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Effects of wind power spectrum analysis over resource assessment

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  • Lopez-Villalobos, C.A.
  • Rodriguez-Hernandez, O.
  • Martínez-Alvarado, O.
  • Hernandez-Yepes, J.G.

Abstract

Based on the Van der Hoven’s seminal work, wind power industry has adopted the 10 min mean time as the proper sampling to estimate resource assessment. However, research within the literature questions the generalization of the 10 min as a standard measure of minima dispersion due to the particular geographic characteristics where the measurements took place. In this work is analyzed the power spectrum of a high-frequency wind speed time series and its influence over the resource assessment in the region of La Ventosa, Oaxaca, Mexico. Power spectrum analysis from a monthly, seasonal, and annual time series results show a defined synoptic-scale, diurnal, and semi-diurnal variations, which changes in amplitude throughout the year. To study the influence of power spectrum in wind resource assessment were estimated and compared the capacity factors of a typical 2 MW wind turbine against measured wind speed with 1, 5, 10, 60, and 360 min mean times, we found that a maximum difference of 1.4 %. Resource assessment was also estimated using reanalysis data and WRF results, finding similar to high-resolution estimations, highlighting bias-corrected WRF performance, offering reliable results to model power performance after a statistical correction.

Suggested Citation

  • Lopez-Villalobos, C.A. & Rodriguez-Hernandez, O. & Martínez-Alvarado, O. & Hernandez-Yepes, J.G., 2021. "Effects of wind power spectrum analysis over resource assessment," Renewable Energy, Elsevier, vol. 167(C), pages 761-773.
    • Handle: RePEc:eee:renene:v:167:y:2021:i:c:p:761-773
    DOI: 10.1016/j.renene.2020.11.147
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    1. Olauson, Jon & Bergström, Hans & Bergkvist, Mikael, 2016. "Restoring the missing high-frequency fluctuations in a wind power model based on reanalysis data," Renewable Energy, Elsevier, vol. 96(PA), pages 784-791.
    2. Al-Yahyai, Sultan & Charabi, Yassine & Gastli, Adel, 2010. "Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3192-3198, December.
    3. C. A. Lopez-Villalobos & O. Rodriguez-Hernandez & R. Campos-Amezcua & Guillermo Hernandez-Cruz & O. A. Jaramillo & J. L. Mendoza, 2018. "Wind Turbulence Intensity at La Ventosa, Mexico: A Comparative Study with the IEC61400 Standards," Energies, MDPI, vol. 11(11), pages 1-19, November.
    4. Jaramillo, O.A. & Borja, M.A., 2004. "Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case," Renewable Energy, Elsevier, vol. 29(10), pages 1613-1630.
    5. Olauson, Jon, 2018. "ERA5: The new champion of wind power modelling?," Renewable Energy, Elsevier, vol. 126(C), pages 322-331.
    6. Telesca, Luciano & Lovallo, Michele & Kanevski, Mikhail, 2016. "Power spectrum and multifractal detrended fluctuation analysis of high-frequency wind measurements in mountainous regions," Applied Energy, Elsevier, vol. 162(C), pages 1052-1061.
    7. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    8. Tabrizi, Amir Bashirzadeh & Whale, Jonathan & Lyons, Thomas & Urmee, Tania, 2015. "Rooftop wind monitoring campaigns for small wind turbine applications: Effect of sampling rate and averaging period," Renewable Energy, Elsevier, vol. 77(C), pages 320-330.
    9. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal," Applied Energy, Elsevier, vol. 117(C), pages 116-126.
    10. Cadenas, Erasmo & Rivera, Wilfrido, 2007. "Wind speed forecasting in the South Coast of Oaxaca, México," Renewable Energy, Elsevier, vol. 32(12), pages 2116-2128.
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    1. Ismail Kamdar & Shahid Ali & Juntakan Taweekun & Hafiz Muhammad Ali, 2021. "Wind Farm Site Selection Using WAsP Tool for Application in the Tropical Region," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    2. He, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).

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    Keywords

    Wind resource assessment; Wind power spectrum; WRF; MERRA-2; ERA5;
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