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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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    1. Emami, Alireza & Noghreh, Pirooz, 2010. "New approach on optimization in placement of wind turbines within wind farm by genetic algorithms," Renewable Energy, Elsevier, vol. 35(7), pages 1559-1564.
    2. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2012. "Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation," Renewable Energy, Elsevier, vol. 38(1), pages 16-30.
    3. Khan, Salman A. & Rehman, Shafiqur, 2013. "Iterative non-deterministic algorithms in on-shore wind farm design: A brief survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 370-384.
    4. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
    5. Edmund K Burke & Michel Gendreau & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & Rong Qu, 2013. "Hyper-heuristics: a survey of the state of the art," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1695-1724, December.
    6. Wagner, Markus & Day, Jareth & Neumann, Frank, 2013. "A fast and effective local search algorithm for optimizing the placement of wind turbines," Renewable Energy, Elsevier, vol. 51(C), pages 64-70.
    7. Kusiak, Andrew & Song, Zhe, 2010. "Design of wind farm layout for maximum wind energy capture," Renewable Energy, Elsevier, vol. 35(3), pages 685-694.
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    Cited by:

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    5. 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).
    6. 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.
    7. Moghadam, Saman Salehi & Gholamian, Mohammad Reza & Zahedi, Rahim & Shaqaqifar, Maziar, 2024. "Designing a multi-purpose network of sustainable and closed-loop renewable energy supply chain, considering reliability and circular economy," Applied Energy, Elsevier, vol. 369(C).
    8. Ahmed Kheiri, 2020. "Heuristic Sequence Selection for Inventory Routing Problem," Transportation Science, INFORMS, vol. 54(2), pages 302-312, March.
    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. Tao, Siyu & Xu, Qingshan & Feijóo, Andrés & Zheng, Gang & Zhou, Jiemin, 2020. "Nonuniform wind farm layout optimization: A state-of-the-art review," Energy, Elsevier, vol. 209(C).
    11. 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.
    12. Wu, Yan & Zhang, Shuai & Wang, Ruiqi & Wang, Yufei & Feng, Xiao, 2020. "A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner," Renewable Energy, Elsevier, vol. 146(C), pages 687-698.
    13. 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).
    14. Prasun BHATTACHARJEE & Rabin K. JANA & Somenath BHATTACHARYA, 2022. "A Relative Study Of Genetic Algorithm And Moth Flame Optimization Algorithm For Multi-Criteria Design Enhancement Of Wind Turbine Actuator Bearing," Journal of Information Systems & Operations Management, Romanian-American University, vol. 16(1), pages 3-15, May.
    15. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    16. Prasun Bhattacharjee & Somenath Bhattacharya, 2022. "Artificial Intelligence-Driven Competent Plan Of An Indian Wind Farm," Journal of Information Systems & Operations Management, Romanian-American University, vol. 16(2), pages 1-11, December.
    17. Siyu Tao & Andrés Feijóo & Jiemin Zhou & Gang Zheng, 2020. "Topology Design of an Offshore Wind Farm with Multiple Types of Wind Turbines in a Circular Layout," Energies, MDPI, vol. 13(3), pages 1-16, January.
    18. 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).
    19. 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).
    20. Yu, Xiaobing & Wu, Zhuolin & Zhang, Hongqian & Li, Yifei, 2025. "Wind farm layout optimization through multi-strategy differential evolution-based reinforcement learning algorithm," Energy, Elsevier, vol. 335(C).
    21. Shen, Wen Zhong & Zhu, Wei Jun & Barlas, Emre & Li, Ye, 2019. "Advanced flow and noise simulation method for wind farm assessment in complex terrain," Renewable Energy, Elsevier, vol. 143(C), pages 1812-1825.

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