diff --git a/.github/workflows/gh_pages.yml b/.github/workflows/gh_pages.yml
index f64fbe6a1..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: "_build/html"
+        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
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/README.md b/README.md
index 7a027675b..d3d3eb2b0 100644
--- a/README.md
+++ b/README.md
@@ -6,12 +6,12 @@
 [![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
 
-- 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
 
diff --git a/RELEASE.md b/RELEASE.md
index 48070d1f0..1dd49ee3a 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -1,5 +1,49 @@
 # Release Notes
 
+## 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`
+    + 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.
+    + 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
 
 * Enhanced PV plant functionality: added tilting solar panel support, improved system design handling, and refined tilt angle calculations.
diff --git a/docs/_toc.yml b/docs/_toc.yml
index 05da9a2d2..5742ceefd 100644
--- a/docs/_toc.yml
+++ b/docs/_toc.yml
@@ -14,6 +14,15 @@ 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/resource/wave_data
+      - file: api/resource/tidal_data
   - file: api/hybrid_simulation
   - file: api/technology/index
     sections:
@@ -23,6 +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
@@ -38,8 +48,11 @@ 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:
   - file: api/technology/flicker
-  - file: api/cost_calculator
\ No newline at end of file
+  - file: api/cost_calculator
+  - file: api/tools/site_shape_tools
+  - file: api/tools/mhk_cost
\ No newline at end of file
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/docs/api/resource/index.md b/docs/api/resource/index.md
new file mode 100644
index 000000000..789fdc819
--- /dev/null
+++ b/docs/api/resource/index.md
@@ -0,0 +1,41 @@
+# 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)
+- [Wave Resource (Data)](resource:wave-resource)
+- [Tidal Resource (Data)](resource:tidal-resource)
+
+## 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/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/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/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/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..643f07fc8
--- /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](tools:mhk_costs).
diff --git a/docs/api/technology/mhk_wave_plant.md b/docs/api/technology/mhk_wave_plant.md
index 9320ae9bb..09990a44f 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](tools:mhk_costs).
 
