针对当前电动汽车充电过程中电网侧以及用户侧的客观需要,设计了优化电动汽车有序充电的一种方法。以最小化电网侧负荷波动和用户侧最大限度地提高电动汽车的充电电量为目标函数,建立了电动汽车的时间维度调度模型。提出了基于改进的多目标粒子群优化算法,通过个体选择、正态分布自适应突变、模糊满意度决策等策略加快搜索效率以及跳出局部极值。实验结果表明,与MOGA和MOAFSA相比,IACO具有更高的搜索效率。

Considering the objective needs of the grid side and user side in the current charging process of electric vehicles,a method for optimizing the orderly charging of electric vehicles has been designed.A time dimension scheduling model for electric vehicles is established with the objective function of minimizing load fluctuations on the grid side and maximizing the charging capacity of electric vehicles on the user side.An improved multi-objectiveparticle swarm optimization algorithm is proposed to speed up the search efficiency and jump out of the local extremum through individual selection,normal distribution adaptive mutation,fuzzy satisfaction decision and other strategies.The experiment results indicate that IACO has higher search efficiency compared to MOGA and MOAFSA.