Offshore wind turbine selection with multi-criteria decision-making techniques involving D numbers and squeeze adversarial interpretive structural modeling method

Xia Li, Li Xu, Jingjing Cai, Cheng Peng and Xiaoyan Bian

Applied Energy, 2024, vol. 368, issue C, No S0306261924007645

Abstract: In the design of wind farms, the selection of wind turbines is crucial for maximizing energy efficiency and economic gain. The conventional approach in offshore wind turbine research has often relied on expert scoring, which can overlook objective data and involve subjective judgment uncertainties. To address these issues, this paper introduces an innovative hybrid multi-criteria decision-making (MCDM) framework, D-SAISM (squeeze adversarial interpretive structural modeling based on D numbers). This framework aims to improve the evaluation process by adding clarity and rectifying prevalent issues. It utilizes the D numbers method, which helps convert qualitative expert evaluations into numerical data, thus reducing uncertainties. Additionally, the framework integrates the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with an adversarial approach into the traditional interpretive structural modeling (ISM). Through three dimensions squeeze, the transformation of the system to be evaluated from a topologically active system to a more definitive and rigid one, achieving a comparison of the advantages and disadvantages of alternative solutions. This enhancement aids in better visualization and understanding of the selection process. The effectiveness and practical utility of the newly developed D-SAISM model were confirmed when applied to a real offshore wind power project. The model’s recommendations closely matched the actual turbine selection, demonstrating its value in decision-making for wind turbine selection in wind farms.

Keywords: Offshore wind turbine; Multi-criteria decision-making; Squeeze adversarial interpretive structural modeling; D numbers (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924007645
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:368:y:2024:i:c:s0306261924007645

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.123381

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().