基于自适应变异粒子群算法的电动出租车充电引导  

An Adaptive Particle Mutation Swarm Optimization Based Electric Taxi Charging Guidance

作　　者：牛利勇[1] 张帝[1] 王晓峰[1] 姜久春[1] 张维戈[1] 黄梅[1] 

机构地区：[1]国家能源主动配电网技术研发中心(北京交通大学),北京市海淀区100044

出　　处：《电网技术》2015年第1期63-68,共6页Power System Technology

基　　金：北京高等学校青年英才计划项目(YETP0570)~~

摘　　要：通过调研分析深圳市电动出租车的实际运营数据,提出充电站充电设备时间利用率的计算方法。针对区域内充电站之间设备时间利用率分布不均衡的问题,提出了基于充电站信息和车辆信息的电动出租车充电引导系统,建立了充电引导模型,并采用改进的自适应变异粒子群算法引导电动出租车的充电行为。根据深圳充电站的实际数据进行算例仿真,仿真结果表明,经过充电引导后的电动出租车能根据充电站内充电桩的规模均匀分布到相应的充电站,实现区域内充电站之间充电设备利用率的均衡分布,从而提高充电设备时间利用率。仿真结果验证了所提充电引导方法的可行性。Through analyzing the actual operating data of electric taxis in Shenzhen, the formula to calculate charging equipment usage time rate is defined. In allusion to the unbalanced time availability of the charging equipment in regional charging stations, based on the information of charging stations and vehicles, the electric taxis charging guidance system is proposed. An electric taxis charging guidance model is established, in which an improved adaptive mutation particle swarm optimization algorithm is used to guide the charging behaviors of electric taxis. The simulation is based on the actual data of Shenzhen charging stations, and the results show that electric taxis can be evenly distributed to the appropriate charging stations according to the number of charging piles in charging stations with the charging guidance. The evenly distribution among the charging stations in the region will be achieved and the utilization of charging equipment will be improved, so the proposed charging guidance method is verified to be feasible.

关 键 词：电动出租车 充电引导 设备利用率 自适应变异粒子群算法 

分 类 号：TM721[电气工程—电力系统及自动化]