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Wind farm layout optimization under uncertainty

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  • MirHassani, S.A.
  • Yarahmadi, A.

Abstract

The optimal design of a wind farm with the aim of increasing the exploitation rate and power production is one of the challenging problems in the field of renewable energies. In this paper, the effect of using different hub height wind turbines in a farm on total power is studied. An exact mathematical formulation is presented in terms of the interaction matrix for multi-turbine wake effects considering different hub height wind turbines, and a new mixed-integer quadratic optimization model is developed. Then, the model is generalized to include the changes in wind characteristics and uncertain parameters. The model is solved using the linearization technique and an iterative method. The performance of the model and solution algorithm is evaluated by solving different problems borrowed from the literature. The computation results show the possibility of having a high-quality solution in a reasonable time in almost all cases.

Suggested Citation

  • MirHassani, S.A. & Yarahmadi, A., 2017. "Wind farm layout optimization under uncertainty," Renewable Energy, Elsevier, vol. 107(C), pages 288-297.
    • Handle: RePEc:eee:renene:v:107:y:2017:i:c:p:288-297
    DOI: 10.1016/j.renene.2017.01.063
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    References listed on IDEAS

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    1. Marmidis, Grigorios & Lazarou, Stavros & Pyrgioti, Eleftheria, 2008. "Optimal placement of wind turbines in a wind park using Monte Carlo simulation," Renewable Energy, Elsevier, vol. 33(7), pages 1455-1460.
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    Citations

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    Cited by:

    1. Song, Jeonghwan & Kim, Taewan & You, Donghyun, 2023. "Particle swarm optimization of a wind farm layout with active control of turbine yaws," Renewable Energy, Elsevier, vol. 206(C), pages 738-747.
    2. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2018. "Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model," Energies, MDPI, vol. 11(12), pages 1-26, November.
    3. 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.
    4. Reddy, Sohail R., 2020. "Wind Farm Layout Optimization (WindFLO) : An advanced framework for fast wind farm analysis and optimization," Applied Energy, Elsevier, vol. 269(C).
    5. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2021. "Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms," Energies, MDPI, vol. 14(14), pages 1-25, July.
    6. 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.
    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. Hejun Yang & Lei Wang & Yeyu Zhang & Xianjun Qi & Lei Wang & Hongbin Wu, 2018. "Reliability Assessment of Wind Farm Electrical System Based on a Probability Transfer Technique," Energies, MDPI, vol. 11(4), pages 1-16, March.
    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).
    10. Shaaban, S. & Albatal, A. & Mohamed, M.H., 2018. "Optimization of H-Rotor Darrieus turbines' mutual interaction in staggered arrangements," Renewable Energy, Elsevier, vol. 125(C), pages 87-99.
    11. 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).

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