Accelerated analytical model for energy storage capacity value assessment

Energy storage systems(ESS) has becoming increasingly important to provide system reliability under elevated renewable penetration. Accurate assessment of the capacity credit of EES has been constrained by computational complexity of operational details related to hourly renewable variability and system flexibility. This paper presents a novel analytical framework for evaluating the capacity credit of ESS incorporating detailed unit commitment model and multi-year renewable variability. The methodology integrates a single-layer optimization framework eliminating iterative convergence, a fast unit commitment-based reliability evaluation, and a scenario reduction algorithm preserving critical statistical characteristics. Case studies on typical Chinese provincial power system demonstrate the proposed model reduce computational time by 86% while maintaining 99.8% accuracy compared with conventional methods. Results show that storage capacity value depends significantly on duration, storage-to-renewable ratio, and inter-annual renewable variability. The framework provides system planners with an efficient tool for ESS capacity adequacy assessments in renewable-dominated power systems.<br/><br/>