * add tidal battery example --------- Co-authored-by: John Jasa * minor progress * test_battery_ldes.py tests are passing * remove init import and debug statements * include duration --------- Co-authored-by: kbrunik <102193481+kbrunik@users.noreply.github.com> Co-authored-by: John Jasa * Feature/ldes framework (#450) * include duration * bug fix --------- Co-authored-by: kbrunik <102193481+kbrunik@users.noreply.github.com> Co-authored-by: John Jasa * Feature/ldes framework (#451) * resolve merge * Feature/ldes framework (#459) * debugging * add ldes dispatch tests * re-set up instantiation * updates to LDES initialization * get to execute * ldes basically working - no degradation or cycle counting * remove unused code * generally working * add efficiency test for ldes * remove debug statements --------- Co-authored-by: bayc * Fix code syntax * Initial cycle counting for ldes * Fix typo in power_storage_dispatch.py lifecyle count for min operating cost objective * Updating tests and remove print statements * Update soc modeling and fix plot_tools.py error * Fix tests * Pinning floris version * Feature/ldes framework (#468) * add ldes dispatch tests * updates to LDES initialization * add efficiency test for ldes * Update pyproject.toml * include subtests * soc test for ldes * add test for battery replacement schedule with custom financial model --------- Co-authored-by: John Jasa * adding battery test with replacement schedule for custom financial model * fix logic in batt replacement list length check * update tests * fix battery replacement schedule function with custom financial model * LDES tests and remove AEF chemistry option --------- Co-authored-by: bayc Co-authored-by: John Jasa * update release.md * add LDES example * remove commented option * remove commented option * remove outdated comment * remove unnecessary inputs and notes * clarify logic and purpose * address minor comments * correct grammar: * clarify the purpose of the battery_om_per_kw zero value in the LDES example * Update release notes with bugfix * Remove commented out test from ldes battery tests * Updated LDES and batt replacement tests due to pysam 7.0.0 * Small cleanup --------- Co-authored-by: kbrunik <102193481+kbrunik@users.noreply.github.com> Co-authored-by: John Jasa Co-authored-by: bayc Co-authored-by: Genevieve Starke --- RELEASE.md | 3 + ...llowing-long-duration-energy-storage.ipynb | 290 ++++++++++++++ ...ollowing-long-duration-energy-storage.yaml | 34 ++ examples/inputs/default_fin_config_ldes.yaml | 49 +++ hopp/simulation/hybrid_simulation.py | 13 +- .../technologies/battery/battery.py | 154 +++++--- .../technologies/dispatch/plot_tools.py | 54 ++- .../heuristic_load_following_dispatch.py | 6 +- .../linear_voltage_convex_battery_dispatch.py | 6 +- ...near_voltage_nonconvex_battery_dispatch.py | 6 +- .../one_cycle_battery_dispatch_heuristic.py | 6 +- .../power_storage/power_storage_dispatch.py | 2 +- .../power_storage/simple_battery_dispatch.py | 10 +- .../simple_battery_dispatch_heuristic.py | 6 +- .../financial/custom_financial_model.py | 22 +- hopp/simulation/technologies/ldes/__init__.py | 0 .../technologies/ldes/ldes_system_model.py | 293 ++++++++++++++ hopp/tools/dispatch/plot_tools.py | 2 +- pyproject.toml | 2 +- tests/hopp/test_battery.py | 31 +- tests/hopp/test_battery_dispatch.py | 87 +++-- tests/hopp/test_battery_ldes.py | 143 +++++++ tests/hopp/test_battery_ldes_dispatch.py | 171 +++++++++ tests/hopp/test_custom_financial.py | 361 ++++++++++++++++++ tests/hopp/test_dispatch.py | 90 +++++ 25 files changed, 1710 insertions(+), 131 deletions(-) create mode 100644 examples/11-load-following-long-duration-energy-storage.ipynb create mode 100644 examples/inputs/11-load-following-long-duration-energy-storage.yaml create mode 100644 examples/inputs/default_fin_config_ldes.yaml create mode 100644 hopp/simulation/technologies/ldes/__init__.py create mode 100644 hopp/simulation/technologies/ldes/ldes_system_model.py create mode 100644 tests/hopp/test_battery_ldes.py create mode 100644 tests/hopp/test_battery_ldes_dispatch.py diff --git a/RELEASE.md b/RELEASE.md index ec2a92cae..977bd9d1a 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -9,6 +9,9 @@ * Added ability to download wind resource data from WTK-LED for Alaska * Added ability to download wind resource data from BC-HRRR CONUS 60-minute (NOAA + NREL) for 2015-2023 * Updated HOPP for pySAM 7.0.0 release +* Add long-duration energy storage (LDES) +* Bugfix for cycle counting in the minimum operating cost objective function - no longer throws an error + ## Version 3.2.0, March 21, 2025 diff --git a/examples/11-load-following-long-duration-energy-storage.ipynb b/examples/11-load-following-long-duration-energy-storage.ipynb new file mode 100644 index 000000000..7626a99e1 --- /dev/null +++ b/examples/11-load-following-long-duration-energy-storage.ipynb @@ -0,0 +1,290 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Long-Duration Energy Storage Example\n", + "---\n", + "In this example, we will show how to use and modify the long duration energy storage (LDES) parameters in the hybrid plant simulation. The LDES model is built inside of the battery module, so the parameters are very similar to the battery storage model. In this example we will model a vanadium redox flow battery (VRFB), but we have written the model in a general way so that many types of long-duration energy storage can be modeled reasonably easily." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Required Modules\n", + "Begin by importing the necessary modules for the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/jthomas2/Documents/programs/HOPP/examples/log/hybrid_systems_2025-04-17T14.54.28.881005.log\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "from hopp import ROOT_DIR\n", + "from hopp.simulation import HoppInterface\n", + "from hopp.utilities import load_yaml\n", + "from hopp.simulation.technologies.sites import SiteInfo, flatirons_site\n", + "from hopp.tools.dispatch.plot_tools import (\n", + " plot_generation_profile\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set Site Information\n", + "Set wind and solar resource data at plant location and load pricing data. In this example, we use the Flatirons site as an example location.\n", + "\n", + "\n", + "\n", + "**NOTE**: For a load following objective function the `desired_schedule` must be set. You can also specify `curtailment_value_type` to either _\"grid\"_ or _\"desired_schedule\"_. If you select _\"grid\"_ the system will curtail energy at the interconnection limit but optimize to meet the `desired_schedule` load and if you select _\"desired_schedule\"_ it curtails energy above the `desired_schedule` and optimizes to meet the `desired_schedule` load." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "DEFAULT_SOLAR_RESOURCE_FILE = ROOT_DIR.parent / \"hopp\" / \"simulation\" / \"resource_files\" / \"solar\" / \"35.2018863_-101.945027_psmv3_60_2012.csv\"\n", + "DEFAULT_WIND_RESOURCE_FILE = ROOT_DIR.parent / \"hopp\" / \"simulation\" / \"resource_files\" / \"wind\" / \"35.2018863_-101.945027_windtoolkit_2012_60min_80m_100m.srw\"\n", + "DEFAULT_PRICE_FILE = ROOT_DIR.parent / \"hopp\" / \"simulation\" / \"resource_files\" / \"grid\" / \"pricing-data-2015-IronMtn-002_factors.csv\"\n", + "\n", + "setpoint_kw = float(10 * 1000)\n", + "DEFAULT_LOAD = setpoint_kw*np.ones((8760))/1000\n", + "\n", + "site = SiteInfo(\n", + " flatirons_site,\n", + " solar_resource_file=DEFAULT_SOLAR_RESOURCE_FILE,\n", + " wind_resource_file=DEFAULT_WIND_RESOURCE_FILE,\n", + " grid_resource_file=DEFAULT_PRICE_FILE,\n", + " desired_schedule=DEFAULT_LOAD,\n", + " curtailment_value_type=\"interconnect_kw\",\n", + " solar=True,\n", + " wind=True,\n", + " wave=False\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the HOPP Model\n", + "To generate the HOPP Model, instantiate the `HoppInterface` class and supply the required YAML configuration.\n", + "\n", + "`HOPPInterface` is capable of handling dictionary input as well as class instances. Here we demonstrate this by loading the YAML file as a dict, modifying it to include our site information, then passing it as an argument to `HoppInterface`. This is useful for programmatic configuration of simulation configs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hopp_config = load_yaml(\"./inputs/11-load-following-long-duration-energy-storage.yaml\")\n", + "\n", + "# alert the model that a replacement schedule will be provided\n", + "hopp_config[\"technologies\"][\"battery\"][\"fin_model\"][\"battery_system\"][\"batt_replacement_option\"] = 2\n", + "# set up the replacement schedule to replace the battery every 15 years at a cost of 50% of the initial capex\n", + "project_life_years = 25\n", + "refurb = [0]*project_life_years\n", + "battery_life_years = 15\n", + "for i in range(battery_life_years-1, project_life_years, battery_life_years):\n", + " refurb[i] = 0.5\n", + "\n", + "# assign replacement schedule to the input config\n", + "hopp_config[\"technologies\"][\"battery\"][\"fin_model\"][\"battery_system\"][\"batt_replacement_schedule_percent\"] = refurb\n", + "\n", + "# set battery om per kw including per kwh cost - om per kwh not included internally\n", + "battery_rating_kw = hopp_config[\"technologies\"][\"battery\"][\"system_capacity_kw\"]\n", + "battery_rating_kwh = hopp_config[\"technologies\"][\"battery\"][\"system_capacity_kwh\"]\n", + "batt_om_per_kwh = hopp_config[\"config\"][\"cost_info\"][\"battery_om_per_kwh\"]\n", + "batt_om_per_kw = hopp_config[\"config\"][\"cost_info\"][\"battery_om_per_kw\"]\n", + "\n", + "# this is how to include om per kwh capacity costs\n", + "total_batt_om_per_kw = (battery_rating_kw*batt_om_per_kw + battery_rating_kwh*batt_om_per_kwh)/battery_rating_kw\n", + "hopp_config[\"config\"][\"cost_info\"][\"battery_om_per_kw\"] = total_batt_om_per_kw\n", + "\n", + "# remove om per kwh input because it is an invalid input to HOPP\n", + "hopp_config[\"config\"][\"cost_info\"].pop(\"battery_om_per_kwh\")\n", + "\n", + "# set SiteInfo instance\n", + "hopp_config[\"site\"] = site" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the Simulation Model\n", + "Instantiate the `HoppInterface` class by providing our modified configuration dict." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "hi = HoppInterface(hopp_config)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "hi.system.battery.dispatch.charge_efficiency\n", + "\n", + "# Note: HOPP LDES does not currently account for self discharge\n", + "\n", + "# set round trip efficiency\n", + "hi.system.battery.dispatch.round_trip_efficiency = 80.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the Simulation\n", + "Simulate the hybrid renewable energy system for a specified number of years (in this case, 25 years)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "hi.simulate(project_life=project_life_years)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Print Simulation Results\n", + "Access and display various simulation results, including annual energies, net present values (NPVs), and total revenues." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annual Energies (kWh):\n", + "{\"pv\": 11234203.208755787, \"wind\": 80310545.9331664, \"battery\": -1214.3240999997406, \"hybrid\": 91543534.81782195}\n", + "\n", + " Percentage of timesteps the load is met:\n", + "78.52739726027397\n", + "\n", + " Total Missed Load:\n", + "417274074.2328715 kWh\n", + "\n", + " Percentage of the load that is missed:\n", + "19.053610695564906\n" + ] + } + ], + "source": [ + "hybrid_plant = hi.system\n", + "\n", + "print(\"Annual Energies (kWh):\")\n", + "print(hybrid_plant.annual_energies)\n", + "\n", + "print(\"\\n Percentage of timesteps the load is met:\")\n", + "print(hybrid_plant.grid.time_load_met)\n", + "\n", + "print(\"\\n Total Missed Load:\")\n", + "print(sum(hybrid_plant.grid.missed_load), \"kWh\")\n", + "\n", + "print(\"\\n Percentage of the load that is missed:\")\n", + "print(hybrid_plant.grid.missed_load_percentage)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize Simulation Results (Optional)\n", + "Optionally, you can visualize the simulation results using plots. Several functions are provided for plotting battery output, generation profiles, and dispatch errors." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_generation_profile(hybrid_plant)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hopp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/inputs/11-load-following-long-duration-energy-storage.yaml b/examples/inputs/11-load-following-long-duration-energy-storage.yaml new file mode 100644 index 000000000..a972f4ec0 --- /dev/null +++ b/examples/inputs/11-load-following-long-duration-energy-storage.yaml @@ -0,0 +1,34 @@ +technologies: + pv: + system_capacity_kw: 5000 + fin_model: !include default_fin_config_ldes.yaml + wind: + num_turbines: 5 + turbine_rating_kw: 5000 + fin_model: !include default_fin_config_ldes.yaml + battery: # VRDB + system_capacity_kwh: 100000 + system_capacity_kw: 10000 + minimum_SOC: 20.0 + maximum_SOC: 100.0 + initial_SOC: 90.0 + system_model_source: "hopp" + chemistry: "LDES" + fin_model: !include default_fin_config_ldes.yaml + grid: + interconnect_kw: 100000 + fin_model: !include default_fin_config_ldes.yaml + +config: + dispatch_options: + battery_dispatch: simple + solver: cbc + n_look_ahead_periods: 48 + grid_charging: true + pv_charging_only: false + include_lifecycle_count: false # assuming that flow batteries have no degradation based on cycles + cost_info: # based on table 6 of "A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration" + storage_installed_cost_mwh: 500000 + storage_installed_cost_mw: 1000000 + battery_om_per_kw: 40.0 + battery_om_per_kwh: 0.0 # this value is here as a place holder for the sake of the example diff --git a/examples/inputs/default_fin_config_ldes.yaml b/examples/inputs/default_fin_config_ldes.yaml new file mode 100644 index 000000000..1ed2a7813 --- /dev/null +++ b/examples/inputs/default_fin_config_ldes.yaml @@ -0,0 +1,49 @@ +battery_system: + batt_replacement_schedule_percent: + - 0 + batt_bank_replacement: + - 0 + batt_replacement_option: 0 + batt_computed_bank_capacity: 0 + batt_meter_position: 0 +system_costs: + om_fixed: + - 0 + om_production: + - 0 + om_capacity: + - 0 + om_batt_fixed_cost: 42.888 + om_batt_variable_cost: + - 0 + om_batt_capacity_cost: 86.559 + om_batt_replacement_cost: 0 + om_replacement_cost_escal: 0 +system_use_lifetime_output: 0 +financial_parameters: + inflation_rate: 2.5 + real_discount_rate: 6.4 + federal_tax_rate: 21.0 + state_tax_rate: 4.4 + property_tax_rate: 1.0 + insurance_rate: 0.5 + debt_percent: 68.5 + term_int_rate: 6.0 + months_working_reserve: 1 + analysis_start_year: 2025 + installation_months: 12 # None + sales_tax_rate_state: 4.5 # none + admin_expense_percent_of_sales: 1.0 #none + capital_gains_tax_rate: 15.0 # none + debt_type: "Revolving debt" # none + depreciation_method: "MACRS" # none handled differently + depreciation_period: 5 # none - handled differently + +cp_capacity_credit_percent: + - 0 +degradation: + - 0 +revenue: + ppa_price_input: + - 0.01 + ppa_escalation: 1 \ No newline at end of file diff --git a/hopp/simulation/hybrid_simulation.py b/hopp/simulation/hybrid_simulation.py index eef290a5b..0e0cf40db 100644 --- a/hopp/simulation/hybrid_simulation.py +++ b/hopp/simulation/hybrid_simulation.py @@ -585,6 +585,7 @@ def set_average_for_hybrid(var_name, weight_factor=None, min_val=None, max_val=N if max_val is not None: hybrid_avg = min(max_val, hybrid_avg) self.grid.value(var_name, hybrid_avg) + return hybrid_avg def set_logical_or_for_hybrid(var_name): @@ -664,6 +665,9 @@ def set_logical_or_for_hybrid(var_name): # Degradation of energy output year after year set_average_for_hybrid("degradation", non_storage_production_ratio) + # Battery replacement schedule + set_average_for_hybrid("batt_replacement_schedule_percent", cost_ratios) + if self.battery: self.grid._financial_model.value('om_batt_replacement_cost', self.battery._financial_model.value('om_batt_replacement_cost')) @@ -764,10 +768,15 @@ def simulate_financials(self, project_life): if self.battery: # Copy over battery replacement information if isinstance(self.battery._financial_model, Singleowner.Singleowner): - self.grid.assign(self.battery._financial_model.BatterySystem.export()) + dict_for_assign = self.battery._financial_model.BatterySystem.export() + self.grid.assign(dict_for_assign) else: try: - self.grid.assign(self.battery._financial_model.export_battery_values()) + dict_for_assign = self.battery._financial_model.export_battery_values() + # do not copy over battery values that were averaged in `calculate_financials` if using CustomFinancial model + if self.battery.value("batt_replacement_option") == 2: + dict_for_assign.pop("batt_replacement_schedule_percent") + self.grid.assign(dict_for_assign) except: raise NotImplementedError("Financial model cannot assign battery values.") diff --git a/hopp/simulation/technologies/battery/battery.py b/hopp/simulation/technologies/battery/battery.py index 12945da7e..280cd8298 100644 --- a/hopp/simulation/technologies/battery/battery.py +++ b/hopp/simulation/technologies/battery/battery.py @@ -2,13 +2,16 @@ from typing import Optional, Sequence, List, Union import numpy as np import pandas as pd +import math + from attrs import define, field -import PySAM.BatteryStateful as BatteryModel +import PySAM.BatteryStateful as PySAMBatteryModel import PySAM.BatteryTools as BatteryTools import PySAM.Singleowner as Singleowner from hopp.simulation.base import BaseClass from hopp.simulation.technologies.financial import FinancialModelType, CustomFinancialModel +from hopp.simulation.technologies.ldes.ldes_system_model import LDES from hopp.simulation.technologies.power_source import PowerSource from hopp.simulation.technologies.sites.site_info import SiteInfo @@ -16,7 +19,6 @@ from hopp.utilities.log import hybrid_logger as logger from hopp.utilities.validators import contains, gt_zero, range_val - @dataclass class BatteryOutputs: I: Sequence @@ -74,16 +76,16 @@ class BatteryConfig(BaseClass): tracking: default True -> `Battery` system_capacity_kwh: Battery energy capacity [kWh] system_capacity_kw: Battery rated power capacity [kW] + system_model_source: software source for the system model, can by 'pysam' or 'hopp' chemistry: Battery chemistry option - - "LFPGraphite" (default) - - - "LMOLTO" - - - "LeadAcid" - - - "NMCGraphite" - + PySAM options: + - "LFPGraphite" (default) + - "LMOLTO" + - "LeadAcid" + - "NMCGraphite" + HOPP options: + - "LDES" generic long-duration energy storage minimum_SOC: Minimum state of charge [%] maximum_SOC: Maximum state of charge [%] initial_SOC: Initial state of charge [%] @@ -93,7 +95,8 @@ class BatteryConfig(BaseClass): """ system_capacity_kwh: float = field(validator=gt_zero) system_capacity_kw: float = field(validator=gt_zero) - chemistry: str = field(default="LFPGraphite", validator=contains(["LFPGraphite", "LMOLTO", "LeadAcid", "NMCGraphite"])) + system_model_source: str = field(default="pysam", validator=contains(["pysam", "hopp"])) + chemistry: str = field(default="LFPGraphite", validator=contains(["LFPGraphite", "LMOLTO", "LeadAcid", "NMCGraphite", "LDES"])) tracking: bool = field(default=True) minimum_SOC: float = field(default=10, validator=range_val(0, 100)) maximum_SOC: float = field(default=90, validator=range_val(0, 100)) @@ -122,7 +125,13 @@ class Battery(PowerSource): def __attrs_post_init__(self): """ """ - system_model = BatteryModel.default(self.config.chemistry) + if self.config.system_model_source == "pysam": + system_model = PySAMBatteryModel.default(self.config.chemistry) + elif self.config.system_model_source == "hopp": + system_model = LDES(self.config, self.site) + self.cycle_count = 0.0 + else: + raise(ValueError("Invalid value for battery system_model_source, must be one of ['pysam', 'hopp']")) if isinstance(self.config.fin_model, dict): financial_model = CustomFinancialModel(self.config.fin_model, name=self.config.name) @@ -130,29 +139,37 @@ def __attrs_post_init__(self): financial_model = self.config.fin_model if financial_model is None: + if self.config.system_model_source == "hopp": + raise(ValueError("Must specify an input dict for the custom financial model when using 'hopp' as the `system_model_source`")) # default - financial_model = Singleowner.from_existing(system_model, self.config_name) + else: + financial_model = Singleowner.from_existing(system_model, self.config_name) else: financial_model = self.import_financial_model(financial_model, system_model, self.config_name) super().__init__("Battery", self.site, system_model, financial_model) self.outputs = BatteryOutputs(n_timesteps=self.site.n_timesteps, n_periods_per_day=self.site.n_periods_per_day) + self.system_capacity_kw = self.config.system_capacity_kw + self.chemistry = self.config.chemistry - BatteryTools.battery_model_sizing(self._system_model, + if self.config.system_model_source == "pysam": + BatteryTools.battery_model_sizing(self._system_model, self.config.system_capacity_kw, self.config.system_capacity_kwh, self.system_voltage_volts, module_specs=Battery.module_specs) - self._system_model.ParamsPack.h = 20 - self._system_model.ParamsPack.Cp = 900 - self._system_model.ParamsCell.resistance = 0.001 - self._system_model.ParamsCell.C_rate = self.config.system_capacity_kw / self.config.system_capacity_kwh + self._system_model.ParamsPack.h = 20 + self._system_model.ParamsPack.Cp = 900 + self._system_model.ParamsCell.resistance = 0.001 + self._system_model.ParamsCell.C_rate = self.config.system_capacity_kw / self.config.system_capacity_kwh + # Minimum set of parameters to set to get statefulBattery to work self._system_model.value("control_mode", 0.0) self._system_model.value("input_current", 0.0) + self._system_model.value("dt_hr", 1.0) self._system_model.value("minimum_SOC", self.config.minimum_SOC) self._system_model.value("maximum_SOC", self.config.maximum_SOC) @@ -162,6 +179,8 @@ def __attrs_post_init__(self): logger.info("Initialized battery with parameters and state {}".format(self._system_model.export())) + + def setup_system_model(self): """Executes Stateful Battery setup""" self._system_model.setup() @@ -169,7 +188,11 @@ def setup_system_model(self): @property def system_capacity_voltage(self) -> tuple: """Battery energy capacity [kWh] and voltage [VDC]""" - return self._system_model.ParamsPack.nominal_energy, self._system_model.ParamsPack.nominal_voltage + + if self.config.system_model_source == "pysam": + return self._system_model.ParamsPack.nominal_energy, self._system_model.ParamsPack.nominal_voltage + elif self.config.system_model_source == "hopp": + return self._system_model.params.nominal_energy, self._system_model.params.nominal_voltage @system_capacity_voltage.setter def system_capacity_voltage(self, capacity_voltage: tuple): @@ -180,18 +203,27 @@ def system_capacity_voltage(self, capacity_voltage: tuple): if size_kwh == 0: size_kwh = 1e-7 - BatteryTools.battery_model_sizing(self._system_model, - 0., - size_kwh, - voltage_volts, - module_specs=Battery.module_specs) + if self.config.system_model_source == "pysam": + BatteryTools.battery_model_sizing(self._system_model, + 0., + size_kwh, + voltage_volts, + module_specs=Battery.module_specs) + else: + self._system_model.params.nominal_voltage = voltage_volts + self._system_model.params.nominal_energy = size_kwh + logger.info("Battery set system_capacity to {} kWh".format(size_kwh)) logger.info("Battery set system_voltage to {} volts".format(voltage_volts)) @property def system_capacity_kwh(self) -> float: """Battery energy capacity [kWh]""" - return self._system_model.ParamsPack.nominal_energy + + if self.config.system_model_source == "pysam": + return self._system_model.ParamsPack.nominal_energy + else: + return self._system_model.params.nominal_energy @system_capacity_kwh.setter def system_capacity_kwh(self, size_kwh: float): @@ -210,7 +242,10 @@ def system_capacity_kw(self, size_kw: float): @property def system_voltage_volts(self) -> float: """Battery bank voltage [VDC]""" - return self._system_model.ParamsPack.nominal_voltage + + if self.config.system_model_source == "pysam": + return self._system_model.ParamsPack.nominal_voltage + # not in LDES/HOPP yet, but could add? May need more specifics about the physics/chemistry @system_voltage_volts.setter def system_voltage_volts(self, voltage_volts: float): @@ -219,7 +254,12 @@ def system_voltage_volts(self, voltage_volts: float): @property def chemistry(self) -> str: """Battery chemistry type""" - model_type = self._system_model.ParamsCell.chem + + if self.config.system_model_source == "pysam": + model_type = self._system_model.ParamsCell.chem + elif self.config.system_model_source == "hopp": + model_type = self._system_model.model_type() #TODO revisit this + if model_type == 0 or model_type == 1: return self._chemistry else: @@ -228,15 +268,20 @@ def chemistry(self) -> str: @property def system_mass(self) -> float: """Battery bank mass [kg]""" - return self._system_model.ParamsPack.mass + + if self.config.system_model_source == "pysam": + return self._system_model.ParamsPack.mass @property def footprint_area(self) -> float: """Battery bank footprint area [m^2]""" - #Battery thermal model assumes a cube for heat exchange - cube_surface_area = self._system_model.ParamsPack.surface_area - footprint = cube_surface_area / 6 # Single side of cube - return footprint + + if self.config.system_model_source == "pysam": + #Battery thermal model assumes a cube for heat exchange + cube_surface_area = self._system_model.ParamsPack.surface_area + footprint = cube_surface_area / 6 # Single side of cube + return footprint + # TODO should be added for individual LDES storage types @property def energy_capital_cost(self) -> Union[float, int]: @@ -259,13 +304,18 @@ def chemistry(self, battery_chemistry: str): - `LeadAcid`: Lead Acid - `NMCGraphite`: Nickel Manganese Cobalt Oxide (Lithium Ion) """ - BatteryTools.battery_model_change_chemistry(self._system_model, battery_chemistry) + + if self.config.system_model_source == "pysam": + BatteryTools.battery_model_change_chemistry(self._system_model, battery_chemistry) + # TODO update LDES in this setter self._chemistry = battery_chemistry logger.info("Battery chemistry set to {}".format(battery_chemistry)) def setup_performance_model(self): """Executes Stateful Battery setup""" - self._system_model.setup() + + if self.config.system_model_source == "pysam": + self._system_model.setup() def simulate_with_dispatch(self, n_periods: int, sim_start_time: Optional[int] = None): """ @@ -300,12 +350,17 @@ def simulate_with_dispatch(self, n_periods: int, sim_start_time: Optional[int] = self.simulate_power(time_step=index_time_step) + if self.config.system_model_source == "hopp" and self.dispatch.options.include_lifecycle_count: + self.cycle_count += self.dispatch.lifecycles[0] + # Store Dispatch model values if sim_start_time is not None: time_slice = slice(sim_start_time, sim_start_time + n_periods) self.outputs.dispatch_SOC[time_slice] = self.dispatch.soc[0:n_periods] self.outputs.dispatch_P[time_slice] = self.dispatch.power[0:n_periods] self.outputs.dispatch_I[time_slice] = self.dispatch.current[0:n_periods] + if self.config.system_model_source == "hopp" and self.dispatch.options.include_lifecycle_count: + self.outputs.n_cycles[time_slice] = [math.floor(self.cycle_count)]*int(n_periods) if self.dispatch.options.include_lifecycle_count: days_in_period = n_periods // (self.site.n_periods_per_day) start_day = sim_start_time // self.site.n_periods_per_day @@ -323,6 +378,7 @@ def simulate_power(self, time_step=None): """ if not self._system_model: return + self._system_model.execute(0) if time_step is not None: @@ -335,11 +391,18 @@ def update_battery_stored_values(self, time_step): Args: time_step: time step where outputs will be stored. """ - for attr in self.outputs.stateful_attributes: - if hasattr(self._system_model.StatePack, attr) or hasattr(self._system_model.StateCell, attr): - getattr(self.outputs, attr)[time_step] = self.value(attr) + for attr in self.outputs.stateful_attributes: # TODO include State in LDES model + if self.config.system_model_source == "pysam": + if hasattr(self._system_model.StatePack, attr) or hasattr(self._system_model.StateCell, attr): + getattr(self.outputs, attr)[time_step] = self.value(attr) + elif attr == 'gen': + getattr(self.outputs, attr)[time_step] = self.value('P') else: - if attr == 'gen': + if hasattr(self._system_model.state, attr): + getattr(self.outputs, attr)[time_step] = self.value(attr) + if attr == 'n_cycles' and self.dispatch.options.include_lifecycle_count: + getattr(self.outputs, attr)[time_step] = math.floor(self.dispatch.lifecycles[0]) + elif attr == 'gen': getattr(self.outputs, attr)[time_step] = self.value('P') def validate_replacement_inputs(self, project_life): @@ -356,14 +419,17 @@ def validate_replacement_inputs(self, project_life): self._financial_model.value('batt_bank_replacement', [0] * (project_life + 1)) if self._financial_model.value('batt_replacement_option') == 2: - if len(self._financial_model.value('batt_replacement_schedule_percent')) != project_life: + if (len(self._financial_model.value('batt_replacement_schedule_percent')) != project_life): raise ValueError(f"Error in Battery model: `batt_replacement_schedule_percent` should be length of project_life {project_life} but is instead {len(self._financial_model.value('batt_replacement_schedule_percent'))}") if len(self._financial_model.value('batt_bank_replacement')) != project_life + 1: - if len(self._financial_model.value('batt_bank_replacement')) == project_life: - # likely an input mistake: add a zero for financial year 0 - self._financial_model.value('batt_bank_replacement', [0] + list(self._financial_model.value('batt_bank_replacement'))) - else: - raise ValueError(f"Error in Battery model: `batt_bank_replacement` should be length of project_life {project_life} but is instead {len(self._financial_model.value('batt_bank_replacement'))}") + + # batt_replacement_option has different behavior in hopp vs in pysam models. The following only applies when using pysam financials version + if (type(self._financial_model) is not CustomFinancialModel): + if len(self._financial_model.value('batt_bank_replacement')) == project_life: + # likely an input mistake: add a zero for financial year 0 + self._financial_model.value('batt_bank_replacement', [0] + list(self._financial_model.value('batt_bank_replacement'))) + else: + raise ValueError(f"Error in Battery model: `batt_bank_replacement` should be length of project_life {project_life} but is instead {len(self._financial_model.value('batt_bank_replacement'))}") def set_overnight_capital_cost(self, energy_capital_cost, power_capital_cost): """Set overnight capital costs [$/kW]. diff --git a/hopp/simulation/technologies/dispatch/plot_tools.py b/hopp/simulation/technologies/dispatch/plot_tools.py index 710477b4a..49037a332 100644 --- a/hopp/simulation/technologies/dispatch/plot_tools.py +++ b/hopp/simulation/technologies/dispatch/plot_tools.py @@ -252,7 +252,9 @@ def plot_generation_profile(hybrid: HybridSimulation, charge_color='r', gen_color='g', price_color='r', - show_price=True + show_price=True, + show_load=False, + super_title=None, ): if not hasattr(hybrid, 'dispatch_builder'): @@ -267,6 +269,7 @@ def plot_generation_profile(hybrid: HybridSimulation, # First sub-plot (resources) gen = [p * power_scale for p in list(hybrid.grid.generation_profile[time_slice])] + des_sched = [p for p in list(hybrid.site.desired_schedule[time_slice])] original_gen = [0]*len(gen) plt.subplot(3, 1, 1) if hybrid.pv: @@ -294,32 +297,40 @@ def plot_generation_profile(hybrid: HybridSimulation, plt.legend(fontsize=font_size-2, loc='upper left') # Battery action - plt.subplot(3, 1, 2) - plt.tick_params(which='both', labelsize=font_size) - discharge = [(p > 0) * p * power_scale for p in hybrid.battery.outputs.P[time_slice]] - charge = [(p < 0) * p * power_scale for p in hybrid.battery.outputs.P[time_slice]] - plt.bar(time, discharge, width=0.9, color=discharge_color, edgecolor='white', label='Battery Discharge') - plt.bar(time, charge, width=0.9, color=charge_color, edgecolor='white', label='Battery Charge') - plt.xlim([start, end]) - ax = plt.gca() - ax.xaxis.set_ticks(list(range(start, end, hybrid.site.n_periods_per_day))) - plt.grid() - ax1 = plt.gca() - ax1.legend(fontsize=font_size-2, loc='upper left') - ax1.set_ylabel('Power (MW)', fontsize=font_size) + ax = plt.subplot(3, 1, 2) + if "battery" in hybrid.technologies: + plt.tick_params(which='both', labelsize=font_size) + discharge = [(p > 0) * p * power_scale for p in hybrid.battery.outputs.P[time_slice]] + charge = [(p < 0) * p * power_scale for p in hybrid.battery.outputs.P[time_slice]] + plt.bar(time, discharge, width=0.9, color=discharge_color, edgecolor='white', label='Battery Discharge') + plt.bar(time, charge, width=0.9, color=charge_color, edgecolor='white', label='Battery Charge') + plt.xlim([start, end]) + ax.xaxis.set_ticks(list(range(start, end, hybrid.site.n_periods_per_day))) + plt.grid() + ax1 = plt.gca() + ax1.legend(fontsize=font_size-2, loc='upper left') + ax1.set_ylabel('Power (MW)', fontsize=font_size) - ax2 = ax1.twinx() - ax2.plot(time, hybrid.battery.outputs.SOC[time_slice], 'k', label='State-of-Charge') - ax2.plot(time, hybrid.battery.outputs.dispatch_SOC[time_slice], '.', label='Dispatch') - ax2.set_ylabel('State-of-Charge (-)', fontsize=font_size) - ax2.legend(fontsize=font_size-2, loc='upper right') - plt.title('Battery Power Flow', fontsize=font_size) + ax2 = ax1.twinx() + ax2.plot(time, hybrid.battery.outputs.SOC[time_slice], 'k', label='State-of-Charge') + ax2.plot(time, hybrid.battery.outputs.dispatch_SOC[time_slice], '.', label='Dispatch') + ax2.set_ylabel('State-of-Charge (-)', fontsize=font_size) + ax2.legend(fontsize=font_size-2, loc='upper right') + plt.title('Battery Power Flow', fontsize=font_size) + else: + # Turn off the axes + ax.set_axis_off() + + # Add text to the axes + ax.text(0.5, 0.5, "No battery in simulation", ha='center', va='center', fontsize=12) # Net action plt.subplot(3, 1, 3) plt.tick_params(which='both', labelsize=font_size) plt.plot(time, original_gen, 'k--', label='Original Generation') plt.plot(time, gen, color=gen_color, label='Optimized Dispatch') + if show_load: + plt.plot(time, des_sched, 'k:', label="Desired Schedule (kW)") plt.xlim([start, end]) ax = plt.gca() ax.xaxis.set_ticks(list(range(start, end, hybrid.site.n_periods_per_day))) @@ -338,6 +349,9 @@ def plot_generation_profile(hybrid: HybridSimulation, plt.xlabel('Time (hours)', fontsize=font_size) plt.title('Net Generation', fontsize=font_size) + if super_title is not None: + plt.suptitle(super_title) + plt.tight_layout() if plot_filename is not None: diff --git a/hopp/simulation/technologies/dispatch/power_storage/heuristic_load_following_dispatch.py b/hopp/simulation/technologies/dispatch/power_storage/heuristic_load_following_dispatch.py index 7f9ead6be..ebb006c0e 100644 --- a/hopp/simulation/technologies/dispatch/power_storage/heuristic_load_following_dispatch.py +++ b/hopp/simulation/technologies/dispatch/power_storage/heuristic_load_following_dispatch.py @@ -2,7 +2,7 @@ import pyomo.environ as pyomo from pyomo.environ import units as u -import PySAM.BatteryStateful as BatteryModel +import PySAM.BatteryStateful as PySAMBatteryModel import PySAM.Singleowner as Singleowner from hopp.simulation.technologies.dispatch.power_storage.simple_battery_dispatch_heuristic import ( @@ -22,7 +22,7 @@ def __init__( self, pyomo_model: pyomo.ConcreteModel, index_set: pyomo.Set, - system_model: BatteryModel.BatteryStateful, + system_model: PySAMBatteryModel.BatteryStateful, financial_model: Singleowner.Singleowner, fixed_dispatch: Optional[List] = None, block_set_name: str = "heuristic_load_following_battery", @@ -33,7 +33,7 @@ def __init__( Args: pyomo_model (pyomo.ConcreteModel): Pyomo concrete model. index_set (pyomo.Set): Indexed set. - system_model (BatteryModel.BatteryStateful): System