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    Rights statement: This is the author’s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy, 126, 2018 DOI: 10.1016/j.renene.2018.03.052

    Accepted author manuscript, 1.1 MB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Evolutionary computation for wind farm layout optimization

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published
  • Dennis Wilson
  • Silvio Rodrigues
  • Carlos Segura
  • Ilya Loshchilov
  • Frank Hutter
  • Guillermo López Buenfil
  • Ahmed Kheiri
  • Ed Keedwell
  • Mario Ocampo-Pineda
  • Ender Özcan
  • Sergio Ivvan Valdez Peña
  • Brian Goldman
  • Salvador Botello Rionda
  • Arturo Hernández-Aguirre
  • Kalyan Veeramachaneni
  • Sylvain Cussat-Blanc
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<mark>Journal publication date</mark>10/2018
<mark>Journal</mark>Renewable Energy
Volume126
Number of pages11
Pages (from-to)681-691
Publication StatusPublished
Early online date23/03/18
<mark>Original language</mark>English

Abstract

This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in C++, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem. This strongly reduces the number of evaluations (limited in the scope of this competition) with extremely promising results.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy, 126, 2018 DOI: 10.1016/j.renene.2018.03.052