Review on Advanced Storage Control Applied to Optimized Operation of Energy Systems for Buildings and Districts: Insights and Perspectives

Maria Ferrara, Matteo Bilardo, Dragos-Ioan Bogatu, Doyun Lee, Mahmood Khatibi, Samira Rahnama, Jun Shinoda, Ying Sun, Yongjun Sun, Alireza Afshari, Fariborz Haghighat, Ongun B. Kazanci, Ryozo Ooka, Enrico Fabrizio*

*Corresponding author for this work

Research output: Contribution to journalReviewpeer-review

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Abstract

In the context of increasing energy demands and the integration of renewable energy sources, this review focuses on recent advancements in energy storage control strategies from 2016 to the present, evaluating both experimental and simulation studies at component, system, building, and district scales. Out of 426 papers screened, 147 were assessed for eligibility, with 56 included in the final review. As a first outcome, this work proposes a novel classification and taxonomy update for advanced storage control systems, aiming to bridge the gap between theoretical research and practical implementation. Furthermore, the study emphasizes experimental case studies, moving beyond numerical analyses to provide practical insights. It investigates how the literature on energy storage is enhancing building flexibility and resilience, highlighting the application of advanced algorithms and artificial intelligence methods and their impact on energy and financial savings. By exploring the correlation between control algorithms and the resulting benefits, this review provides a comprehensive analysis of the current state and future perspectives of energy storage control in smart grids and buildings.
Original languageEnglish
Article number3371
JournalEnergies
Volume17
Issue number14
Number of pages26
ISSN1996-1073
DOIs
Publication statusPublished - 2024

Keywords

  • Thermal storage
  • Energy storage
  • Electric storage
  • Model predictive control
  • Artificial intelligence

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