Dynamic Grid Buffering with Second-Life EV Batteries: A Reinforcement Learning Optimization Approach

The transition to renewable energy sources, such as solar, is crucial for reducing dependence on fossil fuels and mitigating climate change. However, the intermittent nature of solar energy presents significant challenges for grid management. This paper addresses the challenge of optimizing energy storage by integrating second-life electric vehicle (EV) batteries into solar hybrid systems using reinforcement learning (RL). The proposed RL model dynamically adjusts the charging and discharging of second-life EV batteries to match real-time solar generation and energy demand. Our results show that this model improves energy storage efficiency, reduces grid reliance, and enhances overall system performance. Specifically, the agent’s performance improves significantly after 1000 episodes, stabilizing the reward value between -60 and -70, reflecting the optimization of battery usage. This approach contributes to grid stability while promoting the reuse of second-life EV batteries, reducing the environmental impact of battery disposal. The findings suggest that real-time optimization using RL offers a promising solution to integrate renewable energy into the grid effectively. Future work should focus on addressing battery aging effects and further improving the system’s robustness.