model. + system_model (PySAMBatteryModel.BatteryStateful): System model. financial_model (Singleowner.Singleowner): Financial model. fixed_dispatch (Optional[List], optional): List of normalized values [-1, 1] (Charging (-), Discharging (+)). Defaults to None. block_set_name (str, optional): Name of the block set. Defaults to 'heuristic_load_following_battery'. diff --git a/hopp/simulation/technologies/dispatch/power_storage/linear_voltage_convex_battery_dispatch.py b/hopp/simulation/technologies/dispatch/power_storage/linear_voltage_convex_battery_dispatch.py index 4c8e9ddbe..bcbeaa5b8 100644 --- a/hopp/simulation/technologies/dispatch/power_storage/linear_voltage_convex_battery_dispatch.py +++ b/hopp/simulation/technologies/dispatch/power_storage/linear_voltage_convex_battery_dispatch.py @@ -1,7 +1,7 @@ import pyomo.environ as pyomo from pyomo.environ import units as u -import PySAM.BatteryStateful as BatteryModel +import PySAM.BatteryStateful as PySAMBatteryModel import PySAM.Singleowner as Singleowner from hopp.simulation.technologies.dispatch.power_storage.linear_voltage_nonconvex_battery_dispatch import ( @@ -22,7 +22,7 @@ def __init__( self, pyomo_model: pyomo.ConcreteModel, index_set: pyomo.Set, - system_model: BatteryModel.BatteryStateful, + system_model: PySAMBatteryModel.BatteryStateful, financial_model: Singleowner.Singleowner, block_set_name: str = "convex_LV_battery", dispatch_options: dict = None, @@ -33,7 +33,7 @@ def __init__( Args: pyomo_model (pyomo.ConcreteModel): Pyomo concrete model. index_set (pyomo.Set): Indexed set. - system_model (BatteryModel.BatteryStateful): Battery system model. + system_model (PySAMBatteryModel.BatteryStateful): Battery system model. financial_model (Singleowner.Singleowner): Financial model. block_set_name (str, optional): Name of the block set. Defaults to 'convex_LV_battery'. dispatch_options (dict, optional): Dispatch options. Defaults to None. diff --git a/hopp/simulation/technologies/dispatch/power_storage/linear_voltage_nonconvex_battery_dispatch.py b/hopp/simulation/technologies/dispatch/power_storage/linear_voltage_nonconvex_battery_dispatch.py index fccbe7904..b2b5ef00d 100644 --- a/hopp/simulation/technologies/dispatch/power_storage/linear_voltage_nonconvex_battery_dispatch.py +++ b/hopp/simulation/technologies/dispatch/power_storage/linear_voltage_nonconvex_battery_dispatch.py @@ -1,7 +1,7 @@ import pyomo.environ as pyomo from pyomo.environ import units as u -import PySAM.BatteryStateful as BatteryModel +import PySAM.BatteryStateful as PySAMBatteryModel import PySAM.Singleowner as Singleowner from hopp.simulation.technologies.dispatch.power_storage.simple_battery_dispatch import ( @@ -22,7 +22,7 @@ def __init__( self, pyomo_model: pyomo.ConcreteModel, index_set: pyomo.Set, - system_model: BatteryModel.BatteryStateful, + system_model: PySAMBatteryModel.BatteryStateful, financial_model: Singleowner.Singleowner, block_set_name: str = "LV_battery", dispatch_options: dict = None, @@ -33,7 +33,7 @@ def __init__( Args: pyomo_model (pyomo.ConcreteModel): Pyomo concrete model. index_set (pyomo.Set): Indexed set. - system_model (BatteryModel.BatteryStateful): Battery system model. + system_model (PySAMBatteryModel.BatteryStateful): Battery system model. financial_model (Singleowner.Singleowner): Financial model. block_set_name (str, optional): Name of the block set. Defaults to 'LV_battery'. dispatch_options (dict, optional): Dispatch options. Defaults to None. diff --git a/hopp/simulation/technologies/dispatch/power_storage/one_cycle_battery_dispatch_heuristic.py b/hopp/simulation/technologies/dispatch/power_storage/one_cycle_battery_dispatch_heuristic.py index 78a7fee52..2fdea97e9 100644 --- a/hopp/simulation/technologies/dispatch/power_storage/one_cycle_battery_dispatch_heuristic.py +++ b/hopp/simulation/technologies/dispatch/power_storage/one_cycle_battery_dispatch_heuristic.py @@ -2,7 +2,7 @@ import pyomo.environ as pyomo from pyomo.environ import units as u -import PySAM.BatteryStateful as BatteryModel +import PySAM.BatteryStateful as PySAMBatteryModel import PySAM.Singleowner as Singleowner from hopp.simulation.technologies.dispatch.power_storage.simple_battery_dispatch_heuristic import ( @@ -17,7 +17,7 @@ def __init__( self, pyomo_model: pyomo.ConcreteModel, index_set: pyomo.Set, - system_model: BatteryModel.BatteryStateful, + system_model: PySAMBatteryModel.BatteryStateful, financial_model: Singleowner.Singleowner, block_set_name: str = "one_cycle_heuristic_battery", dispatch_options: dict = None, @@ -27,7 +27,7 @@ def __init__( Args: pyomo_model (pyomo.ConcreteModel): Pyomo concrete model. index_set (pyomo.Set): Indexed set. - system_model (BatteryModel.BatteryStateful): Battery system model. + system_model (PySAMBatteryModel.BatteryStateful): Battery system model. financial_model (Singleowner.Singleowner): Financial model. block_set_name (str, optional):Name of the block set. Defaults to 'one_cycle_heuristic_battery'. dispatch_options (dict, optional): Dispatch options. Defaults to None. diff --git a/hopp/simulation/technologies/dispatch/power_storage/power_storage_dispatch.py b/hopp/simulation/technologies/dispatch/power_storage/power_storage_dispatch.py index cb72b49ed..7cf9a7b0d 100644 --- a/hopp/simulation/technologies/dispatch/power_storage/power_storage_dispatch.py +++ b/hopp/simulation/technologies/dispatch/power_storage/power_storage_dispatch.py @@ -111,7 +111,7 @@ def min_operating_cost_objective(self, hybrid_blocks): for t in self.blocks.index_set() ) if self.options.include_lifecycle_count: - objective += self.model.lifecycle_cost * self.model.lifecycles + objective += self.model.lifecycle_cost * sum(self.model.lifecycles) self.obj = objective diff --git a/hopp/simulation/technologies/dispatch/power_storage/simple_battery_dispatch.py b/hopp/simulation/technologies/dispatch/power_storage/simple_battery_dispatch.py index 6587ebf85..2bfa70655 100644 --- a/hopp/simulation/technologies/dispatch/power_storage/simple_battery_dispatch.py +++ b/hopp/simulation/technologies/dispatch/power_storage/simple_battery_dispatch.py @@ -1,13 +1,15 @@ import pyomo.environ as pyomo from pyomo.environ import units as u -import PySAM.BatteryStateful as BatteryModel +import PySAM.BatteryStateful as PySAMBatteryModel +import hopp.simulation.technologies.ldes.ldes_system_model as ldes from hopp.simulation.technologies.dispatch.power_storage.power_storage_dispatch import ( PowerStorageDispatch, ) from hopp.simulation.technologies.financial import FinancialModelType +from typing import Union class SimpleBatteryDispatch(PowerStorageDispatch): """A dispatch class for simple battery operations.""" @@ -16,7 +18,7 @@ def __init__( self, pyomo_model: pyomo.ConcreteModel, index_set: pyomo.Set, - system_model: BatteryModel.BatteryStateful, + system_model: PySAMBatteryModel.BatteryStateful, financial_model: FinancialModelType, block_set_name: str, dispatch_options, @@ -26,7 +28,7 @@ def __init__( Args: pyomo_model (pyomo.ConcreteModel): The Pyomo model instance. index_set (pyomo.Set): The Pyomo index set. - system_model (BatteryModel.BatteryStateful): The battery stateful model. + system_model (PySAMBatteryModel.BatteryStateful): The battery stateful model. financial_model (FinancialModelType): The financial model type. block_set_name (str): Name of the block set. dispatch_options: Dispatch options. @@ -68,7 +70,7 @@ def initialize_parameters(self): def _set_control_mode(self): """Sets control mode.""" - if isinstance(self._system_model, BatteryModel.BatteryStateful): + if isinstance(self._system_model, Union[PySAMBatteryModel.BatteryStateful, ldes.LDES]): self._system_model.value("control_mode", 1.0) # Power control self._system_model.value("input_power", 0.0) self.control_variable = "input_power" diff --git a/hopp/simulation/technologies/dispatch/power_storage/simple_battery_dispatch_heuristic.py b/hopp/simulation/technologies/dispatch/power_storage/simple_battery_dispatch_heuristic.py index 7b2c2ba19..243d4b7ad 100644 --- a/hopp/simulation/technologies/dispatch/power_storage/simple_battery_dispatch_heuristic.py +++ b/hopp/simulation/technologies/dispatch/power_storage/simple_battery_dispatch_heuristic.py @@ -3,7 +3,7 @@ import pyomo.environ as pyomo from pyomo.environ import units as u -import PySAM.BatteryStateful as BatteryModel +import PySAM.BatteryStateful as PySAMBatteryModel import PySAM.Singleowner as Singleowner from hopp.simulation.technologies.dispatch.power_storage.simple_battery_dispatch import ( @@ -22,7 +22,7 @@ def __init__( self, pyomo_model: pyomo.ConcreteModel, index_set: pyomo.Set, - system_model: BatteryModel.BatteryStateful, + system_model: PySAMBatteryModel.BatteryStateful, financial_model: Singleowner.Singleowner, fixed_dispatch: Optional[List] = None, block_set_name: str = "heuristic_battery", @@ -33,7 +33,7 @@ def __init__( Args: pyomo_model (pyomo.ConcreteModel): Pyomo concrete model. index_set (pyomo.Set): Indexed set. - system_model (BatteryModel.BatteryStateful): Battery system model. + system_model (PySAMBatteryModel.BatteryStateful): Battery system model. financial_model (Singleowner.Singleowner): Financial model. fixed_dispatch (Optional[List], optional): List of normalized values [-1, 1] (Charging (-), Discharging (+)). Defaults to None. block_set_name (str, optional): Name of block set. Defaults to 'heuristic_battery'. diff --git a/hopp/simulation/technologies/financial/custom_financial_model.py b/hopp/simulation/technologies/financial/custom_financial_model.py index 65684e9d4..124152ba8 100644 --- a/hopp/simulation/technologies/financial/custom_financial_model.py +++ b/hopp/simulation/technologies/financial/custom_financial_model.py @@ -45,11 +45,11 @@ class BatterySystem(FinancialData): To add any additional system cost, first see if the variable exists in Singleowner, and re-use name. This will simplify interoperability """ - batt_bank_replacement: tuple = field(default=[0]) + batt_bank_replacement: tuple = field(default=[0]) # Battery bank replacements per year [number/year] batt_computed_bank_capacity: tuple = field(default=0) - batt_meter_position: tuple = field(default=0) - batt_replacement_option: float = field(default=0) - batt_replacement_schedule_percent: tuple = field(default=[0]) + batt_meter_position: tuple = field(default=0) # Options: 0=BehindTheMeter,1=FrontOfMeter - not used in custom fin model + batt_replacement_option: float = field(default=0) # [0=none,1=capacity based,2=user schedule] + batt_replacement_schedule_percent: tuple = field(default=[0]) # required if batt_replacement_option is 2, Percentage of battery capacity to replace in each year [%]. For H2I, it is the percent of initial capex replacement cost in each year @define @@ -60,8 +60,8 @@ class SystemCosts(FinancialData): om_batt_fixed_cost: float = field(default=0) om_batt_variable_cost: float = field(default=[0]) om_batt_capacity_cost: float = field(default=0) - om_batt_replacement_cost: float = field(default=0) - om_replacement_cost_escal: float = field(default=0) + om_batt_replacement_cost: float = field(default=0) # Replacement cost 1 [$/kWh] - not used in custom fin model + om_replacement_cost_escal: float = field(default=0) # Production-based O&M escalation [%/year] - not used in custom fin model total_installed_cost: float = field(default=None) @@ -330,7 +330,7 @@ def setup_profast(self, gen_inflation) -> ProFAST.ProFAST: }, ) - if "Battery" in self.name: + if "Battery" in self.name or "LDES" in self.name: pf.set_params( "capacity", max([1E-6, self.value("batt_annual_discharge_energy")[0]/365.0]), @@ -413,14 +413,18 @@ def setup_profast(self, gen_inflation) -> ProFAST.ProFAST: ) # ----------------------------------- Add capital and fixed items to ProFAST ---------------- + + refurb = [0] + if self.BatterySystem.batt_replacement_option == 2: + refurb = list(self.BatterySystem.batt_replacement_schedule_percent) pf.add_capital_item( name="Total installed cost", cost=self.value('total_installed_cost'), depr_type=self.value('depreciation_method'), depr_period=self.value('depreciation_period'), - refurb=[0], + refurb=refurb, ) - + return pf @staticmethod diff --git a/hopp/simulation/technologies/ldes/__init__.py b/hopp/simulation/technologies/ldes/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/hopp/simulation/technologies/ldes/ldes_system_model.py b/hopp/simulation/technologies/ldes/ldes_system_model.py new file mode 100644 index 000000000..9c43cd66d --- /dev/null +++ b/hopp/simulation/technologies/ldes/ldes_system_model.py @@ -0,0 +1,293 @@ +from attrs import define, field, validators, fields_dict +import numpy as np +from typing import Optional, Union +from hopp.simulation.technologies.financial import CustomFinancialModel, FinancialModelType + +from hopp.simulation.technologies.sites.site_info import SiteInfo +from hopp.simulation.technologies.power_source import PowerSource +from hopp.utilities.validators import range_val +from hopp.simulation.base import BaseClass + +@define +class LDESConfig(BaseClass): + """ + Configuration class for `LDES`. + + Args: + tracking: default True -> `LDES` + system_capacity_kwh: LDES energy capacity [kWh] + system_capacity_kw: LDES rated power capacity [kW] + chemistry: LDES chemistry option + - "LDES" generic long-duration energy storage + minimum_SOC: Minimum state of charge [%] + maximum_SOC: Maximum state of charge [%] + initial_SOC: Initial state of charge [%] + fin_model: Financial model. Can be any of the following: + - a dict representing a `CustomFinancialModel` + - an object representing a `CustomFinancialModel` or a `Singleowner.Singleowner` instance + """ + system_capacity_kwh: float = field(validator=validators.gt(0.0)) + system_capacity_kw: float = field(validator=validators.gt(0.0)) + system_model_source: str = field(default="pysam", validator=validators.in_(["pysam", "hopp"])) + chemistry: str = field(default="LDES", validator=validators.in_(["LDES"])) + tracking: bool = field(default=True) + minimum_SOC: float = field(default=10, validator=range_val(0, 100)) + maximum_SOC: float = field(default=90, validator=range_val(0, 100)) + initial_SOC: float = field(default=10, validator=range_val(0, 100)) + fin_model: Optional[Union[str, dict, FinancialModelType]] = field(default=None) + name: str = field(default="LDES") + +@define +class Params: + # cp: float # specific heat capacity [J/KgK] + # h: float # Heat transfer between battery and environment [W/m2K] + + nominal_energy: float # nominal installed energy [kWh] + nominal_voltage: float # nominal DC voltage [V] - > not used for dispatch + duration: float + valid_control_modes = [0.0, 1.0] # control mode 1 is power in kW, control mode 0 is current in A + control_mode: float = field(default=1.0, validator=validators.in_(valid_control_modes)) # TODO how set? + dt_hr: float = field(default=1.0, validator=validators.gt(0.0)) + +@define +class State: + I: float = field(default=None) + P: float = field(default=None) + Q: float = field(default=None) + SOC: float = field(default=None) + T_batt: float = field(default=None) + gen: float = field(default=0) + n_cycles: float = field(default=0) + input_power: float = field(default=None) + input_current: float = field(default=None) + +@define +class LDES(PowerSource): + config: LDESConfig = field() + site: SiteInfo = field() + + def __attrs_post_init__(self): + if self.config.fin_model is None: + raise AttributeError("Financial model must be set in `config.fin_model`") + + if isinstance(self.config.fin_model, dict): + financial_model = CustomFinancialModel(self.config.fin_model, name=self.config.name) + else: + financial_model = self.config.fin_model + + self.financial_model = self.import_financial_model(financial_model, self, self.config.name) + + self._system_capacity_kw = self.config.system_capacity_kw + self._system_capacity_kwh = self.config.system_capacity_kwh + self.initial_SOC = self.config.initial_SOC + + self.state = State() + self.state.SOC = self.initial_SOC + self.state.P = self.SOC*self.system_capacity_kw + + self.params = Params(nominal_energy=self.config.system_capacity_kwh, + nominal_voltage=None, + duration=self.config.system_capacity_kw/self.config.system_capacity_kwh, + ) + + self.params.nominal_energy = self.config.system_capacity_kwh + self.params.duration = self.config.system_capacity_kwh / self.config.system_capacity_kw + + + super().__init__(self.config.name, self.site, self, financial_model) + + def setup(self): + pass + + def export(self): + """Export class data as a dictionary. Method generated using ChatGPT""" + # Create a dictionary of the instance's attributes + data = {name: getattr(self, name) for name in fields_dict(self.__class__) if not name.startswith("_")} + return data + + def model_type(self): + if self.chemistry == "LDES": + return 0 + elif self.chemistry == "AES": + return 1 + + def calc_degradation_rate_eff_per_hour(lifetime_yrs: float, eol_efficiency: float) -> float: + """Calculate the degradation rate per hour of operation + + Args: + lifetime_yrs (float): number of year the battery is expected to operate + eol_efficiency (float): end of life efficiency. Should be between 0 and 1 + + Returns: + float: efficiency loss expected per hour + """ + + days_pr_yr = 365.25 + hours_pr_day = 24.0 + hour_pr_yr = days_pr_yr*hours_pr_day + hour_pr_life = hour_pr_yr*lifetime_yrs + eff_loss_pr_hour = (1.0 - eol_efficiency)/hour_pr_life + + return eff_loss_pr_hour + + def calc_degradation_rate_per_cycle(lifetime_cycles: float, eol_efficiency: float): + """Calculate degradation rate per cycle + + Args: + lifetime_cycles (float): number of cycles for a lifetime + eol_efficiency (float): efficiency at end of life, should be between 0 and 1 + + Returns: + float: efficiency loss expected per cycle + """ + eff_loss_pr_cycle = (1.0 - eol_efficiency)/lifetime_cycles + + return eff_loss_pr_cycle + + def execute(self, verbosity=0): + """Execute battery simulation with the specified level of verbosity. This + mimics the PySAM battery model execute function. + + Args: + verbosity (int, optional): Verbosity level (0, or 1). + 0 means no extra printing, 1 means more printing. Defaults to 0. + """ + + dt_hr = self.value('dt_hr') + max_soc_dec = self.maximum_SOC/100.0 + min_soc_dec = self.minimum_SOC/100.0 + prev_soc_dec = self.state.SOC/100.0 + control_power = self.input_power + + if self.control_mode == 0.0: + raise(ValueError(f"control_mode {self.control_mode} has not been implemented. Must be one of [1.0].")) + elif self.control_mode == 1.0: + + # check power capacity constraint + if abs(control_power) > self.system_capacity_kw: + control_power = np.sign(control_power)*self.system_capacity_kw + + # check energy capacity constraint + if -(control_power*dt_hr) + prev_soc_dec*self.params.nominal_energy > self.params.nominal_energy*max_soc_dec: + control_power = -(max_soc_dec - prev_soc_dec)*self.params.nominal_energy/dt_hr + + elif -(control_power*dt_hr) + prev_soc_dec*self.params.nominal_energy < self.params.nominal_energy*min_soc_dec: + control_power = (prev_soc_dec - min_soc_dec)*self.params.nominal_energy/dt_hr + + else: + raise(ValueError(f"control_mode {self.control_mode} has not been implemented. Must be one of [1.0].")) + + # update state + self.state.P = control_power + self.state.gen = self.state.P + self.state.SOC += (-control_power*dt_hr/self.system_capacity_kwh ) * 100 + + @property + def control_mode(self): + return self.params.control_mode + + @control_mode.setter + def control_mode(self, control_mode_in): + self.params.control_mode = control_mode_in + + @property + def input_current(self): + return self.params.input_current + + @input_current.setter + def input_current(self, input_current_in): + self.state.input_current = input_current_in + + @property + def input_power(self): + return self.state.input_power + + @input_power.setter + def input_power(self, input_power_in): + self.state.input_power = input_power_in + + @property + def dt_hr(self): + return self.params.dt_hr + + @dt_hr.setter + def dt_hr(self, dt_hr_in): + self.params.control_mode = dt_hr_in + + @property + def minimum_SOC(self): + return self.config.minimum_SOC + + @minimum_SOC.setter + def minimum_SOC(self, minimum_SOC_in): + self.config.minimum_SOC = minimum_SOC_in + + @property + def maximum_SOC(self): + return self.config.maximum_SOC + + @maximum_SOC.setter + def maximum_SOC(self, maximum_SOC_in): + self.config.maximum_SOC = maximum_SOC_in + + @property + def initial_SOC(self): + return self.config.initial_SOC + + @initial_SOC.setter + def initial_SOC(self, initial_SOC): + self.config.initial_SOC = initial_SOC + + @property + def chemistry(self): + return self.config.chemistry + + @chemistry.setter + def chemistry(self, chemistry_in): + self.config.chemistry = chemistry_in + + @property + def SOC(self) -> float: + return self.state.SOC + + @SOC.setter + def SOC(self, SOC): + self.state.SOC = SOC + + @property + def nominal_voltage() -> float: + return None + + @property + def nominal_energy(self) -> float: + return self.system_capacity_kwh + + @property + def system_capacity_kw(self) -> float: + """Battery power rating [kW]""" + return self._system_capacity_kw + + @system_capacity_kw.setter + def system_capacity_kw(self, size_kw: float): + self.financial_model.value("system_capacity", size_kw) + self.system_capacity_kw = size_kw + + @property + def system_capacity_kwh(self) -> float: + """Battery energy capacity [kWh]""" + return self._system_capacity_kwh + + @system_capacity_kwh.setter + def system_capacity_kwh(self, size_kwh: float): + self.financial_model.value("batt_computed_bank_capacity", size_kwh) + self.system_capacity_kwh = size_kwh + + @property + def n_cycles(self) -> float: + """ Battery cycle count """ + return self._n_cycles + + @n_cycles.setter + def n_cycles(self, lifecycles: float): + self.dispatch.value("lifecycles", lifecycles) + self.n_cycles = lifecycles diff --git a/hopp/tools/dispatch/plot_tools.py b/hopp/tools/dispatch/plot_tools.py index 87b19118e..9241fc10e 100644 --- a/hopp/tools/dispatch/plot_tools.py +++ b/hopp/tools/dispatch/plot_tools.py @@ -337,7 +337,7 @@ def plot_generation_profile(hybrid: HybridSimulation, if plot_price: ax2 = ax1.twinx() - ax2.tick_params(axis='y', labelsize=font_size) + ax2.tick_params(which='minor', labelsize=font_size) price = [p * hybrid.ppa_price[0] for p in hybrid.site.elec_prices.data[time_slice]] ax2.plot(time, price, color=price_color, label='Price') ax2.set_ylabel('Grid Price ($/kWh)', fontsize=font_size) diff --git a/pyproject.toml b/pyproject.toml index 12149e555..43a33e040 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ requires-python = ">=3.10, <3.12" license = {file = "LICENSE"} dependencies = [ "Cython", - "NREL-PySAM>=6.0.0", + "NREL-PySAM>=6.0.1", "Pillow", "Pyomo>=6.1.2", "fastkml<1", diff --git a/tests/hopp/test_battery.py b/tests/hopp/test_battery.py index 4efa40b37..1e857b22c 100644 --- a/tests/hopp/test_battery.py +++ b/tests/hopp/test_battery.py @@ -21,16 +21,25 @@ def site(): def test_battery_config(subtests): - with subtests.test("with minimal params"): - config = BatteryConfig.from_dict(config_data) + config = BatteryConfig.from_dict(config_data) + + with subtests.test("with minimal params batt_kw"): assert config.system_capacity_kw == batt_kw + with subtests.test("with minimal params system_capacity_kwh"): assert config.system_capacity_kwh == batt_kw * 4 + with subtests.test("with minimal params tracking"): assert config.tracking is True + with subtests.test("with minimal params minimum_SOC"): assert config.minimum_SOC == 10. + with subtests.test("with minimal params maximum_SOC"): assert config.maximum_SOC == 90. + with subtests.test("with minimal params initial_SOC"): assert config.initial_SOC == 10. + with subtests.test("with minimal params fin_model"): assert config.fin_model is None + with subtests.test("with minimal params system_model_source"): + assert config.system_model_source is "pysam" with subtests.test("with invalid capacity"): with pytest.raises(ValueError): @@ -65,12 +74,18 @@ def test_battery_initialization(site, subtests): config = BatteryConfig.from_dict(config_data) battery = Battery(site, config=config) - assert battery._financial_model is not None - assert battery._system_model is not None - assert battery.outputs is not None - assert battery.chemistry == "LFPGraphite" - assert battery.system_capacity_kw == config.system_capacity_kw - assert battery.system_capacity_kwh == config.system_capacity_kwh + with subtests.test("battery attribute not None _financial_model"): + assert battery._financial_model is not None + with subtests.test("battery attribute not None _system_model"): + assert battery._system_model is not None + with subtests.test("battery attribute not None outputs"): + assert battery.outputs is not None + with subtests.test("battery attribute chemistry"): + assert battery.chemistry == "LFPGraphite" + with subtests.test("battery attribute system_capacity_kw"): + assert battery.system_capacity_kw == config.system_capacity_kw + with subtests.test("battery attribute system_capacity_kwh"): + assert battery.system_capacity_kwh == config.system_capacity_kwh with subtests.test("with custom financial model"): data = deepcopy(config_data) diff --git a/tests/hopp/test_battery_dispatch.py b/tests/hopp/test_battery_dispatch.py index e1e7987f1..a83ddc046 100644 --- a/tests/hopp/test_battery_dispatch.py +++ b/tests/hopp/test_battery_dispatch.py @@ -55,7 +55,7 @@ def create_test_objective_rule(m): for t in m.battery.index_set()) -def test_batterystateless_dispatch(): +def test_batterystateless_dispatch(subtests): expected_objective = 28957.15 # Run battery stateful as system model first @@ -88,21 +88,35 @@ def test_batterystateless_dispatch(): assert_units_consistent(model) results = HybridDispatchBuilderSolver.glpk_solve_call(model) - assert results.solver.termination_condition == TerminationCondition.optimal - assert pyomo.value(model.test_objective) == pytest.approx(expected_objective, 1e-5) + with subtests.test("TerminationCondition"): + assert results.solver.termination_condition == TerminationCondition.optimal + + with subtests.test("expected_objective"): + assert pyomo.value(model.test_objective) == pytest.approx(expected_objective, 1e-5) sum_charge_power = sum(battery.dispatch.charge_power) sum_discharge_power = sum(battery.dispatch.discharge_power) - assert sum(battery.dispatch.charge_power) == pytest.approx(sum_charge_power, 1e-2) - assert sum(battery.dispatch.discharge_power) == pytest.approx(sum_discharge_power, 1e-2) + + with subtests.test("sum_charge_power"): + assert sum(battery.dispatch.charge_power) == pytest.approx(sum_charge_power, 1e-2) + + with subtests.test("sum_discharge_power"): + assert sum(battery.dispatch.discharge_power) == pytest.approx(sum_discharge_power, 1e-2) battery.simulate_with_dispatch(48, 0) - for i in range(24): - dispatch_power = battery.dispatch.power[i] * 1e3 - assert battery.outputs.P[i] == pytest.approx(dispatch_power, 1e-3 * abs(dispatch_power)) - assert battery.outputs.dispatch_lifecycles_per_day[0:2] == pytest.approx([0.75048, 1.50096], rel=1e-3) - assert battery.outputs.n_cycles[23] == 0 - assert battery.outputs.n_cycles[47] == 1 + for i in range(45): + with subtests.test(f"battery.dispatch.power[{i}]"): + dispatch_power = battery.dispatch.power[i] * 1e3 + assert battery.outputs.P[i] == pytest.approx(dispatch_power, 1e-3 * abs(dispatch_power)) + + with subtests.test("sum_charge_power"): + assert battery.outputs.dispatch_lifecycles_per_day[0:2] == pytest.approx([0.75048, 1.50096], rel=1e-3) + + with subtests.test("sum_charge_power"): + assert battery.outputs.n_cycles[23] == 0 + + with subtests.test("sum_charge_power"): + assert battery.outputs.n_cycles[47] == 1 # Run battery stateless as system model to compare technologies['battery']['tracking'] = False @@ -132,30 +146,46 @@ def test_batterystateless_dispatch(): assert_units_consistent(model_sl) results = HybridDispatchBuilderSolver.glpk_solve_call(model_sl) - assert results.solver.termination_condition == TerminationCondition.optimal - assert pyomo.value(model_sl.test_objective) == pytest.approx(expected_objective, 1e-5) + with subtests.test("sum_charge_power"): + assert results.solver.termination_condition == TerminationCondition.optimal + + with subtests.test("sum_charge_power"): + assert pyomo.value(model_sl.test_objective) == pytest.approx(expected_objective, 1e-5) - assert sum(battery_sl.dispatch.charge_power) == pytest.approx(sum_charge_power, 1e-2) - assert sum(battery_sl.dispatch.discharge_power) == pytest.approx(sum_discharge_power, 1e-2) + with subtests.test("sum_charge_power"): + assert sum(battery_sl.dispatch.charge_power) == pytest.approx(sum_charge_power, 1e-2) + + with subtests.test("sum_charge_power"): + assert sum(battery_sl.dispatch.discharge_power) == pytest.approx(sum_discharge_power, 1e-2) battery_sl.simulate_with_dispatch(48, 0) for i in range(24): - dispatch_power = battery_sl.dispatch.power[i] * 1e3 - assert battery_sl.outputs.P[i] == pytest.approx(dispatch_power, 1e-3 * abs(dispatch_power)) + with subtests.test(f"battery_sl.dispatch.power[{i}]"): + dispatch_power = battery_sl.dispatch.power[i] * 1e3 + assert battery_sl.outputs.P[i] == pytest.approx(dispatch_power, 1e-3 * abs(dispatch_power)) battery_dispatch = np.array(battery.dispatch.power)[0:48] battery_actual = np.array(battery.generation_profile[0:dispatch_n_look_ahead]) * 1e-3 # convert to MWh battery_sl_dispatch = np.array(battery_sl.dispatch.power)[0:48] battery_sl_actual = np.array(battery_sl.generation_profile)[0:48] * 1e-3 # convert to MWh - assert sum(battery_dispatch - battery_sl_dispatch) == 0 - assert sum(abs(battery_actual - battery_dispatch)) <= 33.5 - assert sum(abs(battery_sl_actual - battery_sl_dispatch)) == 0 - assert sum(abs(battery_actual - battery_sl_actual)) <= 33.5 - assert battery_sl.outputs.lifecycles_per_day[0:2] == pytest.approx([0.75048, 1.50096], rel=1e-3) + with subtests.test("battery_dispatch vs battery_sl_dispatch"): + assert sum(battery_dispatch - battery_sl_dispatch) == 0 + + with subtests.test("battery_actual vs battery_dispatch"): + assert sum(abs(battery_actual - battery_dispatch)) <= 33.5 + + with subtests.test("battery_sl_actual vs battery_sl_dispatch"): + assert sum(abs(battery_sl_actual - battery_sl_dispatch)) == 0 + + with subtests.test("battery_actual vs battery_sl_actual"): + assert sum(abs(battery_actual - battery_sl_actual)) <= 33.5 + + with subtests.test("lifecycles_per_day"): + assert battery_sl.outputs.lifecycles_per_day[0:2] == pytest.approx([0.75048, 1.50096], rel=1e-3) -def test_batterystateless_cycle_limits(): +def test_batterystateless_cycle_limits(subtests): expected_objective = 22513 # objective is less than above due to cycling limits technologies = technologies_input.copy() @@ -186,10 +216,15 @@ def test_batterystateless_cycle_limits(): assert_units_consistent(model_sl) results = HybridDispatchBuilderSolver.glpk_solve_call(model_sl) - assert results.solver.termination_condition == TerminationCondition.optimal - assert pyomo.value(model_sl.test_objective) == pytest.approx(expected_objective, 1e-3) + + with subtests.test("termination_condition"): + assert results.solver.termination_condition == TerminationCondition.optimal + + with subtests.test("test_objective"): + assert pyomo.value(model_sl.test_objective) == pytest.approx(expected_objective, 1e-3) battery_sl.simulate_with_dispatch(48, 0) - assert battery_sl.outputs.lifecycles_per_day[0:2] == pytest.approx([0.75048, 1], rel=1e-3) + with subtests.test("lifecycles_per_day"): + assert battery_sl.outputs.lifecycles_per_day[0:2] == pytest.approx([0.75048, 1], rel=1e-3) diff --git a/tests/hopp/test_battery_ldes.py b/tests/hopp/test_battery_ldes.py new file mode 100644 index 000000000..fb225beca --- /dev/null +++ b/tests/hopp/test_battery_ldes.py @@ -0,0 +1,143 @@ +from unittest.mock import MagicMock +from copy import deepcopy + +import pytest +from pytest import fixture + +from hopp.simulation.technologies.battery import Battery, BatteryConfig +from tests.hopp.utils import create_default_site_info + +from tests.hopp.utils import DEFAULT_FIN_CONFIG + +batt_kw = 5e3 + +config_data = { + 'system_capacity_kwh': batt_kw * 4, + 'system_capacity_kw': batt_kw, + 'system_model_source': "hopp", + 'chemistry': "LDES", + "fin_model": DEFAULT_FIN_CONFIG, +} + +@fixture +def site(): + return create_default_site_info() + + +def test_battery_config(subtests): + + config = BatteryConfig.from_dict(config_data) + + with subtests.test("with minimal params batt_kw"): + assert config.system_capacity_kw == batt_kw + with subtests.test("with minimal params system_capacity_kwh"): + assert config.system_capacity_kwh == batt_kw * 4 + with subtests.test("with minimal params tracking"): + assert config.tracking is True + with subtests.test("with minimal params minimum_SOC"): + assert config.minimum_SOC == 10. + with subtests.test("with minimal params maximum_SOC"): + assert config.maximum_SOC == 90. + with subtests.test("with minimal params initial_SOC"): + assert config.initial_SOC == 10. + with subtests.test("with minimal params system_model_source"): + assert config.system_model_source is "hopp" + + with subtests.test("with invalid capacity"): + with pytest.raises(ValueError): + data = deepcopy(config_data) + data["system_capacity_kw"] = -1. + BatteryConfig.from_dict(data) + + with pytest.raises(ValueError): + data = deepcopy(config_data) + data["system_capacity_kwh"] = -1. + BatteryConfig.from_dict(data) + + with subtests.test("with invalid SOC"): + # SOC values must be between 0-100 + with pytest.raises(ValueError): + data = deepcopy(config_data) + data["minimum_SOC"] = -1. + BatteryConfig.from_dict(data) + + with pytest.raises(ValueError): + data = deepcopy(config_data) + data["maximum_SOC"] = 120. + BatteryConfig.from_dict(data) + + with pytest.raises(ValueError): + data = deepcopy(config_data) + data["initial_SOC"] = 120. + BatteryConfig.from_dict(data) + + +def test_battery_initialization(site, subtests): + config = BatteryConfig.from_dict(config_data) + battery = Battery(site, config=config) + + with subtests.test("battery attribute not None _financial_model"): + assert battery._financial_model is not None + with subtests.test("battery attribute not None _system_model"): + assert battery._system_model is not None + with subtests.test("battery attribute not None outputs"): + assert battery.outputs is not None + with subtests.test("battery attribute chemistry"): + assert battery.chemistry == "LDES" + with subtests.test("battery attribute system_capacity_kw"): + assert battery.system_capacity_kw == config.system_capacity_kw + with subtests.test("battery attribute system_capacity_kwh"): + assert battery.system_capacity_kwh == config.system_capacity_kwh + + with subtests.test("with custom financial model"): + data = deepcopy(config_data) + fin_model = MagicMock() # duck type a financial model for simplicity + data["fin_model"] = fin_model + + config = BatteryConfig.from_dict(data) + battery = Battery(site, config=config) + + assert battery._financial_model == fin_model + +def test_battery_initialization_with_replacement_schedule(site, subtests): + + config_data_local = deepcopy(config_data) + config_data_local["fin_model"]["battery_system"]["batt_replacement_option"] = 2 + length = 25 + refurb = [0]*length + n = 10 + for i in range(n-1, length, n): + refurb[i] = 0.5 + config_data_local["fin_model"]["battery_system"]["batt_replacement_schedule_percent"] = refurb + + config_data_local["fin_model"]["name"] = "LDES" + + config = BatteryConfig.from_dict(config_data_local) + battery = Battery(site, config=config) + + with subtests.test("battery attribute not None _financial_model"): + assert battery._financial_model is not None + with subtests.test("battery attribute not None _system_model"): + assert battery._system_model is not None + with subtests.test("battery attribute not None outputs"): + assert battery.outputs is not None + with subtests.test("battery attribute chemistry"): + assert battery.chemistry == "LDES" + with subtests.test("battery attribute system_capacity_kw"): + assert battery.system_capacity_kw == config.system_capacity_kw + with subtests.test("battery attribute system_capacity_kwh"): + assert battery.system_capacity_kwh == config.system_capacity_kwh + with subtests.test("financial model attribute batt_replacement_option"): + assert battery._financial_model.BatterySystem.batt_replacement_option == 2 + with subtests.test("financial model attribute batt_replacement_option"): + assert battery._financial_model.BatterySystem.batt_replacement_schedule_percent == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0] + + with subtests.test("with custom financial model"): + data = deepcopy(config_data) + fin_model = MagicMock() # duck type a financial model for simplicity + data["fin_model"] = fin_model + + config = BatteryConfig.from_dict(data) + battery = Battery(site, config=config) + + assert battery._financial_model == fin_model \ No newline at end of file diff --git a/tests/hopp/test_battery_ldes_dispatch.py b/tests/hopp/test_battery_ldes_dispatch.py new file mode 100644 index 000000000..eb5560574 --- /dev/null +++ b/tests/hopp/test_battery_ldes_dispatch.py @@ -0,0 +1,171 @@ +from pathlib import Path + +import pytest +import numpy as np +import pyomo.environ as pyomo +from pyomo.environ import units as u +from pyomo.opt import TerminationCondition +from pyomo.util.check_units import assert_units_consistent + +from hopp.simulation.technologies.sites import SiteInfo, flatirons_site +from hopp.simulation.technologies.battery import Battery, BatteryConfig +from hopp.simulation.technologies.dispatch import SimpleBatteryDispatch +from hopp.simulation.technologies.dispatch.hybrid_dispatch_builder_solver import HybridDispatchBuilderSolver, HybridDispatchOptions +from hopp.simulation.technologies.financial.custom_financial_model import CustomFinancialModel +from hopp import ROOT_DIR +from tests.hopp.utils import DEFAULT_FIN_CONFIG +import pytest + +solar_resource_file = ROOT_DIR / "simulation" / "resource_files" / "solar" / "35.2018863_-101.945027_psmv3_60_2012.csv" +wind_resource_file = ROOT_DIR / "simulation" / "resource_files" / "wind" / "35.2018863_-101.945027_windtoolkit_2012_60min_80m_100m.srw" +site = SiteInfo(flatirons_site, solar_resource_file=solar_resource_file, wind_resource_file=wind_resource_file) + +interconnect_mw = 50 +technologies_input = { + 'pv': { + 'system_capacity_kw': 50 * 1000, + }, + 'battery': { + 'system_capacity_kwh': 200 * 1000, + 'system_capacity_kw': 50 * 1000, + 'tracking': False, + 'fin_model': CustomFinancialModel(DEFAULT_FIN_CONFIG, name="Test"), + 'system_model_source': "hopp", + 'chemistry': "LDES", + }, + 'grid': { + 'interconnect_kw': interconnect_mw * 1000 + }} + +# Manually creating objective for testing +dispatch_n_look_ahead = 48 +prices = {} +block_length = 8 +index = 0 +for i in range(int(dispatch_n_look_ahead / block_length)): + for j in range(block_length): + if i % 2 == 0: + prices[index] = 30.0 # assuming low prices + else: + prices[index] = 100.0 # assuming high prices + index += 1 + + +def create_test_objective_rule(m): + return sum((m.battery[t].time_duration * ( + (m.price[t] - m.battery[t].cost_per_discharge) * m.battery[t].discharge_power + - (m.price[t] + m.battery[t].cost_per_charge) * m.battery[t].charge_power)) + for t in m.battery.index_set()) + + +def test_LDES_dispatch(subtests): + expected_objective = 28957.15 + + # Run battery stateful as system model first + technologies = technologies_input.copy() + technologies['battery']['tracking'] = True + model = pyomo.ConcreteModel(name='ldes_only') + model.forecast_horizon = pyomo.Set(initialize=range(dispatch_n_look_ahead)) + model.price = pyomo.Param(model.forecast_horizon, + within=pyomo.Reals, + initialize=prices, + mutable=True, + units=u.USD / u.MWh) + + config = BatteryConfig.from_dict(technologies['battery']) + battery = Battery(site, config=config) + battery._dispatch = SimpleBatteryDispatch(model, + model.forecast_horizon, + battery._system_model, + battery._financial_model, + 'battery', + HybridDispatchOptions()) + + model.test_objective = pyomo.Objective( + rule=create_test_objective_rule, + sense=pyomo.maximize) + + battery.dispatch.initialize_parameters() + battery.dispatch.update_time_series_parameters(0) + battery.dispatch.update_dispatch_initial_soc(battery.dispatch.minimum_soc) # Set initial SOC to minimum + assert_units_consistent(model) + results = HybridDispatchBuilderSolver.glpk_solve_call(model) + + + with subtests.test("TerminationCondition"): + assert results.solver.termination_condition == TerminationCondition.optimal + + with subtests.test("test_objective"): + assert pyomo.value(model.test_objective) == pytest.approx(expected_objective, 1e-5) + + with subtests.test("charge_power"): # TODO this test does not make sense + sum_charge_power = sum(battery.dispatch.charge_power) + assert sum(battery.dispatch.charge_power) == pytest.approx(sum_charge_power, 1e-2) + + with subtests.test("discharge_power"): # TODO this test does not make sense because it just tests the thing against itself + sum_discharge_power = sum(battery.dispatch.discharge_power) + assert sum(battery.dispatch.discharge_power) == pytest.approx(sum_discharge_power, 1e-2) + + with subtests.test("round trip efficiency"): # TODO this test does not make sense because it just tests the thing against itself + assert (sum(battery.dispatch.charge_power) * battery.dispatch.round_trip_efficiency / 100.0 + == pytest.approx(sum(battery.dispatch.discharge_power))) + + battery.simulate_with_dispatch(48, 0) + for i in range(24): + with subtests.test(f"power[{i}]"): + dispatch_power = battery.dispatch.power[i] * 1e3 + assert battery.outputs.P[i] == pytest.approx(dispatch_power, 1e-3 * abs(dispatch_power)) + + for i in range(45): + with subtests.test(f"battery.dispatch.soc[{i}]"): + dispatch_soc = battery.dispatch.soc[i] + assert battery.outputs.SOC[i] == pytest.approx(dispatch_soc, 1e-1 * abs(dispatch_soc)) + + with subtests.test("dispatch_lifecycles_per_day"): + assert battery.outputs.dispatch_lifecycles_per_day[0:2] == pytest.approx([0.75048, 1.50096], rel=1e-3) + + with subtests.test("n_cycles[23]"): + assert battery.outputs.n_cycles[23] == 0 + +def test_LDES_cycle_limits(subtests): + expected_objective = 22513 # objective is less than above due to cycling limits + + technologies = technologies_input.copy() + technologies['battery']['tracking'] = False + model_sl = pyomo.ConcreteModel(name='battery_stateless') + model_sl.forecast_horizon = pyomo.Set(initialize=range(dispatch_n_look_ahead)) + model_sl.price = pyomo.Param(model_sl.forecast_horizon, + within=pyomo.Reals, + initialize=prices, + mutable=True, + units=u.USD / u.MWh) + + config = BatteryConfig.from_dict(technologies['battery']) + battery_sl = Battery(site, config=config) + battery_sl._dispatch = SimpleBatteryDispatch(model_sl, + model_sl.forecast_horizon, + battery_sl._system_model, + battery_sl._financial_model, + 'battery', + HybridDispatchOptions({'max_lifecycle_per_day': 1})) + + model_sl.test_objective = pyomo.Objective( + rule=create_test_objective_rule, + sense=pyomo.maximize) + + battery_sl.dispatch.initialize_parameters() + battery_sl.dispatch.update_time_series_parameters(0) + assert_units_consistent(model_sl) + results = HybridDispatchBuilderSolver.glpk_solve_call(model_sl) + + with subtests.test("termination_condition"): + assert results.solver.termination_condition == TerminationCondition.optimal + + with subtests.test("test_objective"): + assert pyomo.value(model_sl.test_objective) == pytest.approx(expected_objective, 1e-3) + + battery_sl.simulate_with_dispatch(48, 0) + + with subtests.test("dispatch_lifecycles_per_day"): + assert battery_sl.outputs.dispatch_lifecycles_per_day[0:2] == pytest.approx([0.75048, 1], rel=1e-3) + diff --git a/tests/hopp/test_custom_financial.py b/tests/hopp/test_custom_financial.py index bcf962786..426e1f182 100644 --- a/tests/hopp/test_custom_financial.py +++ b/tests/hopp/test_custom_financial.py @@ -511,3 +511,364 @@ def test_hybrid_detailed_pv_with_wind_storage_dispatch(site, subtests): assert npvs.wind == approx(npv_expected_wind, 1e-3) assert npvs.battery == approx(npv_expected_battery, 1e-3) assert npvs.hybrid == approx(npv_expected_hybrid, 1e-3) + +def test_hybrid_simple_pv_with_wind_wave_ldes_storage_dispatch(subtests): + + site_internal = create_default_site_info(wave=True, wave_resource_file=wave_resource_file) + # Test wind + simple PV (pvwattsv8) + LDES storage with dispatch hybrid plant with custom financial model + annual_energy_expected_pv = 10761987 + annual_energy_expected_wind = 31951719 + annual_energy_expected_wave = 12132526 + annual_energy_expected_battery = -990.40 + annual_energy_expected_hybrid = 54847035 + + npv_expected_pv = -1640023 + npv_expected_wind = -5159400 + npv_expected_wave = -57994156 + npv_expected_battery = -8155345 + npv_expected_hybrid = -72948345 + + lcoe_expected_pv = 3.104064331441355 + lcoe_expected_wind = 3.162940789633178 + lcoe_expected_wave = 33.09696662806905 + lcoe_expected_battery = 18.00083292429152 + lcoe_expected_hybrid = 11.14715145739659 + + total_installed_cost_expected = 89050689.65833203 + + interconnect_kw = 20000 + pv_kw = 5000 + wind_kw = 10000 + batt_kw = 5000 + wave_kw = 2860 + + fin_config_local = copy.deepcopy(DEFAULT_FIN_CONFIG) + + fin_config_local["battery_system"]["batt_replacement_option"] = 2 + + length = 25 + refurb = [0]*length + n = 10 + for i in range(n-1, length, n): + refurb[i] = 0.5 + + fin_config_local["battery_system"]["batt_replacement_schedule_percent"] = refurb + + power_sources = { + 'pv': { + 'system_capacity_kw': pv_kw, + 'layout_params': { + "x_position": 0.5, + "y_position": 0.5, + "aspect_power": 0, + "gcr": 0.5, + "s_buffer": 2, + "x_buffer": 2 + }, + 'fin_model': DEFAULT_FIN_CONFIG_LOCAL, + 'dc_degradation': [0] * 25 + }, + 'wind': { + 'num_turbines': 5, + 'turbine_rating_kw': wind_kw / 5, + 'layout_mode': 'boundarygrid', + 'layout_params': { + "border_spacing": 2, + "border_offset": 0.5, + "grid_angle": np.rad2deg(0.5), + "grid_aspect_power": 0.5, + "row_phase_offset": 0.5 + }, + 'fin_model': DEFAULT_FIN_CONFIG_LOCAL, + }, + "wave": { + "device_rating_kw": wave_kw/10, + "num_devices": 10, + "wave_power_matrix": mhk_config["wave_power_matrix"], + "fin_model": DEFAULT_FIN_CONFIG_LOCAL, + }, + 'battery': { + 'system_capacity_kwh': batt_kw * 4, + 'system_capacity_kw': batt_kw, + 'fin_model': fin_config_local, + 'chemistry': "LDES", + 'system_model_source': "hopp", + }, + 'grid': { + 'interconnect_kw': interconnect_kw, + 'fin_model': DEFAULT_FIN_CONFIG_LOCAL, + 'ppa_price': 0.03 + } + } + config = { + "simulation_options": { + "wind": { + "skip_financial": False # test that setting this to false allows financial calculations to run + } + }, + } + + hopp_config = { + "site": site_internal, + "technologies": power_sources, + "config": config + } + + mhk_cost_model_inputs = MHKCostModelInputs.from_dict( + { + "reference_model_num": 3, + "water_depth": 100, + "distance_to_shore": 80, + "number_rows": 10, + "device_spacing": 600, + "row_spacing": 600, + "cable_system_overbuild": 20, + } + ) + + hi = HoppInterface(hopp_config) + hybrid_plant = hi.system + hybrid_plant.layout.plot() + hybrid_plant.battery.dispatch.lifecycle_cost_per_kWh_cycle = 0.01 + hybrid_plant.battery._financial_model.om_batt_variable_cost = [0.75] + hybrid_plant.wave.create_mhk_cost_calculator(mhk_cost_model_inputs) + + hybrid_plant.simulate() + + sizes = hybrid_plant.system_capacity_kw + aeps = hybrid_plant.annual_energies + npvs = hybrid_plant.net_present_values + lcoes = hybrid_plant.lcoe_nom # cents/kWh + + with subtests.test("with minimal params pv size"): + assert sizes.pv == approx(pv_kw, 1e-3) + with subtests.test("with minimal params wind size"): + assert sizes.wind == approx(wind_kw, 1e-3) + with subtests.test("with minimal params wave size"): + assert sizes.wave == approx(wave_kw, 1e-3) + with subtests.test("with minimal params batt kw size"): + assert sizes.battery == approx(batt_kw, 1e-3) + + with subtests.test("with minimal params pv aep"): + assert aeps.pv == approx(annual_energy_expected_pv, 1e-3) + with subtests.test("with minimal params wind aep"): + assert aeps.wind == approx(annual_energy_expected_wind, 1e-3) + with subtests.test("with minimal params wave aep"): + assert aeps.wave == approx(annual_energy_expected_wave, 1e-3) + with subtests.test("with minimal params battery aep"): + assert aeps.battery == approx(annual_energy_expected_battery, 1e-3) + with subtests.test("with minimal params hybrid aep"): + assert aeps.hybrid == approx(annual_energy_expected_hybrid, 1e-3) + + with subtests.test("with minimal params pv npv"): + assert npvs.pv == approx(npv_expected_pv, 1e-3) + with subtests.test("with minimal params wind npv"): + assert npvs.wind == approx(npv_expected_wind, 1e-3) + with subtests.test("with minimal params wave npv"): + assert npvs.wave == approx(npv_expected_wave, 1e-3) + with subtests.test("with minimal params batt npv"): + assert npvs.battery == approx(npv_expected_battery, 1e-3) + with subtests.test("with minimal params hybrid npv"): + assert npvs.hybrid == approx(npv_expected_hybrid, 1e-3) + + with subtests.test("lcoe pv"): + assert lcoes.pv == approx(lcoe_expected_pv, 1e-3) + with subtests.test("lcoe wind"): + assert lcoes.wind == approx(lcoe_expected_wind, 1e-3) + with subtests.test("lcoe wave"): + assert lcoes.wave == approx(lcoe_expected_wave, 1e-3) + with subtests.test("lcoe battery"): + assert lcoes.battery == approx(lcoe_expected_battery, 1e-3) + with subtests.test("lcoe hybrid"): + assert lcoes.hybrid == approx(lcoe_expected_hybrid, 1e-3) + + with subtests.test("total installed cost"): + assert hybrid_plant.grid.total_installed_cost == approx(total_installed_cost_expected, 1E-6) + +def test_hybrid_simple_pv_with_wind_wave_battery_replacement_schedule(subtests): + + site_internal = create_default_site_info(wave=True, wave_resource_file=wave_resource_file) + # Test wind + simple PV (pvwattsv8) + storage with dispatch hybrid plant with custom financial model + annual_energy_expected_pv = 10761987 + annual_energy_expected_wind = 31951719 + annual_energy_expected_wave = 12132526 + annual_energy_expected_battery = -103752 + annual_energy_expected_hybrid = 54747904 + + npv_expected_pv = -1640023 + npv_expected_wind = -5159400 + npv_expected_wave = -57994156 + # npv_expected_battery = -8183543 # value expected if no battery replacement schedule is provided + npv_expected_battery = -8189905 + npv_expected_hybrid = -72978515 + + lcoe_expected_pv = 3.104064331441355 + lcoe_expected_wind = 3.162940789633178 + lcoe_expected_wave = 33.09696662806905 + # lcoe_expected_battery = 13.333128855903514 # value expected if no battery replacement schedule is provided + lcoe_expected_battery = 18.018052581528185 + # lcoe_expected_hybrid = 11.337551789830751 # value expected if no battery replacement schedule is provided + lcoe_expected_hybrid = 11.167675207853105 + + total_installed_cost_expected_pv = 4799592.0 + total_installed_cost_expected_wind = 14540000.0 + total_installed_cost_expected_wave = 61556097.6 + total_installed_cost_expected_battery = 8155000.0 + total_installed_cost_expected_hybrid = 89050689.6 + + interconnect_kw = 20000 + pv_kw = 5000 + wind_kw = 10000 + batt_kw = 5000 + wave_kw = 2860 + + fin_config_local = copy.deepcopy(DEFAULT_FIN_CONFIG) + + fin_config_local["battery_system"]["batt_replacement_option"] = 2 + + length = 25 + refurb = [0]*length + n = 10 + for i in range(n-1, length, n): + refurb[i] = 0.5 + fin_config_local["battery_system"]["batt_replacement_schedule_percent"] = refurb + + power_sources = { + 'pv': { + 'system_capacity_kw': pv_kw, + 'layout_params': { + "x_position": 0.5, + "y_position": 0.5, + "aspect_power": 0, + "gcr": 0.5, + "s_buffer": 2, + "x_buffer": 2 + }, + 'fin_model': DEFAULT_FIN_CONFIG_LOCAL, + 'dc_degradation': [0] * 25 + }, + 'wind': { + 'num_turbines': 5, + 'turbine_rating_kw': wind_kw / 5, + 'layout_mode': 'boundarygrid', + 'layout_params': { + "border_spacing": 2, + "border_offset": 0.5, + "grid_angle": np.rad2deg(0.5), + "grid_aspect_power": 0.5, + "row_phase_offset": 0.5 + }, + 'fin_model': DEFAULT_FIN_CONFIG_LOCAL, + }, + "wave": { + "device_rating_kw": wave_kw/10, + "num_devices": 10, + "wave_power_matrix": mhk_config["wave_power_matrix"], + "fin_model": DEFAULT_FIN_CONFIG_LOCAL, + }, + 'battery': { + 'system_capacity_kwh': batt_kw * 4, + 'system_capacity_kw': batt_kw, + 'fin_model': fin_config_local, + 'system_model_source': "pysam", + }, + 'grid': { + 'interconnect_kw': interconnect_kw, + 'fin_model': DEFAULT_FIN_CONFIG_LOCAL, + 'ppa_price': 0.03 + } + } + config = { + "simulation_options": { + "wind": { + "skip_financial": False # test that setting this to false allows financial calculations to run + } + }, + } + + hopp_config = { + "site": site_internal, + "technologies": power_sources, + "config": config + } + + mhk_cost_model_inputs = MHKCostModelInputs.from_dict( + { + "reference_model_num": 3, + "water_depth": 100, + "distance_to_shore": 80, + "number_rows": 10, + "device_spacing": 600, + "row_spacing": 600, + "cable_system_overbuild": 20, + } + ) + + hi = HoppInterface(hopp_config) + hybrid_plant = hi.system + hybrid_plant.layout.plot() + hybrid_plant.battery.dispatch.lifecycle_cost_per_kWh_cycle = 0.01 + hybrid_plant.battery._financial_model.om_batt_variable_cost = [0.75] + hybrid_plant.wave.create_mhk_cost_calculator(mhk_cost_model_inputs) + + hybrid_plant.simulate() + + sizes = hybrid_plant.system_capacity_kw + aeps = hybrid_plant.annual_energies + npvs = hybrid_plant.net_present_values + lcoes = hybrid_plant.lcoe_nom # cents/kWh + + rtol = 1E-5 + rtol_pv = 1E-3 + with subtests.test("with minimal params pv size"): + assert sizes.pv == approx(pv_kw, rel=rtol_pv) + with subtests.test("with minimal params wind size"): + assert sizes.wind == approx(wind_kw, rel=rtol) + with subtests.test("with minimal params wave size"): + assert sizes.wave == approx(wave_kw, rel=rtol) + with subtests.test("with minimal params batt kw size"): + assert sizes.battery == approx(batt_kw, rel=rtol) + + with subtests.test("with minimal params pv aep"): + assert aeps.pv == approx(annual_energy_expected_pv, rel=rtol_pv) + with subtests.test("with minimal params wind aep"): + assert aeps.wind == approx(annual_energy_expected_wind, rel=rtol) + with subtests.test("with minimal params wave aep"): + assert aeps.wave == approx(annual_energy_expected_wave, rel=rtol) + with subtests.test("with minimal params battery aep"): + assert aeps.battery == approx(annual_energy_expected_battery, rel=rtol) + with subtests.test("with minimal params hybrid aep"): + assert aeps.hybrid == approx(annual_energy_expected_hybrid, rel=rtol_pv) + + with subtests.test("with minimal params pv npv"): + assert npvs.pv == approx(npv_expected_pv, rel=rtol_pv) + with subtests.test("with minimal params wind npv"): + assert npvs.wind == approx(npv_expected_wind, rel=rtol) + with subtests.test("with minimal params wave npv"): + assert npvs.wave == approx(npv_expected_wave, rel=rtol) + with subtests.test("with minimal params batt npv"): + assert npvs.battery == approx(npv_expected_battery, rel=rtol) + with subtests.test("with minimal params hybrid npv"): + assert npvs.hybrid == approx(npv_expected_hybrid, rel=rtol) + + with subtests.test("lcoe pv"): + assert lcoes.pv == approx(lcoe_expected_pv, rel=rtol_pv) + with subtests.test("lcoe wind"): + assert lcoes.wind == approx(lcoe_expected_wind, rel=rtol) + with subtests.test("lcoe wave"): + assert lcoes.wave == approx(lcoe_expected_wave, rel=rtol) + with subtests.test("lcoe battery"): + assert lcoes.battery == approx(lcoe_expected_battery, rel=rtol) + with subtests.test("lcoe hybrid"): + assert lcoes.hybrid == approx(lcoe_expected_hybrid, rel=rtol) + + with subtests.test("total installed cost pv"): + assert hybrid_plant.pv.total_installed_cost == approx(total_installed_cost_expected_pv, rel=rtol) + with subtests.test("total installed cost wind"): + assert hybrid_plant.wind.total_installed_cost == approx(total_installed_cost_expected_wind, rel=rtol) + with subtests.test("total installed cost wave"): + assert hybrid_plant.wave.total_installed_cost == approx(total_installed_cost_expected_wave, rel=rtol) + with subtests.test("total installed cost battery"): + assert hybrid_plant.battery.total_installed_cost == approx(total_installed_cost_expected_battery, rel=rtol) + with subtests.test("total installed cost"): + assert hybrid_plant.grid.total_installed_cost == approx(total_installed_cost_expected_hybrid, rel=rtol) diff --git a/tests/hopp/test_dispatch.py b/tests/hopp/test_dispatch.py index ed819d8b3..27fd715e7 100644 --- a/tests/hopp/test_dispatch.py +++ b/tests/hopp/test_dispatch.py @@ -1045,6 +1045,96 @@ def test_dispatch_load_following_heuristic_with_wave(site, subtests): discharge = [(p > 0) * p * power_scale for p in hi.system.battery.outputs.P] charge = [(p < 0) * p * power_scale for p in hi.system.battery.outputs.P] + with subtests.test("load met"): + assert hi.system.grid.time_load_met == pytest.approx(40.468, 1e-2) + with subtests.test("charge power"): + assert sum(discharge) > 0.0 + with subtests.test("discharge power"): + assert sum(charge) < 0.0 + + +def test_hybrid_dispatch_baseload_heuristic_and_analysis(site): + + desired_schedule = 8760 * [20] + # Using a non-uniform schedule to test the baseload heuristic bugfix + desired_schedule[:2000] = [10.] * 2000 + + desired_schedule_site = SiteInfo(flatirons_site, + desired_schedule=desired_schedule) + wind_solar_battery = {key: technologies[key] for key in ('pv', 'wind', 'battery')} + + dispatch_options = {'battery_dispatch': 'load_following_heuristic', + 'use_higher_hours': True, + 'higher_hours': {'min_regulation_hours': 4, 'min_regulation_power': 5000}} + + hopp_config = { + "site": desired_schedule_site, + "technologies": wind_solar_battery, + "config": { + "dispatch_options": dispatch_options + } + } + hopp_config["technologies"]["grid"] = { + "interconnect_kw": interconnect_mw * 1000 + } + hi = HoppInterface(hopp_config) + hi.simulate(1) + + hybrid_plant = hi.system + + assert hybrid_plant.grid.time_load_met == pytest.approx(94.429, 1e-2) + assert hybrid_plant.grid.capacity_factor_load == pytest.approx(95.659, 1e-2) + assert hybrid_plant.grid.total_number_hours == pytest.approx(4270, 1e-2) + +def test_dispatch_ldes_load_following_heuristic_with_wave(site, subtests): + dispatch_options = {'battery_dispatch': 'load_following_heuristic', 'grid_charging': False} + wave_battery = {key: technologies[key] for key in ['wave', 'battery', 'grid']} + + print(wave_battery) + wave_battery["battery"] = { + 'system_capacity_kwh': 200 * 1000, + 'system_capacity_kw': 50 * 1000, + 'system_model_source': "hopp", + 'chemistry': "LDES", + } + for tech in wave_battery.keys(): + wave_battery[tech]["fin_model"] = DEFAULT_FIN_CONFIG + + + wave_resource_file = ROOT_DIR / "simulation" / "resource_files" / "wave" / "Wave_resource_timeseries.csv" + + desired_schedule = 8760*[20] + site_internal = create_default_site_info(solar=False, wind=False, wave=True, wave_resource_file=wave_resource_file, desired_schedule=desired_schedule) + + hopp_config = { + "site": site_internal, + "technologies": wave_battery, + "config": { + "dispatch_options": dispatch_options + } + } + hi = HoppInterface(hopp_config) + + cost_model_inputs = MHKCostModelInputs.from_dict( + { + "reference_model_num": 3, + "water_depth": 100, + "distance_to_shore": 80, + "number_rows": 10, + "device_spacing": 600, + "row_spacing": 600, + "cable_system_overbuild": 20, + } + ) + + hi.system.wave.create_mhk_cost_calculator(cost_model_inputs) + + hi.simulate(1) + + power_scale = 1.0 + discharge = [(p > 0) * p * power_scale for p in hi.system.battery.outputs.P] + charge = [(p < 0) * p * power_scale for p in hi.system.battery.outputs.P] + with subtests.test("load met"): assert hi.system.grid.time_load_met == pytest.approx(40.468, 1e-2) with subtests.test("charge power"): From b3f0031b3c038b7df130cfecd849c6375accddba Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Thu, 24 Apr 2025 18:36:28 -0600 Subject: [PATCH 32/48] Feature add: Generic Plant (#472) * added simulate_power function to hopp interfacing objects * added ghost plant and integrated physics model * small update to grid.py and power_source.py * updated test for ghost plant * updated ghost plant * added ghost_multi system model * updated generation_profile_wo_battery setter * updated test_ghost for ghost_multi * updated round_digits in dispatch.py to 8 * changed round_digits update to power_source_dispatch instead of dispatch.py * added docstrings and updated some function names in ghost_plant and ghost_multi * udpated generation_profile_wo_battery setter in grid.py so compatible with CustomFinancialModel simulations * updated test_ghost.py * renamed ghost to generic * updated RELEASE.md * updated docstrings and other minor changes to generic plant stuff based on feedback * fixed AttributeError that occurs when initializing generation profile if system capacity hasnt been set * updated RELEASE.md * minor updates to generic plant * update generic_plant * updated generic config so tests pass --------- Co-authored-by: John Jasa --- RELEASE.md | 4 + hopp/simulation/hopp.py | 3 + hopp/simulation/hopp_interface.py | 3 + hopp/simulation/hybrid_simulation.py | 46 +- .../technologies/dispatch/__init__.py | 4 + .../technologies/dispatch/hybrid_dispatch.py | 4 + .../dispatch/power_sources/__init__.py | 4 + .../power_sources/generic_dispatch.py | 114 ++++ .../power_sources/power_source_dispatch.py | 1 + .../technologies/generic/__init__.py | 0 .../technologies/generic/generic_multi.py | 244 ++++++++ .../technologies/generic/generic_plant.py | 348 ++++++++++++ hopp/simulation/technologies/grid.py | 2 + .../technologies/sites/site_info.py | 2 +- tests/hopp/test_generic_plant.py | 534 ++++++++++++++++++ 15 files changed, 1310 insertions(+), 3 deletions(-) create mode 100644 hopp/simulation/technologies/dispatch/power_sources/generic_dispatch.py create mode 100644 hopp/simulation/technologies/generic/__init__.py create mode 100644 hopp/simulation/technologies/generic/generic_multi.py create mode 100644 hopp/simulation/technologies/generic/generic_plant.py create mode 100644 tests/hopp/test_generic_plant.py diff --git a/RELEASE.md b/RELEASE.md index 977bd9d1a..8d1a59e82 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -3,6 +3,9 @@ ## Unreleased, TBD +* Added GenericPlant model which may be used to: + - simulate grid and battery performance without resimulating generation of other technologies + - represent the physics-based performance of a generation technology that is not included in HOPP * Loosened strictness of comparison for wind turbine config checking and added tests * Loosened strictness of comparison for wind turbine hub-height and wind resource hub-height * Updated workflow for specifying wind turbine parameters without specifying a turbine name with PySAM. @@ -13,6 +16,7 @@ * Bugfix for cycle counting in the minimum operating cost objective function - no longer throws an error + ## Version 3.2.0, March 21, 2025 * Updates related to PySAM: diff --git a/hopp/simulation/hopp.py b/hopp/simulation/hopp.py index 2de296256..6f6e46e78 100644 --- a/hopp/simulation/hopp.py +++ b/hopp/simulation/hopp.py @@ -42,6 +42,9 @@ def __attrs_post_init__(self) -> None: def simulate(self, project_life: int = 25, lifetime_sim: bool = False): self.system.simulate(project_life, lifetime_sim) + + def simulate_power(self, project_life: int = 25, lifetime_sim: bool = False): + self.system.simulate_power(project_life, lifetime_sim) # I/O diff --git a/hopp/simulation/hopp_interface.py b/hopp/simulation/hopp_interface.py index 88df556cf..3e0cab729 100644 --- a/hopp/simulation/hopp_interface.py +++ b/hopp/simulation/hopp_interface.py @@ -47,6 +47,9 @@ def reinitialize(self, configuration: Union[dict, str, Path]): def simulate(self, project_life: int = 25, lifetime_sim: bool = False): self.hopp.simulate(project_life, lifetime_sim) + + def simulate_power(self, project_life: int = 25, lifetime_sim: bool = False): + self.hopp.simulate_power(project_life, lifetime_sim) @property def system(self) -> "HybridSimulation": diff --git a/hopp/simulation/hybrid_simulation.py b/hopp/simulation/hybrid_simulation.py index 0e0cf40db..014aa1936 100644 --- a/hopp/simulation/hybrid_simulation.py +++ b/hopp/simulation/hybrid_simulation.py @@ -16,6 +16,7 @@ from hopp.simulation.technologies.csp.trough_plant import TroughConfig, TroughPlant from hopp.simulation.technologies.wave.mhk_wave_plant import MHKWavePlant, MHKConfig from hopp.simulation.technologies.tidal.mhk_tidal_plant import MHKTidalPlant, MHKTidalConfig +from hopp.simulation.technologies.generic.generic_plant import GenericConfig, GenericPlant from hopp.simulation.technologies.battery import Battery, BatteryConfig, BatteryStateless, BatteryStatelessConfig from hopp.simulation.technologies.grid import Grid, GridConfig from hopp.simulation.technologies.reopt import REopt @@ -30,6 +31,7 @@ DetailedPVPlant, WindPlant, MHKWavePlant, + GenericPlant, TowerPlant, TroughPlant, Battery, @@ -39,7 +41,7 @@ class HybridSimulationOutput: """Class for creating :class:`HybridSimulation` output structure""" - _keys = ("pv", "wind", "wave", "tidal", "battery", "tower", "trough", "hybrid") + _keys = ("pv", "wind", "wave", "tidal", "generic", "battery", "tower", "trough", "hybrid") def __init__(self, power_sources): """ @@ -99,6 +101,7 @@ class TechnologiesConfig(BaseClass): wind: Wind config wave: Wave config tidal: Tidal config + generic: Generic config tower: CSP tower config trough: CSP trough config battery: Battery config. If `tracking` is False, uses `BatteryStatelessConfig`. @@ -110,6 +113,7 @@ class TechnologiesConfig(BaseClass): wind: Optional[WindConfig] = field(default=None) wave: Optional[MHKConfig] = field(default=None) tidal: Optional[MHKTidalConfig] = field(default=None) + generic: Optional[Union[GenericConfig,list[GenericConfig]]] = field(default=None) tower: Optional[TowerConfig] = field(default=None) trough: Optional[TroughConfig] = field(default=None) battery: Optional[Union[BatteryConfig, BatteryStatelessConfig]] = field(default=None) @@ -139,6 +143,18 @@ def from_dict(cls, data: dict): if "tidal" in data: config["tidal"] = MHKTidalConfig.from_dict(data["tidal"]) + + if "generic" in data: + if any(isinstance(v,dict) for k,v in data["generic"].items()): + generic_configs = [] + for name,subconfig in data["generic"].items(): + if isinstance(subconfig,dict): + subconfig.setdefault("subsystem_name", name) + generic_config = GenericConfig.from_dict(subconfig) + generic_configs.append(generic_config) + config["generic"] = generic_configs + else: + config["generic"] = GenericConfig.from_dict(data["generic"]) if "tower" in data: config["tower"] = TowerConfig.from_dict(data["tower"]) @@ -191,6 +207,7 @@ class HybridSimulation(BaseClass): wind: Optional[WindPlant] = field(init=False, default=None) wave: Optional[MHKWavePlant] = field(init=False, default=None) tidal: Optional[MHKTidalPlant] = field(init=False, default=None) + generic: Optional[GenericPlant] = field(init=False, default=None) tower: Optional[TowerPlant] = field(init=False, default=None) trough: Optional[TroughPlant] = field(init=False, default=None) battery: Optional[Union[Battery, BatteryStateless]] = field(init=False, default=None) @@ -240,6 +257,14 @@ def __attrs_post_init__(self): self.technologies["tidal"] = self.tidal logger.info("Created HybridSystem.tidal with system size {} mW".format(tidal_config)) + + generic_config = self.tech_config.generic + + if generic_config is not None: + self.generic = GenericPlant(self.site, config=generic_config) + self.technologies["generic"] = self.generic + + logger.info("Created HybridSystem.generic with system size {} mW".format(generic_config)) tower_config = self.tech_config.tower @@ -342,11 +367,13 @@ def setup_cost_calculator(self, cost_calculator: object): def set_om_costs(self, pv_om_per_kw=None, wind_om_per_kw=None, tower_om_per_kw=None, trough_om_per_kw=None, wave_om_per_kw=None, tidal_om_per_kw=None, + generic_om_per_kw=None, battery_om_per_kw=None, hybrid_om_per_kw=None, pv_om_per_mwh=None,wind_om_per_mwh=None, tower_om_per_mwh=None,trough_om_per_mwh=None, wave_om_per_mwh=None,tidal_om_per_mwh=None, + generic_om_per_mwh=None, battery_om_per_mwh=None, hybrid_om_per_mwh=None,): """ @@ -393,6 +420,12 @@ def set_om_costs(self, pv_om_per_kw=None, wind_om_per_kw=None, self.tidal.om_capacity = tidal_om_per_kw if tidal_om_per_mwh: self.tidal.om_production = tidal_om_per_mwh + + if self.generic: + if generic_om_per_kw: + self.generic.om_capacity = generic_om_per_kw + if generic_om_per_mwh: + self.generic.om_production = generic_om_per_mwh if self.battery: if battery_om_per_kw: @@ -458,6 +491,9 @@ def calculate_installed_cost(self): if self.tidal: self.tidal.total_installed_cost = self.tidal.calculate_total_installed_cost() total_cost += self.tidal.total_installed_cost + if self.generic: + self.generic.total_installed_cost = self.generic.calculate_total_installed_cost(cost_kw) + total_cost += self.generic.total_installed_cost if self.tower: self.tower.total_installed_cost = self.tower.calculate_total_installed_cost() total_cost += self.tower.total_installed_cost @@ -692,7 +728,7 @@ def simulate_power(self, project_life: int = 25, lifetime_sim=False): """ self.setup_performance_models() # simulate non-dispatchable systems - non_dispatchable_systems = ['pv', 'wind','wave','tidal'] + non_dispatchable_systems = ['pv', 'wind','wave','tidal','generic'] for system in non_dispatchable_systems: model = getattr(self, system) if model: @@ -919,6 +955,10 @@ def capacity_factors(self) -> HybridSimulationOutput: cf.tidal = self.tidal.capacity_factor hybrid_generation += self.tidal.annual_energy_kwh hybrid_capacity += self.tidal.system_capacity_kw + if self.generic: + cf.generic = self.generic.capacity_factor + hybrid_generation += self.generic.annual_energy_kwh + hybrid_capacity += self.generic.system_capacity_kw if self.tower: cf.tower = self.tower.capacity_factor hybrid_generation += self.tower.annual_energy_kwh @@ -1107,6 +1147,8 @@ def hybrid_simulation_outputs(self, filename: str = "") -> dict: outputs['Wave (MW)'] = self.wave.system_capacity_kw / 1000 if self.tidal: outputs['Tidal (MW)'] = self.tidal.system_capacity_kw / 1000 + if self.generic: + outputs['Generic (MW)'] = self.generic.system_capacity_kw / 1000 if self.tower: outputs['Tower (MW)'] = self.tower.system_capacity_kw / 1000 outputs['Tower Hours of Storage (hr)'] = self.tower.tes_hours diff --git a/hopp/simulation/technologies/dispatch/__init__.py b/hopp/simulation/technologies/dispatch/__init__.py index 4670540f9..b8aa7a2ab 100644 --- a/hopp/simulation/technologies/dispatch/__init__.py +++ b/hopp/simulation/technologies/dispatch/__init__.py @@ -16,6 +16,10 @@ TidalDispatch, ) +from hopp.simulation.technologies.dispatch.power_sources.generic_dispatch import ( + GenericDispatch, +) + from hopp.simulation.technologies.dispatch.grid_dispatch import GridDispatch from hopp.simulation.technologies.dispatch.hybrid_dispatch_options import ( HybridDispatchOptions, diff --git a/hopp/simulation/technologies/dispatch/hybrid_dispatch.py b/hopp/simulation/technologies/dispatch/hybrid_dispatch.py index b73a0d1fc..336ad6499 100644 --- a/hopp/simulation/technologies/dispatch/hybrid_dispatch.py +++ b/hopp/simulation/technologies/dispatch/hybrid_dispatch.py @@ -239,6 +239,10 @@ def wave_generation(self) -> list: @property def tidal_generation(self) -> list: return [self.blocks[t].tidal_generation.value for t in self.blocks.index_set()] + + @property + def generic_generation(self) -> list: + return [self.blocks[t].generic_generation.value for t in self.blocks.index_set()] @property def tower_generation(self) -> list: diff --git a/hopp/simulation/technologies/dispatch/power_sources/__init__.py b/hopp/simulation/technologies/dispatch/power_sources/__init__.py index 94ddf9dcf..492c068e8 100644 --- a/hopp/simulation/technologies/dispatch/power_sources/__init__.py +++ b/hopp/simulation/technologies/dispatch/power_sources/__init__.py @@ -11,3 +11,7 @@ from hopp.simulation.technologies.dispatch.power_sources.tidal_dispatch import ( TidalDispatch, ) + +from hopp.simulation.technologies.dispatch.power_sources.generic_dispatch import ( + GenericDispatch, +) diff --git a/hopp/simulation/technologies/dispatch/power_sources/generic_dispatch.py b/hopp/simulation/technologies/dispatch/power_sources/generic_dispatch.py new file mode 100644 index 000000000..14d97d81f --- /dev/null +++ b/hopp/simulation/technologies/dispatch/power_sources/generic_dispatch.py @@ -0,0 +1,114 @@ +from typing import Union, TYPE_CHECKING +from pyomo.environ import ConcreteModel, Expression, NonNegativeReals, Set, units, Var +from pyomo.network import Port + +if TYPE_CHECKING: + from hopp.simulation.technologies.generic.generic_plant import GenericSystem + from hopp.simulation.technologies.generic.generic_multi import GenericMultiSystem +from hopp.simulation.technologies.financial import FinancialModelType +from hopp.simulation.technologies.dispatch.power_sources.power_source_dispatch import ( + PowerSourceDispatch, +) + + +class GenericDispatch(PowerSourceDispatch): + """Dispatch optimization model for generic power source. + Adapted from tidal_dispatch with minor changes. + """ + + generic_obj: Union[Expression, float] + _system_model: Union["GenericSystem","GenericMultiSystem"] + _financial_model: FinancialModelType + + def __init__( + self, + pyomo_model: ConcreteModel, + indexed_set: Set, + system_model: Union["GenericSystem","GenericMultiSystem"], + financial_model: FinancialModelType, + block_set_name: str = "generic", + ): + """Initialize GenericDispatch. + + Args: + pyomo_model (ConcreteModel): Pyomo concrete model. + indexed_set (Set): Indexed set. + system_model (GenericSystem): System model. + financial_model (FinancialModelType): Financial model. + block_set_name (str): Name of the block set. + + """ + super().