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Publication Date
6 August 2018

CERF – A Geospatial Model for Assessing Future Energy Production Technology Expansion Feasibility



The Capacity Expansion Regional Feasibility (CERF) model is an open-source geospatial model, written in Python and C++, that is designed to determine the on-the-ground feasibility of achieving a projected energy technology expansion plan. Integrated human-Earth systems models and grid expansion models typically do not have sufficient spatial, temporal, or process-level resolution to account for technology-specific siting considerations—for example, the value or costs of connecting a new power plant to the electric grid at a particular location or whether there is sufficient cooling water to support the installation of thermal power plants in a certain region. CERF was developed to specifically examine where power plant locations can be feasibly sited when considering high spatial resolution siting suitability data as well as the net locational costs (i.e., considering both net operating value and interconnection costs), at a spatial resolution of 1 km2. The outputs from CERF can provide insight into factors that influence energy system resilience under a variety of future scenarios can be used to refine model-based projections and be useful for capacity expansion planning exercises. CERF is open-source and publicly available via GitHub. Funding Statement: The original development of the CERF model was conducted under the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830. Further development and ongoing demonstration of CERF is supported by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program.

“Cerf – A Geospatial Model For Assessing Future Energy Production Technology Expansion Feasibility”. 2018. Journal Of Open Research Software 6. doi:10.5334/jors.227.
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