diff --git a/CHANGELOG.md b/CHANGELOG.md
index 30d4744c7..ffdfa7521 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,7 @@
 # Changelog
 
 ## Unreleased
+
 - Change commodity in DRI and EAF model from pig iron to sponge iron based on likely carbon content [PR 670](https://github.com/NatLabRockies/H2Integrate/pull/670)
 - Bugfix for round-trip efficiency handling when calling `check_inputs` around `StoragePerformanceModel` [PR 684](https://github.com/NatLabRockies/H2Integrate/pull/684)
 - Bugfix. Include nuclear in electricity producing tech list and improve error message for zero-length electricity producing techs in model when electricity is specified as the commodity. [PR 685](https://github.com/NatLabRockies/H2Integrate/pull/685)
@@ -17,12 +18,20 @@
 - Add `{commodity}_set_point` as an input to hydrogen fuel cell model [PR 709](https://github.com/NatLabRockies/H2Integrate/pull/709)
 - Rename `n_control_window` to `n_control_window_hours` for unit clarity [PR 712](https://github.com/NatLabRockies/H2Integrate/pull/712)
 - Update N2 diagram for demand openloop control from static and outdated to dynamic and interactive [PR 714](https://github.com/NatLabRockies/H2Integrate/pull/714)
+- `feedstocks.py` has moved from `h2integrate/core/` to `h2_integrate/feedstocks`
+  [PR 719](https://github.com/NatLabRockies/H2Integrate/pull/719)
+- Creates the `EIANaturalGasFeedstockConfig` and `EIANaturalGasFeedstockCostModel` to load EIA
+  natural gas prices from file or to retrieve them from the EIA API. The model is able to retrieve
+  the US or any of the 50 states' annual or monthly values, which will be converted into an hourly timeseries. Additionally, a `EIANaturalGasFeedstockPerformanceConfig` and
+  `EIANaturalGasFeedstockPerformanceModel` are created to be compatible with the hard-coded
+  definitions from the cost model. [PR 719](https://github.com/NatLabRockies/H2Integrate/pull/719)
 - Added basic check of 4-length connections in `technology_interconnections` [PR 720](https://github.com/NatLabRockies/H2Integrate/pull/720)
 - Adds `H2IntegrateModel`, `load_yaml`, `write_yaml`, and `write_readable_yaml` as package-level imports [PR 728](https://github.com/NatLabRockies/H2Integrate/pull/728).
 - Update N2 diagram for Pyomo heuristic control from static image to dynamic and interactive embedded diagram [PR 726](https://github.com/NatLabRockies/H2Integrate/pull/726)
 - Added ability to use timeseries for finance calculations [PR 725](https://github.com/NatLabRockies/H2Integrate/pull/725)
 
 ## 0.8 [April 15, 2026]
+
 - Updated README and docs intro page with expanded H2I description, reorganized sections, and streamlined installation instructions [PR 677](https://github.com/NatLabRockies/H2Integrate/pull/677)
 - Update energy conversion ratio in H2 SMR model [PR 606](https://github.com/NatLabRockies/H2Integrate/pull/606)
 - Update iron models and examples [PR 601](https://github.com/NatLabRockies/H2Integrate/pull/601)
diff --git a/docs/_static/class_hierarchy.html b/docs/_static/class_hierarchy.html
index fea629882..0e7f341f1 100644
--- a/docs/_static/class_hierarchy.html
+++ b/docs/_static/class_hierarchy.html
@@ -380,9 +380,8 @@ <h1></h1>
 
 
                   // parsing and collecting nodes and edges from the python
-                  nodes = new vis.DataSet([{"borderWidth": 4.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "FeedstockCostModel", "label": "FeedstockCostModel", "shape": "ellipse", "size": 18.0, "title": "FeedstockCostModel\ncore/feedstocks.py\n[Core / General]", "x": -79.5125603737886, "y": 584.9567473090942}, {"borderWidth": 5.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SiteBaseComponent", "label": "SiteBaseComponent", "shape": "ellipse", "size": 18.72972972972973, "title": "SiteBaseComponent\ncore/sites.py\n[Core / General]", "x": -51.99787538017026, "y": 668.8874099443384}, {"borderWidth": 4.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SiteLocationComponent", "label": "SiteLocationComponent", "shape": "ellipse", "size": 18.0, "title": "SiteLocationComponent\ncore/sites.py\n[Core / General]", "x": -137.54209445136647, "y": 722.897904528822}, {"borderWidth": 5.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PerformanceModelBaseClass", "label": "PerformanceModelBaseClass", "shape": "ellipse", "size": 40.62162162162162, "title": "PerformanceModelBaseClass\ncore/model_baseclasses.py\n[Core / General]", "x": -243.0155271316636, "y": 685.2630296209401}, {"borderWidth": 5.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CostModelBaseClass", "label": "CostModelBaseClass", "shape": "ellipse", "size": 45.0, "title": "CostModelBaseClass\ncore/model_baseclasses.py\n[Core / General]", "x": -288.0485916668164, "y": 575.9204299065049}, {"borderWidth": 4.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ResizeablePerformanceModelBaseClass", "label": "ResizeablePerformanceModelBaseClass", "shape": "ellipse", "size": 19.45945945945946, "title": "ResizeablePerformanceModelBaseClass\ncore/model_baseclasses.py\n[Core / General]", "x": -237.4418960911029, "y": 464.62515055513364}, {"borderWidth": 5.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CacheBaseClass", "label": "CacheBaseClass", "shape": "ellipse", "size": 19.45945945945946, "title": "CacheBaseClass\ncore/model_baseclasses.py\n[Core / General]", "x": -119.33772505659815, "y": 423.5780806776881}, {"borderWidth": 4.0, "color": {"background": "#F5C542", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GenericConverterCostModel", "label": "GenericConverterCostModel", "shape": "dot", "size": 18.0, "title": "GenericConverterCostModel\nconverters/generic_converter_cost.py\n[Converter / Other]", "x": 428.09388111524015, "y": 469.0988894808179}, {"borderWidth": 3.0, "color": {"background": "#4A90D9", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PYSAMSolarPlantPerformanceModel", "label": "PYSAMSolarPlantPerformanceModel", "shape": "dot", "size": 18.0, "title": "PYSAMSolarPlantPerformanceModel\nconverters/solar/solar_pysam.py\n[Converter / Solar]", "x": 455.6085661088585, "y": 553.0295521160621}, {"borderWidth": 4.0, "color": {"background": "#4A90D9", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ATBUtilityPVCostModel", "label": "ATBUtilityPVCostModel", "shape": "dot", "size": 18.0, "title": "ATBUtilityPVCostModel\nconverters/solar/atb_utility_pv_cost.py\n[Converter / Solar]", "x": 370.0643470376623, "y": 607.0400467005456}, {"borderWidth": 4.0, "color": {"background": "#4A90D9", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ATBResComPVCostModel", "label": "ATBResComPVCostModel", "shape": "dot", "size": 18.0, "title": "ATBResComPVCostModel\nconverters/solar/atb_res_com_pv_cost.py\n[Converter / Solar]", "x": 264.59091435736514, "y": 569.4051717926637}, {"borderWidth": 4.0, "color": {"background": "#4A90D9", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SolarPerformanceBaseClass", "label": "SolarPerformanceBaseClass", "shape": "dot", "size": 18.72972972972973, "title": "SolarPerformanceBaseClass\nconverters/solar/solar_baseclass.py\n[Converter / Solar]", "x": 219.55784982221238, "y": 460.06257207822847}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ElectrolyzerPerformanceBaseClass", "label": "ElectrolyzerPerformanceBaseClass", "shape": "dot", "size": 18.72972972972973, "title": "ElectrolyzerPerformanceBaseClass\nconverters/hydrogen/electrolyzer_baseclass.py\n[Converter / Hydrogen]", "x": 270.16454539792585, "y": 348.7672927268573}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ElectrolyzerCostBaseClass", "label": "ElectrolyzerCostBaseClass", "shape": "dot", "size": 20.18918918918919, "title": "ElectrolyzerCostBaseClass\nconverters/hydrogen/electrolyzer_baseclass.py\n[Converter / Hydrogen]", "x": 388.2687164324306, "y": 307.72022284941175}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SingliticoCostModel", "label": "SingliticoCostModel", "shape": "dot", "size": 18.0, "title": "SingliticoCostModel\nconverters/hydrogen/singlitico_cost_model.py\n[Converter / Hydrogen]", "x": 501.4805766605487, "y": 365.4133442102892}, {"borderWidth": 1, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "WOMBATElectrolyzerModel", "label": "WOMBATElectrolyzerModel", "shape": "dot", "size": 18.0, "title": "WOMBATElectrolyzerModel\nconverters/hydrogen/wombat_model.py\n[Converter / Hydrogen]", "x": 538.9625776291001, "y": 488.44605748599986}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "LinearH2FuelCellPerformanceModel", "label": "LinearH2FuelCellPerformanceModel", "shape": "dot", "size": 18.0, "title": "LinearH2FuelCellPerformanceModel\nconverters/hydrogen/h2_fuel_cell.py\n[Converter / Hydrogen]", "x": 475.93189116793957, "y": 601.9588898891661}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "H2FuelCellCostModel", "label": "H2FuelCellCostModel", "shape": "dot", "size": 18.0, "title": "H2FuelCellCostModel\nconverters/hydrogen/h2_fuel_cell.py\n[Converter / Hydrogen]", "x": 349.5705117805884, "y": 635.8507248662497}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SteamMethaneReformerPerformanceModel", "label": "SteamMethaneReformerPerformanceModel", "shape": "dot", "size": 18.0, "title": "SteamMethaneReformerPerformanceModel\nconverters/hydrogen/steam_methane_reformer.py\n[Converter / Hydrogen]", "x": 236.61361523178945, "y": 568.2423536759715}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SteamMethaneReformerCostModel", "label": "SteamMethaneReformerCostModel", "shape": "dot", "size": 18.0, "title": "SteamMethaneReformerCostModel\nconverters/hydrogen/steam_methane_reformer.py\n[Converter / Hydrogen]", "x": 206.39396475153276, "y": 439.409697663304}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "BasicElectrolyzerCostModel", "label": "BasicElectrolyzerCostModel", "shape": "dot", "size": 18.0, "title": "BasicElectrolyzerCostModel\nconverters/hydrogen/basic_cost_model.py\n[Converter / Hydrogen]", "x": 278.165573095184, "y": 327.54056326717114}, {"borderWidth": 2.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ECOElectrolyzerPerformanceModel", "label": "ECOElectrolyzerPerformanceModel", "shape": "dot", "size": 18.72972972972973, "title": "ECOElectrolyzerPerformanceModel\nconverters/hydrogen/pem_electrolyzer.py\n[Converter / Hydrogen]", "x": 408.929515746538, "y": 301.0719911584519}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CustomElectrolyzerCostModel", "label": "CustomElectrolyzerCostModel", "shape": "dot", "size": 18.0, "title": "CustomElectrolyzerCostModel\nconverters/hydrogen/custom_electrolyzer_cost_model.py\n[Converter / Hydrogen]", "x": 519.3422437620703, "y": 376.741275969993}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GeoH2SubsurfaceCostModel", "label": "GeoH2SubsurfaceCostModel", "shape": "dot", "size": 18.0, "title": "GeoH2SubsurfaceCostModel\nconverters/hydrogen/geologic/mathur_modified.py\n[Converter / Hydrogen]", "x": 541.9933368656093, "y": 509.0547616483423}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GeoH2SubsurfacePerformanceBaseClass", "label": "GeoH2SubsurfacePerformanceBaseClass", "shape": "dot", "size": 19.45945945945946, "title": "GeoH2SubsurfacePerformanceBaseClass\nconverters/hydrogen/geologic/h2_well_subsurface_baseclass.py\n[Converter / Hydrogen]", "x": 462.61867180963463, "y": 617.7338834509918}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GeoH2SubsurfaceCostBaseClass", "label": "GeoH2SubsurfaceCostBaseClass", "shape": "dot", "size": 18.72972972972973, "title": "GeoH2SubsurfaceCostBaseClass\nconverters/hydrogen/geologic/h2_well_subsurface_baseclass.py\n[Converter / Hydrogen]", "x": 329.04975630639217, "y": 636.513453478361}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "AspenGeoH2SurfacePerformanceModel", "label": "AspenGeoH2SurfacePerformanceModel", "shape": "dot", "size": 18.0, "title": "AspenGeoH2SurfacePerformanceModel\nconverters/hydrogen/geologic/aspen_surface_processing.py\n[Converter / Hydrogen]", "x": 222.32576716621537, "y": 553.5863417347226}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "AspenGeoH2SurfaceCostModel", "label": "AspenGeoH2SurfaceCostModel", "shape": "dot", "size": 18.0, "title": "AspenGeoH2SurfaceCostModel\nconverters/hydrogen/geologic/aspen_surface_processing.py\n[Converter / Hydrogen]", "x": 207.46113153897124, "y": 419.00371238109653}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "NaturalGeoH2PerformanceModel", "label": "NaturalGeoH2PerformanceModel", "shape": "dot", "size": 18.0, "title": "NaturalGeoH2PerformanceModel\nconverters/hydrogen/geologic/simple_natural_geoh2.py\n[Converter / Hydrogen]", "x": 293.81016729091453, "y": 314.4201619012048}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "StimulatedGeoH2PerformanceModel", "label": "StimulatedGeoH2PerformanceModel", "shape": "dot", "size": 18.0, "title": "StimulatedGeoH2PerformanceModel\nconverters/hydrogen/geologic/templeton_serpentinization.py\n[Converter / Hydrogen]", "x": 429.1980303433203, "y": 303.5048975398027}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GeoH2SurfacePerformanceBaseClass", "label": "GeoH2SurfacePerformanceBaseClass", "shape": "dot", "size": 18.72972972972973, "title": "GeoH2SurfacePerformanceBaseClass\nconverters/hydrogen/geologic/h2_well_surface_baseclass.py\n[Converter / Hydrogen]", "x": 531.4807512394786, "y": 393.1585470237545}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GeoH2SurfaceCostBaseClass", "label": "GeoH2SurfaceCostBaseClass", "shape": "dot", "size": 18.72972972972973, "title": "GeoH2SurfaceCostBaseClass\nconverters/hydrogen/geologic/h2_well_surface_baseclass.py\n[Converter / Hydrogen]", "x": 538.4198729733245, "y": 529.1652694271473}, {"borderWidth": 3.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ReverseOsmosisPerformanceModel", "label": "ReverseOsmosisPerformanceModel", "shape": "dot", "size": 18.0, "title": "ReverseOsmosisPerformanceModel\nconverters/water/desal/desalination.py\n[Converter / Water]", "x": 445.5710237882473, "y": 629.0047614736493}, {"borderWidth": 3.