基于思维进化算法的电动汽车有序充电控制策略

大量电动汽车充电会加大充电站负荷峰谷差,影响充电站安全稳定运行。因此提出了一种基于思维进化算法(MEA)的电动汽车有序充电控制策略:以用户充电费用最少和充电站负荷峰谷差最小为目标函数,采用MEA算法动态计算接入充电站电动汽车的最优充电时段,由用户自主响应,从而实现充电站内电动汽车的有序充电控制。为验证该策略的有效性,利用蒙特卡洛方法模拟用户充电需求,对算例进行仿真分析。结果表明:与无序充电相比,有序充电控制策略可在降低电动汽车用户费用的基础上实现充电负荷的削峰填谷;相比于使用遗传算法,MEA算法具有一定优势。

A Coordinated Charging Strategy for PEV Charging Stations Based on Mind Evolutionary Algorithm

A large number of electri c vehicles connected to charging stations will increase the peak-valley difference and affect the safe and stable operation of the electric vehicle charging stations. Thus a coordinated charging strategy for plug-in electric vehicle (PEV) charging stations based on Mind Evolutionary Algorithm (MEA) is proposed. The strategy set minimum customer charging costs and minimum peak-valley difference as objective function. The optimal charging period of electric vehicles is calculated dynamically by using MEA. Customers decide whether or not to respond to peak-valley prices and to delay their charging to lower price periods by themselves. The charging coordination of electric vehicles is then realized. In order to verify the effectiveness of the proposed strategy, the Monte Carlo simulation method was utilized to generate the charging needs of customers based on actual customer charging behaviors. The distribution transformer load profiles, customer charging costs were simulated under uncoordinated and coordinated charging scenarios correspondingly. Simulation results indicate that under the proposed coordinated charging control strategy, customer charging costs can be greatly reduced and the peak shaving of distribution transformer loading profile can also be achieved; compared with genetic algorithm, the effect of MEA is better.