Vehicle-to-Grid Demand Response Methodologies: Challenges, Applications, and Future Directions

The rapid growth of electric vehicles (EVs) has driven interest in vehicle-to-grid (V2G) demand response (DR) services. Various frameworks—including agent-based simulations, data-driven scenario modeling, game-theoretic and auction approaches, and heuristic/metaheuristic and deterministic optimization—are employed to optimize EV charging and discharging for grid support. However, method selection, implementation, and performance evaluation vary widely. This paper provides a comprehensive review and comparative analysis of these four categories, detailing their application focus, data requirements, constraints, limitations, and trade-offs. This paper also provides guidelines for matching methods to V2G-DR objectives across residential microgrids, commercial fleets, ancillary services, and industrial facilities, and proposes future research directions using artificial intelligence, digital twins, and hybrid frameworks to address scalability and uncertainty challenges.