Energy Storage

Energy storage research focuses on optimizing the operation and control of various energy storage technologies (batteries, thermal storage, hydrogen systems) to improve grid stability, enhance renewable energy integration, and maximize economic benefits. Current research employs diverse machine learning approaches, including reinforcement learning (with variations like deep deterministic policy gradient and multi-agent reinforcement learning), transformer networks, and deep learning models for accurate state-of-charge estimation and price prediction, often incorporating dynamic programming for optimal decision-making. These advancements are crucial for addressing the intermittency of renewable energy sources and facilitating a transition towards a more sustainable and efficient energy system, impacting both scientific understanding and practical applications in energy markets and electric vehicle technology.