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Optimization of wind farm micro-siting for complex terrain using greedy algorithm

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  • Song, M.X.
  • Chen, K.
  • He, Z.Y.
  • Zhang, X.

Abstract

An optimization approach based on greedy algorithm for optimization of wind farm micro-siting is presented. The key of optimizing wind farm micro-siting is the fast and accurate evaluation of the wake flow interactions of wind turbines. The virtual particle model is employed for wake flow simulation of wind turbines, which makes the present method applicable for non-uniform flow fields on complex terrains. In previous bionic optimization method, within each step of the optimization process, only the power output of the turbine that is being located or relocated is considered. To aim at the overall power output of the wind farm comprehensively, a dependent region technique is introduced to improve the estimation of power output during the optimization procedure. With the technique, the wake flow influences can be reduced more efficiently during the optimization procedure. During the optimization process, the turbine that is being added will avoid being affected other turbines, and avoid affecting other turbine in the meantime. The results from the numerical calculations demonstrate that the present method is effective for wind farm micro-siting on complex terrain, and it produces better solutions in less time than the previous bionic method.

Suggested Citation

  • Song, M.X. & Chen, K. & He, Z.Y. & Zhang, X., 2014. "Optimization of wind farm micro-siting for complex terrain using greedy algorithm," Energy, Elsevier, vol. 67(C), pages 454-459.
    • Handle: RePEc:eee:energy:v:67:y:2014:i:c:p:454-459
    DOI: 10.1016/j.energy.2014.01.082
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    Cited by:

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    3. Song, MengXuan & Wu, BingHeng & Chen, Kai & Zhang, Xing & Wang, Jun, 2016. "Simulating the wake flow effect of wind turbines on velocity and turbulence using particle random walk method," Energy, Elsevier, vol. 116(P1), pages 583-591.
    4. 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.
    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. Radünz, William Corrêa & Mattuella, Jussara M. Leite & Petry, Adriane Prisco, 2020. "Wind resource mapping and energy estimation in complex terrain: A framework based on field observations and computational fluid dynamics," Renewable Energy, Elsevier, vol. 152(C), pages 494-515.
    7. Wędzik, Andrzej & Siewierski, Tomasz & Szypowski, Michał, 2016. "A new method for simultaneous optimizing of wind farm’s network layout and cable cross-sections by MILP optimization," Applied Energy, Elsevier, vol. 182(C), pages 525-538.
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    10. Navarro Diaz, Gonzalo P. & Saulo, A. Celeste & Otero, Alejandro D., 2021. "Full wind rose wind farm simulation including wake and terrain effects for energy yield assessment," Energy, Elsevier, vol. 237(C).
    11. 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.
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    14. Neill, Simon P. & Hashemi, M. Reza & Lewis, Matt J., 2014. "Optimal phasing of the European tidal stream resource using the greedy algorithm with penalty function," Energy, Elsevier, vol. 73(C), pages 997-1006.

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