基于自适应遗传算法的规模化电动汽车智能充电策略研究

摘要

电动汽车充电负荷在时空上具有不确定性，大规模电动汽车无序充电会导致配电网峰值负荷超过设备允许极限，给电网运行带来严重影响。以平滑配电网日负荷曲线为优化目标，建立了考虑各电动汽车用户充电需求约束的规模化电动汽车智能充电控制策略求解模型，并采用自适应遗传算法求解。以 IEEE33节点配电网系统为例，基于蒙特卡洛随机模拟规模化电动汽车并网场景，对比研究了无序充电和智能充电两种控制模式下电动汽车负荷对配电网的影响，验证了利用所提方法对实现平滑负荷的有效性。%Electric vehicles connected to the grid exhibits strong uncertainty in time and space. Dumb charging of large-scale electric vehicles might have adverse impacts on distribution system by causing much high peak load exceeding the supply limits of devices. This paper proposes a model for smart charging control of electric vehicles, which takes smoothing the daily load profile as the objective function and fully accounts the EV owner’s requirement. An adaptive genetic algorithm is applied for solving the model. Using the IEEE 33-bus case as the test systems, scenarios of EVs integration are simulated by Monte Carlo stochastic methods. Smart charging strategy is obtained using the proposed model and method. By comparing with the load profile under dumb charging, the validity of the proposed model is proved.