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ReverseOsmosisCostModel", "label": "ReverseOsmosisCostModel", "shape": "dot", "size": 18.0, "title": "ReverseOsmosisCostModel\nconverters/water/desal/desalination.py\n[Converter / Water]", "x": 309.11651798504033, "y": 631.947653855694}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": 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"shape": "dot", "size": 18.0, "title": "H2FuelCellCostModel\nconverters/hydrogen/h2_fuel_cell.py\n[Converter / Hydrogen]", "x": 349.5705117805884, "y": 635.8507248662497}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SteamMethaneReformerPerformanceModel", "label": "SteamMethaneReformerPerformanceModel", "shape": "dot", "size": 18.0, "title": "SteamMethaneReformerPerformanceModel\nconverters/hydrogen/steam_methane_reformer.py\n[Converter / Hydrogen]", "x": 236.61361523178945, "y": 568.2423536759715}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SteamMethaneReformerCostModel", "label": "SteamMethaneReformerCostModel", 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{"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "LinedRockCavernStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "SaltCavernStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "PipeStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "CompressedGasStorageCostModel"}, {"arrows": "to", "from": "ProFastBase", "to": "ProFastLCO"}, {"arrows": "to", "from": "ProFastBase", "to": "ProFastNPV"}, {"arrows": "to", "from": "ResourceBaseAPIModel", "to": "SolarResourceBaseAPIModel"}, {"arrows": "to", "from": "ResourceBaseAPIModel", "to": "WindResourceBaseAPIModel"}, {"arrows": "to", "from": "SolarResourceBaseAPIModel", "to": "OpenMeteoHistoricalSolarResource"}, {"arrows": "to", "from": "SolarResourceBaseAPIModel", "to": "NLRDeveloperAPISolarResourceBase"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "MeteosatPrimeMeridianSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "MeteosatPrimeMeridianTMYSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "Himawari7SolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "Himawari8SolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "HimawariTMYSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESAggregatedSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESConusSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESFullDiscSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESTMYSolarAPI"}, {"arrows": "to", "from": "WindResourceBaseAPIModel", "to": "OpenMeteoHistoricalWindResource"}, {"arrows": "to", "from": "WindResourceBaseAPIModel", "to": "WTKNLRDeveloperAPIWindResource"}, {"arrows": "to", "from": "DemandComponentBase", "to": "GenericDemandComponent"}, {"arrows": "to", "from": "DemandComponentBase", "to": "FlexibleDemandComponent"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "HeuristicLoadFollowingStorageController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "OptimizedDispatchStorageController"}, {"arrows": "to", "from": "StorageOpenLoopControlBase", "to": "DemandOpenLoopStorageController"}, {"arrows": "to", "from": "StorageOpenLoopControlBase", "to": "SimpleStorageOpenLoopController"}, {"arrows": "to", "from": "StorageOpenLoopControlBase", "to": "PeakLoadManagementHeuristicOpenLoopStorageController"}, {"arrows": "to", "from": "PyomoRuleBaseClass", "to": "PyomoDispatchGenericConverter"}, {"arrows": "to", "from": "PyomoRuleBaseClass", "to": "PyomoRuleStorageBaseclass"}]);
                   nodeColors = {};
                   allNodes = nodes.get({ returnType: "Object" });
                   for (nodeId in allNodes) {
diff --git a/docs/api.md b/docs/api.md
index 6cf44f280..794ce6363 100644
--- a/docs/api.md
+++ b/docs/api.md
@@ -8,6 +8,7 @@
    :recursive:
 
    core
+   feedstocks
    converters
    control
    finances
diff --git a/docs/technology_models/feedstocks.md b/docs/technology_models/feedstocks.md
index fed95df68..dc69b6344 100644
--- a/docs/technology_models/feedstocks.md
+++ b/docs/technology_models/feedstocks.md
@@ -32,6 +32,7 @@ technology_interconnections: [
 ```
 
 Where:
+
 - `name_of_feedstock_source`: Name of your feedstock source
 - `consuming_technology`: Technology that uses the feedstock
 - `commodity`: Type identifier (e.g., "natural_gas", "water", "electricity")
@@ -85,9 +86,66 @@ The `price` parameter is flexible - you can specify constant pricing with a sing
 ```
 
 ### Consumed Feedstock Outputs
+
 The feedstock model outputs cost and performance information about the consumed feedstock. The most notable outputs are:
 - `VarOpEx`: cost of the feedstock consumed (in `USD/yr`)
 - `total_{commodity}_consumed`: total feedstock consumed over simulation (in `commodity_amount_units`)
 - `annual_{commodity}_consumed`: annual feedstock consumed (in `commodity_amount_units/yr`)
 - `rated_{commodity}_production`: this is equal to the the `rated_capacity` of the feedstock model (in `commodity_rate_units`)
 - `capacity_factor`: ratio of the feedstock consumed to the maximum feedstock available
+
+## EIA Natural Gas Pricing
+
+A special case of the feedstock cost model `EIANaturalGasFeedstockCostModel` (see
+[the relevant API docs](https://h2integrate.readthedocs.io/en/latest/_autosummary/h2integrate.feedstocks.eia_ng_pricing.html)
+for complete details) exists to enable users to download data from the EIA API's natural gas price
+portal. Access to the wellhead, import, citygate, residential, commercial, industrial, electrical
+power, and exports price facets are supported for all 50 US states and the US as a whole, though
+it is best to see which data are
+[availble online in the EIA API documentation](https://www.eia.gov/opendata/browser/natural-gas/pri/sum)
+prior to using in an analysis.
+
+Users are expected to get an EIA API key from the
+[EIA Open Data portal](https://www.eia.gov/opendata/), or to download the data as as CSV file for
+the model to load.
+
+At present, the EIA natural gas cost model uses only a single year of price data (annual or monthly)
+and extrapolates it to an hourly timeseries automatically.
+
+### Configuring the EIA Cost Model
+
+Similar to the standard feedstock model, the following variables are able to be set by the user
+
+- `cost_year` (int): Dollar year for cost inputs
+- `annual_cost` (float, optional): Fixed cost per year in USD/year. Defaults to 0.0
+- `start_up_cost` (float, optional): One-time capital cost in USD. Defaults to 0.0
+
+Additionally, there are a few other settings that users will need to provide for the model to work
+that differ from the standard cost model.
+
+:::{important}
+If relying on site coordinate data (`latitude` and `longitude`), then the `reverse_geocoder` package
+is required, which can be pip installed directly or through the `gis` library extras.
+:::
+
+- `resource_year` (int): Which year to obtain the data from in the range [2001, 2026].
+- `monthly` (bool): If True, the monthly data are retrieved, otherwise the annual price is used.
+- location data options 1) use `state` or 2) use `latitude` and `longitude`:
+  - `state` (str): Full name of the state or two-letter state abbreviation, such as "United States" or
+    "US". Only the "US" or all 50 states will produce valid results. When `state` is provided, the
+    site coordinate data will be ignored.
+  - `latitude` (float): Latitude of the natural gas plant site. Only used when `state` is not
+    provided, and will be filled from the plant configuration's site data if not provided.
+  - `longitude` (float): Longitude of the natural gas plant site. Only used when `state` is not
+    provided, and will be filled from the plant configuration's site data if not provided.
+
+- `price_category` (str): One of "wellhead", "imports", "citygate", "residential", "commercial",
+  "industrial", "electrical_power", or "exports". Note that not all categories will return
+  state-level data.
+- `api_key_file` (str, optional): The file where the user's API key is stored. If storing in a file,
+  define it on its own line using the convention "EIA_API_KEY: xxxx", or have the API key defined as
+  an environment variable set as "EIA_API_KEY".
+
+- `commodity`: Set to "natural_gas" internally.
+- `commodity_rate_units`: Set to "MMBtu/h" internally.
