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基于灰狼优化算法的电动汽车充电站选址研究

来源：电工电气发布时间：2025-01-07 16:07 浏览次数：14

基于灰狼优化算法的电动汽车充电站选址研究

黄灿¹，姚泽宇²，葛华锋¹

(1 国网江苏省电力有限公司苏州供电分公司，江苏苏州 215004；

2 河海大学计算机与软件学院，江苏南京 211100)

摘要：随着电动汽车(EV)数量的增加，EV 充电站的不合理选址使得配电网的功率损耗增大、电压分布不均。分别从运营商和投资者角度提出了两种 EV 充电站选址优化目标，然后基于配电网的功率损耗和充电站的安装成本得到综合目标函数，基于灰狼优化算法(GWO)寻找目标函数全局最优解，得到充电站的最优选址配置。以 IEEE 34 节点系统为例，对接入最优选址采用不同控制模式的 EV 充电站的潮流进行对比分析，结果表明优化配置后的配电网功率损耗明显降低，电压分布更加均衡；与粒子群算法和遗传算法的优化结果对比表明，GWO 算法具有更好的收敛性和鲁棒性。

关键词： 电动汽车；充电站；选址优化；灰狼优化算法；配电网；功率损耗；粒子群算法；遗传算法

中图分类号：TM910.6 ；U469.72 文献标识码：A 文章编号：1007-3175(2024)12-0022-05

Research on Location of Electric Vehicle Charging

Station Based on Gray Wolf Optimizer Algorithm

HUANG Can¹, YAO Ze-yu², GE Hua-feng¹

（1 State Grid Jiangsu Electric Power Co., Ltd. Suzhou Power Supply Branch, Suzhou 215004, China;

2 College of Computer Science and Software Engineering, Hohai University, Nanjing 211100, China）

Abstract: With the increase of the number of electric vehicles(EV), the unreasonable location of EV charging stations has led to increased power loss in the distribution network and uneven voltage distribution. In this paper, two location optimization objectives for EV charging stations are proposed from the perspectives of operators and investors respectively. Then, a comprehensive objective function is derived based on the power loss of the distribution network and the installation cost of the charging stations. The global optimal solution of the objective function is sought based on the gray wolf optimizer(GWO) algorithm, and the optimal location configuration of the charging stations is obtained. Taking the IEEE 34-bus system as an example, a comparative analysis of the power flow in EV charging stations with different control modes at the optimal locations is conducted. The results indicate that the power loss in the optimized distribution network is significantly reduced, and the voltage distribution becomes more balanced; comparison of the optimization results with those of the particle swarm optimization(PSO) and genetic algorithm(GA) shows that the GWO algorithm has better convergence and robustness.

Key words: electric vehicle; charging station; optimal location; gray wolf optimizer algorithm; distribution network; power loss; particle swarm optimization; genetic algorithm

参考文献

[1] 韩克勤，丁丹军，钱科军，等. 基于 NSGA-Ⅱ 的电动汽车充电站多目标优化规划[J] . 电力需求侧管理，2017，19(S1) ：72-76.

[2] 方兵，李琳玮，黄亮，等. 计及储能与电动汽车充电站的配电网经济运行研究[J] . 电力需求侧管理，2022，24(2) ：59-64.

[3] 孙铨杰. 大规模电动汽车接入电网的负荷需求及其影响研究[D]. 上海：上海电力大学，2021.

[4] SANGUESA J A, TORRES-SANZ V, GARRIDO P, et al.A Review on Electric Vehicles:Technologies and Challenges[J].Smart Cities，2021，4(1) ：372-404.

[5] 张强. 基于多目标优化的电动汽车充电站选址研究[D].秦皇岛：燕山大学，2021.

[6] ELDEEB H H, FADDEL S, MOHAMMED O A.Multi-Objective Optimization Technique for the Operation of Grid Tied PV Powered EV Charging Station[J].Electric Power Systems Research，2018， 164 ：201-211.

[7] 倪超，王菲，仇经纬，等. 基于 MPGA 的电动汽车充电站选址规划[J] . 电子技术与软件工程，2020(19) ：226-227.

[8] 张艺涵，徐菁，李秋燕，等. 基于密度峰值聚类的电动汽车充电站选址定容方法[J] . 电力系统保护与控制，2021，49(5) ：132-139.

[9] ISLAM M M , SHAREEF H , MOHAMED A . Optimal Location and Sizing of Fast Charging Stations for Electric Vehicles by Incorporating Traffic and Power Networks[J].IET Intelligent Transport Systems， 2018， 12(8) ：947-957.

[10] NEYESTANI N, DAMAVANDI M Y, SHAFIE-KHAH M, et al.Allocation of Plug-In Vehicles' Parking Lots in Distribution Systems Considering Network-Constrained Objectives[J].IEEE Transactions on Power Systems， 2015， 30(5) ：2643-2656.

[11] 程宏波，肖永乐，王勋，等. 考虑低碳收益的电动汽车充电站选址规划[J] . 中国电力，2016，49(7) ：118-121.

[12] 曹佳佳，王淳，霍崇辉，等. 考虑配电网负荷波动和电压偏移的充电站优化规划[J] . 电力科学与技术学报，2021，36(4) ：12-19.

[13] 谢林伟. 基于自适应粒子群算法的电动汽车充电站优化规划[J]. 陕西电力，2012，40(11) ：34-37.

[14] MIRJALILI S, ALJARAH I, MAFARJA M, et al.Grey Wolf Optimizer:Theory， Literature Review，and Application in Computational Fluid Dynamics Problems[J].Nature-Inspired Optimizers，2020，811 ：87-105.

[15] 赵超，王斌，孙志新，等. 基于改进灰狼算法的独立微电网容量优化配置[J] . 太阳能学报，2022，43(1) ：256-262.

[16] NICK M , CHERKAOUI R , PAOLONE M . Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support[J].IEEE Transactions on Power Systems， 2014， 29(5) ：2300-2310.

[17] OWUOR J O, MUNDA J L, JIMOH A A.The IEEE 34 Node Radial Test Feeder as a Simulation Testbench for Distributed Generation[C]//IEEE Africon'11， 2011 ：1-6.

[18] 马临超，杨捷，郭贝贝，等. 考虑电压约束的分布式光伏最大准入功率模型[J] . 河南工学院学报，2022，30(1) ：6-12.

[19] LONG Wen, WU Tiebin, CAI Shaohong, et al.A Novel Grey Wolf Optimizer Algorithm with Refraction Learning[J].IEEE Access， 2019， 7 ：57805-57819.

[20] 王清玉，李宏亮，朱玉，等. 基于牛顿-拉夫逊算法和 P-Q 分解法的潮流计算对比分析[J] . 机电信息，2019(24) ：20-21.

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基于灰狼优化算法的电动汽车充电站选址研究