摘要
针对如何确定电动汽车充电站的位置及充电机数量问题,分析了快速充电站的服务对象,建立了考虑建设成本、充电途中行驶成本、充电站等待成本的综合优化模型。模型考虑了城市地理信息,以充电便捷、等待时间为约束条件,以充电站固定成本、车主充电行驶成本和在充电站等待成本之和最小为目标函数。电动汽车充电站规划问题是非凸、非线性、组合优化问题。为此,对目标函数的求解,提出用差分进化混合粒子群算法,该算法增加了种群的多样性和全局寻优能力。算例分析验证了所提规划方法的有效性和实用性。
A comprehensive objective function was presented to address the problem of locating and sizing of electric vehicle charging stations after analyzing its clients in this paper. The comprehensive objective function includes fixed costs,driving costs on the way to charging stations and waiting costs queuing in charging stations. The objective function,considering the geographic information,treats charging convenience and waiting time as constraint condition. This problem is a classical non-convex,non-linear and combinatorial optimization one. So a differential evolution hybrid particle swarm optimization algorithm which increases the diversity of the population and global optimization searching capability was proposed to solve this problem. The analysis of examples verified the effectiveness and the practicability of the proposed planning approach. Feasibility and effectiveness of the algorithm were verified through contrast calculation and reasonable analysis.
出处
《华北电力大学学报（自然科学版）》
CAS
北大核心
2015年第2期1-7,共7页
Journal of North China Electric Power University：Natural Science Edition
基金
国家高技术研究发展计划(863计划)资助项目(2011AA11A278)
关键词
差分进化混合粒子群算法
充电站规划
电动汽车
排队论
differential evolution hybrid particle swarm optimization algorithm
planning electric vehicle charging station
electric vehicle
queuing theory
