Hycon v0.7

.github/dependabot.yml

@@ -0,0 +1,12 @@
+version: 2
+updates:
+- package-ecosystem: "pip"
+  directory: "/" # Location of package manifests
+  target-branch: "develop"
+  schedule:
+    interval: "monthly"
+  labels:
+    - "package"
+  ignore:
+    - dependency-name: "*"
+      update-types: ["version-update:semver-minor", "version-update:semver-patch"]

.github/workflows/continuous-integration-workflow.yaml

@@ -26,7 +26,7 @@ jobs:
     - name: Run ruff
       run: |
         ruff check .
-        ruff format
+        ruff format --check
     - name: Run tests and collect coverage
       run: |
         # -rA displays the captured output for all tests after they're run

.github/workflows/deploy-pages.yaml

@@ -27,9 +27,8 @@ jobs:
 
     # Build the book
     - name: Build the book
-      working-directory: ${{runner.workspace}}/hycon/docs/
       run: |
-        jupyter-book build .
+        jupyter-book build docs/
 
     # Push the book's HTML to github-pages
     - name: GitHub Pages action

LICENSE.txt

@@ -1,6 +1,6 @@
 BSD 3-Clause License
 
-Copyright (c) 2025 Alliance for Sustainable Energy, LLC and Colorado School of Mines.
+Copyright (c) 2025 Alliance for Energy Innovation, LLC and Colorado School of Mines.
 
 Redistribution and use in source and binary forms, with or without modification, are permitted
 provided that the following conditions are met:

README.md

@@ -11,26 +11,26 @@ and creates an entry point for the development of more advanced controllers.
 
 Hycon will interface with various simulation testbeds and lower level 
 controllers, including:
-- [Hercules](https://github.com/NREL/hercules)
+- [Hercules](https://github.com/NatLabRockies/hercules)
 - [FAST.Farm](https://github.com/OpenFAST/openfast)
-- [ROSCO](https://github.com/NREL/rosco)
+- [ROSCO](https://github.com/NatLabRockies/rosco)
 
 Hycon controllers will also call on design tools such as
-[FLORIS](https://github.com/NREL/floris).
+[FLORIS](https://github.com/NatLabRockies/floris).
 
 ## WETO software
 
 Hycon is primarily developed with the support from the U.S. Department of Energy and
-is part of the [WETO Software Stack](https://nrel.github.io/WETOStack).
+is part of the [WETO Software Stack](https://natlabrockies.github.io/WETOStack).
 For more information and other integrated modeling software, see:
 
-- [Portfolio Overview](https://nrel.github.io/WETOStack/portfolio_analysis/overview.html)
-- [Entry Guide](https://nrel.github.io/WETOStack/_static/entry_guide/index.html)
+- [Portfolio Overview](https://natlabrockies.github.io/WETOStack/portfolio_analysis/overview.html)
+- [Entry Guide](https://natlabrockies.github.io/WETOStack/_static/entry_guide/index.html)
 - [Wind Farm Controls Workshop](https://www.youtube.com/watch?v=f-w6whxIBrA&list=PL6ksUtsZI1dwRXeWFCmJT6cEN1xijsHJz)
 
 NLR's software record for Hycon is SWR-25-54.
 
 ## Documentation
 
 Documentation for Hycon, including installation instructions, can be found
-[here](https://nrel.github.io/hycon/intro.html).
+[here](https://natlabrockies.github.io/hycon/intro.html).

docs/_config.yml

@@ -23,7 +23,7 @@ bibtex_bibfiles:
 
 # Information about where the book exists on the web
 repository:
-  url: https://github.com/NREL/hycon
+  url: https://github.com/NatLabRockies/hycon
   path_to_book: docs
   branch: main
 

docs/code_development.md

@@ -1,6 +1,6 @@
 # Code development
 To contribute to Hycon, please consider forking the main github repository,
-with the [NLR repo](https://github.com/NREL/hycon) as an 
+with the [NLR repo](https://github.com/NatLabRockies/hycon) as an 
 upstream remote. See the [Installation instructions](install_instructions) 
 for details about how to set up your repository as a developer.
 

docs/controllers.md

@@ -43,8 +43,7 @@ However, is a useful comparison case for the WindFarmPowerTrackingController
 Closed-loop wind farm-level power controller that distributes a farm-level 
 power reference among the wind turbines in a farm and adjusts the requests made
 from each turbine depending on whether the power reference has been met. 
-Developed under the [A2e2g project](https://github.com/NREL/a2e2g), with 
-further details provided in 
+Further details provided in 
 [Sinner et al.](https://pubs.aip.org/aip/jrse/article/15/5/053304/2913100).
 
