Renewable Energy Potential Model: Geothermal Supply Curves
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 -
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
Keywords
geothermal, energy, SMU temperatures, supply curve, geospatial, grid infrastructure, exclusions, levelized cost of electricity, EGS, operating costs, feasibility, model, reV, hydrothermal, capital costs, geothermal locationDOE Project Details
Project Name Development of a Geothermal Module in reV
Project Lead Sean Porse
Project Number FY23 AOP 5.4.2.3