GRIDCERF: Geospatial Raster Input Data for Capacity Expansion Regional Feasibility
Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utili...
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Zusammenfassung: | Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 265 suitability layers for use with 56 power plant technology configurations in a harmonized format that can be easily ingested by geospatially-enabled modeling software. It also provides pre-compiled technology-specific suitability layers and allows for user customization to robustly address science objectives when evaluating varying future conditions.
Accompanying GitHub repository:
The following GitHub repository contains the code used to generate the data in this archive: https://github.com/IMMM-SFA/vernon-etal_2023_scidata
Contents:
Note:
GRIDCERF does not provide the source data directly due to some license restrictions related for direct redistribution of the unaltered source data. However, the included file "gridcerf_source_data_description.csv" details the provenance associated with each source dataset and notes their individual licenses/disclaimers.
Common Rasters:
Suitability Layer Type and Source
GRIDCERF Raster Name
Bureau of Land Management (BLM) Surface Management Agency Areas33
gridcerf_blm_surface_management_agency_areas.tif
BLM National Landscape Conservation System (NLCS) - National Monuments34
gridcerf_blm_nlcs_national_monument_conus.tif
BLM NLCS - Outstanding Natural Areas35
gridcerf_blm_nlcs_outstanding_natural_areas_conus.tif
BLM NLCS - Wilderness36
gridcerf_blm_nlcs_wilderness_conus.tif
BLM NLCS - Wilderness Study Areas37
gridcerf_blm_nlcs_wilderness_study_areas_conus.tif
National Park Service (NPS) Class 1 airsheds38
gridcerf_class1_airsheds_conus.tif
NPS Administrative Boundaries39
gridcerf_ |
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DOI: | 10.5281/zenodo.6601789 |