On the optimization of offshore wind farm layouts
Layout optimization of offshore wind farms seeks to automate the design of the wind farm and the placement of wind turbines such that the proposed wind farm maximizes its potential. The optimization of an offshore wind farm layout therefore seeks to minimize the costs of the wind farm while maximizi...
Published in: | Engineering Optimization |
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Format: | Doctoral or Postdoctoral Thesis |
Language: | English |
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The University of Edinburgh
2017
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Online Access: | http://hdl.handle.net/1842/25470 |
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ftunivedinburgh:oai:era.ed.ac.uk:1842/25470 |
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openpolar |
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Open Polar |
collection |
Edinburgh Research Archive (ERA - University of Edinburgh) |
op_collection_id |
ftunivedinburgh |
language |
English |
topic |
wind farm design layout optimization wake modelling cost assessment cable optimization genetic algorithm particle swarm optimizer |
spellingShingle |
wind farm design layout optimization wake modelling cost assessment cable optimization genetic algorithm particle swarm optimizer Pillai, Ajit Chitharanjan On the optimization of offshore wind farm layouts |
topic_facet |
wind farm design layout optimization wake modelling cost assessment cable optimization genetic algorithm particle swarm optimizer |
description |
Layout optimization of offshore wind farms seeks to automate the design of the wind farm and the placement of wind turbines such that the proposed wind farm maximizes its potential. The optimization of an offshore wind farm layout therefore seeks to minimize the costs of the wind farm while maximizing the energy extraction while considering the effects of wakes on the resource; the electrical infrastructure required to collect the energy generated; the cost variation across the site; and all technical and consenting constraints that the wind farm developer must adhere to. As wakes, electrical losses, and costs are non-linear, this produces a complex optimization problem. This thesis describes the design, development, validation, and initial application of a new framework for the optimization of offshore wind farm layouts using either a genetic algorithm or a particle swarm optimizer. The developed methodology and analysis tool have been developed such that individual components can either be used to analyze a particular wind farm layout or used in conjunction with the optimization algorithms to design and optimize wind farm layouts. To accomplish this, separate modules have been developed and validated for the design and optimization of the necessary electrical infrastructure, the assessment of the energy production considering energy losses, and the estimation of the project costs. By including site-dependent parameters and project specific constraints, the framework is capable of exploring the influence the wind farm layout has on the levelized cost of energy of the project. Deploying the integrated framework using two common engineering metaheuristic algorithms to hypothetical, existing, and future wind farms highlights the advantages of this holistic layout optimization framework over the industry standard approaches commonly deployed in offshore wind farm design leading to a reduction in LCOE. Application of the tool to a UK Round 3 site recently under development has also highlighted how the use of this ... |
author2 |
Chick, John Engineering and Physical Sciences Research Council (EPSRC) |
format |
Doctoral or Postdoctoral Thesis |
author |
Pillai, Ajit Chitharanjan |
author_facet |
Pillai, Ajit Chitharanjan |
author_sort |
Pillai, Ajit Chitharanjan |
title |
On the optimization of offshore wind farm layouts |
title_short |
On the optimization of offshore wind farm layouts |
title_full |
On the optimization of offshore wind farm layouts |
title_fullStr |
On the optimization of offshore wind farm layouts |
title_full_unstemmed |
On the optimization of offshore wind farm layouts |
title_sort |
on the optimization of offshore wind farm layouts |
publisher |
The University of Edinburgh |
publishDate |
2017 |
url |
http://hdl.handle.net/1842/25470 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
Ajit C. Pillai, John Chick, Lars Johanning, Mahdi Khorasanchi, and Vincent de Laleu. Offshore wind farm electrical cable layout optimization. Engineering Optimization, 47(12):1689-1708, 2015. ISSN 0305-215X. doi:10.1080/0305215X.2014.992892. Ajit C. Pillai, John Chick, and Vincent de Laleu. Modelling Wind Turbine Wakes at Middelgrunden Wind Farm. In Proceedings of European Wind Energy Conference & Exhibition 2014 Barcelona, Spain, pages 1-10, 2014. Ajit C. Pillai, John Chick, Lars Johanning, Mahdi Khorasanchi, and Sami Barbouchi. Comparison of Offshore Wind Farm Layout Optimization Using a Genetic Algorithm and a Particle Swarm Optimizer. In Proceedings of the ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2016) Busan, South Korea, pages 1-11. ASME, 2016. Ajit C. Pillai, John Chick, Lars Johanning, Mahdi Khorasanchi, and Sebastien Pelissier. Optimisation of Offshore Wind Farms Using a Genetic Algorithm. In Proceedings of the Twenty-Fifth (2015) International Ocean and Polar Engineering Conference, pages 644-652, 2015. ISBN 9781880653890. Ajit C. Pillai, John Chick, Lars Johanning, Mahdi Khorasanchi, and Sebastien Pelissier. Optimisation of Offshore Wind Farms Using a Genetic Algorithm. International Journal of Ocean and Polar Engineering, 26(3):225-234, 2016. doi:10.17736/ijope.2016.mmr16. http://hdl.handle.net/1842/25470 |
