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Improving CFD wind farm simulations incorporating wind direction uncertainty

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  • Antonini, Enrico G.A.
  • Romero, David A.
  • Amon, Cristina H.

Abstract

Accurate quantification of wake losses is crucial in wind farm economics. Computational Fluid Dynamics (CFD) has been proven to be a reliable solution to simulate many complex flows, but several studies showed that its effectiveness in wind farms simulations has not always been consistent. In this work, we investigate the causes for that inconsistency and propose a modeling framework to overcome them. A CFD model was developed using the actuator disk technique to simulate the wind turbines and the surface boundary layer approximation to simulate the ambient conditions. The developed CFD model was implemented for three different wind farms with publicly available experimental measurements. The predictions of CFD model were post-processed with an innovative method that uses a Gaussian-weighted average of a set of CFD results for different wind directions to account for the wind direction uncertainty in the experimental data. Our results show that the proposed method significantly improves the agreement of the CFD predictions with the available experimental observations. These results suggest that the discrepancies between CFD predictions and experimental data reported in previous works, attributed to inaccuracy of the CFD models, can be explained instead by the uncertainty in the wind direction reported in the data sets.

Suggested Citation

  • Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2019. "Improving CFD wind farm simulations incorporating wind direction uncertainty," Renewable Energy, Elsevier, vol. 133(C), pages 1011-1023.
  • Handle: RePEc:eee:renene:v:133:y:2019:i:c:p:1011-1023
    DOI: 10.1016/j.renene.2018.10.084
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    1. Shives, Michael & Crawford, Curran, 2016. "Adapted two-equation turbulence closures for actuator disk RANS simulations of wind & tidal turbine wakes," Renewable Energy, Elsevier, vol. 92(C), pages 273-292.
    2. Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2018. "Continuous adjoint formulation for wind farm layout optimization: A 2D implementation," Applied Energy, Elsevier, vol. 228(C), pages 2333-2345.
    3. Fernando Porté-Agel & Yu-Ting Wu & Chang-Hung Chen, 2013. "A Numerical Study of the Effects of Wind Direction on Turbine Wakes and Power Losses in a Large Wind Farm," Energies, MDPI, vol. 6(10), pages 1-17, October.
    4. Kuo, Jim Y.J. & Romero, David A. & Beck, J. Christopher & Amon, Cristina H., 2016. "Wind farm layout optimization on complex terrains – Integrating a CFD wake model with mixed-integer programming," Applied Energy, Elsevier, vol. 178(C), pages 404-414.
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    Cited by:

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    3. Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2020. "Optimal design of wind farms in complex terrains using computational fluid dynamics and adjoint methods," Applied Energy, Elsevier, vol. 261(C).
    4. Linlin Tian & Yilei Song & Ning Zhao & Wenzhong Shen & Tongguang Wang, 2019. "AD/RANS Simulations of Wind Turbine Wake Flow Employing the RSM Turbulence Model: Impact of Isotropic and Anisotropic Inflow Conditions," Energies, MDPI, vol. 12(21), pages 1-14, October.
    5. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    6. Hornshøj-Møller, Simon D. & Nielsen, Peter D. & Forooghi, Pourya & Abkar, Mahdi, 2021. "Quantifying structural uncertainties in Reynolds-averaged Navier–Stokes simulations of wind turbine wakes," Renewable Energy, Elsevier, vol. 164(C), pages 1550-1558.
    7. Wu, Yan & Zhang, Shuai & Wang, Ruiqi & Wang, Yufei & Feng, Xiao, 2020. "A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner," Renewable Energy, Elsevier, vol. 146(C), pages 687-698.
    8. Antonini, Enrico G.A. & Caldeira, Ken, 2021. "Atmospheric pressure gradients and Coriolis forces provide geophysical limits to power density of large wind farms," Applied Energy, Elsevier, vol. 281(C).
    9. Zheng, Jiancai & Wang, Nina & Wan, Decheng & Strijhak, Sergei, 2023. "Numerical investigations of coupled aeroelastic performance of wind turbines by elastic actuator line model," Applied Energy, Elsevier, vol. 330(PB).
    10. Dhoot, Aditya & Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2021. "Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability," Energy, Elsevier, vol. 223(C).
    11. Eidi, Ali & Ghiassi, Reza & Yang, Xiang & Abkar, Mahdi, 2021. "Model-form uncertainty quantification in RANS simulations of wakes and power losses in wind farms," Renewable Energy, Elsevier, vol. 179(C), pages 2212-2223.
    12. Rizwan Haider & Xin Li & Wei Shi & Zaibin Lin & Qing Xiao & Haisheng Zhao, 2024. "Review of Computational Fluid Dynamics in the Design of Floating Offshore Wind Turbines," Energies, MDPI, vol. 17(17), pages 1-37, August.
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