Ard integration for wind farm combined performance and cost model #312

kbrunik · 2025-10-26T18:47:53Z

Would it be possible to show how a user would create an Ard problem instead of just the H2Integrate problem?

I referenced the Ard docs where the user can find that information if desired, and I also added the command to show how to view the N2 of the Ard sub problem within an H2Integrate model.

elenya-grant · 2025-11-05T18:33:07Z

Could you specify what wind resource data is needed by ard and what the units should be? like that and needs wind speeds in m/s or whatever? Cause if folks are downloading resource data from the openmeteo web API - they may have wind speed in km/h (that's the default unit)

Done, and I added a link to the WindIO docs that define the resource input for Ard.

kbrunik · 2025-10-26T18:52:06Z

Is this because of how the wind resource needs to be formatted? If yes, could you explain the necessary format of the file and what the attributes--or column--requirements are)?

it looks like you're using openmeteo, is this something that you're integrating @elenya-grant?

yes - PR #332

I have not connected Ard to the wind resource capabilities in H2I because it would require passing weather data at run time, but Ard requires the weather data at setup time.

kbrunik · 2025-10-26T19:05:53Z

Could you add a list of the necessary input files for ARD, with maybe a one sentence description about them?

Agreed, and pointing to the Ard docs where possible here would be quite helpful!

kbrunik · 2025-10-26T19:17:32Z

If this example is really similar to 01_onshore in the Ard documentation you could consider linking it for additional explanation about what's happening: https://wisdem.github.io/Ard/examples/01_onshore/optimization_demo.html

kbrunik · 2025-10-26T19:37:13Z

Is there a reason to not set the generation to a value? It seems like it might be easier to check in the future if this test breaks if we have a better idea of the value.

I agree with Kaitlin - I think that having subtests on the value of the total wind generation profile and total solar generation profile would be a good so if other subtests fail (like LCOE or total electricity dispatched, etc), it will be easier to identify what causes other subtest failures.

Good point. I think this was a holdover from just getting it to work. Thanks for calling it out.

Now testing total gen of each tech against saved values

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@@ -1,7 +1,6 @@
     # Changelog
     ## 0.5.x [TBD]
     - Updates models for NumPy version 2.4.0
     - Update test values for WOMBAT update to 0.13.0
     - Added standalone iron DRI and steel EAF performance and cost models
@@ Expand Down Expand Up / @@ -29,6 +28,7 @@ @@
       - Remove unused dependencies.
       - Fixes typos for skipped folders.
       - Fixes missing dependencies for `gis` modifier used in new iron mapping tests.
+    - Added Ard as a combined performance and cost model
     ## 0.5.1 [December 18, 2025]
@@ Expand Down @@

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@@ Expand Up / @@ -115,8 +115,11 @@ pip install h2integrate @@
 . Create a conda environment and install H2Integrate and all its dependencies
+        - for apple silicon
         ```bash
-        conda env create -f environment.yml
+        conda CONDA_SUBDIR=osx-arm64 conda env create -f environment.yml
+        conda activate h2i-ard
+        conda env config vars set CONDA_SUBDIR=osx-arm64 # this command makes the environment permanently native
         ```
 . Install Cbc.
@@ Expand Down @@

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@@ Expand Up / @@ -40,6 +40,7 @@ parts: @@
       - file: technology_models/methanol.md
       - file: technology_models/ammonia.md
       - file: technology_models/pysam_battery.md
+      - file: technology_models/wind_plant_ard.md
       - file: technology_models/geologic_hydrogen.md
       - file: technology_models/grid
       - file: technology_models/hydrogen_storage.md
@@ Expand Down @@

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@@ -0,0 +1,34 @@
+    # Wind Plant - Ard
+    The intent of [Ard](https://github.com/WISDEM/Ard) is to be a modular, full-stack multi-disciplinary optimization tool for wind farms. By incorporating Ard in H2Integrate, we are able to draw on many wind technology models developed at NREL and other institutions without managing them or their connections in Ard. Models connected in Ard include many parts of [WISDEM](https://github.com/WISDEM/WISDEM), [FLORIS](https://github.com/NREL/floris), and [OptiWindNet](https://github.com/DTUWindEnergy/OptiWindNet). Ard also provides constraint functions and wind farm layout generation capabilities among other things. Because Ard has been developed in a modular way, you may extend Ard fairly easily to include other wind models of interest.
+    Ard is included in H2Integrate as an [OpenMDAO sub-model](https://openmdao.org/newdocs/versions/latest/features/building_blocks/components/submodel_comp.html), which means that Ard is treated as a distinct and separate OpenMDAO system within the larger H2I OpenMDAO system. In this way, the user can run an independent wind farm optimization within Ard, or allow H2Integrate to manage the wind farm design variables directly. The drawback of including Ard as a sub-model is that N2 diagrams made from the H2Integrate problems will show Ard only as a single black-box model, rather than showing all the subsystems within Ard. If you wish to view an N2 diagram of Ard, you will need to use the Ard problem instead.
+    ## Some Key Capabilities of Ard
+    - Highly modular to include alternate/new models and capabilities
+        - Built using OpenMDAO
+    - Wind farm layout optimization
+        - Turbine locations
+            - Ordered grid layouts
+            - Continuous locations layouts
+        - Boundaries
+            - Single boundary
+            - Multiple discrete boundary regions
+            - Arbitrary polygonal boundaries shapes
+    - Collection cable array optimization
+        - OptiWindNet
+            - Radial configurations
+            - Branched configurations
+    - Aerodynamics
+        - FLORIS
+    - Cost and finance
+        - WISDEM
+            - LandBOSSE
+            - ORBIT
+            - FinanceSE
+    ## Wind Resource
+    The wind resource capabilities of H2Integrate are not yet connected with Ard, so the user must provide a wind resource file directly to the Ard model inputs.
+    ## Examples
+    For an example of using Ard in an H2Integrate model, see `examples/xx_wind_ard`. Note that Ard uses a combination of input files, including a [wind IO](https://github.com/IEAWindSystems/windIO) file.

