论文部分内容阅读
探讨了在实时电价的环境下,以用户充电费用最小为目的,利用电价调节充电负荷分配,实现有序充电调度的方法。在保证用户充电需求的同时,满足在充电费用最优以及日负荷波动和电网负荷峰谷差尽可能小的前提下,建立了新型的电动汽车充电模型。采用遗传算法求解此充电模型,从而得到一个最优方案,根据此方案合理安排各个时段充电内的车辆数。根据蒙特卡洛模拟的电车运行的相关信息数值,运用得到的最优方法以此来对电车充电模拟仿真。仿真结果表明:考虑了峰谷差和日负荷波动约束的有序充电方案可明显降低电动汽车充电费用,同时也能保证电力系统安全稳定运行。
In the real-time electricity price environment, this paper discusses how to adjust the charging load distribution with the aim of minimizing the user charging cost and to achieve an orderly charging schedule. While guaranteeing the user’s charging requirements, a new model of electric vehicle charging is established on the premise of the optimal charging cost and the smallest fluctuation of daily load and power grid peak-to-valley ratio. The genetic algorithm is used to solve the charging model to get an optimal solution. According to this scheme, the number of vehicles charged in each period can be reasonably arranged. According to Monte Carlo simulation of the value of the relevant information on the operation of trams, using the best way to train simulation of the car. The simulation results show that the ordered charging scheme, which takes the peak-trough difference and daily load fluctuation constraints into account, can significantly reduce the charging cost of electric vehicles and ensure the safe and stable operation of the power system.