Economic and financial benefits of intelligent energy storage dispatch in renewable energy systems

Abstract

The increasing integration of renewable energy sources has rendered the maintenance of grid stability and the optimization of system economics essential problems. Effective energy storage scheduling is crucial in tackling these difficulties, especially in systems that use combined cooling, heating, and power (CCHP) solutions. This research presents a data-driven dispatch approach for storage of compressed air energy systems aimed at enhancing efficiency and minimizing operating expenses. The modular dynamic modeling technique effectively delineates component behavior while streamlining system interactions, resulting in improved performance, optimized energy consumption, and increased reliability under diverse settings. This study employs an advanced model-free deep reinforcement learning (DRL) approach to overcome the limits of existing control techniques, especially an improved variant of the twin delayed deep deterministic policy gradient (TD3) algorithm, designated as TD3-AC. This approach integrates self-attention mechanisms with behavior cloning to optimize real-time energy dispatch, adjusting to fluctuating grid circumstances. The TD3-AC algorithm significantly improves dispatch accuracy (29.13%) and lowers operating costs by 8.3% when compared to traditional approaches, according to experimental data. This study presents a viable technology approach for the intelligent management of extensive energy storage systems, improving both system stability and financial efficiency.