中国电机工程学报2018，Vol.38Issue(2)：514-525,12.DOI:10.13334/j.0258-8013.pcsee.161981
基于改进云自适应粒子群的多DG配电网EV充电站优化配置
Optimization of Electric Vehicle Charging Stations Based on Improved Cloud Adaptive Particle Swarm in Distribution Network With Multiple DG
摘要
Abstract
Considering the prospective of multiple distributed generators (DG) interconnection in the distribution network,a new concept of the fuzzy service radius of electric vehicle (EV) charging station was presented.A new optimal location and parameter setting model of electric vehicle charging station was constructed,considering the factors of DG,service radius,traffic flow,power quality and construction cost,in order to calculate the optimal objective function of annual profits under the condition of multi-objective constraint.An improved cloud adaptive particle swarm algorithm (ICAPSO) was proposed,to make it more suitable for the large-scale optimization model.MATLAB simulation based on IEEE123 node distribution network with multiple DG interconnection,the results verified the feasibility and effectiveness of the proposed optimization model and algorithm.The comparison of convergence procedure shows that the proposed algorithm has better ability of global optimization and preventing premature convergence.关键词
电动汽车/充电站/分布式电源/配电网/云自适应粒子群Key words
electric vehicle/charging stations/distributed generators/distribution network/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
黄飞腾,翁国庆,南余荣,杨晓东,陈鼎..基于改进云自适应粒子群的多DG配电网EV充电站优化配置[J].中国电机工程学报,2018,38(2):514-525,12.基金项目
国家自然科学基金(51507153) （51507153）
浙江省自然科学基金(LY17E070005) （LY17E070005）
浙江省高校实验室工作研究项目(YB201633).Project Supported by National Natural Science Foundation of China (51507153) （YB201633）
Natural Science Foundation of Zhejiang province (LY17E070005) （LY17E070005）
Laboratory Work Research Program of Universities in Zhejiang Province (YB201633). （YB201633）
