Show simple item record

dc.contributor.advisorChick, Johnen
dc.contributor.authorPillai, Ajit Chitharanjanen
dc.date.accessioned2017-11-09T11:28:20Z
dc.date.available2017-11-09T11:28:20Z
dc.date.issued2017-07-10
dc.identifier.urihttp://hdl.handle.net/1842/25470
dc.description.abstractLayout 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 tool can aid in the development of future regulations by considering various constraints on the placement of wind turbines within the site and exploring how these impact the levelized cost of energy.en
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.relation.hasversionAjit 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.en
dc.relation.hasversionAjit 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.en
dc.relation.hasversionAjit 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.en
dc.relation.hasversionAjit 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.en
dc.relation.hasversionAjit 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.en
dc.subjectwind farm designen
dc.subjectlayout optimizationen
dc.subjectwake modellingen
dc.subjectcost assessmenten
dc.subjectcable optimizationen
dc.subjectgenetic algorithmen
dc.subjectparticle swarm optimizeren
dc.titleOn the optimization of offshore wind farm layoutsen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen
dcterms.accessRightsRestricted Accessen


Files in this item

This item appears in the following Collection(s)

Show simple item record