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Optimization of wind turbine placement in a wind farm using a new pseudo-random number generation method

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  • Zergane, Saïd
  • Smaili, Arezki
  • Masson, Christian

Abstract

In this paper, with the goal of maximizing the power production of a wind farm and reducing the wake effect resulting from front-end turbines, we present a new optimization method based on the generation of pseudo-random numbers as a mathematical approach; we have used this method along with the Jensen linear wake model in order to study optimal wind turbine positioning in a farm of given dimensions. For this purpose, a computer program has been developed to carry out numerical simulations based on the maximum total power produced. Using a typical wind turbine for uniform and unidirectional wind speed, the simulation results that we have obtained are presented and discussed. Compared to previous works based on genetic algorithms and viral basis methods, this optimization has yielded recorded enhancements of up to 6.5% on resulting wind farm power. Furthermore, we have found that an optimum number of wind turbines can be properly determined for any given wind farm.

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  • Zergane, Saïd & Smaili, Arezki & Masson, Christian, 2018. "Optimization of wind turbine placement in a wind farm using a new pseudo-random number generation method," Renewable Energy, Elsevier, vol. 125(C), pages 166-171.
    • Handle: RePEc:eee:renene:v:125:y:2018:i:c:p:166-171
    DOI: 10.1016/j.renene.2018.02.082
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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.
    2. DuPont, Bryony & Cagan, Jonathan & Moriarty, Patrick, 2016. "An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm," Energy, Elsevier, vol. 106(C), pages 802-814.
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    4. 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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    4. Mathias Mier & Patrick Hoffmann, 2022. "Wind Turbine Placement and Externalities," ifo Working Paper Series 369, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    5. Zheng, Yue & Wang, Jie-Sheng & Zhu, Jun-Hua & Zhang, Xin-Yue & Xing, Yu-Xuan & Zhang, Yun-Hao, 2024. "MORSA: Multi-objective reptile search algorithm based on elite non-dominated sorting and grid indexing mechanism for wind farm layout optimization problem," Energy, Elsevier, vol. 293(C).
    6. Pollini, Nicolò, 2022. "Topology optimization of wind farm layouts," Renewable Energy, Elsevier, vol. 195(C), pages 1015-1027.
    7. Muhammad Nabeel Hussain & Nadeem Shaukat & Ammar Ahmad & Muhammad Abid & Abrar Hashmi & Zohreh Rajabi & Muhammad Atiq Ur Rehman Tariq, 2022. "Effective Realization of Multi-Objective Elitist Teaching–Learning Based Optimization Technique for the Micro-Siting of Wind Turbines," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    8. Gorg Abdelmassih & Mohammed Al-Numay & Abdelali El Aroudi, 2021. "Map Optimization Fuzzy Logic Framework in Wind Turbine Site Selection with Application to the USA Wind Farms," Energies, MDPI, vol. 14(19), pages 1-15, September.
    9. Ti, Zilong & Deng, Xiao Wei & Zhang, Mingming, 2021. "Artificial Neural Networks based wake model for power prediction of wind farm," Renewable Energy, Elsevier, vol. 172(C), pages 618-631.
    10. Christos A. Christodoulou & Vasiliki Vita & George-Calin Seritan & Lambros Ekonomou, 2020. "A Harmony Search Method for the Estimation of the Optimum Number of Wind Turbines in a Wind Farm," Energies, MDPI, vol. 13(11), pages 1-8, June.

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