+- `commodity_amount_units`: Set to "MMBtu" internally.
diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py
index b2abecd78..2230ef099 100644
--- a/h2integrate/core/supported_models.py
+++ b/h2integrate/core/supported_models.py
@@ -1,6 +1,11 @@
+from h2integrate.feedstocks import (
+    FeedstockCostModel,
+    FeedstockPerformanceModel,
+    EIANaturalGasFeedstockCostModel,
+    EIANaturalGasFeedstockPerformanceModel,
+)
 from h2integrate.resource.river import RiverResource
 from h2integrate.resource.tidal import TidalResource
-from h2integrate.core.feedstocks import FeedstockCostModel, FeedstockPerformanceModel
 from h2integrate.transporters.pipe import PipePerformanceModel
 from h2integrate.transporters.cable import CablePerformanceModel
 from h2integrate.converters.grid.grid import GridCostModel, GridPerformanceModel
@@ -314,6 +319,8 @@
     # Feedstock
     "FeedstockPerformanceModel": FeedstockPerformanceModel,
     "FeedstockCostModel": FeedstockCostModel,
+    "EIANaturalGasFeedstockCostModel": EIANaturalGasFeedstockCostModel,
+    "EIANaturalGasFeedstockPerformanceModel": EIANaturalGasFeedstockPerformanceModel,
     # Grid
     "GridPerformanceModel": GridPerformanceModel,
     "GridCostModel": GridCostModel,
diff --git a/h2integrate/feedstocks/__init__.py b/h2integrate/feedstocks/__init__.py
new file mode 100644
index 000000000..5af123871
--- /dev/null
+++ b/h2integrate/feedstocks/__init__.py
@@ -0,0 +1,5 @@
+from h2integrate.feedstocks.feedstocks import FeedstockPerformanceModel, FeedstockCostModel
+from h2integrate.feedstocks.eia_ng_pricing import (
+    EIANaturalGasFeedstockCostModel,
+    EIANaturalGasFeedstockPerformanceModel,
+)
diff --git a/h2integrate/feedstocks/eia_ng_pricing.py b/h2integrate/feedstocks/eia_ng_pricing.py
new file mode 100644
index 000000000..335ae9a2c
--- /dev/null
+++ b/h2integrate/feedstocks/eia_ng_pricing.py
@@ -0,0 +1,554 @@
+import os
+import json
+from pathlib import Path
+from datetime import datetime
+
+import attrs
+import numpy as np
+import pandas as pd
+import requests
+import openmdao.api as om
+from attrs import field, define
+
+from h2integrate.core.utilities import merge_shared_inputs
+from h2integrate.core.file_utils import get_path
+from h2integrate.core.model_baseclasses import BaseConfig, CostModelBaseClass
+
+
+HOURS_PER_YEAR = 8760
+SECONDS_PER_HOUR = 3600
+MCF_to_MMBTU = 1 / 0.964
+STATE_MAP = {
+    "Alabama": "AL",
+    "Alaska": "AK",
+    "Arizona": "AZ",
+    "Arkansas": "AR",
+    "California": "CA",
+    "Colorado": "CO",
+    "Connecticut": "CT",
+    "Delaware": "DE",
+    "Florida": "FL",
+    "Georgia": "GA",
+    "Hawaii": "HI",
+    "Idaho": "ID",
+    "Illinois": "IL",
+    "Indiana": "IN",
+    "Iowa": "IA",
+    "Kansas": "KS",
+    "Kentucky": "KY",
+    "Louisiana": "LA",
+    "Maine": "ME",
+    "Maryland": "MD",
+    "Massachusetts": "MA",
+    "Michigan": "MI",
+    "Minnesota": "MN",
+    "Mississippi": "MS",
+    "Missouri": "MO",
+    "Montana": "MT",
+    "Nebraska": "NE",
+    "Nevada": "NV",
+    "New Hampshire": "NH",
+    "New Jersey": "NJ",
+    "New Mexico": "NM",
+    "New York": "NY",
+    "North Carolina": "NC",
+    "North Dakota": "ND",
+    "Ohio": "OH",
+    "Oklahoma": "OK",
+    "Oregon": "OR",
+    "Pennsylvania": "PA",
+    "Rhode Island": "RI",
+    "South Carolina": "SC",
+    "South Dakota": "SD",
+    "Tennessee": "TN",
+    "Texas": "TX",
+    "Utah": "UT",
+    "Vermont": "VT",
+    "Virginia": "VA",
+    "Washington": "WA",
+    "West Virginia": "WV",
+    "Wisconsin": "WI",
+    "Wyoming": "WY",
+    "District of Columbia": "DC",
+    "United States": "US",
+}
+CURRENT_YEAR = datetime.now().year
+
+EIA_FACET = {
+    "wellhead": "N9190{}3",
+    "imports": "N9100{}3",
+    "citygate": "N3050{}3",
+    "residential": "N3010{}3",
+    "commercial": "N3020{}3",
+    "industrial": "N3035{}3",
+    "electrical_power": "N3045{}3",
+    "exports": "N9130{}3",
+}
+
+
+def convert_to_monthly(df: pd.DataFrame) -> pd.DataFrame | None:
+    """Converts an annual timeseries to monthly by repeating the one value, or returns
+    the data passed, if already monthly.
+
+    Args:
+        df (pd.DataFrame): The annual or monthly natural gas pricing data.
+
+    Returns:
+        pd.DataFrame | None: Returns back the monthly data if the original data have either
+            1 or 12 data entries, otherwise None is returned.
+    """
+    match df.shape[0]:
+        case 12:
+            return df.resample("MS").bfill()  # ensure it's start of the month
+        case 1:
+            year = df.index.year[0]
+            ix = pd.date_range(f"{year}-01", f"{year}-12", freq="MS")
+            df = df.reindex(ix, method="nearest")
+            return df
+        case _:
+            pass
+
+
+def get_state_from_coords(latitude: float, longitude: float) -> str:
+    """Reverse geocodes a :py:attr:`latitude` and :py:attr:`longitude` pair to get the
+    state containing the coordinate pair.
+
+    Args:
+        latitude (float): Site latitude.
+        longitude (float): Site longitude.
+
+    Returns:
+        str: 2-letter state code (i.e., "Alabama" -> "AL").
+    """
+    try:
+        import reverse_geocoder as rg
+    except ModuleNotFoundError as e:
+        msg = (
+            "EIA natural gas feedstock coordinate input requires `reverse_geocoder` to be"
+            " installed. Directly `pip install reverse_geocoder` or use"
+            " `pip install h2integrate[gis]`."
+        )
+        raise ModuleNotFoundError(msg) from e
+
+    result = rg.search((latitude, longitude))[0]
+    return convert_state_to_code(convert_state_value(result["admin1"]))
+
+
+def convert_state_value(state: str) -> str:
+    """Convert potential two-letter state abbreviations to upper case and all else to title
+    casing to align with the ``STATE_MAP`` keys and values.
+
+    Args:
+        state (str): Either a two-letter state abbreviation or full state name.
+
+    Returns:
+        str: Upper case state abbreviation or title case state name.
+    """
+    if len(state) == 2:
+        return state.upper()
+    return state.title()
+
+
+def convert_state_to_code(state: str) -> str:
+    """Converts the :py:attr:`state` name to a two-letter abbreviation or returns the input value.
+
+    Args:
+        state (str): Full state name in title casing or two-letter state abbreviation in upper case.
+
+    Returns:
+        str: Two-letter state abbreviation.
+    """
+    return STATE_MAP.get(state, state)
+
+
+def get_eia_api_key(api_key_file: Path | None) -> str:
+    """Retrieves the EIA API key from a file, and returns the key following "EIA_API_KEY:".
+
+    Args:
+        api_key_file (Path, optional): Full file path and name of where the EIA API key is located.
+            If none is provided, then the API key is retrieved from the environment variables. Must
+            be encoded as "EIA_API_KEY: xxxxxx"
+
+    Raises:
+        ValueError: Raised either if no file is provided and an environment variable has not be
+            defined, or if a filename is provided but "EIA_API_KEY" is not found.
+
+    Returns:
+        str: The EIA API key.
+    """
+    if api_key_file is None:
+        key = os.environ.get("EIA_API_KEY")
+        if key is None:
+            msg = (
+                "No `api_key_file` provided for the EIA API, and 'EIA_API_KEY' is not defined as an"
+                " environment variable."
+            )
+            raise ValueError(msg)
+        return key
+
+    with api_key_file.open() as f:
+        for line in f.readlines():
+            if ":" in line:
+                name, val = line.strip().split(":")
+                if name == "EIA_API_KEY":
+                    return val.strip()
+    raise ValueError(f"No 'EIA_API_KEY' defined in {api_key_file=}")
+
+
+@define(kw_only=True)
+class EIANaturalGasFeedstockPerformanceConfig(BaseConfig):
+    """Configuration class for the EIA natural gas price feedstock, which uses base units of MMBtu.
