Decentralized rapid optimization strategy for coordinated control of electric vehicles and user load considering travel characteristics

In the field of electric vehicle (EV) energy management, the driving trajectory of EVs significantly influences energy scheduling decisions. Patterns of household electricity usage, particularly from air conditioning systems, can serve as indicators of user presence within residential areas. To alleviate the peak load pressure caused by large-scale EV integration in such regions, this paper proposes a dynamic pricing and distributed load optimization approach based on a Stackelberg game framework. This method jointly optimizes and coordinates the charging behaviours of EVs and residential electricity consumption. A bi-level game-theoretic model is constructed between the distribution network operator and users, capturing the interactive dynamics between operator-driven load shaping and user-side autonomous response. Building on this framework, the Alternating Direction Method of Multipliers (ADMM) is employed to develop an efficient distributed optimization algorithm. The proposed algorithm aims to achieve peak shaving and valley filling, reduce power procurement costs, and maintain user comfort and service quality. Simulation results demonstrate that the proposed strategy effectively reduces computation time, ensures system operational stability, and facilitates real-time coordination of charging and discharging activities. This study offers theoretical insights and technical guidance to enhance the efficiency and adaptability of regional energy management systems.