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Comparing methods to calculate atmospheric stability-dependent wind speed profiles: A case study on coastal location

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  • Gualtieri, Giovanni
  • Secci, Sauro

Abstract

Among all uncertainty factors affecting the wind power assessment at a site, wind speed extrapolation is probably one of most critical ones, particularly if considering the increasing size of modern multi-MW wind turbines, and therefore of their hub height. This work is intended as a contribution towards a possible harmonisation of methods and techniques, necessarily including surface roughness and atmospheric stability, aimed at extrapolating wind speed for wind energy purposes. Through the years, different methods have been used to this end, such as power law (PL), logarithmic law (LogL), and log-linear law (LogLL). Furthermore, aside from applying PL by using a mean wind shear coefficient observed between two heights (α¯), a number of methods have been developed to estimate PL exponent α when only surface data are available, such as those by Spera and Richards (SR), Smedman-Högström and Högström (SH) and Panofsky and Dutton (PD).

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  • Gualtieri, Giovanni & Secci, Sauro, 2011. "Comparing methods to calculate atmospheric stability-dependent wind speed profiles: A case study on coastal location," Renewable Energy, Elsevier, vol. 36(8), pages 2189-2204.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:8:p:2189-2204
    DOI: 10.1016/j.renene.2011.01.023
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    Cited by:

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    2. Gualtieri, Giovanni & Secci, Sauro, 2012. "Methods to extrapolate wind resource to the turbine hub height based on power law: A 1-h wind speed vs. Weibull distribution extrapolation comparison," Renewable Energy, Elsevier, vol. 43(C), pages 183-200.
    3. Pérez Albornoz, C. & Escalante Soberanis, M.A. & Ramírez Rivera, V. & Rivero, M., 2022. "Review of atmospheric stability estimations for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
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    5. Valsaraj, P. & Thumba, Drisya Alex & Asokan, K. & Kumar, K. Satheesh, 2020. "Symbolic regression-based improved method for wind speed extrapolation from lower to higher altitudes for wind energy applications," Applied Energy, Elsevier, vol. 260(C).
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    9. Đurišić, Željko & Mikulović, Jovan, 2012. "A model for vertical wind speed data extrapolation for improving wind resource assessment using WAsP," Renewable Energy, Elsevier, vol. 41(C), pages 407-411.
    10. Gualtieri, Giovanni & Secci, Sauro, 2014. "Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy," Renewable Energy, Elsevier, vol. 62(C), pages 164-176.
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    14. Li, Jiale & Wang, Xuefei & Yu, Xiong (Bill), 2018. "Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment," Applied Energy, Elsevier, vol. 213(C), pages 469-485.
    15. Oner, Yasemin & Ozcira, Selin & Bekiroglu, Nur & Senol, Ibrahim, 2013. "A comparative analysis of wind power density prediction methods for Çanakkale, Intepe region, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 491-502.
    16. Gualtieri, Giovanni, 2015. "Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height," Renewable Energy, Elsevier, vol. 78(C), pages 68-81.
    17. Alina Wilke & Paul J.J. Welfens, 2020. "Urban Wind Energy Production Potential: New Opportunities," EIIW Discussion paper disbei287, Universitätsbibliothek Wuppertal, University Library.
    18. Frank, Christopher W. & Pospichal, Bernhard & Wahl, Sabrina & Keller, Jan D. & Hense, Andreas & Crewell, Susanne, 2020. "The added value of high resolution regional reanalyses for wind power applications," Renewable Energy, Elsevier, vol. 148(C), pages 1094-1109.
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    20. Costoya, X. & Rocha, A. & Carvalho, D., 2020. "Using bias-correction to improve future projections of offshore wind energy resource: A case study on the Iberian Peninsula," Applied Energy, Elsevier, vol. 262(C).

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