-```{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/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)
+<!-- 
+```{eval-rst}
+.. automodule:: hopp.simulation.technologies.sites.site_shape_tools
+``` -->
+
+(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/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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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 4 Axes>"
+      ]
+     },
+     "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": [
+       "<PolarAxes: >"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "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": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "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": [
+       "<matplotlib.colorbar.Colorbar at 0x172525090>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ksWaCukpKV1G99NJLXuf369fPevC41a9f335u3bpVypcvb0HT559/bsGVZouKFi0qDz/8MMvOAQCOxt5YDgh2dBrrbLTj8YsvvmjjTBYuXHjW56lTp44sXrzY72sEACBaEew4bDUWAABAzK7GAgAA/iOzE8bMji4NBwAA4eVyJQQ94oHfwc6oUaNsNZNbt27dbEfyMmXKyOrVq8/19QEAgDPQHjvBjnjgd7AzceJEz/YJ2qtGx5w5c+TSSy+Ve+65JxTXCAAAEL6aHe1l4w52tOuxZnbatWtnS7wbNWoU+JUAAAC/ULMTosyOdhx27xX12WefeXYk12Xkqamp/j4dAAAIEDU7IQp2OnfubB2J27ZtK3/++adNX6mVK1dKpUqV/H06AADgILt375ZevXpZvW7u3Lmldu3a8v3339t9ui3Tvffea8fy5s0rpUuXluuvv1727NnjrGks7Wise1jp/lRPP/205MuXz47/+uuv0r9//1BcIwAAiIJprP3790uzZs2kZcuWVq9brFgx2bhxo2efyb///lt++OEHeeihh6Ru3bp2/p133ilXXnmlJyCK+mBHI7ZbbrnF3kTGTTsHDx58rq8NAAD8i2Cnolx+PlZXZGvdrm7r5JY+HtDtmXThUnrjx4+XCy+80JIk5cqVk6ifxtIdyN9///3QXQ0AAAi7lJQUr6F7UWZm1qxZcsEFF0jXrl2lePHithflyy+//K/PffDgQUlISJCCBQuKY2p2OnXqJB9++GForgYAAPiVmUkLYrj+l9nRbI1mZdxj5MiRmb7eli1bbBPvypUry9y5c+W2226TgQMHytSpUzM9/9ixY1bD06NHD0lMTBTH1OzoG3zsscfkm2++sR3EtQApPX3TAAAg9HQ7bR/21D4j90N1lXX6YCRnzpyZnp+WlmaZnREjRthtzeysXbvWevD17t37tNIXbU+jq7U1QIokv4OdV1991VJRK1assJGepqkIdgAAcJbExESfMi+lSpWSGjVqeB2rXr36aSUu7kBn+/bt8uWXX0Y0qxNQsLN169bQXAkAAPCLbveg/wvm8f7QlVjr16/3OrZhwwZJTk4+LdDRVVoLFiywJeqRxq7nAAA4VLhXYw0ePFiaNm1q01ga0Hz77bcyadIkG+5A55prrrHl57rLgjYb1p0XVOHChSVHjhzimGBn165dVpGty8hOnDjhdd/o0aPP1bUBAIB/oUXGCWHss9OwYUOZOXOmDBs2zOp3ddn52LFjpWfPnp6GgxofqHr16nk9VrM8LVq0EEcEO/Pnz7fmQBUrVpR169ZJrVq1ZNu2bVaA1KBBg9BcJQAAiAodO3a0kRndJ1PjgWjjd7Cj0dzdd98tjz76qOTPn9+KknStvUZ1HTp0CM1VAgCA02hcEdRqLJdEHa0NXrx4sRU3a0dm7dKsq76aNGkiuXLlCk+w88svv8jbb7/9z4OzZZOjR4/alhGazrrqqqtszT0AAIi9mp1Qeuutt2TcuHG2rUSJEiVsXy3de+uvv/6SzZs3W6CjiRXt25O+IDokTQW1r467TkeXoOkFuP3xxx/+Ph0AAIhz9evXl+eff1769OljGR3db1Pb23z99dfy888/W1fnjz76yNPnZ8aMGaHN7DRu3NheXNfVX3bZZXLXXXfJjz/+KB988IHdBwAAwiNWMjtPPfWUtG/f/oz3a5NDLW7W8eSTT1qtcEiDHV1tdfjwYftd63b093feecc6K7MSCwCA2F2NFSr/FuhkpH17/O3d43ewo6uw0k9paYtoAACAc+2TTz6RhQsXWr8ebWjYpUuXgJ7H75oddeDAAXnllVdsZZYWDiltIKTr6wEAQHhXYwUzotVDDz0kQ4cOta2odDm7NjQcMGBAQM/ld2ZnzZo10qZNG9sVVefMbrrpJuuKqDU72mTw9ddfD+hCAACAf/4JWIKp2ZGooauwtPjYTUtkVq9ebSuylBYva83OCy+8EPrMzpAhQ+wFdc+L9OvdtVj5q6++8vsCAAAAbr31Vhk0aJD11nGXzTz33HO2F5cuhNKd06tUqRLQc/sd7Hz33Xdyyy23nHa8TJkynv0vAABA+FZjBTOixfLly62lje7GMHv2bHnttddk5cqVthfXxRdfbFtVTZs2LTzTWLr8S9e7Z6S7nmqXQwAAEB46CxXMTJRLokfWrFmtYWDXrl2tQbEugho/frw1FwyW35kd3RdLuyXrzqZKC4e0VkcvMNAqaQAAEN+ZHTedvpo7d65cffXVcskll8iLL74oYQt2Pv30U/up82faW0f3w9KtIpo3by6VKlWyfbK00Q8AAEAgK7119dUVV1whDz74oAU7OrWl5TPatFjrdgLl8zRW586d5frrr7fGgfPmzbMuyroySwMfnV/TFVoAACCMYmgeq3fv3hbw9OjRQ+bPn29TWW+88YZMmTLFbnfv3t0CoVGjRoUu2NHoSldh1alTx15YU0sXXXSR3y8IAADOkWCnolzRM4315ZdfWkGyzhZpWxv96da6dWvr56dlNIHwOdipW7eupZKeeOIJadeundx+++2WZtKCovQSExMlVmVPSJXsCdHzBwPxoUCWf+rj4L9cCXx2gcqf/VikL8GxTmT7Z7Ns+Ee3nZo0aZL069fPZpAy7myu7W5GjBghIS9QzpYtmwwfPlxmzZpl27AXLVpUChUqZKNgwYL2EwAAhEcsdVB+7bXXLLujO6DrEnPtq3Ou+L30XDsl6zyaTmM98MADFgABAIDwi5Vdz1W9evWsi3Io+BypaNFQ//795aOPPrI00p133hmSCwIAAPHF5XJZK5tQ8Xkaq0aNGrJlyxYrECLQAQAgCmhmJtgRBWrWrCnTp0+XEyf+vd5Jt6rS2aWnnnoqNJkdzeroLucZC5IBAEBkBFt344qSmh3d3FObE2us0bZtW9sQVDsna1Hy/v375eeff7aWNz/99JPccccdFvCEJNjRlVcAACCKxEifndatW1u9jgY0utv5W2+9Jdu3b7fmxboYSouWtddfz549A1oMRXUxAACICtq/LxQ9/Ah2AABwqFhajRVKBDsAADhZlExFRTO/dz1304rp9evXy6lTp87tFQEAAEQy2Pn777+lb9++kidPHlsqtmPHDjs+YMAAv5eCAQCA4KexghnxwO9gR5efr169WhYuXGhLwtx013OtoAYAAGFejRXMiAN+1+x8+OGHFtQ0btzYq9uhZnk2b958rq8PAADEuJSUFJ/PDWTDcb+Dnd9//12KFy9+2vEjR46EtNUzAADISL93g/nuTZBooJuJ+xpDpKamhn4aS7safvLJJ57b7ot75ZVXpEmTJn5fAAAAcM401u7du6VXr15SpEgRyZ07t9SuXdtrA0/dMLxdu3Z2v8YIq1atOutzLliwwHY816G7n2tSZejQoTJz5kwb+nuJEiXsvkD4ndnRTUAvvfRSa92sK7HGjRtnvy9ZskQWLVoU0EUAAIDot3//fmnWrJm0bNlS5syZI8WKFbP9qtJ3NdaZHm0M2K1bN7npppt8et7mzZt7fn/sscdk9OjR0qNHD8+xK6+80oKqSZMmSe/evUMf7Ogb0ChNV17pC3/++efSoEEDWbp0qd0GAACxuV3EqFGjJCkpSSZPnuw5VqFCBa9zrrvuOvu5bdu2gC5J44mJEydmOrPUr1+/8DUVPO+88+Tll18O6AUBAMA5EuzO5a6ETAuEc+bMaSOjWbNmSfv27aVr1642m1OmTBnbvNPXDI4vNJjSGOPpp5/2Oq7lMnpfWIKdTz/91HY+1zeb3ty5cyUtLc2muAAAgHN2PU/KEEQ88sgjMnz48NPO37Jli0yYMEGGDBki999/v3z33XcycOBAyZEjR0DTS5kZM2aMdOnSxabJGjVqZMe+/fZbmy57//33A3pOvwuU77vvvkwroV0ul90HAACcZefOnXLw4EHP0J56mdGkhpauaP2u7kR+8803W1Yns2mnQF122WUW2Gidzl9//WXjiiuukA0bNth9Ycns6AXUqFHjtOPVqlWTTZs2BXQRAAAgcjU7iYmJPvWvKVWq1GkxQPXq1QPOuJxJ2bJl5cknnzxnz+d3sFOgQAFLY5UvX97ruAY6efPmPWcXBgAAwlOz4ytdiaX7YqanGZfk5GQ513R7Kt2SSvfiTK9OnTqhD3auuuoqGTRokK1710Jld6Bz1113WcoJAADEpsGDB0vTpk1tGkuXlmstjS4H1+Gm004apOzZs8duu4OjkiVL2vClefENN9xgNTsRayqo1dGawdFpK11upkNTWNo86Nlnn/X7AgAAQGASXMEPfzRs2NCSHW+//bbUqlVLHn/8cRk7dqz07NnTa8WW1vNcfvnldvs///mP3fa1rkcTKgcOHJDly5db08LPPvtMpk6dKpUrV7bnDts0ljYQnDdvnm0IqheiKaVLLrkkoAsAAADO6LOjOnbsaONM+vTpYyNQ2kX5o48+sr46WbJksSmytm3bWk3RyJEjPUFUyIKdkydPWnCjTQW1FbQOAACAc0U7MLv34NTOzDqtVaVKFWtc/MMPPwT0nH5NY2XPnl3KlSsX0HwZAAAIUYFyMCPKVK1a1VPnU7duXfnvf/9r+3HpNJiuBguE3zU7DzzwgDUS0gIkAAAQXxuBhtqdd94pv/76q6e5oRYqa6Ll+eeft8LosNTsjB8/3lZflS5d2ubRMi43DzTFBAAA0KtXL8/v559/vmzfvl3WrVtnAU/RokXDE+x06tQpoBcCAADOL1AOJ92dQWuFtWtzMPwOdjSlBAAAokCMBjuvv/66PPPMM7Zrg9IC5Xvuucezo3pYdj0HAADx10E5HEaPHi0PPfSQ3HHHHdaxWX399ddy6623yh9//GGNDUMe7Oia94SEM384rNQCAACBeuGFF2xn9euvv95zTHdoqFmzpu3EHpZgRzsnZuy9s3LlSutu+Oijj/p9AQAAIDCBdEFOL5jHhoquxNItKTLSY+5VWmHZGyuja665xiKud955R/r27RvQhQAAAD/FYM1OpUqV5N1337U2N+lpjKFbRoSlz86ZNG7cWObPn+/XY7Tts+6zkT9/fuuWqCu9Mu6meuzYMbn99ttt7618+fJJly5d5LfffvM6Z+DAgbY8LWfOnFKvXr3TXmfhwoUWpGkzIl0qr+e89dZbAb5TAAAQKjpL9PDDD0uHDh1s7y0d+rsef+yxxyIX7Bw9etSa/ZQpU8avxy1atMgCmWXLltleWzolpltQaKtoN52bmz17tsyYMcPO111UO3fufNpz3XjjjdK9e/dMX0f38tL9u95//31Zs2aN7aaqc4Eff/xxAO8WAACEiiY1dBNQ7anz4Ycf2tDfdYf1q6++OjzTWLpPRfoCZV0Df+jQIcmTJ4+8+eabfj2X7mSa3pQpUyzDs2LFCttY9ODBg/Lqq6/KtGnTpFWrVnbO5MmTbZd1DZA0m6Q00FK6f4YGMxllTIVpd8bPP/9cPvjgg3/dzAwAgGim38ZB1exIdNLZmowxxb59+6yDcsbv9JAEO7qVe8bVWcWKFZNGjRpZIBQMDW5U4cKF7acGPZrtadOmjeecatWqWRfFpUuXeoKdQF9Lg6bMHD9+3IZbSkpKwK8DAACCp8XJuiQ9LMFO7969JRTS0tJk0KBBtqa+Vq1admzv3r2SI0cOKViwoNe5JUqUsPsCpYVP3333nW0udqZaIlaWAQCiXgz22QmFgJoKHjhwwKaXfvnlF7utK7G0ZqZAgQIBX4jW7qxdu9YaB4XSggULrGbn5ZdftuvOzLBhw2TIkCFemZ2kpKSQXhcAAH6LwdVYoeB3gfL3338v5513nowZM8Z2Pteh3Q71WKCbgGqXRC0W1kCkbNmynuMlS5aUEydOWHCVnq7G0vv8pQXOV1xxhV17+mZFGemqrsTERK8BAADiJLOjq6O0k6FmRrJl++fhp06dkn79+tk01FdffeXzc2lx84ABA6xRoS4Pr1ChwmkFStmzZ7cl7VqdrXRp+o4dO6RJkyZ+Xbc+vxYjjxo1Sm6++Wa/HgsAQFSKoczOkHQzKpnRRUhhC3Y0s5M+0LEnyZZNhg4dKhdccIHfU1e60uqjjz6yXjvuOhydDtNdTvWnNinUD0CLljXDosGRBjrpi5M3bdokhw8ftsfrMvhVq1bZ8Ro1aljNj2aMNNDRVVgaNLlfR+9zF0MDAOA0sdRBeeXKlWc9R1dqhyXY0YBDMyu6Kiq9nTt3WsDiD937QrVo0cLruC4v79Onj/2uU0664kuDFF0h1b59e3nppZe8zteskk5RudWvX99+bt26VcqXL29bWfz9999WeKzDrXnz5pbxAQDAkWIos7NgwYKQPbffwY427tNsy7PPPuvZu+Kbb76xrdd79Ojh13PpNNbZ5MqVS1588UUbZ3K2gEX79+gAAADxx+9gR4McbSqoBb5aq6O0rua2226Tp556KhTXCAAAYjyzE1XBjta5jBs3zqaDNm/ebMd0JZZ2UAYAAOETSzU7oRTw3lga3NSuXVuSk5Nt6wV3zx0AAABHBzvdunWT8ePH2++68klXYOkx90abAAAgzB2UgxlxwO9pLO2j88ADD9jv2h9Hi4y16Z+ueHriiSc8/XAAAECIxUjNzppMNvE+E02uhDzY0Q003b1pdNdyDW50Suvyyy+3FVkAAAD+qFevni1+0gSK/vw3qampEvJpLN0jSnccP3LkiAU77dq1s+P79++3ZeIAACC8BcrBjGigffG2bNliP7UkRndU0J562mhQh/6ui6ECLZfxO7OjW0L07NlT8uXLJ+XKlfM0BNTpLS1YBgAAYRIj01jJycme37t27SrPP/+8XHbZZV5TV5pseeihh6RTp05+P7/fwU7//v3lwgsvtI7Jbdu2te7GqmLFilazAwAAEKgff/zxtL0ylR77+eefw7f0XFdgaY3O7t27PY0F9XazZs0CuggAABCAYKewXP6/pH739+rVS4oUKWL7WOqsju6b6bkkl0sefvhhKVWqlN3fpk0b2bhxo8/PX716devld+LECc8x/V2P6X1hCXZ0jyndLkKLkmvWrGn7ZCndoJMOygAAhJHrHAw/aH2uJjZ054Q5c+ZYpuW5556TQoUKec55+umnbRpq