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Climatology of wind variability for the Shagaya region in Kuwait

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  • Naegele, S.M.
  • McCandless, T.C.
  • Greybush, S.J.
  • Young, G.S.
  • Haupt, S.E.
  • Al-Rasheedi, M.

Abstract

Electrical system operators utilizing wind energy production need accurate wind power forecasts to prepare for changes in power production. To understand the forecast problem and sources of forecast uncertainty, a climatology of the region of interest is needed. For Shagaya Renewable Energy Park in Kuwait, seasonal and diurnal wind patterns and the atmospheric phenomena that cause them are identified using observations from meteorological towers, surface weather stations, and wind turbines. A setup conducive to shamals increases hub-height wind speed by up to 3 m s-1 from May to August and thereby increases power production of the Shagaya wind turbines by 24%, in a season with higher energy demand for cooling homes and businesses. Near sunset, wind speed ramps up and remains faster throughout the night due to the prevalent nocturnal low-level jet. Wind speed ramps back down after sunrise when the nocturnal boundary layer is eroded by convective turbulence, which leads to more short-term fluctuations in wind speed and wind power during the day. Given knowledge on these seasonal and diurnal cycles of short-term and long-term wind power variability, wind power has clear potential to meet a significant portion of Kuwait's energy needs.

Suggested Citation

  • Naegele, S.M. & McCandless, T.C. & Greybush, S.J. & Young, G.S. & Haupt, S.E. & Al-Rasheedi, M., 2020. "Climatology of wind variability for the Shagaya region in Kuwait," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:rensus:v:133:y:2020:i:c:s1364032120303804
    DOI: 10.1016/j.rser.2020.110089
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    References listed on IDEAS

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    1. Mari R. Tye & Sue Ellen Haupt & Eric Gilleland & Christina Kalb & Tara Jensen, 2019. "Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait," Energies, MDPI, vol. 12(23), pages 1-17, November.
    2. Zhang, Jie & Cui, Mingjian & Hodge, Bri-Mathias & Florita, Anthony & Freedman, Jeffrey, 2017. "Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales," Energy, Elsevier, vol. 122(C), pages 528-541.
    3. Cui, Mingjian & Zhang, Jie & Feng, Cong & Florita, Anthony R. & Sun, Yuanzhang & Hodge, Bri-Mathias, 2017. "Characterizing and analyzing ramping events in wind power, solar power, load, and netload," Renewable Energy, Elsevier, vol. 111(C), pages 227-244.
    4. Wood, Michael & Alsayegh, Osamah A., 2014. "Impact of oil prices, economic diversification policies and energy conservation programs on the electricity and water demands in Kuwait," Energy Policy, Elsevier, vol. 66(C), pages 144-156.
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    Cited by:

    1. Al-Khayat, Mohammad & AL-Rasheedi, Majed, 2024. "A new method for estimating the annual energy production of wind turbines in hot environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
    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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