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Loughney, S, Wang, J, Bashir, M, Armin, M and Yang, Y (2021) Development and application of a multiple-attribute decision-analysis methodology for site selection of floating offshore wind farms on the UK Continental Shelf. Sustainable Energy Technologies and Assessments, 47. ISSN 2213-1388
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Abstract
This research presents the development of a methodology for determining the most suitable floating offshore wind farm locations for the northern coast of Scotland, through the application of multi-attribute decision-analysis. A large area off the northern coast of Scotland is defined and separated into coordinate grids. The environmental, logistical and facilities factors are first analysed in order to remove sites that fall within restricted areas. Following this, data is gathered for the remaining sites in terms of a set of Logistics, Facilities & Environmental, and Met-Ocean criteria. The logistical criterion consists of such factors as, depth, distance to ports and distance to substations. The Met-ocean criterion provides a data analysis of the wind, wave, tidal and current conditions of each site between 2011 and 2016, and the Facilities & Environmental criterion analyses the proximity of the sites to such criteria as Marine Protection Areas, Special Areas of Conservation, military training areas and subsea facilities. The compiled data is then applied to a Multiple Attribute Decision Analysis (MADA) algorithm which aggregates the data for each site and produces a utility ranking in order to determine the most suitable site for floating offshore wind. Validation is conducted through benchmark testing and correlation with government survey sites.
| Item Type: | Article |
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| Uncontrolled Keywords: | 0905 Civil Engineering, 0906 Electrical and Electronic Engineering |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TC Hydraulic engineering. Ocean engineering T Technology > TD Environmental technology. Sanitary engineering |
| Divisions: | Engineering |
| Publisher: | Elsevier |
| Date Deposited: | 08 Jul 2021 08:22 |
| Last Modified: | 03 Jul 2022 00:50 |
| DOI or ID number: | 10.1016/j.seta.2021.101440 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/15261 |
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