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Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes

Author

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  • Zhenzhou Shao

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Ying Wu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Li Li

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Shuang Han

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Yongqian Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

Abstract

In a wind farm some wind turbines may be affected by multiple upwind wakes. The commonly used approach in engineering to simulate the interaction effect of different wakes is to combine the single analytical wake model and the interaction model. The higher turbulence level and shear stress profile generated by upwind turbines in the superposed area leads to faster wake recovery. The existing interaction models are all analytical models based on some simple assumptions of superposition, which cannot characterize this phenomenon. Therefore, in this study, a mixing coefficient is introduced into the classical energy balance interaction model with the aim of reflecting the effect of turbulence intensity on velocity recovery in multiple wakes. An empirical expression is also given to calculate this parameter. The performance of the new model is evaluated using data from the Lillgrund and the Horns Rev I offshore wind farms, and the simulations agree reasonably with the observations. The comparison of different interaction model simulation results with measured data show that the calculation accuracy of this new interaction model is high, and the mean absolute percentage error of wind farm efficiency is reduced by 5.3% and 1.58%, respectively, compared to the most commonly used sum of squares interaction model.

Suggested Citation

  • Zhenzhou Shao & Ying Wu & Li Li & Shuang Han & Yongqian Liu, 2019. "Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes," Energies, MDPI, vol. 12(4), pages 1-14, February.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:680-:d:207581
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    References listed on IDEAS

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    1. Veisi, Amin Allah & Shafiei Mayam, Mohammad Hossein, 2017. "Effects of blade rotation direction in the wake region of two in-line turbines using Large Eddy Simulation," Applied Energy, Elsevier, vol. 197(C), pages 375-392.
    2. Yimei Wang & Yongqian Liu & Li Li & David Infield & Shuang Han, 2018. "Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method," Energies, MDPI, vol. 11(4), pages 1-19, April.
    3. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    4. Jeon, Sanghyeon & Kim, Bumsuk & Huh, Jongchul, 2015. "Comparison and verification of wake models in an onshore wind farm considering single wake condition of the 2 MW wind turbine," Energy, Elsevier, vol. 93(P2), pages 1769-1777.
    5. Charlotte Bay Hasager & Leif Rasmussen & Alfredo Peña & Leo E. Jensen & Pierre-Elouan Réthoré, 2013. "Wind Farm Wake: The Horns Rev Photo Case," Energies, MDPI, vol. 6(2), pages 1-21, February.
    6. José F. Herbert-Acero & Oliver Probst & Pierre-Elouan Réthoré & Gunner Chr. Larsen & Krystel K. Castillo-Villar, 2014. "A Review of Methodological Approaches for the Design and Optimization of Wind Farms," Energies, MDPI, vol. 7(11), pages 1-87, October.
    7. Tian, Linlin & Zhu, Weijun & Shen, Wenzhong & Song, Yilei & Zhao, Ning, 2017. "Prediction of multi-wake problems using an improved Jensen wake model," Renewable Energy, Elsevier, vol. 102(PB), pages 457-469.
    8. Kuo, Jim Y.J. & Romero, David A. & Amon, Cristina H., 2015. "A mechanistic semi-empirical wake interaction model for wind farm layout optimization," Energy, Elsevier, vol. 93(P2), pages 2157-2165.
    9. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
    10. Göçmen, Tuhfe & Laan, Paul van der & Réthoré, Pierre-Elouan & Diaz, Alfredo Peña & Larsen, Gunner Chr. & Ott, Søren, 2016. "Wind turbine wake models developed at the technical university of Denmark: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 752-769.
    11. Huiwen Liu & Imran Hayat & Yaqing Jin & Leonardo P. Chamorro, 2018. "On the Evolution of the Integral Time Scale within Wind Farms," Energies, MDPI, vol. 11(1), pages 1-11, January.
    12. Leonardo P. Chamorro & Fernando Porté-Agel, 2011. "Turbulent Flow Inside and Above a Wind Farm: A Wind-Tunnel Study," Energies, MDPI, vol. 4(11), pages 1-21, November.
    13. 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.
    14. Sang Lee & Peter Vorobieff & Svetlana Poroseva, 2018. "Interaction of Wind Turbine Wakes under Various Atmospheric Conditions," Energies, MDPI, vol. 11(6), pages 1-15, June.
    15. Wim Munters & Johan Meyers, 2018. "Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization," Energies, MDPI, vol. 11(1), pages 1-32, January.
    16. Esteban Ferrer & Oliver M.F. Browne & Eusebio Valero, 2017. "Sensitivity Analysis to Control the Far-Wake Unsteadiness Behind Turbines," Energies, MDPI, vol. 10(10), pages 1-21, October.
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    Cited by:

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    3. Houssem R. E. H. Bouchekara & Yusuf A. Sha’aban & Mohammad S. Shahriar & Makbul A. M. Ramli & Abdullahi A. Mas’ud, 2023. "Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2," Sustainability, MDPI, vol. 15(3), pages 1-32, January.
    4. Dongqin Zhang & Yang Liang & Chao Li & Yiqing Xiao & Gang Hu, 2022. "Applicability of Wake Models to Predictions of Turbine-Induced Velocity Deficit and Wind Farm Power Generation," Energies, MDPI, vol. 15(19), pages 1-26, October.
    5. Yuan Li & Zengjin Xu & Zuoxia Xing & Bowen Zhou & Haoqian Cui & Bowen Liu & Bo Hu, 2020. "A Modified Reynolds-Averaged Navier–Stokes-Based Wind Turbine Wake Model Considering Correction Modules," Energies, MDPI, vol. 13(17), pages 1-19, August.
    6. Yutaka Hara & Yoshifumi Jodai & Tomoyuki Okinaga & Masaru Furukawa, 2021. "Numerical Analysis of the Dynamic Interaction between Two Closely Spaced Vertical-Axis Wind Turbines," Energies, MDPI, vol. 14(8), pages 1-23, April.
    7. Francesco Castellani & Davide Astolfi, 2020. "Editorial on Special Issue “Wind Turbine Power Optimization Technology”," Energies, MDPI, vol. 13(7), pages 1-4, April.
    8. Angel G. Gonzalez-Rodriguez & Javier Serrano-González & Manuel Burgos-Payán & Jesús Manuel Riquelme-Santos, 2021. "Realistic Optimization of Parallelogram-Shaped Offshore Wind Farms Considering Continuously Distributed Wind Resources," Energies, MDPI, vol. 14(10), pages 1-20, May.
    9. Paxis Marques João Roque & Shyama Pada Chowdhury & Zhongjie Huan, 2021. "Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study," Energies, MDPI, vol. 14(14), pages 1-22, July.
    10. Brooks, Sam & Mahmood, Minhal & Roy, Rajkumar & Manolesos, Marinos & Salonitis, Konstantinos, 2023. "Self-reconfiguration simulations of turbines to reduce uneven farm degradation," Renewable Energy, Elsevier, vol. 206(C), pages 1301-1314.

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