__init__( + pyomo_model, + indexed_set, + system_model, + financial_model, + block_set_name=block_set_name, + ) + + def max_gross_profit_objective(self, hybrid_blocks): + """Generic instance of maximum gross profit objective. + + Args: + hybrid_blocks (Pyomo.block): A generalized container for defining hierarchical + models by adding modeling components as attributes. + + """ + self.obj = Expression( + expr=sum( + -(1 / hybrid_blocks[t].time_weighting_factor) + * self.blocks[t].time_duration + * self.blocks[t].cost_per_generation + * hybrid_blocks[t].generic_generation + for t in hybrid_blocks.index_set() + ) + ) + + def min_operating_cost_objective(self, hybrid_blocks): + """Generic instance of minimum operating cost objective. + + Args: + hybrid_blocks (Pyomo.block): A generalized container for defining hierarchical + models by adding modeling components as attributes. + + """ + self.obj = sum( + hybrid_blocks[t].time_weighting_factor + * self.blocks[t].time_duration + * self.blocks[t].cost_per_generation + * hybrid_blocks[t].generic_generation + for t in hybrid_blocks.index_set() + ) + + def _create_variables(self, hybrid): + """Create Generic variables to add to hybrid plant instance. + + Args: + hybrid: Hybrid plant instance. + + Returns: + tuple: Tuple containing created variables. + - generation: Generation from given technology. + - load: Load from given technology. + + """ + hybrid.generic_generation = Var( + doc="Power generation of generic devices [MW]", + domain=NonNegativeReals, + units=units.MW, + initialize=0.0, + ) + return hybrid.generic_generation, 0 + + def _create_port(self, hybrid): + """Create generic port to add to hybrid plant instance. + + Args: + hybrid: Hybrid plant instance. + + Returns: + Port: Generic Port object. + + """ + hybrid.generic_port = Port(initialize={"generation": hybrid.generic_generation}) + return hybrid.generic_port diff --git a/hopp/simulation/technologies/dispatch/power_sources/power_source_dispatch.py b/hopp/simulation/technologies/dispatch/power_sources/power_source_dispatch.py index c1dd1d3fc..fb4e4805f 100644 --- a/hopp/simulation/technologies/dispatch/power_sources/power_source_dispatch.py +++ b/hopp/simulation/technologies/dispatch/power_sources/power_source_dispatch.py @@ -33,6 +33,7 @@ def __init__( financial_model, block_set_name=block_set_name, ) + self.round_digits = int(8) @staticmethod def dispatch_block_rule(gen): diff --git a/hopp/simulation/technologies/generic/__init__.py b/hopp/simulation/technologies/generic/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/hopp/simulation/technologies/generic/generic_multi.py b/hopp/simulation/technologies/generic/generic_multi.py new file mode 100644 index 000000000..157ae5cf1 --- /dev/null +++ b/hopp/simulation/technologies/generic/generic_multi.py @@ -0,0 +1,244 @@ +from typing import Optional, Union, TYPE_CHECKING + +from attrs import define, field +import numpy as np + +from hopp.simulation.base import BaseClass + +if TYPE_CHECKING: + from hopp.simulation.technologies.generic.generic_plant import GenericSystem + +@define +class GenericMultiSystem(BaseClass): + """Represents physics of multiple user-defined generation technologies. + This class combines functionality of both PowerSource and hybrid_simulation.simulate_power() + to ensure that individual technologies (or subsystems) are operated as part of the hybrid_plant + in the same manner that they would be if they were represented as individual PowerSource objects. + + Note: + this functionality is not tested for financial calculations. + + Args: + subsystems (list[GenericSystem]): list of subsystem objects. + subsystem_names (list[str], Optional): list of unique names to identify each subsystem. + If not provided or if duplicate names are used, it will append a number to the end of the names. + system_name (str, Optional): name of the MultiSystem, defaults to "generic_multi". + n_timesteps (float | int, Optional): number of timesteps in the simulation, defaults to 8760. + This attribute is included so that GenericMulti does not require SiteInfo as an input. + + Attributes: + system_capacity (float): system capacity of all subsystems in kW. + system_capacity_ac (float): system capacity of all subsystems in kW-AC. + gen (list[float]): generation profile of all subsystems in kW + annual_energy (float): annual energy production of all subsystems in kWh/year + capacity_factor (float): capacity factor of all the subsystems as a percent + annual_energy_pre_curtailment_ac (float): annual energy production of all subsystems in kWh/year + """ + subsystems: list["GenericSystem"] + subsystem_names: Optional[list[str]] = field(default = []) + system_name: Optional[str] = field(default = "generic_multi") + n_timesteps: Union[float,int] = field(default = 8760) + + # Multi-System aggregated values. + system_capacity: float = field(init = False) + system_capacity_ac: float = field(init = False) + gen: list[float] = field(init = False) + annual_energy: float = field(init = False) + capacity_factor: float = field(init = False) + annual_energy_pre_curtailment_ac: float = field(init = False) + + def __attrs_post_init__(self): + """Initialize some attributes and set defaults as needed. This method does the following: + + 1) ensures that subsystem_names are unique and reassigns names to each subsystem if needed + + 2) updates generation profile and system capacity. This initializes the GenericMultiSystem attributes: + gen, annual_energy, annual_energy_pre_curtailment_ac, system_capacity, system_capacity_ac, and capacity_factor. + """ + if len(self.subsystem_names)==0: + subsystem_names_original = [sub.system_name for sub in self.subsystem_names] + for ni,sub_name in enumerate(subsystem_names_original): + if subsystem_names_original.count(sub_name)>1: + subsystem_names_original[ni] = f"{sub_name}_{ni}" + self.subsystems[ni].value("system_name", f"{sub_name}_{ni}") + self.subsystem_names = subsystem_names_original + self.system_capacity = 0.0 #temporarily set to avoid attribute error when calculating capacity factor + self.update_generation_profile() + self.update_system_capacity(None) + + def value(self, name:str, set_value=None): + """Set or retrieve attribute of `hopp.simulation.technologies.generic.generic_plant.GenericSystem`. + if set_value = None, then retrieve value; otherwise overwrite variable's value. + + Args: + name (str): name of attribute to set or retrieve. + set_value (Optional): value to set for variable `name`. + If `None`, then retrieve value. Defaults to None. + """ + + if set_value is not None: + self.__setattr__(name, set_value) + else: + return self.__getattribute__(name) + + def execute(self, project_life): + """Empty execute function since generation is set during initialization. + + Args: + project_life (int): unused project life in years + """ + return + + def export(self): + """Return all the generic system configuration in a dictionary for the financial model + + Returns: + dict: generic system configuration for the financial model. + """ + + config = { + 'system_capacity': self.system_capacity, + } + return config + + def update_generation_profile(self): + """Recalculate ``gen`` attribute after subsystem generation profiles may have been updated. Also updates ``annual_energy``, + ``annual_energy_pre_curtailment_ac``, and ``capacity_factor``. + """ + + generation_profile_kW = np.zeros(self.n_timesteps) + + for sub in self.subsystems: + generation_profile_kW += np.array(sub.value("gen")) + self.value("annual_energy_pre_curtailment_ac",np.sum(generation_profile_kW)) + self.value("annual_energy",np.sum(generation_profile_kW)) + self.value("gen",generation_profile_kW.tolist()) + + self.update_capacity_factor() + + def update_system_capacity(self, placeholder): + """Recalculate ``system_capacity`` attribute after subsystem system capacities may have been updated. + Also updates ``system_capacity_ac`` and ``capacity_factor``. + + Args: + placeholder (None): unused placeholder value so this function parallels + the function in ``hopp.simulation.technologies.generic.generic_plant.GenericSystem`` + """ + + system_capacity_kw = 0.0 + system_capacity_kwac = 0.0 + for sub in self.subsystems: + system_capacity_kw += np.array(sub.value("system_capacity")) + system_capacity_kwac += np.array(sub.value("system_capacity_ac")) + self.value("system_capacity",system_capacity_kw) + self.value("system_capacity_ac",system_capacity_kwac) + self.update_capacity_factor() + + def update_capacity_factor(self): + """Recalculate and update ``capacity_factor`` as a percent (%) + """ + + if self.system_capacity>0: + capacity_factor = 100*(np.sum(self.gen)/(len(self.gen)*self.system_capacity)) + else: + capacity_factor = 0.0 + self.value("capacity_factor",capacity_factor) + + def get_subsystem_from_name(self, subsystem_name:str): + """Retrieve subsystem object with system_name==subsystem_name. + + Args: + subsystem_name (str): name of subsystem, `corresponding to GenericSystem.system_name` + + Raises: + UserWarning: if subsystem_name doesn't match system_name of any subsystems. + + Returns: + :obj:`hopp.simulation.technologies.generic.generic_plant.GenericSystem`: GenericSystem object + """ + + subs = [sub.system_name for sub in self.subsystems if sub.system_name==subsystem_name] + if len(subs)==1: + return subs[0] + raise UserWarning(f"No subsystems have unique system_name: {subsystem_name}") + + def update_generation_profile_for_subsystem(self, generation_profile_kW:Union[list,np.ndarray], subsystem_name:str): + """Update the generation profile for a single subsystem. + + Args: + generation_profile_kW (Union[list,np.ndarray]): generation profile of subsystem in kW + subsystem_name (str): name of subsystem to set the generation profile for. + """ + + subsystem = self.get_subsystem_from_name(subsystem_name) + subsystem.update_generation_profile(generation_profile_kW) + self.update_generation_profile() + + def update_system_capacity_for_subsystem(self, system_capacity_kw:Union[float,int], subsystem_name:str): + """Update the system capacity for a single subsystem. + + Args: + system_capacity_kw (Union[float,int]): system capacity of subsystem. + subsystem_name (str): name of subsystem to set the system capacity for. + """ + + subsystem = self.get_subsystem_from_name(subsystem_name) + subsystem.update_system_capacity(system_capacity_kw) + self.update_system_capacity(None) + + def set_subsystem_value(self, subsystem_name:str, variable_name:str, value): + """Set attribute of `hopp.simulation.technologies.generic.generic_plant.GenericSystem` + + Args: + subsystem_name (str): name of subsystem, corresponds to `hopp.simulation.technologies.generic.generic_plant.GenericSystem.system_name` + variable_name (str): name of attribute to retrieve. + value (Any): value to set for variable `variable_name`. + """ + + subsystem = self.get_subsystem_from_name(subsystem_name) + subsystem.value(variable_name,value) + + def get_subsystem_value(self, subsystem_name:str, variable_name:str): + """Retrieve attribute of `hopp.simulation.technologies.generic.generic_plant.GenericSystem` + + Args: + subsystem_name (str): name of subsystem, corresponds to `hopp.simulation.technologies.generic.generic_plant.GenericSystem.system_name` + variable_name (str): name of attribute to retrieve. + + Returns: + any: value for ``variable_name`` attribute of subsystem corresponding to ``subsystem_name`` + """ + + subsystem = self.get_subsystem_from_name(subsystem_name) + return subsystem.value(variable_name) + + def calc_nominal_capacity(self, interconnect_kw: float): + """Calculates the nominal AC net system capacity per subsystem. + + Args: + interconnect_kw (float): grid interconnection limit in kW + + Returns: + float: sum of subsystem's nominal AC net capacity [kW] + """ + W_ac_nom = 0.0 + for sub in self.subsystems: + W_ac = sub.calc_nominal_capacity(interconnect_kw) + W_ac_nom += W_ac + return W_ac_nom + + def calc_gen_max_feasible_kwh(self, interconnect_kw: float): + """Calculates the maximum feasible generation profile that could have occurred (year 1) + + Args: + interconnect_kw (float): grid interconnection limit in kW + + Returns: + list[float]: sum of subsystem's maximum feasible generation profile [kWh] + """ + E_net_max_feasible = np.zeros(self.n_timesteps) + + for sub in self.subsystems: + E_net_max_feasible_sub = sub.calc_gen_max_feasible_kwh(interconnect_kw) + E_net_max_feasible += np.array(E_net_max_feasible_sub) + return E_net_max_feasible.tolist() \ No newline at end of file diff --git a/hopp/simulation/technologies/generic/generic_plant.py b/hopp/simulation/technologies/generic/generic_plant.py new file mode 100644 index 000000000..d6b82e4b5 --- /dev/null +++ b/hopp/simulation/technologies/generic/generic_plant.py @@ -0,0 +1,348 @@ +from typing import Optional, Union + +from attrs import define, field +import numpy as np + +from hopp.simulation.technologies.power_source import PowerSource +from hopp.simulation.base import BaseClass +from hopp.simulation.technologies.financial import CustomFinancialModel, FinancialModelType +from hopp.simulation.technologies.sites import SiteInfo +import PySAM.Singleowner as Singleowner +from hopp.simulation.technologies.generic.generic_multi import GenericMultiSystem +from hopp.utilities.validators import gt_zero +import warnings + +@define +class GenericConfig(BaseClass): + """Configuration class for GenericPlant + + Args: + system_capacity_kw (float): system capacity in kW. + system_capacity_kwac (float, Optional): system capacity in kWac. If not provided then defaults to system_capacity_kw. + generation_profile_kw (list[float]): generation profile of system in kW. + subsystem_name (str, Optional): name of subsystem, only used if ``GenericMultiSystem`` is the system_model. + n_timesteps (float | int): number of timesteps in a year, defaults to 8760. + fin_model (obj | dict | str): Optional financial model. Can be any of the following: + + - a string representing an argument to `Singleowner.default` + + - a dict representing a `CustomFinancialModel` + + - an object representing a `CustomFinancialModel` or `Singleowner.Singleowner` instance + + """ + + system_capacity_kw: float = field(default = 0.0) + system_capacity_kwac: Optional[float] = field(default = None) + generation_profile_kw: Optional[list[float]] = field(default = None) + subsystem_name: Optional[str] = field(default="generic_system") + + n_timesteps: Union[float,int] = field(default = 8760) + fin_model: Optional[Union[dict, FinancialModelType]] = field(default=None) + name: str = field(default="GenericPlant") + + + +@define +class GenericSystem(BaseClass): + """Represents a single generic system defined by its system capacity and generation profile. + + Args: + system_capacity (float): system capacity in kW. For DC-systems, + this is likely equal to the system capacity in kW-DC. + gen (list[float]): generation profile in kW. + system_capacity_ac (float, Optional): system capacity in kW-AC. Defaults to system_capacity if not specified. + Should be specified if `system_capacity_ac` is different than `system_capacity`. + This input is helpful when representing DC-systems such as ``PVPlant``. + system_name (str, Optional): name of system, primarily used if using `GenericMultiSystem`. + Defaults to "generic_system". + n_timesteps (float | int, Optional): number of timesteps in a simulation. Defaults to 8760. + t_step (float | int, Optional): time step in hours. Defaults to 1. + + Attributes: + annual_energy (float): annual energy production in kWh/year + capacity_factor (float): capacity factor of the system based on system_capacity as a percent + annual_energy_pre_curtailment_ac (float): annual energy production in kWh/year + """ + + system_capacity: float = field(validator=gt_zero) + gen: list[float] + + system_capacity_ac: Optional[float] = field(default = None) + system_name: Optional[str] = field(default = "generic_system") + n_timesteps: Optional[Union[float,int]] = field(default = 8760) + t_step: Optional[Union[float,int]] = field(default = 1) + + # Calculated values + annual_energy: float = field(init = False) + capacity_factor: float = field(init = False) + annual_energy_pre_curtailment_ac: float = field(init = False) + + def __attrs_post_init__(self): + """Initialize some attributes and set defaults if needed. This method does the following: + + 1) calculate attributes: + - `annual_energy` + - `annual_energy_pre_curtailment_ac` + - `capacity_factor` + + 2) set `system_capacity_ac` to `system_capacity` if `system_capacity_ac` was not input. + + Raises: + ValueError: if length of self.gen is not equal to self.n_timesteps + """ + + self.annual_energy = np.sum(self.gen) + self.annual_energy_pre_curtailment_ac = np.sum(self.gen) + + if self.system_capacity_ac is None: + self.system_capacity_ac = self.system_capacity + + if len(self.gen)!=self.n_timesteps: + msg = ( + f"Generation profile expected to have {self.n_timesteps} values but " + f"has {len(self.gen)} values." + ) + + raise ValueError(msg) + + self.update_capacity_factor() + + def value(self, name: str, set_value=None): + """Set or retrieve attribute of `hopp.simulation.technologies.generic.generic_plant.GenericSystem`. + if set_value = None, then retrieve value; otherwise overwrite variable's value. + + Args: + name (str): name of attribute to set or retrieve. + set_value (Optional): value to set for variable `name`. + If `None`, then retrieve value. Defaults to None. + """ + + if set_value is not None: + self.__setattr__(name, set_value) + else: + return self.__getattribute__(name) + + def execute(self, project_life): + """Empty execute function since generation is set during initialization. + + Args: + project_life (int): currently unused project life in years. + May be used in financial calculation in the future. + """ + return + + def export(self): + """Return all the generic system configuration in a dictionary for the financial model + + Returns: + dict: generic system configuration for the financial model. + """ + + config = { + 'system_capacity': self.system_capacity, + } + return config + + def update_capacity_factor(self): + """Recalculate and update system capacity_factor as a percent (%) + """ + + if self.system_capacity>0: + capacity_factor = 100*(np.sum(self.gen)/(len(self.gen)*self.system_capacity)) + else: + capacity_factor = 0.0 + self.value("capacity_factor",capacity_factor) + + def update_system_capacity(self,system_capacity_kw:Union[float,int]): + """Update ``system_capacity`` attribute and recalculate ``capacity_factor``. + Also updates ``system_capacity_ac`` if it was previously equal to ``system_capacity``. + + Note: + If system_capacity_ac is different than system_capacity, please be sure + to update system_capacity_ac using the `value()` function. + + Args: + system_capacity_kw (float | int): system capacity in kW + """ + + if self.system_capacity!=self.system_capacity_ac: + msg = ( + f"Resetting system_capacity for {self.system_name} but system_capacity_ac ({self.system_capacity_ac}) " + f"is different than system_capacity ({self.system_capacity}). Remember to update system_capacity_ac too." + ) + warnings.warn(msg,UserWarning) + else: + self.value("system_capacity_ac",system_capacity_kw) + + self.value("system_capacity",system_capacity_kw) + self.update_capacity_factor() + + def update_generation_profile(self,generation_profile_kW:Union[list,np.ndarray]): + """Reset the generation profile and update corresponding attributes + (`gen`, `annual_energy_pre_curtailment_ac`, `annual_energy`, and `capacity_factor`). + + Args: + generation_profile_kW (Union[list,np.ndarray]): generation profile in kW + + Raises: + ValueError: if input generation_profile_kW is not same length as gen attribute. + """ + + if len(generation_profile_kW)==self.n_timesteps: + if isinstance(generation_profile_kW,list): + generation_profile_kW = np.array(generation_profile_kW) + + self.value("annual_energy_pre_curtailment_ac",np.sum(generation_profile_kW)) + self.value("annual_energy",np.sum(generation_profile_kW)) + self.value("gen",list(generation_profile_kW)) + self.update_capacity_factor() + return + msg = ( + "Generation profile is not correct length. " + f"Should be length {self.n_timesteps} but is length {len(generation_profile_kW)}") + raise ValueError(msg) + + def calc_nominal_capacity(self,interconnect_kw: float): + """Calculates the nominal AC net system capacity. + + Args: + interconnect_kw (float): grid interconnection limit in kW + + Returns: + float: system's nominal AC net capacity [kW] + """ + + W_ac_nom = min(self.system_capacity_ac, interconnect_kw) + return W_ac_nom + + def calc_gen_max_feasible_kwh(self, interconnect_kw: float): + """Calculates the maximum feasible generation profile that could have occurred (year 1) + + Args: + interconnect_kw (float): grid interconnection limit in kW + + Returns: + list[float]: maximum feasible generation [kWh] + """ + + W_ac_nom = self.calc_nominal_capacity(interconnect_kw) + + E_net_max_feasible = [min(x,W_ac_nom) * self.t_step for x in self.gen[0:self.n_timesteps]] # [kWh] + return E_net_max_feasible + +@define +class GenericPlant(PowerSource): + site: SiteInfo + config: Union[GenericConfig,list[GenericConfig]] + config_name: str = field(init=False, default="CustomGenerationProfileSingleOwner") + + def __attrs_post_init__(self): + t_step = self.site.interval / 60 + + if isinstance(self.config,list): + # requires GenericMultiSystem as system_model + subsystems = [] + subsystem_names = [] + for config in self.config: + sub = GenericSystem( + system_capacity = config.system_capacity_kw, + gen = config.generation_profile_kw, + n_timesteps = config.n_timesteps, + system_capacity_ac = config.system_capacity_kwac, + system_name = config.subsystem_name, + t_step = t_step, + ) + subsystems.append(sub) + subsystem_names.append(config.subsystem_name) + system_model = GenericMultiSystem(subsystems,subsystem_names=subsystem_names) + fin_model = self.config[0].fin_model + fin_model_name = self.config[0].name + else: + # requires GenericSystem as system_model + system_model = GenericSystem( + system_capacity = self.config.system_capacity_kw, + gen = self.config.generation_profile_kw, + n_timesteps = self.config.n_timesteps, + system_capacity_ac = self.config.system_capacity_kwac, + system_name = self.config.subsystem_name, + t_step = t_step, + ) + fin_model = self.config.fin_model + fin_model_name = self.config.name + + + financial_model = None + if isinstance(fin_model, str): + if "singleowner" in fin_model.lower(): + financial_model = Singleowner.default(fin_model) + elif isinstance(fin_model, dict): + financial_model = CustomFinancialModel(fin_model, name=fin_model_name) + else: + financial_model = fin_model + if financial_model is None: + financial_model = Singleowner.default(self.config_name) + else: + financial_model = self.import_financial_model( + financial_model, system_model, self.config_name + ) + + super().__init__("GenericPlant", self.site, system_model, financial_model) + self._dispatch = None + self._layout = None + + @property + def system_capacity_kw(self): + """float: System capacity in kW. + """ + return self._system_model.value("system_capacity") + + @system_capacity_kw.setter + def system_capacity_kw(self, size_kw: float): + self._system_model.update_system_capacity(size_kw) + + @property + def system_capacity_kwac(self): + """float: AC system capacity in kW-AC. + """ + return self._system_model.value("system_capacity_ac") + + @system_capacity_kw.setter + def system_capacity_kwac(self, size_kwac: float): + self._system_model.value("system_capacity_ac",size_kwac) + + @property + def generation_profile(self): + """list[float]: generation profile in kW. + """ + return self._system_model.value("gen") + + @generation_profile.setter + def generation_profile(self, generation_profile_kW:Union[list,np.ndarray]): + self._system_model.update_generation_profile(generation_profile_kW) + + def calc_nominal_capacity(self, interconnect_kw: float): + """Calculates the nominal AC net system capacity. + + Args: + interconnect_kw (float): grid interconnection limit in kW + + Returns: + float: sum of subsystem's nominal AC net capacity [kW] + """ + + W_ac_nom = self._system_model.calc_nominal_capacity(interconnect_kw) + return W_ac_nom + + def calc_gen_max_feasible_kwh(self, interconnect_kw: float): + """Calculates the maximum feasible generation profile that could have occurred (year 1). + + Args: + interconnect_kw (float): grid interconnection limit in kW + + Returns: + list[float]: maximum feasible generation timeseries [kWh] + """ + + E_net_max_feasible = self._system_model.calc_gen_max_feasible_kwh(interconnect_kw) + return E_net_max_feasible diff --git a/hopp/simulation/technologies/grid.py b/hopp/simulation/technologies/grid.py index d54d489be..5f8f6fd39 100644 --- a/hopp/simulation/technologies/grid.py +++ b/hopp/simulation/technologies/grid.py @@ -280,6 +280,8 @@ def generation_profile_wo_battery(self) -> Sequence: @generation_profile_wo_battery.setter def generation_profile_wo_battery(self, system_generation_wo_battery_kw: Sequence): + if isinstance(self._financial_model,Singleowner.Singleowner): + self._financial_model.value('gen_without_battery',system_generation_wo_battery_kw) self._system_model.SystemOutput.gen = system_generation_wo_battery_kw @property diff --git a/hopp/simulation/technologies/sites/site_info.py b/hopp/simulation/technologies/sites/site_info.py index 09f3e4bdf..f8062cdaf 100644 --- a/hopp/simulation/technologies/sites/site_info.py +++ b/hopp/simulation/technologies/sites/site_info.py @@ -154,7 +154,7 @@ class SiteInfo(BaseClass): wave_resource: Optional[WaveResource] = field(init=False, default=None) tidal_resource: Optional[TidalResource] = field(init=False, default=None) elec_prices: Optional[ElectricityPrices] = field(init=False, default=None) - n_timesteps: int = field(init=False, default=None) + n_timesteps: Optional[int] = field(default=8760) n_periods_per_day: int = field(init=False) interval: int = field(init=False) urdb_label: str = field(init=False) diff --git a/tests/hopp/test_generic_plant.py b/tests/hopp/test_generic_plant.py new file mode 100644 index 000000000..0b489c929 --- /dev/null +++ b/tests/hopp/test_generic_plant.py @@ -0,0 +1,534 @@ +from pytest import approx, fixture + +import numpy as np + +from hopp.simulation import HoppInterface + +from hopp import ROOT_DIR +from hopp.utilities import load_yaml + +FLORIS_V4_TEMPLATE_PATH = ROOT_DIR.parent / "tests"/"hopp"/"inputs"/"floris_v4_empty_layout.yaml" +DEFAULT_WIND_RESOURCE_FILE = ROOT_DIR / "simulation" / "resource_files" / "wind" / "35.2018863_-101.945027_windtoolkit_2012_60min_80m_100m.srw" +DEFAULT_SOLAR_RESOURCE_FILE = ROOT_DIR / "simulation" / "resource_files" / "solar" / "35.2018863_-101.945027_psmv3_60_2012.csv" + +@fixture +def site_info(): + site_dict = { + "data": { + "lat": 35.2018863, + "lon": -101.945027, + "year": 2012, + "site_details": { + "site_shape": "square", + "site_area_km2": 2.0, + }, + }, + "solar_resource_file": DEFAULT_SOLAR_RESOURCE_FILE, + "wind_resource_file": DEFAULT_WIND_RESOURCE_FILE, + "solar": True, + "wind": True, + "hub_height": 90.0, + # "follow_desired_schedule": True, + "curtailment_value_type": "interconnect_kw", + "desired_schedule": [18.0]*8760, + } + return site_dict + +@fixture +def generic_site(): + site_dict = { + "data": { + "lat": 35.2018863, + "lon": -101.945027, + "year": 2012, + "site_details": { + "site_shape": "square", + "site_area_km2": 2.0, + }, + }, + "solar": False, + "wind": False, + # "follow_desired_schedule": True, + "curtailment_value_type": "interconnect_kw", + "desired_schedule": [18.0]*8760, + "n_timesteps": 8760, + } + return site_dict + +@fixture +def hybrid_tech_config(): + """Loads the config YAML and updates site info to use resource files.""" + floris_template = load_yaml(str(FLORIS_V4_TEMPLATE_PATH)) + technologies = { + "pv": { + "system_capacity_kw": 120600, + "panel_tilt_angle": "lat-func", + "dc_ac_ratio": 1.34, + "inv_eff": 96.0, + "losses": 14.0757, + }, + "wind": { + "num_turbines": 15, + "turbine_name": "NREL_Reference_5MW_126", + "model_name": "floris", + "floris_config":floris_template, + "layout_mode": "basicgrid", + "layout_params": { + "row_D_spacing": 5.0, + "turbine_D_spacing": 5.0, + } + + }, + "battery": { + "system_capacity_kw": 25000, + "system_capacity_kwh": 100000, + "minimum_SOC": 10.0, + "maximum_SOC": 100.0, + "initial_SOC": 10.0, + }, + "grid": { + "interconnect_kw": 180000.0, + "ppa_price": 0.01, + } + } + return technologies + +@fixture +def dispatch_options(): + dispatch_opt = { + "battery_dispatch": "load_following_heuristic", + "solver": "cbc", + "n_look_ahead_periods": 48, + "grid_charging": False, + "pv_charging_only": False, + "include_lifecycle_count": False, + } + return dispatch_opt + + +def test_generic_hybrid_with_storage_dispatch(hybrid_tech_config,site_info,dispatch_options,generic_site,subtests): + """Test generic plant functionality for a wind, pv, and battery system. + This uses GenericMultiSystem as the GenericPlant system_model. + """ + + hopp_config_renewables = { + "site": site_info, + "technologies": hybrid_tech_config, + "config": {"dispatch_options":dispatch_options}, + } + # simulate renewables + hi = HoppInterface(hopp_config_renewables) + hi.system.simulate(project_life = 1) + hybrid_plant = hi.system + + pv_size_kwac = hybrid_plant.pv._system_model.SystemDesign.system_capacity/hybrid_plant.pv._system_model.SystemDesign.dc_ac_ratio + wind_generation_profile = np.array(hybrid_plant.wind.generation_profile) + pv_generation_profile = np.array(hybrid_plant.pv.generation_profile) + wind_pv_generation = wind_generation_profile + pv_generation_profile + + hopp_config_generic = { + "site": generic_site, + "technologies": { + "generic": { + "pv_system": { + "system_capacity_kw": hybrid_plant.pv._system_model.SystemDesign.system_capacity, + "system_capacity_kwac": pv_size_kwac, + "generation_profile_kw": np.array(hybrid_plant.pv.generation_profile).tolist(), + }, + "wind_system": { + "system_capacity_kw": hybrid_plant.wind.system_capacity_kw, + "system_capacity_kwac": hybrid_plant.wind.system_capacity_kw, + "generation_profile_kw": np.array(hybrid_plant.wind.generation_profile).tolist(), + }, + }, + "battery": hybrid_tech_config["battery"], + "grid": hybrid_tech_config["grid"], + }, + "config": {"dispatch_options":dispatch_options}, + } + + generic_hi = HoppInterface(hopp_config_generic) + generic_hi.system.simulate(project_life = 1) + hybrid_generic_plant = generic_hi.system + + generation_hybrid = np.array(hybrid_plant.generation_profile.grid) + generation_generic = np.array(hybrid_generic_plant.generation_profile.grid) + + # hybrid nominal capacity is set after simulate_grid_connection() + # calculated in calc_nominal_capacity() - which is AC capacity + with subtests.test("hybrid_nominal_capacity"): + assert hybrid_generic_plant.grid.hybrid_nominal_capacity == approx(hybrid_plant.grid.hybrid_nominal_capacity,1e-6) + + # hybrid_size_kw input to simulate_grid_connection() + with subtests.test("hybrid_size_kw"): + assert hybrid_generic_plant.grid.system_capacity_kw == approx(hybrid_plant.grid.system_capacity_kw) + + # check that generation profile was set properly + with subtests.test("Generic Generation Profile"): + np.testing.assert_allclose( + hybrid_generic_plant.generation_profile.generic, + wind_pv_generation, + rtol = 1e-6 + ) + + # check gen max feasible + with subtests.test("total_gen_max_feasible_year1"): + wind_gen_max_feasible = hybrid_plant.wind.calc_gen_max_feasible_kwh(hybrid_plant.interconnect_kw) + pv_gen_max_feasible = hybrid_plant.pv.calc_gen_max_feasible_kwh(hybrid_plant.interconnect_kw) + wind_pv_gen_max_feasible = np.array(wind_gen_max_feasible) + np.array(pv_gen_max_feasible) + np.testing.assert_allclose( + hybrid_generic_plant.generic.gen_max_feasible, + wind_pv_gen_max_feasible, + rtol = 1e-6 + ) + + # based on total_gen_max_feasible_year1 input to simulate_grid_connection() + with subtests.test("grid.gen_max_feasible"): + np.testing.assert_allclose( + hybrid_generic_plant.grid.gen_max_feasible, + hybrid_plant.grid.gen_max_feasible, + rtol = 1e-6 + ) + + # total_gen_max_feasible_year1 input to simulate_grid_connection() + with subtests.test("grid.total_gen_max_feasible_year1"): + np.testing.assert_allclose( + hybrid_generic_plant.grid.total_gen_max_feasible_year1, + hybrid_plant.grid.total_gen_max_feasible_year1, + rtol = 1e-6 + ) + + with subtests.test("system_pre_interconnect_kwac"): + np.testing.assert_allclose( + hybrid_generic_plant.grid._system_model.Outputs.system_pre_interconnect_kwac, + hybrid_plant.grid._system_model.Outputs.system_pre_interconnect_kwac, + rtol = 1e-6, + ) + + with subtests.test("Grid AEP"): + assert np.sum(generation_generic) == approx(np.sum(generation_hybrid),1e-6) + + # total_gen is input to simulate_grid_connection + with subtests.test("generation_profile_wo_battery"): + np.testing.assert_allclose( + hybrid_generic_plant.grid.generation_profile_wo_battery, + hybrid_plant.grid.generation_profile_wo_battery, + rtol = 1e-6, + ) + + # hybrid_plant.grid.generation_profile + with subtests.test("generation_profile.grid"): + np.testing.assert_allclose( + hybrid_generic_plant.generation_profile.grid, + hybrid_plant.generation_profile.grid, + rtol = 1e-6, + ) + +def test_generic_wind_with_storage_dispatch(hybrid_tech_config,site_info,dispatch_options,generic_site,subtests): + """Test generic plant functionality for a wind and battery system. + This uses GenericSystem as the GenericPlant system_model. + """ + + techs = ['wind','battery','grid'] + tech_config = {k:v for k,v in hybrid_tech_config.items() if k in techs} + hopp_config_renewables = { + "site": site_info, + "technologies": tech_config, + "config": {"dispatch_options":dispatch_options}, + } + + # simulate renewables + hi = HoppInterface(hopp_config_renewables) + hi.system.simulate_power(project_life = 1) + hybrid_plant = hi.system + + wind_generation_profile = np.array(hybrid_plant.wind._system_model.gen) + + hopp_config_generic = { + "site": generic_site, + "technologies": { + "generic": { + "system_capacity_kw": hybrid_plant.wind.system_capacity_kw, + "generation_profile_kw": list(wind_generation_profile) + }, + "battery": hybrid_tech_config["battery"], + "grid": hybrid_tech_config["grid"], + }, + "config": {"dispatch_options":dispatch_options}, + } + + generic_hi = HoppInterface(hopp_config_generic) + generic_hi.system.simulate_power(project_life = 1) + hybrid_generic_plant = generic_hi.system + + generation_hybrid = np.array(hybrid_plant.generation_profile.grid) + generation_generic = np.array(hybrid_generic_plant.generation_profile.grid) + + with subtests.test("grid.hybrid_nominal_capacity"): + assert hybrid_generic_plant.grid.hybrid_nominal_capacity == approx(hybrid_plant.grid.hybrid_nominal_capacity,1e-6) + + with subtests.test("hybrid_size_kw"): + assert hybrid_generic_plant.grid.system_capacity_kw == approx(hybrid_plant.grid.system_capacity_kw) + + with subtests.test("Grid AEP"): + assert np.sum(generation_generic) == approx(np.sum(generation_hybrid),1e-6) + + with subtests.test("generation_profile.grid"): + np.testing.assert_allclose(hybrid_generic_plant.generation_profile.grid, hybrid_plant.generation_profile.grid,rtol = 1e-6) + + +def test_generic_pv_with_storage_dispatch(hybrid_tech_config,site_info,dispatch_options,generic_site,subtests): + """Test generic plant functionality for a pv and battery system. + This uses GenericSystem as the GenericPlant system_model. + """ + + techs = ['pv','battery','grid'] + tech_config = {k:v for k,v in hybrid_tech_config.items() if k in techs} + hopp_config_renewables = { + "site": site_info, + "technologies": tech_config, + "config": {"dispatch_options":dispatch_options}, + } + + # simulate renewables + hi = HoppInterface(hopp_config_renewables) + hi.system.simulate_power(project_life = 1) + hybrid_plant = hi.system + pv_size_kwac = hybrid_plant.pv._system_model.SystemDesign.system_capacity/hybrid_plant.pv._system_model.SystemDesign.dc_ac_ratio + hopp_config_generic = { + "site": generic_site, + "technologies": { + "generic": { + "system_capacity_kw": hybrid_plant.pv._system_model.SystemDesign.system_capacity, + "system_capacity_kwac": pv_size_kwac, + "generation_profile_kw": list(np.array(hybrid_plant.pv._system_model.Outputs.gen)) + }, + "battery": hybrid_tech_config["battery"], + "grid": hybrid_tech_config["grid"], + }, + "config": {"dispatch_options":dispatch_options}, + } + + generic_hi = HoppInterface(hopp_config_generic) + generic_hi.system.simulate_power(project_life = 1) + hybrid_generic_plant = generic_hi.system + + generation_hybrid = np.array(hybrid_plant.generation_profile.grid) + generation_generic = np.array(hybrid_generic_plant.generation_profile.grid) + + with subtests.test("grid.hybrid_nominal_capacity"): + assert hybrid_generic_plant.grid.hybrid_nominal_capacity == approx(hybrid_plant.grid.hybrid_nominal_capacity,1e-6) + + with subtests.test("hybrid_size_kw"): + assert hybrid_generic_plant.grid.system_capacity_kw == approx(hybrid_plant.grid.system_capacity_kw) + + with subtests.test("Grid AEP"): + assert np.sum(generation_generic) == approx(np.sum(generation_hybrid),1e-6) + + with subtests.test("Grid generation profile"): + np.testing.assert_allclose(generation_generic, generation_hybrid,rtol = 1e-6) + +def test_generic_hybrid(hybrid_tech_config,site_info,dispatch_options,generic_site,subtests): + """Test generic plant functionality for a wind and pv system. + This uses GenericMultiSystem as the GenericPlant system_model. + """ + + techs = ['pv','wind','grid'] + tech_config = {k:v for k,v in hybrid_tech_config.items() if k in techs} + hopp_config_renewables = { + "site": site_info, + "technologies": tech_config, + "config": {"dispatch_options":dispatch_options}, + } + + # simulate renewables + hi = HoppInterface(hopp_config_renewables) + hi.system.simulate_power(project_life = 1) + hybrid_plant = hi.system + + pv_size_kwac = hybrid_plant.pv._system_model.SystemDesign.system_capacity/hybrid_plant.pv._system_model.SystemDesign.dc_ac_ratio + wind_generation_profile = np.array(hybrid_plant.wind.generation_profile) + pv_generation_profile = np.array(hybrid_plant.pv.generation_profile) + wind_pv_generation = wind_generation_profile + pv_generation_profile + + hopp_config_generic = { + "site": generic_site, + "technologies": { + "generic": { + "pv_system": { + "system_capacity_kw": hybrid_plant.pv._system_model.SystemDesign.system_capacity, + "system_capacity_kwac": pv_size_kwac, + "generation_profile_kw": np.array(hybrid_plant.pv.generation_profile).tolist(), + }, + "wind_system": { + "system_capacity_kw": hybrid_plant.wind.system_capacity_kw, + "system_capacity_kwac": hybrid_plant.wind.system_capacity_kw, + "generation_profile_kw": np.array(hybrid_plant.wind.generation_profile).tolist(), + }, + }, + "grid": hybrid_tech_config["grid"], + }, + "config": {"dispatch_options":dispatch_options}, + } + + generic_hi = HoppInterface(hopp_config_generic) + generic_hi.system.simulate_power(project_life = 1) + hybrid_generic_plant = generic_hi.system + + generation_hybrid = np.array(hybrid_plant.generation_profile.grid) + generation_generic = np.array(hybrid_generic_plant.generation_profile.grid) + + # check hybrid nominal capacity + with subtests.test("grid.hybrid_nominal_capacity"): + assert hybrid_generic_plant.grid.hybrid_nominal_capacity == approx(hybrid_plant.grid.hybrid_nominal_capacity,1e-6) + + # check gen max feasible + with subtests.test("gen_max_feasible"): + wind_gen_max_feasible = hybrid_plant.wind.calc_gen_max_feasible_kwh(hybrid_plant.interconnect_kw) + pv_gen_max_feasible = hybrid_plant.pv.calc_gen_max_feasible_kwh(hybrid_plant.interconnect_kw) + wind_pv_gen_max_feasible = np.array(wind_gen_max_feasible) + np.array(pv_gen_max_feasible) + np.testing.assert_allclose( + hybrid_generic_plant.generic.gen_max_feasible, + wind_pv_gen_max_feasible, + rtol = 1e-6 + ) + + # check that generation profile was set properly + with subtests.test("Generic Generation Profile"): + np.testing.assert_allclose( + hybrid_generic_plant.generation_profile.generic, + wind_pv_generation, + rtol = 1e-6 + ) + + # check total gen max feasible year 1 + with subtests.test("grid.total_gen_max_feasible_year1"): + np.testing.assert_allclose( + hybrid_generic_plant.grid.total_gen_max_feasible_year1, + hybrid_plant.grid.total_gen_max_feasible_year1, + rtol = 1e-6 + ) + + with subtests.test("grid.system_pre_interconnect_kwac"): + np.testing.assert_allclose( + hybrid_generic_plant.grid._system_model.Outputs.system_pre_interconnect_kwac, + hybrid_plant.grid._system_model.Outputs.system_pre_interconnect_kwac, + rtol = 1e-6 + ) + + with subtests.test("Grid AEP"): + assert np.sum(generation_generic) == approx(np.sum(generation_hybrid),1e-6) + + with subtests.test("generation_profile.grid"): + np.testing.assert_allclose(generation_generic, generation_hybrid,rtol = 1e-6) + + +def test_generic_wind_with_pv_and_storage_dispatch(hybrid_tech_config,site_info,dispatch_options,subtests): + """Test generic plant functionality with other generation technologies. GenericPlant is + used to substitute the wind system and runs PV, battery, and grid as normal. + This uses GenericMultiSystem as the GenericPlant system_model. + """ + + hopp_config_renewables = { + "site": site_info, + "technologies": hybrid_tech_config, + "config": {"dispatch_options":dispatch_options}, + } + # simulate renewables + hi = HoppInterface(hopp_config_renewables) + hi.system.simulate(project_life = 1) + hybrid_plant = hi.system + + wind_generation_profile = np.array(hybrid_plant.wind.generation_profile) + + hopp_config_generic = { + "site": site_info, + "technologies": { + "generic": { + "wind_system": { + "system_capacity_kw": hybrid_plant.wind.system_capacity_kw, + "system_capacity_kwac": hybrid_plant.wind.system_capacity_kw, + "generation_profile_kw": np.array(hybrid_plant.wind.generation_profile).tolist(), + }, + }, + "pv": hybrid_tech_config["pv"], + "battery": hybrid_tech_config["battery"], + "grid": hybrid_tech_config["grid"], + }, + "config": {"dispatch_options":dispatch_options}, + } + + generic_hi = HoppInterface(hopp_config_generic) + generic_hi.system.simulate(project_life = 1) + hybrid_generic_plant = generic_hi.system + + generation_hybrid = np.array(hybrid_plant.generation_profile.grid) + generation_generic = np.array(hybrid_generic_plant.generation_profile.grid) + + # hybrid nominal capacity is set after simulate_grid_connection() + # calculated in calc_nominal_capacity() - which is AC capacity + with subtests.test("hybrid_nominal_capacity"): + assert hybrid_generic_plant.grid.hybrid_nominal_capacity == approx(hybrid_plant.grid.hybrid_nominal_capacity,1e-6) + + # hybrid_size_kw input to simulate_grid_connection() + with subtests.test("hybrid_size_kw"): + assert hybrid_generic_plant.grid.system_capacity_kw == approx(hybrid_plant.grid.system_capacity_kw) + + # check that generation profile was set properly + with subtests.test("Generic Generation Profile"): + np.testing.assert_allclose( + hybrid_generic_plant.generation_profile.generic, + wind_generation_profile, + rtol = 1e-6 + ) + + # check gen max feasible + with subtests.test("total_gen_max_feasible_year1"): + wind_gen_max_feasible = hybrid_plant.wind.calc_gen_max_feasible_kwh(hybrid_plant.interconnect_kw) + np.testing.assert_allclose( + hybrid_generic_plant.generic.gen_max_feasible, + wind_gen_max_feasible, + rtol = 1e-6 + ) + + # based on total_gen_max_feasible_year1 input to simulate_grid_connection() + with subtests.test("grid.gen_max_feasible"): + np.testing.assert_allclose( + hybrid_generic_plant.grid.gen_max_feasible, + hybrid_plant.grid.gen_max_feasible, + rtol = 1e-6 + ) + + # total_gen_max_feasible_year1 input to simulate_grid_connection() + with subtests.test("grid.total_gen_max_feasible_year1"): + np.testing.assert_allclose( + hybrid_generic_plant.grid.total_gen_max_feasible_year1, + hybrid_plant.grid.total_gen_max_feasible_year1, + rtol = 1e-6 + ) + + with subtests.test("system_pre_interconnect_kwac"): + np.testing.assert_allclose( + hybrid_generic_plant.grid._system_model.Outputs.system_pre_interconnect_kwac, + hybrid_plant.grid._system_model.Outputs.system_pre_interconnect_kwac, + rtol = 1e-6, + ) + + with subtests.test("Grid AEP"): + assert np.sum(generation_generic) == approx(np.sum(generation_hybrid),1e-6) + + # total_gen is input to simulate_grid_connection + with subtests.test("generation_profile_wo_battery"): + np.testing.assert_allclose( + hybrid_generic_plant.grid.generation_profile_wo_battery, + hybrid_plant.grid.generation_profile_wo_battery, + rtol = 1e-6, + ) + + # hybrid_plant.grid.generation_profile + with subtests.test("generation_profile.grid"): + np.testing.assert_allclose( + hybrid_generic_plant.generation_profile.grid, + hybrid_plant.generation_profile.grid, + rtol = 1e-6, + ) \ No newline at end of file From fa5915fc64dd6a69aa3932288d52e164cd892c01 Mon Sep 17 00:00:00 2001 From: John Jasa Date: Fri, 25 Apr 2025 10:22:58 -0500 Subject: [PATCH 33/48] Bugfix for flicker mismatch (#479) * Bugfix for flicker mismatch * Updated test_flicker_mismatch test --- RELEASE.md | 1 + .../technologies/layout/flicker_mismatch.py | 11 +---------- tests/hopp/test_flicker_mismatch.py | 15 ++++++++++++++- 3 files changed, 16 insertions(+), 11 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 8d1a59e82..525c4f6fb 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -14,6 +14,7 @@ * Updated HOPP for pySAM 7.0.0 release * Add long-duration energy storage (LDES) * Bugfix for cycle counting in the minimum operating cost objective function - no longer throws an error +* Bugfix for flicker mismatch; cases with a single `Point` now correctly work diff --git a/hopp/simulation/technologies/layout/flicker_mismatch.py b/hopp/simulation/technologies/layout/flicker_mismatch.py index 19a270683..c14f5408f 100644 --- a/hopp/simulation/technologies/layout/flicker_mismatch.py +++ b/hopp/simulation/technologies/layout/flicker_mismatch.py @@ -1,5 +1,4 @@ from typing import List, Union, Optional, Sequence -from pathlib import Path import copy from itertools import product import sys @@ -335,9 +334,7 @@ def _calculate_shading(weight: float, intersecting_points = site_points.intersection(shadow) if intersecting_points: if isinstance(intersecting_points, Point): - intersecting_points = (intersecting_points, ) - # else: - # intersecting_points = intersecting_points.geoms + intersecting_points = MultiPoint([intersecting_points]) # break up into separate instructions for minor speed up by vectorization xs = np.array([pt.x for pt in intersecting_points.geoms]) ys = np.array([pt.y for pt in intersecting_points.geoms]) @@ -357,12 +354,6 @@ def _calculate_shading(weight: float, heat_map[y, x] += weight * area_weight else: heat_map[y, x] += weight - # if isinstance(shadow, Polygon): - # shadow = (shadow, ) - # for poly in shadow: - # x, y = poly.exterior.xy - # plt.plot(x, y) - # plt.show() @staticmethod def _calculate_power_loss(poa: float, diff --git a/tests/hopp/test_flicker_mismatch.py b/tests/hopp/test_flicker_mismatch.py index 7b3ffb7d4..1e6f37d92 100644 --- a/tests/hopp/test_flicker_mismatch.py +++ b/tests/hopp/test_flicker_mismatch.py @@ -185,4 +185,17 @@ def test_plot(): flicker = FlickerMismatch(lat, lon, angles_per_step=12) axs = flicker.plot_on_site(False, False) - plot_tiled(flicker_heatmap, flicker, axs) \ No newline at end of file + plot_tiled(flicker_heatmap, flicker, axs) + +def test_single_turbine_point(subtests): + FlickerMismatch.diam_mult_nwe = 3 + FlickerMismatch.diam_mult_s = 1 + flicker = FlickerMismatch(lat, lon, angles_per_step=1, gridcell_width=1, gridcell_height=1, blade_length=1.5) + shadow, loss = flicker.create_heat_maps(range(3185, 3187), ("poa", "power")) + + with subtests.test("max shadow"): + assert(np.max(shadow) == approx(0.8471120495784218, 1e-4)) + with subtests.test("average shadow"): + assert(np.average(shadow) == approx(0.004629629629629629, 1e-4)) + with subtests.test("nonzero shadow count"): + assert(np.count_nonzero(shadow) == approx(2, 1e-4)) \ No newline at end of file From 37358e59cba410ddb8648de3ca2edbadfc806bad Mon Sep 17 00:00:00 2001 From: kbrunik <102193481+kbrunik@users.noreply.github.com> Date: Wed, 30 Apr 2025 13:09:49 -0500 Subject: [PATCH 34/48] Update LDES Example (#480) * update ldes example * change fin file * note on model limitation --------- Co-authored-by: John Jasa --- ...llowing-long-duration-energy-storage.ipynb | 213 ++++++++++++------ ...ollowing-long-duration-energy-storage.yaml | 37 ++- examples/inputs/default_fin_config.yaml | 28 ++- examples/inputs/default_fin_config_ldes.yaml | 49 ---- .../technologies/sites/site_info.py | 2 +- 5 files changed, 196 insertions(+), 133 deletions(-) delete mode 100644 examples/inputs/default_fin_config_ldes.yaml diff --git a/examples/11-load-following-long-duration-energy-storage.ipynb b/examples/11-load-following-long-duration-energy-storage.ipynb index 7626a99e1..3cc425e0e 100644 --- a/examples/11-load-following-long-duration-energy-storage.ipynb +++ b/examples/11-load-following-long-duration-energy-storage.ipynb @@ -6,16 +6,11 @@ "source": [ "# Long-Duration Energy Storage Example\n", "---\n", - "In this example, we will show how to use and modify the long duration energy storage (LDES) parameters in the hybrid plant simulation. The LDES model is built inside of the battery module, so the parameters are very similar to the battery storage model. In this example we will model a vanadium redox flow battery (VRFB), but we have written the model in a general way so that many types of long-duration energy storage can be modeled reasonably easily." + "In this example, we will show how to use and modify the long duration energy storage (LDES) parameters in the hybrid plant simulation. The LDES model is built inside of the battery module, so the parameters are very similar to the battery storage model. In this example we will model a vanadium redox flow battery (VRFB), but we have written the model in a general way so that many types of long-duration energy storage can be modeled reasonably easily.\n", + "\n", + "**NOTE**: Initializing the LDES model may slow down the `HoppInterface` in Jupyter Notebooks. This is a known limitation; initialization is faster when run from a `.py` script." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -33,32 +28,50 @@ "name": "stdout", "output_type": "stream", "text": [ - "/Users/jthomas2/Documents/programs/HOPP/examples/log/hybrid_systems_2025-04-17T14.54.28.881005.log\n" + "/Users/kbrunik/github/HOPP/examples/log/hybrid_systems_2025-04-29T14.35.18.231197.log\n" ] } ], "source": [ "import numpy as np\n", "\n", - "from hopp import ROOT_DIR\n", "from hopp.simulation import HoppInterface\n", "from hopp.utilities import load_yaml\n", - "from hopp.simulation.technologies.sites import SiteInfo, flatirons_site\n", "from hopp.tools.dispatch.plot_tools import (\n", " plot_generation_profile\n", - ")\n" + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Set Site Information\n", - "Set wind and solar resource data at plant location and load pricing data. In this example, we use the Flatirons site as an example location.\n", + "### Create the HOPP Model\n", + "To generate the HOPP Model, instantiate the `HoppInterface` class and supply the required YAML configuration.\n", + "\n", + "`HOPPInterface` is capable of handling dictionary input as well as class instances. Here we demonstrate this by loading the YAML file as a dict, modifying it to include our site information, then passing it as an argument to `HoppInterface`. This is useful for programmatic configuration of simulation configs.\n", + "\n", + "For LDES simulations, the YAML configuration should resemble those used for batteries in the hybrid system examples. The LDES system is defined within the battery section of the technologies block, using the key parameters shown below.\n", "\n", + "The key differences for LDES compared to a standard battery setup are:\n", "\n", + "- Set `system_model_source` to `\"hopp\"`.\n", + "- Set `chemistry` to `\"LDES\"`.\n", "\n", - "**NOTE**: For a load following objective function the `desired_schedule` must be set. You can also specify `curtailment_value_type` to either _\"grid\"_ or _\"desired_schedule\"_. If you select _\"grid\"_ the system will curtail energy at the interconnection limit but optimize to meet the `desired_schedule` load and if you select _\"desired_schedule\"_ it curtails energy above the `desired_schedule` and optimizes to meet the `desired_schedule` load." + "An example YAML snippet for an LDES system:\n", + "\n", + "```yaml\n", + "technologies:\n", + " battery: # VRDB\n", + " system_capacity_kwh: 100000\n", + " system_capacity_kw: 10000\n", + " minimum_SOC: 20.0\n", + " maximum_SOC: 100.0\n", + " initial_SOC: 90.0\n", + " system_model_source: \"hopp\"\n", + " chemistry: \"LDES\"\n", + " fin_model: !include default_fin_config_ldes.yaml\n", + "```" ] }, { @@ -67,56 +80,92 @@ "metadata": {}, "outputs": [], "source": [ - "DEFAULT_SOLAR_RESOURCE_FILE = ROOT_DIR.parent / \"hopp\" / \"simulation\" / \"resource_files\" / \"solar\" / \"35.2018863_-101.945027_psmv3_60_2012.csv\"\n", - "DEFAULT_WIND_RESOURCE_FILE = ROOT_DIR.parent / \"hopp\" / \"simulation\" / \"resource_files\" / \"wind\" / \"35.2018863_-101.945027_windtoolkit_2012_60min_80m_100m.srw\"\n", - "DEFAULT_PRICE_FILE = ROOT_DIR.parent / \"hopp\" / \"simulation\" / \"resource_files\" / \"grid\" / \"pricing-data-2015-IronMtn-002_factors.csv\"\n", + "hopp_config = load_yaml(\"./inputs/11-load-following-long-duration-energy-storage.yaml\")\n", "\n", + "# set SiteInfo default load\n", "setpoint_kw = float(10 * 1000)\n", "DEFAULT_LOAD = setpoint_kw*np.ones((8760))/1000\n", - "\n", - "site = SiteInfo(\n", - " flatirons_site,\n", - " solar_resource_file=DEFAULT_SOLAR_RESOURCE_FILE,\n", - " wind_resource_file=DEFAULT_WIND_RESOURCE_FILE,\n", - " grid_resource_file=DEFAULT_PRICE_FILE,\n", - " desired_schedule=DEFAULT_LOAD,\n", - " curtailment_value_type=\"interconnect_kw\",\n", - " solar=True,\n", - " wind=True,\n", - " wave=False\n", - " )" + "hopp_config[\"site\"][\"desired_schedule\"] = DEFAULT_LOAD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Create the HOPP Model\n", - "To generate the HOPP Model, instantiate the `HoppInterface` class and supply the required YAML configuration.\n", + "### Set Up Battery Replacement and O&M Costs\n", + "In this section, we configure the financial model for the LDES system by specifying a battery replacement schedule and adjusting the battery operation and maintenance (O&M) costs.\n", "\n", - "`HOPPInterface` is capable of handling dictionary input as well as class instances. Here we demonstrate this by loading the YAML file as a dict, modifying it to include our site information, then passing it as an argument to `HoppInterface`. This is useful for programmatic configuration of simulation configs." + "#### 1. Enable a User-Defined Replacement Schedule\n", + "\n", + "First, we alert the model that a custom battery replacement schedule will be provided by setting the `batt_replacement_option` to `2`:\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "hopp_config = load_yaml(\"./inputs/11-load-following-long-duration-energy-storage.yaml\")\n", + "hopp_config[\"technologies\"][\"battery\"][\"fin_model\"][\"battery_system\"][\"batt_replacement_option\"] = 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Define the Replacement Schedule\n", "\n", - "# alert the model that a replacement schedule will be provided\n", - "hopp_config[\"technologies\"][\"battery\"][\"fin_model\"][\"battery_system\"][\"batt_replacement_option\"] = 2\n", - "# set up the replacement schedule to replace the battery every 15 years at a cost of 50% of the initial capex\n", + "Next, we create a replacement schedule where the battery is refurbished every 15 years, and the refurbishment cost is set at 50% of the original capital cost:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ "project_life_years = 25\n", "refurb = [0]*project_life_years\n", "battery_life_years = 15\n", "for i in range(battery_life_years-1, project_life_years, battery_life_years):\n", - " refurb[i] = 0.5\n", + " refurb[i] = 0.5\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This creates a list (`refurb`) where each element represents the replacement cost (as a fraction of initial CAPEX) in each project year.\n", "\n", - "# assign replacement schedule to the input config\n", - "hopp_config[\"technologies\"][\"battery\"][\"fin_model\"][\"battery_system\"][\"batt_replacement_schedule_percent\"] = refurb\n", + "We then assign this schedule to the HOPP configuration:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "hopp_config[\"technologies\"][\"battery\"][\"fin_model\"][\"battery_system\"][\"batt_replacement_schedule_percent\"] = refurb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Adjust Battery Operation and Maintenance (O&M) Costs\n", + "Battery O&M costs can be based on both power (\\\\$ per kW) and energy capacity (\\\\$ per kWh). However here we've chosen to represent the battery O&M as a \\$ per kW value.\n", "\n", + "We first calculate the total O&M cost by combining both per-kW and per-kWh components:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ "# set battery om per kw including per kwh cost - om per kwh not included internally\n", "battery_rating_kw = hopp_config[\"technologies\"][\"battery\"][\"system_capacity_kw\"]\n", "battery_rating_kwh = hopp_config[\"technologies\"][\"battery\"][\"system_capacity_kwh\"]\n", @@ -125,13 +174,34 @@ "\n", "# this is how to include om per kwh capacity costs\n", "total_batt_om_per_kw = (battery_rating_kw*batt_om_per_kw + battery_rating_kwh*batt_om_per_kwh)/battery_rating_kw\n", - "hopp_config[\"config\"][\"cost_info\"][\"battery_om_per_kw\"] = total_batt_om_per_kw\n", - "\n", - "# remove om per kwh input because it is an invalid input to HOPP\n", - "hopp_config[\"config\"][\"cost_info\"].pop(\"battery_om_per_kwh\")\n", - "\n", - "# set SiteInfo instance\n", - "hopp_config[\"site\"] = site" + "hopp_config[\"config\"][\"cost_info\"][\"battery_om_per_kw\"] = total_batt_om_per_kw" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we remove the `battery_om_per_kwh` input from the config, since we've already included O&M costs through the `battery_om_per_kw` input:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hopp_config[\"config\"][\"cost_info\"].pop(\"battery_om_per_kwh\")" ] }, { @@ -144,24 +214,36 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "hi = HoppInterface(hopp_config)\n" + "hi = HoppInterface(hopp_config)" ] }, { - "cell_type": "code", - "execution_count": 5, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ + "### Modify Dispatch Parameter for LDES\n", + "\n", + "When modeling LDES, a dispatch parameter you may want to adjust compared to a shorter-duration battery is:\n", "\n", - "hi.system.battery.dispatch.charge_efficiency\n", + "- `round_trip_efficiency`\n", "\n", - "# Note: HOPP LDES does not currently account for self discharge\n", + "```{note}\n", + "HOPP LDES does not currently account for self discharge.\n", + "```\n", "\n", + "You can modify this parameter directly on the `dispatch` object after creating your HOPP model. Here's an example:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ "# set round trip efficiency\n", "hi.system.battery.dispatch.round_trip_efficiency = 80.0" ] @@ -176,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -201,16 +283,16 @@ "output_type": "stream", "text": [ "Annual Energies (kWh):\n", - "{\"pv\": 11234203.208755787, \"wind\": 80310545.9331664, \"battery\": -1214.3240999997406, \"hybrid\": 91543534.81782195}\n", + "{\"pv\": 11234203.2087558, \"wind\": 80310545.93316662, \"battery\": -1214.332420000269, \"hybrid\": 91543534.80950251}\n", "\n", " Percentage of timesteps the load is met:\n", "78.52739726027397\n", "\n", " Total Missed Load:\n", - "417274074.2328715 kWh\n", + "417465809.7516137 kWh\n", "\n", " Percentage of the load that is missed:\n", - "19.053610695564906\n" + "19.062365742082815\n" ] } ], @@ -240,12 +322,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -257,13 +339,6 @@ "source": [ "plot_generation_profile(hybrid_plant)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/inputs/11-load-following-long-duration-energy-storage.yaml b/examples/inputs/11-load-following-long-duration-energy-storage.yaml index a972f4ec0..af6102e5b 100644 --- a/examples/inputs/11-load-following-long-duration-energy-storage.yaml +++ b/examples/inputs/11-load-following-long-duration-energy-storage.yaml @@ -1,11 +1,40 @@ +# SiteInfo +site: + data: + lat: 35.2018863 + lon: -101.945027 + elev: 1099 + year: 2012 + tz: -6 + site_boundaries: + verts: + - [3.06, 288.87] + - [0.0, 1084.03] + - [1784.05, 1084.24] + - [1794.09, 999.64] + - [1494.34, 950.97] + - [712.64, 262.8] + - [1216.98, 272.36] + - [1217.76, 151.62] + - [708.14, 0.0] + urdb_label: "5ca4d1175457a39b23b3d45e" + hub_height: 97.0 + solar_resource_file: "resource_files/solar/35.2018863_-101.945027_psmv3_60_2012.csv" + wind_resource_file: "resource_files/wind/35.2018863_-101.945027_windtoolkit_2012_60min_80m_100m.srw" + grid_resource_file: "resource_files/grid/pricing-data-2015-IronMtn-002_factors.csv" + curtailment_value_type: "interconnect_kw" + solar: true + wind: true + wave: false + technologies: pv: system_capacity_kw: 5000 - fin_model: !include default_fin_config_ldes.yaml + fin_model: !include default_fin_config.yaml wind: num_turbines: 5 turbine_rating_kw: 5000 - fin_model: !include default_fin_config_ldes.yaml + fin_model: !include default_fin_config.yaml battery: # VRDB system_capacity_kwh: 100000 system_capacity_kw: 10000 @@ -14,10 +43,10 @@ technologies: initial_SOC: 90.0 system_model_source: "hopp" chemistry: "LDES" - fin_model: !include default_fin_config_ldes.yaml + fin_model: !include default_fin_config.yaml grid: interconnect_kw: 100000 - fin_model: !include default_fin_config_ldes.yaml + fin_model: !include default_fin_config.yaml config: dispatch_options: diff --git a/examples/inputs/default_fin_config.yaml b/examples/inputs/default_fin_config.yaml index b11a61841..c43b7207a 100644 --- a/examples/inputs/default_fin_config.yaml +++ b/examples/inputs/default_fin_config.yaml @@ -1,16 +1,22 @@ battery_system: - batt_replacement_schedule_percent: [0] - batt_bank_replacement: [0] + batt_replacement_schedule_percent: + - 0 + batt_bank_replacement: + - 0 batt_replacement_option: 0 batt_computed_bank_capacity: 0 batt_meter_position: 0 -system_costs: - om_fixed: [1] - om_production: [2] - om_capacity: [0] +system_costs: + om_fixed: + - 0 + om_production: + - 0 + om_capacity: + - 0 om_batt_fixed_cost: 0 - om_batt_variable_cost: [0.75] - om_batt_capacity_cost: 0 + om_batt_variable_cost: + - 0 + om_batt_capacity_cost: 0 om_batt_replacement_cost: 0 om_replacement_cost_escal: 0 revenue': @@ -35,5 +41,7 @@ financial_parameters: debt_type: "Revolving debt" depreciation_method: "MACRS" depreciation_period: 5 -cp_capacity_credit_percent: [0] -degradation: [0] \ No newline at end of file +cp_capacity_credit_percent: + - 0 +degradation: + - 0 \ No newline at end of file diff --git a/examples/inputs/default_fin_config_ldes.yaml b/examples/inputs/default_fin_config_ldes.yaml deleted file mode 100644 index 1ed2a7813..000000000 --- a/examples/inputs/default_fin_config_ldes.yaml +++ /dev/null @@ -1,49 +0,0 @@ -battery_system: - batt_replacement_schedule_percent: - - 0 - batt_bank_replacement: - - 0 - batt_replacement_option: 0 - batt_computed_bank_capacity: 0 - batt_meter_position: 0 -system_costs: - om_fixed: - - 0 - om_production: - - 0 - om_capacity: - - 0 - om_batt_fixed_cost: 42.888 - om_batt_variable_cost: - - 0 - om_batt_capacity_cost: 86.559 - om_batt_replacement_cost: 0 - om_replacement_cost_escal: 0 -system_use_lifetime_output: 0 -financial_parameters: - inflation_rate: 2.5 - real_discount_rate: 6.4 - federal_tax_rate: 21.0 - state_tax_rate: 4.4 - property_tax_rate: 1.0 - insurance_rate: 0.5 - debt_percent: 68.5 - term_int_rate: 6.0 - months_working_reserve: 1 - analysis_start_year: 2025 - installation_months: 12 # None - sales_tax_rate_state: 4.5 # none - admin_expense_percent_of_sales: 1.0 #none - capital_gains_tax_rate: 15.0 # none - debt_type: "Revolving debt" # none - depreciation_method: "MACRS" # none handled differently - depreciation_period: 5 # none - handled differently - -cp_capacity_credit_percent: - - 0 -degradation: - - 0 -revenue: - ppa_price_input: - - 0.01 - ppa_escalation: 1 \ No newline at end of file diff --git a/hopp/simulation/technologies/sites/site_info.py b/hopp/simulation/technologies/sites/site_info.py index f8062cdaf..e008decf4 100644 --- a/hopp/simulation/technologies/sites/site_info.py +++ b/hopp/simulation/technologies/sites/site_info.py @@ -183,7 +183,7 @@ def __attrs_post_init__(self): interval (int): Number of minutes per time interval. urdb_label (str): Link to `Utility Rate DataBase `_ label for REopt runs. follow_desired_schedule (bool): Indicates if a desired schedule was provided. Defaults to False. - kml_data (KML, Optional): KML data to be used when definining site boundaries. + kml_data (KML, Optional): KML data to be used when defining site boundaries. """ if self.renewable_resource_origin=="API": set_nrel_key_dot_env() From ade31db0ecb6d970906c2c2cbc484188f8b22a92 Mon Sep 17 00:00:00 2001 From: John Jasa Date: Wed, 30 Apr 2025 17:52:01 -0500 Subject: [PATCH 35/48] Bumped version and changelog (#482) --- RELEASE.md | 13 ++++++------- hopp/__init__.py | 2 +- 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 525c4f6fb..59f3e898e 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,23 +1,22 @@ # Release Notes -## Unreleased, TBD +## Version 3.3.0, April 30, 2025 -* Added GenericPlant model which may be used to: +* Added GenericPlant model ([PR #472](https://github.com/NREL/HOPP/pull/472)) which may be used to: - simulate grid and battery performance without resimulating generation of other technologies - represent the physics-based performance of a generation technology that is not included in HOPP * Loosened strictness of comparison for wind turbine config checking and added tests * Loosened strictness of comparison for wind turbine hub-height and wind resource hub-height * Updated workflow for specifying wind turbine parameters without specifying a turbine name with PySAM. -* Added ability to download wind resource data from WTK-LED for Alaska -* Added ability to download wind resource data from BC-HRRR CONUS 60-minute (NOAA + NREL) for 2015-2023 -* Updated HOPP for pySAM 7.0.0 release -* Add long-duration energy storage (LDES) +* Added ability to download wind resource data from WTK-LED for Alaska ([PR #461](https://github.com/NREL/HOPP/pull/461)) +* Added ability to download wind resource data from BC-HRRR CONUS 60-minute (NOAA + NREL) for 2015-2023 ([PR #474](https://github.com/NREL/HOPP/pull/474)) +* Updated HOPP for pySAM 7.0.0 release ([PR #477](https://github.com/NREL/HOPP/pull/477)) +* Add long-duration energy storage (LDES) ([PR #471](https://github.com/NREL/HOPP/pull/471)) * Bugfix for cycle counting in the minimum operating cost objective function - no longer throws an error * Bugfix for flicker mismatch; cases with a single `Point` now correctly work - ## Version 3.2.0, March 21, 2025 * Updates related to PySAM: diff --git a/hopp/__init__.py b/hopp/__init__.py index 7518ef071..cdd3d4ba6 100644 --- a/hopp/__init__.py +++ b/hopp/__init__.py @@ -1,7 +1,7 @@ from pathlib import Path -__version__ = "3.2.0" +__version__ = "3.3.0" ROOT_DIR = Path(__file__).resolve().parent From 5277c20dade31ab5eb9df70b35a47025955b3b7d Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Mon, 12 May 2025 11:25:58 -0600 Subject: [PATCH 36/48] adding WETO stack blurb to readme (#485) adding WETO stack blurb to readme (#485) --- README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/README.md b/README.md index 830ae6b8d..7c4ec1b8f 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,16 @@ As part of NREL's [Hybrid Energy Systems Research](https://www.nrel.gov/wind/hyb software assesses optimal designs for the deployment of distributed, commercial, and utility-scale hybrid energy plants, particularly considering wind, solar and storage. + +## Part of the WETO Stack + +HOPP is primarily developed with the support of the U.S. Department of Energy and is part of the [WETO Software Stack](https://nrel.github.io/WETOStack). For more information and other integrated modeling software, see: +- [Portfolio Overview](https://nrel.github.io/WETOStack/portfolio_analysis/overview.html) +- [Entry Guide](https://nrel.github.io/WETOStack/_static/entry_guide/index.html) +- [Techno-Economic Modeling Workshop](https://nrel.github.io/WETOStack/workshops/user_workshops_2024.html#tea-and-cost-modeling) +- [Systems Engineering Workshop](https://nrel.github.io/WETOStack/workshops/user_workshops_2024.html#systems-engineering) + + ## Software requirements - Python version 3.10, and 3.11 only From b8972bb90c3ef1a5277632b735e28ddd763ec8b6 Mon Sep 17 00:00:00 2001 From: John Jasa Date: Wed, 28 May 2025 11:32:46 -0500 Subject: [PATCH 37/48] Bumping Python version (#488) * Bumped python versions * python 3.14 isn't out yet * Updated AEP comparison to use approx --- .github/ISSUE_TEMPLATE/bug_report.md | 4 ++-- .github/workflows/ci.yml | 2 +- README.md | 2 +- pyproject.toml | 5 +++-- tests/hopp/test_hybrid.py | 2 +- 5 files changed, 8 insertions(+), 7 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 7bcc5622a..bba3d7a64 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -49,7 +49,7 @@ the problem. All code and full tracebacks should be properly markdown formatted. - OS: -- Python version: <3.10.4> +- Python version: <3.11.1> - HOPP version: <0.1.1> | Package | Version | diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 7291c50d1..7e5afebf5 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -11,7 +11,7 @@ jobs: shell: bash -el {0} strategy: matrix: - python-version: ["3.10", "3.11"] + python-version: ["3.11", "3.12", "3.13"] steps: - uses: actions/checkout@v4 diff --git a/README.md b/README.md index 7c4ec1b8f..7bed01d77 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ HOPP is primarily developed with the support of the U.S. Department of Energy an ## Software requirements -- Python version 3.10, and 3.11 only +- Python version 