Evolutionary computation for wind farm layout optimization

Wilson, Dennis; Rodrigues, Silvio; Segura, Carlos; Loshchilov, Ilya; Huttor, Frank; Buenfil, Guillermo López; Kheiri, Ahmed; Keedwell, Ed; Ocampo-Pineda, Mario; Özcan, Ender; Peña, Sergio Ivvan Valdez; Goldman, Brian; Rionda, Salvador Botello; Hernández-Aguirre, Arturo; Veeramachaneni, Kalyan; Sylvain, Cussat-Blanc

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.

Citation

Wilson, D., Rodrigues, S., Segura, C., Loshchilov, I., Huttor, F., Buenfil, G. L., …Sylvain, C.-B. (2018). Evolutionary computation for wind farm layout optimization. Renewable Energy, 126, https://doi.org/10.1016/j.renene.2018.03.052

• Journal Article Type: Article
• Acceptance Date: Mar 20, 2018
• Online Publication Date: Mar 23, 2018
• Publication Date: Oct 1, 2018
• Deposit Date: Apr 4, 2018
• Publicly Available Date: Mar 24, 2019
• Journal: Renewable Energy
• Print ISSN: 0960-1481
• Electronic ISSN: 1879-0682
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 126
• DOI: https://doi.org/10.1016/j.renene.2018.03.052
• Keywords: wind farm layout optimization, evolutionary algorithm,; competition
• Public URL: https://nottingham-repository.worktribe.com/output/950327
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S096014811830363X
• Contract Date: Apr 4, 2018