diff --git a/CHANGELOG.md b/CHANGELOG.md
index 67f80cdc6..f91081115 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -73,6 +73,7 @@
 - Updated `commodity_sell_price` input to `ProFastNPV` to be per year of the plant life. Also updated `BasicProFASTParameterConfig.as_dict()` so explicitly input escalation values are not overwritten to the general inflation rate [PR 799](https://github.com/NatLabRockies/H2Integrate/pull/799)
 - Added `calc_azimuth_angle()` to `PYSAMSolarPlantPerformanceModel` to provide default azimuth angle based on whether the site is in the northern or southern hemisphere [PR 806](https://github.com/NatLabRockies/H2Integrate/pull/806)
 - Corrected water rate units in pipe feedstock from galUS to galUS/h [PR 813](https://github.com/NatLabRockies/H2Integrate/pull/813)
+- Added a thermal-nuclear (light-water reactor) model and a high-temperature steam electrolysis model. [PR 807](https://github.com/NatLabRockies/H2Integrate/pull/807)
 
 ## 0.8 [April 15, 2026]
 
diff --git a/docs/_static/class_hierarchy.html b/docs/_static/class_hierarchy.html
index 99ed86a84..9c6751d65 100644
--- a/docs/_static/class_hierarchy.html
+++ b/docs/_static/class_hierarchy.html
@@ -380,8 +380,8 @@ <h1></h1>
 
 
                   // parsing and collecting nodes and edges from the python
-                  nodes = new vis.DataSet([{"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoRuleBaseClass", "label": "PyomoRuleBaseClass", "shape": "hexagon", "size": 19.45945945945946, "title": "PyomoRuleBaseClass\ncontrol/control_rules/pyomo_rule_baseclass.py\n[Control / General]", "x": 654.0, "y": 0.0}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoDispatchGenericConverter", "label": "PyomoDispatchGenericConverter", "shape": "dot", "size": 18.0, "title": "PyomoDispatchGenericConverter\ncontrol/control_rules/converters/generic_converter.py\n[Converter / Other]", "x": 428.09388111524015, "y": 469.0988894808179}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoRuleStorageBaseclass", "label": "PyomoRuleStorageBaseclass", "shape": "diamond", "size": 18.0, "title": "PyomoRuleStorageBaseclass\ncontrol/control_rules/storage/pyomo_storage_rule_baseclass.py\n[Storage / General]", "x": 428.09388111524004, "y": -469.09888948081795}, {"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoStorageControllerBaseClass", "label": "PyomoStorageControllerBaseClass", "shape": "diamond", "size": 20.18918918918919, "title": "PyomoStorageControllerBaseClass\ncontrol/control_strategies/pyomo_storage_controller_baseclass.py\n[Storage / General]", "x": 455.6085661088584, "y": -385.16822684557377}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DemandOpenLoopStorageController", "label": "DemandOpenLoopStorageController", "shape": "diamond", "size": 18.0, "title": "DemandOpenLoopStorageController\ncontrol/control_strategies/storage/demand_openloop_storage_controller.py\n[Storage / General]", "x": 370.0643470376622, "y": -331.15773226109025}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HeuristicLoadFollowingStorageController", "label": "HeuristicLoadFollowingStorageController", "shape": "diamond", "size": 18.0, "title": "HeuristicLoadFollowingStorageController\ncontrol/control_strategies/storage/heuristic_pyomo_controller.py\n[Storage / General]", "x": 264.590914357365, "y": -368.7926071689721}, {"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "StorageOpenLoopControlBase", "label": "StorageOpenLoopControlBase", "shape": "diamond", "size": 20.18918918918919, "title": "StorageOpenLoopControlBase\ncontrol/control_strategies/storage/openloop_storage_control_base.py\n[Storage / General]", "x": 219.55784982221226, "y": -478.13520688340736}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OptimizedDispatchStorageController", "label": "OptimizedDispatchStorageController", "shape": "diamond", "size": 18.0, "title": "OptimizedDispatchStorageController\ncontrol/control_strategies/storage/optimized_pyomo_controller.py\n[Storage / General]", "x": 270.16454539792574, "y": -589.4304862347785}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PeakLoadManagementHeuristicOpenLoopStorageController", "label": "PeakLoadManagementHeuristicOpenLoopStorageController", "shape": "diamond", "size": 18.0, "title": "PeakLoadManagementHeuristicOpenLoopStorageController\ncontrol/control_strategies/storage/plm_openloop_storage_controller.py\n[Storage / General]", "x": 388.2687164324305, "y": -630.4775561122241}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PeakLoadManagementOptimizedStorageController", "label": "PeakLoadManagementOptimizedStorageController", "shape": "diamond", "size": 18.0, "title": "PeakLoadManagementOptimizedStorageController\ncontrol/control_strategies/storage/plm_optimized_storage_controller.py\n[Storage / General]", "x": 501.4805766605486, "y": -572.7844347513467}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SimpleStorageOpenLoopController", "label": "SimpleStorageOpenLoopController", "shape": "diamond", "size": 18.0, "title": "SimpleStorageOpenLoopController\ncontrol/control_strategies/storage/simple_openloop_controller.py\n[Storage / General]", "x": 538.9625776291, "y": -449.75172147563603}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CostMinimizationControl", "label": "CostMinimizationControl", "shape": "hexagon", "size": 18.0, "title": "CostMinimizationControl\ncontrol/control_strategies/system_level/cost_minimization_control.py\n[Control / General]", "x": 681.5146849936184, "y": 83.93066263524416}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DemandFollowingControl", "label": "DemandFollowingControl", "shape": "hexagon", "size": 18.0, "title": "DemandFollowingControl\ncontrol/control_strategies/system_level/demand_following_control.py\n[Control / General]", "x": 595.9704659224221, "y": 137.9411572197277}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ProfitMaximizationControl", "label": "ProfitMaximizationControl", "shape": "hexagon", "size": 18.0, "title": "ProfitMaximizationControl\ncontrol/control_strategies/system_level/profit_maximization_control.py\n[Control / General]", "x": 490.497033242125, "y": 100.30628231184586}, {"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SystemLevelControlBase", "label": "SystemLevelControlBase", "shape": "hexagon", "size": 20.18918918918919, "title": "SystemLevelControlBase\ncontrol/control_strategies/system_level/system_level_control_base.py\n[Control / General]", "x": 445.4639687069722, "y": -9.036317402589397}, {"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": 455.6085661088585, "y": 553.0295521160621}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "AmmoniaSynLoopCostModel", "label": "AmmoniaSynLoopCostModel", "shape": "dot", "size": 18.0, "title": "AmmoniaSynLoopCostModel\nconverters/ammonia/ammonia_synloop_cost.py\n[Converter / Ammonia]", "x": 370.0643470376623, "y": 607.0400467005456}, {"borderWidth": 3.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "AmmoniaSynLoopPerformanceModel", "label": "AmmoniaSynLoopPerformanceModel", "shape": "dot", "size": 18.0, "title": "AmmoniaSynLoopPerformanceModel\nconverters/ammonia/ammonia_synloop_performance.py\n[Converter / Ammonia]", "x": 264.59091435736514, "y": 569.4051717926637}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SimpleAmmoniaPerformanceModel", "label": "SimpleAmmoniaPerformanceModel", "shape": "dot", "size": 18.0, "title": "SimpleAmmoniaPerformanceModel\nconverters/ammonia/simple_ammonia_model.py\n[Converter / Ammonia]", "x": 219.55784982221238, "y": 460.06257207822847}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SimpleAmmoniaCostModel", "label": "SimpleAmmoniaCostModel", "shape": "dot", "size": 18.0, "title": "SimpleAmmoniaCostModel\nconverters/ammonia/simple_ammonia_model.py\n[Converter / Ammonia]", "x": 270.16454539792585, "y": 348.7672927268573}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DOCPerformanceModel", "label": "DOCPerformanceModel", "shape": "dot", "size": 18.0, "title": "DOCPerformanceModel\nconverters/co2/marine/direct_ocean_capture.py\n[Converter / CO2]", "x": 388.2687164324306, "y": 307.72022284941175}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DOCCostModel", "label": "DOCCostModel", "shape": "dot", "size": 18.0, "title": "DOCCostModel\nconverters/co2/marine/direct_ocean_capture.py\n[Converter / CO2]", "x": 501.4805766605487, "y": 365.4133442102892}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OAEPerformanceModel", "label": "OAEPerformanceModel", "shape": "dot", "size": 18.0, "title": "OAEPerformanceModel\nconverters/co2/marine/ocean_alkalinity_enhancement.py\n[Converter / CO2]", "x": 538.9625776291001, "y": 488.44605748599986}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OAECostModel", "label": "OAECostModel", "shape": "dot", "size": 18.0, "title": "OAECostModel\nconverters/co2/marine/ocean_alkalinity_enhancement.py\n[Converter / CO2]", "x": 475.93189116793957, "y": 601.9588898891661}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OAECostAndFinancialModel", "label": "OAECostAndFinancialModel", "shape": "dot", "size": 18.0, "title": "OAECostAndFinancialModel\nconverters/co2/marine/ocean_alkalinity_enhancement.py\n[Converter / CO2]", "x": 349.5705117805884, "y": 635.8507248662497}, {"borderWidth": 4.0, "color": {"background": "#1B3A5C", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GridPerformanceModel", "label": "GridPerformanceModel", "shape": "dot", "size": 18.0, "title": "GridPerformanceModel\nconverters/grid/grid.py\n[Converter / Grid]", "x": 236.61361523178945, "y": 568.2423536759715}, {"borderWidth": 4.0, "color": {"background": "#1B3A5C", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GridCostModel", "label": "GridCostModel", "shape": "dot", "size": 18.0, "title": "GridCostModel\nconverters/grid/grid.py\n[Converter / Grid]", "x": 206.39396475153276, "y": 439.409697663304}, {"borderWidth": 4.0, "color": {"background": "#4A90D9", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HOPPComponent", "label": "HOPPComponent", "shape": "dot", "size": 18.0, "title": "HOPPComponent\nconverters/hopp/hopp_wrapper.py\n[Converter / HOPP]", "x": 278.165573095184, "y": 327.54056326717114}, {"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": 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": "ElectrolyzerPerformanceBaseClass", "label": "ElectrolyzerPerformanceBaseClass", "shape": "dot", "size": 18.72972972972973, "title": "ElectrolyzerPerformanceBaseClass\nconverters/hydrogen/electrolyzer_baseclass.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": "ElectrolyzerCostBaseClass", "label": "ElectrolyzerCostBaseClass", "shape": "dot", "size": 20.18918918918919, "title": "ElectrolyzerCostBaseClass\nconverters/hydrogen/electrolyzer_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": "LinearH2FuelCellPerformanceModel", "label": "LinearH2FuelCellPerformanceModel", "shape": "dot", "size": 18.0, "title": "LinearH2FuelCellPerformanceModel\nconverters/hydrogen/h2_fuel_cell.py\n[Converter / Hydrogen]", "x": 329.04975630639217, "y": 636.513453478361}, {"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": 222.32576716621537, "y": 553.5863417347226}, {"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": 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": "SingliticoCostModel", "label": "SingliticoCostModel", "shape": "dot", "size": 18.0, "title": "SingliticoCostModel\nconverters/hydrogen/singlitico_cost_model.py\n[Converter / Hydrogen]", "x": 293.81016729091453, "y": 314.4201619012048}, {"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": 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": "SteamMethaneReformerCostModel", "label": "SteamMethaneReformerCostModel", "shape": "dot", "size": 18.0, "title": "SteamMethaneReformerCostModel\nconverters/hydrogen/steam_methane_reformer.py\n[Converter / Hydrogen]", "x": 531.4807512394786, "y": 393.1585470237545}, {"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": 538.4198729733245, "y": 529.1652694271473}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": 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"PipeStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "CompressedGasStorageCostModel"}]);
