A Prediction Method of Electric Vehicle Charging Load Considering Traffic Network and Travel Rules

被引:0
|
作者
Long, Xuemei [1 ]
Yang, Jun [1 ]
Wang, Yang
Dai, Xianzhong
Zhan, Xiangpeng
Rao, Yingqing
机构
[1] Wuhan Univ, Coll Elect Engn, Wuhan 430072, Hubei, Peoples R China
关键词
EV; traffic network; cellular automata; charging load prediction;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Predicting the electric vehicle (EV) charging load is important to the power grid security. Current literature that researches EV charging load prediction rarely considers the specific traffic network and the trip characteristics of the drivers. This paper proposes a prediction method of electric vehicle charging load considering traffic network and travel rules. First, considering the travel rules of taxis and private cars, the travel information is obtained through the Monte Carlo simulation. Then, based on the road topology modeling and the cellular automata driving rules, we can simulate the driving process of the vehicle on the road. This paper mainly focuses on electricity consumption caused by the different speed and the use of air conditioner. The charging decision is determined by specific circumstances. Finally, by analyzing the charging information, we can predict the space-time distribution of the EV charging load.
引用
收藏
页码:930 / 937
页数:8
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