Offshore wind turbine selection with a novel multi-criteria decision-making method based on Dempster-Shafer evidence theory. (June 2022)

• Record Type: Journal Article
• Authors: Wang, Jin; Xu, Li; Cai, Jingjing; Fu, Yang; Bian, Xiaoyan
• Is Part Of: Sustainable energy technologies and assessments. Volume 51(2022)
• Publication Date: 2022-06
• Subjects: Offshore wind turbine -- Decision making -- Dempster-Shafer evidence theory -- Multi-criteria decision-making; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.101951
• Languages: English
• ISSNs: 2213-1388

Abstract

Graphical abstract: Highlights: A new decision-making model for offshore wind turbine selection is proposed. The factors affecting offshore wind turbine selection is established. D-S evidence theory is used to express and process the uncertainty of expert judgments. MCDM method in connection with D-S evidence theory to rank the wind turbines. Abstract: The selection of the optimal wind turbines is a crucial issue in the construction of offshore wind farms and is a multi-criteria decision-making problem. Decision-makers usually depend on their own subjective judgement when evaluating alternatives with respect to decision criteria. Such an approach leads to unpredictable uncertainty. One main objective of this work is to deal with and express uncertain information effectively on the basis of the Dempster-Shafer evidence theory. Then, a new decision-making model for offshore wind turbine selection is established using the Dempster-Shafer evidence theory in connection with the multi-criteria decision-making method. In the process of selecting offshore wind turbines, 22 decision criteria under five main criteria are adopted, and the criterion weights are determined using the stepwise weighted assessment ratio analysis method. Expert judgement on alternatives is represented as a basic probability assignment and fused by the synthesis rules based on the credibility of the evidence. The best wind turbine is selected using the technique for order preference by similarity to ideal solution. The proposed method is applied to an actual offshore wind farm, and the results reveal that the method can effectively select the optimal scheme from four different types of offshore wind turbines.