+
+    Attributes:
+        rated_capacity (float):  The rated capacity of the feedstock in `commodity_rate_units`.
+            This is used to size the feedstock supply to meet the plant's needs.
+    """
+
+    rated_capacity: float = field()
+    commodity: str = field(default="natural_gas", init=False)
+    commodity_rate_units: str = field(default="MMBtu/h", init=False)
+
+
+class EIANaturalGasFeedstockPerformanceModel(om.ExplicitComponent):
+    """Feedstock performance model compatible with the hard-coded units and commodity inputs
+    from the :py:class:`EIANaturalGasFeedstockCostModel` and
+    :py:class:`EIANaturalGasFeedstockConfig`.
+    """
+
+    _time_step_bounds = (3600, 3600)  # (min, max) time step lengths (seconds) allowed
+
+    def initialize(self):
+        self.options.declare("driver_config", types=dict)
+        self.options.declare("plant_config", types=dict)
+        self.options.declare("tech_config", types=dict)
+
+    def setup(self):
+        self.config = EIANaturalGasFeedstockPerformanceConfig.from_dict(
+            merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"),
+            additional_cls_name=self.__class__.__name__,
+        )
+        self.n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]
+        self.add_input(
+            f"{self.config.commodity}_capacity",
+            val=self.config.rated_capacity,
+            units=self.config.commodity_rate_units,
+        )
+
+        self.add_output(
+            f"{self.config.commodity}_out",
+            shape=self.n_timesteps,
+            units=self.config.commodity_rate_units,
+        )
+
+    def compute(self, inputs, outputs):
+        """Generates the feedstock array operating at full capacity for a full year."""
+        outputs[f"{self.config.commodity}_out"] = np.full(
+            self.n_timesteps, inputs[f"{self.config.commodity}_capacity"][0]
+        )
+
+
+@define
+class EIANaturalGasFeedstockConfig(BaseConfig):
+    """EIA Industrial Natural Gas Pricing API configuration and downloader for the US and all 50 US
+    states, in $/MCF, converted to $/MMBtu. Please see
+    https://www.eia.gov/opendata/browser/natural-gas/pri/sum for further details about data
+    availability.
+
+    Args:
+        state (str): Full name of the state or two-letter state abbreviation, such as
+            "United States" or "US". Only the "US" or all 50 states will produce valid results.
+        resource_year (int): The YYYY-format year whose data should be retrieved. Must be between
+            2001 and the current year, inclusive of endpoints.
+        cost_year (int): dollar-year for costs. Defaults to the current year.
+        monthly (Path): True, if monthly data is desired, False if annual data is desired.
+        price_category (str): One of "wellhead", "imports", "citygate", "residential", "commercial",
+            "industrial", "electrical_power", or "exports". Note that not all categories will return
+            state-level data.
+        api_key_file (Path, optional): Full file name of the file where the API key is located. If
+            no file name is provided, then the environment variables ``EIA_API_KEY`` is used.
+        filename (str, optional): Filename for where to save the data or where the data may
+            already be located. If the file exists, the columns "period", "state", and "price" must
+            exist, otherwise the file will not be used. "period" should be of the form YYYY or
+            YYYY-MM, and state should be either the full state name or the two-letter abbreviation.
+        annual_cost (float, optional): fixed cost associated with the feedstock in USD/year.
+            Defaults to 0.0.
+        start_up_cost (float, optional): one-time capital cost associated with the feedstock in USD.
+            Defaults to 0.0.
+    """
+
+    resource_year: int = field(validator=attrs.validators.in_(range(2001, CURRENT_YEAR + 1)))
+    monthly: bool = field(validator=attrs.validators.instance_of(bool))
+    price_category: str = field(converter=str.lower, validator=attrs.validators.in_(EIA_FACET))
+    url: str = field(default=None, init=False)
+    series: str = field(init=False)
+    price: pd.DataFrame = field(init=False, validator=attrs.validators.instance_of(pd.DataFrame))
+    api_key_file: str | None = field(default=None, converter=attrs.converters.optional(get_path))
+    state: str = field(
+        default=None,
+        converter=attrs.converters.optional(
+            attrs.converters.pipe(convert_state_value, convert_state_to_code)
+        ),
+        validator=attrs.validators.optional(
+            attrs.validators.in_([*STATE_MAP, *STATE_MAP.values()])
+        ),
+    )
+    latitude: float = field(default=999.9, validator=attrs.validators.instance_of(float))
+    longitude: float = field(default=999.9, validator=attrs.validators.instance_of(float))
+    cost_year: int = field(default=CURRENT_YEAR)
+    annual_cost: float = field(default=0.0, converter=float)
+    start_up_cost: float = field(default=0.0, converter=float)
+    commodity: str = field(default="natural_gas", init=False)
+    commodity_rate_units: str = field(default="MMBtu/h", init=False)
+    commodity_amount_units: str = field(default="MMBtu", init=False)
+    filename: str = field(default=None)
+
+    def __attrs_post_init__(self):
+        """Creates the EIA natural gas facet series code based on validated user inputs, sets the
+        :py:attr:`commodity_amount_units` if not given a value, and fetches the EIA natural gas
+        price.
+        """
+        if self.filename is not None:
+            try:
+                self.filename = get_path(self.filename)
+            except FileNotFoundError:
+                self.filename = Path(self.filename).resolve()
+
+        if self.state is None:
+            if self.latitude == 999.9 or self.longitude == 999.9:
+                msg = (
+                    "The EIA natural gas feedstock model require one of `state` or"
+                    " `latitude` and `longitude`."
+                )
+                raise ValueError(msg)
+
+            self.state = get_state_from_coords(self.latitude, self.longitude)
+
+        self.series = EIA_FACET[self.price_category].format(self.state)
+        if self.commodity_amount_units is None:
+            self.commodity_amount_units = f"({self.commodity_rate_units})*h"
+        self.url = self.create_eia_api_url()
+        self.price = self.get_data()
+
+    def create_eia_api_url(self):
+        """Creates the full EIA natural gas API url."""
+        api_key = get_eia_api_key(self.api_key_file)
+
+        year = self.resource_year
+        base_url = "https://api.eia.gov/v2/natural-gas/pri/sum/data/"
+        frequency = f"frequency={'monthly' if self.monthly else 'annual'}"
+        data = "data[0]=value"
+        facet = f"facets[series][]={self.series}"
+        start = f"start={year}"
+        end = f"end={year}"
+        if self.monthly:
+            start = f"{start}-01"
+            end = f"{end}-12"
+        sort_col = "sort[0][column]=period"
+        sort_dir = "sort[0][direction]=asc"
+        api_key = f"api_key={api_key}"
+
+        url_opts = "&".join((frequency, data, facet, start, end, sort_col, sort_dir, api_key))
+        url = f"{base_url}?{url_opts}"
+        return url
+
+    def get_data(self, filename: Path | None = None) -> pd.DataFrame:
+        """Loads the previously saved data from :py:attr:`filename` if ``resource_year``
+        is available as either annual or monthly data, otherwise data is retrieved from the EIA API.
+
+        Args:
+            filename (Path | None, optional): The full filename where the natural gas pricing data
+                should be saved to or loaded from, if available. Must have columns "period" and
+                "price". Defaults to None.
+
+        Raises:
+            requests.exceptions.HTTPError: Raised if an unsuccessful API query result is returned.
+
+        Returns:
+            pandas.DataFrame: DataFrame with index "period" and column "value" with natural gas
+                pricing in $/MMBtu (converted from the EIA's USD per thousands of cubic feet) as
+                either the monthly value or extrapolated annual values to a monthly resolution.
