基于多目标粒子群的电动汽车优化充电策略  

Research on Optimal Charging Strategy of Electric Vehicle Based on Multi-Objective Particle Swarm Optimization

作　　者：李婷婷[1] 娄柯 王园 徐华超 LI Tingting;LOU Ke;WANG Yuan;XU Huachao(School of Mechanical Engineering,Anhui Polytechnic University,Wuhu 241000,China;School of Electrical Engineering,Anhui Polytechnic University,Wuhu 241000,China)

机构地区：[1]安徽工程大学机械工程学院,安徽芜湖241000 [2]安徽工程大学电气工程学院,安徽芜湖241000

出　　处：《电子科技》2024年第3期51-56,共6页Electronic Science and Technology

基　　金：安徽省高校自然科学研究重点项目(KJ2019A0151)。

摘　　要：居民区家用电动汽车充电具有较强的集中性,大规模电动汽车充电负荷会对配电网系统造成负荷峰谷差过大等问题。文中提出一种基于多目标粒子群(Multi-Objctive Particle Swarm Optimization,MPSO)算法的用户充电选择控制策略,通过分析预测电动汽车充电负荷建立以系统总负荷方差和调度成本最小为目标函数的多目标优化模型,同时考虑了电动汽车电池及系统功率等约束条件,采用多目标粒子群优化算法求解电动汽车最优起始充电时刻。仿真结果表明,相比居民区内电动汽车无序充电,文中所提电动汽车充电策略能有效降低负荷峰值和调度成本。Household electric vehicle charging in residential areas has a strong centrality.Large-scale electric vehicle charging load causes large peak-valley load difference and other problems in the distribution network system.This study proposes a user charging selection control strategy based on Multi-Objective Particle Swarm Optimization(MPSO)algorithm.Through the analysis and prediction of electric vehicle charging load,a multi-objective optimization model is established with the minimum variance of the total system load and scheduling cost as the objective function.Meanwhile,considering the constraints of electric vehicle battery and system power,the MPSO algorithm is used to solve the optimal initial charging time of electric vehicles.The simulation results show that compared with unordered charging of EVs in residential areas,the EV charging strategy proposed in this study can effectively reduce load peak and dispatch cost.

关 键 词：电动汽车 粒子群算法 有序充电 负荷 充电功率 峰谷差 电网安全 多目标优化 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]