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"hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ResizeablePerformanceModelBaseClass", "label": "ResizeablePerformanceModelBaseClass", "shape": "ellipse", "size": 19.42105263157895, "title": "ResizeablePerformanceModelBaseClass\ncore/model_baseclasses.py\n[Core / General]", "x": -288.0485916668164, "y": 575.9204299065049}, {"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": 19.42105263157895, "title": "CacheBaseClass\ncore/model_baseclasses.py\n[Core / General]", "x": -237.4418960911029, "y": 464.62515055513364}, {"borderWidth": 4.0, "color": {"background": "#F5C542", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": 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"ElectrolyzerCostBaseClass", "label": "ElectrolyzerCostBaseClass", "shape": "dot", "size": 20.842105263157894, "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": 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"size": 18.0, "title": "HTSEPerformanceModel\nconverters/hydrogen/htse_electrolyzer.py\n[Converter / Hydrogen]", "x": 541.9933368656093, "y": 509.0547616483423}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HTSECostModel", "label": "HTSECostModel", "shape": "dot", "size": 18.0, "title": "HTSECostModel\nconverters/hydrogen/htse_electrolyzer.py\n[Converter / Hydrogen]", "x": 462.61867180963463, "y": 617.7338834509918}, {"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": 329.04975630639217, "y": 636.513453478361}, {"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": 19.42105263157895, "title": "GeoH2SubsurfacePerformanceBaseClass\nconverters/hydrogen/geologic/h2_well_subsurface_baseclass.py\n[Converter / Hydrogen]", "x": 222.32576716621537, "y": 553.5863417347226}, {"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": 18.710526315789473, "title": "GeoH2SubsurfaceCostBaseClass\nconverters/hydrogen/geologic/h2_well_subsurface_baseclass.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": "AspenGeoH2SurfacePerformanceModel", "label": "AspenGeoH2SurfacePerformanceModel", "shape": "dot", "size": 18.0, "title": "AspenGeoH2SurfacePerformanceModel\nconverters/hydrogen/geologic/aspen_surface_processing.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": 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"id": "StimulatedGeoH2PerformanceModel", "label": "StimulatedGeoH2PerformanceModel", "shape": "dot", "size": 18.0, "title": "StimulatedGeoH2PerformanceModel\nconverters/hydrogen/geologic/templeton_serpentinization.py\n[Converter / Hydrogen]", "x": 538.4198729733245, "y": 529.1652694271473}, {"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": 18.710526315789473, "title": "GeoH2SurfacePerformanceBaseClass\nconverters/hydrogen/geologic/h2_well_surface_baseclass.py\n[Converter / Hydrogen]", "x": 445.5710237882473, "y": 629.0047614736493}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", 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{"arrows": "to", "from": "CostModelBaseClass", "to": "MCHTOLStorageCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "HydrogenStorageBaseCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBBatteryCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "FeedstockCostModel"}, {"arrows": "to", "from": "ResizeablePerformanceModelBaseClass", "to": "ElectrolyzerPerformanceBaseClass"}, {"arrows": "to", "from": "ResizeablePerformanceModelBaseClass", "to": "AmmoniaSynLoopPerformanceModel"}, {"arrows": "to", "from": "CacheBaseClass", "to": "FlorisWindPlantPerformanceModel"}, {"arrows": "to", "from": "CacheBaseClass", "to": "HOPPComponent"}, {"arrows": "to", "from": "SolarPerformanceBaseClass", "to": "PYSAMSolarPlantPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerPerformanceBaseClass", "to": "ECOElectrolyzerPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerPerformanceBaseClass", "to": "HTSEPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "SingliticoCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "BasicElectrolyzerCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "CustomElectrolyzerCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "HTSECostModel"}, {"arrows": "to", "from": "ECOElectrolyzerPerformanceModel", "to": "WOMBATElectrolyzerModel"}, {"arrows": "to", "from": "GeoH2SubsurfacePerformanceBaseClass", "to": "NaturalGeoH2PerformanceModel"}, {"arrows": "to", "from": "GeoH2SubsurfacePerformanceBaseClass", "to": "StimulatedGeoH2PerformanceModel"}, {"arrows": "to", "from": "GeoH2SubsurfaceCostBaseClass", "to": "GeoH2SubsurfaceCostModel"}, {"arrows": "to", "from": "GeoH2SurfacePerformanceBaseClass", "to": "AspenGeoH2SurfacePerformanceModel"}, {"arrows": "to", "from": "GeoH2SurfaceCostBaseClass", "to": "AspenGeoH2SurfaceCostModel"}, {"arrows": "to", "from": "DesalinationPerformanceBaseClass", "to": "ReverseOsmosisPerformanceModel"}, {"arrows": "to", "from": "DesalinationCostBaseClass", "to": "ReverseOsmosisCostModel"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBasePerformanceComponent", "to": "HydrogenEAFPlantPerformanceComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBasePerformanceComponent", "to": "NaturalGasEAFPlantPerformanceComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBaseCostComponent", "to": "HydrogenEAFPlantCostComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBaseCostComponent", "to": "NaturalGasEAFPlantCostComponent"}, {"arrows": "to", "from": "SteelPerformanceBaseClass", "to": "SteelPerformanceModel"}, {"arrows": "to", "from": "SteelCostBaseClass", "to": "SteelCostAndFinancialModel"}, {"arrows": "to", "from": "WindPerformanceBaseClass", "to": "PYSAMWindPlantPerformanceModel"}, {"arrows": "to", "from": "WindPerformanceBaseClass", "to": "FlorisWindPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolPerformanceBaseClass", "to": "SMRMethanolPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolPerformanceBaseClass", "to": "CO2HMethanolPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolCostBaseClass", "to": "SMRMethanolPlantCostModel"}, {"arrows": "to", "from": "MethanolCostBaseClass", "to": "CO2HMethanolPlantCostModel"}, {"arrows": "to", "from": "MethanolFinanceBaseClass", "to": "SMRMethanolPlantFinanceModel"}, {"arrows": "to", "from": "MethanolFinanceBaseClass", "to": "CO2HMethanolPlantFinanceModel"}, {"arrows": "to", "from": "IronReductionPlantBasePerformanceComponent", "to": "HydrogenIronReductionPlantPerformanceComponent"}, {"arrows": "to", "from": "IronReductionPlantBasePerformanceComponent", "to": "NaturalGasIronReductionPlantPerformanceComponent"}, {"arrows": "to", "from": "IronReductionPlantBaseCostComponent", "to": "HydrogenIronReductionPlantCostComponent"}, {"arrows": "to", "from": "IronReductionPlantBaseCostComponent", "to": "NaturalGasIronReductionPlantCostComponent"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "StoragePerformanceModel"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "StorageAutoSizingModel"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "PySAMBatteryPerformanceModel"}, {"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": "FeedstockCostModel", "to": "EIANaturalGasFeedstockCostModel"}, {"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": "PeakLoadManagementOptimizedStorageController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "HeuristicLoadFollowingStorageController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "OptimizedDispatchStorageController"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "ProfitMaximizationControl"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "DemandFollowingControl"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "CostMinimizationControl"}, {"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" });
diff --git a/docs/technology_models/h2_htse.md b/docs/technology_models/h2_htse.md
new file mode 100644
index 000000000..2e9c57ba0
--- /dev/null
+++ b/docs/technology_models/h2_htse.md
@@ -0,0 +1,103 @@
+# High-Temperature Steam Electrolysis (HTSE) Model
+
+The HTSE model in H2Integrate represents hydrogen production from high-temperature steam electrolysis using electricity and thermal input. It is implemented as two components:
+
+- `HTSEPerformanceModel`
+- `HTSECostModel`
+
+The performance model converts electricity and heat into hydrogen, water demand, and operating signals for connected technologies. The cost model computes installed capital cost and fixed operating cost from installed HTSE size.
+
+## Model Overview
+
+This is a simplified HTSE representation with constant nominal specific energy requirements:
+
+- `nominal_electricity_required` in `kWh/kg`
+- `nominal_heat_required` in `kWh/kg`
+
+At each timestep, hydrogen production is determined from:
+
+- installed HTSE size
+- available `electricity_in`
+- available `heat_in`
+- optional `hydrogen_command_value` when system-level control is enabled
+- turndown behavior
+
+The model also exposes internal operating signals that are useful for coupled systems, including:
+
+- `heat_demand`
+- `electricity_demand`
+- `electricity_consumed`
+- `water_demand`
+
+```{note}
+The current implementation uses `electricity_demand` to report installed electrical demand equal to nameplate size, while `electricity_consumed` reports the timestep electricity required by the energy balance. For coupled analyses, `electricity_consumed` is the more literal consumption signal.
+```
+
+## Performance Model
+
+Use this model by setting:
+
+- performance model: `HTSEPerformanceModel`
+
+The HTSE performance model inherits from the electrolyzer base classes, so it is treated as a hydrogen-producing, dispatchable technology with an `electricity_in` input and a `hydrogen_out` output.
+
+```{figure} images/HTSE.png
+:alt: HTSE schematic
+:width: 100%
+:align: center
+```
+
+### Dispatch and sizing behavior
+
+Installed size is first inferred from:
+
+$$
+\text{electrolyzer\_size\_mw} = n_{clusters} \times cluster\_rating\_MW
+$$
+
+The model supports additional sizing modes inherited from the resizeable performance base class:
+
+- `normal`
+- `resize_by_max_feedstock`
+- `resize_by_max_commodity`
+
+In the current implementation:
+
+- `resize_by_max_feedstock` supports sizing from `electricity`
+- `resize_by_max_commodity` supports sizing from `hydrogen`
+
+When system-level control is enabled, hydrogen demand is taken from `hydrogen_command_value`. Otherwise, the model assumes demand equal to rated hydrogen production implied by installed electrical size.
+
+### Energy balance behavior
+
+The model forms:
+
+$$
+\text{total\_specific\_energy} = nominal\_heat\_required + nominal\_electricity\_required
+$$
+
+and computes a nominal heat-to-electricity ratio:
+
+$$
+\text{ratio\_heat\_elec\_nom} = \frac{nominal\_heat\_required}{nominal\_electricity\_required}
+$$
+
+Available heat is used first up to the requested `heat_demand`. The remaining required energy is supplied electrically when possible. Hydrogen production is then limited by the combined energy available and by the turndown threshold.
+
+```{note}
+The current implementation is intentionally simple and should be interpreted as a reduced-order plant representation, not a detailed SOEC stack model with thermal transients, degradation coupling, startup dynamics, or detailed balance-of-plant behavior.
+```
+
+### API details
+For API details, see the [`HTSEPerformanceModel` and `HTSECostModel` API documentation](../_autosummary/h2integrate.converters.hydrogen.htse_electrolyzer).
+
+## Cost Model
+
+Use this model by setting:
+
+- cost model: `HTSECostModel`
+
+The cost model is size-based and currently depends only on installed HTSE size.
+
+### API details
+For API details, see the [`HTSEPerformanceModel` and `HTSECostModel` API documentation](../_autosummary/h2integrate.converters.hydrogen.htse_electrolyzer).
diff --git a/docs/technology_models/images/HTSE.png b/docs/technology_models/images/HTSE.png
new file mode 100644
index 000000000..fb33470f5
Binary files /dev/null and b/docs/technology_models/images/HTSE.png differ
diff --git a/docs/technology_models/images/ThermalNucReactor-H2I.png b/docs/technology_models/images/ThermalNucReactor-H2I.png
new file mode 100644
index 000000000..63db37880
Binary files /dev/null and b/docs/technology_models/images/ThermalNucReactor-H2I.png differ
diff --git a/docs/technology_models/nuclear.md b/docs/technology_models/nuclear.md
index e793d1429..0036d679c 100644
--- a/docs/technology_models/nuclear.md
+++ b/docs/technology_models/nuclear.md
@@ -1,83 +1,33 @@
-# Nuclear power plant model
-
-The nuclear power plant model provides a simple, size-based performance model and a type-based cost model.
-Cost defaults are intended to be populated from literature, such as Quinn et al. (2023) on SMR LWR techno-economic analysis.
-See the paper here: [Quinn et al. (2023)](#references).
-
-To use this model, set the performance model to `QuinnNuclearPerformanceModel` and the cost model to `QuinnNuclearCostModel` in your `tech_config`.
-
-## Performance model
-
-The performance model limits electricity production by the rated capacity and an optional demand signal.
-
-**Inputs**
-| Name | Shape | Units | Description |
-| --- | --- | --- | --- |
-| `system_capacity` | scalar | kW | Rated electrical capacity. |
-| `electricity_set_point` | array[n_timesteps] | kW | Optional set point profile; defaults to rated capacity. |
-
-**Outputs**
-| Name | Shape | Units | Description |
-| --- | --- | --- | --- |
-| `electricity_out` | array[n_timesteps] | kW | Electricity produced, capped at `system_capacity`. |
-| `rated_electricity_production` | scalar | kW | Rated production (capacity). |
-| `total_electricity_produced` | scalar | kW*h | Sum of production over the simulation. |
-| `annual_electricity_produced` | array[plant_life] | kW*h/year | Annualized production. |
-| `capacity_factor` | array[plant_life] | unitless | Ratio of actual to maximum production. |
-| `replacement_schedule` | array[plant_life] | unitless | Placeholder replacement schedule (zeros). |
-| `operational_life` | scalar | yr | Operational life (defaults to plant life). |
-
-## Cost model
-
-The cost model uses direct cost parameters to compute capital and operating costs.
-It supports optional scaling of capex with size using a reference capacity and scaling exponent.
