Renewable Energy Potential Model: Geothermal Supply Curves

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The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies.

To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. The included paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure.

Our preliminary supply curves and levelized cost of electricity (LCOE) results provided here should be considered with care due to the high uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for three scenarios: two hydrothermal (3.5km depth, USGS heat flow & SMU temperatures respectively) and one EGS (4.5km depth, SMU temperatures). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

Citation Formats

TY - DATA AB - The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. The included paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results provided here should be considered with care due to the high uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for three scenarios: two hydrothermal (3.5km depth, USGS heat flow & SMU temperatures respectively) and one EGS (4.5km depth, SMU temperatures). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources. AU - Trainor-Guitton, Whitney A2 - Thomson, Sophie-Min A3 - Pinchuk, Pavlo A4 - Maclauren, Galen A5 - Buster, Grant DB - Geothermal Data Repository DP - Open EI | National Renewable Energy Laboratory DO - 10.15121/2008490 KW - geothermal KW - energy KW - SMU temperatures KW - supply curve KW - geospatial KW - grid infrastructure KW - exclusions KW - levelized cost of electricity KW - EGS KW - operating costs KW - feasibility KW - model KW - reV KW - hydrothermal KW - capital costs KW - geothermal location LA - English DA - 2023/08/21 PY - 2023 PB - National Renewable Energy Laboratory T1 - Renewable Energy Potential Model: Geothermal Supply Curves UR - https://doi.org/10.15121/2008490 ER -
Export Citation to RIS
Trainor-Guitton, Whitney, et al. Renewable Energy Potential Model: Geothermal Supply Curves. National Renewable Energy Laboratory, 21 August, 2023, Geothermal Data Repository. https://doi.org/10.15121/2008490.
Trainor-Guitton, W., Thomson, S., Pinchuk, P., Maclauren, G., & Buster, G. (2023). Renewable Energy Potential Model: Geothermal Supply Curves. [Data set]. Geothermal Data Repository. National Renewable Energy Laboratory. https://doi.org/10.15121/2008490
Trainor-Guitton, Whitney, Sophie-Min Thomson, Pavlo Pinchuk, Galen Maclauren, and Grant Buster. Renewable Energy Potential Model: Geothermal Supply Curves. National Renewable Energy Laboratory, August, 21, 2023. Distributed by Geothermal Data Repository. https://doi.org/10.15121/2008490
@misc{GDR_Dataset_1549, title = {Renewable Energy Potential Model: Geothermal Supply Curves}, author = {Trainor-Guitton, Whitney and Thomson, Sophie-Min and Pinchuk, Pavlo and Maclauren, Galen and Buster, Grant}, abstractNote = {The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies.

To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. The included paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure.

Our preliminary supply curves and levelized cost of electricity (LCOE) results provided here should be considered with care due to the high uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for three scenarios: two hydrothermal (3.5km depth, USGS heat flow & SMU temperatures respectively) and one EGS (4.5km depth, SMU temperatures). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.
}, url = {https://gdr.openei.org/submissions/1549}, year = {2023}, howpublished = {Geothermal Data Repository, National Renewable Energy Laboratory, https://doi.org/10.15121/2008490}, note = {Accessed: 2025-04-24}, doi = {10.15121/2008490} }
https://dx.doi.org/10.15121/2008490

Details

Data from Aug 21, 2023

Last updated Oct 12, 2023

Submitted Sep 19, 2023

Organization

National Renewable Energy Laboratory

Contact

Whitney Trainor-Guitton

Authors

Whitney Trainor-Guitton

National Renewable Energy Laboratory

Sophie-Min Thomson

National Renewable Energy Laboratory

Pavlo Pinchuk

National Renewable Energy Laboratory

Galen Maclauren

National Renewable Energy Laboratory

Grant Buster

National Renewable Energy Laboratory

DOE Project Details

Project Name Development of a Geothermal Module in reV

Project Lead Sean Porse

Project Number FY23 AOP 5.4.2.3

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