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Abstract As renewable energy, characterised by its intermittent nature, increasingly penetrates the conventional power grid, the role of energy storage systems (ESS) in maintaining energy balance becomes paramount. This dynamic necessitates a rigorous reliability assessment of ESS to ensure consistent energy availability and system stability. The authors provide a review of the existing research on ESS reliability assessment, encompassing various methods, models, reliability indicators, and offers an analysis of future research trends in ESS reliability. Firstly, the authors summarise the different types of ESS and their characteristics, analysing the trends in ESS reliability research and the unique characteristics of ESS compared to conventional power systems. Secondly, the methods used for the assessment are reviewed, including Markov methods, generalised generating functions, Monte Carlo simulations etc. The shortcomings and characteristics of these methods are discussed. The key reliability indicators, such as Mean Time Between Failures and Mean Time to Repair are emphasised. The applied role of reliability studies is summarised. Finally, the perspective of new research trends in ESS reliability assessment are identified, especially the integration of artificial intelligence and machine learning, and emphasises their potential to further improve the robustness and effectiveness of ESS reliability.
Yan et al. (Mon,) studied this question.