Meta Reinforcement Learning for Optimal Control of Battery Energy Storage Systems in Distributed Energy Resources

Battery Energy Storage Systems (BESS) play a crucial role in enhancing Distributed Energy Resources (DERs) efficiency and reliability. Managing these systems across diverse DER environments presents challenges due to the dynamic nature of the grid, market fluctuations, and the inherent complexities of both DERs and the batteries themselves. This paper proposes a new approach for adaptive battery management in DERs, utilizing meta learning for Deep Q Networks. We trained an autonomous agent in a reinforcement learning environment, enabling it to optimize battery operations across multiple DER locations with minimal training data. The effectiveness of the proposed method is validated through a two-stage process. First, the agent undergoes meta-learning training in the reinforcement environment, equipping it with the necessary decision-making capabilities. Second, its performance is evaluated through a simulation using real world data on energy consumption, generation, and pricing. The agent excels at handling multiple objectives simultaneously and pursues three key goals: maximizing renewable energy usage, maintaining healthy battery states of charge, and potentially reducing energy costs for consumers.