改进粒子群算法的电动汽车时空优化分配策略

兼顾待充电汽车的时间分配和空间分配,以每个时段每个充电站的充电电动汽车数量为决策变量,建立了集中充电时段内充电负荷方差和充电站充电汽车数量方差的数学模型.提出时空优化分配策略,使待充电汽车在时空上达到均衡分配,并在基本粒子群算法基础上结合了线性递减权重和异步变化学习因子方法.基于纽约州独立系统交易运行机构(NYISO)的原始负荷数据进行算例仿真.结果表明,文中提出的电动汽车集中充电调度策略在时空上优化分配待充电汽车,达到了降低负荷峰谷差、减小负荷波动的目的.

A space-time allocation strategy optimized by improved particle swarm algorithm for electric vehicle

This paper proposes a new concentrated charging scheduling strategy,which considers both spatial al-location and temporal allocation of electric vehicle and uses the number of charging electric in each time interval and each charging station vehicles as decision variables.For achieving averagly distributed charging electric vehi-cles in space-time,the mathematical models of load variance and the number of charging vehicles variance are set up,an optimized space-time allocation strategy is proposed to make loads reached equilibrium in time and space, and basic particle swarm optimization （PSO）algorithm combines the linear decreasing weight and asynchronous change learning factor methods to solve the models.Finally,a simulation based on a certain area＇s load curve is made,revealing that the poposed centralized charging scheduling strategy can not only lower the peak-valley difference,but aslo reduce the load fluctuation.