Prediction of Electric Vehicle Charging Load Considering Interaction Between Road Network and Power Grid and User's Psychology

被引：0
|
作者：
Long X. [1 ]
Yang J. [1 ]
Wu F. [1 ]
Zhan X. [1 ]
Lin Y. [1 ]
Xu J. [1 ]
机构：
[1] School of Electrical Engineering and Automation, Wuhan University, Wuhan
来源：
Yang, Jun (JYang@whu.edu.cn) | 1600年 / Automation of Electric Power Systems Press卷 / 44期
基金：
中国国家自然科学基金;
关键词：
Charging load prediction; Electric vehicle; Interaction framework of road network and power grid; Microscopic traffic modeling; Regret theory;
D O I：
10.7500/AEPS20191011008
中图分类号：
学科分类号：
摘要：
A prediction framework of electric vehicle (EV) charging load is proposed, which considers user's psychology and information interaction between road network and power grid. Firstly, the destination of EV is obtained through trip chain and origin-destination (OD) matrix. Secondly, considering driving time, queuing time and charging price, a model of choosing charging station based on regret theory is proposed. Thirdly, based on the following model, the microscopic traffic analysis on vehicle driving process in the road network is carried out, and the framework of charging load prediction considering the interaction between road network and power grid driven by charging price is established. Finally, Monte Carlo method is used to simulate the travelling and charging situations of EVs, so as to predict the time-space distribution of EV charging load. Through the simulation on the Third Ring Road Network in Beijing, China and the corresponding power grid, the effectiveness of the proposed EV charging load prediction framework is verified. The simulation results also show that the interaction between road network and power grid through charging price make the charging load distribution of electric private cars and taxis significantly different in time and space. © 2020 Automation of Electric Power Systems Press.
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页码：86 / 93
页数：7
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