基于改进粒子群算法的电动汽车群充电优化方法  

Optimization Method of Electric Vehicle Group Charging Based on Improved Particle Swarm Optimization Algorithm

作　　者：王文超 刘天羽[1] 汪笃军 袁硕[1] 

机构地区：[1]上海电机学院电气学院,上海201306

出　　处：《电力学报》2017年第6期439-446,共8页Journal of Electric Power

基　　金：上海自然科学基金(14ZR1417200;15ZR1417300)

摘　　要：针对大量电动汽车充电引起局部配电网日负荷峰谷差变大这一问题,研究提出了改进约束处理机制的改进粒子群算法对问题进行优化,并通过蒙特卡洛法对电动汽车无序充电和有序充电分别进行了仿真建模,最后使用Matlab软件进行了仿真计算,结果证明了研究提出算法的有效性。Aimed at the problem,which is caused by a large number of electric vehicles charging,that the difference between peak point and valley point of the power grid load curve becomes large,an improved particle swarm optimization（PSO）algorithm with the improved constraint processing mechanism is proposed,and models of the disordered and ordered EV charging process are built respectively by the Monte Carlo method.Finally,the simulation is carried out using Matlab software.The results show that the proposed algorithm is effective.

关 键 词：改进粒子群算法 电动汽车 蒙特卡洛法 

分 类 号：TM910.6[电气工程—电力电子与电力传动]