Optimized charging strategy of community electric vehicle charging station based on improved NSGA-II

被引：0
|
作者：
Wang Y. [1 ]
Cai C. [1 ]
Xue H. [1 ]
机构：
[1] College of Electrical Engineering, Shanghai University of Electric Power, Shanghai
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2017年 / 37卷 / 12期
基金：
上海市自然科学基金; 中国国家自然科学基金;
关键词：
Charging strategy; Community charging station; Electric vehicles; Multi-objective optimization; NSGA-II; Pareto optimality;
D O I：
10.16081/j.issn.1006-6047.2017.12.015
中图分类号：
学科分类号：
摘要：
An optimized charging strategy for community electric vehicle charging station based on improved NSGA-II(Nondominated Sorting Genetic Algorithm II) is proposed. Firstly, the multi-objective charging model of electric vehicle charging station is established to minimize the charging cost of per unit electric energy and the load variance of grid side, with the capacity limitation of electric vehicle charging and distribution transformers as constraints. Then, aiming at the shortcomings of traditional NSGA-II, such as difficulties for generating the initial populations satisfying the constraints, uneven distribution of Pareto solution sets and low performance of optimal solution sets, an improved NSGA-II, combining improved initial population generation method with comparison operator of crowding distance, is proposed to solve the model. The optimal compromise charging scheme is selected from the final Pareto solution sets by TOPSIS(Technique for Order Performance by Similarity to Ideal Solution) based on information entropy. Finally, simulative results of examples verify the effectiveness of the proposed algorithm and show that the improved NSGA-II can improve the grid-side load level and charging cost performance of customers in large extent. © 2017, Electric Power Automation Equipment Press. All right reserved.
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页码：109 / 115
页数：6
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