Work

Accelerated analytical model for energy storage capacity value assessment

Year: 2025

Type: article

Abstract: 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.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2025.111434

Authors: Yuxin Zhang, Xinyu Chen, Jingzuo Han, Junxuan Zou, Qiuwei Wu

Institutions: Huazhong University of Science and Technology, Queen's University Belfast

Language: English

Topic: Power System Reliability and Maintenance

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,920