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

Akintayo T. Abolude and Wen Zhou
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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

Energies, 2018, vol. 12, issue 1, 1-14

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.

Keywords: site-specific effects; turbulence intensity; wind speed-up; hub-height wind shear; power output fluctuations (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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