3.11 or higher ## Installing from Package Repositories diff --git a/pyproject.toml b/pyproject.toml index 43a33e040..02a1522fc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ dynamic = ["version"] authors = [{name = "NREL", email = "dguittet@nrel.gov"}] readme = {file = "README.md", content-type = "text/markdown"} description = "Hybrid Systems Optimization and Performance Platform." -requires-python = ">=3.10, <3.12" +requires-python = ">=3.11" license = {file = "LICENSE"} dependencies = [ "Cython", @@ -69,8 +69,9 @@ classifiers = [ # https://pypi.org/classifiers/ "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 3 :: Only", - "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", "Topic :: Scientific/Engineering", "Topic :: Software Development :: Libraries :: Python Modules", ] diff --git a/tests/hopp/test_hybrid.py b/tests/hopp/test_hybrid.py index 3c065d4e8..153250ebc 100644 --- a/tests/hopp/test_hybrid.py +++ b/tests/hopp/test_hybrid.py @@ -777,7 +777,7 @@ def test_hybrid_pv_only_custom_fin(hybrid_config, subtests): with subtests.test("aep"): assert aeps.pv == approx(10789795.03, 1e-3) - assert aeps.hybrid == aeps.pv + assert aeps.hybrid == approx(aeps.pv) def test_hybrid_pv_battery_custom_fin(hybrid_config, subtests): From 85dc876a3b140604428b25451e5e0fa9bed85b8b Mon Sep 17 00:00:00 2001 From: Jared Thomas Date: Wed, 4 Jun 2025 12:41:46 -0600 Subject: [PATCH 38/48] Bring in function from H2I to copy cost info across both input methods (#491) * move overwrite_fin_values to hopp from h2integrate * update pysam version to 7.0.0 --- RELEASE.md | 5 +- hopp/simulation/hopp.py | 194 +++++++++++++++++- hopp/simulation/hopp_interface.py | 3 +- .../financial/custom_financial_model.py | 4 +- pyproject.toml | 2 +- 5 files changed, 201 insertions(+), 7 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 59f3e898e..82b9e5314 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,5 +1,8 @@ # Release Notes - +## Unreleased +* Add `overwrite_fin_values` from H2Integrate to sync cost input methods +* Bump minimum NREL-PySAM version to 7.0.0 +* Clarify that the `nominal_discount_rate` method of the `CustomFinancialModel` uses the Fisher equation ## Version 3.3.0, April 30, 2025 diff --git a/hopp/simulation/hopp.py b/hopp/simulation/hopp.py index 6f6e46e78..5a07df428 100644 --- a/hopp/simulation/hopp.py +++ b/hopp/simulation/hopp.py @@ -1,9 +1,10 @@ from __future__ import annotations import yaml +import warnings from attrs import define, field from pathlib import Path from typing import Optional, Union - +import numpy as np from hopp.simulation.base import BaseClass from hopp.simulation.hybrid_simulation import HybridSimulation, TechnologiesConfig from hopp.simulation.technologies.sites import SiteInfo @@ -69,6 +70,8 @@ def from_file(cls, input_file_path: Union[str, Path], filetype: Optional[str] = input_dict = load_yaml(input_file_path) else: raise ValueError("Supported import filetype is YAML") + + input_dict = overwrite_fin_values(input_dict) return Hopp.from_dict(input_dict) def to_file(self, output_file_path: str, filetype: str="YAML") -> None: @@ -83,3 +86,192 @@ def to_file(self, output_file_path: str, filetype: str="YAML") -> None: yaml.dump(self.as_dict(), f, default_flow_style=False) else: raise ValueError("Supported export filetype is YAML") + + + +def overwrite_fin_values(hopp_config): + """ + Overrides specific financial model values in the HOPP configuration with values from the `cost_info` section. + + This function ensures that the financial model values for technologies (e.g., wind, PV, battery) are updated + with the corresponding values provided in the `cost_info` section of the HOPP configuration. If discrepancies + are found between the values in the financial model and the `cost_info`, the financial model values are + overwritten, and a warning is issued to notify the user. + + Args: + hopp_config (dict): The HOPP configuration dictionary containing information about technologies, + financial models, and cost information. + + Expected structure: + - `technologies`: Contains technology-specific financial models (e.g., wind, PV, battery). + - `config`: Contains the `cost_info` section with updated cost values. + + Returns: + dict: The updated HOPP configuration dictionary with overwritten financial model values. + + Raises: + UserWarning: If a financial model value is overwritten due to a mismatch with the `cost_info` value. + + Notes: + - This function supports the following technologies: wind, PV (solar), and battery. + - The following financial model values can be overwritten in individual technology financial models: + - `om_capacity`: Fixed O&M costs per unit capacity [$/kW]. + - `om_production`: Variable O&M costs per unit production [$/MWh]. + + Example: + If the `cost_info` section specifies a new value for `wind_om_per_kw`, and this value differs from the + existing `om_capacity` value in the wind financial model, the `om_capacity` value will be updated, and + a warning will be issued. + """ + # override individual fin_model values with cost_info values + if ("config" in hopp_config.keys()) and ("cost_info" in hopp_config["config"]) and (hopp_config["config"]["cost_info"] is not None): + if "wind" in hopp_config["technologies"]: + if ("wind_om_per_kw" in hopp_config["config"]["cost_info"]) and ( + np.any(hopp_config["technologies"]["wind"]["fin_model"]["system_costs"]["om_capacity"] + != hopp_config["config"]["cost_info"]["wind_om_per_kw"]) + ): + for i in range( + len(hopp_config["technologies"]["wind"]["fin_model"]["system_costs"]["om_capacity"]) + ): + hopp_config["technologies"]["wind"]["fin_model"]["system_costs"]["om_capacity"][ + i + ] = hopp_config["config"]["cost_info"]["wind_om_per_kw"] + + om_fixed_wind_fin_model = hopp_config["technologies"]["wind"]["fin_model"][ + "system_costs" + ]["om_capacity"][i] + wind_om_per_kw = hopp_config["config"]["cost_info"]["wind_om_per_kw"] + msg = ( + f"'om_capacity[{i}]' in the wind 'fin_model' was {om_fixed_wind_fin_model}," + f" but 'wind_om_per_kw' in 'cost_info' was {wind_om_per_kw}. The 'om_capacity'" + " value in the wind 'fin_model' is being overwritten with the value from the" + " 'cost_info'" + ) + warnings.warn(msg, UserWarning) + if ("wind_om_per_mwh" in hopp_config["config"]["cost_info"]) and ( + hopp_config["technologies"]["wind"]["fin_model"]["system_costs"]["om_production"][0] + != hopp_config["config"]["cost_info"]["wind_om_per_mwh"] + ): + # Use this to set the Production-based O&M amount [$/MWh] + for i in range( + len( + hopp_config["technologies"]["wind"]["fin_model"]["system_costs"][ + "om_production" + ] + ) + ): + hopp_config["technologies"]["wind"]["fin_model"]["system_costs"]["om_production"][ + i + ] = hopp_config["config"]["cost_info"]["wind_om_per_mwh"] + om_wind_variable_cost = hopp_config["technologies"]["wind"]["fin_model"][ + "system_costs" + ]["om_production"][i] + wind_om_per_mwh = hopp_config["config"]["cost_info"]["wind_om_per_mwh"] + msg = ( + f"'om_production' in the wind 'fin_model' was {om_wind_variable_cost}, but" + f" 'wind_om_per_mwh' in 'cost_info' was {wind_om_per_mwh}. The 'om_production'" + " value in the wind 'fin_model' is being overwritten with the value from the" + " 'cost_info'" + ) + warnings.warn(msg, UserWarning) + + if "pv" in hopp_config["technologies"]: + if ("pv_om_per_kw" in hopp_config["config"]["cost_info"]) and ( + hopp_config["technologies"]["pv"]["fin_model"]["system_costs"]["om_capacity"][0] + != hopp_config["config"]["cost_info"]["pv_om_per_kw"] + ): + for i in range( + len(hopp_config["technologies"]["pv"]["fin_model"]["system_costs"]["om_capacity"]) + ): + hopp_config["technologies"]["pv"]["fin_model"]["system_costs"]["om_capacity"][i] = ( + hopp_config["config"]["cost_info"]["pv_om_per_kw"] + ) + + om_fixed_pv_fin_model = hopp_config["technologies"]["pv"]["fin_model"][ + "system_costs" + ]["om_capacity"][i] + pv_om_per_kw = hopp_config["config"]["cost_info"]["pv_om_per_kw"] + msg = ( + f"'om_capacity[{i}]' in the pv 'fin_model' was {om_fixed_pv_fin_model}, but" + f" 'pv_om_per_kw' in 'cost_info' was {pv_om_per_kw}. The 'om_capacity' value" + " in the pv 'fin_model' is being overwritten with the value from the" + " 'cost_info'" + ) + warnings.warn(msg, UserWarning) + if ("pv_om_per_mwh" in hopp_config["config"]["cost_info"]) and ( + hopp_config["technologies"]["pv"]["fin_model"]["system_costs"]["om_production"][0] + != hopp_config["config"]["cost_info"]["pv_om_per_mwh"] + ): + # Use this to set the Production-based O&M amount [$/MWh] + for i in range( + len(hopp_config["technologies"]["pv"]["fin_model"]["system_costs"]["om_production"]) + ): + hopp_config["technologies"]["pv"]["fin_model"]["system_costs"]["om_production"][ + i + ] = hopp_config["config"]["cost_info"]["pv_om_per_mwh"] + om_pv_variable_cost = hopp_config["technologies"]["pv"]["fin_model"]["system_costs"][ + "om_production" + ][i] + pv_om_per_mwh = hopp_config["config"]["cost_info"]["pv_om_per_mwh"] + msg = ( + f"'om_production' in the pv 'fin_model' was {om_pv_variable_cost}, but" + f" 'pv_om_per_mwh' in 'cost_info' was {pv_om_per_mwh}. The 'om_production' value" + " in the pv 'fin_model' is being overwritten with the value from the 'cost_info'" + ) + warnings.warn(msg, UserWarning) + + if "battery" in hopp_config["technologies"]: + if ("battery_om_per_kw" in hopp_config["config"]["cost_info"]) \ + and (hopp_config["technologies"]["battery"]["fin_model"]["system_costs"]["om_capacity"][0] + != hopp_config["config"]["cost_info"]["battery_om_per_kw"] + ): + for i in range( + len( + hopp_config["technologies"]["battery"]["fin_model"]["system_costs"][ + "om_capacity" + ] + ) + ): + hopp_config["technologies"]["battery"]["fin_model"]["system_costs"]["om_capacity"][ + i + ] = hopp_config["config"]["cost_info"]["battery_om_per_kw"] + + om_batt_fixed_cost = hopp_config["technologies"]["battery"]["fin_model"][ + "system_costs" + ]["om_capacity"][i] + battery_om_per_kw = hopp_config["config"]["cost_info"]["battery_om_per_kw"] + msg = ( + f"'om_capacity' in the battery 'fin_model' was {om_batt_fixed_cost}, but" + f" 'battery_om_per_kw' in 'cost_info' was {battery_om_per_kw}. The" + " 'om_capacity' value in the battery 'fin_model' is being overwritten with the" + " value from the 'cost_info'" + ) + warnings.warn(msg, UserWarning) + if ("battery_om_per_mwh" in hopp_config["config"]["cost_info"]) and ( + hopp_config["technologies"]["battery"]["fin_model"]["system_costs"]["om_production"][0] + != hopp_config["config"]["cost_info"]["battery_om_per_mwh"] + ): + # Use this to set the Production-based O&M amount [$/MWh] + for i in range( + len( + hopp_config["technologies"]["battery"]["fin_model"]["system_costs"][ + "om_production" + ] + ) + ): + hopp_config["technologies"]["battery"]["fin_model"]["system_costs"][ + "om_production" + ][i] = hopp_config["config"]["cost_info"]["battery_om_per_mwh"] + om_batt_variable_cost = hopp_config["technologies"]["battery"]["fin_model"][ + "system_costs" + ]["om_production"][i] + battery_om_per_mwh = hopp_config["config"]["cost_info"]["battery_om_per_mwh"] + msg = ( + f"'om_production' in the battery 'fin_model' was {om_batt_variable_cost}, but" + f" 'battery_om_per_mwh' in 'cost_info' was {battery_om_per_mwh}. The" + " 'om_production' value in the battery 'fin_model' is being overwritten with the" + " value from the 'cost_info'", + ) + warnings.warn(msg, UserWarning) + + return hopp_config diff --git a/hopp/simulation/hopp_interface.py b/hopp/simulation/hopp_interface.py index 3e0cab729..271f509aa 100644 --- a/hopp/simulation/hopp_interface.py +++ b/hopp/simulation/hopp_interface.py @@ -2,7 +2,7 @@ from pathlib import Path from typing import Union, TYPE_CHECKING -from hopp.simulation.hopp import Hopp, SiteInfo +from hopp.simulation.hopp import Hopp, overwrite_fin_values # avoid potential circular dep if TYPE_CHECKING: @@ -43,6 +43,7 @@ def reinitialize(self, configuration: Union[dict, str, Path]): self.hopp = Hopp.from_file(self.configuration) elif isinstance(self.configuration, dict): + self.configuration = overwrite_fin_values(self.configuration) self.hopp = Hopp.from_dict(self.configuration) def simulate(self, project_life: int = 25, lifetime_sim: bool = False): diff --git a/hopp/simulation/technologies/financial/custom_financial_model.py b/hopp/simulation/technologies/financial/custom_financial_model.py index 124152ba8..6a13adfc0 100644 --- a/hopp/simulation/technologies/financial/custom_financial_model.py +++ b/hopp/simulation/technologies/financial/custom_financial_model.py @@ -450,7 +450,7 @@ def npv(rate: float, net_cash_flow: List[float]): @staticmethod def nominal_discount_rate(inflation_rate: float, real_discount_rate: float): """ - Computes the nominal discount rate [%] + Computes the nominal discount rate [%] using the Fisher equation. :param inflation_rate: inflation rate [%] :param real_discount_rate: real discount rate [%] @@ -470,7 +470,6 @@ def nominal_discount_rate(inflation_rate: float, real_discount_rate: float): return ( (1 + real_discount_rate / 100) * (1 + inflation_rate / 100) - 1 ) * 100 - def net_cash_flow(self, project_life=25): """ Computes the net cash flow timeseries of annual values over lifetime @@ -508,7 +507,6 @@ def o_and_m_cost(self): """ Computes the annual O&M cost from the fixed, per capacity and per production costs """ - return self.value('om_fixed')[0] \ + self.value('om_capacity')[0] * self.value('system_capacity') \ + self.value('om_production')[0] * self.value('annual_energy_kwh') * 1e-3 diff --git a/pyproject.toml b/pyproject.toml index 02a1522fc..8877a5ce6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ requires-python = ">=3.11" license = {file = "LICENSE"} dependencies = [ "Cython", - "NREL-PySAM>=6.0.1", + "NREL-PySAM>=7.0.0", "Pillow", "Pyomo>=6.1.2", "fastkml<1", From 64d4c6297b17be9006ffa150ab427b9816f122fa Mon Sep 17 00:00:00 2001 From: John Jasa Date: Thu, 26 Jun 2025 15:42:56 -0500 Subject: [PATCH 39/48] Doc URL fix (#493) --- docs/api/hopp_interface.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/api/hopp_interface.md b/docs/api/hopp_interface.md index da8e4c233..83bf291c2 100644 --- a/docs/api/hopp_interface.md +++ b/docs/api/hopp_interface.md @@ -1,6 +1,6 @@ # HOPP Simulation Interface -This class is the main interaction point for HOPP simulations. See [Examples](https://github.com/NREL/HOPP/tree/main/examples/workshop) for in-depth notebooks and configuration files. +This class is the main interaction point for HOPP simulations. See [Examples](https://github.com/NREL/HOPP/tree/main/examples) for in-depth notebooks and configuration files. ```{eval-rst} .. currentmodule:: hopp.simulation.hopp_interface From ea79be6af0e946530789b9fb651c11920b8baf55 Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Sun, 6 Jul 2025 11:41:51 -0600 Subject: [PATCH 40/48] Bugfix: user provided turbine in PySAM format (#494) * updated workflow for user-provided turbine file for pysam * updated handling user input wind model parameters if using pysam * added tests for user provided wind turbine parameters * updated RELEASE.md * Apply suggestions from code review --------- Co-authored-by: John Jasa --- RELEASE.md | 1 + .../technologies/layout/wind_layout.py | 9 +- .../technologies/wind/wind_plant.py | 45 ++- tests/hopp/inputs/pysam_turbine_input.yaml | 329 ++++++++++++++++++ tests/hopp/test_wind.py | 40 +++ 5 files changed, 409 insertions(+), 15 deletions(-) create mode 100644 tests/hopp/inputs/pysam_turbine_input.yaml diff --git a/RELEASE.md b/RELEASE.md index 82b9e5314..f8659f854 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -3,6 +3,7 @@ * Add `overwrite_fin_values` from H2Integrate to sync cost input methods * Bump minimum NREL-PySAM version to 7.0.0 * Clarify that the `nominal_discount_rate` method of the `CustomFinancialModel` uses the Fisher equation +* Bug-fix in `WindPlant` for handling model_input_file for PySAM simulations. ## Version 3.3.0, April 30, 2025 diff --git a/hopp/simulation/technologies/layout/wind_layout.py b/hopp/simulation/technologies/layout/wind_layout.py index 54436aec9..a1aa3d47c 100644 --- a/hopp/simulation/technologies/layout/wind_layout.py +++ b/hopp/simulation/technologies/layout/wind_layout.py @@ -246,9 +246,12 @@ def __attrs_post_init__(self): if isinstance(self._system_model, Floris): self.turb_pos_x, self.turb_pos_y = self._system_model.wind_farm_layout else: - self.turb_pos_x = self._system_model.value("wind_farm_xCoordinates") - self.turb_pos_y = self._system_model.value("wind_farm_yCoordinates") - + if 'wind_farm_xCoordinates' in self._system_model.Farm.export(): + self.turb_pos_x = self._system_model.value("wind_farm_xCoordinates") + self.turb_pos_y = self._system_model.value("wind_farm_yCoordinates") + else: + self.turb_pos_x = [0] + self.turb_pos_y = [0] if isinstance(self.parameters, dict): if self.layout_mode == 'boundarygrid': self.parameters = WindBoundaryGridParameters.from_dict(self.parameters) diff --git a/hopp/simulation/technologies/wind/wind_plant.py b/hopp/simulation/technologies/wind/wind_plant.py index 57f21f527..b7a65c3b2 100644 --- a/hopp/simulation/technologies/wind/wind_plant.py +++ b/hopp/simulation/technologies/wind/wind_plant.py @@ -105,7 +105,7 @@ class WindConfig(BaseClass): validator=contains(["pysam", "floris"]), converter=(str.strip, str.lower) ) - model_input_file: Optional[str] = field(default=None) + model_input_file: Optional[Union[str,dict]] = field(default=None) rating_range_kw: Tuple[int, int] = field(default=(1000, 3000)) floris_config: Optional[Union[dict, str, Path]] = field(default=None) adjust_air_density_for_elevation: Optional[bool] = field(default=False) @@ -186,19 +186,40 @@ def __attrs_post_init__(self): system_model = Windpower.default(self.config_name) else: # initialize system using pysam input file - input_file_path = resource_file_converter(self.config.model_input_file) - input_dict = load_yaml(input_file_path) - - system_model = Windpower.new() + if isinstance(self.config.model_input_file,str): + input_dict = load_yaml(self.config.model_input_file) + else: + input_dict = self.config.model_input_file + try: + nTurbs = len(input_dict['Farm']['wind_farm_xCoordinates']) + except KeyError: + nTurbs = 0 + if nTurbs==self.config.num_turbines: + self.config.layout_mode = 'custom' + self.config.layout_params = { + 'layout_x': input_dict['Farm']['wind_farm_xCoordinates'], + 'layout_y': input_dict['Farm']['wind_farm_yCoordinates'], + } + print("Using wind layout found in model_input_file, changing layout_mode to custom.") + + resource = input_dict.get("Resource", {}) + user_provided_data = False if resource.get("wind_resource_data", None) is None else True + user_provided_distribution = False if resource.get("wind_resource_distribution", None) is None else True + user_provided_weibull = False if resource.get("weibull_wind_speed:", None) is None else True + input_dict.setdefault('Resource',{}) + if not user_provided_data and not user_provided_distribution and not user_provided_weibull: + input_dict['Resource'].update({"wind_resource_data": self.site.wind_resource.data}) + user_provided_data = True + if user_provided_data: + input_dict['Resource'].setdefault("wind_resource_model_choice",0) + if user_provided_weibull: + input_dict['Resource'].setdefault("wind_resource_model_choice",1) + if user_provided_distribution: + input_dict['Resource'].setdefault("wind_resource_model_choice",2) + + system_model = Windpower.default(self.config_name) system_model.assign(input_dict) - wind_farm_xCoordinates = input_dict['Farm']['wind_farm_xCoordinates'] - nTurbs = len(wind_farm_xCoordinates) - system_model.value("wind_resource_data", self.site.wind_resource.data) - - # turbine power curve (array of kW power outputs) - self.wind_turbine_powercurve_powerout = [1] * nTurbs - if financial_model is None: # default financial_model = Singleowner.from_existing(system_model, self.config_name) diff --git a/tests/hopp/inputs/pysam_turbine_input.yaml b/tests/hopp/inputs/pysam_turbine_input.yaml new file mode 100644 index 000000000..8c398eb0f --- /dev/null +++ b/tests/hopp/inputs/pysam_turbine_input.yaml @@ -0,0 +1,329 @@ +Turbine: + wind_resource_shear: 0.14 + wind_turbine_hub_ht: 80.0 + wind_turbine_max_cp: 0.45 + wind_turbine_powercurve_powerout: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 25.71 + - 37.14 + - 54.29 + - 77.14 + - 111.43 + - 145.71 + - 185.71 + - 231.43 + - 300.0 + - 334.29 + - 391.43 + - 454.29 + - 511.43 + - 602.85 + - 654.29 + - 734.29 + - 820.0 + - 905.71 + - 1000.0 + - 1070.0 + - 1170.0 + - 1280.0 + - 1380.0 + - 1510.0 + - 1640.0 + - 1790.0 + - 1920.0 + - 2010.0 + - 2110.0 + - 2190.0 + - 2260.0 + - 2320.0 + - 2370.0 + - 2420.0 + - 2460.0 + - 2480.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 2500.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + wind_turbine_powercurve_windspeeds: + - 0.0 + - 0.25 + - 0.5 + - 0.75 + - 1.0 + - 1.25 + - 1.5 + - 1.75 + - 2.0 + - 2.25 + - 2.5 + - 2.75 + - 3.0 + - 3.25 + - 3.5 + - 3.75 + - 4.0 + - 4.25 + - 4.5 + - 4.75 + - 5.0 + - 5.25 + - 5.5 + - 5.75 + - 6.0 + - 6.25 + - 6.5 + - 6.75 + - 7.0 + - 7.25 + - 7.5 + - 7.75 + - 8.0 + - 8.25 + - 8.5 + - 8.75 + - 9.0 + - 9.25 + - 9.5 + - 9.75 + - 10.0 + - 10.25 + - 10.5 + - 10.75 + - 11.0 + - 11.25 + - 11.5 + - 11.75 + - 12.0 + - 12.25 + - 12.5 + - 12.75 + - 13.0 + - 13.25 + - 13.5 + - 13.75 + - 14.0 + - 14.25 + - 14.5 + - 14.75 + - 15.0 + - 15.25 + - 15.5 + - 15.75 + - 16.0 + - 16.25 + - 16.5 + - 16.75 + - 17.0 + - 17.25 + - 17.5 + - 17.75 + - 18.0 + - 18.25 + - 18.5 + - 18.75 + - 19.0 + - 19.25 + - 19.5 + - 19.75 + - 20.0 + - 20.25 + - 20.5 + - 20.75 + - 21.0 + - 21.25 + - 21.5 + - 21.75 + - 22.0 + - 22.25 + - 22.5 + - 22.75 + - 23.0 + - 23.25 + - 23.5 + - 23.75 + - 24.0 + - 24.25 + - 24.5 + - 24.75 + - 25.0 + - 25.25 + - 25.5 + - 25.75 + - 26.0 + - 26.25 + - 26.5 + - 26.75 + - 27.0 + - 27.25 + - 27.5 + - 27.75 + - 28.0 + - 28.25 + - 28.5 + - 28.75 + - 29.0 + - 29.25 + - 29.5 + - 29.75 + - 30.0 + - 30.25 + - 30.5 + - 30.75 + - 31.0 + - 31.25 + - 31.5 + - 31.75 + - 32.0 + - 32.25 + - 32.5 + - 32.75 + - 33.0 + - 33.25 + - 33.5 + - 33.75 + - 34.0 + - 34.25 + - 34.5 + - 34.75 + - 35.0 + - 35.25 + - 35.5 + - 35.75 + - 36.0 + - 36.25 + - 36.5 + - 36.75 + - 37.0 + - 37.25 + - 37.5 + - 37.75 + - 38.0 + - 38.25 + - 38.5 + - 38.75 + - 39.0 + - 39.25 + - 39.5 + - 39.75 + - 40.0 + wind_turbine_rotor_diameter: 100.0 \ No newline at end of file diff --git a/tests/hopp/test_wind.py b/tests/hopp/test_wind.py index 455205c41..9ea7a5ea3 100644 --- a/tests/hopp/test_wind.py +++ b/tests/hopp/test_wind.py @@ -75,6 +75,46 @@ def test_wind_powercurve_pysam(): assert all([a == b for a, b in zip(windspeeds_truth, windspeeds_calc)]) assert all([a == b for a, b in zip(powercurve_truth, powercurve_calc)]) +def test_user_input_turbine_dict_pysam(site): + nTurbs = 10 + pysam_model = windpower.default("WindpowerSingleowner") + pysam_default_model = pysam_model.export() + input_turbine_config = {'Turbine':pysam_default_model['Turbine']} + + config = WindConfig.from_dict({'num_turbines': nTurbs, "model_input_file": input_turbine_config}) + model = WindPlant(site, config=config) + + turbine_rated_power_kW = max(pysam_default_model['Turbine']['wind_turbine_powercurve_powerout']) + assert model.system_capacity_kw == nTurbs*turbine_rated_power_kW + + model._system_model.execute(0) + assert model._system_model.Outputs.capacity_factor == approx(36.5,abs = 0.5) + +def test_user_input_turbine_file_pysam(site): + nTurbs = 10 + pysam_turbine_input_filepath = str(ROOT_DIR.parent/"tests"/"hopp"/"inputs"/"pysam_turbine_input.yaml") + config = WindConfig.from_dict({'num_turbines': nTurbs, "model_input_file": pysam_turbine_input_filepath}) + model = WindPlant(site, config=config) + + testing_pysam_model = load_yaml(pysam_turbine_input_filepath) + turbine_rated_power_kW = max(testing_pysam_model['Turbine']['wind_turbine_powercurve_powerout']) + assert model.system_capacity_kw == nTurbs*turbine_rated_power_kW + model._system_model.execute(0) + assert model._system_model.Outputs.capacity_factor == approx(36.5,abs = 0.5) + + +def test_user_input_pysam_file(site): + nTurbs = 32 + pysam_input_filepath = str(ROOT_DIR.parent/"tests"/"hopp"/"inputs"/"pysam_simulation_input.yaml") + config = WindConfig.from_dict({'num_turbines': nTurbs, "model_input_file": pysam_input_filepath}) + model = WindPlant(site, config=config) + + testing_pysam_model = load_yaml(pysam_input_filepath) + turbine_rated_power_kW = max(testing_pysam_model['Turbine']['wind_turbine_powercurve_powerout']) + assert model.system_capacity_kw == nTurbs*turbine_rated_power_kW + + model._system_model.execute(0) + assert model._system_model.Outputs.capacity_factor == approx(35.0,abs = 0.5) def test_changing_n_turbines_pysam(site): # test with gridded layout From bab5e02c6fdb9fdfd7de49f299068e1b5a8021db Mon Sep 17 00:00:00 2001 From: kbrunik <102193481+kbrunik@users.noreply.github.com> Date: Thu, 24 Jul 2025 14:55:26 -0500 Subject: [PATCH 41/48] Remove tidal_resource from Tidal Model (#495) * remove tidal_resource since no longer required for SSC sim --- RELEASE.md | 1 + examples/10-tidal-battery.ipynb | 19 +++-- examples/inputs/10-tidal-battery.yaml | 70 +++++++++---------- .../technologies/tidal/mhk_tidal_plant.py | 6 -- tests/hopp/inputs/tidal/tidal_device.yaml | 41 +---------- tests/hopp/test_hybrid.py | 2 - 6 files changed, 50 insertions(+), 89 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index f8659f854..8f8709752 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -4,6 +4,7 @@ * Bump minimum NREL-PySAM version to 7.0.0 * Clarify that the `nominal_discount_rate` method of the `CustomFinancialModel` uses the Fisher equation * Bug-fix in `WindPlant` for handling model_input_file for PySAM simulations. +* Remove `tidal_resource` as a required input to the tidal model because it's no longer required in the SSC model ([NREL/SSC PR #1305](https://github.com/NREL/ssc/pull/1305)) ## Version 3.3.0, April 30, 2025 diff --git a/examples/10-tidal-battery.ipynb b/examples/10-tidal-battery.ipynb index 10f6c4afd..aaddbb0dc 100644 --- a/examples/10-tidal-battery.ipynb +++ b/examples/10-tidal-battery.ipynb @@ -19,9 +19,17 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/kbrunik/github/HOPP/examples/log/hybrid_systems_2025-07-23T14.55.37.333918.log\n" + ] + } + ], "source": [ "from hopp.simulation import HoppInterface" ] @@ -35,8 +43,7 @@ "\n", "For the site information, the tidal resource data **must be pre-loaded** in the format given in the `Tidal_resource_timeseries.csv`.\n", "\n", - "The tidal technology configuration requires the device rating (kw), power curve of tidal energy device as function of stream speeds (kW), and number of devices. Additionally there's a variable called `tidal_resource`, which is required for model instantiation but doesn't impact a timeseries simulation.\n", - "\n", + "The tidal technology configuration requires the device rating (kw), power curve of tidal energy device as function of stream speeds (kW), and number of devices.\n", "Note that the tidal model doesn't come with a default financial model. To address this, you must establish the `CustomFinancialModel` from HOPP.\n", "\n", "The `default_fin_config` contains all of the necessary parameters for the financial calculations.\n", @@ -46,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -144,7 +151,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pysam6", + "display_name": "pysam7", "language": "python", "name": "python3" }, diff --git a/examples/inputs/10-tidal-battery.yaml b/examples/inputs/10-tidal-battery.yaml index 60149d306..b8d6f3db4 100644 --- a/examples/inputs/10-tidal-battery.yaml +++ b/examples/inputs/10-tidal-battery.yaml @@ -72,41 +72,41 @@ technologies: # this is a dummy resource profile and does not # impact simulation when using timeseries data # TODO: Remove once PySAM Pypi updates - tidal_resource: - - [0.000000, 0.009000] - - [0.100000, 0.031000] - - [0.200000, 0.042000] - - [0.300000, 0.044000] - - [0.400000, 0.048000] - - [0.500000, 0.049000] - - [0.600000, 0.053000] - - [0.700000, 0.051000] - - [0.800000, 0.052000] - - [0.900000, 0.056000] - - [1.000000, 0.050000] - - [1.100000, 0.052000] - - [1.200000, 0.050000] - - [1.300000, 0.048000] - - [1.400000, 0.047000] - - [1.500000, 0.043000] - - [1.600000, 0.042000] - - [1.700000, 0.040000] - - [1.800000, 0.034000] - - [1.900000, 0.031000] - - [2.000000, 0.026000] - - [2.100000, 0.023000] - - [2.200000, 0.020000] - - [2.300000, 0.016000] - - [2.400000, 0.013000] - - [2.500000, 0.011000] - - [2.600000, 0.007000] - - [2.700000, 0.005000] - - [2.800000, 0.004000] - - [2.900000, 0.002000] - - [3.000000, 0.001000] - - [3.100000, 0.000000] - - [3.200000, 0.000000] - - [3.300000, 0.000000] + # tidal_resource: + # - [0.000000, 0.009000] + # - [0.100000, 0.031000] + # - [0.200000, 0.042000] + # - [0.300000, 0.044000] + # - [0.400000, 0.048000] + # - [0.500000, 0.049000] + # - [0.600000, 0.053000] + # - [0.700000, 0.051000] + # - [0.800000, 0.052000] + # - [0.900000, 0.056000] + # - [1.000000, 0.050000] + # - [1.100000, 0.052000] + # - [1.200000, 0.050000] + # - [1.300000, 0.048000] + # - [1.400000, 0.047000] + # - [1.500000, 0.043000] + # - [1.600000, 0.042000] + # - [1.700000, 0.040000] + # - [1.800000, 0.034000] + # - [1.900000, 0.031000] + # - [2.000000, 0.026000] + # - [2.100000, 0.023000] + # - [2.200000, 0.020000] + # - [2.300000, 0.016000] + # - [2.400000, 0.013000] + # - [2.500000, 0.011000] + # - [2.600000, 0.007000] + # - [2.700000, 0.005000] + # - [2.800000, 0.004000] + # - [2.900000, 0.002000] + # - [3.000000, 0.001000] + # - [3.100000, 0.000000] + # - [3.200000, 0.000000] + # - [3.300000, 0.000000] fin_model: !include default_fin_config.yaml battery: system_capacity_kwh: 80000 diff --git a/hopp/simulation/technologies/tidal/mhk_tidal_plant.py b/hopp/simulation/technologies/tidal/mhk_tidal_plant.py index ae6d8fd3a..899249bdf 100644 --- a/hopp/simulation/technologies/tidal/mhk_tidal_plant.py +++ b/hopp/simulation/technologies/tidal/mhk_tidal_plant.py @@ -19,10 +19,6 @@ class MHKTidalConfig(BaseClass): device_rating_kw (float): Rated power of the MHK device [kW] num_devices (int): Number of MHK tidal devices in the system tidal_power_curve (List[List[float]]): Power curve of tidal energy device as function of stream speeds [kW] - tidal_resource (List[List[float]]): Required by the PySAM MhkTidal module for initialization. Although this parameter - is not actively used in HOPP's timeseries simulation mode, it must still be provided to fully - instantiate the PySAM MhkTidal model. - Frequency distribution of resource as a function of stream speeds. fin_model (obj | dict): Optional financial model. Can be any of the following: - a dict representing a `CustomFinancialModel` - an object representing a `CustomFinancialModel` instance @@ -36,7 +32,6 @@ class MHKTidalConfig(BaseClass): device_rating_kw: float = field(validator=gt_zero) num_devices: int = field(validator=gt_zero) tidal_power_curve: List[List[float]] - tidal_resource: List[List[float]] fin_model: Union[dict, CustomFinancialModel] loss_array_spacing: float = field(default=0., validator=range_val(0, 100)) loss_resource_overprediction: float = field(default=0., validator=range_val(0, 100)) @@ -93,7 +88,6 @@ def __attrs_post_init__(self): self._system_model.device_rated_power = self.config.device_rating_kw self._system_model.value("number_devices", self.config.num_devices) self._system_model.value("tidal_power_curve", self.config.tidal_power_curve) - self._system_model.value("tidal_resource", self.config.tidal_resource) # Losses loss_attributes = [ diff --git a/tests/hopp/inputs/tidal/tidal_device.yaml b/tests/hopp/inputs/tidal/tidal_device.yaml index 4c2d24a6a..6b5b1209e 100644 --- a/tests/hopp/inputs/tidal/tidal_device.yaml +++ b/tests/hopp/inputs/tidal/tidal_device.yaml @@ -45,43 +45,4 @@ tidal_power_curve: - [3.200000, 1085.370000] - [3.300000, 1055.730000] -num_devices: 20 -# Tidal resource is required in PySAM prechecks -# this is a dummy resource profile and does not -# impact simulation when using timeseries data -# TODO: Remove once PySAM Pypi updates -tidal_resource: -- [0.000000, 0.009000] -- [0.100000, 0.031000] -- [0.200000, 0.042000] -- [0.300000, 0.044000] -- [0.400000, 0.048000] -- [0.500000, 0.049000] -- [0.600000, 0.053000] -- [0.700000, 0.051000] -- [0.800000, 0.052000] -- [0.900000, 0.056000] -- [1.000000, 0.050000] -- [1.100000, 0.052000] -- [1.200000, 0.050000] -- [1.300000, 0.048000] -- [1.400000, 0.047000] -- [1.500000, 0.043000] -- [1.600000, 0.042000] -- [1.700000, 0.040000] -- [1.800000, 0.034000] -- [1.900000, 0.031000] -- [2.000000, 0.026000] -- [2.100000, 0.023000] -- [2.200000, 0.020000] -- [2.300000, 0.016000] -- [2.400000, 0.013000] -- [2.500000, 0.011000] -- [2.600000, 0.007000] -- [2.700000, 0.005000] -- [2.800000, 0.004000] -- [2.900000, 0.002000] -- [3.000000, 0.001000] -- [3.100000, 0.000000] -- [3.200000, 0.000000] -- [3.300000, 0.000000] \ No newline at end of file +num_devices: 20 \ No newline at end of file diff --git a/tests/hopp/test_hybrid.py b/tests/hopp/test_hybrid.py index 153250ebc..ff1543c4d 100644 --- a/tests/hopp/test_hybrid.py +++ b/tests/hopp/test_hybrid.py @@ -449,7 +449,6 @@ def test_hybrid_tidal_only(hybrid_config, mhk_tidal_config, tidalsite, subtests) "device_rating_kw": mhk_tidal_config["device_rating_kw"], "num_devices": 2, "tidal_power_curve": mhk_tidal_config["tidal_power_curve"], - "tidal_resource": mhk_tidal_config["tidal_resource"], "fin_model": DEFAULT_FIN_CONFIG, }, "grid": { @@ -598,7 +597,6 @@ def test_hybrid_tidal_battery(hybrid_config, mhk_tidal_config,tidalsite, subtest "device_rating_kw": mhk_tidal_config["device_rating_kw"], "num_devices": 2, "tidal_power_curve": mhk_tidal_config["tidal_power_curve"], - "tidal_resource": mhk_tidal_config["tidal_resource"], "fin_model": DEFAULT_FIN_CONFIG, }, "battery": { From 3426b8ea156f33ca7cefb6cb4b7dfb1db02c1f39 Mon Sep 17 00:00:00 2001 From: Chris Bay <12664940+bayc@users.noreply.github.com> Date: Fri, 29 Aug 2025 15:20:19 -0600 Subject: [PATCH 42/48] add test for load following heuristic dispatch (#497) --- RELEASE.md | 1 + .../heuristic_load_following_dispatch.py | 9 ++- tests/hopp/test_dispatch.py | 70 +++++++++++++++++++ 3 files changed, 77 insertions(+), 3 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 8f8709752..a21404b49 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -5,6 +5,7 @@ * Clarify that the `nominal_discount_rate` method of the `CustomFinancialModel` uses the Fisher equation * Bug-fix in `WindPlant` for handling model_input_file for PySAM simulations. * Remove `tidal_resource` as a required input to the tidal model because it's no longer required in the SSC model ([NREL/SSC PR #1305](https://github.com/NREL/ssc/pull/1305)) +* Add load following heuristic dispatch test ## Version 3.3.0, April 30, 2025 diff --git a/hopp/simulation/technologies/dispatch/power_storage/heuristic_load_following_dispatch.py b/hopp/simulation/technologies/dispatch/power_storage/heuristic_load_following_dispatch.py index ebb006c0e..a753d679f 100644 --- a/hopp/simulation/technologies/dispatch/power_storage/heuristic_load_following_dispatch.py +++ b/hopp/simulation/technologies/dispatch/power_storage/heuristic_load_following_dispatch.py @@ -1,4 +1,4 @@ -from typing import Optional, List +from typing import Optional, List, Dict, Union, TYPE_CHECKING import pyomo.environ as pyomo from pyomo.environ import units as u @@ -8,7 +8,10 @@ from hopp.simulation.technologies.dispatch.power_storage.simple_battery_dispatch_heuristic import ( SimpleBatteryDispatchHeuristic, ) - +if TYPE_CHECKING: + from hopp.simulation.technologies.dispatch.hybrid_dispatch_builder_solver import ( + HybridDispatchOptions + ) class HeuristicLoadFollowingDispatch(SimpleBatteryDispatchHeuristic): """Operates the battery based on heuristic rules to meet the demand profile based power available from power generation profiles and @@ -26,7 +29,7 @@ def __init__( financial_model: Singleowner.Singleowner, fixed_dispatch: Optional[List] = None, block_set_name: str = "heuristic_load_following_battery", - dispatch_options: Optional[dict] = None, + dispatch_options: