风光-电动汽车协同调度能够有效降低风光出力和电动汽车无序充电等多重不确定性对电力系统的不利影响.现有优化调度模型多以等效负荷波动最小为优化目标,仅考虑了等效负荷的整体波动性,无法衡量风光出力与负荷的匹配度且并未考虑不同出力场景下风光出力的差异性.针对上述问题,提出一种面向高比例新能源并网场景的风光-电动车协同调度方法.构建基于蒙特卡罗模拟的电动汽车无序充电模型;基于风光出力预测数据,构建基于Gap statistic和K-means++算法的风光出力典型日划分模型;以等效负荷方差和负荷追踪系数最小为双优化目标,构建风光-电动汽车协同调度模型,并采用NSGA-II算法求解.结果表明:所提模型能够有效提升风光出力与负荷的匹配度,降低等效负荷波动性,从而缓解风光出力和电动汽车无序充电等多重不确定性对电力系统的不利影响.

Wind-solar-electric vehicles coordinated optimization scheduling can effectively reduce the adverse effects of multiple uncertainties of wind-solar output and disorderly charging of electric vehicles on the power system. Most of the existing optimization scheduling models take the minimum equivalent load fluctuation as the optimization objective, which, only considering the overall fluctuation of equivalent load, cannot measure the matching degree of output-load, and do not consider the difference of output in different output scenarios. Therefore, a wind-solar-electric vehicles coordination scheduling method for high proportion new energy grid-connected scenarios is proposed. First, the disordered charging model of electric vehicles by Monte Carlo simulation is constructed. Then, a wind-solar output typical day classification model using Gap statistical and K-means++ is constructed based on the forecasting data of wind and solar power. Finally, taking the minimum equivalent load variance and load tracking coefficient as the double optimization objectives, a wind-solar-electric vehicles coordination optimization scheduling model is established, and the NSGA-II algorithm is used to solve it. The results demonstrate that the proposed model can effectively improve the matching degree of wind-solar output and load, and reduce the fluctuation of equivalent load, so as to reduce the adverse effects of multiple uncertainties of wind-solar output and disorderly charging of electric vehicles on the power system.