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@@ Expand Up @@
             + `'floris_wind_plant_performance'`
         - cost models:
             + `'atb_wind_cost'`
+        - combined models:
+            + `'wind_plant_ard'`
     - `solar`: solar-PV panels
         - performance models:
             + `'pysam_solar_plant_performance'`
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Ard integration for wind farm combined performance and cost model #312

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kbrunik Oct 26, 2025

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jaredthomas68 Jan 29, 2026

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elenya-grant Nov 5, 2025

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-    name: h2integrate
+    name: h2i-ard
     channels:
       - conda-forge
       - defaults
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@@ Expand Up / @@ -1142,6 +1142,78 @@ def test_simple_dispatch_example(subtests): @@
             assert pytest.approx(electricity_lcoe, rel=1e-6) == 78.01723
+    def test_windard_pv_battery_dispatch_example(subtests):
+        # Change the current working directory to the example's directory
+        os.chdir(EXAMPLE_DIR / "xx_wind_ard")
+        # Create the model
+        model = H2IntegrateModel("./h2i_inputs/wind_pv_battery.yaml")
+        # Run the model
+        model.run()
+        # Post-process the results
+        model.post_process()
+        with subtests.test("Check wind generation"):
+            # Wind should generate some electricity
+            wind_electricity = model.prob.get_val("wind.electricity_out", units="MW")
+            assert wind_electricity.sum() > 0
+        with subtests.test("Check solar generation"):
+            # Solar should generate some electricity
+            solar_electricity = model.prob.get_val("solar.electricity_out", units="MW")
+            assert solar_electricity.sum() > 0
+        with subtests.test("Check battery gets wind and solar output"):
+            # Wind plus solar electricity should match battery input (direct connection)
+            battery_electricity_in = model.prob.get_val("battery.electricity_in", units="MW")
+            assert wind_electricity.sum() + solar_electricity.sum() == pytest.approx(
+                battery_electricity_in.sum(), rel=1e-6
+            )
+        with subtests.test("Check demand satisfaction"):
+            dispatched_electricity = model.prob.get_val("battery.electricity_out", units="MW")
+            # Demand should be met for the last part of the year
+            assert np.allclose(
+                dispatched_electricity[8700:],
+                model.prob.get_val("battery.electricity_demand", units="MW")[8700:],
+            )
+        # Subtest for LCOE
+        with subtests.test("Check dispatched LCOE value"):
+            lcoe = model.prob.get_val("finance_subgroup_dispatched_electricity.LCOE")[0]
+            assert pytest.approx(lcoe, rel=1e-6) == 0.22171693744337054
+        with subtests.test("Check generation LCOE value (excludes battery)"):
+            lcoe = model.prob.get_val("finance_subgroup_produced_electricity.LCOE")[0]
+            assert pytest.approx(lcoe, rel=1e-6) == 0.06770722531229562
+        # Subtest for total electricity produced
+        with subtests.test("Check total electricity dispatched"):
+            total_electricity = model.prob.get_val(
+                "finance_subgroup_dispatched_electricity.electricity_sum.total_electricity_produced",
+                units="MW*h/year",
+            ).sum()
+            assert total_electricity == pytest.approx(dispatched_electricity.sum())
+        # Subtest for electricity curtailed
+        with subtests.test("Check electricity curtailed"):
+            electricity_curtailed = model.prob.get_val(
+                "battery.electricity_unused_commodity", units="MW"
+            ).sum()
+            # import pdb; pdb.set_trace()
+            assert electricity_curtailed == pytest.approx(525088.2729268058, rel=1e-6)
+        # Subtest for missed load
+        with subtests.test("Check electricity missed load"):
+            electricity_missed_load = np.linalg.norm(
+                model.prob.get_val("battery.electricity_unmet_demand", units="MW")
+            )
+            assert electricity_missed_load == pytest.approx(468.28057304873026)
     def test_csvgen_design_of_experiments(subtests):
         os.chdir(EXAMPLE_DIR / "20_solar_electrolyzer_doe")
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