United States Department of Transportation
i
Intermodel Comparison Between Switch 2.0 and GE MAPS: Evaluating a New Tool for Integrated Modeling of Electric Vehicles and High-Renewable Power Systems
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2018-07-01
[PDF-1.46 MB]
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Alternative Title:EVTC Project 21 - Effect of Electric Vehicles on Power System Expansion and Operation
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Edition:Final Research Project Report
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Abstract:Electric Vehicles (EVs) could help increase energy security and reduce greenhouse gas emissions by using electricity produced from clean, domestic sources instead of imported oil. This benefit could be enhanced if EVs are adopted in high-renewable power systems and are charged at the times when renewable power is most abundant, producing a win-win arrangement in which EVs enable greater adoption of renewable power in the grid. With its unique geography and current fossil fuel based energy infrastructure combined with its aggressive renewable energy goals, Hawaii forms an ideal site for large-scale adoption of EVs in the future. This report presents results from the Hawaii Natural Energy Institute (HNEI) at the University of Hawaii’s (UH’s) project on the “Effect of Electric Vehicles on Power System Expansion and Operation,” in partnership with the Electric Vehicle Transportation Center at the University of Central Florida. This project’s overall focus is on studying the synergies between well-timed EV charging and the design and operation of high-renewable power systems. This work requires high-quality, validated models of electric power systems. To support this effort, UH investigators configured the Switch power system model similarly to the GE Multi-Area Production Simulation (GE MAPS) model, as it was used by GE Energy Consulting for HNEI’s recent Hawaii Renewable Portfolio Standards Study. GE MAPS is a widely respected and frequently used production-cost model for power systems. When configured with similar input data, researchers found that the two models agree very closely on how high-renewable power systems would be operated, including hourly production from individual power plants, annual curtailment rates for renewable energy facilities, and total annual production from different power sources. The models agree on 97 percent of the variation in curtailment and 65–100 percent of the variation in generator usage across 17 diverse scenarios of renewable energy and transmission deployment on Oahu and Maui. This work gave investigators confidence to continue using Switch to investigate interactions and synergies between EV charging and high-renewable power systems.
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