Building a grey-based multi-criteria decision-making model for offshore wind farm site selection

doi:10.1016/j.seta.2020.100935

機構典藏 > College of Business and Management > Graduate Institute & Department of Transportation Management > Journal Article > Item 987654321/121020

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/121020

Title:	Building a grey-based multi-criteria decision-making model for offshore wind farm site selection
Authors:	Hsu, Chao-che
Keywords:	Sustainable energy;Offshore wind farm;Site selection;MCDM
Date:	2020-12-16
Issue Date:	2021-08-24 12:15:14 (UTC+8)
Abstract:	The selection of suitable sites is a critical step for the successful development of offshore wind power. This study aims to establish an assessment framework to select the optimal alternative under multiple conflicting criteria. A hybrid model that combines the grey decision-making trial-and-evaluation laboratory-based analytic network process (grey DANP) and probability-based grey relational analysis (P-GRA) methods is proposed. Grey DANP can not only map the influential network relationship of the criteria, but also generate a set of criteria influence weights. In addition, P-GRA, which uses the concept of interval analysis, can be used to determine the gap between the alternatives and the ideal referential solution (IRS). P-GRA retains the interval value type in the calculation process to avoid the loss of potential information. The proposed model optimizes the traditional DANP and GRA methods, which improving their effectiveness and practicability for practical applications. This paper contributes to solving the problem of farm site selection based on the potential for sustainable development and the novelty of the analysis model. A case study of a Taiwan example is used to examine the feasibility of the proposed model. Finally, a sensitivity analysis was conducted to demonstrate the robustness and innovativeness of the proposed model.
Relation:	Sustainable Energy Technologies and Assessments,43,100935
DOI:	10.1016/j.seta.2020.100935
Appears in Collections:	[Graduate Institute & Department of Transportation Management] Journal Article

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