+        """
+        if filename is None:
+            filename = self.filename
+
+        if filename is not None:
+            filename = Path(filename).resolve()
+            if filename.exists():
+                df = pd.read_csv(filename, parse_dates=["period"]).set_index("period")
+                df = df.loc[
+                    (df.index.year == self.resource_year) & df.state.eq(self.state), ["price"]
+                ]
+                df = convert_to_monthly(df)
+                if df is not None:
+                    return df
+
+        r = requests.get(self.url)
+        if r.status_code != 200:
+            err = json.loads(r.text)["error"]
+            raise requests.exceptions.HTTPError(err)
+
+        df = pd.DataFrame.from_dict(json.loads(r.text)["response"]["data"])
+        if df.size == 0:
+            raise ValueError(f"No data for combination {self.state=}, {self.price_category=}")
+
+        df.period = pd.to_datetime(df.period)
+        df.value = df.value.astype(float)
+        df = (
+            df.set_index("period")
+            .rename(columns={"value": "price", "area-name": "state"})
+            .replace("U.S.", "US")
+        )[["state", "price"]]
+        df = convert_to_monthly(df)
+        df.price *= MCF_to_MMBTU
+
+        if filename is not None:
+            df.to_csv(filename, index_label="period")
+        return df[["price"]]
+
+
+class EIANaturalGasFeedstockCostModel(CostModelBaseClass):
+    """Feedstock cost model based on the EIA natural gas price API results that uses
+    annual or monthly data to model an hourly time step for a single year to model the
+    price of natural gas used in the model.
+    """
+
+    _time_step_bounds = (3600, 3600)  # (min, max) time step lengths (seconds) allowed
+
+    def _extrapolate_price_to_hourly(self) -> pd.DataFrame:
+        """Converts the monthly EIA price timeseries to an hourly time series for ``plant_life``
+        number of years.
+        """
+        price = self.config.price.copy()
+
+        last = price.iloc[[-1]].resample("ME").ffill()
+        last.index = [pd.to_datetime(last.index[0].to_pydatetime().replace(hour=23))]
+        price = (
+            pd.concat((price, last))
+            .resample("h")
+            .ffill()
+            .drop(price.loc[(price.index.month == 2) & (price.index.day == 29)].index)
+        )
+        if price.shape[0] != self.n_timesteps:
+            msg = (
+                "An error occurred converting EIA data to hourly to match size:"
+                f" {price.shape[0]} to simulation {self.n_timesteps=}"
+            )
+            raise ValueError(msg)
+        return price
+
+    def setup(self):
+        """Defines the inputs and outputs of the model and converts the
+        :py:attr:`EIANaturalGasFeedstockConfig.price` to an hourly timeseries for the
+        ``plant_life``.
+        """
+        # TODO: figure out mult-site or single site usage for coordinates input
+        # if (site_config := self.options["plant_config"].get("site")) is None:
+        #     raise ValueError("Single-site definition is missing from the plant configuration.")
+        self.config = EIANaturalGasFeedstockConfig.from_dict(
+            merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"),
+            # merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost") | site_config,  # noqa: E501
+            additional_cls_name=self.__class__.__name__,
+            strict=False,
+        )
+        self.n_timesteps = int(self.options["plant_config"]["plant"]["simulation"]["n_timesteps"])
+
+        super().setup()
+
+        self.dt = self.options["plant_config"]["plant"]["simulation"]["dt"]
+        self.plant_life = int(self.options["plant_config"]["plant"]["plant_life"])
+        self.fraction_of_year_simulated = (
+            self.dt / SECONDS_PER_HOUR * self.n_timesteps / HOURS_PER_YEAR
+        )
+        price = self._extrapolate_price_to_hourly()
+
+        self.add_input(
+            f"{self.config.commodity}_consumed",
+            val=0.0,
+            shape=self.n_timesteps,
+            units=self.config.commodity_rate_units,
+            desc=f"Consumption profile of {self.config.commodity}",
+        )
+        self.add_input(
+            f"{self.config.commodity}_out",
+            val=0,
+            shape=self.n_timesteps,
+            units=self.config.commodity_rate_units,
+        )
+        self.add_input(
+            "price",
+            val=price.price.to_numpy(),
+            units=f"USD/({self.config.commodity_amount_units})",
+            desc=f"Price profile of {self.config.commodity}",
+        )
+
+        self.add_output(
+            f"total_{self.config.commodity}_consumed",
+            val=0.0,
+            units=self.config.commodity_amount_units,
+        )
+        self.add_output(
+            f"annual_{self.config.commodity}_consumed",
+            val=0.0,
+            shape=self.plant_life,
+            units=f"({self.config.commodity_amount_units})/year",
+        )
+        self.add_output(
+            "capacity_factor",
+            val=0.0,
+            shape=self.plant_life,
+            units="unitless",
+            desc="Capacity factor",
+        )
+        self.add_output(
+            "replacement_schedule",
+            val=0.0,
+            shape=self.plant_life,
+            units="unitless",
+            desc="Lifetime estimate of item replacements as a fraction of capacity",
+        )
+
+        # TODO: Update to the commodity_capacity input of the FeedstockPerformanceModel
+        # NOTE: Should I set this to rated_capacity if it's available?
+        self.add_output(
+            f"rated_{self.config.commodity}_production",
+            val=0,
+            units=self.config.commodity_rate_units,
+        )
+
+    def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
+        """Calculates the following outputs:
+
+        - ``capacity_factor``: commodity_consumed / commodity_out
+        - ``total_commodity_consumed``: sum of commodity_consumed divided by number
+          of hours simulated.
+        - ``annual_commodity_consumed``: :py:attr:`total_commodity_consumed` * (1 / years simulated)
+        - ``rated_commodity_production``: maximum input ``commodity_out``.
+        - ``CapEx``: :py:attr:`FeedstockCostConfig.start_up_cost`.
+        - ``OpEx``: :py:attr:`FeedstockCostConfig.annual_cost`.
+        - ``VarOpEx``: sum of (:py:attr:`FeedstockCostConfig.price` * input ``commodity_consumed``).
+        """
+        outputs["capacity_factor"] = (
+            inputs[f"{self.config.commodity}_consumed"].sum()
+            / inputs[f"{self.config.commodity}_out"].sum()
+        )
+        outputs[f"total_{self.config.commodity}_consumed"] = inputs[
+            f"{self.config.commodity}_consumed"
+        ].sum() * (self.dt / 3600)
+
+        # TODO: update to handle varying consumption levels when feedstock consumption is available
+        outputs[f"annual_{self.config.commodity}_consumed"] = outputs[
+            f"total_{self.config.commodity}_consumed"
+        ] * (1 / self.fraction_of_year_simulated)
+
+        outputs[f"rated_{self.config.commodity}_production"] = inputs[
+            f"{self.config.commodity}_out"
+        ].max()
+
+        # TODO: Calculate costs
+        price = inputs["price"]
+        hourly_consumption = inputs[f"{self.config.commodity}_consumed"]
+        cost_per_year = sum(price * hourly_consumption)
+
+        outputs["CapEx"] = self.config.start_up_cost
+        outputs["OpEx"] = self.config.annual_cost
+        outputs["VarOpEx"] = cost_per_year
diff --git a/h2integrate/core/feedstocks.py b/h2integrate/feedstocks/feedstocks.py
similarity index 87%
rename from h2integrate/core/feedstocks.py
rename to h2integrate/feedstocks/feedstocks.py
index 594817356..687545b64 100644
--- a/h2integrate/core/feedstocks.py
+++ b/h2integrate/feedstocks/feedstocks.py
@@ -155,15 +155,12 @@ def setup(self):
             val=0.0,
             units=self.config.commodity_amount_units,
         )
-
         self.add_output(
             f"annual_{self.config.commodity}_consumed",
             val=0.0,
             shape=self.plant_life,
             units=f"({self.config.commodity_amount_units})/year",
         )
-
-        # Capacity factor is feedstock_consumed/max_feedstock_available
         self.add_output(
             "capacity_factor",
             val=0.0,
@@ -171,32 +168,43 @@ def setup(self):
             units="unitless",
             desc="Capacity factor",
         )
+        self.add_output(
+            "replacement_schedule",
+            val=0.0,
+            shape=self.plant_life,
+            units="unitless",
+            desc="Lifetime estimate of item replacements as a fraction of capacity",
+        )
 
-        # The should be equal to the commodity_capacity input of the FeedstockPerformanceModel
+        # TODO: Update to the commodity_capacity input of the FeedstockPerformanceModel
+        # NOTE: Should I set this to rated_capacity if it's available?
         self.add_output(
             f"rated_{self.config.commodity}_production",
             val=0,
             units=self.config.commodity_rate_units,
         )
 
-        # lifetime estimate of item replacements, represented as a fraction of the capacity.
