JOURNAL ARTICLE
Site selection of offshore wind-wave-hydrogen energy coupling system based on improved WHFS-TOPSIS: A case study in China.
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: Du, Yan-dong; Dong, Yao; Wu, Zheng-long; Wang, Han-wen; Wu, Yang-wen; Lu, Qiang 3 of 3
Abstract
This article focuses on developing a multi-criteria decision-making (MCDM) framework for site selection of offshore wind-wave-hydrogen energy coupling system (OWWHECS) projects. It introduces an improved technique for order preference by similarity to ideal solution (TOPSIS) method extended with weighted hesitant fuzzy sets (WHFS) to better capture expert evaluations and uncertainties in criteria assessment. The framework integrates subjective weights from the best-worst method (BWM) and objective weights from the entropy method to prioritize potential sites, demonstrated through a case study of offshore regions in Bohai Bay and the Shandong Peninsula, China. Results highlight economic factors, especially initial investment cost and proximity to ports, as critical criteria, and the WHFS-TOPSIS method shows greater sensitivity and reliability compared to other ranking methods. The study addresses a research gap in OWWHECS site selection by providing a comprehensive criteria system and a novel decision-making approach tailored to the unique characteristics of these emerging renewable energy projects.
Additional Information
- Source:Journal of Renewable & Sustainable Energy. 2025/03, Vol. 17, Issue 2, p1
- Document Type:Article
- Subject Area:Power and Energy
- Publication Date:2025
- ISSN:1941-7012
- DOI:10.1063/5.0243994
- Accession Number:184884758
- 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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