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The lazy greedy algorithm for power optimization of wind turbine positioning on complex terrain

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  • Song, M.X.
  • Chen, K.
  • Zhang, X.
  • Wang, J.

Abstract

Wind farm micro-siting is to determine the optimal positions of wind turbines within the wind farm, with the target of maximizing total power output or profit. This paper studies the performance of the lazy greedy algorithm on optimization of wind turbine positions above complex terrain. Instead of the traditional linear models, computational fluid dynamics and virtual particle wake flow model are employed in the present study for a more accurate evaluation of wind energy distribution and wind power output of wind farm on complex terrain. The validity of the submodular property used by the lazy greedy algorithm is discussed for the wind farm micro-siting optimization problem. By conducting the numerical tests, results demonstrate that the combination of the lazy greedy algorithm and the virtual particle wake model is effective in optimizing wind turbine positioning on complex terrain, for it produces better solution in less time comparing to the previous bionic method.

Suggested Citation

  • Song, M.X. & Chen, K. & Zhang, X. & Wang, J., 2015. "The lazy greedy algorithm for power optimization of wind turbine positioning on complex terrain," Energy, Elsevier, vol. 80(C), pages 567-574.
    • Handle: RePEc:eee:energy:v:80:y:2015:i:c:p:567-574
    DOI: 10.1016/j.energy.2014.12.012
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    References listed on IDEAS

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    1. González, J. Serrano & Rodríguez, Á.G. González & Mora, J. Castro & Burgos Payán, M. & Santos, J. Riquelme, 2011. "Overall design optimization of wind farms," Renewable Energy, Elsevier, vol. 36(7), pages 1973-1982.
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    4. Song, M.X. & Chen, K. & He, Z.Y. & Zhang, X., 2012. "Wake flow model of wind turbine using particle simulation," Renewable Energy, Elsevier, vol. 41(C), pages 185-190.
    5. Song, M.X. & Chen, K. & He, Z.Y. & Zhang, X., 2013. "Bionic optimization for micro-siting of wind farm on complex terrain," Renewable Energy, Elsevier, vol. 50(C), pages 551-557.
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    Citations

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    Cited by:

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    3. Song, Mengxuan & Chen, Kai & Zhang, Xing & Wang, Jun, 2016. "Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction," Renewable Energy, Elsevier, vol. 85(C), pages 57-65.
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    5. Wang, Longyan & Cholette, Michael E. & Tan, Andy C.C. & Gu, Yuantong, 2017. "A computationally-efficient layout optimization method for real wind farms considering altitude variations," Energy, Elsevier, vol. 132(C), pages 147-159.
    6. Kuo, Jim Y.J. & Romero, David A. & Beck, J. Christopher & Amon, Cristina H., 2016. "Wind farm layout optimization on complex terrains – Integrating a CFD wake model with mixed-integer programming," Applied Energy, Elsevier, vol. 178(C), pages 404-414.
    7. Abdullah Al Shereiqi & Amer Al-Hinai & Mohammed Albadi & Rashid Al-Abri, 2020. "Optimal Sizing of a Hybrid Wind-Photovoltaic-Battery Plant to Mitigate Output Fluctuations in a Grid-Connected System," Energies, MDPI, vol. 13(11), pages 1-21, June.
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    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. Froese, Gabrielle & Ku, Shan Yu & Kheirabadi, Ali C. & Nagamune, Ryozo, 2022. "Optimal layout design of floating offshore wind farms," Renewable Energy, Elsevier, vol. 190(C), pages 94-102.
    11. Zhikun Luo & Zhifeng Sun & Fengli Ma & Yihan Qin & Shihao Ma, 2020. "Power Optimization for Wind Turbines Based on Stacking Model and Pitch Angle Adjustment," Energies, MDPI, vol. 13(16), pages 1-15, August.
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