-        self.add_output("replacement_schedule", val=0.0, shape=plant_life, units="unitless")
-
     def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
-        # Capacity factor is the total amount consumed / the total amount available
+        """Calculates the following outputs:
+
+        - ``capacity_factor``: commodity_consumed / commodity_out
+        - ``total_commodity_consumed``: sum of commodity_consumed divided by number
+          of hours simulated.
+        - ``annual_commodity_consumed``: :py:attr:`total_commodity_consumed` * (1 / years simulated)
+        - ``rated_commodity_production``: maximum input ``commodity_out``.
+        - ``CapEx``: :py:attr:`FeedstockCostConfig.start_up_cost`.
+        - ``OpEx``: :py:attr:`FeedstockCostConfig.annual_cost`.
+        - ``VarOpEx``: sum of (:py:attr:`FeedstockCostConfig.price` * input ``commodity_consumed``).
+        """
         outputs["capacity_factor"] = (
             inputs[f"{self.config.commodity}_consumed"].sum()
             / inputs[f"{self.config.commodity}_out"].sum()
         )
-
-        # Sum the amount consumed
         outputs[f"total_{self.config.commodity}_consumed"] = inputs[
             f"{self.config.commodity}_consumed"
         ].sum() * (self.dt / 3600)
 
-        # Estimate annual consumption based on consumption over the simulation
-        # NOTE: once we standardize feedstock consumption outputs in models, this should
-        # be updated to handle consumption that varies over years of operation
+        # TODO: update to handle varying consumption levels when feedstock consumption is available
         outputs[f"annual_{self.config.commodity}_consumed"] = outputs[
             f"total_{self.config.commodity}_consumed"
         ] * (1 / self.fraction_of_year_simulated)
@@ -205,7 +213,6 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
             f"{self.config.commodity}_out"
         ].max()
 
-        # Calculate costs
         price = inputs["price"]
         hourly_consumption = inputs[f"{self.config.commodity}_consumed"]
 
diff --git a/h2integrate/feedstocks/test/__init__.py b/h2integrate/feedstocks/test/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/h2integrate/feedstocks/test/conftest.py b/h2integrate/feedstocks/test/conftest.py
new file mode 100644
index 000000000..378efd555
--- /dev/null
+++ b/h2integrate/feedstocks/test/conftest.py
@@ -0,0 +1,68 @@
+"""
+Pytest configuration file.
+"""
+
+import os
+
+from h2integrate import EXAMPLE_DIR
+
+from test.conftest import (  # noqa: F401
+    temp_dir,
+    temp_dir_module,
+    temp_copy_of_example,
+    pytest_collection_modifyitems,
+    temp_copy_of_example_module_scope,
+)
+
+
+def pytest_sessionstart(session):
+    initial_om_report_setting = os.getenv("OPENMDAO_REPORTS")
+    if initial_om_report_setting is not None:
+        os.environ["TMP_OPENMDAO_REPORTS"] = initial_om_report_setting
+
+    os.environ["OPENMDAO_REPORTS"] = "none"
+
+    # set environment variables used for
+    # tests in h2integrate/core/test/test_recorder.py
+    os.environ["TEST_RECORDER_OUTPUT_EXAMPLE"] = "05_wind_h2_opt"
+    os.environ["TEST_RECORDER_OUTPUT_DIR"] = "testingtesting_output_dir"
+    os.environ["TEST_RECORDER_OUTPUT_FILE0"] = "testingtesting_filename.sql"
+    os.environ["TEST_RECORDER_OUTPUT_FILE1"] = "testingtesting_filename0.sql"
+    os.environ["TEST_RECORDER_OUTPUT_FILE2"] = "testingtesting_filename1.sql"
+
+
+def pytest_sessionfinish(session, exitstatus):
+    initial_om_report_setting = os.getenv("TMP_OPENMDAO_REPORTS")
+    if initial_om_report_setting is not None:
+        os.environ["OPENMDAO_REPORTS"] = initial_om_report_setting
+    os.environ.pop("TMP_OPENMDAO_REPORTS", None)
+
+    # remove files that were created in h2integrate/core/test/test_recorder.py
+    if os.getenv("TEST_RECORDER_OUTPUT_EXAMPLE") is not None:
+        test_dir = (
+            EXAMPLE_DIR
+            / os.getenv("TEST_RECORDER_OUTPUT_EXAMPLE")
+            / os.getenv("TEST_RECORDER_OUTPUT_DIR")
+        )
+        file0path = test_dir / os.getenv("TEST_RECORDER_OUTPUT_FILE0")
+        file1path = test_dir / os.getenv("TEST_RECORDER_OUTPUT_FILE1")
+        file2path = test_dir / os.getenv("TEST_RECORDER_OUTPUT_FILE2")
+        if file0path.exists():
+            file0path.unlink()
+        if file1path.exists():
+            file1path.unlink()
+        if file2path.exists():
+            file2path.unlink()
+        # remove folder created in h2integrate/core/test/test_recorder.py
+        if test_dir.exists():
+            files_in_test_folder = list(test_dir.iterdir())
+            if len(files_in_test_folder) == 0:
+                test_dir.rmdir()
+
+        # remove environment variables used for tests in
+        # h2integrate/core/test/test_recorder.py
+        os.environ.pop("TEST_RECORDER_OUTPUT_EXAMPLE", None)
+        os.environ.pop("TEST_RECORDER_OUTPUT_DIR", None)
+        os.environ.pop("TEST_RECORDER_OUTPUT_FILE0", None)
+        os.environ.pop("TEST_RECORDER_OUTPUT_FILE1", None)
+        os.environ.pop("TEST_RECORDER_OUTPUT_FILE2", None)
diff --git a/h2integrate/feedstocks/test/test_eia_ng_feedstock.py b/h2integrate/feedstocks/test/test_eia_ng_feedstock.py
new file mode 100644
index 000000000..b1ab09101
--- /dev/null
+++ b/h2integrate/feedstocks/test/test_eia_ng_feedstock.py
@@ -0,0 +1,189 @@
+import os
+import importlib
+
+import numpy as np
+import pandas as pd
+import pytest
+from requests.exceptions import HTTPError
+
+from h2integrate.feedstocks import eia_ng_pricing as eia
+
+
+DUMMY_KEY = "xxxxxx"
+
+
+@pytest.fixture
+def EIA_API_key_file(temp_dir):
+    """Creates a dummy EIA API key configuration file and returns the file path object."""
+    good_api_fn = temp_dir / ".eiarc"
+    bad_api_fn = temp_dir / ".badeiarc"
+    with good_api_fn.open("w") as f:
+        f.write(f"EIA_API_KEY: {DUMMY_KEY}")
+    with bad_api_fn.open("w") as f:
+        f.write(f"EIA_API: {DUMMY_KEY}")
+    return good_api_fn, bad_api_fn
+
+
+@pytest.mark.unit
+def test_convert_to_monthly(subtests):
+    """Test the annual and month-start conversions."""