-
-**Inputs**
-| Name | Shape | Units | Description |
-| --- | --- | --- | --- |
-| `system_capacity` | scalar | kW | Plant capacity used for cost scaling. |
-| `electricity_out` | array[n_timesteps] | kW | Output from performance model. |
-
-**Cost parameters (tech_config)**
-| Key | Type | Description |
-| --- | --- | --- |
-| `system_capacity_kw` | float | Rated electrical capacity (kW). |
-| `capex_per_kw` | float | Capital cost per kW. |
-| `fixed_opex_per_kw_year` | float | Fixed O&M per kW per year. |
-| `variable_opex_per_mwh` | float | Variable O&M per MWh. |
-| `reference_capacity_kw` | float | Reference capacity for capex scaling (defaults to `system_capacity_kw`). |
-| `capex_scaling_exponent` | float | Capex scaling exponent (defaults to 1.0). |
-| `cost_year` | int | Dollar year for the input costs. |
-
-The capex calculation follows:
+# Nuclear power plant models
 
-$$
-C_{\text{capex}} = (c_{\text{capex}} \cdot (P / P_{\text{ref}})^{(k-1)}) \cdot P
-$$
+H2Integrate currently includes two nuclear converter options:
+
+- `QuinnNuclearPerformanceModel` with `QuinnNuclearCostModel` for an electricity-only nuclear plant
+- `SimpleThermalNuclearReactorPerformanceModel` with `SimpleThermalNuclearReactorCostModel` for a thermal reactor that can trade off electricity production and process heat delivery
+
+The first model is based on Quinn et al. (2023). The second is a simplified thermal reactor representation intended for coupled workflows such as nuclear plus HTSE.
 
-Where $c_{\text{capex}}$ is `capex_per_kw`, $P$ is plant capacity (kW), $P_{\text{ref}}$ is `reference_capacity_kw`, and $k$ is `capex_scaling_exponent`.
+## Quinn electricity-only nuclear model
 
-**Outputs**
-| Name | Shape | Units | Description |
-| --- | --- | --- | --- |
-| `CapEx` | scalar | USD | Total capital expenditure. |
-| `OpEx` | scalar | USD/year | Fixed plus variable O&M. |
-| `VarOpEx` | array[plant_life] | USD/year | Variable O&M (repeated each year). |
-| `cost_year` | scalar | year | Dollar year of costs. |
+Use this model by setting:
 
-## Example tech_config
+- performance model: `QuinnNuclearPerformanceModel`
+- cost model: `QuinnNuclearCostModel`
+
+This model produces electricity only and clips commanded output to rated plant capacity.
+
+### Example `tech_config`
 
 ```yaml
 technologies:
   nuclear:
     performance_model:
-      model: "QuinnNuclearPerformanceModel"
+      model: QuinnNuclearPerformanceModel
     cost_model:
-      model: "QuinnNuclearCostModel"
+      model: QuinnNuclearCostModel
     model_inputs:
       performance_parameters:
         system_capacity_kw: 300000.0
-        capacity_factor: 0.9
       cost_parameters:
         system_capacity_kw: 450000.0
         capex_per_kw: 6000.0
@@ -88,7 +38,51 @@ technologies:
         cost_year: 2023
 ```
 
-(references)=
-## References
+### API details
+For API details, see the [`QuinnNuclearPerformanceModel` and `QuinnNuclearCostModel` API documentation](../_autosummary/h2integrate.converters.nuclear.nuclear_plant).
 
+(references)=
+### References
 - Quinn, J. et al., 2023. Small modular reactor light water reactor techno-economic analysis. Applied Energy 120669. https://doi.org/10.1016/j.apenergy.2023.120669
+
+## Simple thermal nuclear reactor model
+
+Use this model by setting:
+
+- performance model: `SimpleThermalNuclearReactorPerformanceModel`
+- cost model: `SimpleThermalNuclearReactorCostModel`
+
+This model represents a reactor with:
+
+- a high-pressure electric conversion stage
+- a low-pressure electric conversion stage
+- an extractable process heat stream, extracted upstream of the low-pressure turbine stages (dashed red arrow in the figure)
+
+It supports two operating modes:
+
+- `heat`: In `heat` mode, delivered heat is limited by available process heat and requested heat demand. Remaining low-pressure heat is converted to electricity.
+
+- `electricity`: In `electricity` mode, electricity is limited by the command value and rated capacity. Remaining process heat is then sent as `heat_out`.
+
+```{figure} images/ThermalNucReactor-H2I.png
+:alt: Thermal nuclear reactor schematic
+:width: 100%
+:align: center
+```
+
+### Thermal reactor dispatch logic
+
+The model computes a combined electric efficiency:
+
+$$
+\eta_{combined} = \eta_{hp} + (1 - \eta_{hp}) \eta_{lp}
+$$
+
+Then infers thermal capacity from rated electrical capacity:
+
+$$
+P_{thermal} = \frac{P_{electric,rated}}{\eta_{combined}}
+$$
+
+### API details
+For API details, see the [`SimpleThermalNuclearReactorPerformanceModel` and `SimpleThermalNuclearReactorCostModel` API documentation](../_autosummary/h2integrate.converters.nuclear.nuclear_plant_thermal).
diff --git a/docs/user_guide/model_overview.md b/docs/user_guide/model_overview.md
index 8c943e7a7..e2cee6f50 100644
--- a/docs/user_guide/model_overview.md
+++ b/docs/user_guide/model_overview.md
@@ -150,12 +150,14 @@ auto-generated API page.
 - `hydrogen`: hydrogen production
   - performance models:
     + {py:class}`~h2integrate.converters.hydrogen.pem_electrolyzer.ECOElectrolyzerPerformanceModel` - An OpenMDAO component that wraps the PEM electrolyzer model.
+    + {py:class}`~h2integrate.converters.hydrogen.htse_electrolyzer.HTSEPerformanceModel` - A simplified high-temperature steam electrolysis (HTSE) model.
     + {py:class}`~h2integrate.converters.hydrogen.h2_fuel_cell.LinearH2FuelCellPerformanceModel` - Performance model for a hydrogen fuel cell.
     + {py:class}`~h2integrate.converters.hydrogen.steam_methane_reformer.SteamMethaneReformerPerformanceModel` - Performance model for steam methane reforming (SMR) hydrogen production plants.
   - cost models:
     + {py:class}`~h2integrate.converters.hydrogen.basic_cost_model.BasicElectrolyzerCostModel` - An OpenMDAO component that computes the cost of a PEM electrolyzer.
     + {py:class}`~h2integrate.converters.hydrogen.custom_electrolyzer_cost_model.CustomElectrolyzerCostModel` - An OpenMDAO component that computes the cost of a PEM electrolyzer.
     + {py:class}`~h2integrate.converters.hydrogen.h2_fuel_cell.H2FuelCellCostModel` - Cost model for a hydrogen fuel cell system.
+    + {py:class}`~h2integrate.converters.hydrogen.htse_electrolyzer.HTSECostModel` - A simple size-based cost model for HTSE.
     + {py:class}`~h2integrate.converters.hydrogen.singlitico_cost_model.SingliticoCostModel` - An OpenMDAO component that computes the cost of a PEM electrolyzer.
     + {py:class}`~h2integrate.converters.hydrogen.steam_methane_reformer.SteamMethaneReformerCostModel` - Cost model for steam methane reforming hydrogen production plants.
   - combined performance and cost models:
@@ -202,8 +204,10 @@ auto-generated API page.
 - `nuclear`: nuclear power plants
   - performance models:
     + {py:class}`~h2integrate.converters.nuclear.nuclear_plant.QuinnNuclearPerformanceModel` - Simple nuclear performance model producing electricity.
+    + {py:class}`~h2integrate.converters.nuclear.nuclear_plant_thermal.SimpleThermalNuclearReactorPerformanceModel` - Simple thermal nuclear reactor model with heat and electricity outputs.
   - cost models:
     + {py:class}`~h2integrate.converters.nuclear.nuclear_plant.QuinnNuclearCostModel` - Cost model for nuclear power plants.
+    + {py:class}`~h2integrate.converters.nuclear.nuclear_plant_thermal.SimpleThermalNuclearReactorCostModel` - Simple cost model for the thermal nuclear reactor.
 
 - `solar`: solar-PV panels
   - performance models:
diff --git a/examples/36_nuclear_reactor_htse/driver_config.yaml b/examples/36_nuclear_reactor_htse/driver_config.yaml
new file mode 100644
index 000000000..edf1a9391
--- /dev/null
+++ b/examples/36_nuclear_reactor_htse/driver_config.yaml
@@ -0,0 +1,52 @@
+name: driver_config
+description: Runs a thermal nuclear plant with simple optimization
+general:
+  folder_output: nuclear_thermal_plant
+### Use This For Parameter Sweep
+# driver:
+#   design_of_experiments:
+#     flag: true
+#     generator: "FullFact" #type of generator to use
+#     levels: 3  # input is specific to this generator
+#     debug_print: true
+driver:
+  optimization:
+    flag: false
+    solver: COBYLA
+    tol: 0.1
+    catol: 100000
+    max_iter: 100
+    rhobeg: 0.1
+    disp: 3
+    debug_print: true
+design_variables:
+  nuclear:
+    electricity_command_value:
+      flag: true
+      lower: 1000
+      upper: 500000
+      units: kW
+  htse:
+    n_clusters:
+      flag: true
+      lower: 2.0
+      upper: 40.0
+      units: unitless
+# Add constraints later to the code
+# constraints:
+  # nuclear:
+    # electricity_out:
+      # flag: true
+      # upper: 5000.0
+      # units: MW
+  #   ramping_rate:
+  #     flag: true
+  #     upper: 500
+  #     units: kW/hr
+  # electrolyzer:
+  #   total_hydrogen_produced:
+  #     flag: false
+  #     lower: 60500000.
+  #     units: kg/year
+objective:
+  name: finance_subgroup_hydrogen.LCOH
diff --git a/examples/36_nuclear_reactor_htse/nuclear_reactor_thermal_htse.yaml b/examples/36_nuclear_reactor_htse/nuclear_reactor_thermal_htse.yaml
new file mode 100644
index 000000000..874e6169f
--- /dev/null
+++ b/examples/36_nuclear_reactor_htse/nuclear_reactor_thermal_htse.yaml
@@ -0,0 +1,6 @@
+name: H2Integrate_config
+system_summary: This is an example plant with a nuclear plant capable of dispatching thermal and electrical energy to an HTSE
+  system
+driver_config: driver_config.yaml
+technology_config: tech_config.yaml
+plant_config: plant_config.yaml
diff --git a/examples/36_nuclear_reactor_htse/plant_config.yaml b/examples/36_nuclear_reactor_htse/plant_config.yaml
new file mode 100644
index 000000000..e37f37a79
--- /dev/null
+++ b/examples/36_nuclear_reactor_htse/plant_config.yaml
@@ -0,0 +1,87 @@
+name: plant_config
+description: This plant is located in CO, USA...
+sites:
+  site:
+    latitude: 35.2018863
+    longitude: -101.945027
+plant:
+  plant_life: 60
+# array of arrays containing left-to-right technology
+# interconnections; can support bidirectional connections
+# with the reverse definition.
+# this will naturally grow as we mature the interconnected tech
+technology_interconnections:
+  - [nuclear, htse, [heat_out, heat_in]]
+  - [nuclear, electrical_load_demand, electricity, cable]
+  - [htse, nuclear, [heat_demand, heat_command_value]]
+  - [htse, electrical_load_demand, [electricity_demand, electricity_demand]]
+  - [water_feedstock, htse, water, pipe]
+  - [htse_electricity_feedstock, htse, electricity, cable]
+  - [finance_subgroup_electricity, grid_sell, [LCOE, electricity_sell_price]]
+  - [finance_subgroup_electricity, htse_electricity_feedstock, [LCOE, price]]
+  - [electrical_load_demand, grid_sell, [unused_electricity_out, electricity_set_point]]
+  - [electrical_load_demand, grid_sell, [unused_electricity_out, electricity_in]]
+tech_to_dispatch_connections: []
+resource_to_tech_connections: []
+finance_parameters:
+  finance_groups:
+    nuclear_finance:
+      commodity: electricity
+      finance_model: ProFastLCO
+      model_inputs:
+        params:
+          analysis_start_year: 2030
+          installation_time: 84  # months - estimating 7 years for construction
+          inflation_rate: 0.0  # 0 for nominal analysis
+          discount_rate: 0.105  # nominal equity return based on 2024 ATB baseline workbook for nuclear
+          debt_equity_ratio: 2.26  # 2024 ATB uses 69.4% debt for nuclear
+          property_tax_and_insurance: 0.03  # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf  https://www.house.mn.gov/hrd/issinfo/clsrates.aspx
+          total_income_tax_rate: 0.257  # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8)
+          capital_gains_tax_rate: 0.15  # H2FAST default
+          sales_tax_rate: 0.07375  # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/
+          debt_interest_rate: 0.08  # based on 2024 ATB nominal interest rate for nuclear
+          debt_type: Revolving debt  # can be "Revolving debt" or "One time loan". Revolving debt is H2FAST default and leads to much lower LCOH
+          loan_period_if_used: 0  # H2FAST default, not used for revolving debt
+          cash_onhand_months: 1  # H2FAST default
+          admin_expense: 0.00  # percent of sales H2FAST default
+        capital_items:
+          depr_type: MACRS   # can be "MACRS" or "Straight line"
+          depr_period: 15  # 15 years is typical, some advanced nuclear facilities may be eligible for a 5 year MACRS
+          refurb: [0.]
