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Stochastic modelling of wind speeds based on turbulence intensity

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  • Arenas-López, J. Pablo
  • Badaoui, Mohamed

Abstract

In this article, we propose a model of wind speed on a scale of seconds based on a parametrization of the Ornstein-Uhlenbeck (OU) process, that allows the characterization of wind turbulence. The determination of the accurate parametrization of the OU process that best fits the standard deviation of the wind speed follows the physical principle of wind, which states a relationship of proportionality between turbulence intensity and mean wind speed. The approach addressed in this paper consists in defining the coefficients in the OU process capable to capture the real data properties. To validate the proposed model, a turbulence intensity analysis was performed on a set of wind speed data in seconds provided by NCAR/EOL. The parameters of mean wind speed and turbulence intensity are used to configure each resulting model. The best parametrization of the OU process is chosen by comparing the set of wind speeds in seconds and their characteristics, and is determined by a turbulence intensity analysis. Finally, the proposed methodology is applied to describe a set of mean wind speeds measured every 10 min from a specific location in Mexico.

Suggested Citation

  • Arenas-López, J. Pablo & Badaoui, Mohamed, 2020. "Stochastic modelling of wind speeds based on turbulence intensity," Renewable Energy, Elsevier, vol. 155(C), pages 10-22.
    • Handle: RePEc:eee:renene:v:155:y:2020:i:c:p:10-22
    DOI: 10.1016/j.renene.2020.03.104
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    1. Zárate-Miñano, Rafael & Anghel, Marian & Milano, Federico, 2013. "Continuous wind speed models based on stochastic differential equations," Applied Energy, Elsevier, vol. 104(C), pages 42-49.
    2. 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.
    3. Casella, Livio, 2015. "Performance analysis of the first method for long-term turbulence intensity estimation at potential wind energy sites," Renewable Energy, Elsevier, vol. 74(C), pages 106-115.
    4. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    5. Yao, Jinran & Gan, Siqing, 2018. "Stability of the drift-implicit and double-implicit Milstein schemes for nonlinear SDEs," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 294-301.
    6. Ren, Guorui & Liu, Jinfu & Wan, Jie & Li, Fei & Guo, Yufeng & Yu, Daren, 2018. "The analysis of turbulence intensity based on wind speed data in onshore wind farms," Renewable Energy, Elsevier, vol. 123(C), pages 756-766.
    7. Loukatou, Angeliki & Howell, Sydney & Johnson, Paul & Duck, Peter, 2018. "Stochastic wind speed modelling for estimation of expected wind power output," Applied Energy, Elsevier, vol. 228(C), pages 1328-1340.
    8. Calif, Rudy, 2012. "PDF models and synthetic model for the wind speed fluctuations based on the resolution of Langevin equation," Applied Energy, Elsevier, vol. 99(C), pages 173-182.
    9. 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.
    10. Morales, J.M. & Mínguez, R. & Conejo, A.J., 2010. "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, Elsevier, vol. 87(3), pages 843-855, March.
    11. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    12. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    13. Iversen, Emil B. & Morales, Juan M. & Møller, Jan K. & Madsen, Henrik, 2016. "Short-term probabilistic forecasting of wind speed using stochastic differential equations," International Journal of Forecasting, Elsevier, vol. 32(3), pages 981-990.
    14. Wang, Jianzhou & Niu, Tong & Lu, Haiyan & Guo, Zhenhai & Yang, Wendong & Du, Pei, 2018. "An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms," Applied Energy, Elsevier, vol. 211(C), pages 492-512.
    15. Zárate-Miñano, Rafael & Milano, Federico, 2016. "Construction of SDE-based wind speed models with exponentially decaying autocorrelation," Renewable Energy, Elsevier, vol. 94(C), pages 186-196.
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