4sSJsnz5csmbN6/ta3ns2DGfXkMfN3fuXClbtqwFSjr0dz2m9wXC72msYcOGyerVq20/qg4dOniO68UMHz5c7rvvvoAuBAAARHfNzqhRo6x2Rjfsdks/5aRZnbFjx8qDDz4oV111lR17/fXXpUSJEvLhhx/Kf/7zn7O+hpbKaLHyW2+9JevWrfPsy3nttdda4BSWYEcv9p133pHGjRt7LQ/TLI97+wgAAOAcKSkpXrdz5sxpI6NZs2ZZlkaLiBctWiRlypSxWt6bbrrJ7tfVVHv37rUEiFuBAgWkUaNGtpLbl2BHaVBz8803y7ni9zTW77//LsWLFz/tuC5FP9vaeAAAEH1Lz5OSkiwocQ+tj8mMZlwmTJgglStXtmkl3QR84MCB1lhYaaCjNJOTnt523+eLN954Qy666CIpXbq0bN++3Y6NGTNGPvroo/AEO1qc/Mknn3huuwOcV155RZo0aRLQRQAAgMjZuXOnNQ12Dy1ZyUxaWpo0aNBARowYIfXr17fsi2Z1Aq2lyYwGU0OGDJFLL73UaoTcTQS1LkinyMIyjaVvUC9Ai5J0JZbugK6/L1myxFJaAADAWRITE22cja6wqlGjhtcxXSHlbvZXsmRJ+/nbb7/ZuW56W7sk++KFF16Ql19+2frppF/4pMmWu+++W8KS2dG00qpVqyzQ0eVmuuO5TmvpXNz5558f0EUAAIDoX43VrFkzWb9+vdexDRs2eJoCarGyBjzz58/3qgfSVVm+zv5o3Y9mjTLSGiItmQlLZkdpy2aNujJ677335JprrgnoQgAAgH+C3fIhwc/HDh48WJo2bWqzPN26dZNvv/1WJk2aZMOeLyHBdlrQJsNa16PBj3Y91tobXzsf62M0qZK+q7LSLaoC7bPjV7Cj2RxdBpYjRw6pUqWK57gWDGkDIb2PYAcAgNjUsGFDmTlzptX0PPbYYxaYaB2NbiPlNnToUMvAaD3PgQMHbEZIAxVf98/Uep3bb7/d+vLoUnYNqN5++20rmtb64JAGO2vXrpWOHTtaEZPS9fNaRKSRnd6nBUrpC5cBAEAYhHl/q44dO9o4E83uaCCkIxD9+vWzzsvaq0cbGWt/Hc0MaY2wr0vXAw527r33XqlUqZKMHz/eIiwdv/zyi3VT1ohNLwwAAIRRjGwEmpFminRosHP48OFMW96EJNj57rvvrBhZq6kvvvhiC3buv/9+ue6664K6AAAAALejR4/a9JVuS6VD+/vpVJmuAmvXrp2EdDXWH3/8YWkkpQ2HtLuhdlEGAADObioYTbRMRreYUFrzo9tH6P5b7vKZkAY7Ogd36NAhW0KmDYf0tkZfejv9AAAAsbn0PBx++OEHm0Fyr/LWpezaRVkDIN1gNKTTWJpSSr8CS2+nXwevtzUAcnc6BAAAsbX0PBy0Tid//vz2u5bPdO7cWbJkyWKzSe6tI0IW7CxYsCCgFwAAAPCVLobSTcevvvpq239Le/uoffv2+dTlOahgp3nz5gG9AAAACJEYXI318MMP23JzDXJat27t6bysWZ7MOiuHrIMyAACIAjEY7FxzzTXWiPDXX3+VunXreo5r4KPZnkAQ7AAAgKiiRcnuTUV18dOXX34pVatWlWrVqoVnI1AAABAdYnHpebdu3ayBsdJV37rbuR6rU6eOZ3d1fxHsAADgVDG49Pyrr77yLD3Xfbh0tbf229Fl57rBaCAIdgAAQNTQXn6FCxe233U7qi5dulgn5csvv1w2btwYupodXePuqw8++EBi1Qf9W0u2bL7t2or/l5qH0rBgHC2aPdKX4Fh59p2I9CU4Vs4tv0f6EhzrVNrx8L1YDBYoJyUlydKlSy3g0WBn+vTpdnz//v0+75yekU/fQro9hJumkzStpMd0Hk2tWLHCUkz+BEUAACA4sdhUcNCgQbYJaL58+SQ5OVlatGjhmd6qXbt26IKdyZMne+1+roVCEydOlKxZs9ox7Zrcv3//gJv9AAAAKI0ndD+snTt3Stu2ba17sqpYsWLANTt+zy+89tpr8vXXX3sCHaW/DxkyRJo2bSrPPPNMQBcCAAD8FIPTWEpnjtyzR25asxMov4OdU6dOybp162y9e3p6LC0tLeALAQAA8TmNNWTIEHn88cclb9689vu/GT16dOiDnRtuuEH69u0rmzdvtjSTWr58uTz11FN2HwAACJMYyeysXLlSTp486fn9THTD8UD4Hew8++yz1tXwueees1bOqlSpUnLPPffIXXfdFdBFAACA+LUg3Wbjodh43O9gRwuFhg4dakNbOCsKkwEAiIAYyeyEWlANUAhyAACIHJ3USQjy8dHixhtv9HmhVMiDnd9++03uvvtumT9/vuzbt8/67qSny9ABAAD8MWXKFOurU79+/dNii2D5Hez06dNHduzYIQ899JDV6gRaLAQAAIIUQ9NYt912m7z99tuydetWW/DUq1cvz7YRYQ92tMfO4sWLpV69eufkAgAAQHwvPVcvvviiLSvXbad0qmrYsGHWW0dXgLdr1y6o5EqWQPasONfpJQAAgJw5c0qPHj1k3rx58vPPP0vNmjWto3L58uXl8OHD4Qt2xo4dK/fdd59s27Yt4BcFAADncBormBGldPW3ZnM0wRJsPbDf01jdu3eXv//+W8477zzbcj17du8dmf/666+gLggAAPghigMWfx0/ftwzjaVlMx07dpTx48dLhw4dPHtkhSXY0cwOAADAuaTTVdOnT7dyGV2GrsXKRYsWPSfP7Xew07t373PywgAAIDixVKA8ceJEKVeunO1uvmjRIhuZ0cxPSIId7ZTsbiDo7pp8JjQaBAAgTGJo6fn1118fsnY2PgU7hQoVsn2wihcvLgULFsz0YrSASI/TVBAAgNjM7AwfPlweffRRr2NVq1aVdevW2e+6Sbg2HtZ6G62/0VqbF154QUqUKOFTU8FQ8SnY+fLLLz2NffR3GgkCABCfatasKV988YXndrZs/4QSR44csX44devWtVhBaQPiK664QpYtWxZUgXFYgp3mzZtbR8MKFSpIixYtQn9VAAAgKqexsmXLJiVLljzt+DfffGNtaVauXOkpaZk6darNDmnw06ZNG4kUn8MsXWquwY5WSL/55puya9eu0F4ZAADwaRormOGux00/dArqTDZu3CilS5e2QuKePXvaFlJKH6MzP9oY0C1XrlyW0dFprUjyOdjRqExXYm3ZskVuuukm26yrcuXKcsstt9hSMd0gFAAAOE9SUpIUKFDAM0aOHJnpeY0aNbLams8++0wmTJhgsz4XX3yxHDp0SBo3bix58+aVe++91/rx6bSW1u9oLa/W/UaSz0vPdfrKPYV17NgxWbJkiSxcuNCGpqlOnjwp1apVk59++imU1wsAAM7xNNbOnTu9VlOnz86kd+mll3p+r1OnjgU/mvx49913bQ+rGTNm2Iaezz//vGV0dOuHBg0aRLReJ6A+O+60VKtWreSiiy6Sli1bypw5c+S///2vpxobAAA4J9hJTEwMqHWMrtCuUqWKbNq0yW5rgbKuyPrjjz+stkfv1/oenfKKJL9CrRMnTshXX31ly840yNE3ceutt8r+/futnbOmswAAQHw4fPiwBTelSpXyOq6djzVG0BKYffv2yZVXXimOyOxoJmf58uVWpKyrs7RWZ9q0aae9QQAAEJt9du6++25bSq5TV3v27JFHHnlEsmbNatNVavLkyVK9enUpVqyYLF26VO68804ZPHiw9eJxRLCzePFiC2w06NHaHQ14ihQpEtqrAwAAUbP0fNeuXRbY/PnnnxbQaDmL9tDR39X69etl2LBhtil4+fLl5YEHHrBgJ9J8DnYOHDhgAY8WJI8aNcrerM7TadDjDn7cbxYAAMSe6dOn/+v9Tz31lI1o43Owo8vJtO2zDqXLzHTd/IIFC+Tpp5+2tfa6FH3t2rWhvF4AAPA/CbpVkyvw1E5CEI91koBWY7mDH91CQod2R9Sq619++eXcXh0AAIiLjUCjIthJS0uT77//3qaxNJujbaG1YVCZMmVsZdaLL75oPwEAQGwWKMd8sKNLyDS40fXyGtSMGTPGanV0GwkAAADHBzvPPPOMBTlalAwAAKIA01jnNtjRvjoAACB6MI3lm8huVgEAABDLwY7uqtqwYUPJnz+/FC9eXDp16mQNidLTTUdvv/12a2CYL18+6dKly2k7rA8cOFDOP/9827isXr16//qaun+Hvp7WIAEAEBPTWMGMOBDRYGfRokUWyGj3xXnz5tnO6bqJmBZCu2nnxdmzZ9tOqnq+tqfu3Lnzac914403Svfu3f/19fT5tRmibkcPAECsTGMFM+JBwH12zoXPPvvM6/aUKVMsw7NixQq55JJL5ODBg/Lqq6/aHly6TUX6fTc0QGrcuLEd063k1e+//y5r1qw54+s9+OCDUq1aNWndurUsWbIkpO8NAABEh6iq2dHgRmmjQqVBj2Zj2rRp4zlHg5Vy5crZBmP+0J1XNTuk/YDO5vjx45KSkuI1AACIOkxjOSvY0aaFgwYNkmbNmkmtWrXs2N69eyVHjhyn1deUKFHC7vOVbljWp08fyxwlJib6VEtUoEABz0hKSgrgHQEAEHpMYTko2NHaHd1X62ybjAXipptukmuvvdamxnyhO7Zqlsk9du7cec6vCQAAxFGwc8cdd8jHH39s21CULVvWc1y7NZ84ccJ2XE9PV2Ppff5MYT377LO2f5eOvn37WhCjv7/22munna+rujQDlH4AABB1dCPPYEcciGiBssvlkgEDBsjMmTNtz60KFSp43a/LybNnzy7z58+3JedKl6bv2LFDmjRp4vPraH1Pamqq5/ZHH30ko0aNsiJl3dsLAAAnoqmgA4IdnbrSlVYafGjvG3cdjtbJ5M6d235qFmbIkCFWtKwZFg2ONNBxr8Ry9845fPiwPf7o0aOyatUqO16jRg2r+dHVW+nphqZZsmTx1AYBAOBIbBcR/cHOhAkT7KduKJqeLi/XgmKlG45qYKKZHV0l1b59e3nppZe8zu/Xr5/14HGrX7++/dy6dauUL18+DO8EAABEq4hPY51Nrly5bLn4vy0Z1ykwf2gg5Q6mAABwqoS0f0Ywj48HEQ12AABAEJjGcs5qLAAAgFAhswMAgEOxGss3BDsAADhVsL1yXPER7TCNBQAAYhqZHQAAHIppLN8Q7AAA4FSsxvIJ01gAACCmkdkBAMChmMbyDcEOAABOxWosnxDsAADgUGR2fEPNDgAAiGkEOwAAOH01VjDDD8OHD5eEhASvUa1aNc/9e/fuleuuu05KliwpefPmlQYNGsj7778vkcY0FgAADhWJaayaNWvKF1984bmdLdv/hxLXX3+9HDhwQGbNmiVFixaVadOmSbdu3eT777+X+vXrS6SQ2QEAAD7T4EYzN+6hQY3bkiVLZMCAAXLhhRdKxYoV5cEHH5SCBQvKihUrJJIIdgAAcKo0V/BDRFJSUrzG8ePHz/iSGzdulNKlS1sw07NnT9mxY4fnvqZNm8o777wjf/31l6Slpcn06dPl2LFj0qJFC4kkgh0AAOK8ZicpKUkKFCjgGSNHjsz05Ro1aiRTpkyRzz77TCZMmCBbt26Viy++WA4dOmT3v/vuu3Ly5EkpUqSI5MyZU2655RaZOXOmVKpUSSKJmh0AAOLczp07JTEx0XNbA5XMXHrppZ7f69SpY8FPcnKyBTl9+/aVhx56yGp2tKZHp7c+/PBDq9lZvHix1K5dWyKFYAcAAIdKCLJXTsL/fmqgkz7Y8ZXW41SpUkU2bdokmzdvlvHjx8vatWutiFnVrVvXAp0XX3xRJk6cKJHCNBYAAE7voBzMCMLhw4ctyClVqpT8/fffdixLFu/QImvWrFa/E0kEOwAAwCd33323LFq0SLZt22Yrr66++moLZnr06GH9drQ2R+t0vv32WwuCnnvuOZk3b5506tRJIolpLAAAHCrcfXZ27dplgc2ff/4pxYoVk4suukiWLVtmv6tPP/1U7rvvPrniiiss66PBz9SpU+Wyyy6TSCLYAQDAqQLoguzFz8fqUvJ/U7ly5ajomJwRwQ4AAA6V4HLZCObx8YCaHQAAENPI7AAA4FS6yCmYhU5pEhcIdgAAcCimsXzDNBYAAIhpZHYAAHCqMK/GciqCHQAAnCrYLsiu+Ih2mMYCAAAxjcwOAAAOFe4Oyk5FsOMD1//SfKdSj0f6Uhwp9RR/zIJx6mRqpC/BsU6dOhHpS3CsrGn8fReoU2knvL47QoppLJ/wLeSDQ4cO2c9vVjwb6UsBADjou6NAgQKRvgwQ7PimdOnSsnPnTsmfP78kJCRItElJSZGkpCS7xsTExEhfjqPw2QWHzy9wfHax+/lpRkcDHf3uCLWEtH9GMI+PBwQ7PsiSJYuULVtWop3+Cx9t/9I7BZ9dcPj8AsdnF5ufX9gyOkxj+YRgBwAAp6LPjk9Yeg4AAGIamZ0YkDNnTnnkkUfsJ/zDZxccPr/A8dkFh8/vH+yN5ZsEV1jWxgEAgHNZoK11QS3PHybZsuUK+HlOnTomC1aMlIMHD0Zl7dO5wjQWAACIaUxjAQDgVDo3E8zycZfEBYIdAAAcipod3zCNBQAAYhrBTpQYOXKkNGzY0Lo0Fy9eXDp16iTr16/3OufYsWNy++23S5EiRSRfvnzSpUsX+e2337zO2bFjh1x++eWSJ08ee5577rlHTp065XXOwoULpUGDBraKoVKlSjJlyhRxsnB9dvq5aQftjGPv3r0S75/dwIED5fzzz7c/U/Xq1cv0tdasWSMXX3yx5MqVyzrfPv300+J04fr8tm3blumfvWXLlkk8f3arV6+WHj162J+n3LlzS/Xq1WXcuHGnvVas/Z13ep8dVxBD4gLBTpRYtGiR/Uutf3nNmzdPTp48Ke3atZMjR454zhk8eLDMnj1bZsyYYefv2bNHOnfu7Lk/NTXVvqxPnDghS5YskalTp9q/1A8//LDnnK1bt9o5LVu2lFWrVsmgQYOkX79+MnfuXHGqcH12bvoX8q+//uoZ+hd1PH92bjfeeKN07979jCtH9HmTk5NlxYoV8swzz8jw4cNl0qRJ4mTh+vzcvvjiC68/exogxfNnp3+W9N+/N998U3766Sd54IEHZNiwYTJ+/PiY/jvPS1CBjituOijrHh6IQvv27bN4fdGiRXb7wIEDruzZs7tmzJjhOeeXX36xc5YuXWq3P/30U1eWLFlce/fu9ZwzYcIEV2Jiouv48eN2e+jQoa6aNWt6vVb37t1d7du3d8WKUH12CxYssMfs37/fFasC+ezSe+SRR1x169Y97fhLL73kKlSokOezVPfee6+ratWqrlgSqs9v69at9piVK1e6YlWwn51b//79XS1btvTcjtW/8w4ePGifRau697raNXg44NGq7r32PPp8sYzMTpTSngeqcOHCnv+C0f/yadOmjeecatWqSbly5WTp0qV2W3/Wrl1bSpQo4Tmnffv29l/V+l897nPSP4f7HPdzxIJQfXZuOs1QqlQpadu2rXzzzTcSSwL57Hyh515yySWSI0cOr89Xs2T79++XWBGqz8/tyiuvtEzGRRddJLNmzZJYcq4+O30e93PExd95aedgxAGCnSiUlpZmqdZmzZpJrVq17JjWhegXRcGCBb3O1S9nd82I/kz/Ze2+333fv52jX+pHjx4VpwvlZ6cBzsSJE+X999+3oXUCLVq0kB9++EFiQaCfnS98+XydLpSfn9arPPfcczad88knn1iwozUusRLwnKvPTqeg33nnHbn55ps9x2L97zz3aqxgRjxg6XkU0nnstWvXytdffx3pS3GcUH52VatWteHWtGlT2bx5s4wZM0beeOMNcTr+3EXv51e0aFEZMmSI57YW9mr9itY+abbH6c7FZ6ePv+qqq2wLCa39iRvseu4TMjtR5o477pCPP/5YFixYIGXLlvUcL1mypBXPHjhwwOt8XZmg97nPybjKw337bOdom3BdzeBkof7sMnPhhRfKpk2bxOmC+ex8Eejn6xSh/vwy06hRI/7s/c/PP/8srVu3tozOgw8+6HVfLP+dB98R7EQJ3aJM/6WfOXOmfPnll1KhQgWv+3XVRfbs2WX+/PmeY1rvoMulmzRpYrf1548//ij79u3znKOrHPRf6ho1anjOSf8c7nPcz+FE4frsMqOrO3R6K54/O1/ouV999ZXVYKT/fDVTVqhQIXGqcH1+meHP3j+0pk5XWvXu3VuefPLJ014nFv/O88JqLJ8wjRVFadxp06bJRx99ZH0n3HPSutGb/teH/uzbt6+lsrX4Tr+EBwwYYP/CNm7c2M7V1K1+MV933XXWw0SfQ/8rR5/bvTPwrbfeassyhw4daktd9S+Zd9991+oAnCpcn93YsWPtL+SaNWta/49XXnnFPr/PP/9c4vmzU5phOHz4sD1e6yD0i1jpZ6p1F9dee608+uij9lz33nuvTTloPxSdAnSycH1+2gpBf9avX9+Of/DBB/Laa6/Zn8F4/uz0z1GrVq2s4FjPcz9H1qxZpVixYjH7d54XprF8E+nlYPiHuzVUxjF58mTPOUePHrVllbqEN0+ePK6rr77a9euvv3o9z7Zt21yXXnqpK3fu3K6iRYu67rrrLtfJkye9ztEl1PXq1XPlyJHDVbFiRa/XcKJwfXajRo1ynXfeea5cuXK5Chcu7GrRooXryy+/dDnZufrsmjdvnunz6JJpt9WrV7suuugiV86cOV1lypRxPfXUUy6nC9fnN2XKFFf16tXt8doO4cILL/Rakh2vn50u1c/sOZKTk2P677z0S89bV7/L1b7W/QGP1tXvioul5wn6fz7GRQAAIAroajLNfrWuepdky/pP9jkQp1KPy/z1z9mSfc2enY02A9UsbXo6Hb1u3Trr9J1xOtJNs2ldu3aVSGEaCwAAh4rERqA1a9a0bt5u2bL9E0poOw7t7J2edknXVYOXXnqpRBLBDgAAcS4lJcXrttYquusVM9LgJrPVhForlfG4FqB369bNekVFEquxAACI89VYSUlJNi3mHrpR65ls3LhRSpcuLRUrVpSePXvaCrnMaBdsLbbXQvNII7MDAIBTpbl0Liq4x4vIzp07vWp2zpTV0f5Oukmy1unolJXW71x88cW2Mk5X1aX36quv2k702oA10gh2AACIc4mJiT4VKKevvalTp44FP8nJyVaAnD6Doy0UtLXAQw89JNGAYAcAAKeKcJ+dggULSpUqVU7r5v3ee+/J33//Lddff71EA2p2AABwrGDrdVxBvbo2w9Q9AjN289YpLN23zd3cMdLI7AAA4FRhzuzcfffdcsUVV9jUlW5Gqxuv6iqsHj16eM7RLI9uD/Ppp59KtCCzA4SJNuOqV69epC8DAAK2a9cuC2y0QFmXlBcpUkSWLVvmlcHRrUx0U9do2n2eYAfwwe+//y633XablCtXzlYpaC8J3Y/nm2++kWjQokULSUhIsJErVy6bQ9elo/HWIP2PP/6wfzYjRow47T79i1n3VEpNTY3ItQEhoaupgh1+mD59umV0jh8/boGP3j7vvPO8ztF//3Q5epYs0RNiMI0F+KBLly5y4sQJ25BRe0v89ttvtpPyn3/+Gdbr0F3DdSfozNx0003y2GOP2V9CutnhzTffbMWDGqRFq397P4EoWrSodWzVtvSaaq9du7YdnzFjhnz88ceycuVKS7mfSxo8aZAZTX+xI4640v4ZwTw+DvBvJ3AWBw4ckMWLF8uoUaOkZcuWNld94YUXyrBhw6wAz03/S+aqq66yTqG6hFMzCRoUncl3330nbdu2tS9obeLVvHlz+eGHH7zO0S/RCRMm2OvkzZtXnnzyyTM+X548eSyrodd3ww032LLQefPmee7XIEjn28uUKWPPpUtGFy5c6Ll/+/btFiAUKlTI7teW8Onn3BctWmTvWzNbWox43333yalTpzz3ly9f3naGT0+n7XT67mzvZ/bs2dKwYUPLSunncfXVV/t83Rnpc+su671797ZgSrNyusP2U089Zal33WW7QYMG9loauGqfkPTvY/To0RYk6Wtpo7X+/ftbEaab9hjRIHLWrFm2K7l+HvrPXq9JPx99nN7frFkz+0wBRB7BDnAWGrzo+PDDD+2LNzNpaWkW6Pz1118WFGiQsWXLFunevfsZn/fQoUP2hfz111/bnHflypXlsssus+PpabCgX/4//vij3HjjjWe9Xp260uBMN+bLkSOH5/gdd9whS5cutbTzmjVrLPvRoUMH64aqNCDQ96eFhfpaGty5W7zv3r3brk0DktWrV1vAoqstnnjiCZ8/xzO9n08++cRu6/Nr5kUzZho0+HrdmRk3bpxl3R5//HELVmrVqiUDBgywz0WXwt55553y888/y3//+18LXtIHkZqhef755+Wnn36yTJ5myYYOHer1/LqkVj+fV155xc4rXLiwdOrUyQJWvUa9Xs2saXAHOKGDcsyL9LbrgBO89957rkKFCrly5crlatq0qWvYsGGu1atXe+7//PPPXVmzZnXt2LHDc+ynn36ydZ3ffvut3X7kkUdcdevWPeNrpKamuvLnz++aPXu255g+ftCgQWe9vubNm7uyZ8/uyps3r/3Ux+m1fvPNN3b/9u3b7fp2797t9bjWrVvbe1G1a9d2DR8+PNPnv//++11Vq1Z1paWleY69+OKLrnz58tl1q+TkZNeYMWO8HqfvV9/3v72fJk2auHr27Jnp6/py3Wcyf/58e2xiYqJr27ZtnseNGDHC67w33njDVapUqTM+z4wZM1xFihTx3J48ebK9j1WrVnmO/fnnn3Zs4cKF/3pNwLly8OBB+zPXpsytrg5JdwY82pS51Z5Hny+WkdkBfKzZ0aI8nbrQrIJOWehUiGYF1C+//GJTHjrcdIpDpzP0vszoFJfW2WhGR6exdOpLp0sy7jNzwQUX+HSNukeN7kOjRdPa5fSBBx7wtGnXLIrWlmjhsjtTpUOzUNojQw0cONAyNTr9ostJNUPhpu+hSZMmXpkKPU+vV4sU/ZHx/eg1t27dOtNzfbnuM2nVqpUVJF933XU2tac0K6V1TemfS/8ZaNt7zdYo3c1Zr0enzbT9vT5es0Tu+5VmzHSa0E0zO3369LGidZ0K1MxSxt2fAUQOBcqAj7TGQ2tsdGgL9H79+llQoF9ygdApLP0S1S9G/TLW2g8NKLQQOj2tAfGFBkyVKlWy37V1u/6uX/Zt2rSxoEQLc3VjvowFuu6pKn0/+mWt00qff/65reZ67rnnbPrHFzr9k3H1l9bMZJTx/eTOnfuMz+nLdf8b3Z1ZR/rn0xqdzp07Z/rPd9u2bdKxY0cr6tapLQ1idJpR2+DrPxeti3Jfc8YpqsmTJ1vA+Nlnn8k777wjDz74oE1n6j8DIFY7KDsFmR0gQJq5OXLkiP2um93pRno63LQmRIub9bzMaAZGvxy1VkWLgTXY0aXT54IGAlqXooW9GoDUr1/fMiT79u2zICj90KJmN81M3XrrrfLBBx/IXXfdJS+//LLn/WkdSvpgRq9fMx/aT0Npn4302YyUlBTZunXrWa9VMyRap5MZX6/bV5qNW79+/WnPpUODNQ2qtP5KgzwNUjSjpBk9X+n1auH6kiVLrE5I9wYCQsomoYKp2ZG4QLADnIVmX3RK5M0337SpHf0C16XMTz/9tBUlK82e6AoenUrSFVXffvutFcJqweqZpqF0+uqNN96wKaLly5fbY/8ty+GvW265RTZs2CDvv/++fWnr8+s1aSCj70GvUbM3mslRgwYNkrlz59p9+h4WLFhgQY7SIl8N5DTLo4XPuqJJs1pDhgzxLLnWz0jfjxYB6/STZq58Weatz/P222/bT/0s3MXRypfr9sfDDz8sr7/+umV3tLBYX08LnzULozTo0WzUCy+8YAXm+n4mTpx41ufV69IgRwNCXYGlmTEtoHZ/fgAii2AH8CFLosudx4wZI5dccon9F7tOY2mtx/jx4+0cndLQAECXbes5GvzosmadzjgTXc20f/9+yzZoXYhmeYoXL37OrlunYDRI0NVPmq3QaRa9rRkbXYKtq4d0+bs2SlSaQdEVWfoFrXVJGmi89NJLdp/Wr+gydA006tata9kfndpxBwlKv+w1uNNpoMsvv9yeP2OzsTM1RNTgUeuhdKm6Bk36Om5nu25/6DSd9tvRYERXlmn2Rv+5umt69L3p0nMNtvSf81tvvWWB1dno9JYGgVrbpZ+brsTSz1IDTiCkWI3lkwStUvbtVAAAEA10mljr9NoU7yfZsvx/iwl/nUo7IV/se0UOHjxoiyRiFQXKAAA4FQXKPmEaCwAAxDQyOwAAOBWZHZ8Q7AAA4FS2a3kQAUtafAQ7TGMBAICYRmYHAACHcrnSbATz+HhAsAMAgFNpzU0wU1EuprEAAAAcj8wOAABOZZkZMjtnQ7ADAIBTpaWJJARRd+OKj5odprEAAEBMI7MDAIBTMY3lE4IdAAAcypWWJq4gprFccTKNRbADAIBTkdnxCTU7AAAgppHZAQDAqbShYAKZnbMh2AEAwKksWAlm6blL4gHTWAAAIKaR2QEAwKFcaS5xBTGN5YqTzA7BDgAATmVLx+mgfDZMYwEAAJ8MHz5cEhISvEa1atW8zlm6dKm0atVK8ubNK4mJiXLJJZfI0aNHJZLI7AAA4FCRmMaqWbOmfPHFF57b2bJl8wp0OnToIMOGDZMXXnjB7lu9erVkyRLZ3ArBDgAAThWBaaxs2bJJyZIlM71v8ODBMnDgQLnvvvs8x6pWrSqRxjQWAAAOdUpOyilXEENO2vOkpKR4jePHj5/xNTdu3CilS5eWihUrSs+ePWXHjh12fN++fbJ8+XIpXry4NG3aVEqUKCHNmzeXr7/+WiItwRUvpdgAAMSIY8eOSYUKFWTv3r1BP1e+fPnk8OHDXsceeeQRq8/JaM6cOXauZmt+/fVXefTRR2X37t2ydu1a+emnn6RJkyZSuHBhefbZZ6VevXry+uuvy0svvWT3V65cWSKFYAcAAIcGPCdOnAj6eVwulxUap5czZ04bZ3PgwAFJTk6W0aNHS/Xq1aVZs2ZWrzNixAjPOXXq1JHLL79cRo4cKZFCzQ4AAA6UK1cuG5FUsGBBqVKlimzatMlWYKkaNWp4naNBkHuqK1Ko2QEAAAHRKa3NmzdLqVKlpHz58lbLs379eq9zNmzYYNmfSCKzAwAAfHL33XfLFVdcYcHLnj17rLYna9as0qNHD5sKu+eee+xY3bp1rWZn6tSpsm7dOnnvvfckkgh2AACAT3bt2mWBzZ9//inFihWTiy66SJYtW2a/q0GDBlktkS5B/+uvvyzomTdvnpx33nkSSRQoAwCAmEbNDgAAiGkEOwAAIKYR7AAAgJhGsAMAAGIawQ4AAIhpBDsAACCmEewAAICYRrADAABiGsEOAACIaQQ7AAAgphHsAAAAiWX/BwQ0sSqqKfGrAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "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": [
+       "<PolarAxes: >"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "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/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/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/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/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/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/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
+- 11.997341633816305
+- 12.579838122549745
+- 13.247951471095586
+- 14.107296080317507
+- 14.47287343839896
+- 13.719413985576479
+- 11.84661882167167
+- 10.052948877564068
+- 8.63865137251234
+- 7.59913682246736
+- 6.521809820530946
+- 5.528022431020552
+- 5.117208091823769
+- 5.867047609524703
+- 6.91327432934153
+- 8.180885048185145
+- 9.03461986033867
+- 10.032262351951072
+- 10.550891175500922
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+- 10.785793502434629
+- 10.657366532111869
+- 10.665446787780278
+- 10.653577600484013
+- 10.945713136347475
+- 11.171028325337137
+- 11.842390092644134
+- 12.710701573202284
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+- 8.992125596533384
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+- 7.710513663779936
+- 7.721916706816427
+- 7.797829836498241
+- 8.04541107117756
+- 8.977524724383565
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+- 10.42050406857797
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+- 7.909569726509561
+- 7.123524765282463
+- 7.2249446822797445
+- 8.126998987077291
+- 9.433293148387824
+- 10.21360485968953
+- 10.6639117591067
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+- 11.770872163864512
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+- 12.158424009879202
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diff --git a/examples/inputs/floris_v4_template.yaml b/examples/inputs/floris_v4_template.yaml
new file mode 100644
index 000000000..0cc74b6dc
--- /dev/null
+++ b/examples/inputs/floris_v4_template.yaml
@@ -0,0 +1,101 @@
+
+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
+    # 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 @@
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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/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/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/__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
 
