Reinforcement Learning in Action: Optimal Scheduling for Efficient Battery Management

Abstract

This paper reviews the latest trends and challenges in battery management systems (BMS) using reinforcement learning (RL) techniques. RL has gained attention as an effective solution to address the volatility of renewable energy sources and the need for advanced power demand-supply balancing. Unlike traditional methods such as linear programming and model predictive control, RL demonstrates superior flexibility and adaptability in dynamic and uncertain environments. Key topics covered include the application of RL algorithms like Proximal Policy Optimization (PPO) in energy storage systems, highlighting their advantages in optimizing battery charge and discharge schedules. However, practical implementation faces challenges such as computational resource requirements and integration with real-world systems. Future directions include integrating RL-based BMS with electric vehicles and smart appliances to achieve more efficient energy management. This study aims to contribute to the development of sustainable and intelligent energy systems for future power grids.