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A Comparative Computational Fluid Dynamic Study on the Effects of Terrain Type on Hub-Height Wind Aerodynamic Properties

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  • Akintayo T. Abolude

    (School of Energy and Environment, City University of Hong Kong, Hong Kong 00852, China)

  • Wen Zhou

    (Guy Carpenter Asia–Pacific Climate Impact Center, School of Energy and Environment, City University of Hong Kong, Hong Kong 00852, China)

Abstract

The increased adoption of wind power has generated global discourse in wind energy meteorology. Studies based on turbine performances show a deviation of actual output from power curve output, thereby yielding errors irrespective of the turbine site. Understanding the cause of these errors is essential for wind power optimization, thus necessitating investigation into site-specific effects on turbine performance and operation. Therefore, Computational Fluid Dynamics simulations of hub-height wind aerodynamic properties were conducted based on the k-ε turbulence closure model Reynolds Averaged Navier Stokes equations for three terrains. To isolate terrain-induced effects, the same 40 m above mean sea level wind climatology was imposed on all three terrains. For the four wind directions considered, turbulence intensity (TI) was least in the offshore terrain at about 5–7% but ranged considerably higher from 4–18% for the coastal and island terrain. TI on crests also increased significantly by up to 15% upstream of wind direction for the latter terrains. Inflow angle ranged from −15° to +15° in both coastal and island terrains but remained at <+1° in the offshore terrain. Hellman exponent increased from between factors of 2–4 in the other two terrains relative to that of the offshore terrain. Wind speed-up varied from about 1.06–1.13, accounting for a range of 17–30% difference in power output from a hypothetical operational 2 MW turbine output placed in the three different terrains. Turbine loading, fatigue, efficiency, and life cycle can also be impacted by the variations noted. While adopting a site-specific power curve may help minimize errors and losses, collecting these aerodynamic data alongside wind speed and direction is the future for wind power optimization under big data and machine learning.

Suggested Citation

  • Akintayo T. Abolude & Wen Zhou, 2018. "A Comparative Computational Fluid Dynamic Study on the Effects of Terrain Type on Hub-Height Wind Aerodynamic Properties," Energies, MDPI, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:83-:d:193669
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    References listed on IDEAS

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    1. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Wu, Yuan-Kang, 2016. "Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1048-1059.
    2. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2014. "Study on offshore wind power potential and wind farm optimization in Hong Kong," Applied Energy, Elsevier, vol. 130(C), pages 519-531.
    3. Farrugia, R.N., 2003. "The wind shear exponent in a Mediterranean island climate," Renewable Energy, Elsevier, vol. 28(4), pages 647-653.
    4. Dhunny, A.Z. & Lollchund, M.R. & Rughooputh, S.D.D.V., 2017. "Wind energy evaluation for a highly complex terrain using Computational Fluid Dynamics (CFD)," Renewable Energy, Elsevier, vol. 101(C), pages 1-9.
    5. A.Z. Dhunny & M.R. Lollchund & S.D.D.V. Rughooputh, 2016. "Numerical analysis of wind flow patterns over complex hilly terrains: comparison between two commonly used CFD software," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 39(3/4), pages 181-203.
    6. 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.
    7. Takanori Uchida, 2018. "LES Investigation of Terrain-Induced Turbulence in Complex Terrain and Economic Effects of Wind Turbine Control," Energies, MDPI, vol. 11(6), pages 1-15, June.
    8. Bilal, Muhammad & Birkelund, Yngve & Homola, Matthew & Virk, Muhammad Shakeel, 2016. "Wind over complex terrain – Microscale modelling with two types of mesoscale winds at Nygårdsfjell," Renewable Energy, Elsevier, vol. 99(C), pages 647-653.
    9. Zanon, Alessandro & De Gennaro, Michele & Kühnelt, Helmut, 2018. "Wind energy harnessing of the NREL 5 MW reference wind turbine in icing conditions under different operational strategies," Renewable Energy, Elsevier, vol. 115(C), pages 760-772.
    10. Whale, J. & McHenry, M.P. & Malla, A., 2013. "Scheduling and conducting power performance testing of a small wind turbine," Renewable Energy, Elsevier, vol. 55(C), pages 55-61.
    11. Jafarian, M. & Ranjbar, A.M., 2010. "Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine," Renewable Energy, Elsevier, vol. 35(9), pages 2008-2014.
    12. Akintayo Temiloluwa Abolude & Wen Zhou, 2018. "Assessment and Performance Evaluation of a Wind Turbine Power Output," Energies, MDPI, vol. 11(8), pages 1-15, August.
    13. Lubitz, William David, 2014. "Impact of ambient turbulence on performance of a small wind turbine," Renewable Energy, Elsevier, vol. 61(C), pages 69-73.
    14. Cetinay, Hale & Kuipers, Fernando A. & Guven, A. Nezih, 2017. "Optimal siting and sizing of wind farms," Renewable Energy, Elsevier, vol. 101(C), pages 51-58.
    15. Jan Konopka & António Lopes & Andreas Matzarakis, 2018. "An Original Approach Combining CFD, Linearized Models, and Deformation of Trees for Urban Wind Power Assessment," Sustainability, MDPI, vol. 10(6), pages 1-24, June.
    16. Shu, Z.R. & Li, Q.S. & He, Y.C. & Chan, P.W., 2016. "Observations of offshore wind characteristics by Doppler-LiDAR for wind energy applications," Applied Energy, Elsevier, vol. 169(C), pages 150-163.
    17. Yan, Shu & Shi, Shaoping & Chen, Xinming & Wang, Xiaodong & Mao, Linzhi & Liu, Xiaojie, 2018. "Numerical simulations of flow interactions between steep hill terrain and large scale wind turbine," Energy, Elsevier, vol. 151(C), pages 740-747.
    18. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
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