基于GA-GWO算法的电动汽车有序充放电两阶段优化策略  

A Two-Stage Optimization Strategy for Orderly Charging and Discharging of Electric Vehicles Based on GA-GWO Algorithm

作　　者：闫丽梅[1] 王登银 洪益民 刘继翔 YAN Li-mei;WANG Deng-yin;HONG Yi-min;LIU Ji-xiang(School of Electrical&Information Engineering,Northeast Petroleum University,Daqing 163318,China;Xinxian Power Supply Company of State Grid Henan Electric Power Company,Xinxian 465550,China)

机构地区：[1]东北石油大学电气信息工程学院,黑龙江大庆163318 [2]国网河南省电力公司新县供电公司,河南新县465550

出　　处：《电工电气》2025年第2期24-31,共8页Electrotechnics Electric

摘　　要：电动汽车(EV)聚集性无序充电会对电力系统的安全与稳定性运行产生不良影响。考虑电网侧的调峰需求和EV用户的充电需求及充电成本,在基于分时电价的基础上,提出最小临界电量对EV向电网进行馈电进行限制,并给出一种基于最小临界电量的两阶段有序充放电控制策略,以EV用户充电费用最小与电网负荷波动最小为目标,建立EV充放电优化模型。利用遗传-灰狼优化算法(GA-GWO)对EV的充放电行为进行优化,采用蒙特卡洛法模拟某居民区450辆EV的充电需求,与其他充电策略在不同渗透率的场景下进行了对比仿真,结果表明,所提出充放电优化策略能起到降低负荷方差以及削峰填谷作用,且随着参与调度的电动汽车数量增多,优化效果更明显。Aggregate disordered charging of electric vehicles(EV)can adversely affect the safe and stable operation of power systems.Considering the peak shaving demand on the grid side,the charging demand and charging cost of EV users,the minimum critical amount is proposed to limit the feeding of EV to the grid based on the time-of-use tariff.A two-stage orderly charging and discharging control strategy based on the minimum critical power is proposed,and an EV charging and discharging optimization model is established with the goal of minimizing the charging cost of EV users and minimizing the fluctuation of grid load.The genetic algorithm-gray wolf(GA-GWO)was used to optimize the charging and discharging behavior of EV,and the Monte Carlo method was used to simulate the charging demand of 450 EV in a residential area,and the simulation was compared with other charging strategies in different penetration scenarios.The results show that the proposed charging and discharging optimization strategy can reduce the load variance and peak shaving and valley filling,and the optimization effect is more obvious with the increase of the number of electric vehicles participating in the scheduling.

关 键 词：电动汽车 分时电价 最小临界电量 两阶段有序充放电 遗传-灰狼优化算法 

分 类 号：TM734[电气工程—电力系统及自动化] U469.72[机械工程—车辆工程]