Energy storage systems (ESSs) are essential in stabilizing energy systems, improving reliability, enabling the effective integration of renewable energy sources, addressing sustainability challenges, providing power backup, and strengthen energy security. This study uses bibliometric analysis methodology to explore research trends in ESSs supply chain modeling and optimization, analyzing 6,793 documents published between 2014-2024 using tools such as VOSviewer and OpenRefine. Findings reveal rapid growth in ESS supply chain publications , driven by the global transition to renewable energy, increased adoption of electric vehicles (EVs), and emphasis on supply chain sustainability. The current ESSs research progression analysis identified the future research direction focusing on machine learning (ML) and deep reinforcement learning (DRL) technologies integration and optimization of multiple renewable energy systems, ESSs supply chain sustainability, distributed and decentralized ESSs supply chain, and large-scale ESSs technologies. To the best of our knowledge, this study is the first that systematically outline the entire research landscape of ESS supply chain modeling and optimization utilizing a detailed bibliometric methodology. This study contributes to the literature by outlining the progression of ESSs research, underlining significant trends, and suggesting future research directions.

Dalal Bamufleh

Affiliation

Refine Results

Optimizing Energy Storage Systems Supply Chains: A Bibliometric Analysis (2014-2024)