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Offshore wind farm design with the Coral Reefs Optimization algorithm

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  • Salcedo-Sanz, S.
  • Gallo-Marazuela, D.
  • Pastor-Sánchez, A.
  • Carro-Calvo, L.
  • Portilla-Figueras, A.
  • Prieto, L.

Abstract

This paper presents a novel algorithm for wind farm design and layout optimization: the Coral Reefs Optimization algorithm (CRO). The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction. The CRO is fully described and detailed in this paper, and then applied to the design of a real offshore wind farm in northern Europe. It is shown that the CRO outperforms the results of alternative algorithms in this problem, such as Evolutionary Approaches, Differential Evolution or Harmony Search algorithms.

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  • Salcedo-Sanz, S. & Gallo-Marazuela, D. & Pastor-Sánchez, A. & Carro-Calvo, L. & Portilla-Figueras, A. & Prieto, L., 2014. "Offshore wind farm design with the Coral Reefs Optimization algorithm," Renewable Energy, Elsevier, vol. 63(C), pages 109-115.
    • Handle: RePEc:eee:renene:v:63:y:2014:i:c:p:109-115
    DOI: 10.1016/j.renene.2013.09.004
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    Cited by:

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    2. Salcedo-Sanz, S. & Pastor-Sánchez, A. & Del Ser, J. & Prieto, L. & Geem, Z.W., 2015. "A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction," Renewable Energy, Elsevier, vol. 75(C), pages 93-101.
    3. Ju, Xinglong & Liu, Feng, 2019. "Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation," Applied Energy, Elsevier, vol. 248(C), pages 429-445.
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    6. Serrano González, Javier & Burgos Payán, Manuel & Riquelme Santos, Jesús Manuel, 2018. "Optimal design of neighbouring offshore wind farms: A co-evolutionary approach," Applied Energy, Elsevier, vol. 209(C), pages 140-152.
    7. Angel G. Gonzalez-Rodriguez & Javier Serrano-González & Manuel Burgos-Payán & Jesús Manuel Riquelme-Santos, 2021. "Realistic Optimization of Parallelogram-Shaped Offshore Wind Farms Considering Continuously Distributed Wind Resources," Energies, MDPI, vol. 14(10), pages 1-20, May.
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    10. Díaz, H. & Silva, D. & Bernardo, C. & Guedes Soares, C., 2023. "Micro sitting of floating wind turbines in a wind farm using a multi-criteria framework," Renewable Energy, Elsevier, vol. 204(C), pages 449-474.
    11. Behera, Sasmita & Sahoo, Subhrajit & Pati, B.B., 2015. "A review on optimization algorithms and application to wind energy integration to grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 214-227.
    12. Javier Serrano González & Manuel Burgos Payán & Jesús Manuel Riquelme Santos & Ángel Gaspar González Rodríguez, 2021. "Optimal Micro-Siting of Weathervaning Floating Wind Turbines," Energies, MDPI, vol. 14(4), pages 1-19, February.
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    15. 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.
    16. Cuadra, L. & Salcedo-Sanz, S. & Nieto-Borge, J.C. & Alexandre, E. & Rodríguez, G., 2016. "Computational intelligence in wave energy: Comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1223-1246.
    17. Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).

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