+    htse_finance:
+      commodity: hydrogen
+      finance_model: ProFastLCO
+      model_inputs:
+        params:
+          analysis_start_year: 2036  # delayed so nuclear plant and HTSE plant come online the same year
+          installation_time: 12  # months - estimating 1 year for construction
+          inflation_rate: 0.0  # 0 for nominal analysis
+          discount_rate: 0.15  # nominal equity return based on 2024 ATB baseline workbook for nuclear
+          debt_equity_ratio: 2.26  # 2024 ATB uses 69.4% debt for nuclear
+          property_tax_and_insurance: 0.03  # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf  https://www.house.mn.gov/hrd/issinfo/clsrates.aspx
+          total_income_tax_rate: 0.257  # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8)
+          capital_gains_tax_rate: 0.15  # H2FAST default
+          sales_tax_rate: 0.07375  # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/
+          debt_interest_rate: 0.08  # based on 2024 ATB nominal interest rate for nuclear
+          debt_type: Revolving debt  # can be "Revolving debt" or "One time loan". Revolving debt is H2FAST default and leads to much lower LCOH
+          loan_period_if_used: 0  # H2FAST default, not used for revolving debt
+          cash_onhand_months: 1  # H2FAST default
+          admin_expense: 0.00  # percent of sales H2FAST default
+        capital_items:
+          depr_type: MACRS   # can be "MACRS" or "Straight line"
+          depr_period: 7  # 15 years is typical, some advanced nuclear facilities may be eligible for a 5 year MACRS
+          refurb: [0.]
+  cost_adjustment_parameters:
+    cost_year_adjustment_inflation: 0.025  # used to adjust modeled costs to target_dollar_year
+    target_dollar_year: 2022
+  finance_subgroups:
+    electricity:
+      commodity: electricity
+      commodity_stream: nuclear
+      finance_groups: [nuclear_finance]
+      technologies: [nuclear]
+    hydrogen:
+      commodity: hydrogen
+      commodity_stream: htse
+      finance_groups: [htse_finance]
+      technologies: [htse_electricity_feedstock, water_feedstock, htse]
diff --git a/examples/36_nuclear_reactor_htse/run_nuclear_reactor_thermal.py b/examples/36_nuclear_reactor_htse/run_nuclear_reactor_thermal.py
new file mode 100644
index 000000000..620a0282b
--- /dev/null
+++ b/examples/36_nuclear_reactor_htse/run_nuclear_reactor_thermal.py
@@ -0,0 +1,90 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+from h2integrate.core.file_utils import load_yaml
+from h2integrate.core.h2integrate_model import H2IntegrateModel
+
+
+# load config and update htse refurb profile
+config = load_yaml("nuclear_reactor_thermal_htse.yaml")
+plant_config = load_yaml(config["plant_config"])
+plant_life = plant_config["plant"]["plant_life"]
+tech_config = load_yaml(config["technology_config"])
+
+refurb = np.zeros(plant_life)
+refurb[3::4] = 1
+tech_config["technologies"]["htse"]["model_inputs"]["capital_items"]["refurb"] = refurb
+
+config["technology_config"] = tech_config
+
+# Create a GreenHEART model
+h2i = H2IntegrateModel(config)
+
+# generate N2 diagram
+# om.n2(h2i.prob)
+
+# Run and process the model
+h2i.run()
+
+h2i.post_process()
+# expected water kg/h 4,347.8261
+# generate plots of the output
+
+e_nuclear = h2i.prob.get_val("nuclear.annual_electricity_produced", units="TW*h/year")[0]
+e_htse = h2i.prob.get_val("htse.electricity_demand", units="TW*h/year")[0]
+e_sold = h2i.prob.get_val("grid_sell.annual_electricity_sold", units="TW*h/year")[0]
+
+heat_nuclear = np.sum(h2i.prob.get_val("nuclear.heat_out", units="GW"))
+heat_htse = np.sum(h2i.prob.get_val("htse.heat_demand", units="GW"))
+
+h2_htse = h2i.prob.get_val("htse.annual_hydrogen_produced", units="kt/year")[0]
+
+# Print levelized cost outputs for each finance model/subgroup
+lcoe_electricity = h2i.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0]
+lcoh_hydrogen = h2i.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0]
+
+print(f"LCOE (electricity finance subgroup): {lcoe_electricity:.2f} USD/MWh")
+print(f"LCOH (hydrogen finance subgroup): {lcoh_hydrogen:.4f} USD/kg")
+
+# Prepare data for bar charts
+labels = ["Nuclear Plant", "HTSE Plant", "Grid Sold"]
+electricity = [e_nuclear, e_htse, e_sold]
+
+heat_labels = ["Nuclear Plant", "HTSE Plant"]
+heat = [heat_nuclear, heat_htse]
+
+h2_labels = ["HTSE Plant"]
+h2 = [h2_htse]
+
+fig, axs = plt.subplots(1, 3, figsize=(15, 5))
+
+# Electricity bar chart: Nuclear Generation (left), Stacked HTSE+Grid (right)
+bar_width = 0.6
+x = np.arange(2)
+
+# Bar 0: Total Nuclear Generation
+axs[0].bar([0], [e_nuclear], color="deepskyblue", width=bar_width, label="Nuclear generation")
+
+# Bar 1: Stacked HTSE + Grid Sold
+axs[0].bar([1], [e_sold], color="green", width=bar_width, label="Sold to grid")
+axs[0].bar([1], [e_htse], color="orange", width=bar_width, label="HTSE demand", bottom=e_sold)
+
+axs[0].set_xticks([0, 1])
+axs[0].set_xticklabels(["Produced", "Used"])
+axs[0].set_ylabel("Annual Energy (TW*h/year)")
+axs[0].set_title("Electricity")
+axs[0].set(ylim=[0, 10])
+axs[0].legend(ncol=2, frameon=False)
+
+# Heat bar chart
+axs[1].bar(heat_labels, heat, color=["red", "purple"])
+axs[1].set_ylabel("Annual Heat (GWh/year)")
+axs[1].set_title("Heat")
+
+# H2 bar chart
+axs[2].bar(h2_labels, h2, color=["gold"])
+axs[2].set_ylabel("Annual Hydrogen (kt/year)")
+axs[2].set_title("Hydrogen")
+
+plt.tight_layout()
+plt.show()
diff --git a/examples/36_nuclear_reactor_htse/tech_config.yaml b/examples/36_nuclear_reactor_htse/tech_config.yaml
new file mode 100644
index 000000000..7116fb56c
--- /dev/null
+++ b/examples/36_nuclear_reactor_htse/tech_config.yaml
@@ -0,0 +1,107 @@
+name: technology_config
+description: This is a nuclear plant generating electricity and can also provide heat
+general:
+  folder_output: nuclear_htse
+technologies:
+  water_feedstock:
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: water
+        commodity_rate_units: galUS/h
+      performance_parameters:
+        rated_capacity: 2000.0  # gal water per hour
+      cost_parameters:
+        cost_year: 2021
+        commodity_amount_units: galUS
+        price: 0.0035  # 3.5 USD/kgal
+        annual_cost: 0.
+        start_up_cost: 0.0
+  nuclear:
+    performance_model:
+      model: SimpleThermalNuclearReactorPerformanceModel
+    cost_model:
+      model: SimpleThermalNuclearReactorCostModel
+    model_inputs:
+      performance_parameters:
+        operating_mode: heat
+        electricity_command_value: 500000  # kW
+        high_pressure_electrical_efficiency: 0.12
+        low_pressure_electrical_efficiency: 0.22
+        minimum_heat_extract: 1  # MW
+      cost_parameters:
+        cost_year: 2022
+        nuclear_reactor_rated_capacity: 5000000.0
+        nuclear_reactor_upfront_cost: 5750.0  # $/kWe  # includes significant labor as per (Moneghan and Abou-Jaoude, 2024).
+        nuclear_reactor_fixed_om_cost: 2.64 # can adjust with labor  # (Moneghan and Abou-Jaoude, 2024).
+        nuclear_reactor_variable_om_cost: 0.0145
+        # Values from Table 38, Meta-Analysis of Advanced Nuclear Reactor Cost Estimations,
+        # slightly adjusted away from 93% capacity factor
+      shared_parameters:
+        rated_capacity: 1000000.0
+  htse_electricity_feedstock:
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: electricity
+        commodity_rate_units: kW
+      performance_parameters:
+        rated_capacity: 30000.0  # kW
+      cost_parameters:
+        cost_year: 2022
+        commodity_amount_units: kW*h
+        price: 0.0  # USD/(kW*h)
+        annual_cost: 0.
+        start_up_cost: 0.0
+  htse:
+    performance_model:
+      model: HTSEPerformanceModel
+    cost_model:
+      model: HTSECostModel
+    model_inputs:
+      performance_parameters:
+        nominal_heat_required: 6.4  # kWh/kg
+        nominal_electricity_required: 36.8  # kWh/kg
+        # https://lwrs.inl.gov/content/uploads/11/2024/03/TechnicalEconomicAssessment-LWR-SupportedHydrogen.pdf  # INL/RPT-23-75604
+        cluster_rating_MW: 1  # Electric-only sizing component, so capacity = rating*clusters/nom_elec
+        n_clusters: 25
+        turndown_ratio: 0.1  # minimum operating power
+        uptime_hours_until_eol: 35040  # 4 years as per  Diaz et al. 2023
+      cost_parameters:
+        cost_year: 2022
+        unit_capex: 1417  # $/kW based on Diaz et al. 2023: Technical Economic Assessment of LWR-Supported Hydrogen Markets in Gulf Coast Regions
+        fixed_capex: 0.0
+      capital_items:
+        depr_type: MACRS     # can be "MACRS" or "Straight line"
+        depr_period: 7
+        refurb: null  # to be assigned at run rime
+        replacement_cost_percent: 0.1077  # based on table 2 in Diaz et al. 2023
+  electrical_load_demand:
+    performance_model:
+      model: GenericDemandComponent
+    model_inputs:
+      performance_parameters:
+        commodity: electricity
+        commodity_rate_units: kW
+        demand_profile: 100000  # 100 MW
+  grid_sell:
+    performance_model:
+      model: GridPerformanceModel
+    cost_model:
+      model: GridCostModel
+    model_inputs:
+      shared_parameters:
+        interconnection_size: 5000000  # 5 GW
+      cost_parameters:
+        cost_year: 2022
+        electricity_sell_price: 0.0  # $/kWh - only selling, no buying
+        sell_price_mode: constant
+        interconnection_capex_per_kw: 0.0  # $/kW capital cost
+        interconnection_opex_per_kw: 0.0  # $/kW/year O&M cost
+        fixed_interconnection_cost: 0.0  # $ one-time fixed cost
diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py
index d02a1c52a..d113a9542 100644
--- a/examples/test/test_all_examples.py
+++ b/examples/test/test_all_examples.py
@@ -3050,3 +3050,56 @@ def test_plm_optimized_dispatch_example(subtests, temp_copy_of_example):
         battery_charge = model.prob.get_val("battery.storage_electricity_charge", units="kW")
         total_energy_charged = battery_charge.sum() * (1 / 60)  # kWh, 1 min timestep
         assert pytest.approx(total_energy_charged, rel=1e-3) == -2663.0
+
+
+@pytest.mark.integration
+@pytest.mark.parametrize(
+    "example_folder,resource_example_folder", [("36_nuclear_reactor_htse", None)]
+)
+def test_nuclear_reactor_htse_example(subtests, temp_copy_of_example):
+    example_folder = temp_copy_of_example
+
+    model = H2IntegrateModel(example_folder / "nuclear_reactor_thermal_htse.yaml")
+    model.run()
+    annual_nuclear_electricity = model.prob.get_val(
+        "nuclear.annual_electricity_produced", units="TW*h/year"
+    )[0]
+    model.prob.get_val("htse.electricity_demand", units="TW*h/year")[0]
+    annual_htse_electricity = model.prob.get_val("htse.electricity_demand", units="TW*h/year")[0]
+    annual_grid_sell = model.prob.get_val("grid_sell.annual_electricity_sold", units="TW*h/year")[0]
+    annual_hydrogen = model.prob.get_val("htse.annual_hydrogen_produced", units="kt/year")[0]
+
+    with subtests.test("Nuclear annual electricity is positive"):
+        assert annual_nuclear_electricity == pytest.approx(8.75162086757)
+
+    with subtests.test("HTSE annual hydrogen production is positive"):
+        assert annual_hydrogen == pytest.approx(5.951086956521741)
+
+    with subtests.test("Grid annual electricity sold is non-negative"):
+        assert annual_grid_sell == pytest.approx(8.53262086956522)
+
+    with subtests.test("Electricity balance between HTSE demand and grid sales"):
+        assert pytest.approx(annual_nuclear_electricity, rel=1e-4) == (
+            annual_htse_electricity + annual_grid_sell
+        )
+
+    high_pressure_heat = model.prob.get_val("nuclear.high_pressure_heat", units="MW")
+    low_pressure_heat = model.prob.get_val("nuclear.low_pressure_heat", units="MW")
+    extracted_heat = model.prob.get_val("nuclear.heat_out", units="MW")
+
+    with subtests.test("Nuclear thermal split is conserved"):
+        assert np.allclose(high_pressure_heat, low_pressure_heat + extracted_heat, rtol=1e-6)
+
+    rated_nuclear_output = model.prob.get_val("nuclear.rated_electricity_production", units="MW")[0]
+    nuclear_electricity_out = model.prob.get_val("nuclear.electricity_out", units="MW")
+
+    with subtests.test("Nuclear electricity output is within rated limit"):
+        assert np.all(nuclear_electricity_out <= rated_nuclear_output + 1e-6)
+
+    unused_electricity = model.prob.get_val(
+        "electrical_load_demand.unused_electricity_out", units="MW"
+    )
+    grid_electricity_in = model.prob.get_val("grid_sell.electricity_in", units="MW")
+
+    with subtests.test("Unused electricity is routed to grid sell"):
+        assert pytest.approx(unused_electricity.sum(), rel=1e-6) == grid_electricity_in.sum()
diff --git a/h2integrate/converters/hydrogen/__init__.py b/h2integrate/converters/hydrogen/__init__.py
index b39fb2556..0bf80327d 100644
--- a/h2integrate/converters/hydrogen/__init__.py
+++ b/h2integrate/converters/hydrogen/__init__.py
@@ -13,3 +13,7 @@
     SteamMethaneReformerPerformanceModel,
     SteamMethaneReformerCostModel,
 )
+from h2integrate.converters.hydrogen.htse_electrolyzer import (
+    HTSEPerformanceModel,
+    HTSECostModel,
+)
diff --git a/h2integrate/converters/hydrogen/htse_electrolyzer.py b/h2integrate/converters/hydrogen/htse_electrolyzer.py
new file mode 100644
index 000000000..c0903dc9e
--- /dev/null
+++ b/h2integrate/converters/hydrogen/htse_electrolyzer.py
@@ -0,0 +1,340 @@
+import numpy as np
+from attrs import field, define
+from CoolProp.CoolProp import PropsSI
+
+from h2integrate.core.utilities import merge_shared_inputs
+from h2integrate.core.validators import gt_zero, contains
+from h2integrate.core.model_baseclasses import (
+    CostModelBaseConfig,
+    ResizeablePerformanceModelBaseConfig,
+)
+from h2integrate.converters.hydrogen.utilities import size_electrolyzer_for_hydrogen_demand
+from h2integrate.converters.hydrogen.electrolyzer_baseclass import (
+    ElectrolyzerCostBaseClass,
+    ElectrolyzerPerformanceBaseClass,
+)
+
+
+@define(kw_only=True)
+class HTSEElectrolyzerPerformanceModelConfig(ResizeablePerformanceModelBaseConfig):
+    """Configuration class for the HTSE performance model.
