Abstract
Wind farm power production is known to be strongly affected by turbine wake effects. The purpose of this study is to develop and test a new analytical model for the prediction of wind turbine wakes and the associated power losses in wind farms. The new model is an extension of the one recently proposed by Bastankhah and Porté-Agel for the wake of stand-alone wind turbines. It satisfies the conservation of mass and momentum and assumes a self-similar Gaussian shape of the velocity deficit. The local wake growth rate is estimated based on the local streamwise turbulence intensity. Superposition of velocity deficits is used to model the interaction of the multiple wakes. Furthermore, the power production from the wind turbines is calculated using the power curve. The performance of the new analytical wind farm model is validated against power measurements and large-eddy simulation (LES) data from the Horns Rev wind farm for a wide range of wind directions, corresponding to a variety of full-wake and partial-wake conditions. A reasonable agreement is found between the proposed analytical model, LES data, and power measurements. Compared with a commonly used wind farm wake model, the new model shows a significant improvement in the prediction of wind farm power.
Highlights
Renewable energies play an increasingly important role in the global energy market as sources of sustainable and clean energy
Since the constant wake growth rate assumption is likely unrealistic, we propose an empirical equation for the local wake growth rate that is based on the local streamwise turbulence intensity to consider the turbulence effect in wind farms
For the local streamwise turbulence intensity, we propose to use a top-hat distribution with a the local streamwise turbulence intensity, we propose to large-eddy simulation (LES)
Summary
Renewable energies play an increasingly important role in the global energy market as sources of sustainable and clean energy. Wind energy is witnessing continuous growth at an average annual rate of approximately 25% and currently contributes to more than 2.6% of electricity generation worldwide This contribution is expected to increase to 18% of the world’s electricity generation by 2050 [1]. Recent advances in turbulence-resolving computational fluid dynamics methods, such as large-eddy simulation (LES) and cutting-edge experimental techniques, have allowed detailed characterization of wind turbine wake flows. Both experimental and numerical approaches have the potential of providing accurate results, the simplicity and low computational cost associated with analytical models make them appealing for wind farm optimization purposes [3,4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
