IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v132y2019icp921-930.html
   My bibliography  Save this article

Prediction of the wind speed probabilities in the atmospheric surface layer

Author

Listed:
  • Efthimiou, G.C.
  • Kumar, P.
  • Giannissi, S.G.
  • Feiz, A.A.
  • Andronopoulos, S.

Abstract

Accurate prediction of the wind speed probabilities in the atmospheric surface layer is very important for wind energy assessment studies and many other practical applications such as the design and operation of wind turbines and human exposure to wind extremes. In a recent study, an optimized beta distribution was developed for the prediction of the wind speed probabilities in the atmospheric surface layer. Various uncertainties arise in real scenarios due to the composite atmospheric variability, the topography of the terrain, nearby obstacles, orographical features, and other synoptic conditions. Thus, in the first part of this study, the beta distribution is validated further with the wind speed database of the FUSION Field Trial 2007 (FFT-07) tracer field experiment for various atmospheric stability conditions. The model is applied without any change in its constants and a high degree of agreement with the field experiment is achieved. One main advantage of the proposed beta distribution is that it can be incorporated in computational models that are able to predict the mean, the variance and the integral time scale of the wind speed. The second part of the paper includes the incorporation of the beta distribution in the Reynolds Averaged Navier Stokes (RANS) methodology. Initially, the “RANS-beta” model is validated against wind speed measurements performed in a wind tunnel over a rough ground. The wind speed 25th, 50th and 75th percentiles were found to be highly dependent on the height and the model gave comparable results with the experiment. Then, the wind speed database of the field experiment JU2003 is used to examine the “RANS-beta” model's performance. The 25th, 50th, 75th and 95th model percentiles at 20 sensors located inside the complex urban area were found to be in good agreement with the experimental ones (FAC2 = 0.8).

Suggested Citation

  • Efthimiou, G.C. & Kumar, P. & Giannissi, S.G. & Feiz, A.A. & Andronopoulos, S., 2019. "Prediction of the wind speed probabilities in the atmospheric surface layer," Renewable Energy, Elsevier, vol. 132(C), pages 921-930.
    • Handle: RePEc:eee:renene:v:132:y:2019:i:c:p:921-930
    DOI: 10.1016/j.renene.2018.08.060
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118310085
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.08.060?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. Dabbaghiyan, Amir & Fazelpour, Farivar & Abnavi, Mohhamadreza Dehghan & Rosen, Marc A., 2016. "Evaluation of wind energy potential in province of Bushehr, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 455-466.
    2. Ramenah, H. & Tanougast, C., 2016. "Reliably model of microwind power energy output under real conditions in France suburban area," Renewable Energy, Elsevier, vol. 91(C), pages 1-10.
    3. 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.
    4. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang, 2016. "Wind speed probability distribution estimation and wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 881-899.
    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. Elena García Bustamante & J. Fidel González Rouco & Jorge Navarro & Etor E. Lucio Eceiza & Cristina Rojas Labanda, 2021. "Expected Recurrence of Extreme Winds in Northwestern Sahara and Associated Uncertainties," Energies, MDPI, vol. 14(21), pages 1-32, October.

    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. 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.
    2. Allouhi, A. & Zamzoum, O. & Islam, M.R. & Saidur, R. & Kousksou, T. & Jamil, A. & Derouich, A., 2017. "Evaluation of wind energy potential in Morocco's coastal regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 311-324.
    3. Ali Mostafaeipour & Mostafa Rezaei & Mehdi Jahangiri & Mojtaba Qolipour, 2020. "Feasibility analysis of a new tree-shaped wind turbine for urban application: A case study," Energy & Environment, , vol. 31(7), pages 1230-1256, November.
    4. Kam, Olle Michel & Noël, Stéphane & Ramenah, Harry & Kasser, Pierre & Tanougast, Camel, 2021. "Comparative Weibull distribution methods for reliable global solar irradiance assessment in France areas," Renewable Energy, Elsevier, vol. 165(P1), pages 194-210.
    5. Siyavash Filom & Soheil Radfar & Roozbeh Panahi & Erfan Amini & Mehdi Neshat, 2021. "Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    6. Bahrami, Arian & Teimourian, Amir & Okoye, Chiemeka Onyeka & Khosravi, Nima, 2019. "Assessing the feasibility of wind energy as a power source in Turkmenistan; a major opportunity for Central Asia's energy market," Energy, Elsevier, vol. 183(C), pages 415-427.
    7. Pan, Yue & Qin, Jianjun, 2022. "A novel probabilistic modeling framework for wind speed with highlight of extremes under data discrepancy and uncertainty," Applied Energy, Elsevier, vol. 326(C).
    8. Munir Ali Elfarra & Mustafa Kaya, 2018. "Comparison of Optimum Spline-Based Probability Density Functions to Parametric Distributions for the Wind Speed Data in Terms of Annual Energy Production," Energies, MDPI, vol. 11(11), pages 1-15, November.
    9. 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.
    10. Aiguo Tan & Xiangning Lin & Jinwen Sun & Ran Lyu & Zhengtian Li & Long Peng & Muhammad Shoaib Khalid, 2016. "A Novel DFIG Damping Control for Power System with High Wind Power Penetration," Energies, MDPI, vol. 9(7), pages 1-15, July.
    11. Han, Qinkai & Chu, Fulei, 2021. "Directional wind energy assessment of China based on nonparametric copula models," Renewable Energy, Elsevier, vol. 164(C), pages 1334-1349.
    12. Lidong Zhang & Qikai Li & Yuanjun Guo & Zhile Yang & Lei Zhang, 2018. "An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    13. Yahya Z. Alharthi & Mahbube K. Siddiki & Ghulam M. Chaudhry, 2018. "Resource Assessment and Techno-Economic Analysis of a Grid-Connected Solar PV-Wind Hybrid System for Different Locations in Saudi Arabia," Sustainability, MDPI, vol. 10(10), pages 1-22, October.
    14. 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.
    15. 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).
    16. Guedes, Kevin S. & de Andrade, Carla F. & Rocha, Paulo A.C. & Mangueira, Rivanilso dos S. & de Moura, Elineudo P., 2020. "Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions," Applied Energy, Elsevier, vol. 268(C).
    17. Minaeian, Ali & Sedaghat, Ahmad & Mostafaeipour, Ali & Akbar Alemrajabi, Ali, 2017. "Exploring economy of small communities and households by investing on harnessing wind energy in the province of Sistan-Baluchestan in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 835-847.
    18. Eslami, M. & Nahani, P., 2021. "How policies affect the cost-effectiveness of residential renewable energy in Iran: A techno-economic analysis for optimization," Utilities Policy, Elsevier, vol. 72(C).
    19. Krishnamoorthy R & Udhayakumar K & Kannadasan Raju & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2020. "An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization," Energies, MDPI, vol. 13(12), pages 1-41, June.
    20. Khojasteh, Danial & Khojasteh, Davood & Kamali, Reza & Beyene, Asfaw & Iglesias, Gregorio, 2018. "Assessment of renewable energy resources in Iran; with a focus on wave and tidal energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2992-3005.

    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:132:y:2019:i:c:p:921-930. 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.