基于差分进化粒子群优化算法的光伏发电园区与电动汽车充放电协同优化调度研究

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关键词：负荷波动；协同优化；全局搜索；种群多样性

中图分类号：TM76 文献标志码：A 文章编号：2095-8188（2026）02-0019-05

DOI： 10.16628/j.cnki.2095-8188.2026.02.003

Abstract：To tackle the loadfluctuation issues inapark causedbythe intermitencyof photovoltaic（PV） system power generation and the randomness of electric vehicle（EV）loads，a coordinatedoptimization scheduling model based onthe particle swarm optimization-diferential evolution（PSO-DE）algorithm is proposed.The model adoptsa jointobjective optimization functionaimed at minimizing the park’soperating costs while maximizing the benefits for EVusers.Byintegrating the global fastconvergence characteristics ofthe particle swarmoptimization （PSO）algorithm with the local search capabilityand population diversity maintenance mechanism of the diferential evolution（DE）algorithm，the proposed approach effctively enhancesconvergence speed by approximately 30%. Using the typical dailyload data for a summer day in a park in Shanghai asa simulationcase study，the results demonstrate that the proposed coodinated optimization model effctively reduces the park’soperating costs， increases EV user benefits by 7.7% ，and improves both economic performance and operational stability of the system. The simulation outcomes validate the accuracy and effectiveness of the model.

Key words： load fluctuation；coordinated optimization；global search；population diversity

0引言

近年来，由于生产制造业的持续发展，中国电动汽车生产量已多年居世界首位，展现了我国在全球新能源汽车领域的领先地位[1]。（剩余5547字）