Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results

Smith, Faith; Ho, Jonathan; Trainor-Guitton, Whitney; Thomson, Sophie-Min; Smith, Ligia E. P.; Heimiller, Donna

doi:10.15121/2516751

Title: Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results

Dataset
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Abstract

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.

Authors:

Smith, Faith; Ho, Jonathan; Trainor-Guitton, Whitney; Thomson, Sophie-Min; Smith, Ligia E. P.; Heimiller, Donna

National Renewable Energy Laboratory

Publication Date:: Mon May 20 00:00:00 EDT 2024

Other Number(s):: 1604

Research Org.:: DOE Geothermal Data Repository; National Renewable Energy Laboratory

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)

Collaborations:: National Renewable Energy Laboratory

Subject:: 15 GEOTHERMAL ENERGY; 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

OSTI Identifier:: 2516751

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

Citation Formats


                    Smith, Faith, Ho, Jonathan, Trainor-Guitton, Whitney, Thomson, Sophie-Min, Smith, Ligia E. P., and Heimiller, Donna. Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results.  United States: N. p., 2024. 
        Web.  doi:10.15121/2516751.

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                    Smith, Faith, Ho, Jonathan, Trainor-Guitton, Whitney, Thomson, Sophie-Min, Smith, Ligia E. P., & Heimiller, Donna. Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results.  United States.  doi:https://doi.org/10.15121/2516751

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                    Smith, Faith, Ho, Jonathan, Trainor-Guitton, Whitney, Thomson, Sophie-Min, Smith, Ligia E. P., and Heimiller, Donna. 2024.  
"Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results".  United States.  doi:https://doi.org/10.15121/2516751.  https://www.osti.gov/servlets/purl/2516751. Pub date:Mon May 20 00:00:00 EDT 2024

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@article{osti_2516751,

  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.},

  doi          = {10.15121/2516751},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon May 20 00:00:00 EDT 2024},

  month        = {Mon May 20 00:00:00 EDT 2024}

}

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

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