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DOE OSTI · 2282550

Data-driven method for electric vehicle charging demand analysis: Case study in Virginia

Liu, Zhaocai·Borlaug, Brennan·Meintz, Andrew·Neuman, Christopher·Wood, Eric·Bennett, Jesse
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Abstract

Electric vehicle (EV) adoption in the U.S. will be accelerated by the historic $7.5 billion public investments in EV charging infrastructure. Careful analysis of EV charging demands plays a vital role in understanding the energy requirements, power grid impact, and smart charging management opportunities of EVs. To this end, this paper develops a data-driven trip-chaining-based modeling framework including five steps: Trip data acquisition and preprocessing, EV adoption modeling, travel itinerary synthesis, EV charging demand simulation and EV load profile generation. The developed analysis framework was demonstrated using real-world data for one region in Virginia, U.S. The results show that the proposed modeling framework can work effectively. For the study region in 2040, the predicted number of plug-in EVs is 470,114, resulting in a weekly charging demand of 38,078,127 kWh (55% home, 9% work, and 36% public) in September and 45,920,358 kWh (61% home, 9% work, and 30% public) in February.

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29 ENERGY PLANNING, POLICY, AND ECONOMY33 ADVANCED PROPULSION SYSTEMSconnected vehicle dataelectric vehicle charging demandland use datatrip chaining

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Liu, Zhaocai, Borlaug, Brennan, Meintz, Andrew, Neuman, Christopher, Wood, Eric, Bennett, Jesse. 2023-11-26. Data-driven method for electric vehicle charging demand analysis: Case study in Virginia. https://doi.org/10.1016/j.trd.2023.103994

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