Edge computing-based optimal dispatching of charging loads considering dynamic hosting capacity

Owing to the rapid increase in electric vehicle integration and the uncoordinated charging behaviour of electric vehicles, the overloading risk of distribution transformers has deteriorated. This impact caused by large-scale electric vehicle integration can be effectively reduced through the orderly guidance of electric vehicle charging behaviours. Here, a dispatching strategy for charging loads is proposed to address the problems of the uncoordinated charging demand in electric vehicles and overloading risk of distribution transformers in residential areas. First, an edge-side dynamic index of the electric vehicle hosting capacity is proposed to guide the optimal dispatching of charging loads. Subsequently, an optimal dispatching model of the charging loads is established based on edge computing. The edge-side dispatching strategy for the charging loads is then further updated considering the participation willingness of electric vehicle users. Finally, the effectiveness of the proposed control strategy is validated using a modified residential distribution network in Tianjin. The results show that the proposed strategy can effectively decrease the overloading risk of distribution transformers while realizing the efficient operation of electric vehicles on the edge side. This study proposes an edge computing-based dispatching strategy for EVs that considers the dynamic hosting capacity of distribution networks. First, an edge-side dynamic index is established to dynamically reflect the EV hosting capacity. Subsequently, an optimal dispatching model for EVs is proposed by integrating the dynamic index. Furthermore, the dispatching strategy is updated by considering EV users' willingness. image