To mitigate the adverse impacts of disorderly electric vehicle (EV) cluster integration on the stable operation of the power grid

road network

and charging stations

this study proposes a cooperative game optimization strategy for multi-type charging stations based on dynamic attraction aggregation under the "vehicle-road-station-grid" coupling framework. Firstly

an interaction framework of the coupled system is constructed

using a dynamic Johnson's shortest-time path method combined with a semi-dynamic traffic flow equilibrium model to determine EV travel routes. Then

a dynamic attraction model is developed for various charging station types

considering multiple factors that influence users' charging decisions. Minkowski sum theory is applied to aggregate heterogeneous EV clusters into equivalent representations

thereby predicting the spatiotemporal distribution of charging loads. Finally

a multi-type charging station cooperative alliance is formed. A cooperative game-based optimal scheduling model is established with the maximization of total alliance revenue and incentives as the objective. The Shapley value method is used to allocate the cooperative alliance revenue. Simulation results demonstrate that the proposed scheduling strategy effectively guides EV charging behavior and enhances the synergistic operation level of the "vehicle-road-station-grid" system.