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电动私家车充电负荷中长期推演模型
引用本文:于海东,张焰,潘爱强.电动私家车充电负荷中长期推演模型[J].电力系统自动化,2019,43(21):80-87.
作者姓名:于海东  张焰  潘爱强
作者单位:上海交通大学电子信息与电气工程学院,上海市,200240;国网上海市电力公司电力科学研究院,上海市,200090
基金项目:国家电网公司科技项目(520940170022)
摘    要:针对现有研究中电动汽车规模预测及其充电负荷预测相互独立的现状,提出了一种电动私家车充电负荷中长期推演模型。首先,综合考虑电动私家车使用人群的创新效应、模仿效应及电动私家车的价格因素,对Bass模型进行一定的改进以实现对电动私家车中长期保有量的预测进行动态建模。其次,基于描述车辆日出行行为特征的出行链模型,对私家车车主的出行活动的起止时间、行驶里程及停留时间进行概率建模,并采用蒙特卡洛模拟仿真城市海量的车辆行为。最后,采用上海市实际地理信息及行车数据,通过车辆规模推演与车辆行为仿真的交替进行,分析了社会宣传与政策补贴、车主充电习惯以及充电引导措施等对上海未来数年电动私家车充电负荷需求时空分布的影响。

关 键 词:电动汽车  负荷预测  Bass模型  出行链  蒙特卡洛
收稿时间:2019/2/21 0:00:00
修稿时间:2019/8/22 0:00:00

Medium- and Long-term Evolution Model of Charging Load for Private Electric Vehicle
YU Haidong,ZHANG Yan and PAN Aiqiang.Medium- and Long-term Evolution Model of Charging Load for Private Electric Vehicle[J].Automation of Electric Power Systems,2019,43(21):80-87.
Authors:YU Haidong  ZHANG Yan and PAN Aiqiang
Affiliation:School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China,School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China and Electric Power Research Institute of State Grid Shanghai Municipal Electric Power Company, Shanghai 200090, China
Abstract:Aiming at the situation of scale prediction and independent charging load prediction of electric vehicles(EVs)in current researches, a medium- and long-term charging load prediction model for private EVs is proposed. Firstly, this paper improves the dynamic model of the medium- and long-term ownership prediction of private electric vehicles using Bass model with innovation, imitation and price effect of users for private EVs. Secondly, the daily travel behavior is described based on trip chain model. Probabilistic model is established to describe the starting and stopping time, daily mileage and residence time of users for private cars. Monte Carlo method is adopted to simulate the behavior of massive private electric vehicles. Finally, with realistic geographical information and vehicle travel data of Shanghai, scale prediction and behavior simulation of private EVs are conducted alternately. Various factors influencing spatial and temporal distribution of charging load in the coming years are analyzed, including media promotion about electric vehicle, subsidies for purchasing electric vehicles, charging habits of car owner and charging control strategy.
Keywords:electric vehicles(EVs)  load prediction  Bass model  trip chain  Monte Carlo
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