diff --git a/hopp/simulation/hybrid_simulation.py b/hopp/simulation/hybrid_simulation.py
index 4a59dabd3..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
@@ -1085,9 +1117,9 @@ 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/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,,,,,,,,,,,,,,
+2008,1,1,0,30,0.91814244,,,,,,,,,,,,,,
+2008,1,1,1,30,0.276233,,,,,,,,,,,,,,
+2008,1,1,2,30,0.5840359,,,,,,,,,,,,,,
+2008,1,1,3,30,1.1303853,,,,,,,,,,,,,,
+2008,1,1,4,30,1.2114222,,,,,,,,,,,,,,
+2008,1,1,5,30,0.92221373,,,,,,,,,,,,,,
+2008,1,1,6,30,0.3877952,,,,,,,,,,,,,,
+2008,1,1,7,30,0.23787645,,,,,,,,,,,,,,
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+2012,12,25,22,30,0.276592,,,,,,,,,,,,,,
+2012,12,25,23,30,0.5265774,,,,,,,,,,,,,,
+2012,12,26,0,30,1.3502867,,,,,,,,,,,,,,
+2012,12,26,1,30,1.8845301,,,,,,,,,,,,,,
+2012,12,26,2,30,1.9909881,,,,,,,,,,,,,,
+2012,12,26,3,30,1.8519971,,,,,,,,,,,,,,
+2012,12,26,4,30,1.4663675,,,,,,,,,,,,,,
+2012,12,26,5,30,0.5838932,,,,,,,,,,,,,,
+2012,12,26,6,30,0.67075914,,,,,,,,,,,,,,
+2012,12,26,7,30,1.593084,,,,,,,,,,,,,,
+2012,12,26,8,30,2.02019,,,,,,,,,,,,,,
+2012,12,26,9,30,2.0636897,,,,,,,,,,,,,,
+2012,12,26,10,30,1.7511959,,,,,,,,,,,,,,
+2012,12,26,11,30,1.1206958,,,,,,,,,,,,,,
+2012,12,26,12,30,0.30764878,,,,,,,,,,,,,,
+2012,12,26,13,30,0.49886507,,,,,,,,,,,,,,
+2012,12,26,14,30,0.9808444,,,,,,,,,,,,,,
+2012,12,26,15,30,1.0206741,,,,,,,,,,,,,,
+2012,12,26,16,30,0.813046,,,,,,,,,,,,,,
+2012,12,26,17,30,0.4599557,,,,,,,,,,,,,,
+2012,12,26,18,30,0.13805327,,,,,,,,,,,,,,
+2012,12,26,19,30,0.7114486,,,,,,,,,,,,,,
+2012,12,26,20,30,1.0892869,,,,,,,,,,,,,,
+2012,12,26,21,30,1.1093041,,,,,,,,,,,,,,
+2012,12,26,22,30,0.7356413,,,,,,,,,,,,,,
+2012,12,26,23,30,0.06414558,,,,,,,,,,,,,,
+2012,12,27,0,30,0.8369628,,,,,,,,,,,,,,
+2012,12,27,1,30,1.6413647,,,,,,,,,,,,,,
+2012,12,27,2,30,2.0653872,,,,,,,,,,,,,,
+2012,12,27,3,30,2.0723891,,,,,,,,,,,,,,
+2012,12,27,4,30,1.8460125,,,,,,,,,,,,,,
+2012,12,27,5,30,1.282018,,,,,,,,,,,,,,
+2012,12,27,6,30,0.23820905,,,,,,,,,,,,,,
+2012,12,27,7,30,1.1288313,,,,,,,,,,,,,,
+2012,12,27,8,30,1.9693073,,,,,,,,,,,,,,
+2012,12,27,9,30,2.28124,,,,,,,,,,,,,,
+2012,12,27,10,30,2.1969907,,,,,,,,,,,,,,
+2012,12,27,11,30,1.7151874,,,,,,,,,,,,,,
+2012,12,27,12,30,0.9188081,,,,,,,,,,,,,,
+2012,12,27,13,30,0.03931398,,,,,,,,,,,,,,
+2012,12,27,14,30,0.78050506,,,,,,,,,,,,,,
+2012,12,27,15,30,1.1568899,,,,,,,,,,,,,,
+2012,12,27,16,30,1.1056887,,,,,,,,,,,,,,
+2012,12,27,17,30,0.8368942,,,,,,,,,,,,,,
+2012,12,27,18,30,0.37860316,,,,,,,,,,,,,,
+2012,12,27,19,30,0.31498963,,,,,,,,,,,,,,
+2012,12,27,20,30,0.93121994,,,,,,,,,,,,,,
+2012,12,27,21,30,1.207523,,,,,,,,,,,,,,
+2012,12,27,22,30,1.0727575,,,,,,,,,,,,,,
+2012,12,27,23,30,0.5446761,,,,,,,,,,,,,,
+2012,12,28,0,30,0.2877285,,,,,,,,,,,,,,
+2012,12,28,1,30,1.2208331,,,,,,,,,,,,,,
+2012,12,28,2,30,1.9481701,,,,,,,,,,,,,,
+2012,12,28,3,30,2.2204504,,,,,,,,,,,,,,
+2012,12,28,4,30,2.1106884,,,,,,,,,,,,,,
+2012,12,28,5,30,1.7520916,,,,,,,,,,,,,,
+2012,12,28,6,30,0.95436734,,,,,,,,,,,,,,
+2012,12,28,7,30,0.400908,,,,,,,,,,,,,,
+2012,12,28,8,30,1.6405461,,,,,,,,,,,,,,
+2012,12,28,9,30,2.3052678,,,,,,,,,,,,,,
+2012,12,28,10,30,2.4343765,,,,,,,,,,,,,,
+2012,12,28,11,30,2.1692524,,,,,,,,,,,,,,
+2012,12,28,12,30,1.4958693,,,,,,,,,,,,,,
+2012,12,28,13,30,0.5636828,,,,,,,,,,,,,,
+2012,12,28,14,30,0.38063878,,,,,,,,,,,,,,
+2012,12,28,15,30,1.0720801,,,,,,,,,,,,,,
+2012,12,28,16,30,1.3017758,,,,,,,,,,,,,,
+2012,12,28,17,30,1.1349887,,,,,,,,,,,,,,
+2012,12,28,18,30,0.752421,,,,,,,,,,,,,,
+2012,12,28,19,30,0.16158755,,,,,,,,,,,,,,
+2012,12,28,20,30,0.6396095,,,,,,,,,,,,,,
+2012,12,28,21,30,1.1836387,,,,,,,,,,,,,,
+2012,12,28,22,30,1.2828197,,,,,,,,,,,,,,
+2012,12,28,23,30,0.9603896,,,,,,,,,,,,,,
+2012,12,29,0,30,0.26178962,,,,,,,,,,,,,,
+2012,12,29,1,30,0.69813836,,,,,,,,,,,,,,
+2012,12,29,2,30,1.6374747,,,,,,,,,,,,,,
+2012,12,29,3,30,2.2183669,,,,,,,,,,,,,,
+2012,12,29,4,30,2.314495,,,,,,,,,,,,,,
+2012,12,29,5,30,2.0756822,,,,,,,,,,,,,,
+2012,12,29,6,30,1.5335062,,,,,,,,,,,,,,
+2012,12,29,7,30,0.48185268,,,,,,,,,,,,,,
+2012,12,29,8,30,1.0067841,,,,,,,,,,,,,,
+2012,12,29,9,30,2.115291,,,,,,,,,,,,,,
+2012,12,29,10,30,2.520619,,,,,,,,,,,,,,
+2012,12,29,11,30,2.4364574,,,,,,,,,,,,,,
+2012,12,29,12,30,1.9818037,,,,,,,,,,,,,,
+2012,12,29,13,30,1.1377373,,,,,,,,,,,,,,
+2012,12,29,14,30,0.1352413,,,,,,,,,,,,,,
+2012,12,29,15,30,0.776847,,,,,,,,,,,,,,
+2012,12,29,16,30,1.3205049,,,,,,,,,,,,,,
+2012,12,29,17,30,1.3789655,,,,,,,,,,,,,,
+2012,12,29,18,30,1.0752852,,,,,,,,,,,,,,
+2012,12,29,19,30,0.5459529,,,,,,,,,,,,,,
+2012,12,29,20,30,0.23477249,,,,,,,,,,,,,,
+2012,12,29,21,30,0.998391,,,,,,,,,,,,,,
+2012,12,29,22,30,1.3782885,,,,,,,,,,,,,,
+2012,12,29,23,30,1.2648858,,,,,,,,,,,,,,
+2012,12,30,0,30,0.75284576,,,,,,,,,,,,,,
+2012,12,30,1,30,0.11360959,,,,,,,,,,,,,,
+2012,12,30,2,30,1.1574237,,,,,,,,,,,,,,
+2012,12,30,3,30,2.018153,,,,,,,,,,,,,,
+2012,12,30,4,30,2.3985085,,,,,,,,,,,,,,
+2012,12,30,5,30,2.3171878,,,,,,,,,,,,,,
+2012,12,30,6,30,1.9325832,,,,,,,,,,,,,,
+2012,12,30,7,30,1.1619494,,,,,,,,,,,,,,
+2012,12,30,8,30,0.21490929,,,,,,,,,,,,,,
+2012,12,30,9,30,1.6198349,,,,,,,,,,,,,,
+2012,12,30,10,30,2.46051,,,,,,,,,,,,,,
+2012,12,30,11,30,2.575246,,,,,,,,,,,,,,
+2012,12,30,12,30,2.2979085,,,,,,,,,,,,,,
+2012,12,30,13,30,1.6750126,,,,,,,,,,,,,,
+2012,12,30,14,30,0.6992261,,,,,,,,,,,,,,
+2012,12,30,15,30,0.34813854,,,,,,,,,,,,,,
+2012,12,30,16,30,1.1356553,,,,,,,,,,,,,,
+2012,12,30,17,30,1.4935455,,,,,,,,,,,,,,
+2012,12,30,18,30,1.3733197,,,,,,,,,,,,,,
+2012,12,30,19,30,0.92785186,,,,,,,,,,,,,,
+2012,12,30,20,30,0.26132327,,,,,,,,,,,,,,
+2012,12,30,21,30,0.60957134,,,,,,,,,,,,,,
+2012,12,30,22,30,1.2952645,,,,,,,,,,,,,,
+2012,12,30,23,30,1.4625921,,,,,,,,,,,,,,
+2012,12,31,0,30,1.1452478,,,,,,,,,,,,,,
+2012,12,31,1,30,0.46553835,,,,,,,,,,,,,,
+2012,12,31,2,30,0.5316762,,,,,,,,,,,,,,
+2012,12,31,3,30,1.5886081,,,,,,,,,,,,,,
+2012,12,31,4,30,2.2899947,,,,,,,,,,,,,,
+2012,12,31,5,30,2.4501832,,,,,,,,,,,,,,
+2012,12,31,6,30,2.2045972,,,,,,,,,,,,,,
+2012,12,31,7,30,1.6543872,,,,,,,,,,,,,,
+2012,12,31,8,30,0.65037763,,,,,,,,,,,,,,
+2012,12,31,9,30,0.8383921,,,,,,,,,,,,,,
+2012,12,31,10,30,2.12047,,,,,,,,,,,,,,
+2012,12,31,11,30,2.620574,,,,,,,,,,,,,,
+2012,12,31,12,30,2.4828808,,,,,,,,,,,,,,
+2012,12,31,13,30,2.058798,,,,,,,,,,,,,,
+2012,12,31,14,30,1.2911476,,,,,,,,,,,,,,
+2012,12,31,15,30,0.23505875,,,,,,,,,,,,,,
+2008,12,31,16,30,0.78573483,,,,,,,,,,,,,,
+2008,12,31,17,30,1.423305,,,,,,,,,,,,,,
+2008,12,31,18,30,1.5817568,,,,,,,,,,,,,,
+2008,12,31,19,30,1.2973683,,,,,,,,,,,,,,
+2008,12,31,20,30,0.7285456,,,,,,,,,,,,,,
+2008,12,31,21,30,0.13518524,,,,,,,,,,,,,,
+2008,12,31,22,30,0.93706787,,,,,,,,,,,,,,
+2008,12,31,23,30,1.4786706,,,,,,,,,,,,,,
diff --git a/hopp/simulation/technologies/csp/pySSC_daotk/ssc_wrap.py b/hopp/simulation/technologies/csp/pySSC_daotk/ssc_wrap.py
index 6c931d28f..6d68ba11f 100644
--- a/hopp/simulation/technologies/csp/pySSC_daotk/ssc_wrap.py
+++ b/hopp/simulation/technologies/csp/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/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/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/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/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/grid.py b/hopp/simulation/technologies/grid.py
index d654f0dab..d54d489be 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):
         """
@@ -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/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_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/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/layout/wind_layout.py b/hopp/simulation/technologies/layout/wind_layout.py
index b0e046a0a..54436aec9 100644
--- a/hopp/simulation/technologies/layout/wind_layout.py
+++ b/hopp/simulation/technologies/layout/wind_layout.py
@@ -1,101 +1,326 @@
 from __future__ import annotations
-from typing import Union, NamedTuple
-import numpy as np
+from typing import Union, Optional
+
 import matplotlib.pyplot as plt
+import numpy as np
+from attrs import define, field, validators
 from shapely.geometry import Polygon, Point, MultiPolygon
+from shapely.geometry.base import BaseGeometry
 from shapely.affinity import scale
-import PySAM.Windpower as windpower
 
