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Wind resource assessment using SODAR and meteorological mast – A case study of Pakistan

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  • Khan, Komal S.
  • Tariq, Muhammad

Abstract

A wind assessment process can make or break the economics of wind plant development. Lack of credible data is one of the major reasons for faulty predictions and inaccurate estimations of the energy production from wind farms. This paper provides a concise, yet comprehensive analysis of state-of-the-art wind site assessment techniques, including a detailed survey of their strengths and pitfalls. The analysis of each technique addresses issues that may affect the power production estimates to an undesirable degree. It also overviews parameters such as survey time, coverage area, cost, and feasibility for each technique according to the site chosen for the assessment. A case study at the end presents independent surveys carried out in the Kallarkahar region of Pakistan using the latest site assessment techniques. The collected data sets are examined in order to unearth discrepancies affecting the assessment process in the surveys.

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  • Khan, Komal S. & Tariq, Muhammad, 2018. "Wind resource assessment using SODAR and meteorological mast – A case study of Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2443-2449.
    • Handle: RePEc:eee:rensus:v:81:y:2018:i:p2:p:2443-2449
    DOI: 10.1016/j.rser.2017.06.050
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    Cited by:

    1. Juan, Y.-H. & Wen, C.-Y. & Chen, W.-Y. & Yang, A.-S., 2021. "Numerical assessments of wind power potential and installation arrangements in realistic highly urbanized areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Majidi Nezhad, M. & Groppi, D. & Marzialetti, P. & Fusilli, L. & Laneve, G. & Cumo, F. & Garcia, D. Astiaso, 2019. "Wind energy potential analysis using Sentinel-1 satellite: A review and a case study on Mediterranean islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 499-513.
    3. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
    4. He, Yuhang & Han, Xingxing & Xu, Chang & Cheng, Zhe & Wang, Jincheng & Liu, Wei & Xu, Dong, 2023. "Sensitivity of simulated wind power under diverse spatial scales and multiple terrains using the weather research and forecasting model," Energy, Elsevier, vol. 285(C).
    5. Jamshid Ali Turi & Joanna Rosak-Szyrocka & Maryam Mansoor & Hira Asif & Ahad Nazir & Daniel Balsalobre-Lorente, 2022. "Assessing Wind Energy Projects Potential in Pakistan: Challenges and Way Forward," Energies, MDPI, vol. 15(23), pages 1-21, November.
    6. Sumair, Muhammad & Aized, Tauseef & Aslam Bhutta, Muhammad Mahmood & Siddiqui, Farrukh Arsalan & Tehreem, Layba & Chaudhry, Abduallah, 2022. "Method of Four Moments Mixture-A new approach for parametric estimation of Weibull Probability Distribution for wind potential estimation applications," Renewable Energy, Elsevier, vol. 191(C), pages 291-304.
    7. Majidi Nezhad, M. & Heydari, A. & Groppi, D. & Cumo, F. & Astiaso Garcia, D., 2020. "Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands," Renewable Energy, Elsevier, vol. 155(C), pages 212-224.
    8. Olaofe, Z.O., 2019. "Quantification of the near-surface wind conditions of the African coast: A comparative approach (satellite, NCEP CFSR and WRF-based)," Energy, Elsevier, vol. 189(C).
    9. 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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