Optional[Union[Dict, "HybridDispatchOptions"]] = None, ): """Initialize HeuristicLoadFollowingDispatch. diff --git a/tests/hopp/test_dispatch.py b/tests/hopp/test_dispatch.py index 27fd715e7..705c13c2a 100644 --- a/tests/hopp/test_dispatch.py +++ b/tests/hopp/test_dispatch.py @@ -1,6 +1,7 @@ from pathlib import Path import pytest import pyomo.environ as pyomo +import numpy as np from pyomo.environ import units as u from pyomo.opt import TerminationCondition from pyomo.util.check_units import assert_units_consistent @@ -22,6 +23,7 @@ from hopp.simulation.technologies.dispatch.power_storage.linear_voltage_convex_battery_dispatch import ConvexLinearVoltageBatteryDispatch from hopp.simulation.technologies.dispatch.power_storage.simple_battery_dispatch import SimpleBatteryDispatch +from hopp.simulation.technologies.dispatch.power_storage.heuristic_load_following_dispatch import HeuristicLoadFollowingDispatch from hopp.simulation.technologies.dispatch.hybrid_dispatch_builder_solver import HybridDispatchBuilderSolver, HybridDispatchOptions from hopp.simulation.technologies.dispatch.power_sources.pv_dispatch import PvDispatch from hopp.simulation.technologies.dispatch.power_sources.wind_dispatch import WindDispatch @@ -524,6 +526,74 @@ def create_test_objective_rule(m): assert battery.outputs.P[i] == pytest.approx(dispatch_power, 1e-3 * abs(dispatch_power)) +def test_heuristic_load_following_dispatch(site): + dispatch_n_look_ahead = 48 # note this must be an even number for this test + + # Load in battery config + config = BatteryConfig.from_dict(technologies['battery']) + battery = Battery(site, config=config) + + # Create pyomo model + model = pyomo.ConcreteModel(name='battery_only') + model.forecast_horizon = pyomo.Set(initialize=range(dispatch_n_look_ahead)) + + # Instantiate battery dispatch + battery._dispatch = HeuristicLoadFollowingDispatch( + pyomo_model=model, + index_set=model.forecast_horizon, + system_model=battery._system_model, + financial_model=battery._financial_model, + block_set_name="heuristic_load_following_battery", + dispatch_options=HybridDispatchOptions( + { + 'include_lifecycle_count': False, + 'battery_dispatch': 'load_following_heuristic', + } + ), + ) + + # Setup dispatch for battery + battery.dispatch.initialize_parameters() + battery.dispatch.update_time_series_parameters(0) + battery.dispatch.update_dispatch_initial_soc(battery.dispatch.minimum_soc) # Set initial SOC to minimum + assert_units_consistent(model) + + # Generate test data for n horizon, charging for first half of the horizon, + # then discharging for the latter half. + tot_gen = np.ones(dispatch_n_look_ahead) + n_look_ahead_half = int(dispatch_n_look_ahead / 2) + grid_limit = np.concatenate( + (np.ones(n_look_ahead_half) * 0.9, np.ones(n_look_ahead_half) * 1.1) + ) + + # Set the dispatch pyomo variables + battery.dispatch.set_fixed_dispatch(tot_gen, grid_limit, grid_limit) + + # Simulate the battery with the heuristic dispatch + battery.simulate_with_dispatch(n_periods=dispatch_n_look_ahead, sim_start_time=0) + + # Check that the power is being assigned correctly to the battery outputs + dispatch_power = np.empty(dispatch_n_look_ahead) + for i in range(dispatch_n_look_ahead): + dispatch_power[i] = battery.dispatch.power[i] * 1e3 + assert battery.outputs.P[i] == pytest.approx( + dispatch_power[i], 1e-3 * abs(dispatch_power[i]) + ) + + # Check that the has non-zero charge and discharge power, and that the sum of charge + # and discharge power are equal. + assert sum(battery.dispatch.charge_power) > 0.0 + assert sum(battery.dispatch.discharge_power) > 0.0 + assert (sum(battery.dispatch.charge_power) #* battery.dispatch.round_trip_efficiency / 100.0 + == pytest.approx(sum(battery.dispatch.discharge_power))) + + # Check that the dispatch values have not changed + expected_dispatch_power = np.concatenate( + (np.ones(n_look_ahead_half) * -100., np.ones(n_look_ahead_half) * 100.) + ) + np.testing.assert_allclose(dispatch_power, expected_dispatch_power) + + def test_simple_battery_dispatch_lifecycle_count(site): expected_objective = 24378.6 expected_lifecycles = [0.75048, 1.50096] From ab2cd194d97276c634018ad1bbebcd72c083c00e Mon Sep 17 00:00:00 2001 From: John Jasa Date: Wed, 22 Oct 2025 10:30:39 -0500 Subject: [PATCH 43/48] Pin timezonefinder, consolidate utilities, rename MHKConfig, removed defunct conda build (#500) * Removing timezonefinder * Consolidate utilities; rename MHKWaveConfig * Pinned timezonefinder * Removed defunct conda build * Fixed import --- .github/workflows/conda_build.yml | 24 ------ RELEASE.md | 1 + conda.recipe/meta.yaml | 57 ------------- conda_build.sh | 10 --- docs/api/technology/mhk_wave_plant.md | 2 +- examples/legacy/analysis/main_usa_new.py | 1 + examples/legacy/analysis/multi_location.py | 1 + examples/legacy/analysis/single_location.py | 1 + hopp/simulation/hybrid_simulation.py | 6 +- .../financial/custom_financial_model.py | 2 +- .../technologies/financial/mhk_cost_model.py | 4 +- .../technologies/layout/pv_inverter.py | 2 +- .../technologies/layout/pv_module.py | 2 +- .../technologies/layout/shadow_flicker.py | 2 +- hopp/simulation/technologies/power_source.py | 2 +- .../technologies/pv/detailed_pv_plant.py | 2 +- .../technologies/wave/mhk_wave_plant.py | 4 +- hopp/tools/resource/__init__.py | 2 +- hopp/tools/resource/resource_tools.py | 5 +- hopp/tools/utils.py | 81 ------------------- hopp/utilities/__init__.py | 2 +- hopp/utilities/utilities.py | 43 ++++++++++ pyproject.toml | 3 +- tests/hopp/test_dispatch.py | 4 +- tests/hopp/test_wave.py | 6 +- 25 files changed, 71 insertions(+), 198 deletions(-) delete mode 100644 .github/workflows/conda_build.yml delete mode 100644 conda.recipe/meta.yaml delete mode 100644 conda_build.sh delete mode 100644 hopp/tools/utils.py diff --git a/.github/workflows/conda_build.yml b/.github/workflows/conda_build.yml deleted file mode 100644 index 8d5400d9d..000000000 --- a/.github/workflows/conda_build.yml +++ /dev/null @@ -1,24 +0,0 @@ -name: Conda Build and Upload - -on: - release: - types: [published] - -jobs: - build: - name: Conda - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - uses: conda-incubator/setup-miniconda@v3 - with: - auto-update-conda: true - python-version: 3.11 - - name: Build and upload conda package - shell: bash -l {0} - env: - ANACONDA_TOKEN: ${{ secrets.ANACONDA_TOKEN }} - run: | - conda install --yes --quiet conda-build conda-verify anaconda-client - conda build conda.recipe/ -c nrel -c conda-forge -c sunpower - anaconda -t $ANACONDA_TOKEN upload -u nrel $(conda build conda.recipe/ -c nrel -c conda-forge -c sunpower --output) diff --git a/RELEASE.md b/RELEASE.md index a21404b49..f90f67454 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -6,6 +6,7 @@ * Bug-fix in `WindPlant` for handling model_input_file for PySAM simulations. * Remove `tidal_resource` as a required input to the tidal model because it's no longer required in the SSC model ([NREL/SSC PR #1305](https://github.com/NREL/ssc/pull/1305)) * Add load following heuristic dispatch test +* Consolidated utilities, renamed MHKConfig to MHKWaveConfig ## Version 3.3.0, April 30, 2025 diff --git a/conda.recipe/meta.yaml b/conda.recipe/meta.yaml deleted file mode 100644 index ae1e00a04..000000000 --- a/conda.recipe/meta.yaml +++ /dev/null @@ -1,57 +0,0 @@ -package: - name: hopp - version: {{ environ.get('GIT_DESCRIBE_TAG','').replace('v', '', 1) }} - -source: - git_url: ../ - -build: - number: 0 - noarch: python - script: python setup.py install --single-version-externally-managed --record=record.txt - -requirements: - host: - - python - - pip - - setuptools - - matplotlib - - nrel-pysam>=2.1.4 - - numpy>=1.16 - - pandas - - pillow - - pvmismatch - - pysolar - - python-dotenv - - pytz - - requests - - scipy - - shapely - - timezonefinder - - urllib3 - run: - - python - - pip - - matplotlib - - nrel-pysam>=2.1.4 - - {{ pin_compatible('numpy') }} - - pandas - - pillow - - pvmismatch - - pysolar - - python-dotenv - - pytz - - requests - - scipy - - shapely - - timezonefinder - - urllib3 - run-constrained: - - global_land_mask - -about: - home: "https://github.com/NREL/HOPP" - license: BSD 3-Clause - summary: "Hybrid Systems Optimization and Performance Platform" - doc_url: "https://www.nrel.gov/wind/hybrid-energy-systems-research.html" - dev_url: "https://github.com/NREL/HOPP" diff --git a/conda_build.sh b/conda_build.sh deleted file mode 100644 index 7855e78f9..000000000 --- a/conda_build.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash - -set -e - -conda build conda.recipe/ -c nrel -c conda-forge -c sunpower - -anaconda upload -u nrel $(conda build conda.recipe/ -c nrel -c conda-forge -c sunpower --output) - -echo "Building and uploading conda package done!" - diff --git a/docs/api/technology/mhk_wave_plant.md b/docs/api/technology/mhk_wave_plant.md index 09990a44f..cac035a76 100644 --- a/docs/api/technology/mhk_wave_plant.md +++ b/docs/api/technology/mhk_wave_plant.md @@ -14,7 +14,7 @@ MHK Wave Generator class ## Wave Plant Configuration ```{eval-rst} -.. autoclass:: hopp.simulation.technologies.wave.mhk_wave_plant.MHKConfig +.. autoclass:: hopp.simulation.technologies.wave.mhk_wave_plant.MHKWaveConfig :members: :undoc-members: ``` diff --git a/examples/legacy/analysis/main_usa_new.py b/examples/legacy/analysis/main_usa_new.py index 5d454431e..5005b0cd4 100644 --- a/examples/legacy/analysis/main_usa_new.py +++ b/examples/legacy/analysis/main_usa_new.py @@ -23,6 +23,7 @@ from hopp.tools.analysis import create_cost_calculator from hopp.tools.resource import * +from hopp.tools.resource.resource_tools import get_offset from hopp import ROOT_DIR diff --git a/examples/legacy/analysis/multi_location.py b/examples/legacy/analysis/multi_location.py index 0efe21ba7..2aeac3dee 100644 --- a/examples/legacy/analysis/multi_location.py +++ b/examples/legacy/analysis/multi_location.py @@ -27,6 +27,7 @@ from hopp.simulation.hybrid_simulation import HybridSimulation from hopp.tools.analysis import create_cost_calculator from hopp.tools.resource import * +from hopp.tools.resource.resource_tools import get_offset from hopp.tools.resource.resource_loader import site_details_creator from hopp import ROOT_DIR resource_dir = ROOT_DIR / "simulation" / "resource_files" diff --git a/examples/legacy/analysis/single_location.py b/examples/legacy/analysis/single_location.py index 60a13b9e4..94ae16b39 100644 --- a/examples/legacy/analysis/single_location.py +++ b/examples/legacy/analysis/single_location.py @@ -24,6 +24,7 @@ from hopp.simulation.hybrid_simulation import HybridSimulation from hopp.tools.analysis import create_cost_calculator from hopp.tools.resource import * +from hopp.tools.resource.resource_tools import get_offset from hopp import ROOT_DIR resource_dir = ROOT_DIR / "simulation" / "resource_files" diff --git a/hopp/simulation/hybrid_simulation.py b/hopp/simulation/hybrid_simulation.py index 014aa1936..6c6e3b93f 100644 --- a/hopp/simulation/hybrid_simulation.py +++ b/hopp/simulation/hybrid_simulation.py @@ -14,7 +14,7 @@ from hopp.simulation.technologies.wind.wind_plant import WindPlant, WindConfig from hopp.simulation.technologies.csp.tower_plant import TowerConfig, TowerPlant from hopp.simulation.technologies.csp.trough_plant import TroughConfig, TroughPlant -from hopp.simulation.technologies.wave.mhk_wave_plant import MHKWavePlant, MHKConfig +from hopp.simulation.technologies.wave.mhk_wave_plant import MHKWavePlant, MHKWaveConfig from hopp.simulation.technologies.tidal.mhk_tidal_plant import MHKTidalPlant, MHKTidalConfig from hopp.simulation.technologies.generic.generic_plant import GenericConfig, GenericPlant from hopp.simulation.technologies.battery import Battery, BatteryConfig, BatteryStateless, BatteryStatelessConfig @@ -111,7 +111,7 @@ class TechnologiesConfig(BaseClass): """ pv: Optional[Union[PVConfig, DetailedPVConfig]] = field(default=None) wind: Optional[WindConfig] = field(default=None) - wave: Optional[MHKConfig] = field(default=None) + wave: Optional[MHKWaveConfig] = field(default=None) tidal: Optional[MHKTidalConfig] = field(default=None) generic: Optional[Union[GenericConfig,list[GenericConfig]]] = field(default=None) tower: Optional[TowerConfig] = field(default=None) @@ -139,7 +139,7 @@ def from_dict(cls, data: dict): config["wind"] = WindConfig.from_dict(data["wind"]) if "wave" in data: - config["wave"] = MHKConfig.from_dict(data["wave"]) + config["wave"] = MHKWaveConfig.from_dict(data["wave"]) if "tidal" in data: config["tidal"] = MHKTidalConfig.from_dict(data["tidal"]) diff --git a/hopp/simulation/technologies/financial/custom_financial_model.py b/hopp/simulation/technologies/financial/custom_financial_model.py index 6a13adfc0..c7de16039 100644 --- a/hopp/simulation/technologies/financial/custom_financial_model.py +++ b/hopp/simulation/technologies/financial/custom_financial_model.py @@ -3,7 +3,7 @@ import inspect from typing import Sequence, List import numpy as np -from hopp.tools.utils import flatten_dict, equal +from hopp.utilities import flatten_dict, equal from hopp.simulation.base import BaseClass import ProFAST diff --git a/hopp/simulation/technologies/financial/mhk_cost_model.py b/hopp/simulation/technologies/financial/mhk_cost_model.py index 40ae1cccd..221d279b4 100644 --- a/hopp/simulation/technologies/financial/mhk_cost_model.py +++ b/hopp/simulation/technologies/financial/mhk_cost_model.py @@ -8,7 +8,7 @@ # avoid circular dep if TYPE_CHECKING: - from hopp.simulation.technologies.wave.mhk_wave_plant import MHKConfig + from hopp.simulation.technologies.wave.mhk_wave_plant import MHKWaveConfig @define class MHKCostModelInputs(BaseClass): @@ -65,7 +65,7 @@ class MHKCosts(BaseClass): ValueError: If any of the required keys in `mhk_config` or `cost_model_inputs` are missing. """ - mhk_config: "MHKConfig" + mhk_config: "MHKWaveConfig" cost_model_inputs: MHKCostModelInputs _device_rated_power: float = field(init=False) diff --git a/hopp/simulation/technologies/layout/pv_inverter.py b/hopp/simulation/technologies/layout/pv_inverter.py index 930876b6f..350dbf1be 100644 --- a/hopp/simulation/technologies/layout/pv_inverter.py +++ b/hopp/simulation/technologies/layout/pv_inverter.py @@ -2,7 +2,7 @@ import PySAM.Pvsamv1 as pv_detailed import PySAM.Pvwattsv8 as pv_simple -from hopp.tools.utils import flatten_dict +from hopp.utilities import flatten_dict def get_inverter_attribs(model: Union[pv_simple.Pvwattsv8, pv_detailed.Pvsamv1, dict], only_ref_values=True) -> dict: """ diff --git a/hopp/simulation/technologies/layout/pv_module.py b/hopp/simulation/technologies/layout/pv_module.py index d2fdd7064..d26da2048 100644 --- a/hopp/simulation/technologies/layout/pv_module.py +++ b/hopp/simulation/technologies/layout/pv_module.py @@ -4,7 +4,7 @@ import PySAM.Pvsamv1 as pv_detailed import PySAM.Pvwattsv8 as pv_simple -from hopp.tools.utils import flatten_dict +from hopp.utilities import flatten_dict # PVWatts default module # pvmismatch standard module description diff --git a/hopp/simulation/technologies/layout/shadow_flicker.py b/hopp/simulation/technologies/layout/shadow_flicker.py index 30a13b44b..7b8d7443a 100644 --- a/hopp/simulation/technologies/layout/shadow_flicker.py +++ b/hopp/simulation/technologies/layout/shadow_flicker.py @@ -8,9 +8,9 @@ from shapely.geometry import Point from shapely.geometry import Polygon, MultiPolygon, MultiPoint from shapely.ops import unary_union -import timezonefinder from pysolar.solar import * from pvmismatch import * +import timezonefinder from hopp.simulation.technologies.layout.pv_module import * diff --git a/hopp/simulation/technologies/power_source.py b/hopp/simulation/technologies/power_source.py index bf31f91db..6d99b1ffb 100644 --- a/hopp/simulation/technologies/power_source.py +++ b/hopp/simulation/technologies/power_source.py @@ -7,7 +7,7 @@ from hopp.simulation.technologies.sites.site_info import SiteInfo from hopp.utilities.log import hybrid_logger as logger from hopp.simulation.technologies.dispatch.power_sources.power_source_dispatch import PowerSourceDispatch -from hopp.tools.utils import array_not_scalar, equal +from hopp.utilities import array_not_scalar, equal from hopp.utilities.log import hybrid_logger as logger from hopp.simulation.base import BaseClass diff --git a/hopp/simulation/technologies/pv/detailed_pv_plant.py b/hopp/simulation/technologies/pv/detailed_pv_plant.py index 91f45253d..90db16ec5 100644 --- a/hopp/simulation/technologies/pv/detailed_pv_plant.py +++ b/hopp/simulation/technologies/pv/detailed_pv_plant.py @@ -19,7 +19,7 @@ ) from hopp.simulation.base import BaseClass -from hopp.tools.utils import flatten_dict +from hopp.utilities import flatten_dict @define diff --git a/hopp/simulation/technologies/wave/mhk_wave_plant.py b/hopp/simulation/technologies/wave/mhk_wave_plant.py index 71da8986a..c8844fd31 100644 --- a/hopp/simulation/technologies/wave/mhk_wave_plant.py +++ b/hopp/simulation/technologies/wave/mhk_wave_plant.py @@ -11,7 +11,7 @@ @define -class MHKConfig(BaseClass): +class MHKWaveConfig(BaseClass): """ Configuration class for MHKWavePlant. @@ -56,7 +56,7 @@ class MHKWavePlant(PowerSource): """ site: SiteInfo - config: MHKConfig + config: MHKWaveConfig cost_model_inputs: Optional[MHKCostModelInputs] = field(default=None) config_name: str = field(default="MhkWave") diff --git a/hopp/tools/resource/__init__.py b/hopp/tools/resource/__init__.py index e21bf6934..eb49d1637 100644 --- a/hopp/tools/resource/__init__.py +++ b/hopp/tools/resource/__init__.py @@ -1,2 +1,2 @@ -from .resource_tools import get_country, filter_sites, get_offset, extrapolate_wind_speed +from .resource_tools import get_country, filter_sites, extrapolate_wind_speed from .resource_loader.resource_loader_files import resource_loader_file diff --git a/hopp/tools/resource/resource_tools.py b/hopp/tools/resource/resource_tools.py index 286bec65d..4c6338523 100644 --- a/hopp/tools/resource/resource_tools.py +++ b/hopp/tools/resource/resource_tools.py @@ -10,13 +10,12 @@ from datetime import datetime from pytz import timezone, utc -from timezonefinder import TimezoneFinder from global_land_mask import globe from shapely.geometry import shape from shapely.prepared import prep from shapely.geometry import Point import requests -import pandas as pd +import timezonefinder def get_country(lat, lon, geo_data): @@ -79,7 +78,7 @@ def get_offset(lat, long): :return: """ today = datetime.now() - tf = TimezoneFinder() + tf = timezonefinder.TimezoneFinder() tz_target = timezone(tf.timezone_at(lng=long, lat=lat)) if not tz_target: raise ValueError("tz_target error") diff --git a/hopp/tools/utils.py b/hopp/tools/utils.py deleted file mode 100644 index 6a067003d..000000000 --- a/hopp/tools/utils.py +++ /dev/null @@ -1,81 +0,0 @@ -import numpy as np -from typing import Sequence - - -def flatten_dict(d): - def get_key_values(d): - for key, value in d.items(): - if isinstance(value, dict): - yield from get_key_values(value) - else: - yield key, value - - return {key:value for (key,value) in get_key_values(d)} - -def equal(a, b): - """Determines whether integers, floats, lists, tupes or dictionaries are equal""" - if isinstance(a, (int, float)): - return np.isclose(a, b) - elif isinstance(a, (list, tuple)): - if len(a) != len(b): - return False - else: - for i in range(len(a)): - if not np.isclose(a[i], b[i]): - return False - return True - elif isinstance(a, dict): - if len(a) != len(b): - return False - else: - for key in a.keys(): - if key not in b.keys(): - return False - if not np.isclose(a[key], b[key]): - return False - return True - else: - raise Exception('Type not recognized') - -def export_all(obj): - """ - Exports all variables from pysam objects including those not assigned - - Assumes the object is a collection of objects with all the variables within them: - obj: - object1: - variable1: - variable2: - - """ - output_dict = {} - for attribute_name in dir(obj): - try: - attribute = getattr(obj, attribute_name) - except: - continue - if not callable(attribute) and not attribute_name.startswith('__'): - output_dict[attribute_name] = {} - for subattribute_name in dir(attribute): - if subattribute_name.startswith('__'): - continue - try: - subattribute = getattr(attribute, subattribute_name) - except Exception as e: - if 'not assigned' in str(e): - output_dict[attribute_name][subattribute_name] = None - continue - else: - continue - if not callable(subattribute): - output_dict[attribute_name][subattribute_name] = subattribute - - # Remove dictionary if empty - if len(output_dict[attribute_name]) == 0: - del output_dict[attribute_name] - - return output_dict - -def array_not_scalar(array): - """Return True if array is array-like and not a scalar""" - return isinstance(array, Sequence) or (isinstance(array, np.ndarray) and hasattr(array, "__len__")) \ No newline at end of file diff --git a/hopp/utilities/__init__.py b/hopp/utilities/__init__.py index bdb1fb062..e3a5ce5b9 100644 --- a/hopp/utilities/__init__.py +++ b/hopp/utilities/__init__.py @@ -1 +1 @@ -from .utilities import load_yaml \ No newline at end of file +from .utilities import load_yaml, write_yaml, check_create_folder, flatten_dict, equal, array_not_scalar \ No newline at end of file diff --git a/hopp/utilities/utilities.py b/hopp/utilities/utilities.py index 116e77ecc..8892c5b11 100644 --- a/hopp/utilities/utilities.py +++ b/hopp/utilities/utilities.py @@ -1,5 +1,8 @@ import os import yaml +import numpy as np +from typing import Sequence + class Loader(yaml.SafeLoader): @@ -38,3 +41,43 @@ def write_yaml(filename,data): with open(filename, 'w+') as file: yaml.dump(data, file,sort_keys=False,encoding = None,default_flow_style=False) + + +def flatten_dict(d): + def get_key_values(d): + for key, value in d.items(): + if isinstance(value, dict): + yield from get_key_values(value) + else: + yield key, value + + return {key:value for (key,value) in get_key_values(d)} + +def equal(a, b): + """Determines whether integers, floats, lists, tuples or dictionaries are equal""" + if isinstance(a, (int, float)): + return np.isclose(a, b) + elif isinstance(a, (list, tuple)): + if len(a) != len(b): + return False + else: + for i in range(len(a)): + if not np.isclose(a[i], b[i]): + return False + return True + elif isinstance(a, dict): + if len(a) != len(b): + return False + else: + for key in a.keys(): + if key not in b.keys(): + return False + if not np.isclose(a[key], b[key]): + return False + return True + else: + raise Exception('Type not recognized') + +def array_not_scalar(array): + """Return True if array is array-like and not a scalar""" + return isinstance(array, Sequence) or (isinstance(array, np.ndarray) and hasattr(array, "__len__")) \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 8877a5ce6..50fd61dbc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,8 +42,7 @@ dependencies = [ "scipy", "shapely>=2", "setuptools", - "timezonefinder", - "urllib3", + "timezonefinder==6.5.9", "openpyxl", "attrs", "utm", diff --git a/tests/hopp/test_dispatch.py b/tests/hopp/test_dispatch.py index 705c13c2a..25363a377 100644 --- a/tests/hopp/test_dispatch.py +++ b/tests/hopp/test_dispatch.py @@ -13,7 +13,7 @@ from hopp.simulation.technologies.pv.pv_plant import PVPlant, PVConfig from hopp.simulation.technologies.csp.tower_plant import TowerPlant, TowerConfig from hopp.simulation.technologies.csp.trough_plant import TroughPlant, TroughConfig -from hopp.simulation.technologies.wave.mhk_wave_plant import MHKWavePlant, MHKConfig +from hopp.simulation.technologies.wave.mhk_wave_plant import MHKWavePlant, MHKWaveConfig from hopp.simulation.technologies.financial.mhk_cost_model import MHKCostModelInputs from hopp.simulation.technologies.dispatch.power_sources.csp_dispatch import CspDispatch from hopp.simulation.technologies.dispatch.power_sources.tower_dispatch import TowerDispatch @@ -361,7 +361,7 @@ def test_wave_dispatch(): financial_model = {'fin_model': DEFAULT_FIN_CONFIG} mhk_config.update(financial_model) - config = MHKConfig.from_dict(mhk_config) + config = MHKWaveConfig.from_dict(mhk_config) cost_model_input = MHKCostModelInputs.from_dict({ 'reference_model_num':3, diff --git a/tests/hopp/test_wave.py b/tests/hopp/test_wave.py index 627ff6de4..da555d5b7 100644 --- a/tests/hopp/test_wave.py +++ b/tests/hopp/test_wave.py @@ -3,7 +3,7 @@ from pathlib import Path from hopp.simulation.technologies.sites import SiteInfo -from hopp.simulation.technologies.wave.mhk_wave_plant import MHKWavePlant, MHKConfig +from hopp.simulation.technologies.wave.mhk_wave_plant import MHKWavePlant, MHKWaveConfig from hopp.simulation.technologies.financial.mhk_cost_model import MHKCostModelInputs from hopp.simulation.technologies.financial.custom_financial_model import CustomFinancialModel from hopp.utilities import load_yaml @@ -35,7 +35,7 @@ def mhk_config(): def waveplant(mhk_config, site): financial_model = {'fin_model': DEFAULT_FIN_CONFIG} mhk_config.update(financial_model) - config = MHKConfig.from_dict(mhk_config) + config = MHKWaveConfig.from_dict(mhk_config) cost_model_input = MHKCostModelInputs.from_dict({ 'reference_model_num':3, @@ -54,7 +54,7 @@ def test_mhk_config(mhk_config, subtests): financial_model = {'fin_model': DEFAULT_FIN_CONFIG} mhk_config.update(financial_model) - config = MHKConfig.from_dict(mhk_config) + config = MHKWaveConfig.from_dict(mhk_config) assert config.device_rating_kw == 286. assert config.num_devices == 100 From e0250ee18123462b7f2db83dcd50d4c9226b85a8 Mon Sep 17 00:00:00 2001 From: John Jasa Date: Wed, 5 Nov 2025 18:51:33 -0600 Subject: [PATCH 44/48] Bumping version for release (#504) --- hopp/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hopp/__init__.py b/hopp/__init__.py index cdd3d4ba6..4816ef04f 100644 --- a/hopp/__init__.py +++ b/hopp/__init__.py @@ -1,7 +1,7 @@ from pathlib import Path -__version__ = "3.3.0" +__version__ = "3.4.0" ROOT_DIR = Path(__file__).resolve().parent From 6039ec9dc07fbae32648139cf1d49cd77a31d4e8 Mon Sep 17 00:00:00 2001 From: Jonathan Martin <94018654+jmartin4nrel@users.noreply.github.com> Date: Wed, 5 Nov 2025 17:54:59 -0700 Subject: [PATCH 45/48] Solar api fix (#502) * Change API URL * Updated release notes --------- Co-authored-by: John Jasa --- RELEASE.md | 1 + hopp/simulation/technologies/resource/solar_resource.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/RELEASE.md b/RELEASE.md index f90f67454..110df35e9 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -7,6 +7,7 @@ * Remove `tidal_resource` as a required input to the tidal model because it's no longer required in the SSC model ([NREL/SSC PR #1305](https://github.com/NREL/ssc/pull/1305)) * Add load following heuristic dispatch test * Consolidated utilities, renamed MHKConfig to MHKWaveConfig +* Updated solar resource download to use GOES Aggregated PSM v4 download ## Version 3.3.0, April 30, 2025 diff --git a/hopp/simulation/technologies/resource/solar_resource.py b/hopp/simulation/technologies/resource/solar_resource.py index ac16a3c36..8fd6299c5 100644 --- a/hopp/simulation/technologies/resource/solar_resource.py +++ b/hopp/simulation/technologies/resource/solar_resource.py @@ -12,7 +12,7 @@ from hopp import ROOT_DIR -BASE_URL = "https://developer.nrel.gov/api/nsrdb/v2/solar/psm3-2-2-download.csv" +BASE_URL = "https://developer.nrel.gov/api/nsrdb/v2/solar/nsrdb-GOES-aggregated-v4-0-0-download.csv" class SolarResource(Resource): From a95c7d2b040e71ea35e8f695fb4900994b1e79e8 Mon Sep 17 00:00:00 2001 From: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Date: Wed, 5 Nov 2025 17:55:14 -0700 Subject: [PATCH 46/48] updated hopp logger (#501) --- hopp/utilities/log.py | 72 +++++++++++++++++++++---------------------- 1 file changed, 35 insertions(+), 37 deletions(-) diff --git a/hopp/utilities/log.py b/hopp/utilities/log.py index c6e2b0327..61b81cb54 100644 --- a/hopp/utilities/log.py +++ b/hopp/utilities/log.py @@ -3,50 +3,50 @@ import logging from datetime import datetime from pathlib import Path - -try: - from mpi4py import MPI -except: - MPI = False - -# set to logging.WARNING for fewer messages -logging_level = logging.INFO - -# level to print to console -console_level = logging.WARNING - +from hopp import ROOT_DIR # set up logging to file - see previous section for more details formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s') -if MPI: - print("logging to stdout") - logging.basicConfig(level=logging_level, - datefmt='%m-%d %H:%M', - stream=sys.stdout) - handler = logging.StreamHandler() - handler.setFormatter(formatter) +if os.getenv("ENABLE_HOPP_LOGGING",default=False): + log_level = os.getenv("HOPP_LOG_LEVEL",default="INFO") + if log_level.upper() == "INFO": + logging_level = logging.INFO + if log_level.upper() == "WARNING": + logging_level = logging.WARNING + if log_level.upper() == "DEBUG": + logging_level = logging.DEBUG + + # setup logging to file + if os.getenv("HOPP_LOG_TO_FILE",default=True): + run_suffix = '_' + datetime.now().isoformat().replace(':', '.') + log_path = Path.cwd() / "log" + if not os.path.isdir(log_path): + os.mkdir(log_path) + log_path = log_path / ("hybrid_systems" + run_suffix + ".log") + + logging.basicConfig(level=logging_level, + datefmt='%m/%d/%Y %I:%M:%S %p', + filename=str(log_path), + filemode='w') + + handler = logging.FileHandler(str(log_path)) + handler.setFormatter(formatter) + else: + # setup logging to console + logging.basicConfig(level=logging_level, + datefmt='%m-%d %H:%M', + stream=sys.stdout) + handler = logging.StreamHandler() + handler.setFormatter(formatter) + handler.setLevel(logging_level) else: - run_suffix = '_' + datetime.now().isoformat().replace(':', '.') - log_path = Path.cwd() / "log" + log_path = ROOT_DIR.parent / "log" if not os.path.isdir(log_path): os.mkdir(log_path) - log_path = log_path / ("hybrid_systems" + run_suffix + ".log") - print(log_path) - # logging.basicConfig(level=logging_level, - # datefmt='%m-%d %H:%M', - # filename=str(log_path), - # filemode='w') + log_path = log_path / ("empty_log.log") handler = logging.FileHandler(str(log_path)) handler.setFormatter(formatter) -# define a Handler which writes WARNING messages or higher to the sys.stderr -console = logging.StreamHandler() -console.setLevel(console_level) - - -# Now, define a couple of other loggers which might represent areas in your -# application: - hybrid_logger = logging.getLogger('HybridSim') flicker_logger = hybrid_logger bos_logger = hybrid_logger @@ -55,8 +55,6 @@ hybrid_logger.addHandler(handler) opt_logger.addHandler(handler) -hybrid_logger.addHandler(console) -opt_logger.addHandler(console) logging.getLogger('').propagate = False logging.getLogger('HybridSim').propagate = False From 7fd29a6d377e00b3a429049e5108464cd0f46233 Mon Sep 17 00:00:00 2001 From: John Jasa Date: Wed, 5 Nov 2025 18:56:54 -0600 Subject: [PATCH 47/48] Rebasing dev and main (#505) * v3.3.0 develop -> main (#483) * minor clean-ups to floris.py (#419) * removed writing wind data to file in floris.py and added verbose as attr * minor clean up to applying losses in floris execute function * added floris clean-up to RELEASE.md * patch for readthedocs build using sphinx * fixed typo in config filepath from previous commit * fixed typo in config filepath from previous commit again * feature: option to load wind and solar resource data from HPC (#414) * added ability to grab wind and solar resource data off the HPC and updated upstream functions * updated doc strings and comments for wtk_data and nsrdb_data scripts * added comments to site_info for added options, fixed bug of resource year being set to 2012 * updated formatting and docstrings for previous commit * updated RELEASE.md with recent changes * changed input variable name in HPCWindData to align with WindResource and updated doc strings * added high level test for hpc resource data * added sphinx config path in readthedocs to fix build * updated doc strings and minor updates to hpc resource functions * updated docstrings and added valid year check and tests for hpc resource data * changed site_info api doc file to markdown * added doc files for wind and solar resource * minor updates to doc strings in hpc resource classes * updated string formatting * added tests for wtk and nsrdb resource if filepath is provided * split long comments into multiple lines * Bugfix: Load following heuristic method for variable load signals only using the beginning of the load signal (#421) * Fixing bug in heuristic method that affects variable load following * Updating release notes * Updated test for varied heuristics --------- Co-authored-by: John Jasa * feature: option to initialize site_info with resource data (#415) * added ability to grab wind and solar resource data off the HPC and updated upstream functions * updated doc strings and comments for wtk_data and nsrdb_data scripts * added comments to site_info for added options, fixed bug of resource year being set to 2012 * updated formatting and docstrings for previous commit * updated RELEASE.md with recent changes * changed input variable name in HPCWindData to align with WindResource and updated doc strings * added high level test for hpc resource data * added ability to supply preloaded and formatted resource data to site_info and updated doc strings * added tests for using preloaded resource data and updated RELEASE.md * tried to improve the site_info documentation but it aint much better * added elevation as site_info attribute and updated with solar resource elevation if called * added sphinx config path in readthedocs to fix build * updated doc strings and minor updates to hpc resource functions * split out tests for initializing site_info with resource data * updated docstrings and added valid year check and tests for hpc resource data * added sphinx config path in readthedocs to fix build * updated doc strings and minor updates to hpc resource functions * updated docstrings and added valid year check and tests for hpc resource data * changed site_info api doc file to markdown * added doc files for wind and solar resource * minor updates to doc strings in hpc resource classes * resolved merge conflict in doc string of wind_resource.py * updated some formatting in resource files for readthedocs build * updated string formatting * added tests for wtk and nsrdb resource if filepath is provided * split long comments into multiple lines * minor clean ups to wind and solar resource functions * now passing tests --------- Co-authored-by: John Jasa * Feature add: alternative to define site boundaries (#426) - Added different ways to define site boundaries; square, circle, etc - Expanded docs and tests accordingly --------- Co-authored-by: bayc * Remove Wave Resource Deprecated Methods (#430) * update deprecated methods * H to h * update RELEASE.md * update wave resource docstring * fix docstring spacing * wave resource doc * fix doc build * PySAM 6.0.1 (#425) * update pysam to 6.0.0 * update grid default json to CustomGenerationProfile json * update wave plant loading resource file to handle 1hr timesteps by default * Updated for pysam 6.0.0 * wave cost model updates. update test values add additional test for costs. * Update regression test values based on updates to wind and solar pysam default jsons and updates to singleowner model. * Reopt test: update default json and financial value for wind * update test values in test_capacity_credit. changes due to json defaults and impact on battery optimization * CSP update. Update lcoe value because of changes to SingleOwner financial model * WIP; updating test values for detailed PV * Bringing tests back for detailed PV * update regression test results * update RELEASE.md * update example default fin config * remove outdated comments * update docstrings to google format * update RELEASE.md with PR number. Force update for NREL-PySAM dependency * remove commented code * update detailed pv attribute for pysam 6.0.1 * update release notes --------- Co-authored-by: John Jasa * Feature add: option to adjust air density for site elevation (#427) * added site shape tools script * updated site_info for new option of site boundary definition * updated doc strings for site info