+    correct_ix = pd.DatetimeIndex(pd.date_range("2000-01", "2000-12", freq="MS"), name="period")
+
+    annual_df = pd.DataFrame(
+        [[10]], columns=["price"], index=pd.Index(pd.to_datetime(["2000-01-01"]), name="period")
+    )
+    with subtests.test("Convert annual to monthly value"):
+        df = eia.convert_to_monthly(annual_df)
+        assert (df.index == correct_ix).all()
+        assert all(df.price.values == 10)
+
+    ms_ix = pd.to_datetime([f"2000-{x:02d}-01" for x in range(1, 13)])
+    me_ix = pd.to_datetime([f"2000-{x:02d}-28" for x in range(1, 13)])
+    correct_monthly_vals = np.arange(1, 13)
+
+    with subtests.test("Test month start inputs for monthly conversion to month starts"):
+        df = pd.DataFrame(
+            correct_monthly_vals, columns=["price"], index=pd.Index(ms_ix, name="period")
+        )
+        df = eia.convert_to_monthly(df)
+        assert (df.index == correct_ix).all()
+        assert (df.price.to_numpy() == correct_monthly_vals).all()
+
+    with subtests.test("Test month start inputs for monthly conversion to month starts"):
+        df = pd.DataFrame(
+            correct_monthly_vals, columns=["price"], index=pd.Index(me_ix, name="period")
+        )
+        df = eia.convert_to_monthly(df)
+        assert (df.index == correct_ix).all()
+        assert (df.price.to_numpy() == correct_monthly_vals).all()
+
+
+@pytest.mark.unit
+@pytest.mark.skipif(
+    importlib.util.find_spec("reverse_geocoder") is None, reason="reverse_geocoder is not installed"
+)
+def test_get_state_from_coords(subtests):
+    """Test the reverse geocoding for state data functionality."""
+    best_trailer_in_colorado_coords = (39.9140081, -105.2249155)
+    definitely_not_the_us_coords = (53.5265263, -113.657807)
+
+    with subtests.test("Test valid US coordinate pair"):
+        assert "CO" == eia.get_state_from_coords(*best_trailer_in_colorado_coords)
+
+    with subtests.test("Test invalid US coordinate pair"):
+        result = eia.get_state_from_coords(*definitely_not_the_us_coords)
+        assert result not in eia.STATE_MAP.values()
+        assert result == "Alberta"
+
+
+@pytest.mark.unit
+@pytest.mark.skipif(
+    importlib.util.find_spec("reverse_geocoder") is not None, reason="reverse_geocoder is installed"
+)
+def test_get_state_from_coords_fail():
+    """Tests that the correct error is raised when ``reverse_geocoder` is missing."""
+    msg = "EIA natural gas feedstock coordinate input requires `reverse_geocoder`"
+    with pytest.raises(ModuleNotFoundError, match=msg):
+        eia.get_state_from_coords(0, 0)
+
+
+@pytest.mark.unit
+def test_convert_state_value():
+    """Tests the conversion of the state value to a compliant name or code format."""
+    assert eia.convert_state_value("united states") == "United States"
+    assert eia.convert_state_value("us") == "US"
+
+
+@pytest.mark.unit
+def test_convert_state_to_code():
+    """Tests the conversion of a state name to a 2 letter code."""
+    assert eia.convert_state_to_code("Washington") == "WA"
+    assert eia.convert_state_to_code("DC") == "DC"
+    assert eia.convert_state_to_code("JK") == "JK"
+    assert eia.convert_state_to_code("washington") == "washington"
+
+
+@pytest.mark.unit
+def test_get_eia_api_key(subtests, EIA_API_key_file):
+    """Tests the API Key retrieval."""
+    good_api_fn, bad_api_fn = EIA_API_key_file
+
+    with subtests.test("Use a dummy file"):
+        assert eia.get_eia_api_key(good_api_fn) == DUMMY_KEY
+
+    if (api_key := os.environ.get("EIA_API_KEY")) is None:
+        api_key = DUMMY_KEY
+        os.environ["EIA_API_KEY"] = api_key
+    with subtests.test("Use the environment variable"):
+        assert eia.get_eia_api_key(None) == api_key
+        del os.environ["EIA_API_KEY"]
+
+    with subtests.test("Error is raised for no file nor env variable"):
+        msg = "No `api_key_file` provided for the EIA API, and 'EIA_API_KEY'"
+        with pytest.raises(ValueError, match=msg):
+            eia.get_eia_api_key(None)
+
+    with subtests.test("Error is raised for file with bad key name"):
+        msg = "No 'EIA_API_KEY' defined"
+        with pytest.raises(ValueError, match=msg):
+            eia.get_eia_api_key(bad_api_fn)
+
+
+@pytest.mark.unit
+def test_EIANaturalGasFeedstockConfig(subtests, EIA_API_key_file):
+    """Tests a failed API for basic parameterizations."""
+    if (api_key := os.environ.get("EIA_API_KEY")) is None:
+        api_key = DUMMY_KEY
+        os.environ["EIA_API_KEY"] = api_key
+
+    correct_url = (
+        "https://api.eia.gov/v2/natural-gas/pri/sum/data/"
+        "?frequency=monthly"
+        "&data[0]=value"
+        "&facets[series][]=N3035AK3"
+        "&start=2022-01"
+        "&end=2022-12"
+        "&sort[0][column]=period"
+        "&sort[0][direction]=asc"
+        f"&api_key={api_key}"
+    )
+    if api_key == DUMMY_KEY:
+        with subtests.test("Ensure API URL is correct for no API key"):
+            with pytest.raises(HTTPError):
+                config = eia.EIANaturalGasFeedstockConfig(
+                    state="ak",
+                    resource_year=2022,
+                    cost_year=2025,
+                    monthly=True,
+                    price_category="industrial",
+                )
+                assert config.url == correct_url
+        del os.environ["EIA_API_KEY"]
+    else:
+        with subtests.test("Ensure API works if a valid API environment variable exists"):
+            config = eia.EIANaturalGasFeedstockConfig(
+                state="ak",
+                resource_year=2022,
+                cost_year=2025,
+                monthly=True,
+                price_category="industrial",
+            )
+            assert config.url == correct_url
+            assert isinstance(config.price, pd.DataFrame)
+            assert config.price.shape == (12, 1)
+            assert (
+                config.price.index == pd.Index(pd.date_range("2022-01", "2022-12", freq="MS"))
+            ).all()
+
+    with subtests.test("Check no location data failure"):
+        msg = (
+            "The EIA natural gas feedstock model require one of `state` or"
+            " `latitude` and `longitude`."
+        )
+        with pytest.raises(ValueError, match=msg):
+            eia.EIANaturalGasFeedstockConfig(
+                resource_year=2022,
+                cost_year=2025,
+                monthly=True,
+                price_category="industrial",
+            )
diff --git a/h2integrate/core/test/test_feedstocks.py b/h2integrate/feedstocks/test/test_feedstocks.py
similarity index 99%
rename from h2integrate/core/test/test_feedstocks.py
rename to h2integrate/feedstocks/test/test_feedstocks.py
index bbf4772e1..760bd3b7d 100644
--- a/h2integrate/core/test/test_feedstocks.py
+++ b/h2integrate/feedstocks/test/test_feedstocks.py
@@ -10,7 +10,7 @@
 import openmdao.api as om
 from pytest import fixture
 
-from h2integrate.core.feedstocks import FeedstockCostModel, FeedstockPerformanceModel
+from h2integrate.feedstocks.feedstocks import FeedstockCostModel, FeedstockPerformanceModel
 
 
 @fixture
diff --git a/pyproject.toml b/pyproject.toml
index 0a67eb712..a83fe08d5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -50,6 +50,7 @@ dependencies = [
   "ProFAST",
   "Pyomo>=6.1.2",
   "rainflow",
+  "requests",
   "requests-cache",
   "retry_requests",
   "rich>=13.7.0",
@@ -110,7 +111,8 @@ examples = [
 ]
 gis = [
   "geopandas",
-  "contextily"
+  "contextily",
+  "reverse_geocoder"
 ]
 ard = ["ard-nrel"]
 extras = ["h2integrate[gis,ard]"]