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Evolutionary computation for wind farm layout optimization

Author

Listed:
  • Wilson, Dennis
  • Rodrigues, Silvio
  • Segura, Carlos
  • Loshchilov, Ilya
  • Hutter, 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
  • Cussat-Blanc, Sylvain

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.

Suggested Citation

  • Wilson, Dennis & Rodrigues, Silvio & Segura, Carlos & Loshchilov, Ilya & Hutter, Frank & Buenfil, Guillermo López & Kheiri, Ahmed & Keedwell, Ed & Ocampo-Pineda, Mario & Özcan, Ender & Peña, Sergio Iv, 2018. "Evolutionary computation for wind farm layout optimization," Renewable Energy, Elsevier, vol. 126(C), pages 681-691.
    • Handle: RePEc:eee:renene:v:126:y:2018:i:c:p:681-691
    DOI: 10.1016/j.renene.2018.03.052
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    Cited by:

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    7. Wang, Longyan & Zuo, Ming J. & Xu, Jian & Zhou, Yunkai & Tan, Andy C., 2019. "Optimizing wind farm layout by addressing energy-variance trade-off: A single-objective optimization approach," Energy, Elsevier, vol. 189(C).
    8. Wu, Yan & Xia, Tianqi & Wang, Yufei & Zhang, Haoran & Feng, Xiao & Song, Xuan & Shibasaki, Ryosuke, 2022. "A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network," Renewable Energy, Elsevier, vol. 185(C), pages 302-320.
    9. Cuadra, L. & Ocampo-Estrella, I. & Alexandre, E. & Salcedo-Sanz, S., 2019. "A study on the impact of easements in the deployment of wind farms near airport facilities," Renewable Energy, Elsevier, vol. 135(C), pages 566-588.
    10. Butterwick, Thomas & Kheiri, Ahmed & Lulli, Guglielmo & Gromicho, Joaquim & Kreeft, Jasper, 2023. "Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm," Renewable Energy, Elsevier, vol. 208(C), pages 1-16.
    11. Dhunny, A.Z. & Timmons, D.S. & Allam, Z. & Lollchund, M.R. & Cunden, T.S.M., 2020. "An economic assessment of near-shore wind farm development using a weather research forecast-based genetic algorithm model," Energy, Elsevier, vol. 201(C).
    12. Masoudi, Seiied Mohsen & Baneshi, Mehdi, 2022. "Layout optimization of a wind farm considering grids of various resolutions, wake effect, and realistic wind speed and wind direction data: A techno-economic assessment," Energy, Elsevier, vol. 244(PB).
    13. Tao, Siyu & Xu, Qingshan & Feijóo-Lorenzo, Andrés E. & Zheng, Gang & Zhou, Jiemin, 2021. "Optimal layout of a Co-Located wind/tidal current farm considering forbidden zones," Energy, Elsevier, vol. 228(C).
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