Application of Reinforcement Learning in Energy Storage Management

Abstract

Adding renewable energy sources to the power grid has made it necessary to have effective energy storage management systems to deal with problems like power outages and changes in the amount of energy available. Reinforcement learning (RL) has become a potential way to improve how energy storage works in this situation. This essay looks at how RL methods can be used in managing energy storage, with a focus on how they might improve the cost-effectiveness and efficiency of energy storage systems (ESS). RL algorithms, like Q-learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO), can figure out the best ways to handle things by dealing with their surroundings and getting input on how well they did. RL agents can change how they act in changing and unclear situations by learning from their mistakes. This lets real-time decisions be made about how to send and schedule energy storage. RL-based ESS managers can find the best charging and dumping plans by looking at things like power prices, demand patterns, predictions for renewable energy production, and system limits. This helps them make the most money, keep the grid stable, and reduce running costs. RL methods are also flexible enough to meet a wide range of goals, such as lowering frequencies, moving loads, and shaving off peak power, all while taking long-term performance measures and practical limits into account. This essay talks about the latest improvements in RL-based energy storage management systems, the problems and benefits of using them, and possible directions for future study. Overall, using RL for managing energy storage has a lot of potential to make adding green energy sources to the power grid more efficient and long-lasting.