Evolutionary Analysis of Electric Vehicle to Grid (V2G) Strategies Based on Game Learning^*

In the V2G (Vehicle to Grid) interaction scenario, there is a learning process for obtaining optimal strategies due to the gradual rationality of electric vehicles and micro-grids. This paper compares four evolutionary algorithms-replicator dynamics, reinforcement learning, belief learning, and experience-weighted attraction (EWA)-in V2G interactions according to different interaction scenarios. By constructing a V2G game model and performing simulations, we evaluate these algorithms in terms of information processing, learning speed, and equilibrium results. The EWA algorithm demonstrates superior efficiency and stability, making it a promising tool for V2G strategy optimization. This study provides insights into the application of learning algorithms in V2G games, offering guidance for future IoV (Internet of Vehicles) developments, especially in grid load management and energy dispatch.