-from hopp.utilities.log import hybrid_logger as logger
+import PySAM.Windpower as windpower
+from hopp.simulation.base import BaseClass
 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):
-    """
-    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)
+from hopp.simulation.technologies.sites.site_shape_tools import plot_site_polygon
+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_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: float
-    border_offset: float
-    grid_angle: float
-    grid_aspect_power: float
-    row_phase_offset: float
 
+    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. 
+            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. 
+            ``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
+    """
 
-class WindCustomParameters(NamedTuple):
+    #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 = 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(
+                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
+
+    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
     """
-    direct user input of the x and y coordinates
+    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):
     """
+    Configuration class for 'custom' wind layout.
 
-    layout_x: list
-    layout_y: list
-
-
-class WindLayout:
+    Args:
+        layout_x (list[float]): x-coordinates of turbines
+        layout_y (list[float]): y-coordinates of turbines
     """
 
+    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, 
+                    WindGridParameters,
+                    None, 
+                    dict
+                ]
+            ): wind layout parameters for the corresponding `layout_mode`
+        turbine_rating_kW (float, Optional): rating of a single turbine in kW. if not provided, 
+            turbine power is estimated from the power-curve.
     """
-    def __init__(self,
-                 site_info: SiteInfo,
-                 wind_source: windpower.Windpower,
-                 layout_mode: str,
-                 parameters: Union[WindBoundaryGridParameters, WindCustomParameters, None],
-                 min_spacing: float = 200.,
-                 ):
-        """
-
+    site_polygon: Union[Polygon, BaseGeometry] 
+    _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,
+        WindGridParameters,
+        dict,
+    ]
+    
+    turbine_rating_kW: Optional[float] = field(default=None)
+
+    turb_pos_x: list[float] = field(init=False)
+    turb_pos_y: list[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
+        
+        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.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
-
         # 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':
+                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)
+            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):
-        self.min_spacing = max(self.min_spacing, self._system_model.value("wind_turbine_rotor_diameter") * 2)
+        """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`.
+        """
+    
+        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):
-        self._system_model.value("wind_farm_xCoordinates", self.turb_pos_x)
-        self._system_model.value("wind_farm_yCoordinates", self.turb_pos_y)
+        """Set the number of turbines. System capacity gets modified as a result.
+        """
+        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)
-        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))
+        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 * self.turbine_rating_kW
+                )
+            )
+        
 
     @property
     def rotor_diameter(self):
         return self._system_model.value("wind_turbine_rotor_diameter")
 
-    def reset_boundarygrid(self,
-                           n_turbines,
-                           parameters: WindBoundaryGridParameters,
-                           exclusions: Polygon = None):
-        """
+    def reset_boundarygrid(self, n_turbines, 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
@@ -103,29 +328,32 @@ 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.parameters.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),
-                ))
+                )
+            )
 
-        valid_wind_shape = subtract_turbine_exclusion_zone(self.min_spacing, wind_shape, turbine_positions)
+        valid_wind_shape = subtract_turbine_exclusion_zone(
+            self.parameters.min_spacing,
+            wind_shape, turbine_positions,
+        )
 
         # 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.min_spacing,
+            self.parameters.grid_angle,
+            self.parameters.grid_aspect_ratio,
+            self.parameters.row_phase_offset,
+            self.parameters.max_spacing,
+            self.parameters.min_spacing,
             max_num_interior_turbines,
         )
         turbine_positions.extend(grid_sites)
@@ -137,40 +365,43 @@ def reset_boundarygrid(self,
         self.turb_pos_x, self.turb_pos_y = xcoords, ycoords
         self._set_system_layout()
 
-    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
+    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.
 
