JOURNAL ARTICLE

Parametric assessment and multi-objective optimization of an ejector-enhanced compressed air energy storage system based on conventional and advanced exergy.

  • Published In: Journal of Renewable & Sustainable Energy, 2024, v. 16, n. 5. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Liu, Tongqing; Wu, Shuhong; Zhong, Like; Yao, Erren; Hu, Yang; Xi, Guang 3 of 3

Abstract

This article focuses on the development and thermodynamic analysis of an ejector-enhanced compressed air energy storage system (EA-CAES) designed to reduce exergy destruction during the throttling process and improve round-trip efficiency (RTE) compared to conventional adiabatic compressed air energy storage (A-CAES) systems. The study employs both conventional and advanced exergy analyses, sensitivity analysis, and multi-objective optimization to evaluate system performance and identify key components for improvement. Results indicate that the EA-CAES system achieves a 3.07% higher RTE (63.47%) than the A-CAES system, with significant reductions in exergy destruction, particularly in the ejector and throttling valve. Advanced exergy analysis reveals that endogenous exergy destruction dominates and that the ejector, turbine, and compressor components should be prioritized for efficiency enhancements, while sensitivity analysis highlights the influence of air storage and throttling pressures on system performance.

Additional Information

  • Source:Journal of Renewable & Sustainable Energy. 2024/09, Vol. 16, Issue 5, p1
  • Document Type:Article
  • Subject Area:Engineering
  • Publication Date:2024
  • ISSN:1941-7012
  • DOI:10.1063/5.0226187
  • Accession Number:180632695
  • 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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