Related Datasets

Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results

DOI 10.15121/2516751

Publicly accessible License

This dataset contains the results of a study conducted by the National Renewable Energy Laboratory (NREL) to identify potential future priority geothermal leasing areas on Bureau of Land Management (BLM) and United States Forest Service (USFS) lands. The analysis uses the Regional Energy Deployment System (ReEDS) model to evaluate geothermal resource potential under different scenarios of resource depth and technology combinations through the year 2050. The study considers geothermal resource potential, natural resource conflicts, and transmission access to categorize areas into near, mid, and far deployment priorities.

The dataset includes outputs from the ReEDS model, such as geothermal capacity, generation, system costs, and emissions under various economic and technical scenarios. Favorability site data with geographic coordinates and site-specific attributes (e.g., resource favorability, land type) are also provided. Supporting resources include a technical report detailing methodologies and assumptions, along with a link to the ReEDS model GitHub repository, which requires GAMS and Python software for execution.

Associated Technical Report

This report describes the methodology, analysis, and results of the study, including identification of high-priority geothermal leasing areas and their economic potenti... more

View

Favorability Sites.xlsx

This file includes data on potential geothermal leasing sites, categorized by favorability levels (near, mid, and far priorities), with geographic coordinates and wheth... more

Download 38.07 kB

ReEDS Model GitHub

Python GAMS

This link provides access to the ReEDS model repository, which includes the full model code, documentation, and input data required to replicate the analysis. The repos... more

View Repository

ReEDS Results.xlsx

This file contains model outputs from the ReEDS system, providing scenarios for geothermal energy deployment, including generation, capacity, system costs, electricity ... more

Download 821.05 kB

Citation Formats

TY  - DATA
AB  - This dataset contains the results of a study conducted by the National Renewable Energy Laboratory (NREL) to identify potential future priority geothermal leasing areas on Bureau of Land Management (BLM) and United States Forest Service (USFS) lands. The analysis uses the Regional Energy Deployment System (ReEDS) model to evaluate geothermal resource potential under different scenarios of resource depth and technology combinations through the year 2050. The study considers geothermal resource potential, natural resource conflicts, and transmission access to categorize areas into near, mid, and far deployment priorities.

The dataset includes outputs from the ReEDS model, such as geothermal capacity, generation, system costs, and emissions under various economic and technical scenarios. Favorability site data with geographic coordinates and site-specific attributes (e.g., resource favorability, land type) are also provided. Supporting resources include a technical report detailing methodologies and assumptions, along with a link to the ReEDS model GitHub repository, which requires GAMS and Python software for execution.
AU  - Smith, Faith
A2  - Ho, Jonathan
A3  - Trainor-Guitton, Whitney
A4  - Thomson, Sophie-Min
A5  - Smith, Ligia E. P.
A6  - Heimiller, Donna
DB  - Open Energy Data Initiative (OEDI)
DP  - Open EI | National Renewable Energy Laboratory
DO  - 10.15121/2516751
KW  - geothermal
KW  - energy
KW  - ReEDs
KW  - GAMS
KW  - Python
KW  - BLM
KW  - USFS
KW  - Renewable Energy Potential Model
KW  - geothermal leasing areas
KW  - priority leasing
KW  - resource potential
KW  - feasibility
KW  - technology combination
KW  - natural resource conflicts
KW  - transmission
KW  - geothermal capacity
KW  - generation
KW  - system cost
KW  - emissions
KW  - technical report
KW  - processed data
KW  - modeling
KW  - GitHub
KW  - model results
LA  - English
DA  - 2024/05/20
PY  - 2024
PB  - National Renewable Energy Laboratory
T1  - Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results
UR  - https://doi.org/10.15121/2516751
ER  -

Export Citation to RIS

Smith, Faith, et al. Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results. National Renewable Energy Laboratory, 20 May, 2024, GDR. https://doi.org/10.15121/2516751.

Smith, F., Ho, J., Trainor-Guitton, W., Thomson, S., Smith, L., & Heimiller, D. (2024). Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results. [Data set]. GDR. National Renewable Energy Laboratory. https://doi.org/10.15121/2516751

Smith, Faith, Jonathan Ho, Whitney Trainor-Guitton, Sophie-Min Thomson, Ligia E. P. Smith, and Donna Heimiller. Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results. National Renewable Energy Laboratory, May, 20, 2024.  Distributed by GDR. https://doi.org/10.15121/2516751

@misc{OEDI_Dataset_8320,
title = {Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results},
author = {Smith, Faith and Ho, Jonathan and Trainor-Guitton, Whitney and Thomson, Sophie-Min and Smith, Ligia E. P. and Heimiller, Donna},
abstractNote = {This dataset contains the results of a study conducted by the National Renewable Energy Laboratory (NREL) to identify potential future priority geothermal leasing areas on Bureau of Land Management (BLM) and United States Forest Service (USFS) lands. The analysis uses the Regional Energy Deployment System (ReEDS) model to evaluate geothermal resource potential under different scenarios of resource depth and technology combinations through the year 2050. The study considers geothermal resource potential, natural resource conflicts, and transmission access to categorize areas into near, mid, and far deployment priorities.



The dataset includes outputs from the ReEDS model, such as geothermal capacity, generation, system costs, and emissions under various economic and technical scenarios. Favorability site data with geographic coordinates and site-specific attributes (e.g., resource favorability, land type) are also provided. Supporting resources include a technical report detailing methodologies and assumptions, along with a link to the ReEDS model GitHub repository, which requires GAMS and Python software for execution.},
url = {https://gdr.openei.org/submissions/1604},
year = {2024},
howpublished = {GDR, National Renewable Energy Laboratory, https://doi.org/10.15121/2516751},
note = {Accessed: 2025-04-25},
doi = {10.15121/2516751}
}

https://dx.doi.org/10.15121/2516751

Details

Data from May 20, 2024

Last updated Feb 14, 2025

Submitted May 29, 2024