Open Access

ARTICLE

Research on Operational Reliability Assessment of New Energy Storage Based on Grey-Fuzzy Theory and Extreme Scenarios

Haiyun An¹, Tianhui Zhao¹, Jingbo Zhao¹, Zhe Chen¹, Hailong Zhang², Yunting Yao^2,*
1 State Grid Jiangsu Electric Power Co., Ltd. Electric Power Science Research Institute, Nanjing, China
2 School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China
* Corresponding Author: Yunting Yao. Email:
(This article belongs to the Special Issue: Grid Integration of Intermittent Renewable Energy Resources: Technologies, Policies, and Operational Strategies)

Energy Engineering https://doi.org/10.32604/ee.2026.079900

Received 30 January 2026; Accepted 12 March 2026; Published online 17 April 2026

Abstract

To address the limitations of existing energy storage technology evaluations—specifically the lack of horizontal comparisons under unified scenarios and inadequate research on adaptability under extreme conditions. This paper constructs a comprehensive utility evaluation system for new energy storage based on Grey Fuzzy Theory. This system comprises four core criterion layers, namely technical performance, economy, reliability and environmental impact, and scenario adaptability, along with 11 quantitative indicators. To enhance the objectivity of the evaluation, a combined weighting method integrating Grey Relational Analysis (GRA) and the Entropy Weight Method (EWM) is adopted to determine the indicator weights. To adapt to complex practical operating conditions, a dynamic update mechanism for energy storage evaluation factors is established, which considers both the service demand structure and physical operating environment; correspondingly, a scenario correction coefficient matrix and an extreme environmental risk correction matrix are constructed. Taking four mainstream energy storage technologies—Electrochemical Energy Storage (EES), Compressed Air Energy Storage (CAES), Gravity Energy Storage (GES), and Flywheel Energy Storage (FES) as research objects, case studies are carried out under multiple typical scenarios. The results indicate that in balanced service scenarios, EES achieves the highest comprehensive score of 0.7912; in high-ramping scenarios, GES attains the highest comprehensive score of 0.8457; and in extreme high-temperature and high-frequency regulation scenarios, FES achieves the highest comprehensive score of 0.7372. The research findings provide a solid theoretical basis for energy storage technology selection, safety management, and policy formulation in new power systems.

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Keywords

New energy storage; grey fuzzy theory; entropy weight method; extreme scenario

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