A synergistic EV charging framework for smart cities with commitment-driven penalty mechanism and preference-based optimal charging source selection

The rapid growth of electric vehicle (EV) adoption poses significant challenges to the existing grid infrastructure and demands various advanced energy management strategies. Among the emerging solutions, coordinated charging frameworks like Grid-to-Vehicle (G2V) and Vehicle-to-Vehicle (V2V) paradigms have proven considerable potential in optimizing energy distribution, reducing peak demand, and enhancing the flexibility and resilience of smart energy systems. However, relying solely on G2V could lead to congestion during peak hours, and V2V risks unreliable participation. Despite progress in both domains, integrating their trading mechanisms for optimal pricing remains a challenge. This study presents a novel synergistic energy management framework that combines the cooperative G2V and V2V energy trading with penalty enforcement and a user preference-based charging source selection mechanism to ensure reliable participation. A dynamic pricing mechanism is formulated using a multi-armed bandit reinforcement learning model to optimize economic outcomes for both energy demanding EVs and energy-supplying entities, such as supplying electric vehicles and charging stations. The proposed framework employs a Gale-Shapley based cooperative matching protocol enhanced with preference-based charging source selection, and a novel penalty model based on EV default behavior to ensure efficient and stable pairings while incorporating individual rationality. Simulation results across multiple case scenarios demonstrate that the proposed framework significantly improves schedule adherence, participant's welfare, matching optimality, and energy trading reliability. The findings underscore the potential of the framework for real-world implementation in achieving cost-effective, practical, and reliable energy trading across dynamic mobility scenarios.