Optimization scheduling method for multiple flexibility resources considering electric vehicle cluster

Abstract

To unleash the scheduling potential of flexible resources, an enhanced flexibility approach is proposed, which takes into account various flexible resources including electric vehicle (EV) clusters. Initially, the dispatch potential of individual EV users is predicted based on historical data regarding travel habits and charging station parameters. Subsequently, the power and energy-feasible regions are constructed to quantify the flexibility of the EV clusters. To enhance the overall system’s operational efficiency and flexibility, various constraints related to flexibility resources are taken into consideration, including the source-grid-load-storage aspects. As a result, a multidimensional flexibility resource supply-demand model is established and the analysis target cascading method is applied to solve this sophisticated model. Finally, the modified system is used to test and analyze the proposed model and method. The results demonstrate the proposed model and method can enhance the operational flexibility of high-proportion new energy distribution networks.