Energy storage supply chain modeling and optimization: A systematic review

Abstract

This paper provides a comprehensive review of Energy Storage System (ESS) supply chain modeling and optimization over the past decade (2014–2024). Motivated by the increasing demand for ESS integration with renewable energy sources and the complexities of battery energy storage systems (BESSs), this study employs a systematic literature review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The review results indicated that multi-objective optimization models dominate ESS and BESS supply chain studies, due to their capability to manage the trade-offs between these chains' economic performance, environmental sustainability, and operational efficiency. The analysis identifies China's dominance in ESS research because of the Chinese government's extensive investments in renewable energy and electric vehicle (EV) production and characterizes 2019 as the most productive year for publications, given the global legislative changes and technological advancements. The review recognizes the future direction of ESS research related to integrating multiple optimization techniques, optimizing ESS supply chain environmental impacts, hybrid renewable ESSs, and shared ESSs. Also, it emphasizes the growing significance of artificial intelligence (AI), machine learning (ML), and deep reinforcement learning (DRL), as emerging methodologies for improving ESS supply chain optimization. This review paper contributes to the literature by providing practical insights related to ESS supply chain optimization, aligning with global decarbonization targets, and highlighting ESSs' future research approaches. Policymakers, manufacturers, energy providers, and researchers can utilize these findings to design sustainable ESS supply chains that optimize costs, environmental impacts, and social aspects.