 Integral action, as well as gain scheduling based on turbine saturation, has been disabled as 

docs/install_instructions.md

@@ -3,9 +3,9 @@
 
 Hycon is _not_ designed to be used as a stand-alone package. Most likely, 
 you'll want to add Hycon to an existing conda environment that contains your
-simulation testbed, such as [Hercules](https://github.com/NREL/hercules). 
+simulation testbed, such as [Hercules](https://github.com/NatLabRockies/hercules). 
 For example, see the 
-[Hercules installation instructions](https://nrel.github.io/hercules/install_instructions.html)
+[Hercules installation instructions](https://natlabrockies.github.io/hercules/install_instructions.html)
 for how to set up an appropriate conda environment.
 
 (installation_general_users)=
@@ -16,7 +16,7 @@ be sufficient to install Hycon (presumably, after activating your conda
 environment):
 
 ```
-git clone https://github.com/NREL/hycon
+git clone https://github.com/NatLabRockies/hycon
 pip install hycon/
 ```
 
@@ -32,7 +32,7 @@ git clone https://github.com/your-github-id/hycon
 pip install -e "hycon/[develop]"
 ```
 To contribute back to the base repository 
-https://github.com/NREL/hycon, please do the following:
+https://github.com/NatLabRockies/hycon, please do the following:
 - Create a branch from the base repository's `develop` branch on your fork 
 containing your code changes (e.g. `your-github-id:feature/your-new-feature`)
 - Open a pull request into the base repository's `NREL:develop` branch, and provide 
@@ -49,7 +49,7 @@ For more information on what your pull request should contain, see
 (installation_examples)=
 ## To run examples
 
-All Hycon examples run in the [Hercules](https://github.com/NREL/hercules) simulation environment.
+All Hycon examples run in the [Hercules](https://github.com/NatLabRockies/hercules) simulation environment.
 To run the examples, you will need to additionally install Hercules. See the 
-[Hercules installation instructions](https://nrel.github.io/hercules/install_instructions.html)
+[Hercules installation instructions](https://natlabrockies.github.io/hercules/install_instructions.html)
 for details.

docs/wake_steering_design.md

@@ -3,11 +3,11 @@
 The `hycon.design_tools.wake_steering_design` module provides various tools for the design of yaw
 offset lookup tables for "open-loop" wake steering. The two primary functions are `build_simple_wake_steering_lookup_table` and `build_uncertain_wake_steering_lookup_table`, both of
 which take an instantiated
-[`FlorisModel`](https://nrel.github.io/floris/_autosummary/floris.floris_model.html),
+[`FlorisModel`](https://natlabrockies.github.io/floris/_autosummary/floris.floris_model.html),
 along with various design parameters, and return a pandas DataFrame `df_opt` containing the optimal
 yaw offset angles for each wind turbine. Under the hood, both functions run an optimization using
 FLORIS'
-[`YawOptimizerSR`](https://nrel.github.io/floris/_autosummary/floris.optimization.yaw_optimization.yaw_optimizer_sr.html) methodology. The `uncertain` version takes into account wind direction
+[`YawOptimizerSR`](https://natlabrockies.github.io/floris/_autosummary/floris.optimization.yaw_optimization.yaw_optimizer_sr.html) methodology. The `uncertain` version takes into account wind direction
 uncertainty via the second required argument `wd_std`, representing the wind direction standard
 deviation.
 

examples/battery_control_comparison/runscript.py

@@ -17,7 +17,7 @@
 df = pd.read_csv("../example_inputs/lmp_rt.csv")
 df = df.rename(columns={"interval_start_utc": "time_utc"}).drop(columns=["market", "lmp"])
 # Create reference that steps up and down each five minutes
-reference_input_sequence = np.tile(np.array([20000, 0]), int(len(df)/2))
+reference_input_sequence = np.tile(np.array([20000, 0]), int(len(df) / 2))
 df["battery_power_reference"] = reference_input_sequence
 # Add end of step info
 df["time_utc"] = pd.to_datetime(df["time_utc"])
@@ -26,9 +26,9 @@
 df = pd.merge(df, df_2, how="outer").sort_values("time_utc").reset_index(drop=True)
 df.to_csv("power_reference.csv", index=False)
 
+
 # Create some functions for simulating for simplicity
 def simulate(soc_0, clipping_thresholds, gain):
