Ard integration for wind farm combined performance and cost model

CHANGELOG.md

@@ -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
@@ -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]
 

README.md

@@ -115,8 +115,11 @@ pip install h2integrate
 
 3. 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
     ```
 
 4. Install Cbc.

docs/_toc.yml

@@ -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

docs/technology_models/wind_plant_ard.md

@@ -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.

docs/user_guide/model_overview.md

@@ -136,6 +136,8 @@ Below summarizes the available performance, cost, and financial models for each
         + `'floris_wind_plant_performance'`
     - cost models:
         + `'atb_wind_cost'`
+    - combined models:
+        + `'wind_plant_ard'`
 - `solar`: solar-PV panels
     - performance models:
         + `'pysam_solar_plant_performance'`

environment.yml

@@ -1,4 +1,4 @@
-name: h2integrate
+name: h2i-ard
 channels:
   - conda-forge
   - defaults

examples/test/test_all_examples.py

@@ -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")
 

examples/xx_wind_ard/ard_inputs/NREL_Reference_5MW_126.csv

@@ -0,0 +1,51 @@
+Wind Speed [m/s],Power [kW],Cp [-],Thrust [kN],Ct [-]
+3,40.52,0.208546508,77.66,1.132034888
+4,177.67,0.385795061,121.90,0.999470963
+5,403.9,0.449038264,174.88,0.917697381
+6,737.59,0.474546985,236.23,0.860849503
+7,1187.18,0.480994449,304.55,0.815371198
+7.1,1239.25,0.481172749,311.87,0.811614904
+7.2,1292.52,0.481235678,319.27,0.807939328
+7.3,1347.32,0.481305875,326.77,0.80443352
+7.4,1403.26,0.481238912,334.35,0.800993851
+7.5,1460.7,0.481167356,342.03,0.79768116
+7.6,1519.64,0.481081935,349.82,0.794529244
+7.7,1580.17,0.481007003,357.72,0.791495834
+7.8,1642.11,0.480880409,365.71,0.788560434
+7.9,1705.76,0.480789285,374.51,0.787217182
+8,1771.17,0.480737341,384.00,0.787127977
+9,2518.55,0.480111543,485.21,0.785839257
+10,3448.38,0.479218839,597.48,0.783812219
+10.1,3552.14,0.479120347,609.30,0.783568108
+10.2,3657.95,0.479022984,621.23,0.783328285
+10.3,3765.12,0.478834971,631.75,0.781194418
+10.4,3873.93,0.478597234,640.86,0.777292539
+10.5,3984.48,0.478324162,650.02,0.773464375
+10.6,4096.58,0.477994289,659.23,0.769690236
+10.7,4210.72,0.477665338,668.51,0.766001924
+10.8,4326.15,0.477253698,677.81,0.762348072
+10.9,4443.4,0.476819542,687.17,0.758760824
+11,4562.5,0.476368667,696.60,0.755242872
+11.1,4683.42,0.475896732,706.08,0.751792927
+11.2,4806.16,0.475404347,715.65,0.748434131
+11.3,4929.93,0.474814698,725.25,0.745113997
+11.4,5000.92,0.469087611,711.09,0.717806682
+11.5,5000.16,0.456886723,677.65,0.672204789
+11.6,4999.98,0.445156758,654.72,0.63831272
+11.7,4999.96,0.433837552,636.70,0.610176496
+11.8,4999.98,0.422902868,621.40,0.585456847
+11.9,5000,0.412332387,607.97,0.563222111
+12,5000,0.402110045,595.94,0.542912273
+13,5000.01,0.316270768,514.41,0.399312061
+14,5000.01,0.253224057,463.93,0.310517829
+15,5000.02,0.205881042,426.43,0.248633226
+16,5000,0.169640239,397.20,0.203543725
+17,5000.02,0.141430529,373.66,0.169616419
+18,5000.03,0.119144335,354.36,0.143478955
+19,5000.02,0.101304591,338.30,0.122938861
+20,5000.04,0.086856409,324.77,0.106515296
+21,5000.02,0.075029591,312.72,0.093026095
+22,5000.05,0.065256635,301.23,0.081648606
+23,5000.01,0.057109143,291.13,0.072197368
+24,5000,0.050263779,282.71,0.064388275
+25,5000.04,0.044470536,275.29,0.057782745

examples/xx_wind_ard/ard_inputs/open-meteo-56.20N8.54E86m-short.yaml

@@ -0,0 +1,47 @@
+name: Test case
+wind_resource:
+  time:
+  - '2023-01-01T00:00:00Z'
+  - '2023-01-01T01:00:00Z'
+  - '2023-01-01T02:00:00Z'
+  - '2023-01-01T03:00:00Z'
+  - '2023-01-01T04:00:00Z'
+  - '2023-01-01T05:00:00Z'
+  - '2023-01-01T06:00:00Z'
+  - '2023-01-01T07:00:00Z'
+  - '2023-01-01T08:00:00Z'
+  - '2023-01-01T09:00:00Z'
+  turbulence_intensity:
+    data:
+    - 0.1
+    - 0.1
+    - 0.1
+    - 0.1
+    - 0.1
+    - 0.1
+    - 0.1
+    - 0.1
+    - 0.1
+    - 0.1
+  wind_direction:
+  - 54
+  - 42
+  - 71
+  - 67
+  - 66
+  - 65
+  - 69
+  - 76
+  - 84
+  - 100
+  wind_speed:
+  - 1.36
+  - 4.46
+  - 7.18
+  - 7.08
+  - 6.67
+  - 6.94
+  - 6.96
+  - 6.5
+  - 6.13
+  - 5.18