-        :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.parameters.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:
-                        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 = []
@@ -181,42 +412,175 @@ def reset_grid(self,
         self.turb_pos_x, self.turb_pos_y = xcoords, ycoords
         self._set_system_layout()
 
-    def set_layout_params(self,
-                          wind_kw,
-                          params: Union[WindBoundaryGridParameters, WindCustomParameters, None],
-                          exclusions: Polygon = None):
-        self.parameters = params
-        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':
+    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:
+                # 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]
+            ycoords_grid = [point.y for point in grid_position_square]
+            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, WindGridParameters]
+            ],
+            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,
+                        WindGridParameters,
+                    ]
+                ]
+            ): 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"
+            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(
+                self._system_model.value("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)
+        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
+    def set_num_turbines(self, n_turbines: int):
+        """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,
-             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 = 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")
@@ -224,12 +588,11 @@ def plot(self,
             x, y = turb_pos_x[n], turb_pos_y[n]
             circle = plt.Circle(
                 (x, y),
-                radius=self.rotor_diameter/2.0,
-                # linewidth=linewidth * 10,
+                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/layout/wind_layout_tools.py b/hopp/simulation/technologies/layout/wind_layout_tools.py
index a2336ff20..a35204df6 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,
@@ -40,25 +41,40 @@ 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
+    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(
@@ -264,3 +280,172 @@ 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))<tol:
+                x_coords.append(np.round(x,n_decimals))
+                y_coords.append(np.round(y,n_decimals))
+    return x_coords,y_coords
+
+
+def adjust_site_for_box_grid_layout(site_polygon, nturbs, interrow_spacing, intrarow_spacing, row_phase_offset, grid_angle):
+    """Calculate gridded wind-turbine layout with turbines starting at bottom left corner of
+        site (xmin, ymin) and able to be placed along boundary.
+
+    Args:
+        site_polygon (BaseGeometry): Site polygon.
+        nturbs (int): number of wind turbines
+        interrow_spacing (float): distance between rows in meters
+        intrarow_spacing (float): distance between turbines along same row in meters
+        row_phase_offset (float): offset ratio of turbines along row from one row to the next.
+            Must be within range (0,1).
+        grid_angle (float | int): grid rotation angle in degrees where 0 is North, increasing clockwise. 
+
+    Returns:
+        2-element tuple containing
+
+        - **x** (List[float]): x-coordinates of turbines within site boundaries.
+        - **y** (List[float]): y-coordinates of turbines within site boundaries.
+    """
+    # NOTE: only works if row_phase_offset and grid_angle are both zero!
+    # shift params for y coordinates
+    # center_shift_y = site_polygon.centroid.y%interrow_spacing
+    if row_phase_offset!=0 and grid_angle!=0:
+        print("warning - this function is not validated for nonzero `row_phase_offset` and `grid_angle` (in `adjust_site_for_box_grid_layout()`)")
+    site_polygon,site_verts = rotate_shape(site_polygon,rotation_angle_deg=grid_angle)
+
+    diagonal_distance = calc_dist_between_two_points_cartesian(*site_polygon.bounds)
+    center_shift_y = site_polygon.centroid.y%interrow_spacing #-diagonal_distance/interrow_spacing
+    center_shift_x = -diagonal_distance/intrarow_spacing
+    center_point = Point(site_polygon.centroid.x + center_shift_x, site_polygon.centroid.y + center_shift_y)
+
+    site_polygon_adj = site_polygon.buffer(max([interrow_spacing,intrarow_spacing])/2)
+    turbine_locs = create_grid(site_polygon_adj,
+            center_point,
+            grid_angle,
+            intrarow_spacing,
+            interrow_spacing,
+            row_phase_offset,
+            nturbs
+            )
+    xcoords_grid = [point.x for point in turbine_locs]
+    ycoords_grid = [point.y for point in turbine_locs]
+    
+    site_boundaries = site_polygon_adj = site_polygon.buffer(min([interrow_spacing,intrarow_spacing])/2)
+    x,y = check_turbines_in_site(xcoords_grid,ycoords_grid,site_boundaries)
+    return x,y
+
+def check_layout_for_unique_points(layout_x,layout_y):
+    """Remove duplicate coordinates.
+
+    Args:
+        layout_x (List[float]): x-coordinates of turbines
+        layout_y (List[float]): y-coordinates of turbines
+
+    Returns:
+        2-element tuple containing
+
+        - **x_coords** (List[float]): x-coordinates of turbines with unique coordinates.
+        - **y_coords** (List[float]): y-coordinates of turbines with unique coordinates.
+    """
+    df = pd.DataFrame({"x": layout_x, "y": layout_y}).drop_duplicates()
+    return df["x"].to_list(),df["y"].to_list()
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/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 7a8ea8f0b..a26621e34 100644
--- a/hopp/simulation/technologies/resource/__init__.py
+++ b/hopp/simulation/technologies/resource/__init__.py
@@ -1,7 +1,10 @@
 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
 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/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/tidal_resource.py b/hopp/simulation/technologies/resource/tidal_resource.py
new file mode 100644
index 000000000..13d87cefa
--- /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/resource/wave_resource.py b/hopp/simulation/technologies/resource/wave_resource.py
index 370df958c..a71f8afbf 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.
+        
+        Notes:
+            Future task: 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()
diff --git a/hopp/simulation/technologies/resource/wind_resource.py b/hopp/simulation/technologies/resource/wind_resource.py
index 34c60f4b4..2110d27a0 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, 
@@ -33,29 +36,38 @@ 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
+            self.hub_height_meters = wind_turbine_hub_ht
+            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')
-
-        self.__dict__.update(kwargs)
+        if path_resource.parts[-1]!="wind":
+            self.path_resource = self.path_resource / 'wind'
 
         self.file_resource_heights = None
         self.update_height(wind_turbine_hub_ht)
@@ -96,18 +108,18 @@ 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 +195,11 @@ 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/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..9ae4a5cf2 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
@@ -17,7 +17,10 @@
     SolarResource,
     WindResource,
     WaveResource,
-    ElectricityPrices
+    TidalResource,
+    ElectricityPrices,
+    HPCWindData,
+    HPCSolarData,
 )
 from hopp.tools.layout.plot_tools import plot_shape
 from hopp.utilities.log import hybrid_logger as logger
@@ -28,7 +31,8 @@
 )
 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)):
         if i == 0:
@@ -48,7 +52,15 @@ 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 "".
+        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.
+        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".
         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 [].
@@ -57,14 +69,57 @@ 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
+        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.
+            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.
     """
     # User provided
     data: dict
+    """dictionary of site info data with key as:
+
+        - **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.
+    """
+
+    
     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")
+    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())
@@ -73,23 +128,30 @@ 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"]))
 
+    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)
-    solar_resource: Optional[SolarResource] = field(init=False, default=None)
-    wind_resource: Optional[WindResource] = field(init=False, default=None)
-    wave_resoure: Optional[WaveResource] = 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_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)
     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
@@ -99,30 +161,32 @@ 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.
+            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.
             interval (int): Number of minutes per time interval.
             urdb_label (str): Link to `Utility Rate DataBase <https://openei.org/wiki/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.
         """
-        set_nrel_key_dot_env()
+        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")
@@ -130,21 +194,34 @@ 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 'elev' in data:
+            self.elev = data['elev']
+        
         if self.solar:
-            self.solar_resource = SolarResource(data['lat'], data['lon'], data['year'], filepath=self.solar_resource_file)
+            self.solar_resource = self.initialize_solar_resource(data)
             self.n_timesteps = len(self.solar_resource.data['gh']) // 8760 * 8760
+            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.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 = WindResource(data['lat'], data['lon'], data['year'], wind_turbine_hub_ht=self.hub_height,
-                                                filepath=self.wind_resource_file, source=self.wind_resource_origin)
+            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
@@ -166,14 +243,153 @@ 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.
 
-    # 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..73893ffd5
--- /dev/null
+++ b/hopp/simulation/technologies/sites/site_shape_tools.py
@@ -0,0 +1,256 @@
+from shapely.geometry import Polygon, MultiPolygon, box
+import numpy as np
+import pandas as pd
+from hopp.tools.layout.plot_tools import plot_shape
+import matplotlib.pyplot as plt
+
+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
+
+
+def plot_site_polygon(
+    site_polygon,
+    figure=None,
+    axes=None,
+    border_color=(0, 0, 0),
+    alpha=0.95,
+    linewidth=1.0
+    ):
+        bounds = site_polygon.bounds
+        site_sw_bound = np.array([bounds[0], bounds[1]])
+        site_ne_bound = np.array([bounds[2], bounds[3]])
+        site_center = .5 * (site_sw_bound + site_ne_bound)
+        max_delta = max(site_ne_bound - site_sw_bound)
+        reach = (max_delta / 2) * 1.3
+        min_plot_bound = site_center - reach
+        max_plot_bound = site_center + reach
+
+        if not figure and not axes:
+            figure = plt.figure(1)
+            axes = figure.add_subplot(111)
+
+        axes.set_aspect('equal')
+        axes.set(xlim=(min_plot_bound[0], max_plot_bound[0]), ylim=(min_plot_bound[1], max_plot_bound[1]))
+        plot_shape(figure, axes, site_polygon, '--', color=border_color, alpha=alpha, linewidth=linewidth / 2)
+        if isinstance(site_polygon, Polygon):
+            shape = [site_polygon]
+        elif isinstance(site_polygon, MultiPolygon):
+            shape = site_polygon.geoms
+        for geom in shape:    
+            xs, ys = geom.exterior.xy    
+            plt.fill(xs, ys, alpha=0.3, fc='g', ec='none')
+
+        plt.tick_params(which='both', labelsize=15)
+        plt.xlabel('x (m)', fontsize=15)
+        plt.ylabel('y (m)', fontsize=15)
+
+        return figure, axes
\ No newline at end of file
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 0af69c911..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
@@ -18,22 +16,20 @@ 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 +51,7 @@ 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 +150,7 @@ 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 +176,7 @@ 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 +186,7 @@ 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
@@ -217,27 +212,23 @@ 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/hopp/simulation/technologies/wind/floris.py b/hopp/simulation/technologies/wind/floris.py
index a507c706e..a8fe10b6b 100644
--- a/hopp/simulation/technologies/wind/floris.py
+++ b/hopp/simulation/technologies/wind/floris.py
@@ -1,68 +1,93 @@
-# 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 hopp.simulation.base import BaseClass
 from hopp.simulation.technologies.sites import SiteInfo
-from hopp.type_dec import resource_file_converter
-
 # 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
+)
+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()
 
     _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 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)
+    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)
+    
+    turb_velocities: np.ndarray = field(init = False)
+    turb_powers: np.ndarray = 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!!!!!
+        """Set-up and initialize floris_config and floris model. This method does the following:
 
-        if floris_input_file is None:
+        1) check that floris config is provided
+        2) load floris config if needed
+        3) modify air density in floris config if needed
+        4) initialize attributes from floris config and update floris config as needed
+        5) initialize floris model
+
+        Raises:
+            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:
             raise ValueError("A timestep is required.")
 
