摘要：

随着电动汽车的快速发展, 大量的充电需求将带来配电网负荷峰谷差加剧、充电负荷的不确定性等问题。为此, 面向电网负荷稳定提出了自适应离散充电调度策略。构建离散充电调度模型, 通过对电动汽车充电过程中的状态决策变量和充电功率进行联合优化, 以最小化配电网负荷的峰谷差。为了满足电动汽车的实时充电需求, 设计车辆充电区间的自适应调整方法, 根据不同电动汽车的到达时间和离开时间, 实时调整电动汽车的充电调度区间。然而, 离散充电调度模型中的状态决策变量和充电功率具有高耦合性, 是混合整数非线性规划(MINLP)问题。为解决该问题, 首先, 通过计算充电负荷裕度求解时隙的充电负荷分配率; 然后, 基于不同时隙的负荷动态分配, 对充电状态决策变量进行迭代更新; 最后, 基于更新的状态决策变量, 优化时间离散的充电功率。仿真结果表明, 提出的调度策略可以有效降低配电网负荷的峰谷差, 提高电网稳定性, 并可灵活满足电动汽车的实时充电需求。

关键词: 电动汽车, 峰谷差, 实时充电, 自适应离散, 状态决策

Abstract:

With the rapid development of electric vehicles, a large number of charging demand will bring about problems such as increased peak-valley difference of distribution network load and uncertainty of charging load. To this end, an adaptive discrete charging scheduling strategy is proposed for power grid load stability. A discrete charging scheduling model is constructed to minimize the peak-valley difference in distribution network load by jointly optimizing the state decision variables and charging power during the charging process of electric vehicles. In order to meet the real-time charging demand of electric vehicles, an adaptive adjustment method of vehicle charging interval is designed. According to the arrival time and departure time of different electric vehicles, the charging scheduling interval of electric vehicles is adjusted in real time. However, the state decision variables and charging power in the discrete charging scheduling model are highly coupled, which is a Mixed Integer Nonlinear Programming (MINLP) problem. In order to solve this problem, first, the charging load allocation rate of the time slot is solved by calculating the charging load margin; then, based on the dynamic allocation of loads in different time slots, the charging state decision variables are iteratively updated; finally, based on the updated state decision variables, the time-discrete charging power is optimized. Simulation results show that the proposed scheduling strategy can effectively reduce the peak-valley difference of distribution network load, improve power grid stability, and flexibly meet the real-time charging needs of electric vehicles.

Key words: electric vehicle, peak-valley difference, real-time charging, adaptive discrete, state decision-making