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Wind characteristics and wind energy potential in western Nevada

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  • Belu, Radian
  • Koracin, Darko

Abstract

The wind potential in western Nevada was assessed by using wind, temperature, and pressure data over a period of four and half years from four 50m tall towers. The seasonal wind patterns for all towers show a maximum during the spring season. Diurnal wind speed patterns for all seasons and months showed a minimum during the late morning and a maximum during the late afternoon. The highest values are during the spring season with multi-annual hourly wind speeds at or above 8m/s and relative frequency of the wind speed in the optimum turbine range (5–25m/s) of 70% or higher for the Tonopah tower, with lower values for the other three towers. The monthly power law index values are lower than the standard value 0.147 (in general 0.13 or lower). The hourly turbulence intensities were higher at lower elevations, with values of about 0.35 or higher at the 10m level and at lower wind speed range (5.0m/s or less). Higher turbulence intensities were found for all towers and heights during the spring and summer seasons and lower values during the rest of the year. The daily gust factor for the 2003–2007 composite data sets shows low probabilities (2% or less) of the wind gusts exceeding 25m/s.

Suggested Citation

  • Belu, Radian & Koracin, Darko, 2009. "Wind characteristics and wind energy potential in western Nevada," Renewable Energy, Elsevier, vol. 34(10), pages 2246-2251.
  • Handle: RePEc:eee:renene:v:34:y:2009:i:10:p:2246-2251
    DOI: 10.1016/j.renene.2009.02.024
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    Cited by:

    1. Irwanto, M. & Gomesh, N. & Mamat, M.R. & Yusoff, Y.M., 2014. "Assessment of wind power generation potential in Perlis, Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 296-308.
    2. Wu, Zhong-Qiang & Jia, Wen-Jing & Zhao, Li-Ru & Wu, Chang-Han, 2016. "Maximum wind power tracking based on cloud RBF neural network," Renewable Energy, Elsevier, vol. 86(C), pages 466-472.
    3. Ciulla, G. & D’Amico, A. & Di Dio, V. & Lo Brano, V., 2019. "Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks," Renewable Energy, Elsevier, vol. 140(C), pages 477-492.
    4. Gualtieri, Giovanni, 2018. "Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height: method's test at a mountain site," Renewable Energy, Elsevier, vol. 120(C), pages 457-467.
    5. Diaf, S. & Notton, G., 2013. "Technical and economic analysis of large-scale wind energy conversion systems in Algeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 37-51.
    6. Bilal, Boudy & Adjallah, Kondo Hloindo & Yetilmezsoy, Kaan & Bahramian, Majid & Kıyan, Emel, 2021. "Determination of wind potential characteristics and techno-economic feasibility analysis of wind turbines for Northwest Africa," Energy, Elsevier, vol. 218(C).
    7. Miao, Shuwei & Yang, Hejun & Gu, Yingzhong, 2018. "A wind vector simulation model and its application to adequacy assessment," Energy, Elsevier, vol. 148(C), pages 324-340.
    8. Abul Kalam Azad & Mohammad Golam Rasul & Talal Yusaf, 2014. "Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications," Energies, MDPI, vol. 7(5), pages 1-30, May.
    9. Ucar, Aynur & Balo, Figen, 2010. "Assessment of wind power potential for turbine installation in coastal areas of Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1901-1912, September.
    10. de la Rosa, Juan José González & Pérez, Agustín Agüera & Palomares Salas, José Carlos & Ramiro Leo, José Gabriel & Muñoz, Antonio Moreno, 2011. "A novel inference method for local wind conditions using genetic fuzzy systems," Renewable Energy, Elsevier, vol. 36(6), pages 1747-1753.

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