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Maximizing the overall production of wind farms by setting the individual operating point of wind turbines

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  • Serrano González, Javier
  • Burgos Payán, Manuel
  • Riquelme Santos, Jesús
  • González Rodríguez, Ángel Gaspar

Abstract

The classical operation strategy of wind farms seeks each wind turbine to convert as much aerodynamic power as available from the incoming airflow. But this does not warranty that the power converted by the whole wind farm be a maximum due to the interaction between turbines (wake effect). Unlike the conventional operation, this paper proposes the individual selection of the operation point of each turbine so that the overall production of the wind farm is maximized. To reach that goal, the power produced by some upwind turbines is slightly reduced in order to increase the available aerodynamic power for the downwind turbines, which results in an increase of the overall wind farm energy extraction. The optimization is performed by means of a genetic algorithm that selects the optimal pitch angle and tip speed ratio of each individual wind turbine, in order to maximize the overall wind farm production.

Suggested Citation

  • Serrano González, Javier & Burgos Payán, Manuel & Riquelme Santos, Jesús & González Rodríguez, Ángel Gaspar, 2015. "Maximizing the overall production of wind farms by setting the individual operating point of wind turbines," Renewable Energy, Elsevier, vol. 80(C), pages 219-229.
    • Handle: RePEc:eee:renene:v:80:y:2015:i:c:p:219-229
    DOI: 10.1016/j.renene.2015.02.009
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    1. Saavedra-Moreno, B. & Salcedo-Sanz, S. & Paniagua-Tineo, A. & Prieto, L. & Portilla-Figueras, A., 2011. "Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms," Renewable Energy, Elsevier, vol. 36(11), pages 2838-2844.
    2. Serrano González, J. & Burgos Payán, M. & Riquelme Santos, J., 2013. "Optimum design of transmissions systems for offshore wind farms including decision making under risk," Renewable Energy, Elsevier, vol. 59(C), pages 115-127.
    3. 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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    5. Lee, Jaejoon & Son, Eunkuk & Hwang, Byungho & Lee, Soogab, 2013. "Blade pitch angle control for aerodynamic performance optimization of a wind farm," Renewable Energy, Elsevier, vol. 54(C), pages 124-130.
    6. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2012. "Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation," Renewable Energy, Elsevier, vol. 38(1), pages 16-30.
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    Cited by:

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    3. Van-Hai Bui & Akhtar Hussain & Thai-Thanh Nguyen & Hak-Man Kim, 2021. "Multi-Objective Stochastic Optimization for Determining Set-Point of Wind Farm System," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    4. Tanvir Ahmad & Abdul Basit & Juveria Anwar & Olivier Coupiac & Behzad Kazemtabrizi & Peter C. Matthews, 2019. "Fast Processing Intelligent Wind Farm Controller for Production Maximisation," Energies, MDPI, vol. 12(3), pages 1-17, February.
    5. Jie Tian & Dao Zhou & Chi Su & Mohsen Soltani & Zhe Chen & Frede Blaabjerg, 2017. "Wind Turbine Power Curve Design for Optimal Power Generation in Wind Farms Considering Wake Effect," Energies, MDPI, vol. 10(3), pages 1-19, March.
    6. Moghadam, Saman Salehi & Gholamian, Mohammad Reza & Zahedi, Rahim & Shaqaqifar, Maziar, 2024. "Designing a multi-purpose network of sustainable and closed-loop renewable energy supply chain, considering reliability and circular economy," Applied Energy, Elsevier, vol. 369(C).
    7. Wang, Yu & Wei, Shanbi & Yang, Wei & Chai, Yi, 2023. "Adaptive economic predictive control for offshore wind farm active yaw considering generation uncertainty," Applied Energy, Elsevier, vol. 351(C).
    8. Rafael V. Rodrigues & Corinne Lengsfeld, 2019. "Development of a Computational System to Improve Wind Farm Layout, Part I: Model Validation and Near Wake Analysis," Energies, MDPI, vol. 12(5), pages 1-24, March.
    9. Hou, Peng & Enevoldsen, Peter & Hu, Weihao & Chen, Cong & Chen, Zhe, 2017. "Offshore wind farm repowering optimization," Applied Energy, Elsevier, vol. 208(C), pages 834-844.
    10. 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.
    11. Gionfra, Nicolò & Sandou, Guillaume & Siguerdidjane, Houria & Faille, Damien & Loevenbruck, Philippe, 2019. "Wind farm distributed PSO-based control for constrained power generation maximization," Renewable Energy, Elsevier, vol. 133(C), pages 103-117.
    12. Liao, Hao & Hu, Weihao & Wu, Xiawei & Wang, Ni & Liu, Zhou & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Active power dispatch optimization for offshore wind farms considering fatigue distribution," Renewable Energy, Elsevier, vol. 151(C), pages 1173-1185.
    13. Santos, Marllen & González, Mario, 2019. "Factors that influence the performance of wind farms," Renewable Energy, Elsevier, vol. 135(C), pages 643-651.
    14. Javier Serrano González & Bruno López & Martín Draper, 2021. "Optimal Pitch Angle Strategy for Energy Maximization in Offshore Wind Farms Considering Gaussian Wake Model," Energies, MDPI, vol. 14(4), pages 1-18, February.
    15. Lo Brutto, Ottavio A. & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2017. "Assessing the effectiveness of a global optimum strategy within a tidal farm for power maximization," Applied Energy, Elsevier, vol. 204(C), pages 653-666.

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