+
+    Args:
+        n_clusters (int): Number of HTSE clusters in the system.
+        nominal_heat_required (float): Nominal thermal energy required per kg of hydrogen
+            in kWh/kg.
+        nominal_electricity_required (float): Nominal electrical energy required per kg of
+            hydrogen in kWh/kg.
+        location (str): Deployment location, either ``"onshore"`` or ``"offshore"``.
+            Present in config but not used directly in the current performance calculation.
+            Defaults to ``"onshore"``.
+        cluster_rating_MW (float): Nameplate electrical rating per cluster in MW.
+        eol_eff_percent_loss (float): End-of-life efficiency loss in percent. Present in
+            config but not used directly in the current timestep energy balance. Defaults
+            to ``10.0``.
+        uptime_hours_until_eol (int): Hours of operation between replacement events.
+            Defaults to ``80000``.
+        include_degradation_penalty (bool): Whether to apply a degradation penalty. Present
+            in config but not used directly in the current timestep energy balance. Defaults
+            to ``False``.
+        turndown_ratio (float): Minimum fraction of rated hydrogen production required to
+            stay on (unitless). Defaults to ``0.1``.
+        pressure_H2 (float): Hydrogen pressure setting. Present in config but not used
+            directly in the current timestep energy balance. Defaults to ``1.0``.
+    """
+
+    n_clusters: int = field(validator=gt_zero)
+    nominal_heat_required: float = field(validator=gt_zero)
+    nominal_electricity_required: float = field(validator=gt_zero)
+    location: str = field(default="onshore", validator=contains(["onshore", "offshore"]))
+    cluster_rating_MW: float = field(validator=gt_zero)
+    eol_eff_percent_loss: float = field(default=10.0, validator=gt_zero)
+    uptime_hours_until_eol: int = field(default=80000, validator=gt_zero)
+    include_degradation_penalty: bool = field(default=False)
+    turndown_ratio: float = field(default=0.1, validator=gt_zero)
+    pressure_H2: float = field(default=1.0, validator=gt_zero)
+
+
+class HTSEPerformanceModel(ElectrolyzerPerformanceBaseClass):
+    """A simplified high-temperature steam electrolysis (HTSE) model.
+
+    This model represents hydrogen production from high-temperature steam electrolysis
+    using constant nominal specific energy requirements for electricity and heat. It
+    inherits from the electrolyzer base classes, so it is treated as a hydrogen-producing,
+    dispatchable technology with an ``electricity_in`` input and a ``hydrogen_out`` output.
+
+    Installed size is inferred from ``n_clusters`` and ``cluster_rating_MW``, and the model
+    also supports the ``resize_by_max_feedstock`` (from electricity) and
+    ``resize_by_max_commodity`` (from hydrogen) sizing modes inherited from the resizeable
+    performance base class. At each timestep the model uses available heat first, supplies
+    the remaining required energy electrically when possible, and limits hydrogen
+    production by the combined available energy and the turndown threshold. It also exposes
+    operating signals useful for coupled systems, including ``heat_demand``,
+    ``electricity_demand``, ``electricity_consumed``, and ``water_consumed``.
+
+    In the current implementation, ``electricity_demand`` reports installed electrical
+    demand equal to nameplate size, while ``electricity_consumed`` reports timestep
+    electricity required by the energy balance.
+
+    The implementation is intentionally simple and should be interpreted as a reduced-order
+    plant representation, not a detailed SOEC stack model with thermal transients,
+    degradation coupling, startup dynamics, or detailed balance-of-plant behavior.
+    """
+
+    def setup(self) -> None:
+        self.config = HTSEElectrolyzerPerformanceModelConfig.from_dict(
+            merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"),
+            strict=False,
+            additional_cls_name=self.__class__.__name__,
+        )
+        super().setup()
+
+        self.add_output(
+            "efficiency",
+            val=0.0,
+            units="unitless",
+            desc="Average first-law efficiency based on utilized input energy",
+        )
+        self.add_output(
+            "time_until_replacement",
+            val=float(self.config.uptime_hours_until_eol),
+            units="h",
+            desc="Operating hours until replacement",
+        )
+        self.add_input(
+            "n_clusters",
+            val=self.config.n_clusters,
+            units="unitless",
+            desc="Number of HTSE clusters in the system",
+        )
+        self.add_input(
+            "heat_in",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="kW",
+            desc="Thermal energy supplied to the HTSE system",
+        )
+        self.add_output(
+            "electrolyzer_size_mw",
+            val=0.0,
+            units="MW",
+            desc="Installed HTSE nameplate capacity",
+        )
+        self.add_input("cluster_size", val=1.0, units="MW")
+        self.add_input("max_hydrogen_capacity", val=1000.0, units="kg/h")
+
+        self.add_input(
+            "water_in",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="galUS/h",
+            desc="Water supply",
+        )
+
+        self.add_output(
+            "water_consumed",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="galUS/h",
+            desc="Water consumption",
+        )
+        self.add_output(
+            "heat_demand",
+            val=self.config.n_clusters
+            * self.config.cluster_rating_MW
+            * 1000
+            * self.config.nominal_heat_required
+            / self.config.nominal_electricity_required,
+            shape=self.n_timesteps,
+            units="kW",
+            desc="Thermal demand by the HTSE system",
+        )
+        self.add_output(
+            "electricity_demand",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="kW",
+            desc="Electric demand by the HTSE system",
+        )
+        self.add_output(
+            "electricity_consumed",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="kW",
+            desc="Electricity consumed by the HTSE",
+        )
+
+    def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
+        electrolyzer_size_mw = float(inputs["n_clusters"][0]) * self.config.cluster_rating_MW
+
+        size_mode = discrete_inputs["size_mode"]
+        if size_mode != "normal":
+            size_flow = discrete_inputs["flow_used_for_sizing"]
+            if size_mode == "resize_by_max_feedstock":
+                feed_ratio = max(float(inputs["max_feedstock_ratio"][0]), 1.0e-6)
+                if size_flow == "electricity":
+                    electrolyzer_size_mw = np.max(inputs["electricity_in"]) / 1000.0 * feed_ratio
+                else:
+                    raise ValueError(f"Cannot resize for '{size_flow}' feedstock")
+            elif size_mode == "resize_by_max_commodity":
+                comm_ratio = max(float(inputs["max_commodity_ratio"][0]), 1.0e-6)
+                if size_flow == "hydrogen":
+                    electrolyzer_size_mw = size_electrolyzer_for_hydrogen_demand(
+                        float(inputs["max_hydrogen_capacity"][0]) * comm_ratio
+                    )
+                else:
+                    raise ValueError(f"Cannot resize for '{size_flow}' commodity")
+            else:
+                raise NotImplementedError(f"Sizing mode '{size_mode}' not implemented")
+
+        n_clusters = inputs["n_clusters"]
+        electrolyzer_size_mw = n_clusters * self.config.cluster_rating_MW
+        electrolyzer_size_kw = electrolyzer_size_mw * 1000.0
+
+        if "system_level_control" in self.options["plant_config"]:
+            hydrogen_demand = inputs["hydrogen_command_value"]  # kg/hr?
+        else:
+            hydrogen_demand = (
+                electrolyzer_size_kw / self.config.nominal_electricity_required
+            )  # kW/(kWh/kg) = kg/hr
+        ratio_heat_elec_nom = (
+            self.config.nominal_heat_required / self.config.nominal_electricity_required
+        )
+
+        heat_available_kw = inputs["heat_in"]
+        electricity_available_kw = inputs["electricity_in"]
+        # hydrogen_demand = inputs["hydrogen_command_value"]
+
+        total_specific_energy = (
+            self.config.nominal_heat_required + self.config.nominal_electricity_required
+        )
+        rated_hydrogen_production = electrolyzer_size_kw / self.config.nominal_electricity_required
+
+        # note here that the RATED production is based purely on the electrical requirement.
+        # The heat input is a bonus amount. Units here are kg/hr
+        heat_demand_kw = hydrogen_demand * self.config.nominal_heat_required
+
+        actual_heat_kw = np.minimum(heat_demand_kw, heat_available_kw)
+        electricity_demand_kw = total_specific_energy * hydrogen_demand - actual_heat_kw
+        ratio_in = heat_available_kw / electricity_available_kw
+        hydrogen_produced = np.where(
+            electricity_available_kw < electricity_demand_kw,
+            np.where(
+                ratio_in > ratio_heat_elec_nom,
+                electricity_available_kw / self.config.nominal_electricity_required,
+                (heat_available_kw + electricity_available_kw) / total_specific_energy,
+            ),
+            (actual_heat_kw + electricity_demand_kw) / total_specific_energy,
+        )
+
+        actual_heat_kw / ratio_heat_elec_nom
+
+        # in this case, the electricity is insufficient compared to the heat, so we'll use
+        # actual_electricity_kw = np.minimum(electricity_demand_kw, electricity_available_kw)
+        min_turn_down = self.config.turndown_ratio * rated_hydrogen_production
+        hydrogen_produced = np.where(hydrogen_produced >= min_turn_down, hydrogen_produced, 0.0)
+
+        # Density of liquid water at 20°C (293.15 K) and 1 atm (101325 Pa)
+        PropsSI("D", "T", 293.15, "P", 101325, "Water")
+        kg_per_liter_water = 1.0
+        water_demand_kg_per_h = hydrogen_produced * 18.015 / 2.016
+        liters_per_galUS = 3.785411
+        water_demand_gal_per_h = (
+            water_demand_kg_per_h * (1.0 / kg_per_liter_water) * (1.0 / liters_per_galUS)
+        )
+
+        # heat_demand_kw = hydrogen_out * self.config.nominal_heat_required
+        # electricity_demand_kw = hydrogen_out * total_specific_energy - actual_heat_kw
+        # electricity_demand_kw = np.minimum(electricity_demand_kw, electricity_available_kw)
+        outputs["electricity_consumed"] = electricity_demand_kw
+        outputs["hydrogen_out"] = hydrogen_produced
+        # outputs["heat_demand"] = np.minimum(rated_hydrogen_production,
+        # inputs["hydrogen_command_value"]) * self.config.nominal_heat_required
+        outputs["heat_demand"] = hydrogen_demand * self.config.nominal_heat_required
+        outputs["electricity_demand"] = electrolyzer_size_kw
+        outputs["water_consumed"] = water_demand_gal_per_h
+        outputs["rated_hydrogen_production"] = rated_hydrogen_production
+        outputs["electrolyzer_size_mw"] = electrolyzer_size_mw
+
+        total_hydrogen_produced = np.sum(hydrogen_produced) * (self.dt / 3600.0)
+        outputs["total_hydrogen_produced"] = total_hydrogen_produced
+        annual_hydrogen = total_hydrogen_produced / self.fraction_of_year_simulated
+        outputs["annual_hydrogen_produced"] = np.full(self.plant_life, annual_hydrogen)
+
+        max_production = rated_hydrogen_production * self.n_timesteps * (self.dt / 3600.0)
+        capacity_factor = total_hydrogen_produced / max_production if max_production > 0 else 0.0
+        outputs["capacity_factor"] = np.full(self.plant_life, capacity_factor)
+
+        utilized_input_kw = actual_heat_kw + electricity_demand_kw
+        available_input_kw = heat_available_kw + electricity_available_kw
+        with np.errstate(divide="ignore", invalid="ignore"):
+            timestep_efficiency = np.divide(
+                utilized_input_kw,
+                available_input_kw,
+                out=np.zeros_like(utilized_input_kw),
+                where=available_input_kw > 0,
+            )
+        outputs["efficiency"] = float(np.mean(timestep_efficiency))
+
+        refurb_schedule = np.zeros(self.plant_life)
+        refurb_period = max(1, round(self.config.uptime_hours_until_eol / 8760))
+        refurb_schedule[refurb_period : self.plant_life : refurb_period] = 1.0
+        outputs["replacement_schedule"] = refurb_schedule
+        outputs["time_until_replacement"] = float(self.config.uptime_hours_until_eol)
+
+
+@define(kw_only=True)
+class HTSECostModelConfig(CostModelBaseConfig):
+    """Configuration class for the HTSE cost model.
