电子科技 ›› 2024, Vol. 37 ›› Issue (3): 51-56.doi: 10.16180/j.cnki.issn1007-7820.2024.03.007

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基于多目标粒子群的电动汽车优化充电策略

李婷婷1, 娄柯2, 王园1, 徐华超1   

  1. 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
  • 收稿日期:2022-09-28 出版日期:2024-03-15 发布日期:2024-03-11
  • 作者简介:李婷婷(1993-),女,助教。研究方向:电动汽车入网充电策略。
    娄柯(1979-),男,博士,副教授。研究方向:多智能体分布式协调控制、智能微电网能量管理与调度。
  • 基金资助:
    安徽省高校自然科学研究重点项目(KJ2019A0151)

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

LI Tingting1, LOU Ke2, WANG Yuan1, XU Huachao1   

  1. 1. School of Mechanical Engineering,Anhui Polytechnic University,Wuhu 241000,China
    2. School of Electrical Engineering,Anhui Polytechnic University,Wuhu 241000,China
  • Received:2022-09-28 Online:2024-03-15 Published:2024-03-11
  • Supported by:
    Key Project of Natural Science Research in Universities of Anhui(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。

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

Abstract:

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.

Key words: electric vehicle, particle swarm optimization, orderly charging, load, charging power, peak-valley difference, power grid security, multi-objective optimization

中图分类号: 

  • TP29