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Large-eddy simulation study of wind turbine array above swell sea

Author

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  • Yang, Haoze
  • Ge, Mingwei
  • Abkar, Mahdi
  • Yang, Xiang I.A.

Abstract

Swells are common in sea areas, but how swells affect the operation of an offshore wind farm is poorly understood. To fill in this knowledge gap, large-eddy simulations of turbine arrays above swells are performed. Specifically, downwind, upwind, and lateral swells with three different wave ages are considered. The results show that downwind and upwind swells respectively lead to increased and decreased wind speeds with weakened and enhanced turbulence. The wakes above high-wave-age downwind swells go through three distinct phases, featuring slow, fast, and moderate wake recovery rates and low, high, and moderate turbulence levels. Compared to the no swell case, downwind swells lead to higher power outputs far downstream, whereas upwind swells lead to lower or slightly higher power outputs. Lateral swells give rise to spanwise motions at sea level, resulting in a wind profile that changes direction as a function of the height. The nonzero spanwise velocity deflects the wakes and reduces sheltering among the wind turbines. The net effect is increased turbine power outputs. In addition, a peak emerges at the swells’ intrinsic frequency in the power output spectra of the first turbines above high-wave-age upwind and downwind swells and all the turbines above lateral swells.

Suggested Citation

  • Yang, Haoze & Ge, Mingwei & Abkar, Mahdi & Yang, Xiang I.A., 2022. "Large-eddy simulation study of wind turbine array above swell sea," Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s0360544222015778
    DOI: 10.1016/j.energy.2022.124674
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    References listed on IDEAS

    as
    1. Li, Qing'an & Murata, Junsuke & Endo, Masayuki & Maeda, Takao & Kamada, Yasunari, 2016. "Experimental and numerical investigation of the effect of turbulent inflow on a Horizontal Axis Wind Turbine (Part I: Power performance)," Energy, Elsevier, vol. 113(C), pages 713-722.
    2. Meysonnat, Pascal S. & Koh, Seong Ryong & Roidl, Benedikt & Schröder, Wolfgang, 2016. "Impact of transversal traveling surface waves in a non-zero pressure gradient turbulent boundary layer flow," Applied Mathematics and Computation, Elsevier, vol. 272(P2), pages 498-507.
    3. Wu, Yu-Ting & Porté-Agel, Fernando, 2015. "Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm," Renewable Energy, Elsevier, vol. 75(C), pages 945-955.
    4. Castro-Santos, Laura & Martins, Elson & Guedes Soares, C., 2017. "Economic comparison of technological alternatives to harness offshore wind and wave energies," Energy, Elsevier, vol. 140(P1), pages 1121-1130.
    5. Yang, Haoze & Ge, Mingwei & Gu, Bo & Du, Bowen & Liu, Yongqian, 2022. "The effect of swell on marine atmospheric boundary layer and the operation of an offshore wind turbine," Energy, Elsevier, vol. 244(PB).
    6. Li, Qing'an & Murata, Junsuke & Endo, Masayuki & Maeda, Takao & Kamada, Yasunari, 2016. "Experimental and numerical investigation of the effect of turbulent inflow on a Horizontal Axis Wind Turbine (part II: Wake characteristics)," Energy, Elsevier, vol. 113(C), pages 1304-1315.
    7. Yu-Ting Wu & Fernando Porté-Agel, 2012. "Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study," Energies, MDPI, vol. 5(12), pages 1-23, December.
    8. Fleming, Paul A. & Gebraad, Pieter M.O. & Lee, Sang & van Wingerden, Jan-Willem & Johnson, Kathryn & Churchfield, Matt & Michalakes, John & Spalart, Philippe & Moriarty, Patrick, 2014. "Evaluating techniques for redirecting turbine wakes using SOWFA," Renewable Energy, Elsevier, vol. 70(C), pages 211-218.
    9. Zhang, Shuaibin & Du, Bowen & Ge, Mingwei & Zuo, Yingtao, 2022. "Study on the operation of small rooftop wind turbines and its effect on the wind environment in blocks," Renewable Energy, Elsevier, vol. 183(C), pages 708-718.
    10. Yang, Di & Meneveau, Charles & Shen, Lian, 2014. "Effect of downwind swells on offshore wind energy harvesting – A large-eddy simulation study," Renewable Energy, Elsevier, vol. 70(C), pages 11-23.
    11. 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.
    12. Esteban, M. Dolores & Diez, J. Javier & López, Jose S. & Negro, Vicente, 2011. "Why offshore wind energy?," Renewable Energy, Elsevier, vol. 36(2), pages 444-450.
    13. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
    14. Sun, Xiaojing & Huang, Diangui & Wu, Guoqing, 2012. "The current state of offshore wind energy technology development," Energy, Elsevier, vol. 41(1), pages 298-312.
    15. Zheng, Chong-wei & Wu, Di & Wu, Hai-lang & Guo, Jing & Shen, Chong & Tian, Chuan & Tian, Xin-long & Xiao, Zi-niu & Zhou, Wen & Li, Chong-yin, 2022. "Propagation and attenuation of swell energy in the Pacific Ocean," Renewable Energy, Elsevier, vol. 188(C), pages 750-764.
    16. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    17. Zheng, C.W. & Li, C.Y., 2017. "Propagation characteristic and intraseasonal oscillation of the swell energy of the Indian Ocean," Applied Energy, Elsevier, vol. 197(C), pages 342-353.
    18. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Li, Qi, 2019. "A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes," Applied Energy, Elsevier, vol. 233, pages 975-984.
    19. Abkar, Mahdi & Porté-Agel, Fernando, 2014. "Mean and turbulent kinetic energy budgets inside and above very large wind farms under conventionally-neutral condition," Renewable Energy, Elsevier, vol. 70(C), pages 142-152.
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