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The decision model of 3-dimensional wind farm layout design

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  • Song, Zhe
  • Zhang, Zijun
  • Chen, Xingying

Abstract

This research investigates the maximization of the expected wind farm power output through optimizing the layout of wind turbines as well as their heights. A model for determining the wind farm layout in 3-dimensional (3-d) space is introduced. The objective of the layout design is to maximize the expected wind farm power output. To estimate the wind deficit in 3-d space, a linear 3-d wake loss model is developed and a criterion for identifying wakes affecting on a targeted wind turbine is constructed. The proposed model is complex and, therefore, solved by an evolutionary strategy algorithm. A comparative analysis of the proposed model and a benchmark model is conducted. In the computational study, the design solution offered by the proposed model indicates that the expected wind farm power output could be increased by choosing wind turbines with different heights.

Suggested Citation

  • Song, Zhe & Zhang, Zijun & Chen, Xingying, 2016. "The decision model of 3-dimensional wind farm layout design," Renewable Energy, Elsevier, vol. 85(C), pages 248-258.
    • Handle: RePEc:eee:renene:v:85:y:2016:i:c:p:248-258
    DOI: 10.1016/j.renene.2015.06.036
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Sun, Haiying & Yang, Hongxing & Gao, Xiaoxia, 2019. "Investigation into spacing restriction and layout optimization of wind farm with multiple types of wind turbines," Energy, Elsevier, vol. 168(C), pages 637-650.
    2. Mayo, Michael & Daoud, Maisa, 2016. "Informed mutation of wind farm layouts to maximise energy harvest," Renewable Energy, Elsevier, vol. 89(C), pages 437-448.
    3. Croonenbroeck, Carsten & Hennecke, David, 2021. "A comparison of optimizers in a unified standard for optimization on wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    4. Hu, Weicheng & Yang, Qingshan & Chen, Hua-Peng & Guo, Kunpeng & Zhou, Tong & Liu, Min & Zhang, Jian & Yuan, Ziting, 2022. "A novel approach for wind farm micro-siting in complex terrain based on an improved genetic algorithm," Energy, Elsevier, vol. 251(C).
    5. Kyoungboo Yang & Kyungho Cho, 2019. "Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study," Energies, MDPI, vol. 12(23), pages 1-15, November.
    6. Lo Brutto, Ottavio A. & Nguyen, Van Thinh & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2016. "Tidal farm analysis using an analytical model for the flow velocity prediction in the wake of a tidal turbine with small diameter to depth ratio," Renewable Energy, Elsevier, vol. 99(C), pages 347-359.
    7. Mingcan Li & Hanbin Xiao & Lin Pan & Chengjun Xu, 2019. "Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms," Energies, MDPI, vol. 12(5), pages 1-32, March.
    8. Yang, Kyoungboo & Kwak, Gyeongil & Cho, Kyungho & Huh, Jongchul, 2019. "Wind farm layout optimization for wake effect uniformity," Energy, Elsevier, vol. 183(C), pages 983-995.
    9. 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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