基于改进人工蜂群算法的电动汽车充电策略优化  

Optimization of Electric Vehicle Charging Strategy Based on Improved Artificial Bee Colony Algorithm

作　　者：刘华锋 张翀 张迪 许寅皓 顾一鸣 刘秦娥 LIU Huafeng;ZHANG Chong;ZHANG Di;XU Yinhao;GU Yiming;LIU Qine(Xiangyang Power Supply Company,State Grid Hubei Electric Power Co.,Ltd.,Xiangyang 441200,Hubei,China;State Key Laboratory of Advanced Power Transmission Technology(State Grid Smart Grid Research Institute Co.,Ltd.),Beijing 102209,China;Beijing Key Laboratory of DC Grid Technology and Simulation(State Grid Smart Grid Research Institute Co.,Ltd.),Beijing 102209,China)

机构地区：[1]国网湖北省电力有限公司襄阳供电公司,湖北襄阳441002 [2]先进输电技术国家重点实验室(国网智能电网研究院有限公司),北京102209 [3]直流电网技术与仿真北京市重点实验室(国网智能电网研究院有限公司),北京102209

出　　处：《电气传动自动化》2022年第6期6-10,共5页Electric Drive Automation

摘　　要：由于大规模电动汽车接入电网的无序充电,导致电网安全稳定的运行状态受到冲击,提出了基于改进人工蜂群算法的电动汽车充电策略优化。以获取电动汽车出行时间概率密度与电池充电特性作为关键因素,计算电动汽车充电负荷需求,基于改进人工蜂群算法建立优化模型来实现电动汽车充电策略优化。仿真实验结果表明本文所提策略优化后的电动汽车充电负荷峰谷差降低449.2kW,电网负荷波动优化效果明显。Due to the disordered charging of large-scale electric vehicles connected to the power grid,the safe and stable operation of the power grid has been impacted. An electric vehicle charging strategy optimization based on an improved artificial bee colony algorithm is proposed. The electric vehicle travel time probability density and battery charging characteristics are obtained as key factors to calculate the electric vehicle charging load demand,and an optimization model is established based on the improved artificial bee colony algorithm to optimize the electric vehicle charging strategy. The simulation results show that the peak-to-valley difference of electric vehicle charging load is reduced by 449.2kW after the optimization of the strategy proposed in this paper,and the optimization effect of power grid load fluctuation is obvious.

关 键 词：改进人工蜂群算法 电动汽车 充电策略 优化方法 

分 类 号：TM910.6[电气工程—电力电子与电力传动]