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Comparison of Offshore Wind Farm Layout Optimization Using a Genetic Algorithm and a Particle Swarm Optimizer


Citation Pillai, A.C., Chick, J., Johanning, L., Khorasanchi, M. and Barbouchi, S. Comparison of Offshore Wind Farm Layout Optimization Using a Genetic Algorithm and a Particle Swarm Optimizer, ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering Volume 6: Ocean Space Utilization; Ocean Renewable Energy Busan, South Korea, June 19?24, 2016, 2016. https://dx.doi.org/10.1115/OMAE2016-54145.
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Author(s) Pillai, A.C., Chick, J., Johanning, L., Khorasanchi, M. and Barbouchi, S.
Project partner(s) University of Edinburgh, University of Exeter, University of Strathclyde, Electricité de France
Publisher ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering Volume 6: Ocean Space Utilization; Ocean Renewable Energy Busan, South Korea, June 19?24, 2016
DOI https://dx.doi.org/10.1115/OMAE2016-54145
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Abstract This article explores the application of a binary genetic algorithm and a binary particle swarm optimizer to the optimization of an offshore wind farm layout. The framework developed as part of this work makes use of a modular design to include a detailed assessment of a wind farm’s layout including validated analytic wake modelling, cost assessment, and the design of the necessary electrical infrastructure considering constraints. This study has found that both algorithms are capable of optimizing wind farm layouts with respect to levelised cost of energy when using a detailed, complex evaluation function. Both are also capable of identifying layouts with lower levelised costs of energy than similar studies that have been published in the past and are therefore both applicable to this problem. The performance of both algorithms has highlighted that both should be further tuned and benchmarked in order to better characterize their performance.
Associated Project(s) ETI-MA2003: Industrial Doctorate Centre for Offshore Renewable Energy (IDCORE)
Associated Dataset(s) EDC0000134: Tidal Energy Site Environmental Conditions Measured During the ReDAPT Tidal Project 2011-2014
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