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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    Titel :

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


    Beteiligte:
    Liu, Zhaocai (Autor:in) / Borlaug, Brennan (Autor:in) / Meintz, Andrew (Autor:in) / Neuman, Christopher (Autor:in) / Wood, Eric (Autor:in) / Bennett, Jesse (Autor:in)


    Erscheinungsdatum :

    2023-11-19




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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