基于遗传算法计及充电行驶距离的电动汽车充电网络规划

doi:10.3969/j.issn.1008-0198.2023.03.005

湖南电力 ›› 2023, Vol. 43 ›› Issue (3): 29-36.doi: 10.3969/j.issn.1008-0198.2023.03.005

• “用电能效与综合能源”专栏 • 上一篇下一篇

基于遗传算法计及充电行驶距离的电动汽车充电网络规划

谢鹰¹, 郑众¹, 刘剑峰¹, 高希², 李大祥²

1.国网江苏省电力有限公司苏州供电分公司,江苏苏州215000;
2.南通大学电气工程学院,江苏南通 226019

收稿日期:2023-03-13 修回日期:2023-04-23 出版日期:2023-06-25 发布日期:2023-06-25
通信作者: 李大祥(1997),男,硕士研究生,研究方向为新型电力系统规划。
作者简介:谢鹰(1977),男,工程师,研究方向为电动汽车充电网络规划。
基金资助:
国家自然科学基金项目(51877112);江苏省高等学校自然科学研究重大项目(22KJA470006)

Electric Vehicle Charging Network Planning Considering Charging Driving Distance Based on Genetic Algorithm

XIE Ying¹, ZHENG Zhong¹, LIU Jianfeng¹, GAO Xi², LI Daxiang²

1. State Grid Suzhou Power Supply Company,Suzhou 215000, China;
2. College of Electrical Engineering, Nantong University, Nantong 226019, China

Received:2023-03-13 Revised:2023-04-23 Online:2023-06-25 Published:2023-06-25

摘要/Abstract

摘要： 为缩短电动汽车充电行驶距离,提升车主充电便利性,建立了基于流量需求模型(flow capturing location model,FCLM)的电动汽车充电网络随机规划模型。在充电站建造数目给定的前提下,通过优化充电站建设地址,在保证电动汽车充电行驶距离满足机会约束的同时,最小化整个交通网络中的电动汽车平均充电行驶里程。所建立的规划模型为考虑机会约束的0-1整数规划问题,采用基于可行性法则的遗传算法(genetic algorithm,GA)对其进行求解,为提高求解性能对遗传算法中的交叉、变异算子进行改进。最后,采用基于25节点交通网络的算例验证所提模型与求解方法的有效性,并对不同规划边界条件下充电行驶距离概率分布特性、置信度与充电站建造数目对规划结果的影响进行了分析。

关键词: 电动汽车, 充电网络, 充电行驶距离, 机会约束, 遗传算法

Abstract: In order to shorten the charging distance of electric vehicles and improve the charging convenience of car owners, a stochastic planning model for charging electric vehicle electrical network based on flow capturing location model(FCLM) is established. Under the premise of a given number of charging stations, by optimizing the construction address of charging stations, while ensuring that the charging distance of electric vehicles meets opportunity constraints, the average charging distance of electric vehicles in the entire transportation network is minimized. The established programming model is a 0-1 integer programming problem with opportunity constraints. The genetic algorithm based on the feasibility rule is used to solve it. In order to improve the solution performance, the crossover and mutation operators in the genetic algorithm(GA)are improved. Finally, an example based on a 25 node transportation network is used to validate the effectiveness of the proposed model and solution method. The probability distribution characteristics of charging distance under different planning boundary conditions, as well as the impact of confidence and the number of charging stations built on the planning results, are analyzed.

Key words: electric vehicle, charging network, charging driving distance, chance constraint, genetic algorithm

中图分类号:

U491
TM732

谢鹰, 郑众, 刘剑峰, 高希, 李大祥. 基于遗传算法计及充电行驶距离的电动汽车充电网络规划[J]. 湖南电力, 2023, 43(3): 29-36.

XIE Ying, ZHENG Zhong, LIU Jianfeng, GAO Xi, LI Daxiang. Electric Vehicle Charging Network Planning Considering Charging Driving Distance Based on Genetic Algorithm[J]. Hunan Electric Power, 2023, 43(3): 29-36.

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基于遗传算法计及充电行驶距离的电动汽车充电网络规划

Electric Vehicle Charging Network Planning Considering Charging Driving Distance Based on Genetic Algorithm

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