基于灰狼优化算法的电动汽车充电站选址研究  

Research on Location of Electric Vehicle Charging Station Based on Gray Wolf Optimizer Algorithm

作　　者：黄灿 姚泽宇 葛华锋 HUANG Can;YAO Ze-yu;GE Hua-feng(State Grid Jiangsu Electric Power Co.,Ltd.Suzhou Power Supply Branch,Suzhou 215004,China;College of Computer Science and Software Engineering,Hohai University,Nanjing 211100,China)

机构地区：[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

出　　处：《电工电气》2024年第12期22-26,53,共6页Electrotechnics Electric

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。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 ob-jective 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 signifi-cantly 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.

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 

分 类 号：TM910.6[电气工程—电力电子与电力传动] U469.72[机械工程—车辆工程]