Aggregation Model-Based Optimization for Electric Vehicle Charging Strategy

被引：89

作者：

Zheng, Jinghong ^{[1
]}

Wang, Xiaoyu ^{[2
]}

Men, Kun ^{[3
]}

Zhu, Chun ^{[4
]}

Zhu, Shouzhen ^{[1
]}

机构：

[1] Tsinghua Univ, State Key Lab Power Syst, Dept Elect Engn, Beijing 100084, Peoples R China

[2] Carleton Univ, Dept Elect, Ottawa, ON K1S 5B6, Canada

[3] China Southern Power Grid Corp, Guangzhou 510080, Guangdong, Peoples R China

[4] Microsoft Corp, Sunnyvale, CA 94089 USA

来源：

IEEE TRANSACTIONS ON SMART GRID | 2013年 / 4卷 / 02期

关键词：

Aggregation model; electric vehicle; optimal charging; parameter estimation; stochastic distribution;

D O I：

10.1109/TSG.2013.2242207

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents an aggregation charging model for large numbers of electric vehicles (EVs). A genetic algorithm (GA) is employed to obtain the stochastic feature parameters of the aggregation model, and a charging strategy based on the aggregation model is developed to reduce the power fluctuation level caused by EV charging. In addition, an updatable optimization method is proposed to track the variation of the EV charging characteristics. The proposed charging strategy and optimization method are validated by the simulation results.

引用

页码：1058 / 1066

页数：9

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