JOURNAL ARTICLE

Analysis of coupling characteristics of clean heating systems based on complementary solar, geothermal, and wind energy.

  • Published In: Journal of Renewable & Sustainable Energy, 2024, v. 16, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Geng, Zhi; Chen, Keyu; Li, Junfen; Wang, Jianli; Shi, Tianqing; Gu, Yujiong 3 of 3

Abstract

This article focuses on the development and simulation of a multi-energy complementary clean heating system integrating solar, geothermal, and wind energy to enhance heating efficiency and stability during the heating season in Zhengzhou, China. Using Ebsilon simulation software and local meteorological and geothermal data, the study modeled four coupled subsystems—solar collector, geothermal, wind energy, and two-stage reheating—and analyzed key performance indicators such as photothermal conversion efficiency, heating capacity, and heat transfer efficiency. Results showed that solar thermal efficiency peaked at 76.013%, geothermal heating capacity remained stable between 123.637 and 125.556 kW with increases linked to geothermal water temperature, and wind turbines provided variable power output up to 3000 kW to support system operation. The integrated system demonstrated effective complementarity among energy sources, improving clean energy utilization and reducing reliance on coal, thereby offering significant environmental benefits and practical guidance for renewable energy applications in clean heating.

Additional Information

  • Source:Journal of Renewable & Sustainable Energy. 2024/03, Vol. 16, Issue 2, p1
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2024
  • ISSN:1941-7012
  • DOI:10.1063/5.0192512
  • Accession Number:176929541
  • 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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