Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results
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.
Resources
4 resources available
-
Favorability Sites.xlsx
XLSX -
ReEDS Results.xlsx
XLSX -
ReEDS Model GitHub
APPLICATION/OCTET-STREAM -
Associated Technical Report
PDF
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
{
"fn": "Sophie-Min Thomson",
"@type": "vcard:Contact",
"hasEmail": "mailto:sophiemin.thomson@nlr.gov"
}
|
| dataQuality |
true
|
| description | 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. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Favorability Sites.xlsx",
"format": "xlsx",
"accessURL": "https://gdr.openei.org/files/1604/Favorability_Sites.xlsx",
"mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"description": "This file includes data on potential geothermal leasing sites, categorized by favorability levels (near, mid, and far priorities), with geographic coordinates and whether the land is managed by the Bureau of Land Management (BLM). Each record identifies geothermal site characteristics, including a unique identifier, latitude, and longitude."
},
{
"@type": "dcat:Distribution",
"title": "ReEDS Results.xlsx",
"format": "xlsx",
"accessURL": "https://gdr.openei.org/files/1604/ReEDS%20Results.xlsx",
"mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"description": "This file contains model outputs from the ReEDS system, providing scenarios for geothermal energy deployment, including generation, capacity, system costs, electricity prices, and emissions. The data is segmented by year, technology, and economic metrics such as system costs and capacity in gigawatts."
},
{
"@type": "dcat:Distribution",
"title": "ReEDS Model GitHub",
"accessURL": "https://github.com/NREL/ReEDS-2.0?tab=readme-ov-file",
"mediaType": "application/octet-stream",
"description": "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 repository details the functionality of the ReEDS system for modeling energy deployment scenarios and related analyses."
},
{
"@type": "dcat:Distribution",
"title": "Associated Technical Report",
"format": "pdf",
"accessURL": "https://www.nrel.gov/docs/fy24osti/88247.pdf",
"mediaType": "application/pdf",
"description": "This report describes the methodology, analysis, and results of the study, including identification of high-priority geothermal leasing areas and their economic potential. It also includes detailed modeling assumptions, deployment categories, and breakdowns of geothermal resource capacity across different scenarios."
}
]
|
| DOI | 10.15121/2516751 |
| identifier | https://data.openei.org/submissions/8320 |
| issued | 2024-05-20T06:00:00Z |
| keyword |
[
"BLM",
"GAMS",
"GitHub",
"Python",
"ReEDs",
"Renewable Energy Potential Model",
"USFS",
"emissions",
"energy",
"feasibility",
"generation",
"geothermal",
"geothermal capacity",
"geothermal leasing areas",
"model results",
"modeling",
"natural resource conflicts",
"priority leasing",
"processed data",
"resource potential",
"system cost",
"technical report",
"technology combination",
"transmission"
]
|
| landingPage | https://gdr.openei.org/submissions/1604 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2025-02-14T21:03:43Z |
| programCode |
[
"019:006"
]
|
| projectLead | Sean Porse |
| projectNumber | FY24 AOP 5.3.1.4 |
| projectTitle | Geothermal Leasing Analysis |
| publisher |
{
"name": "National Renewable Energy Laboratory",
"@type": "org:Organization"
}
|
| spatial |
"{"type":"Polygon","coordinates":[[[-125.4514,24.5873],[-66.5318,24.5873],[-66.5318,49.2637],[-125.4514,49.2637],[-125.4514,24.5873]]]}"
|
| title | Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results |
