A framework for the selection of optimum offshore wind farm locations for deployment

Date

2018-07-16

Supervisor/s

Journal Title

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Volume Title

Publisher

MDPI

Department

Type

Article

ISSN

1996-1073

Format

Citation

Varvara Mytilinou, Estivaliz Lozano-Minguez and Athanasios Kolios. A framework for the selection of optimum offshore wind farm locations for deployment. Energies, 2018, Volume 11, Issue 7, Article number 1855

Abstract

This research develops a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 available zones in the UK. The framework includes optimization techniques, decision-making methods and experts’ input in order to support investment decisions. Further, techno-economic evaluation, life cycle costing (LCC) and physical aspects for each location are considered along with experts’ opinions to provide deeper insight into the decision-making process. A process on the criteria selection is also presented and seven conflicting criteria are being considered for implementation in the technique for the order of preference by similarity to the ideal solution (TOPSIS) method in order to suggest the optimum location that was produced by the nondominated sorting genetic algorithm (NSGAII). For the given inputs, Seagreen Alpha, near the Isle of May, was found to be the most probable solution, followed by Moray Firth Eastern Development Area 1, near Wick, which demonstrates by example the effectiveness of the newly introduced framework that is also transferable and generic. The outcomes are expected to help stakeholders and decision makers to make better informed and cost-effective decisions under uncertainty when investing in offshore wind energy in the UK.

Description

Software Description

Software Language

Github

Keywords

multi-objective optimization, nondominated sorting genetic algorithm (NSGA), multi-criteria decision making (MCDM), technique for the order of preference by similarity to the ideal solution (TOPSIS), life cycle cost

DOI

Rights

Attribution 4.0 International

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