Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (4): 1192-1198.

Special Issue: 前沿与综合应用

• Frontier and comprehensive applications •

Fuzzy multi-objective charging scheduling algorithm for electric vehicle based on load balance

1. Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
• Received:2020-07-13 Revised:2020-09-10 Online:2021-04-10 Published:2020-10-10
• Supported by:
This work is partially supported by the Foundation of the Humanities and Social Sciences of Ministry of Education (20YJC630215).

基于负荷平衡的电动汽车模糊多目标充电调度算法

1. 上海海事大学 物流科学与工程研究院, 上海 201306
• 通讯作者: 周美玲
• 作者简介:周美玲（1996—），女，江西抚州人，硕士研究生，主要研究方向：电动汽车充电；陈淮莉（1970—），女，安徽合肥人，教授，博士，主要研究方向：高级计划与排程、供应链管理。
• 基金资助:
教育部人文社会科学基金资助项目（20YJC630215）。

Abstract: Three-phase imbalance and load peak-valley difference in the distribution network were caused by single-phase charging of Electric Vehicle(EV) in residential area. Therefore, amulti-objective charging scheduling strategy for EV considering load balance was proposed. Based on the three-phase network, the total delay time and charge balance were used as the objective function, and constraints such as load peak-valley difference and three-phase imbalance were taken into account to establish the scheduling model of EV charging for static and online scheduling problems. The multi-objective solution was obtained by the improved Non-dominated Sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ), and the results were optimized by designing crossover operators, adaptively adjusting mutation probability and local optimization. The Pareto optimal frontier was obtained by setting a certain volume of external archives and crowding distance, and the fuzzy membership method was used to obtain the compromise optimal solution. The influence of number of simultaneously active charging points and three-phase imbalance value on the optimization results was analyzed through an example.The proposed strategy was compared with the disorderly charging strategy so that the validity of the proposed model and strategy was proved.

CLC Number: