IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v87y2016ip1p376-390.html
   My bibliography  Save this article

Atmospheric stability varying wind shear coefficients to improve wind resource extrapolation: A temporal analysis

Author

Listed:
  • Gualtieri, Giovanni

Abstract

Based on a 3-year (2011–2013) dataset of 10-min records collected at 10, 20, 40, and 80 m from the met mast of Cabauw, a time-varying investigation of the wind shear coefficient (WSC) relationship with atmospheric stability was addressed. WSC interdaily and interannual variability was analysed according to a 2-D combined representation, which confirmed a clear oval-shaped “solar shadow” caused by solar warming observed during diurnal unstable hours, and large WSCs occurring under strong stable conditions during the summer nights.

Suggested Citation

  • Gualtieri, Giovanni, 2016. "Atmospheric stability varying wind shear coefficients to improve wind resource extrapolation: A temporal analysis," Renewable Energy, Elsevier, vol. 87(P1), pages 376-390.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:376-390
    DOI: 10.1016/j.renene.2015.10.034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148115303876
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2015.10.034?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. Đurišić, Željko & Mikulović, Jovan, 2012. "A model for vertical wind speed data extrapolation for improving wind resource assessment using WAsP," Renewable Energy, Elsevier, vol. 41(C), pages 407-411.
    2. Fox, Neil I., 2011. "A tall tower study of Missouri winds," Renewable Energy, Elsevier, vol. 36(1), pages 330-337.
    3. Kubik, M.L. & Coker, P.J. & Barlow, J.F. & Hunt, C., 2013. "A study into the accuracy of using meteorological wind data to estimate turbine generation output," Renewable Energy, Elsevier, vol. 51(C), pages 153-158.
    4. Gualtieri, Giovanni, 2015. "Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height," Renewable Energy, Elsevier, vol. 78(C), pages 68-81.
    5. Pneumatikos, John D., 1991. "An experimental test of the empirical formulae commonly used to represent wind speed profiles near the ground," Renewable Energy, Elsevier, vol. 1(5), pages 623-628.
    6. Gualtieri, Giovanni & Secci, Sauro, 2014. "Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy," Renewable Energy, Elsevier, vol. 62(C), pages 164-176.
    7. Gualtieri, Giovanni & Secci, Sauro, 2012. "Methods to extrapolate wind resource to the turbine hub height based on power law: A 1-h wind speed vs. Weibull distribution extrapolation comparison," Renewable Energy, Elsevier, vol. 43(C), pages 183-200.
    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. Pérez Albornoz, C. & Escalante Soberanis, M.A. & Ramírez Rivera, V. & Rivero, M., 2022. "Review of atmospheric stability estimations for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    2. Nedaei, Mojtaba & Assareh, Ehsanolah & Walsh, Philip R., 2018. "A comprehensive evaluation of the wind resource characteristics to investigate the short term penetration of regional wind power based on different probability statistical methods," Renewable Energy, Elsevier, vol. 128(PA), pages 362-374.
    3. Liu, Yongqian & Qiao, Yanhui & Han, Shuang & Tao, Tao & Yan, Jie & Li, Li & Bekhbat, Galsan & Munkhtuya, Erdenebat, 2021. "Rotor equivalent wind speed calculation method based on equivalent power considering wind shear and tower shadow," Renewable Energy, Elsevier, vol. 172(C), pages 882-896.
    4. Geon Hwa Ryu & Young-Gon Kim & Sung Jo Kwak & Man Soo Choi & Moon-Seon Jeong & Chae-Joo Moon, 2022. "Atmospheric Stability Effects on Offshore and Coastal Wind Resource Characteristics in South Korea for Developing Offshore Wind Farms," Energies, MDPI, vol. 15(4), pages 1-23, February.
    5. Christy Pérez & Michel Rivero & Mauricio Escalante & Victor Ramirez & Damien Guilbert, 2023. "Influence of Atmospheric Stability on Wind Turbine Energy Production: A Case Study of the Coastal Region of Yucatan," Energies, MDPI, vol. 16(10), pages 1-20, May.
    6. 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.
    7. Bahamonde, Manuel Ignacio & Litrán, Salvador P., 2019. "Study of the energy production of a wind turbine in the open sea considering the continuous variations of the atmospheric stability and the sea surface roughness," Renewable Energy, Elsevier, vol. 135(C), pages 163-175.
    8. Li, Jiale & Wang, Xuefei & Yu, Xiong (Bill), 2018. "Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment," Applied Energy, Elsevier, vol. 213(C), pages 469-485.
    9. Crippa, Paola & Alifa, Mariana & Bolster, Diogo & Genton, Marc G. & Castruccio, Stefano, 2021. "A temporal model for vertical extrapolation of wind speed and wind energy assessment," Applied Energy, Elsevier, vol. 301(C).
    10. 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).
    11. Gualtieri, Giovanni, 2019. "A comprehensive review on wind resource extrapolation models applied in wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 215-233.

    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. Gualtieri, Giovanni, 2019. "A comprehensive review on wind resource extrapolation models applied in wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 215-233.
    2. Li, Jiale & Wang, Xuefei & Yu, Xiong (Bill), 2018. "Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment," Applied Energy, Elsevier, vol. 213(C), pages 469-485.
    3. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    4. Crippa, Paola & Alifa, Mariana & Bolster, Diogo & Genton, Marc G. & Castruccio, Stefano, 2021. "A temporal model for vertical extrapolation of wind speed and wind energy assessment," Applied Energy, Elsevier, vol. 301(C).
    5. Kapica, Jacek & Pawlak, Halina & Ścibisz, Marek, 2015. "Carbon dioxide emission reduction by heating poultry houses from renewable energy sources in Central Europe," Agricultural Systems, Elsevier, vol. 139(C), pages 238-249.
    6. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    7. Gualtieri, Giovanni & Secci, Sauro, 2014. "Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy," Renewable Energy, Elsevier, vol. 62(C), pages 164-176.
    8. 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).
    9. Gualtieri, Giovanni, 2015. "Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height," Renewable Energy, Elsevier, vol. 78(C), pages 68-81.
    10. Valsaraj, P. & Thumba, Drisya Alex & Asokan, K. & Kumar, K. Satheesh, 2020. "Symbolic regression-based improved method for wind speed extrapolation from lower to higher altitudes for wind energy applications," Applied Energy, Elsevier, vol. 260(C).
    11. Christopher Jung, 2016. "High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series," Energies, MDPI, vol. 9(5), pages 1-20, May.
    12. 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.
    13. Ayman Al-Quraan & Bashar Al-Mhairat, 2022. "Intelligent Optimized Wind Turbine Cost Analysis for Different Wind Sites in Jordan," Sustainability, MDPI, vol. 14(5), pages 1-24, March.
    14. Bahamonde, Manuel Ignacio & Litrán, Salvador P., 2019. "Study of the energy production of a wind turbine in the open sea considering the continuous variations of the atmospheric stability and the sea surface roughness," Renewable Energy, Elsevier, vol. 135(C), pages 163-175.
    15. Chandel, S.S. & Ramasamy, P. & Murthy, K.S.R, 2014. "Wind power potential assessment of 12 locations in western Himalayan region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 530-545.
    16. Castro-Santos, Laura & Martins, Elson & Guedes Soares, C., 2016. "Cost assessment methodology for combined wind and wave floating offshore renewable energy systems," Renewable Energy, Elsevier, vol. 97(C), pages 866-880.
    17. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    18. J. Wang, 2016. "Reviews of seismicity around Taiwan: Weibull distribution," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1651-1668, February.
    19. Katinas, Vladislovas & Gecevicius, Giedrius & Marciukaitis, Mantas, 2018. "An investigation of wind power density distribution at location with low and high wind speeds using statistical model," Applied Energy, Elsevier, vol. 218(C), pages 442-451.
    20. Dongbum Kang & Kyungnam Ko & Jongchul Huh, 2018. "Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea," Energies, MDPI, vol. 11(2), pages 1-19, February.

    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:renene:v:87:y:2016:i:p1:p:376-390. 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.journals.elsevier.com/renewable-energy .

    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.