and site shape tool functions * added tests for site shape tools functions * added site shape tools to documentation * added another optional input for site_details if using circle as shape * removed checking vertices so tests pass * added function to rotate site * added site boundary buffer for verts back in * added site polygon buffer as optional input to site info * added wind resource tools script * added test for air density adjustment for elevation calc * integrated adjusting for elevation in wind models * added weighted parser, updated tests, updated wind_resource.py * minor docstring updates to wind resource tools * minor fix to wind_plant and made regression test for elevation adjustment option * removed unnecessary lines and comments * integrated weighted parse resource data method into wind plant --------- Co-authored-by: John Jasa * Intermediate: update to wind layout and floris functions (1/2) (#429) * added site shape tools script * updated site_info for new option of site boundary definition * updated doc strings for site info and site shape tool functions * added tests for site shape tools functions * added site shape tools to documentation * added another optional input for site_details if using circle as shape * updated RELEASE.md with new feature * removed checking vertices so tests pass * added function to rotate site * added site boundary buffer for verts back in * added site polygon buffer as optional input to site info * added wind resource tools script * added test for air density adjustment for elevation calc * integrated adjusting for elevation in wind models * updated doc strings for recent changes * updated release file * fixed bug in make_grid_lines * fixed input to create_grid to be in degrees instead of radians * added new wind layout tools and fixed doc strings related to bug fix * updated call to wind layout tools and fixed inputs for test_custom_financial * adding in option for basicgrid layout option * renamed test_layout test * added weighted parser, updated tests, updated wind_resource.py * added plot function to site_shape_tools * added new wind layout tools function and cleaned up wind_layout a bit * added more functionality to floris.py and added some optional parameters to windconfig * added layout test for basicgrid layout * added doc strings to wind layout files and wind plant files * added warning if user inputs incorrect turbine rating with floris * updated tests that were failing because of floris update * minor fix to wind_plant and made regression test for elevation adjustment option * added in integrated of weighted average resource data from v3/elevated_wind --------- Co-authored-by: John Jasa * Feature add: integrated wind layout methods when using Floris (2/2) (#431) * added site shape tools script * updated site_info for new option of site boundary definition * updated doc strings for site info and site shape tool functions * added tests for site shape tools functions * added site shape tools to documentation * added another optional input for site_details if using circle as shape * updated RELEASE.md with new feature * removed checking vertices so tests pass * added function to rotate site * added site boundary buffer for verts back in * added site polygon buffer as optional input to site info * added wind resource tools script * added test for air density adjustment for elevation calc * integrated adjusting for elevation in wind models * updated doc strings for recent changes * updated release file * fixed bug in make_grid_lines * fixed input to create_grid to be in degrees instead of radians * added new wind layout tools and fixed doc strings related to bug fix * updated call to wind layout tools and fixed inputs for test_custom_financial * adding in option for basicgrid layout option * renamed test_layout test * added weighted parser, updated tests, updated wind_resource.py * added plot function to site_shape_tools * added new wind layout tools function and cleaned up wind_layout a bit * added more functionality to floris.py and added some optional parameters to windconfig * updated example 05 input file so it wont cause a warning * added layout test for basicgrid layout * added comments to tests that will fail because of floris update * updated tests that were failing because of floris update * minor fix to wind_plant and made regression test for elevation adjustment option * adjusted basic grid to center layout on site center and integrated layout with floris * integrated wind layout with floris and updated wind layout mode in floris test in test_hybrid * updated tests for floris because of recent feature-adds * added in integrated of weighted average resource data from v3/elevated_wind --------- Co-authored-by: John Jasa Co-authored-by: bayc * Wind Layout Clean-up (#433) * added site shape tools script * updated site_info for new option of site boundary definition * updated doc strings for site info and site shape tool functions * added tests for site shape tools functions * added site shape tools to documentation * added another optional input for site_details if using circle as shape * updated RELEASE.md with new feature * removed checking vertices so tests pass * added function to rotate site * added site boundary buffer for verts back in * added site polygon buffer as optional input to site info * added wind resource tools script * added test for air density adjustment for elevation calc * integrated adjusting for elevation in wind models * updated doc strings for recent changes * updated release file * fixed bug in make_grid_lines * fixed input to create_grid to be in degrees instead of radians * added new wind layout tools and fixed doc strings related to bug fix * updated call to wind layout tools and fixed inputs for test_custom_financial * adding in option for basicgrid layout option * renamed test_layout test * added weighted parser, updated tests, updated wind_resource.py * added plot function to site_shape_tools * added new wind layout tools function and cleaned up wind_layout a bit * added more functionality to floris.py and added some optional parameters to windconfig * updated example 05 input file so it wont cause a warning * added layout test for basicgrid layout * added test that shows how floris breaks - other floris tests will fail * added comments to tests that will fail because of floris update * added doc strings to wind layout files and wind plant files * added minor comment to floris.py * updated README with recent changes * added warning if user inputs incorrect turbine rating with floris * updated tests that were failing because of floris update * added some extra asserts to floris test in test_hybrid * minor docstring updates to wind resource tools * minor fix to wind_plant and made regression test for elevation adjustment option * removed unnecessary lines and comments * added some property and setters to floris * bug fix in test_hybrid for floris run * fixed failing test in test_hybrid * adjusted basic grid to center layout on site center and integrated layout with floris * fixed typo in floris.py * integrated wind layout with floris and updated wind layout mode in floris test in test_hybrid * updated tests for floris because of recent feature-adds * updated RELEASE.md with feature-add description * minor clean ups to scripts * cleaned up wind layout script * added function to modify layout params in wind_plant * minor update to wind_layout.py set_layout_params function * bug fix for failing tests * added in integrated of weighted average resource data from v3/elevated_wind * updated RELEASE.md * updated release.md * made changes based on review feedback * updated release file and removed unnecessary comments in some scripts * further workflow and comment clean-up from feedback * updated doc string and comments in make_grid_lines() * updated some doc strings in floris.py * updated handling if layout_params is None for wind * Minor docstring update * Fixed merge bug * Modifications based on Rob's PR * Fixed validators call * Updated spacing calls --------- Co-authored-by: John Jasa * Missed load (#432) * update print/save statements to not multiply missed load by 100 since it is already a percentage and not a decimal * correct setting and printing of 'schedule_curtailed_percentage' with removing extra 100 multiples at print and including brackets for clarification * add clarifying parentheses * update RELEASE.md * MHK Tidal Plant (#444) * update pysam to 6.0.0 * update grid default json to CustomGenerationProfile json * update wave plant loading resource file to handle 1hr timesteps by default * Updated for pysam 6.0.0 * wave cost model updates. update test values add additional test for costs. * Update regression test values based on updates to wind and solar pysam default jsons and updates to singleowner model. * Reopt test: update default json and financial value for wind * update test values in test_capacity_credit. changes due to json defaults and impact on battery optimization * CSP update. Update lcoe value because of changes to SingleOwner financial model * WIP; updating test values for detailed PV * Bringing tests back for detailed PV * update regression test results * update RELEASE.md * update example default fin config * remove outdated comments * update docstrings to google format * update deprecated methods * H to h * update RELEASE.md * initial tidal model * update RELEASE.md with PR number. Force update for NREL-PySAM dependency * update tidal model with tests * add ability to interpolate resource data * add validators back * update documentation * Converted tabs to spaces * change resource import * update docstrings and documentation * update description of tidal resource * reduce attrs logic Co-authored-by: John Jasa * reduce logic Co-authored-by: John Jasa * check identity with is not None * update path handling * fixing docs * fix description * clean up readthedoc warnings * fix typo --------- Co-authored-by: John Jasa * Integrate Tidal into Hybrid Simulation (#446) * integrate mhk_tidal into HybridSimulation * update tidal and wave docs * add tidal test to test_hybrid.py * add robust_approx method Co-authored-by: John Jasa * Add Tidal Dispatch (#448) * add tidal dispatch * add tidal battery example --------- Co-authored-by: John Jasa * Feature add: integration with turbine-models library for wind simulations (#435) * Added initial functionality for integrating turbine-models library * Integrated with turbine-models library and FLORIS and made integration test * Integrated turbine-models library with PySAM simulation and made integration test * Replaced UserWarning about wind turbine hub-height and site info discrepancy with ability to redownload wind resource data * Added initialize_pysam_turbine() function * Made turbine_rating_kw an optional input to WindConfig * Updated README.md, RELEASE.md, and pyproject.toml * Updated wind resource parsing methods to work if more than 2 resource heights are input * Changed UserWarnings to ValueErrors in wind_plant.py and floris.py * Added turbine_group as optional input to check_turbine_library_for_turbine function in turbine_library_tools.py * Updated Ct function in power_curve_tools.py to not fail if multiple roots --------- Co-authored-by: Gen Starke and John Jasa * Distributed wind-hybrid examples (#452) * updated examples for battery is actually used * updated RELEASE.md for new examples * Minor typographical edits * updated example 9 and minor update to plot_generation_profile function * updated tick labelsize in plot_generation_profile for bottom subplot * updated example 9 with feedback from kbrunik --------- Co-authored-by: John Jasa * Update README.md * Version bump for 3.2.0 release (#453) * Version bump for release * Update RELEASE.md * Update doc book deployment * Update wind tests for floats (#460) * Loosened wind turbine property checks * Loosened wind turbine property checks and fixed wind initialization logic * Added to release.md * Removed unnecessary logic for assigning financial values * Hotfix for GreenHEART plotting (#462) * Loosen turbine rating check (#464) * Made turbine rating check +/- 10% * Loosened strictness of wind turbine rating comparison and added tests * Added coke_supply_EI to greet_data.py parsing and greet_2023_processed.yaml. Updated test_cost_calculator.py > test_bos_calculate_bos_cost_interpolate assertions failing locally because of floating point precision issues, changed assert X == Y == Z to 3 separate assert X == pytest.approx(Y) statements (#466) * Update pyproject.toml * Update README.md to not specify `coin-or-cbc` version. (#470) * Update README.md to not specify `coin-or-cbc` version. --------- Co-authored-by: John Jasa * Bug-fix: WindPlant update for checking inputs (#469) * fixed issues for pysam related to specifying the turbine hub-height * updated pysam wind turbine initialization steps * updated default value in windplant --------- Co-authored-by: John Jasa * Feature add: Download wind resource for Alaska (#461) * added alaska wind resource download tools and class * updated alaska wind filel and resource.py for handling non alphanumeric characters * updated parsing methods in case pressure or temperature data is given for other resource heights * updated pysam wind resource tools to be flexible whether pressure is provided or not * integrated alaska wind into workflow * updated RELEASE.md * added tests for AK wind and updated site_info and alaska_wind file * added new resource file for alaska wind resource data used for new tests * updated doc strings and added documentation for Alaska wind API call * actually added alaska documentation file * fixed latitude for alaska test site * remove whitespace in resource.py Co-authored-by: John Jasa * updated doc string in site_info.py Co-authored-by: John Jasa * updated doc string in site_info.py Co-authored-by: John Jasa * updated pysam_wind_tools.py for docstrings and handling resource year * updated wind_resource.py combine_files function to use function in pysam_wind_tools.py * added missing return value in combine_wind_files --------- Co-authored-by: John Jasa * Feature add: Download wind data from Bias-Corrected HRRR Dataset (#474) * added alaska wind resource download tools and class * updated alaska wind filel and resource.py for handling non alphanumeric characters * updated parsing methods in case pressure or temperature data is given for other resource heights * updated pysam wind resource tools to be flexible whether pressure is provided or not * integrated alaska wind into workflow * updated RELEASE.md * added tests for AK wind and updated site_info and alaska_wind file * added new resource file for alaska wind resource data used for new tests * updated doc strings and added documentation for Alaska wind API call * actually added alaska documentation file * fixed latitude for alaska test site * Add BC-HRRR dataset to HOPP * Upate docs * Finish updating docs * Add testing * Make dataset title shorter for docs * BCHRRR file cleanup * Debugging adding precipitation rate * Add precipitation rate back into the data * Update doc strings * Apply suggestions from code review * Approximate surface to 100m pressure * Updated the test_wind BCHRRR test * Updated BCHRRR test * Update after pressure conversion * Enable running pysam again --------- Co-authored-by: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Co-authored-by: John Jasa * Updates for pySAM 7.0.0 (#477) * WIP: updating for pysam 7 * Updates for pysam 7 * Updated release.md * Updated test_hybrid based on tidal changes in pysam * Add long duration energy storage (LDES) (#471) * Feature/ldes framework (#443) * rename pysam battery model from BatteryModel to PySAMBatteryModel to make room for LDES * create space for LDES system model integration * switch from LDES to AEF * isolate assert statements in subtests * start tests for LDES and start LDES initialization * work on integrating LDES * ldes initial tests passing * add comments/TODO * Feature/ldes framework working framework, need to fill in execute (#449) * rename pysam battery model from BatteryModel to PySAMBatteryModel to make room for LDES * create space for LDES system model integration * switch from LDES to AEF * isolate assert statements in subtests * start tests for LDES and start LDES initialization * work on integrating LDES * ldes initial tests passing * add comments/TODO * Missed load (#432) * update print/save statements to not multiply missed load by 100 since it is already a percentage and not a decimal * correct setting and printing of 'schedule_curtailed_percentage' with removing extra 100 multiples at print and including brackets for clarification * add clarifying parentheses * update RELEASE.md * MHK Tidal Plant (#444) * update pysam to 6.0.0 * update grid default json to CustomGenerationProfile json * update wave plant loading resource file to handle 1hr timesteps by default * Updated for pysam 6.0.0 * wave cost model updates. update test values add additional test for costs. * Update regression test values based on updates to wind and solar pysam default jsons and updates to singleowner model. * Reopt test: update default json and financial value for wind * update test values in test_capacity_credit. changes due to json defaults and impact on battery optimization * CSP update. Update lcoe value because of changes to SingleOwner financial model * WIP; updating test values for detailed PV * Bringing tests back for detailed PV * update regression test results * update RELEASE.md * update example default fin config * remove outdated comments * update docstrings to google format * update deprecated methods * H to h * update RELEASE.md * initial tidal model * update RELEASE.md with PR number. Force update for NREL-PySAM dependency * update tidal model with tests * add ability to interpolate resource data * add validators back * update documentation * Converted tabs to spaces * change resource import * update docstrings and documentation * update description of tidal resource * reduce attrs logic Co-authored-by: John Jasa * reduce logic Co-authored-by: John Jasa * check identity with is not None * update path handling * fixing docs * fix description * clean up readthedoc warnings * fix typo --------- Co-authored-by: John Jasa * working on fixing setters * save comments from discussion with gen * include tests for battery with ldes * Integrate Tidal into Hybrid Simulation (#446) * integrate mhk_tidal into HybridSimulation * update tidal and wave docs * add tidal test to test_hybrid.py * add robust_approx method Co-authored-by: John Jasa * Add Tidal Dispatch (#448) * add tidal dispatch * add tidal battery example --------- Co-authored-by: John Jasa * minor progress * test_battery_ldes.py tests are passing * remove init import and debug statements * include duration --------- Co-authored-by: kbrunik <102193481+kbrunik@users.noreply.github.com> Co-authored-by: John Jasa * Feature/ldes framework (#450) * include duration * bug fix --------- Co-authored-by: kbrunik <102193481+kbrunik@users.noreply.github.com> Co-authored-by: John Jasa * Feature/ldes framework (#451) * resolve merge * Feature/ldes framework (#459) * debugging * add ldes dispatch tests * re-set up instantiation * updates to LDES initialization * get to execute * ldes basically working - no degradation or cycle counting * remove unused code * generally working * add efficiency test for ldes * remove debug statements --------- Co-authored-by: bayc * Fix code syntax * Initial cycle counting for ldes * Fix typo in power_storage_dispatch.py lifecyle count for min operating cost objective * Updating tests and remove print statements * Update soc modeling and fix plot_tools.py error * Fix tests * Pinning floris version * Feature/ldes framework (#468) * add ldes dispatch tests * updates to LDES initialization * add efficiency test for ldes * Update pyproject.toml * include subtests * soc test for ldes * add test for battery replacement schedule with custom financial model --------- Co-authored-by: John Jasa * adding battery test with replacement schedule for custom financial model * fix logic in batt replacement list length check * update tests * fix battery replacement schedule function with custom financial model * LDES tests and remove AEF chemistry option --------- Co-authored-by: bayc Co-authored-by: John Jasa * update release.md * add LDES example * remove commented option * remove commented option * remove outdated comment * remove unnecessary inputs and notes * clarify logic and purpose * address minor comments * correct grammar: * clarify the purpose of the battery_om_per_kw zero value in the LDES example * Update release notes with bugfix * Remove commented out test from ldes battery tests * Updated LDES and batt replacement tests due to pysam 7.0.0 * Small cleanup --------- Co-authored-by: kbrunik <102193481+kbrunik@users.noreply.github.com> Co-authored-by: John Jasa Co-authored-by: bayc Co-authored-by: Genevieve Starke * Feature add: Generic Plant (#472) * added simulate_power function to hopp interfacing objects * added ghost plant and integrated physics model * small update to grid.py and power_source.py * updated test for ghost plant * updated ghost plant * added ghost_multi system model * updated generation_profile_wo_battery setter * updated test_ghost for ghost_multi * updated round_digits in dispatch.py to 8 * changed round_digits update to power_source_dispatch instead of dispatch.py * added docstrings and updated some function names in ghost_plant and ghost_multi * udpated generation_profile_wo_battery setter in grid.py so compatible with CustomFinancialModel simulations * updated test_ghost.py * renamed ghost to generic * updated RELEASE.md * updated docstrings and other minor changes to generic plant stuff based on feedback * fixed AttributeError that occurs when initializing generation profile if system capacity hasnt been set * updated RELEASE.md * minor updates to generic plant * update generic_plant * updated generic config so tests pass --------- Co-authored-by: John Jasa * Bugfix for flicker mismatch (#479) * Bugfix for flicker mismatch * Updated test_flicker_mismatch test * Update LDES Example (#480) * update ldes example * change fin file * note on model limitation --------- Co-authored-by: John Jasa * Bumped version and changelog (#482) --------- Co-authored-by: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Co-authored-by: genevievestarke <103534902+genevievestarke@users.noreply.github.com> Co-authored-by: bayc Co-authored-by: kbrunik <102193481+kbrunik@users.noreply.github.com> Co-authored-by: Jared Thomas Co-authored-by: Dakota Sky Ramos <85905407+dakotaramos@users.noreply.github.com> Co-authored-by: Genevieve Starke * Fix typos (#487) * Update README.md (#489) * Bumping version for release --------- Co-authored-by: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Co-authored-by: genevievestarke <103534902+genevievestarke@users.noreply.github.com> Co-authored-by: bayc Co-authored-by: kbrunik <102193481+kbrunik@users.noreply.github.com> Co-authored-by: Jared Thomas Co-authored-by: Dakota Sky Ramos <85905407+dakotaramos@users.noreply.github.com> Co-authored-by: Genevieve Starke Co-authored-by: omahs <73983677+omahs@users.noreply.github.com> Co-authored-by: Olexandr88 --- README.md | 2 +- RELEASE.md | 17 ++++++ examples/legacy/analysis/main_usa_new.py | 58 +++++++++---------- examples/legacy/analysis/multi_location.py | 2 +- examples/legacy/analysis/single_location.py | 2 +- .../technologies/csp/tower_plant.py | 4 +- .../technologies/csp/trough_plant.py | 2 +- .../power_storage/power_storage_dispatch.py | 2 +- 8 files changed, 53 insertions(+), 36 deletions(-) diff --git a/README.md b/README.md index 7bed01d77..a3b227264 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # HOPP: Hybrid Optimization and Performance Platform [![PyPI version](https://badge.fury.io/py/hopp.svg)](https://badge.fury.io/py/hopp) -![CI Tests](https://github.com/NREL/HOPP/actions/workflows/ci.yml/badge.svg) +[![CI Tests](https://github.com/NREL/HOPP/actions/workflows/ci.yml/badge.svg)](https://github.com/NREL/HOPP/actions/workflows/ci.yml) [![image](https://img.shields.io/pypi/pyversions/hopp.svg)](https://pypi.python.org/pypi/hopp) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) diff --git a/RELEASE.md b/RELEASE.md index 110df35e9..6f6717872 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -25,6 +25,23 @@ * Bugfix for flicker mismatch; cases with a single `Point` now correctly work + +## Version 3.3.0, April 30, 2025 + +* Added GenericPlant model ([PR #472](https://github.com/NREL/HOPP/pull/472)) which may be used to: + - simulate grid and battery performance without resimulating generation of other technologies + - represent the physics-based performance of a generation technology that is not included in HOPP +* Loosened strictness of comparison for wind turbine config checking and added tests +* Loosened strictness of comparison for wind turbine hub-height and wind resource hub-height +* Updated workflow for specifying wind turbine parameters without specifying a turbine name with PySAM. +* Added ability to download wind resource data from WTK-LED for Alaska ([PR #461](https://github.com/NREL/HOPP/pull/461)) +* Added ability to download wind resource data from BC-HRRR CONUS 60-minute (NOAA + NREL) for 2015-2023 ([PR #474](https://github.com/NREL/HOPP/pull/474)) +* Updated HOPP for pySAM 7.0.0 release ([PR #477](https://github.com/NREL/HOPP/pull/477)) +* Add long-duration energy storage (LDES) ([PR #471](https://github.com/NREL/HOPP/pull/471)) +* Bugfix for cycle counting in the minimum operating cost objective function - no longer throws an error +* Bugfix for flicker mismatch; cases with a single `Point` now correctly work + + ## Version 3.2.0, March 21, 2025 * Updates related to PySAM: diff --git a/examples/legacy/analysis/main_usa_new.py b/examples/legacy/analysis/main_usa_new.py index 5005b0cd4..05e482cfc 100644 --- a/examples/legacy/analysis/main_usa_new.py +++ b/examples/legacy/analysis/main_usa_new.py @@ -297,48 +297,48 @@ def run_hybrid_calc(site_num, scenario_descriptions, results_dir, load_resource_ key=operator.itemgetter(1)) # Determine the differential between standalone wind + standalone solar vs. wind + adding solar - hopp_outputs['Hybrid vs. Seperate'] = establish_save_output_dict() - hopp_outputs['Hybrid vs. Seperate']['Scenario Description'].append( + hopp_outputs['Hybrid vs. Separate'] = establish_save_output_dict() + hopp_outputs['Hybrid vs. Separate']['Scenario Description'].append( '(Combined Wind & Solar) - (Standalone Wind + Standalone Solar)') - hopp_outputs['Hybrid vs. Seperate']['Solar (%)'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['Solar (MW)'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['Wind (MW)'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['AEP (GWh)'].append((hopp_outputs['Hybrid']['AEP (GWh)'][0]) + hopp_outputs['Hybrid vs. Separate']['Solar (%)'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['Solar (MW)'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['Wind (MW)'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['AEP (GWh)'].append((hopp_outputs['Hybrid']['AEP (GWh)'][0]) - (hopp_outputs['Wind']['AEP (GWh)'][0] + hopp_outputs['Solar']['AEP (GWh)'][0])) - hopp_outputs['Hybrid vs. Seperate']['Solar AEP (GWh)'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['Wind AEP (GWh)'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['Solar Capacity Factor'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['Capacity Factor'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['Wind Capacity Factor'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['Capacity Factor of Interconnect'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['Percentage Curtailment'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['NPV ($-million)'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['LCOE - Nominal'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['LCOE - Real'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['IRR (%)'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['PPA Price Used'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['TOD Profile Used'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['Revenue (PPA)'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['Revenue (TOD)'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['Pearson R Wind V Solar'].append(float('nan')) - hopp_outputs['Hybrid vs. Seperate']['BOS Cost'].append((hopp_outputs['Hybrid']['BOS Cost'][0]) + hopp_outputs['Hybrid vs. Separate']['Solar AEP (GWh)'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['Wind AEP (GWh)'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['Solar Capacity Factor'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['Capacity Factor'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['Wind Capacity Factor'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['Capacity Factor of Interconnect'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['Percentage Curtailment'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['NPV ($-million)'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['LCOE - Nominal'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['LCOE - Real'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['IRR (%)'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['PPA Price Used'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['TOD Profile Used'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['Revenue (PPA)'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['Revenue (TOD)'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['Pearson R Wind V Solar'].append(float('nan')) + hopp_outputs['Hybrid vs. Separate']['BOS Cost'].append((hopp_outputs['Hybrid']['BOS Cost'][0]) - (hopp_outputs['Wind']['BOS Cost'][0] + hopp_outputs['Solar']['BOS Cost'][0])) - hopp_outputs['Hybrid vs. Seperate']['BOS Cost percent reduction'].append(100 * (( + hopp_outputs['Hybrid vs. Separate']['BOS Cost percent reduction'].append(100 * (( hopp_outputs[ - 'Hybrid vs. Seperate'][ + 'Hybrid vs. Separate'][ 'BOS Cost'][0]) / ((hopp_outputs['Wind']['BOS Cost'][0] + hopp_outputs['Solar']['BOS Cost'][ 0])))) - hopp_outputs['Hybrid vs. Seperate']['Cost / MWh Produced'].append((hopp_outputs['Hybrid'] + hopp_outputs['Hybrid vs. Separate']['Cost / MWh Produced'].append((hopp_outputs['Hybrid'] ['Cost / MWh Produced'][0]) - (hopp_outputs['Wind']['Cost / MWh Produced'][0] + hopp_outputs['Solar']['Cost / MWh Produced'][ 0]) / 2) - hopp_outputs['Hybrid vs. Seperate']['Cost / MWh Produced percent reduction']. \ - append(100 * ((hopp_outputs['Hybrid vs. Seperate'] + hopp_outputs['Hybrid vs. Separate']['Cost / MWh Produced percent reduction']. \ + append(100 * ((hopp_outputs['Hybrid vs. Separate'] ['Cost / MWh Produced'][0]) / ((hopp_outputs['Wind']['Cost / MWh Produced'][0] + hopp_outputs['Solar'] @@ -346,7 +346,7 @@ def run_hybrid_calc(site_num, scenario_descriptions, results_dir, load_resource_ cost_per_mw_reduction_hybrid_vs_standalone = ((hopp_outputs['Hybrid']['Cost / MWh Produced'][0]) - (hopp_outputs['Wind']['Cost / MWh Produced'][0] + hopp_outputs['Solar']['Cost / MWh Produced'][0]) / 2) - cost_per_mw_reduction_hybrid_vs_standalone_percent = (100 * ((hopp_outputs['Hybrid vs. Seperate'] + cost_per_mw_reduction_hybrid_vs_standalone_percent = (100 * ((hopp_outputs['Hybrid vs. Separate'] ['Cost / MWh Produced'][0]) / ((hopp_outputs['Wind'] ['Cost / MWh Produced'][0] diff --git a/examples/legacy/analysis/multi_location.py b/examples/legacy/analysis/multi_location.py index 2aeac3dee..9a83b1aed 100644 --- a/examples/legacy/analysis/multi_location.py +++ b/examples/legacy/analysis/multi_location.py @@ -569,7 +569,7 @@ def eprint(*args): desired_lons = np.linspace(-129.22923, -65.7146, N_lon) # Load wind and solar resource files for location nearest desired lats and lons - # NB this resource information will be overriden by API retrieved data if load_resource_from_file is set to False + # NB this resource information will be overridden by API retrieved data if load_resource_from_file is set to False sitelist_name = 'filtered_site_details_{}_lats_{}_lons_{}_year'.format(N_lat, N_lon, year) if load_resource_from_file: # Loads resource files in 'resource_files', finds nearest files to 'desired_lats' and 'desired_lons' diff --git a/examples/legacy/analysis/single_location.py b/examples/legacy/analysis/single_location.py index 94ae16b39..ba57bfbb6 100644 --- a/examples/legacy/analysis/single_location.py +++ b/examples/legacy/analysis/single_location.py @@ -506,7 +506,7 @@ def run_all_hybrid_calcs(site_details, scenario_descriptions, results_dir, load_ desired_lons = -40.94 # Load wind and solar resource files for location nearest desired lats and lons - # NB this resource information will be overriden by API retrieved data if load_resource_from_file is set to False + # NB this resource information will be overridden by API retrieved data if load_resource_from_file is set to False if load_resource_from_file: site_details = resource_loader_file(resource_dir, desired_lats, desired_lons, year) # Return contains site_details.to_csv(os.path.join(resource_dir, 'site_details.csv')) diff --git a/hopp/simulation/technologies/csp/tower_plant.py b/hopp/simulation/technologies/csp/tower_plant.py index 0a6f8a712..7a6f4f304 100644 --- a/hopp/simulation/technologies/csp/tower_plant.py +++ b/hopp/simulation/technologies/csp/tower_plant.py @@ -324,7 +324,7 @@ def estimate_receiver_pumping_parasitic(self, nonheated_length=0.2): if nperpath % 2 == 1: dp += rho * 9.8 * Htot - # Pumping parasitic at design point reciever mass flow rate (MWe) + # Pumping parasitic at design point receiver mass flow rate (MWe) wdot = dp * m_rec_design / rho / self.value('eta_pump') / 1.e6 return wdot / self.field_thermal_rating # MWe / MWt @@ -416,7 +416,7 @@ def solar_multiple(self) -> float: @solar_multiple.setter def solar_multiple(self, solar_multiple: float): """ - Set the solar multiple and updates the system model. Solar multiple is defined as the the ratio of receiver + Set the solar multiple and updates the system model. Solar multiple is defined as the ratio of receiver design thermal power over power cycle design thermal power. """ self.ssc.set({'solarm': solar_multiple}) diff --git a/hopp/simulation/technologies/csp/trough_plant.py b/hopp/simulation/technologies/csp/trough_plant.py index af72f85d5..e32b6ce9e 100644 --- a/hopp/simulation/technologies/csp/trough_plant.py +++ b/hopp/simulation/technologies/csp/trough_plant.py @@ -149,7 +149,7 @@ def solar_multiple(self) -> float: @solar_multiple.setter def solar_multiple(self, solar_multiple: float): """ - Set the solar multiple and updates the system model. Solar multiple is defined as the the ratio of receiver + Set the solar multiple and updates the system model. Solar multiple is defined as the ratio of receiver design thermal power over power cycle design thermal power. Args: diff --git a/hopp/simulation/technologies/dispatch/power_storage/power_storage_dispatch.py b/hopp/simulation/technologies/dispatch/power_storage/power_storage_dispatch.py index 7cf9a7b0d..5137b3bd7 100644 --- a/hopp/simulation/technologies/dispatch/power_storage/power_storage_dispatch.py +++ b/hopp/simulation/technologies/dispatch/power_storage/power_storage_dispatch.py @@ -18,7 +18,7 @@ def __init__( block_set_name: str, dispatch_options, ): - """Intialize PowerStorageDispatch. + """Initialize PowerStorageDispatch. Args: pyomo_model (pyomo.ConcreteModel): Pyomo concrete model. From 663b2cb4f571870dad42afe1eadf0907e3bfeff4 Mon Sep 17 00:00:00 2001 From: John Jasa Date: Mon, 1 Dec 2025 12:51:32 -0600 Subject: [PATCH 48/48] Pinning jupyterbook <2 (#508) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 50fd61dbc..5056ee3fa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -87,7 +87,7 @@ develop = [ "pytest-subtests", "pytest-dependency", "responses", - "jupyter-book", + "jupyter-book<2", "sphinxcontrib-napoleon", ] examples = ["jupyterlab"]