IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v213y2018icp469-485.html
   My bibliography  Save this article

Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment

Author

Listed:
  • Li, Jiale
  • Wang, Xuefei
  • Yu, Xiong (Bill)

Abstract

Wind shear models are commonly used to predict the wind speed at wind turbine hub heights from the wind data collected at the elevation of the monitoring station. In many cases, the model parameters are based on empirical values recommended by design specifications that reflect the site conditions. This paper evaluates the benefits of incorporating site-specific wind data to calibrate the wind shear model parameters. Wind speed data collected by a ZephIR® Light Detection and Ranging (LiDAR) system over a 2-year (Oct. 2010–Sept. 2012) period are used for the analyses. The atmospheric stability is found to have appreciable effects on the wind shear parameters, i.e. wind shear coefficients (WSC) for the power law model and roughness lengths for the logarithmic law wind shear models. The calibrated wind shear model parameters by the monitored wind data during the first year are presented in the format of a contour map to demonstrate the spatio-temporal variations, which shows daily and seasonal variations. The calibrated wind shear models are then validated by the wind data collected during the second year, which demonstrates decent performance. The accuracy and performance of incorporating site-specific wind shear model calibration to predict the wind energy resource is evaluated, where six different methods are compared. The results show that the consideration of spatio-temporal variations of wind shear model parameters achieved improve performance over the application of the empirical or yearly-averaged wind shear model parameters in extrapolating the wind speed. It is also found that the performance of considering spatio-temporal wind shear parameters are even better at higher elevations. Furthermore, the analyses find that the use of empirical wind shear model parameters underestimates the wind energy output at the studied sites. Site-specific calibration of the wind shear models could further improve the accuracy of wind energy assessment by considering the site condition and the variability in the atmospheric stability.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:213:y:2018:i:c:p:469-485
    DOI: 10.1016/j.apenergy.2018.01.063
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918300606
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2018.01.063?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. 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.
    3. Islam, M.R. & Saidur, R. & Rahim, N.A., 2011. "Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function," Energy, Elsevier, vol. 36(2), pages 985-992.
    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. Gualtieri, Giovanni & Secci, Sauro, 2011. "Wind shear coefficients, roughness length and energy yield over coastal locations in Southern Italy," Renewable Energy, Elsevier, vol. 36(3), pages 1081-1094.
    6. Feng, Ju & Shen, Wen Zhong, 2017. "Design optimization of offshore wind farms with multiple types of wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 1283-1297.
    7. Herrero-Novoa, Cristina & Pérez, Isidro A. & Sánchez, M. Luisa & García, Ma Ángeles & Pardo, Nuria & Fernández-Duque, Beatriz, 2017. "Wind speed description and power density in northern Spain," Energy, Elsevier, vol. 138(C), pages 967-976.
    8. Ohunakin, O.S. & Adaramola, M.S. & Oyewola, O.M., 2011. "Wind energy evaluation for electricity generation using WECS in seven selected locations in Nigeria," Applied Energy, Elsevier, vol. 88(9), pages 3197-3206.
    9. 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.
    10. Rehman, Shafiqur & Al-Abbadi, Naif M., 2007. "Wind shear coefficients and energy yield for Dhahran, Saudi Arabia," Renewable Energy, Elsevier, vol. 32(5), pages 738-749.
    11. Kwon, Soon-Duck, 2010. "Uncertainty analysis of wind energy potential assessment," Applied Energy, Elsevier, vol. 87(3), pages 856-865, March.
    12. Bañuelos-Ruedas, F. & Angeles-Camacho, C. & Rios-Marcuello, S., 2010. "Analysis and validation of the methodology used in the extrapolation of wind speed data at different heights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(8), pages 2383-2391, October.
    13. 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.
    14. Mekonnen, Addisu D. & Gorsevski, Pece V., 2015. "A web-based participatory GIS (PGIS) for offshore wind farm suitability within Lake Erie, Ohio," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 162-177.
    15. Prasad, Abhnil A. & Taylor, Robert A. & Kay, Merlinde, 2017. "Assessment of solar and wind resource synergy in Australia," Applied Energy, Elsevier, vol. 190(C), pages 354-367.
    16. Farrugia, R.N., 2003. "The wind shear exponent in a Mediterranean island climate," Renewable Energy, Elsevier, vol. 28(4), pages 647-653.
    17. Sklenicka, Petr & Zouhar, Jan, 2018. "Predicting the visual impact of onshore wind farms via landscape indices: A method for objectivizing planning and decision processes," Applied Energy, Elsevier, vol. 209(C), pages 445-454.
    18. Shu, Z.R. & Li, Q.S. & Chan, P.W., 2015. "Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function," Applied Energy, Elsevier, vol. 156(C), pages 362-373.
    19. 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.
    20. Wang, Xuefei & Yang, Xu & Zeng, Xiangwu, 2017. "Seismic centrifuge modelling of suction bucket foundation for offshore wind turbine," Renewable Energy, Elsevier, vol. 114(PB), pages 1013-1022.
    21. Gualtieri, Giovanni & Secci, Sauro, 2011. "Comparing methods to calculate atmospheric stability-dependent wind speed profiles: A case study on coastal location," Renewable Energy, Elsevier, vol. 36(8), pages 2189-2204.
    22. 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.
    23. Rehman, Shafiqur & Al-Abbadi, Naif M., 2008. "Wind shear coefficient, turbulence intensity and wind power potential assessment for Dhulom, Saudi Arabia," Renewable Energy, Elsevier, vol. 33(12), pages 2653-2660.
    24. Chancham, Chana & Waewsak, Jompob & Gagnon, Yves, 2017. "Offshore wind resource assessment and wind power plant optimization in the Gulf of Thailand," Energy, Elsevier, vol. 139(C), pages 706-731.
    25. Koh, J.H. & Ng, E.Y.K., 2016. "Downwind offshore wind turbines: Opportunities, trends and technical challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 797-808.
    26. 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.
    27. Saleh, H. & Abou El-Azm Aly, A. & Abdel-Hady, S., 2012. "Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt," Energy, Elsevier, vol. 44(1), pages 710-719.
    28. Wang, Xuefei & Zeng, Xiangwu & Yang, Xu & Li, Jiale, 2018. "Feasibility study of offshore wind turbines with hybrid monopile foundation based on centrifuge modeling," Applied Energy, Elsevier, vol. 209(C), pages 127-139.
    29. Jiale Li & Xiong (Bill) Yu, 2017. "Analyses of the Extensible Blade in Improving Wind Energy Production at Sites with Low-Class Wind Resource," Energies, MDPI, vol. 10(9), pages 1-24, August.
    30. Simões, Teresa & Estanqueiro, Ana, 2016. "A new methodology for urban wind resource assessment," Renewable Energy, Elsevier, vol. 89(C), pages 598-605.
    31. Akdag, S.A. & Bagiorgas, H.S. & Mihalakakou, G., 2010. "Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean," Applied Energy, Elsevier, vol. 87(8), pages 2566-2573, August.
    32. Vasel-Be-Hagh, Ahmadreza & Archer, Cristina L., 2017. "Wind farm hub height optimization," Applied Energy, Elsevier, vol. 195(C), pages 905-921.
    33. Solyali, Davut & Altunç, Mustafa & Tolun, Süleyman & Aslan, Zafer, 2016. "Wind resource assessment of Northern Cyprus," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 180-187.
    34. Fant, Charles & Gunturu, Bhaskar & Schlosser, Adam, 2016. "Characterizing wind power resource reliability in southern Africa," Applied Energy, Elsevier, vol. 161(C), pages 565-573.
    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. Wang, Xuefei & Zeng, Xiangwu & Yang, Xu & Li, Jiale, 2019. "Seismic response of offshore wind turbine with hybrid monopile foundation based on centrifuge modelling," Applied Energy, Elsevier, vol. 235(C), pages 1335-1350.
    2. Zhang, Jincheng & Zhao, Xiaowei, 2021. "Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning," Applied Energy, Elsevier, vol. 300(C).
    3. Jin, Yuqing & Ju, Ping & Rehtanz, Christian & Wu, Feng & Pan, Xueping, 2018. "Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm," Applied Energy, Elsevier, vol. 222(C), pages 485-496.
    4. 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).
    5. 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).
    6. Wang, Qiang & Yi, Xian & Liu, Yu & Ren, Jinghao & Yang, Jianjun & Chen, Ningli, 2024. "Numerical investigation of dynamic icing of wind turbine blades under wind shear conditions," Renewable Energy, Elsevier, vol. 227(C).
    7. Han, Qinkai & Wang, Tianyang & Chu, Fulei, 2022. "Nonparametric copula modeling of wind speed-wind shear for the assessment of height-dependent wind energy in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    8. Yang, Zihao & Dong, Sheng, 2024. "A novel framework for wind energy assessment at multi-time scale based on non-stationary wind speed models: A case study in China," Renewable Energy, Elsevier, vol. 226(C).
