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County-Level Hourly Renewable Capacity Factor Dataset for the ReEDS Model

Publicly accessible License 

This dataset contains hourly capacity factors for each renewable resource class and region (in this case, county). Technologies like large-scale utility PV (UPV), onshore wind, offshore wind, and concentrating solar power (CSP) are included. The dataset contains 7 years of hourly weather data (2007-2013) for different sites across the US and is used as one of the inputs to the ReEDS-2.0 model (see the "ReEDS 2.0 GitHub Repository" resource link below), developed by NREL. The weather profiles apply to any capacity that exists or is built in each region and class. This helps calculate the generation that can be provided using these resources.

Open, reference, and limited are 3 scenarios based on land-use allowance, derived from the Renewable Energy Potential (reV) model developed by NREL, which helps generate supply curves for renewable technologies and assess the maximum potential of renewable resources in a designated area. Each zipped file in this dataset corresponds to a technology and contains the respective land-use scenario files required to run that technology in ReEDS.

To use this dataset, download and place the extracted files in the locally cloned ReEDS repository inside one of the folders (inputs/variability/multi_year). After completing this copy, upon running the ReEDS model at the county-level spatial resolution for respective analysis purposes, the program will detect the presence of these files and will not fail.

Citation Formats

National Renewable Energy Laboratory (NREL). (2023). County-Level Hourly Renewable Capacity Factor Dataset for the ReEDS Model [data set]. Retrieved from https://dx.doi.org/10.25984/2282347.
Export Citation to RIS
Cole, Wesley, Brown, Maxwell, Sergi, Brian, Carag, Vincent, Serpe, Louisa, Karmakar, Akash, Lopez, Anthony, Williams, Travis, and Pinchuk, Paul. County-Level Hourly Renewable Capacity Factor Dataset for the ReEDS Model. United States: N.p., 01 Aug, 2023. Web. doi: 10.25984/2282347.
Cole, Wesley, Brown, Maxwell, Sergi, Brian, Carag, Vincent, Serpe, Louisa, Karmakar, Akash, Lopez, Anthony, Williams, Travis, & Pinchuk, Paul. County-Level Hourly Renewable Capacity Factor Dataset for the ReEDS Model. United States. https://dx.doi.org/10.25984/2282347
Cole, Wesley, Brown, Maxwell, Sergi, Brian, Carag, Vincent, Serpe, Louisa, Karmakar, Akash, Lopez, Anthony, Williams, Travis, and Pinchuk, Paul. 2023. "County-Level Hourly Renewable Capacity Factor Dataset for the ReEDS Model". United States. https://dx.doi.org/10.25984/2282347. https://data.openei.org/submissions/5986.
@div{oedi_5986, title = {County-Level Hourly Renewable Capacity Factor Dataset for the ReEDS Model}, author = {Cole, Wesley, Brown, Maxwell, Sergi, Brian, Carag, Vincent, Serpe, Louisa, Karmakar, Akash, Lopez, Anthony, Williams, Travis, and Pinchuk, Paul.}, abstractNote = {This dataset contains hourly capacity factors for each renewable resource class and region (in this case, county). Technologies like large-scale utility PV (UPV), onshore wind, offshore wind, and concentrating solar power (CSP) are included. The dataset contains 7 years of hourly weather data (2007-2013) for different sites across the US and is used as one of the inputs to the ReEDS-2.0 model (see the "ReEDS 2.0 GitHub Repository" resource link below), developed by NREL. The weather profiles apply to any capacity that exists or is built in each region and class. This helps calculate the generation that can be provided using these resources.

Open, reference, and limited are 3 scenarios based on land-use allowance, derived from the Renewable Energy Potential (reV) model developed by NREL, which helps generate supply curves for renewable technologies and assess the maximum potential of renewable resources in a designated area. Each zipped file in this dataset corresponds to a technology and contains the respective land-use scenario files required to run that technology in ReEDS.

To use this dataset, download and place the extracted files in the locally cloned ReEDS repository inside one of the folders (inputs/variability/multi_year). After completing this copy, upon running the ReEDS model at the county-level spatial resolution for respective analysis purposes, the program will detect the presence of these files and will not fail.
}, doi = {10.25984/2282347}, url = {https://data.openei.org/submissions/5986}, journal = {}, number = , volume = , place = {United States}, year = {2023}, month = {08}}
https://dx.doi.org/10.25984/2282347

Details

Data from Aug 1, 2023

Last updated Jan 23, 2024

Submitted Jan 18, 2024

Organization

National Renewable Energy Laboratory (NREL)

Contact

Wesley Cole

Authors

Wesley Cole

National Renewable Energy Laboratory NREL

Maxwell Brown

National Renewable Energy Laboratory NREL

Brian Sergi

National Renewable Energy Laboratory NREL

Vincent Carag

National Renewable Energy Laboratory NREL

Louisa Serpe

National Renewable Energy Laboratory NREL

Akash Karmakar

National Renewable Energy Laboratory NREL

Anthony Lopez

National Renewable Energy Laboratory NREL

Travis Williams

National Renewable Energy Laboratory NREL

Paul Pinchuk

National Renewable Energy Laboratory NREL

DOE Project Details

Project Name ReEDS Spatial Flexibility

Project Number FY23 AOP 2.4.0.1

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