+
+    Args:
+        unit_capex (float): Installed capital cost per kW of HTSE electrical size in USD/kW.
+        fixed_opex (float, optional): Fixed annual operating cost per kW in USD/(kW*year).
+            If omitted, it is populated from ``fixed_capex`` (or ``0.0`` when that is also
+            omitted).
+        fixed_capex (float, optional): Fallback value used to populate ``fixed_opex`` when
+            ``fixed_opex`` is not provided.
+        cost_year (int): Dollar year corresponding to the input costs. Defaults to ``2025``.
+    """
+
+    unit_capex: float = field(validator=gt_zero)
+    fixed_opex: float | None = field(default=None)
+    fixed_capex: float | None = field(default=None)
+    cost_year: int = field(default=2025, converter=int)
+
+    def __attrs_post_init__(self) -> None:
+        if self.fixed_opex is None:
+            self.fixed_opex = 0.0 if self.fixed_capex is None else float(self.fixed_capex)
+
+
+class HTSECostModel(ElectrolyzerCostBaseClass):
+    """A simple size-based cost model for HTSE.
+
+    The model computes installed capital cost and fixed operating cost from the installed
+    HTSE electrical size reported by the performance model:
+
+    - ``CapEx`` from ``unit_capex * electrolyzer_size_kw``
+    - ``OpEx`` from ``fixed_opex * electrolyzer_size_kw``
+
+    The current model does not set a nonzero variable operating cost.
+    """
+
+    def setup(self) -> None:
+        self.config = HTSECostModelConfig.from_dict(
+            merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"),
+            strict=False,
+            additional_cls_name=self.__class__.__name__,
+        )
+        super().setup()
+
+        self.add_input(
+            "electrolyzer_size_mw",
+            val=0.0,
+            units="MW",
+            desc="Installed HTSE nameplate capacity",
+        )
+
+    def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
+        electrolyzer_size_kw = float(inputs["electrolyzer_size_mw"][0]) * 1000.0
+        outputs["CapEx"] = self.config.unit_capex * electrolyzer_size_kw
+        outputs["OpEx"] = self.config.fixed_opex * electrolyzer_size_kw
diff --git a/h2integrate/converters/hydrogen/test/test_htse_electrolyzer.py b/h2integrate/converters/hydrogen/test/test_htse_electrolyzer.py
new file mode 100644
index 000000000..eb1ed17df
--- /dev/null
+++ b/h2integrate/converters/hydrogen/test/test_htse_electrolyzer.py
@@ -0,0 +1,242 @@
+import numpy as np
+import pytest
+import openmdao.api as om
+from pytest import fixture
+
+from h2integrate.converters.hydrogen.htse_electrolyzer import HTSECostModel, HTSEPerformanceModel
+
+
+@fixture
+def plant_config():
+    return {
+        "plant": {
+            "plant_life": 30,
+            "simulation": {
+                "n_timesteps": 8760,
+                "dt": 3600,
+            },
+        },
+    }
+
+
+@fixture
+def htse_performance_params():
+    return {
+        "n_clusters": 25,
+        "cluster_rating_MW": 1.0,
+        "nominal_heat_required": 6.4,
+        "nominal_electricity_required": 36.8,
+        "turndown_ratio": 0.1,
+        "uptime_hours_until_eol": 35040,
+    }
+
+
+@fixture
+def htse_cost_params():
+    return {
+        "unit_capex": 1417.0,
+        "cost_year": 2022,
+    }
+
+
+def _build_performance_problem(plant_config, performance_params):
+    tech_config_dict = {
+        "model_inputs": {
+            "performance_parameters": performance_params,
+        }
+    }
+
+    prob = om.Problem()
+    perf_comp = HTSEPerformanceModel(
+        plant_config=plant_config,
+        tech_config=tech_config_dict,
+        driver_config={},
+    )
+    prob.model.add_subsystem("htse_perf", perf_comp, promotes=["*"])
+    prob.setup()
+    return prob
+
+
+@pytest.mark.unit
+def test_htse_performance_full_production(plant_config, htse_performance_params, subtests):
+    n_timesteps = plant_config["plant"]["simulation"]["n_timesteps"]
+    nom_elec = htse_performance_params["nominal_electricity_required"]
+    nom_heat = htse_performance_params["nominal_heat_required"]
+
+    electrolyzer_size_kw = (
+        htse_performance_params["n_clusters"]
+        * htse_performance_params["cluster_rating_MW"]
+        * 1000.0
+    )
+    rated_hydrogen_production = electrolyzer_size_kw / nom_elec
+
+    # Supply exactly the heat and electricity demanded so the model runs at rated production
+    heat_demand_kw = rated_hydrogen_production * nom_heat
+    electricity_demand_kw = electrolyzer_size_kw
+
+    prob = _build_performance_problem(plant_config, htse_performance_params)
+    prob.set_val("heat_in", np.full(n_timesteps, heat_demand_kw), units="kW")
+    prob.set_val("electricity_in", np.full(n_timesteps, electricity_demand_kw), units="kW")
+    prob.run_model()
+
+    hydrogen_out = prob.get_val("hydrogen_out", units="kg/h")
+
+    with subtests.test("Hydrogen produced at rated capacity"):
+        assert pytest.approx(hydrogen_out, rel=1e-6) == np.full(
+            n_timesteps, rated_hydrogen_production
+        )
+
+    with subtests.test("Rated hydrogen production reported"):
+        assert pytest.approx(prob.get_val("rated_hydrogen_production", units="kg/h")[0]) == (
+            rated_hydrogen_production
+        )
+
+    with subtests.test("Installed HTSE size reported"):
+        assert pytest.approx(prob.get_val("electrolyzer_size_mw", units="MW")[0]) == 25.0
+
+    with subtests.test("Electricity demand equals installed electrical size"):
+        assert pytest.approx(prob.get_val("electricity_demand", units="kW")) == (
+            np.full(n_timesteps, electrolyzer_size_kw)
+        )
+
+    with subtests.test("Heat demand based on hydrogen demand"):
+        assert pytest.approx(prob.get_val("heat_demand", units="kW")) == (
+            np.full(n_timesteps, heat_demand_kw)
+        )
+
+    with subtests.test("Capacity factor is one at full production"):
+        assert pytest.approx(prob.get_val("capacity_factor")) == np.ones(
+            plant_config["plant"]["plant_life"]
+        )
+
+    with subtests.test("Efficiency is one when input matches demand"):
+        assert pytest.approx(prob.get_val("efficiency")[0], rel=1e-6) == 1.0
+
+    with subtests.test("Water consumption follows stoichiometry"):
+        expected_water_gal = rated_hydrogen_production * (18.015 / 2.016) / 3.785411
+        assert pytest.approx(prob.get_val("water_consumed", units="galUS/h"), rel=1e-6) == (
+            np.full(n_timesteps, expected_water_gal)
+        )
+
+    with subtests.test("Annual hydrogen production"):
+        assert pytest.approx(prob.get_val("annual_hydrogen_produced", units="kg/year")) == np.full(
+            plant_config["plant"]["plant_life"], rated_hydrogen_production * n_timesteps
+        )
+
+
+@pytest.mark.unit
+def test_htse_performance_turndown_shutoff(plant_config, htse_performance_params, subtests):
+    n_timesteps = plant_config["plant"]["simulation"]["n_timesteps"]
+
+    prob = _build_performance_problem(plant_config, htse_performance_params)
+    # Provide electricity well below the turndown threshold and no heat
+    prob.set_val("heat_in", np.zeros(n_timesteps), units="kW")
+    prob.set_val("electricity_in", np.full(n_timesteps, 100.0), units="kW")
+    prob.run_model()
+
+    with subtests.test("Hydrogen production is zero below turndown"):
+        assert pytest.approx(prob.get_val("hydrogen_out", units="kg/h")) == np.zeros(n_timesteps)
+
+    with subtests.test("Total hydrogen produced is zero"):
+        assert prob.get_val("total_hydrogen_produced", units="kg")[0] == pytest.approx(0.0)
+
+    with subtests.test("Capacity factor is zero"):
+        assert pytest.approx(prob.get_val("capacity_factor")) == np.zeros(
+            plant_config["plant"]["plant_life"]
+        )
+
+
+@pytest.mark.unit
+def test_htse_cost_model(plant_config, htse_cost_params, subtests):
+    tech_config_dict = {
+        "model_inputs": {
+            "cost_parameters": htse_cost_params,
+        }
+    }
+
+    electrolyzer_size_mw = 25.0
+    electrolyzer_size_kw = electrolyzer_size_mw * 1000.0
+
+    prob = om.Problem()
+    cost_comp = HTSECostModel(
+        plant_config=plant_config,
+        tech_config=tech_config_dict,
+        driver_config={},
+    )
+    prob.model.add_subsystem("htse_cost", cost_comp, promotes=["*"])
+    prob.setup()
+
+    prob.set_val("electrolyzer_size_mw", electrolyzer_size_mw, units="MW")
+    prob.run_model()
+
+    with subtests.test("Capital cost scales with installed size"):
+        expected_capex = htse_cost_params["unit_capex"] * electrolyzer_size_kw
+        assert pytest.approx(prob.get_val("CapEx", units="USD")[0], rel=1e-6) == expected_capex
+
+    with subtests.test("Operating cost defaults to zero when fixed_opex omitted"):
+        assert prob.get_val("OpEx", units="USD/year")[0] == pytest.approx(0.0)
+
+    with subtests.test("Cost year is reported"):
+        assert prob.get_val("cost_year") == htse_cost_params["cost_year"]
+
+
+@pytest.mark.unit
+def test_htse_cost_model_fixed_opex(plant_config, subtests):
+    electrolyzer_size_mw = 25.0
+    electrolyzer_size_kw = electrolyzer_size_mw * 1000.0
+
+    tech_config_dict = {
+        "model_inputs": {
+            "cost_parameters": {
+                "unit_capex": 1417.0,
+                "fixed_opex": 10.0,
+            },
+        }
+    }
+
+    prob = om.Problem()
+    cost_comp = HTSECostModel(
+        plant_config=plant_config,
+        tech_config=tech_config_dict,
+        driver_config={},
+    )
+    prob.model.add_subsystem("htse_cost", cost_comp, promotes=["*"])
+    prob.setup()
+    prob.set_val("electrolyzer_size_mw", electrolyzer_size_mw, units="MW")
+    prob.run_model()
+
+    with subtests.test("Operating cost uses provided fixed_opex"):
+        assert pytest.approx(prob.get_val("OpEx", units="USD/year")[0], rel=1e-6) == (
+            10.0 * electrolyzer_size_kw
+        )
+
+
+@pytest.mark.unit
+def test_htse_cost_model_fixed_capex_fallback(plant_config, subtests):
+    electrolyzer_size_mw = 25.0
+    electrolyzer_size_kw = electrolyzer_size_mw * 1000.0
+
+    tech_config_dict = {
+        "model_inputs": {
+            "cost_parameters": {
+                "unit_capex": 1417.0,
+                "fixed_capex": 5.0,
+            },
+        }
+    }
+
+    prob = om.Problem()
+    cost_comp = HTSECostModel(
+        plant_config=plant_config,
+        tech_config=tech_config_dict,
+        driver_config={},
+    )
+    prob.model.add_subsystem("htse_cost", cost_comp, promotes=["*"])
+    prob.setup()
+    prob.set_val("electrolyzer_size_mw", electrolyzer_size_mw, units="MW")
+    prob.run_model()
+
+    with subtests.test("fixed_capex populates fixed_opex when fixed_opex omitted"):
+        assert pytest.approx(prob.get_val("OpEx", units="USD/year")[0], rel=1e-6) == (
+            5.0 * electrolyzer_size_kw
+        )
diff --git a/h2integrate/converters/nuclear/__init__.py b/h2integrate/converters/nuclear/__init__.py
index 3bab04d7e..409ebc15f 100644
--- a/h2integrate/converters/nuclear/__init__.py
+++ b/h2integrate/converters/nuclear/__init__.py
@@ -2,3 +2,7 @@
     QuinnNuclearPerformanceModel,
     QuinnNuclearCostModel,
 )
+from h2integrate.converters.nuclear.nuclear_plant_thermal import (
+    SimpleThermalNuclearReactorPerformanceModel,
+    SimpleThermalNuclearReactorCostModel,
+)
diff --git a/h2integrate/converters/nuclear/nuclear_plant_thermal.py b/h2integrate/converters/nuclear/nuclear_plant_thermal.py
new file mode 100644
index 000000000..df9a2e414
--- /dev/null
+++ b/h2integrate/converters/nuclear/nuclear_plant_thermal.py
@@ -0,0 +1,270 @@
+import numpy as np
+from attrs import field, define
+
+from h2integrate.core.utilities import BaseConfig, merge_shared_inputs
+from h2integrate.core.validators import gt_zero, contains
+from h2integrate.core.model_baseclasses import (
+    CostModelBaseClass,
+    CostModelBaseConfig,
+    PerformanceModelBaseClass,
+)
+
+
+@define(kw_only=True)
+class SimpleThermalNuclearReactorConfig(BaseConfig):
+    """Configuration class for the thermal nuclear reactor performance model.
