JOURNAL ARTICLE

Preliminary design and techno-economic assessment of a trigeneration system integrated with compressed air and chemical energy storage.

  • Published In: Journal of Renewable & Sustainable Energy, 2023, v. 15, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Yao, Erren; Zhong, Like; Li, Ruixiong; Xi, Guang; Zou, Hansen; Wang, Huanran 3 of 3

Abstract

This article focuses on a novel trigeneration system integrating compressed air energy storage (CAES) with chemical energy storage via methanol decomposition to enhance energy utilization efficiency. The system converts compression heat into syngas (H₂ and CO) through an endothermic methanol decomposition reaction during charging, which is then used for air preheating and combustion during discharging, enabling simultaneous production of electricity, heating, and cooling. A comprehensive techno-economic analysis, including sensitivity and multi-objective optimization using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), identifies optimal operating parameters achieving an exergy efficiency of 43.31% and a levelized cost of energy (LCOE) of 97.53 $/MWh. The study highlights the system’s potential for high energy conversion efficiency and cost-effectiveness, with key factors such as air–methanol ratio, pressure ratios, and isentropic efficiencies significantly influencing performance.

Additional Information

  • Source:Journal of Renewable & Sustainable Energy. 2023/05, Vol. 15, Issue 3, p1
  • Document Type:Article
  • Subject Area:Engineering
  • Publication Date:2023
  • ISSN:1941-7012
  • DOI:10.1063/5.0144607
  • Accession Number:164665933
  • 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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