Optimizing Second-Life Battery Use in Renewable Energy Storage: A Deep Reinforcement Learning Approach

With the rising global prevalence of electric vehicles, a significant influx of end-of-life (EOL) lithium-ion batteries is anticipated in the recycling market. Although no longer meeting the performance standards for electric vehicles, these batteries can be repurposed for less demanding applications like stationary storage for renewable energy. This addresses the disposal of second-life batteries and supports the development of clean energy, contributing to sustainable development and climate change mitigation. This paper presents a machine learning model employing Deep Reinforcement Learning (DRL) to aid energy merchants in decision-making regarding battery charging/discharging and energy market bidding, thus maximizing profitability while considering battery wear and tear costs. The methodology combines Long Short-Term Memory (LSTM) networks with Proximal Policy Optimization (PPO) algorithms to manage the complex dynamics of energy pricing and battery state of charge (SoC). Experimental results demonstrate the model’s effectiveness in enhancing the profitability of energy storage using second-life batteries, offering a novel contribution to the field of renewable energy storage and battery reuse.