基于粒子群算法的电动汽车有序充放电优化

Sequential Charging and Discharging Optimization of Electric Vehicles Based on Particle Swarm Optimization

为解决用户在某一时间段集中充电造成电网负荷压力增大的问题,提出基于分时电价制度,结合用户的出行特性,通过改进蒙特卡洛,得到居民无序充电负荷,然后以解决峰谷差和节约用户成本为目标,构建归一化目标函数,以充电功率和电池电量为约束,通过改进粒子群算法优化,得到电动汽车有序充电负荷,降低电网负荷峰谷差。基于粒子群算法的有序充电与无序充电的仿真结果对比表明,有序充电在应对电网负荷压力与节约居民成本有显著效果。

In order to solve the problem of increasing power grid load pressure caused by users’centralized charging in a certain period of time,the optimization of sequential charging and discharging of electric vehicles based on time-of-use price was proposed.Firstly,the disordered charging load of residents was obtained by improving Monte Carlo method combined with the travel characteristics of users.Then,with the goal of solving the peak valley difference and saving user costs,a normalized objective function was constructed,and the charging power and battery power were taken as constraints.Finally,through the improved particle swarm optimization algorithm,the sequential charging load of electric vehicles was optimized to reduce the peak valley difference of power grid load.The comparison of simulation results shows that sequential charging has a significant effect on coping with the load pressure of the power grid and saving the cost of residents.