IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v133y2020ics1364032120303804.html
   My bibliography  Save this article

Climatology of wind variability for the Shagaya region in Kuwait

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032120303804
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2020.110089?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lee, Joseph C.Y. & Draxl, Caroline & Berg, Larry K., 2022. "Evaluating wind speed and power forecasts for wind energy applications using an open-source and systematic validation framework," Renewable Energy, Elsevier, vol. 200(C), pages 457-475.
    2. Gomez, William & Wang, Fu-Kwun & Lo, Shih-Che, 2024. "A hybrid approach based machine learning models in electricity markets," Energy, Elsevier, vol. 289(C).
    3. EunJi Ahn & Jin Hur, 2022. "A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems," Energies, MDPI, vol. 15(7), pages 1-16, April.
    4. Mohammad Al-Zuhair & Talal AL-Bazali, 2022. "Causality Between Energy Consumption and Economic Growth: The Case of Kuwait," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 22-29, November.
    5. Osama Alfalah, 2021. "Estimating Residential Demand for Water in Kuwait: A Cointegration Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 283-287.
    6. Mejia, Mario A. & Melo, Joel D. & Zambrano-Asanza, Sergio & Padilha-Feltrin, Antonio, 2020. "Spatial-temporal growth model to estimate the adoption of new end-use electric technologies encouraged by energy-efficiency programs," Energy, Elsevier, vol. 191(C).
    7. Cui, Mingjian & Zhang, Jie, 2018. "Estimating ramping requirements with solar-friendly flexible ramping product in multi-timescale power system operations," Applied Energy, Elsevier, vol. 225(C), pages 27-41.
    8. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren & Söder, Lennart, 2018. "Analysis of wind power intermittency based on historical wind power data," Energy, Elsevier, vol. 150(C), pages 482-492.
    9. Yun, Eunjeong & Hur, Jin, 2021. "Probabilistic estimation model of power curve to enhance power output forecasting of wind generating resources," Energy, Elsevier, vol. 223(C).
    10. Ayele Gelan & Geoffrey J. D. Hewings & Ahmad Alawadhi, 2021. "Diversifying a resource-dependent economy: private–public relationships in the Kuwaiti economy," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 10(1), pages 1-22, December.
    11. Edalati, Saeed & Ameri, Mehran & Iranmanesh, Masoud & Sadeghi, Zeinolabedin, 2017. "Solar photovoltaic power plants in five top oil-producing countries in Middle East: A case study in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1271-1280.
    12. Marta Poncela & Arturs Purvins & Stamatios Chondrogiannis, 2018. "Pan-European Analysis on Power System Flexibility," Energies, MDPI, vol. 11(7), pages 1-19, July.
    13. Alshawaf, Mohammad & Poudineh, Rahmatallah & Alhajeri, Nawaf S., 2020. "Solar PV in Kuwait: The effect of ambient temperature and sandstorms on output variability and uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    14. Shang, Chuanfu & Wei, Pengcheng, 2018. "Enhanced support vector regression based forecast engine to predict solar power output," Renewable Energy, Elsevier, vol. 127(C), pages 269-283.
    15. Sewdien, V.N. & Preece, R. & Torres, J.L. Rueda & Rakhshani, E. & van der Meijden, M., 2020. "Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting," Renewable Energy, Elsevier, vol. 161(C), pages 878-892.
    16. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    17. Xin Fang & Venkat Krishnan & Bri-Mathias Hodge, 2018. "Strategic Offering for Wind Power Producers Considering Energy and Flexible Ramping Products," Energies, MDPI, vol. 11(5), pages 1-19, May.
    18. Alasseri, Rajeev & Rao, T. Joji & Sreekanth, K.J., 2020. "Institution of incentive-based demand response programs and prospective policy assessments for a subsidized electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    19. Jie Wan & Yanjia Wang & Guorui Ren & Jinfu Liu & Wei Wang & Jilai Yu, 2020. "An Integrated Evaluation Method of the Wind Power Ramp Event Based on Generalized Information of the Source, Grid, and Load," Energies, MDPI, vol. 13(24), pages 1-19, December.
    20. Hugo Algarvio & António Couto & Fernando Lopes & Ana Estanqueiro, 2019. "Changing the Day-Ahead Gate Closure to Wind Power Integration: A Simulation-Based Study," Energies, MDPI, vol. 12(14), pages 1-20, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:133:y:2020:i:c:s1364032120303804. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.