    9. Zhang, Jincheng & Zhao, Xiaowei, 2021. "Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements," Applied Energy, Elsevier, vol. 288(C).
    10. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    11. Liu, Yichao & Chen, Daoyi & Li, Sunwei & Chan, P.W., 2018. "Discerning the spatial variations in offshore wind resources along the coast of China via dynamic downscaling," Energy, Elsevier, vol. 160(C), pages 582-596.
    12. Li, Jiale & Wang, Xuefei & Guo, Yuan & Yu, Xiong Bill, 2020. "The loading behavior of innovative monopile foundations for offshore wind turbine based on centrifuge experiments," Renewable Energy, Elsevier, vol. 152(C), pages 1109-1120.
    13. 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).
    14. Yang, Xinrong & Jiang, Xin & Liang, Shijing & Qin, Yingzuo & Ye, Fan & Ye, Bin & Xu, Jiayu & He, Xinyue & Wu, Jie & Dong, Tianyun & Cai, Xitian & Xu, Rongrong & Zeng, Zhenzhong, 2024. "Spatiotemporal variation of power law exponent on the use of wind energy," Applied Energy, Elsevier, vol. 356(C).
    15. 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).
    16. 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.
    17. Li, Jiale & Song, Zihao & Wang, Xuefei & Wang, Yanru & Jia, Yaya, 2022. "A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD," Energy, Elsevier, vol. 251(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. Li, Jiale & Yu, Xiong (Bill), 2018. "Onshore and offshore wind energy potential assessment near Lake Erie shoreline: A spatial and temporal analysis," Energy, Elsevier, vol. 147(C), pages 1092-1107.
    2. 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.
    3. 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).
    4. 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.
    5. 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).
    6. Aliashim Albani & Mohd Zamri Ibrahim, 2017. "Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia," Energies, MDPI, vol. 10(3), pages 1-21, March.
    7. Wang, Xuefei & Zeng, Xiangwu & Yang, Xu & Li, Jiale, 2019. "Seismic response of offshore wind turbine with hybrid monopile foundation based on centrifuge modelling," Applied Energy, Elsevier, vol. 235(C), pages 1335-1350.
    8. Đurišić, Željko & Mikulović, Jovan, 2012. "Assessment of the wind energy resource in the South Banat region, Serbia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3014-3023.
    9. 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.
    10. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
    11. Chadee, Xsitaaz T. & Clarke, Ricardo M., 2018. "Wind resources and the levelized cost of wind generated electricity in the Caribbean islands of Trinidad and Tobago," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2526-2540.
    12. 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.
    13. 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.
    14. Houssem R. E. H. Bouchekara & Yusuf A. Sha’aban & Mohammad S. Shahriar & Saad M. Abdullah & Makbul A. Ramli, 2023. "Sizing of Hybrid PV/Battery/Wind/Diesel Microgrid System Using an Improved Decomposition Multi-Objective Evolutionary Algorithm Considering Uncertainties and Battery Degradation," Sustainability, MDPI, vol. 15(14), pages 1-38, July.
    15. Jiang, Haiyan & Wang, Jianzhou & Wu, Jie & Geng, Wei, 2017. "Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1199-1217.
    16. 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).
    17. Arslan, Talha & Bulut, Y. Murat & Altın Yavuz, Arzu, 2014. "Comparative study of numerical methods for determining Weibull parameters for wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 820-825.
    18. Emilio Gómez-Lázaro & María C. Bueso & Mathieu Kessler & Sergio Martín-Martínez & Jie Zhang & Bri-Mathias Hodge & Angel Molina-García, 2016. "Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures," Energies, MDPI, vol. 9(2), pages 1-15, February.
    19. Hu, Qinghua & Wang, Yun & Xie, Zongxia & Zhu, Pengfei & Yu, Daren, 2016. "On estimating uncertainty of wind energy with mixture of distributions," Energy, Elsevier, vol. 112(C), pages 935-962.
    20. Mazhar Hussain Baloch & Dahaman Ishak & Sohaib Tahir Chaudary & Baqir Ali & Ali Asghar Memon & Touqeer Ahmed Jumani, 2019. "Wind Power Integration: An Experimental Investigation for Powering Local Communities," Energies, MDPI, vol. 12(4), pages 1-24, 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:appene:v:213:y:2018:i:c:p:469-485. 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/405891/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.