电动汽车
连锁
充电站
工作(物理)
需求管理
智能电网
能源管理系统
公共交通
运输工程
环境经济学
电力需求
计算机科学
能源管理
电信
功率(物理)
工程类
电气工程
能量(信号处理)
经济
机械工程
心理学
统计
物理
数学
量子力学
心理治疗师
宏观经济学
功率消耗
作者
Zhaocai Liu,Brennan Borlaug,Andrew Meintz,Christopher Neuman,Eric Wood,Jesse Bennett
标识
DOI:10.1016/j.trd.2023.103994
摘要
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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