-
     h_dict = load_hercules_input("hercules_input.yaml")
     h_dict["battery"]["initial_conditions"]["SOC"] = soc_0
 
@@ -42,11 +42,9 @@ def simulate(soc_0, clipping_thresholds, gain):
         controller_parameters={"k_batt": gain, "clipping_thresholds": clipping_thresholds},
     )
     controller = HybridSupervisoryControllerMultiRef(
-        battery_controller=battery_controller,
-        interface=interface,
-        input_dict=hmodel.h_dict
+        battery_controller=battery_controller, interface=interface, input_dict=hmodel.h_dict
     )
-    
+
     hmodel.assign_controller(controller)
 
     # Run the simulation
@@ -61,16 +59,19 @@ def simulate(soc_0, clipping_thresholds, gain):
 
     return time, power_sequence, soc_sequence, reference_sequence
 
+
 def plot_results_soc(ax, color, time, power_sequence, soc_sequence):
-    ax[0].plot(time, power_sequence, color=color,
-               label="SOC initial: {:.3f}".format(soc_sequence[0]))
+    ax[0].plot(
+        time, power_sequence, color=color, label="SOC initial: {:.3f}".format(soc_sequence[0])
+    )
     ax[1].plot(time, soc_sequence, color=color, label="SOC")
 
+
 def plot_results_gain(ax, color, time, power_sequence, soc_sequence, gain):
-    ax[0].plot(time, power_sequence, color=color,
-               label="Gain: {:.3f}".format(gain))
+    ax[0].plot(time, power_sequence, color=color, label="Gain: {:.3f}".format(gain))
     ax[1].plot(time, soc_sequence, color=color, label="SOC")
 
+
 ### SOC clipping
 
 # Establish simulation options for demonstrating SOC clipping
@@ -79,66 +80,76 @@ def plot_results_gain(ax, color, time, power_sequence, soc_sequence, gain):
 clipping_thresholds = [0.1, 0.2, 0.8, 0.9]
 
 # Run simulations and create plots for SOC clipping
-fig, ax = plt.subplots(2,1,sharex=True)
-fig.set_size_inches(10,5)
+fig, ax = plt.subplots(2, 1, sharex=True)
+fig.set_size_inches(10, 5)
 for soc_0, col in zip(starting_socs, colors):
     time, pow, soc, ref = simulate(soc_0, clipping_thresholds, 0.01)
-    plot_results_soc(ax, col, time/60, pow, soc)
+    plot_results_soc(ax, col, time / 60, pow, soc)
 
 # Add references and plot aesthetics
-ax[0].plot(time/60, ref, color="black", linestyle="dashed", label="Reference")
+ax[0].plot(time / 60, ref, color="black", linestyle="dashed", label="Reference")
 ax[0].set_ylabel("Power [kW]")
 ax[0].legend()
 
 ax[1].set_ylabel("SOC [-]")
 ax[1].set_xlabel("Time [min]")
-ax[1].set_xlim([time[0]/60, time[-1]/60])
+ax[1].set_xlim([time[0] / 60, time[-1] / 60])
 ax[0].grid()
 ax[1].grid()
-ax[0].plot([time[0]/60, time[-1]/60], [20000, 20000], color="black", linestyle="dotted")
-ax[0].plot([time[0]/60, time[-1]/60], [-20000, -20000], color="black", linestyle="dotted")
+ax[0].plot([time[0] / 60, time[-1] / 60], [20000, 20000], color="black", linestyle="dotted")
+ax[0].plot([time[0] / 60, time[-1] / 60], [-20000, -20000], color="black", linestyle="dotted")
 
 # Add shading for the different clipping regions
-ax[1].fill_between(time/60, 0, clipping_thresholds[0], color="black", alpha=0.2, edgecolor=None)
-ax[1].fill_between(time/60, clipping_thresholds[0], clipping_thresholds[1], color="black",
-                   alpha=0.1, edgecolor=None)
-ax[1].fill_between(time/60, clipping_thresholds[2], clipping_thresholds[3], color="black",
-                   alpha=0.1, edgecolor=None)
-ax[1].fill_between(time/60, clipping_thresholds[3], 1, color="black", alpha=0.2, edgecolor=None)
-ax[1].set_ylim([0,1])
+ax[1].fill_between(time / 60, 0, clipping_thresholds[0], color="black", alpha=0.2, edgecolor=None)
+ax[1].fill_between(
+    time / 60,
+    clipping_thresholds[0],
+    clipping_thresholds[1],
+    color="black",
+    alpha=0.1,
+    edgecolor=None,
+)
+ax[1].fill_between(
+    time / 60,
+    clipping_thresholds[2],
+    clipping_thresholds[3],
+    color="black",
+    alpha=0.1,
+    edgecolor=None,
+)
+ax[1].fill_between(time / 60, clipping_thresholds[3], 1, color="black", alpha=0.2, edgecolor=None)
+ax[1].set_ylim([0, 1])
 if save_figs:
     fig.savefig(
-        "../../docs/graphics/battery-soc-clipping.png",
-        format="png", bbox_inches="tight", dpi=300
