基于负荷平衡的电动汽车模糊多目标充电调度算法

doi:10.11772/j.issn.1001-9081.2020071013

计算机应用 ›› 2021, Vol. 41 ›› Issue (4): 1192-1198.DOI: 10.11772/j.issn.1001-9081.2020071013

所属专题：前沿与综合应用

基于负荷平衡的电动汽车模糊多目标充电调度算法

周美玲, 陈淮莉

上海海事大学物流科学与工程研究院, 上海 201306

收稿日期:2020-07-13 修回日期:2020-09-10 出版日期:2021-04-10 发布日期:2020-10-10
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）。

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

ZHOU Meiling, CHEN Huaili

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).

摘要/Abstract

摘要： 居民小区电动汽车（EV）的单相充电方式导致配电网出现三相不平衡和负荷峰谷差问题，因此提出基于负荷平衡的EV模糊多目标充电调度策略。基于三相网络，将总延迟时间和充电平衡作为目标函数，考虑三相不平衡度和负荷峰谷差等约束，建立静态和在线调度问题下EV充电调度模型。采用改进非支配排序遗传算法-Ⅱ（NSGA-Ⅱ）进行多目标求解，通过设计交叉算子、自适应调整变异概率和局部优化等来优化结果。通过设置一定容量的外部档案和拥挤距离判定来获得Pareto最优前沿，并用模糊隶属度方法得到折中最优解。最后，通过算例分析可同时活动充电点和三相不平衡度的不同取值对优化结果的影响，并与无序充电进行比较，验证了所提模型和策略的有效性。

关键词: 三相不平衡, 在线调度, 帕累托最优前沿, 电动汽车, 遗传算法, 模糊隶属度

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.

Key words: three-phase imbalance, real-time scheduling, Pareto optimal frontier, Electric Vehicle (EV), Genetic Algorithm (GA), fuzzy membership

中图分类号:

TM73

周美玲, 陈淮莉. 基于负荷平衡的电动汽车模糊多目标充电调度算法[J]. 计算机应用, 2021, 41(4): 1192-1198.

ZHOU Meiling, CHEN Huaili. Fuzzy multi-objective charging scheduling algorithm for electric vehicle based on load balance[J]. Journal of Computer Applications, 2021, 41(4): 1192-1198.

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基于负荷平衡的电动汽车模糊多目标充电调度算法

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

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