Energy-Aware Vehicle-to-Grid (V2G) Scheduling with Reinforcement Learning for Renewable Energy Integration

Abstract

This research explores the significant impact of reinforcement learning (RL) on improving the efficiency and stability of Vehicle-to-Grid (V2G) systems in the context of renewable energy integration. Through extensive simulations and analyses, our study contrasts RL-based V2G scheduling against traditional deterministic and rule-based approaches. The outcomes reveal a notable 15.3% enhancement in renewable energy utilization through RL scheduling, equating to an additional 120 MWh annually. Sensitivity analyses affirm the resilience of the RL model to variations in parameters, ensuring its adaptability to changing conditions. Furthermore, RL-based V2G scheduling proves instrumental in elevating grid stability, achieving a substantial 19.8% reduction in frequency deviations and a 12.4% decline in voltage variations. These results underscore the tangible implications of RL in addressing challenges related to both energy efficiency and grid stability. The study provides valuable insights into sustainable energy practices, positioning RL-based V2G scheduling as a promising avenue for advancing resilient and effective energy infrastructures. Future research directions are outlined, emphasizing scalability, economic feasibility, and the refinement of advanced RL algorithms tailored to specific V2G scenarios.