+        "../../docs/graphics/battery-soc-clipping.png", format="png", bbox_inches="tight", dpi=300
     )
 
 ### k_batt gain
 
 # Demonstrate different gains
 gains = [0.001, 0.01, 0.1]
-fig, ax = plt.subplots(2,1,sharex=True)
-fig.set_size_inches(10,5)
+fig, ax = plt.subplots(2, 1, sharex=True)
+fig.set_size_inches(10, 5)
 for gain, col in zip(gains, colors):
     time, pow, soc, ref = simulate(0.5, clipping_thresholds, gain)
-    plot_results_gain(ax, col, time/60, pow, soc, gain)
+    plot_results_gain(ax, col, time / 60, pow, soc, gain)
 
 # Add references and plot aesthetics
-ax[0].plot(time/60, ref, color="black", linestyle="dashed", label="Reference")
+ax[0].plot(time / 60, ref, color="black", linestyle="dashed", label="Reference")
 ax[0].set_ylabel("Power [kW]")
 ax[0].legend()
 
 ax[1].set_ylabel("SOC [-]")
 ax[1].set_xlabel("Time [min]")
-ax[1].set_xlim([0, 15]) # Show only the first 15 to highlight differences
+ax[1].set_xlim([0, 15])  # Show only the first 15 to highlight differences
 ax[1].set_ylim([0.45, 0.51])
 ax[0].grid()
 ax[1].grid()
-ax[0].plot([time[0]/60, time[-1]/60], [20000, 20000], color="black", linestyle="dotted")
-ax[0].plot([time[0]/60, time[-1]/60], [-20000, -20000], color="black", linestyle="dotted")
+ax[0].plot([time[0] / 60, time[-1] / 60], [20000, 20000], color="black", linestyle="dotted")
+ax[0].plot([time[0] / 60, time[-1] / 60], [-20000, -20000], color="black", linestyle="dotted")
 if save_figs:
     fig.savefig(
-        "../../docs/graphics/battery-varying-gains.png",
-        format="png", bbox_inches="tight", dpi=300
+        "../../docs/graphics/battery-varying-gains.png", format="png", bbox_inches="tight", dpi=300
     )
 
 plt.show()

examples/battery_market_revenue_control/plot_outputs.py

@@ -110,14 +110,14 @@ def plot_outputs():
     ax = axarr[1]
     ax.plot(
         df["time"],
-        df["battery.power"]/1e3, # Base unit: kW
+        df["battery.power"] / 1e3,  # Base unit: kW
         label="Battery output",
         color="k",
         linewidth=1.0,
     )
     ax.plot(
         df["time"],
-        df["battery.power_setpoint"]/1e3, # Base unit: kW
+        df["battery.power_setpoint"] / 1e3,  # Base unit: kW
         label="Battery setpoint",
         color="k",
         linestyle=":",
@@ -129,27 +129,20 @@ def plot_outputs():
 
     color = "C0"
     ax2.set_ylabel("State of charge [-]", color=color)
-    ax2.plot(
-        df["time"],
-        df["battery.soc"],
-        color=color
-    )
+    ax2.plot(df["time"], df["battery.soc"], color=color)
     ax2.tick_params(axis="y", labelcolor=color)
 
     for ax in axarr:
         ax.grid(True)
         ax.legend(loc="upper right")
 
     # Compute total revenue on real-time market
-    df["revenue_rt"] = (
-        df["battery.power"]/1e3
-        * df["external_signals.lmp_rt"]
-        / 3600
-    )
+    df["revenue_rt"] = df["battery.power"] / 1e3 * df["external_signals.lmp_rt"] / 3600
     print("Real-time revenue over simulation: ${:.1f}".format(df["revenue_rt"].sum()))
 
     return fig
 
+
 if __name__ == "__main__":
     fig = plot_outputs()
     # fig.savefig("../../docs/graphics/battery-market.png", dpi=300, format="png")

examples/battery_market_revenue_control/runscript.py

@@ -17,20 +17,17 @@
 
 # Generate the LMP data needed for the simulation
 df_lmp = generate_locational_marginal_price_dataframe_from_gridstatus(
-    pd.read_csv("../example_inputs/lmp_da.csv"),
-    pd.read_csv("../example_inputs/lmp_rt.csv")
+    pd.read_csv("../example_inputs/lmp_da.csv"), pd.read_csv("../example_inputs/lmp_rt.csv")
 )
 df_lmp.to_csv("lmp_data.csv", index=False)
 
 # Load the input file and establish the Hercules model
 hmodel = HerculesModel("hercules_input.yaml")
 
 # Establish the interface and controller, assign to the Hercules model
-interface=HerculesInterface(hmodel.h_dict)
+interface = HerculesInterface(hmodel.h_dict)
 controller = HybridSupervisoryControllerMultiRef(
-    battery_controller=BatteryPriceSOCController(
-        interface=interface, input_dict=hmodel.h_dict
-    ),
+    battery_controller=BatteryPriceSOCController(interface=interface, input_dict=hmodel.h_dict),
     interface=HerculesInterface(hmodel.h_dict),
     input_dict=hmodel.h_dict,
 )