+
+    Args:
+        operating_mode (str): Dispatch mode of the reactor. Must be ``"heat"`` or
+            ``"electricity"``. In ``heat`` mode the reactor satisfies process heat demand
+            first and converts the remaining low-pressure heat to electricity; in
+            ``electricity`` mode it satisfies the electricity command first and delivers
+            the remaining available heat.
+        electricity_command_value (float): Requested electrical output in kW.
+        high_pressure_electrical_efficiency (float): Fraction of total thermal input
+            converted to electricity in the high-pressure stage (unitless).
+        low_pressure_electrical_efficiency (float): Efficiency applied to the remaining
+            low-pressure heat when generating electricity (unitless).
+        rated_capacity (float): Rated electrical capacity in kW, used to infer the reactor
+            thermal capacity.
+        minimum_heat_extract (float): Minimum process heat reserved for extraction in kW.
+            Defaults to ``0.0``.
+    """
+
+    operating_mode: str = field(validator=contains(["heat", "electricity"]))
+    electricity_command_value: float = field(validator=gt_zero)
+    high_pressure_electrical_efficiency: float = field(validator=gt_zero)
+    low_pressure_electrical_efficiency: float = field(validator=gt_zero)
+    rated_capacity: float = field(validator=gt_zero)
+    minimum_heat_extract: float = field(default=0.0)
+
+
+class SimpleThermalNuclearReactorPerformanceModel(PerformanceModelBaseClass):
+    """Simple thermal nuclear reactor model with heat and electricity outputs.
+
+    This model represents a reactor with a high-pressure electric conversion stage, a
+    low-pressure electric conversion stage, and an extractable process heat stream taken
+    upstream of the low-pressure turbine stages. It trades off electricity production and
+    process heat delivery according to the selected operating mode, making it suitable for
+    coupled workflows such as nuclear plus HTSE.
+
+    The reactor infers its thermal capacity from the rated electrical capacity using a
+    combined electric efficiency and supports two operating modes:
+
+    - ``heat``: satisfy heat demand first, then convert the remaining low-pressure heat to
+      electricity.
+    - ``electricity``: satisfy the electricity command first, then send the remaining
+      available process heat to ``heat_out``.
+    """
+
+    _time_step_bounds = (3600, 3600)
+
+    def initialize(self) -> None:
+        super().initialize()
+        self.commodity = "electricity"
+        self.commodity_rate_units = "kW"
+        self.commodity_amount_units = "kW*h"
+
+    def setup(self):
+        self.config = SimpleThermalNuclearReactorConfig.from_dict(
+            merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"),
+            strict=False,
+            additional_cls_name=self.__class__.__name__,
+        )
+        super().setup()
+
+        self.add_discrete_input("operating_mode", val=self.config.operating_mode)
+        self.add_input(
+            f"{self.commodity}_command_value",
+            val=self.config.electricity_command_value,
+            shape=self.n_timesteps,
+            units=self.commodity_rate_units,
+            desc="Requested electric power setpoint",
+        )
+        self.add_input(
+            "rated_capacity",
+            val=self.config.rated_capacity,
+            units=self.commodity_rate_units,
+            desc="Available reactor thermal capacity",
+        )
+        self.add_input(
+            "high_pressure_electrical_efficiency",
+            val=self.config.high_pressure_electrical_efficiency,
+            units="unitless",
+        )
+        self.add_input(
+            "low_pressure_electrical_efficiency",
+            val=self.config.low_pressure_electrical_efficiency,
+            units="unitless",
+        )
+        self.add_input(
+            "minimum_heat_extract",
+            val=self.config.minimum_heat_extract,
+            units="kW",
+            desc="Minimum thermal output reserved for process heat extraction",
+        )
+        self.add_input(
+            "heat_command_value",
+            val=6400,
+            shape=self.n_timesteps,
+            units="kW",
+            desc="Requested process heat demand from downstream technologies",
+        )
+
+        self.add_output("high_pressure_heat_demanded", val=0.0, shape=self.n_timesteps, units="kW")
+        self.add_output("high_pressure_heat", val=0.0, shape=self.n_timesteps, units="kW")
+        self.add_output("low_pressure_heat", val=0.0, shape=self.n_timesteps, units="kW")
+        self.add_output("heat_out", val=0.0, shape=self.n_timesteps, units="kW")
+
+    def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
+        operating_mode = discrete_inputs["operating_mode"]
+        hp_eff = float(inputs["high_pressure_electrical_efficiency"][0])
+        lp_eff = float(inputs["low_pressure_electrical_efficiency"][0])
+        electric_capacity_mw = float(inputs["rated_capacity"][0]) * 1e-3  # convert kW to MW
+        minimum_heat_extract_kw = np.maximum(
+            inputs["minimum_heat_extract"], 0.0
+        )  # maintain kW rating
+        requested_power_mw = (
+            np.maximum(inputs["electricity_command_value"], 0.0) * 1e-3
+        )  # convert kW to MW, fix >0
+        external_heat_demand_kw = np.maximum(
+            inputs["heat_command_value"], 0.0
+        )  # maintaining kW rating
+
+        combined_efficiency = hp_eff + (1.0 - hp_eff) * lp_eff
+        if combined_efficiency <= 0.0:
+            raise ValueError("Combined nuclear electric efficiency must be greater than zero")
+        if lp_eff <= 0.0:
+            raise ValueError("Low-pressure electrical efficiency must be greater than zero")
+
+        thermal_capacity_mw = electric_capacity_mw / combined_efficiency
+        high_pressure_electricity_mw = thermal_capacity_mw * hp_eff
+        available_process_heat_mw = thermal_capacity_mw * (1.0 - hp_eff)
+        heat_demand_mw = (
+            np.maximum(external_heat_demand_kw, minimum_heat_extract_kw) * 1e-3
+        )  # convert to MW from kW
+
+        if operating_mode == "heat":
+            heat_out_mw = np.minimum(heat_demand_mw, available_process_heat_mw)
+            electricity_out_mw = (
+                high_pressure_electricity_mw + (available_process_heat_mw - heat_out_mw) * lp_eff
+            )
+        elif operating_mode == "electricity":
+            electricity_out_mw = np.minimum(requested_power_mw, electric_capacity_mw)
+            heat_out_mw = (
+                available_process_heat_mw
+                - (electricity_out_mw - high_pressure_electricity_mw) / lp_eff
+            )
+            heat_out_mw = np.clip(heat_out_mw, 0.0, available_process_heat_mw)
+        else:
+            raise NotImplementedError(
+                "The nuclear operating_mode must be either 'heat' or 'electricity'"
+            )
+
+        electricity_out_mw = np.clip(electricity_out_mw, 0.0, electric_capacity_mw)
+        low_pressure_heat_remaining_mw = available_process_heat_mw - heat_out_mw
+
+        high_pressure_heat_kw = np.full(self.n_timesteps, available_process_heat_mw * 1000.0)
+        low_pressure_heat_kw = low_pressure_heat_remaining_mw * 1000.0
+        electricity_out_kw = electricity_out_mw * 1000.0
+        heat_out_kw = heat_out_mw * 1000.0
+
+        outputs["high_pressure_heat_demanded"] = heat_demand_mw * 1000.0
+        outputs["high_pressure_heat"] = high_pressure_heat_kw
+        outputs["low_pressure_heat"] = low_pressure_heat_kw
+        outputs["heat_out"] = heat_out_kw
+        outputs["electricity_out"] = electricity_out_kw
+        outputs["rated_electricity_production"] = electric_capacity_mw * 1000.0
+
+        total_electricity = np.sum(electricity_out_kw) * (self.dt / 3600.0)
+        outputs["total_electricity_produced"] = total_electricity
+        annual_electricity = total_electricity / self.fraction_of_year_simulated
+        outputs["annual_electricity_produced"] = np.full(self.plant_life, annual_electricity)
+
+        avg_electricity_out_mw = float(np.mean(electricity_out_mw))
+        capacity_factor = (
+            avg_electricity_out_mw / electric_capacity_mw if electric_capacity_mw > 0.0 else 0.0
+        )
+        outputs["capacity_factor"] = np.full(self.plant_life, capacity_factor)
+        outputs["replacement_schedule"] = np.zeros(self.plant_life)
+
+
+@define(kw_only=True)
+class SimpleThermalNuclearReactorCostConfig(CostModelBaseConfig):
+    """Configuration class for the thermal nuclear reactor cost model.
+
+    Args:
+        rated_capacity (float): Rated capacity used for cost calculations in kW.
+        nuclear_reactor_upfront_cost (float): Capital cost per kW in USD/kW.
+        nuclear_reactor_fixed_om_cost (float): Fixed annual O&M in USD/(kW*year).
+        nuclear_reactor_variable_om_cost (float): Variable O&M applied to the simulated
+            electricity production in USD/(kW*h).
+        cost_year (int): Dollar year corresponding to the input costs. Defaults to ``2025``.
+    """
+
+    rated_capacity: float = field(validator=gt_zero)
+    nuclear_reactor_upfront_cost: float = field(validator=gt_zero)
+    nuclear_reactor_fixed_om_cost: float = field(validator=gt_zero)
+    nuclear_reactor_variable_om_cost: float = field(validator=gt_zero)
+    cost_year: int = field(default=2025, converter=int)
+
+
+class SimpleThermalNuclearReactorCostModel(CostModelBaseClass):
+    """Simple cost model for the thermal nuclear reactor.
+
+    The model applies capacity-based capital and fixed O&M costs and computes variable O&M
+    from the delivered electricity:
+
+    - ``CapEx`` from ``rated_capacity * nuclear_reactor_upfront_cost``
+    - ``OpEx`` from ``rated_capacity * nuclear_reactor_fixed_om_cost``
+    - ``VarOpEx`` from ``nuclear_reactor_variable_om_cost`` applied to the simulated
+      electricity output, repeated across the plant life.