-        # the above change is a temporary patch to bridge to refactor floris
+        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)
+        if self.config.adjust_air_density_for_elevation and self.site.elev is not None:
+            rho = calculate_air_density(self.site.elev)
+            floris_config["flow_field"].update({"air_density":rho})
+        
+        floris_config = self.initialize_from_floris(floris_config)
+        
+        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()
-
-        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)
-        self.turb_rating = self.config.turbine_rating_kw
         
-        self.wind_turbine_rotor_diameter = self.fi.core.farm.rotor_diameters[0]
+        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.system_capacity = self.nTurbs * self.turb_rating
 
-        # turbine power curve (array of kW power outputs)
-        self.wind_turbine_powercurve_powerout = []
-
         # time to simulate
         if len(self.config.timestep) > 0:
             self.start_idx = self.config.timestep[0]
@@ -70,58 +95,153 @@ 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 initialize_from_floris(self, floris_config):
+        """Initialize wind turbine parmeters and set in floris_config.
 
-        self.initialize_from_floris()
+        Args:
+            floris_config (dict): floris input dictionary
 
-    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
+        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
 
-    def value(self, name: str, set_value=None):
+
+        Returns:
+            dict: updated floris_config
         """
-        if set_value = None, then retrieve value; otherwise overwrite variable's value
+        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)
+        # 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)
+        
+        # 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:
+                msg = (
+                    f"Input turbine rating ({self.config.turbine_rating_kw} kW) does not match "
+                    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
+        
+       
+        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:
+        if set_value is not None:
             self.__setattr__(name, set_value)
         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]
+    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):
+        """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.
 
-        return speeds, wind_dirs
+        Args:
+            param (list[str]): list of parameter keys in FlorisModel to retrieve.
 
-    def execute(self, project_life):
+        Returns:
+            any: value of FlorisModel parameter 
+        """
+        return self.fi.get_param(param)
 
-        print('Simulating wind farm output in FLORIS...')
+    def execute(self, project_life):
+        """Simulate wind farm performance using floris.
 
+        Args:
+            project_life (int): unused project life in years
+        """
+        
+        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 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)
@@ -135,17 +255,24 @@ 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)
-        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_velocities = self.fi.turbine_average_velocities
-        self.annual_energy_pre_curtailment_ac = self.annual_energy
+        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):
         """
@@ -155,4 +282,76 @@ def export(self):
             'system_capacity': self.system_capacity,
             'annual_energy': self.annual_energy,
         }
-        return config
\ No newline at end of file
+        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):
+        """Set wind farm layout coordinates and update system capacity and number 
+        of turbines.
+
+        Args:
+            xcoords (list[float]): x-coordinates of wind turbines in meters.
+            ycoords (list[float]): y-coordinates of wind turbines in meters.
+
+        Raises:
+            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.nTurbs = len(xcoords)
+        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 6b512439b..4616a9fdd 100644
--- a/hopp/simulation/technologies/wind/wind_plant.py
+++ b/hopp/simulation/technologies/wind/wind_plant.py
@@ -1,20 +1,30 @@
 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 numpy as np
 
+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.financial import CustomFinancialModel, FinancialModelType
+from hopp.simulation.technologies.layout.wind_layout import (
+    WindLayout, 
+    WindBoundaryGridParameters, 
+    WindBasicGridParameters,
+    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.layout.wind_layout import WindLayout, WindBoundaryGridParameters
-from hopp.simulation.technologies.financial import CustomFinancialModel, FinancialModelType
+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
@@ -23,49 +33,95 @@ 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'
-        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)
-        fin_model: Optional financial model. Can be any of the following:
+        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
+            - '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.
+        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)
+        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
+        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]] = field(default=None)
+    layout_params: Optional[
+        Union[
+            dict, WindBoundaryGridParameters, WindBasicGridParameters, WindCustomParameters, WindGridParameters
+        ]
+    ] = 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)
+    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"]),
+        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)
-    operational_losses: float = field(default = 12.83, validator=range_val(0, 100))
-    timestep: Optional[Tuple[int, int]] = 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))
+    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
@@ -85,7 +141,8 @@ 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)
@@ -95,14 +152,26 @@ 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)
+                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)
@@ -125,28 +194,96 @@ 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)
-
-        if isinstance(self.config.layout_params, dict):
-            layout_params = WindBoundaryGridParameters(**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, system_model, self.config.layout_mode, layout_params)
+        self._layout = WindLayout(self.site.polygon, system_model, layout_mode, layout_params)
 
         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.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.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}."
+                )
+
+                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:
         try:
@@ -178,6 +315,20 @@ def num_turbines(self):
 
     @num_turbines.setter
     def num_turbines(self, n_turbines: int):
+        
+        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
@@ -207,9 +358,14 @@ 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):
         """
@@ -229,20 +385,24 @@ 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))
@@ -253,9 +413,12 @@ 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))
@@ -266,7 +429,8 @@ 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
         """
@@ -295,3 +459,26 @@ 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:
+                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/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")
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)
 
diff --git a/hopp/tools/resource/wind_tools.py b/hopp/tools/resource/wind_tools.py
new file mode 100644
index 000000000..1bd12a10a
--- /dev/null
+++ b/hopp/tools/resource/wind_tools.py
@@ -0,0 +1,203 @@
+from scipy.constants import R, g, convert_temperature
+import numpy as np
+
+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
+    """
+    
+    # Reference elevation at sea level (m)
+    elevation_sea_level = 0.0  
+    
+    # Convert temperature to Kelvin
+    T_ref_K = convert_temperature([T_REF], "C", "K")[0]
+    
+    # Exponent value used in equation below
+    e = g * (MOLAR_MASS_AIR / 1e3) / (R * LAPSE_RATE)
+    
+    # Calculate air density at site elevation
+    rho = RHO_0 * ((T_ref_K - ((elevation_m - elevation_sea_level) * LAPSE_RATE)) / T_ref_K) ** (e - 1)
+    return rho
+
+def calculate_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:
+        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
+
+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 and wind direction data
+    idx_ws = [ii for ii, field in enumerate(wind_resource.data['fields']) if field == 3]
+    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:
+        speeds = data[:, idx_ws[0]]
+        wind_dirs = data[:, idx_wd[0]]
+        return speeds, wind_dirs
+
+    # 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 
+    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 and wind direction data
+    idx_ws = [ii for ii, field in enumerate(wind_resource.data['fields']) if field == 3]
+    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:
+        speeds = data[:, idx_ws[0]]
+        wind_dirs = data[:, idx_wd[0]]
+        return speeds, wind_dirs
+    
+    # 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'])
+
+    # 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/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 76156a499..0c95f2caf 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",
@@ -48,7 +48,9 @@ dependencies = [
     "attrs",
     "utm",
     "pyyaml-include",
-    "profast"
+    "profast",
+    "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/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/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/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..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
@@ -46,8 +47,8 @@ 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)
@@ -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
             }, 
@@ -193,23 +194,18 @@ 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, 
@@ -235,7 +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
             },
@@ -263,10 +259,10 @@ 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)
+        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)
@@ -279,22 +275,22 @@ 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
@@ -323,7 +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
             },
@@ -433,14 +429,14 @@ 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 +444,6 @@ 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': {
@@ -476,7 +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
             },
@@ -509,7 +500,7 @@ 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 8ffbaa85c..ed819d8b3 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)
@@ -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)
 
@@ -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 = {
@@ -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}
diff --git a/tests/hopp/test_hybrid.py b/tests/hopp/test_hybrid.py
index 50d28d597..24816801f 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")
@@ -329,16 +362,17 @@ 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)
 
 
-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"],
@@ -359,10 +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,
@@ -384,7 +417,7 @@ def test_hybrid_wave_battery(hybrid_config, 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):
@@ -405,21 +438,223 @@ 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_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,
+        },
+    }
 
-def test_hybrid_wind_only_floris(hybrid_config, subtests):
+    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_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"
     )
     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
     hi = HoppInterface(hybrid_config)
@@ -430,15 +665,18 @@ 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(3592293, 1e-3)
+        assert npvs.wind == approx(-9193785, 1e-3)
     with subtests.test("hybrid npv"):
-        assert npvs.hybrid == approx(2108687, 1e-3)
+        assert npvs.hybrid == approx(-10847221, 1e-3)
 
 def test_hybrid_pv_only(hybrid_config, subtests):
     technologies = hybrid_config["technologies"]
@@ -631,8 +869,8 @@ 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 +882,7 @@ 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 +909,10 @@ 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 +928,7 @@ 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 +942,7 @@ 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 +951,11 @@ 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 +976,8 @@ 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 +987,7 @@ 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 +1025,8 @@ 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 +1058,10 @@ 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 +1075,7 @@ 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 +1084,8 @@ 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 +1135,7 @@ def test_hybrid_user_instantiated(site, subtests):
             },
             "grid": {
                 "interconnect_kw": interconnect_kw,
-                "fin_model": "GenericSystemSingleOwner",
+                "fin_model": "CustomGenerationProfileSingleOwner",
                 "ppa_price": 0.01,
             },
         }
@@ -990,22 +1222,22 @@ 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 +1267,13 @@ 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 +1285,9 @@ 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 +1303,13 @@ 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 +1576,7 @@ 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 +1672,8 @@ 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 +1714,104 @@ 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 +1830,39 @@ 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)
+
+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
+    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)
+    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_layout.py b/tests/hopp/test_layout.py
index b0d531928..52bb1e9e2 100644
--- a/tests/hopp/test_layout.py
+++ b/tests/hopp/test_layout.py
@@ -11,13 +11,17 @@
 
 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
 
 from hopp.utilities.utils_for_tests import create_default_site_info
-
+from hopp import ROOT_DIR
 
 @pytest.fixture
 def site():
@@ -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 +88,87 @@ 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, subtests):
+    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)
+
+    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()
+    }
+    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
+    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)):
+        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_solar_layout(site):
@@ -260,14 +343,14 @@ 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 +386,30 @@ 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 +419,14 @@ 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 +439,14 @@ 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 +468,6 @@ 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 +488,14 @@ 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 +534,7 @@ 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 +547,7 @@ 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 +577,12 @@ 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 +598,27 @@ 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))
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
diff --git a/tests/hopp/test_site_info.py b/tests/hopp/test_site_info.py
index ce3698791..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
@@ -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 +158,374 @@ 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
+
+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..e186ce83f
--- /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)
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_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_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
diff --git a/tests/hopp/test_wind.py b/tests/hopp/test_wind.py
index 00caa7784..d76ee0181 100644
--- a/tests/hopp/test_wind.py
+++ b/tests/hopp/test_wind.py
@@ -1,11 +1,11 @@
-from pytest import fixture, approx
 import math
 
 import PySAM.Windpower as windpower
-
+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
@@ -21,25 +21,30 @@ 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 +52,17 @@ 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]
@@ -129,46 +136,74 @@ 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"
+    )
+    
+    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
+    
 
 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})
+    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_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"
+    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': 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)
+    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)
+    
+    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):
+    # this will fail now
     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})
+    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)
-    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)
+    
+    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 
 
-    # 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_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
diff --git a/tests/hopp/test_wind_resource_tools.py b/tests/hopp/test_wind_resource_tools.py
new file mode 100644
index 000000000..40cd2ec0a
--- /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,
+    calculate_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(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(elevation)
+    assert air_dens == approx(1.05, rel = 1e-3)
+
+def test_sea_level_air_density_losses():
+    elevation = 0.0 #meters
+    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_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