Demand Response Decision Optimization for EV Aggregators in V2G Systems via Embedded-Crossed Graph Attention Reinforcement Learning

With the increasing penetration of electric vehicles (EVs), vehicle-to-grid (V2G) technology enables them to act as flexible resources within the power grid. Electric vehicle aggregators (EVAs) play a crucial role in coordinating largescale EV charging and discharging, yet they face the dual challenge of maximizing economic benefits of EVAs while maintaining user satisfaction. To address this issue, this paper develops a solution method based on Stackelberg game theory. As leaders, EVAs determine charging prices and V2G compensation to optimize profits and achieve peak shaving and valley filling, while users, as followers, respond to regulatory signals and adjust their charging behaviors accordingly. An embedded cross multi-agent actor-critic (EC-MAAC) algorithm is further proposed to address the scalability issues arising from large-scale EV participation in V2G systems, while alleviating the reliance of conventional game-theoretic solvers on convexity and differentiability assumptions for tractable equilibrium computation. Simulation results demonstrate that the proposed EC-MAAC approach achieves a 15.1% reduction in EVA operating costs, a 44.6% increase in user rewards, and an improvement in SoC satisfaction from 91.2% to 96.8%, effectively balancing economic efficiency and user satisfaction.