Fundamentals of AI in Energy Storage Applications

This chapter is dedicated to unpacking the foundational concepts of AI and its transformative impact on the domain of energy storage, particularly within the context of renewable energy integration. The chapter commences by providing a primer on AI, elucidating its basic principles, algorithms, and machine learning techniques. It then explores how these AI methodologies are being applied to optimize energy storage solutions. Key focus areas include AI&s;s role in predicting energy demand and supply patterns, enhancing battery management systems, and optimizing charge/discharge cycles to extend the lifespan and efficiency of energy storage units. A significant segment of the chapter delves into the application of AI in thermal energy storage (TES) systems. Here, the discussion revolves around how AI algorithms can be leveraged for thermal load forecasting, efficient energy dispatch, and maintenance of TES systems. The chapter also examines the integration of AI with TES in various temperature ranges, highlighting specific case studies and current research developments. Moreover, the chapter addresses the challenges and opportunities presented by the integration of AI in energy storage. This includes an examination of data management, algorithmic complexity, and the need for robust and adaptive AI systems capable of handling the dynamic nature of energy storage in renewable energy systems.