Intelligent Assessment of Multi-scenario Regulation Capability for Lithium-Ion BESS Using an Improved DDQN Algorithm

Introduction: Aiming at the problems of unclear regulatory capability boundaries and incomplete state perception in multi-scenario assessment methods for lithium-ion battery energy storage power stations, this paper proposes an intelligent assessment method based on an attention mechanism-improved double deep Q-network (ATT-DDQN). Methods: First, a comprehensive multi-scenario assessment indicator system covering capability, economy, and safety is constructed. The analytic hierarchy process and entropy weight method are combined to determine indicator weights, and a multi-objective assessment model is established with the goals of minimizing comprehensive costs and scenario deviations while maximizing response. Next, the assessment problem is modeled as a Markov decision process. By introducing an attention mechanism, the DDQN algorithm's ability to dynamically focus on key features in high-dimensional state spaces is enhanced, improving its decision-making accuracy and convergence speed. Results: Simulation analysis based on actual industrial energy storage data is conducted. The results verify that the proposed method achieves high accuracy and efficiency in assessing multi-scenario regulation capability. Discussion: The method effectively addresses the limitations of traditional approaches by integrating dynamic feature prioritization, leading to more precise state perception and boundary delineation. But the reliance on historical data quality may affect generalizability; future work should explore transfer learning and broader scenario coverage. Conclusion: This study presents a novel intelligent assessment framework that accurately evaluates the regulation capability of lithium-ion battery energy storage power stations across multiple scenarios, offering a reliable tool for practical industrial applications.