JOURNAL ARTICLE

A comprehensive review of compressed air energy storage technologies: Current status and future trends.

  • Published In: Journal of Renewable & Sustainable Energy, 2025, v. 17, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhang, Ruicheng; Zhao, Guoxian 3 of 3

Abstract

This article provides a comprehensive review of compressed air energy storage (CAES) technology, focusing on its principles, technological variants, application scenarios, gas storage methods, economic analysis, and current global projects. CAES is highlighted as a promising large-scale, long-duration energy storage solution with advantages such as large capacity, long lifespan, high safety, and environmental friendliness, with advanced adiabatic CAES (A-CAES) systems achieving efficiencies over 70% and levelized costs of storage (LCOS) comparable to pumped hydro storage (PHS). The review details major CAES projects, including the Huntorf plant in Germany, McIntosh plant in the USA, and the recently commissioned Jintan plant in China, illustrating technological evolution from fossil-fuel-dependent diabatic CAES (D-CAES) to more efficient and cleaner A-CAES and isothermal CAES (I-CAES) systems. It also discusses the critical role of underground salt caverns for air storage, the economic competitiveness of CAES, and future trends emphasizing efficiency improvements, large-scale commercialization, integration with renewable energy sources, smart grid systems, and diversified applications.

Additional Information

  • Source:Journal of Renewable & Sustainable Energy. 2025/03, Vol. 17, Issue 2, p1
  • Document Type:Literature Review
  • Subject Area:Engineering
  • Publication Date:2025
  • ISSN:1941-7012
  • DOI:10.1063/5.0246214
  • Accession Number:184884740
  • Copyright Statement:Copyright of Journal of Renewable & Sustainable Energy is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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