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A comprehensive evaluation of the wind resource characteristics to investigate the short term penetration of regional wind power based on different probability statistical methods

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  • Nedaei, Mojtaba
  • Assareh, Ehsanolah
  • Walsh, Philip R.

Abstract

In the current analysis, an attempt has been made, for the first time in its kind, to perform a comprehensive evaluation of wind resource through employing various statistical approaches. An extensive analysis of 46 probability methods has suggested the Wakeby outperforming other distribution functions in terms of compatibility among the actual and predicted wind data. Another challenging phase of the research was performed with a motivation to study the vertical wind shear profile based on two extrapolation methods, power and logarithmic laws. The estimated wind speed at the 80 m height was ranged from 5.58 to 12.24 m/s with an annual mean wind speed of 7.72 m/s. Through using the power law, the Gumbel distribution function, and Harris algorithm, the probability analysis for extreme wind events at higher altitudes was also performed, which led to the 50-year extreme wind speed being 44.262 m/s indicating a low risk of potential damages from the wind storms in the studied area. Extensive analysis of the wind resource characteristics such as seasonal wind speed profiles, the intensity and direction of regional wind turbulence, together with graphical distribution of wind resource highlighted a strong potential of using wind for power production in the investigated region.

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  • 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.
    • Handle: RePEc:eee:renene:v:128:y:2018:i:pa:p:362-374
    DOI: 10.1016/j.renene.2018.05.077
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    2. Kim, Ji-Young & Oh, Ki-Yong & Kim, Min-Suek & Kim, Kwang-Yul, 2019. "Evaluation and characterization of offshore wind resources with long-term met mast data corrected by wind lidar," Renewable Energy, Elsevier, vol. 144(C), pages 41-55.
    3. Yun-Tao Shi & Yuan Zhang & Xiang Xiang & Li Wang & Zhen-Wu Lei & De-Hui Sun, 2018. "Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs," Energies, MDPI, vol. 11(9), pages 1-22, August.
    4. Xiaosheng Peng & Kai Cheng & Jianxun Lang & Zuowei Zhang & Tao Cai & Shanxu Duan, 2021. "Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning," Energies, MDPI, vol. 14(7), pages 1-18, March.
    5. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.

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