op_doi |
https://doi.org/10.1080/0305215X.2014.99289210.17736/ijope.2016.mmr16 |
container_title |
Engineering Optimization |
container_volume |
47 |
container_issue |
12 |
container_start_page |
1689 |
op_container_end_page |
1708 |
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1772810722002075648 |
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ftunivedinburgh:oai:era.ed.ac.uk:1842/25470 2023-07-30T04:00:08+02:00 On the optimization of offshore wind farm layouts Pillai, Ajit Chitharanjan Chick, John Engineering and Physical Sciences Research Council (EPSRC) 2017-07-10 application/pdf http://hdl.handle.net/1842/25470 en eng The University of Edinburgh Ajit C. Pillai, John Chick, Lars Johanning, Mahdi Khorasanchi, and Vincent de Laleu. Offshore wind farm electrical cable layout optimization. Engineering Optimization, 47(12):1689-1708, 2015. ISSN 0305-215X. doi:10.1080/0305215X.2014.992892. Ajit C. Pillai, John Chick, and Vincent de Laleu. Modelling Wind Turbine Wakes at Middelgrunden Wind Farm. In Proceedings of European Wind Energy Conference & Exhibition 2014 Barcelona, Spain, pages 1-10, 2014. Ajit C. Pillai, John Chick, Lars Johanning, Mahdi Khorasanchi, and Sami Barbouchi. Comparison of Offshore Wind Farm Layout Optimization Using a Genetic Algorithm and a Particle Swarm Optimizer. In Proceedings of the ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2016) Busan, South Korea, pages 1-11. ASME, 2016. Ajit C. Pillai, John Chick, Lars Johanning, Mahdi Khorasanchi, and Sebastien Pelissier. Optimisation of Offshore Wind Farms Using a Genetic Algorithm. In Proceedings of the Twenty-Fifth (2015) International Ocean and Polar Engineering Conference, pages 644-652, 2015. ISBN 9781880653890. Ajit C. Pillai, John Chick, Lars Johanning, Mahdi Khorasanchi, and Sebastien Pelissier. Optimisation of Offshore Wind Farms Using a Genetic Algorithm. International Journal of Ocean and Polar Engineering, 26(3):225-234, 2016. doi:10.17736/ijope.2016.mmr16. http://hdl.handle.net/1842/25470 wind farm design layout optimization wake modelling cost assessment cable optimization genetic algorithm particle swarm optimizer Thesis or Dissertation Doctoral PhD Doctor of Philosophy 2017 ftunivedinburgh https://doi.org/10.1080/0305215X.2014.99289210.17736/ijope.2016.mmr16 2023-07-09T20:29:19Z Layout optimization of offshore wind farms seeks to automate the design of the wind farm and the placement of wind turbines such that the proposed wind farm maximizes its potential. The optimization of an offshore wind farm layout therefore seeks to minimize the costs of the wind farm while maximizing the energy extraction while considering the effects of wakes on the resource; the electrical infrastructure required to collect the energy generated; the cost variation across the site; and all technical and consenting constraints that the wind farm developer must adhere to. As wakes, electrical losses, and costs are non-linear, this produces a complex optimization problem. This thesis describes the design, development, validation, and initial application of a new framework for the optimization of offshore wind farm layouts using either a genetic algorithm or a particle swarm optimizer. The developed methodology and analysis tool have been developed such that individual components can either be used to analyze a particular wind farm layout or used in conjunction with the optimization algorithms to design and optimize wind farm layouts. To accomplish this, separate modules have been developed and validated for the design and optimization of the necessary electrical infrastructure, the assessment of the energy production considering energy losses, and the estimation of the project costs. By including site-dependent parameters and project specific constraints, the framework is capable of exploring the influence the wind farm layout has on the levelized cost of energy of the project. Deploying the integrated framework using two common engineering metaheuristic algorithms to hypothetical, existing, and future wind farms highlights the advantages of this holistic layout optimization framework over the industry standard approaches commonly deployed in offshore wind farm design leading to a reduction in LCOE. Application of the tool to a UK Round 3 site recently under development has also highlighted how the use of this ... Doctoral or Postdoctoral Thesis Arctic Edinburgh Research Archive (ERA - University of Edinburgh) Engineering Optimization 47 12 1689 1708 |