计及供需两侧的电动汽车有序充放电优化算法研究

Optimization algorithm research on ordered charge and discharge of electric vehicles considering supply and demand sides

大量电动汽车进行无序充电将给电网的安全运行带来"峰上加峰"的运行风险,作为一种移动的储能设备,大量电动汽车的无序放电也会对电网的稳定性造成重要影响,因此对电动汽车的充放电行为进行有序引导十分必要。首先,分析了某小区电动汽车无序充放电的负荷情况,并以峰谷分时电价为引导,研究不同响应度的下的日负荷情况;在此基础上综合考虑用户侧和电网侧利益,以电动汽车用户成本最低和小区日负荷峰谷差率最小为优化目标,选择峰谷分时区间为优化变量,构建电动汽车最优充放电模型,分别采用基于Pareto最优的多目标遗传算法NSGA-Ⅱ和基于Pareto最优的粒子群算法求解,得到最优充放电时段,并对二者的优化结果进行比较。最后利用蒙特卡洛算法对算例进行仿真和分析验证,结果表明,利用所提出的有序充放电优化算法,用户可通过放电补偿充电费用,且NSGA-Ⅱ算法更优。

The disorderly charging of large-scale electric vehicles(EVs)will increase the operation risk of peak-up-peak to the power grid.In addition,as a mobile energy storage device,the disordered discharge of a large number of EVs will also have an important impact on the stability of the power grid.Therefore,it is necessary to guide the charge and discharge behavior of EVs in an orderly manner.First of all,the general loads of disordered charge and discharge of electric vehicles in a residential area are analyzed,and the daily load under different response is studied under the guidance of peak-valley time-of-use electricity price.On this basis,considering both the benefits of the divers and the power grid,the optimal charge and discharge model of the EVs are constructed,which takes the lowest charging cost of the EVs and the minimum peak-valley difference of the daily load in the community as the optimization objectives,and selecting the peak-valley time-sharing interval as the optimization variable.The optimal charge and discharge time intervals are found by Pareto-based optimization multi-objective genetic NSGA-Ⅱand Pareto-based optimization particle swarm(PSO),respectively.The results by the different optimization algorithms are compared.Finally,the Monte Carlo algorithm is used to simulate and analyze the model.Simulation results show that users can reduce the charging cost to some extent through the discharge compensation,and NSGA-Ⅱalgorithm is better than PSO.