+    """
+
+    _time_step_bounds = (3600, 3600)
+
+    def setup(self) -> None:
+        self.dt = self.options["plant_config"]["plant"]["simulation"]["dt"]
+        self.plant_life = int(self.options["plant_config"]["plant"]["plant_life"])
+        n_timesteps = int(self.options["plant_config"]["plant"]["simulation"]["n_timesteps"])
+        self.config = SimpleThermalNuclearReactorCostConfig.from_dict(
+            merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"),
+            strict=False,
+            additional_cls_name=self.__class__.__name__,
+        )
+        super().setup()
+
+        self.add_input(
+            "rated_capacity",
+            val=self.config.rated_capacity,
+            units="kW",
+        )
+        self.add_input(
+            "nuclear_reactor_upfront_cost",
+            val=self.config.nuclear_reactor_upfront_cost,
+            units="USD/kW",
+        )
+        self.add_input(
+            "nuclear_reactor_fixed_om_cost",
+            val=self.config.nuclear_reactor_fixed_om_cost,
+            units="USD/(kW*year)",
+        )
+        self.add_input(
+            "nuclear_reactor_variable_om_cost",
+            val=self.config.nuclear_reactor_variable_om_cost,
+            units="USD/(kW*h)",
+        )
+        self.add_input("electricity_out", val=0.0, shape=n_timesteps, units="kW")
+
+    def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
+        rated_capacity_kw = float(inputs["rated_capacity"][0])
+        upfront_cost_per_kw = float(inputs["nuclear_reactor_upfront_cost"][0])
+        fixed_om_per_kw_year = float(inputs["nuclear_reactor_fixed_om_cost"][0])
+        variable_om_per_kwh = float(inputs["nuclear_reactor_variable_om_cost"][0])
+
+        outputs["CapEx"] = rated_capacity_kw * upfront_cost_per_kw
+        outputs["OpEx"] = fixed_om_per_kw_year * rated_capacity_kw
+
+        delivered_electricity_kwh = np.sum(inputs["electricity_out"]) * (self.dt / 3600.0)
+        annual_variable_om = variable_om_per_kwh * delivered_electricity_kwh
+        outputs["VarOpEx"] = np.full(self.plant_life, annual_variable_om)
diff --git a/h2integrate/converters/nuclear/test/test_nuclear_plant_thermal.py b/h2integrate/converters/nuclear/test/test_nuclear_plant_thermal.py
new file mode 100644
index 000000000..05898741e
--- /dev/null
+++ b/h2integrate/converters/nuclear/test/test_nuclear_plant_thermal.py
@@ -0,0 +1,223 @@
+import numpy as np
+import pytest
+import openmdao.api as om
+from pytest import fixture
+
+from h2integrate.converters.nuclear.nuclear_plant_thermal import (
+    SimpleThermalNuclearReactorCostModel,
+    SimpleThermalNuclearReactorPerformanceModel,
+)
+
+
+@fixture
+def plant_config():
+    return {
+        "plant": {
+            "plant_life": 30,
+            "simulation": {
+                "n_timesteps": 8760,
+                "dt": 3600,
+            },
+        },
+    }
+
+
+@fixture
+def thermal_performance_params():
+    return {
+        "operating_mode": "heat",
+        "electricity_command_value": 500000.0,
+        "high_pressure_electrical_efficiency": 0.2,
+        "low_pressure_electrical_efficiency": 0.5,
+        "rated_capacity": 600000.0,
+        "minimum_heat_extract": 0.0,
+    }
+
+
+@fixture
+def thermal_cost_params():
+    return {
+        "rated_capacity": 600000.0,
+        "nuclear_reactor_upfront_cost": 5750.0,
+        "nuclear_reactor_fixed_om_cost": 2.64,
+        "nuclear_reactor_variable_om_cost": 0.0145,
+        "cost_year": 2022,
+    }
+
+
+def _build_performance_problem(plant_config, performance_params):
+    tech_config_dict = {
+        "model_inputs": {
+            "performance_parameters": performance_params,
+        }
+    }
+
+    prob = om.Problem()
+    perf_comp = SimpleThermalNuclearReactorPerformanceModel(
+        plant_config=plant_config,
+        tech_config=tech_config_dict,
+        driver_config={},
+    )
+    prob.model.add_subsystem("nuc_thermal_perf", perf_comp, promotes=["*"])
+    prob.setup()
+    return prob
+
+
+@pytest.mark.unit
+def test_thermal_performance_heat_mode(plant_config, thermal_performance_params, subtests):
+    n_timesteps = plant_config["plant"]["simulation"]["n_timesteps"]
+    plant_life = plant_config["plant"]["plant_life"]
+
+    prob = _build_performance_problem(plant_config, thermal_performance_params)
+    prob.set_val("heat_command_value", np.full(n_timesteps, 400000.0), units="kW")
+    prob.run_model()
+
+    # thermal_capacity = 600 / (0.2 + 0.8 * 0.5) = 1000 MW
+    # available process heat = 1000 * 0.8 = 800 MW
+    # heat_out = min(400, 800) = 400 MW
+    # electricity_out = 200 (HP) + (800 - 400) * 0.5 = 400 MW
+    with subtests.test("Delivered process heat matches demand"):
+        assert pytest.approx(prob.get_val("heat_out", units="kW")) == np.full(n_timesteps, 400000.0)
+
+    with subtests.test("Electricity output in heat mode"):
+        assert pytest.approx(prob.get_val("electricity_out", units="kW")) == (
+            np.full(n_timesteps, 400000.0)
+        )
+
+    with subtests.test("High-pressure heat stream"):
+        assert pytest.approx(prob.get_val("high_pressure_heat", units="kW")) == (
+            np.full(n_timesteps, 800000.0)
+        )
+
+    with subtests.test("Low-pressure heat remaining"):
+        assert pytest.approx(prob.get_val("low_pressure_heat", units="kW")) == (
+            np.full(n_timesteps, 400000.0)
+        )
+
+    with subtests.test("Thermal split is conserved"):
+        hp_heat = prob.get_val("high_pressure_heat", units="kW")
+        lp_heat = prob.get_val("low_pressure_heat", units="kW")
+        heat_out = prob.get_val("heat_out", units="kW")
+        assert np.allclose(hp_heat, lp_heat + heat_out, rtol=1e-6)
+
+    with subtests.test("Rated electricity production"):
+        assert (
+            pytest.approx(prob.get_val("rated_electricity_production", units="kW")[0]) == 600000.0
+        )
+
+    with subtests.test("Capacity factor in heat mode"):
+        assert pytest.approx(prob.get_val("capacity_factor"), rel=1e-6) == np.full(
+            plant_life, 400.0 / 600.0
+        )
+
+    with subtests.test("Annual electricity production"):
+        assert pytest.approx(
+            prob.get_val("annual_electricity_produced", units="kW*h/year")
+        ) == np.full(plant_life, 400000.0 * n_timesteps)
+
+
+@pytest.mark.unit
+def test_thermal_performance_electricity_mode(plant_config, thermal_performance_params, subtests):
+    n_timesteps = plant_config["plant"]["simulation"]["n_timesteps"]
+    plant_life = plant_config["plant"]["plant_life"]
+
+    performance_params = dict(thermal_performance_params)
+    performance_params["operating_mode"] = "electricity"
+
+    prob = _build_performance_problem(plant_config, performance_params)
+    prob.set_val("electricity_command_value", np.full(n_timesteps, 500000.0), units="kW")
+    prob.run_model()
+
+    # electricity_out = min(500, 600) = 500 MW
+    # heat_out = 800 - (500 - 200) / 0.5 = 200 MW
+    with subtests.test("Electricity output tracks command"):
+        assert pytest.approx(prob.get_val("electricity_out", units="kW")) == (
+            np.full(n_timesteps, 500000.0)
+        )
+
+    with subtests.test("Process heat after electricity dispatch"):
+        assert pytest.approx(prob.get_val("heat_out", units="kW")) == np.full(n_timesteps, 200000.0)
+
+    with subtests.test("Thermal split is conserved"):
+        hp_heat = prob.get_val("high_pressure_heat", units="kW")
+        lp_heat = prob.get_val("low_pressure_heat", units="kW")
+        heat_out = prob.get_val("heat_out", units="kW")
+        assert np.allclose(hp_heat, lp_heat + heat_out, rtol=1e-6)
+
+    with subtests.test("Capacity factor in electricity mode"):
+        assert pytest.approx(prob.get_val("capacity_factor"), rel=1e-6) == np.full(
+            plant_life, 500.0 / 600.0
+        )
+
+
+@pytest.mark.unit
+def test_thermal_performance_electricity_capped_at_capacity(
+    plant_config, thermal_performance_params, subtests
+):
+    n_timesteps = plant_config["plant"]["simulation"]["n_timesteps"]
+
+    performance_params = dict(thermal_performance_params)
+    performance_params["operating_mode"] = "electricity"
+
+    prob = _build_performance_problem(plant_config, performance_params)
+    # Command more than the rated electrical capacity (600 MW)
+    prob.set_val("electricity_command_value", np.full(n_timesteps, 900000.0), units="kW")
+    prob.run_model()
+
+    with subtests.test("Electricity output clipped to rated capacity"):
+        assert pytest.approx(prob.get_val("electricity_out", units="kW")) == (
+            np.full(n_timesteps, 600000.0)
+        )
+
+
+@pytest.mark.unit
+def test_thermal_cost_model(plant_config, thermal_cost_params, subtests):
+    n_timesteps = plant_config["plant"]["simulation"]["n_timesteps"]
+    plant_life = plant_config["plant"]["plant_life"]
+
+    tech_config_dict = {
+        "model_inputs": {
+            "cost_parameters": thermal_cost_params,
+        }
+    }
+
+    electricity_out = np.full(n_timesteps, 400000.0)
+
+    prob = om.Problem()
+    cost_comp = SimpleThermalNuclearReactorCostModel(
+        plant_config=plant_config,
+        tech_config=tech_config_dict,
+        driver_config={},
+    )
+    prob.model.add_subsystem("nuc_thermal_cost", cost_comp, promotes=["*"])
+    prob.setup()
+
+    prob.set_val("electricity_out", electricity_out, units="kW")
+    prob.run_model()
+
+    rated_capacity_kw = thermal_cost_params["rated_capacity"]
+    upfront_cost_per_kw = thermal_cost_params["nuclear_reactor_upfront_cost"]
+    fixed_om_per_kw_year = thermal_cost_params["nuclear_reactor_fixed_om_cost"]
+    variable_om_per_kwh = thermal_cost_params["nuclear_reactor_variable_om_cost"]
+
+    dt = plant_config["plant"]["simulation"]["dt"]
+    delivered_electricity_kwh = electricity_out.sum() * (dt / 3600.0)
+
+    with subtests.test("Thermal reactor capital cost"):
+        assert pytest.approx(prob.get_val("CapEx", units="USD")[0], rel=1e-6) == (
+            rated_capacity_kw * upfront_cost_per_kw
+        )
+
+    with subtests.test("Thermal reactor fixed operating cost"):
+        assert pytest.approx(prob.get_val("OpEx", units="USD/year")[0], rel=1e-6) == (
+            fixed_om_per_kw_year * rated_capacity_kw
+        )
+
+    with subtests.test("Thermal reactor variable operating cost"):
+        expected_varopex = variable_om_per_kwh * delivered_electricity_kwh
+        assert pytest.approx(prob.get_val("VarOpEx", units="USD/year"), rel=1e-6) == np.full(
+            plant_life, expected_varopex
+        )
+
+    with subtests.test("Thermal reactor cost year"):
+        assert prob.get_val("cost_year") == thermal_cost_params["cost_year"]
diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py
index f9e43ce89..51fa624bd 100644
--- a/h2integrate/core/supported_models.py
+++ b/h2integrate/core/supported_models.py
@@ -75,6 +75,8 @@ def copy(self):
         "RunOfRiverHydroPerformanceModel": "converters.water_power:RunOfRiverHydroPerformanceModel",
         "RunOfRiverHydroCostModel": "converters.water_power:RunOfRiverHydroCostModel",
         "ECOElectrolyzerPerformanceModel": "converters.hydrogen:ECOElectrolyzerPerformanceModel",
+        "HTSEPerformanceModel": "converters.hydrogen:HTSEPerformanceModel",
+        "HTSECostModel": "converters.hydrogen:HTSECostModel",
         "SingliticoCostModel": "converters.hydrogen:SingliticoCostModel",
         "BasicElectrolyzerCostModel": "converters.hydrogen:BasicElectrolyzerCostModel",
         "CustomElectrolyzerCostModel": "converters.hydrogen:CustomElectrolyzerCostModel",
@@ -128,6 +130,8 @@ def copy(self):
         "NaturalGasPerformanceModel": "converters.natural_gas:NaturalGasPerformanceModel",
         "QuinnNuclearPerformanceModel": "converters.nuclear:QuinnNuclearPerformanceModel",
         "QuinnNuclearCostModel": "converters.nuclear:QuinnNuclearCostModel",
+        "SimpleThermalNuclearReactorCostModel": "converters.nuclear:SimpleThermalNuclearReactorCostModel",
+        "SimpleThermalNuclearReactorPerformanceModel": "converters.nuclear:SimpleThermalNuclearReactorPerformanceModel",
         "NaturalGasCostModel": "converters.natural_gas:NaturalGasCostModel",
         # Transport
         "cable": "transporters:CablePerformanceModel",
@@ -188,12 +192,10 @@ def copy(self):
     }
 )
 
-
 # This next section is to demarcate specific models that belong to certain categories that are
 # relevant for processing in the model stackup. Right now, these designations are
 # used in `h2integrate_model.py`.
 
-
 # Model classes that do not contribute costs to the finance stackup because they are essentially
 # internal-only models that aren't categorized as a specific technology (e.g. a generic combiner
 # or splitter, or a model that is only used for performance modeling within another model and