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Optimal approach for wind resource assessment using Kolmogorov–Smirnov statistic: A case study for large-scale wind farm in Pakistan

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  • Saeed, Muhammad Abid
  • Ahmed, Zahoor
  • Zhang, Weidong

Abstract

Weibull distribution has been widely utilized for better understanding, quantification, and optimal utilization of wind energy globally. Although numerical approaches are widely used for Weibull parameters estimation (WPE), their results still lack consistency. In this situation, Artificial Intelligence Optimization Techniques (AIOT) might be a powerful tool to attaining high precision; however, in the case of WPE, standard methods may not ensure convergence. In this work, a generalized mathematical model is derived for AIOT convergence in the case of WPE. Based on the mathematical model Kolmogorov–Smirnov statistic is utilized to establish a convergence optimizer function (Copt) for WPE. The Weibull fitness tests are computed using real-time data from 13 different locations of Pakistan to check the effeteness of Copt that illustrates that Copt shows better results than numerical methods. For the economic aspect, Levelized economic cost is carried out for all the targeted sites using ten commercially available wind turbines. It is observed that five of the targeted sites are very promising for wind power production. The study is expected to provide an alternative method for WPE and the deployment of wind energy technology as a future power source in the country.

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  • Saeed, Muhammad Abid & Ahmed, Zahoor & Zhang, Weidong, 2021. "Optimal approach for wind resource assessment using Kolmogorov–Smirnov statistic: A case study for large-scale wind farm in Pakistan," Renewable Energy, Elsevier, vol. 168(C), pages 1229-1248.
    • Handle: RePEc:eee:renene:v:168:y:2021:i:c:p:1229-1248
    DOI: 10.1016/j.renene.2021.01.008
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    as
    1. Wais, Piotr, 2017. "Two and three-parameter Weibull distribution in available wind power analysis," Renewable Energy, Elsevier, vol. 103(C), pages 15-29.
    2. Bahrami, Arian & Okoye, Chiemeka Onyeka & Atikol, Ugur, 2017. "Technical and economic assessment of fixed, single and dual-axis tracking PV panels in low latitude countries," Renewable Energy, Elsevier, vol. 113(C), pages 563-579.
    3. Katinas, Vladislovas & Marčiukaitis, Mantas & Gecevičius, Giedrius & Markevičius, Antanas, 2017. "Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania," Renewable Energy, Elsevier, vol. 113(C), pages 190-201.
    4. Guangqian, Du & Bekhrad, Kaveh & Azarikhah, Pouria & Maleki, Akbar, 2018. "A hybrid algorithm based optimization on modeling of grid independent biodiesel-based hybrid solar/wind systems," Renewable Energy, Elsevier, vol. 122(C), pages 551-560.
    5. Jiang, Ping & Yang, Hufang & Heng, Jiani, 2019. "A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting," Applied Energy, Elsevier, vol. 235(C), pages 786-801.
    6. Shuai Li & Lubing Xie & Xiaoming Rui, 2018. "Development and Analysis of the Wind Power Industry in Xinjiang, China," Journal of Management and Sustainability, Canadian Center of Science and Education, vol. 8(2), pages 51-60, June.
    7. Usta, Ilhan, 2016. "An innovative estimation method regarding Weibull parameters for wind energy applications," Energy, Elsevier, vol. 106(C), pages 301-314.
    8. 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.
    9. Jahangiri, Mehdi & Haghani, Ahmad & Mostafaeipour, Ali & Khosravi, Adel & Raeisi, Heidar Ali, 2019. "Assessment of solar-wind power plants in Afghanistan: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 169-190.
    10. 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.
    11. Saeed, Muhammad Abid & Ahmed, Zahoor & Zhang, Weidong, 2020. "Wind energy potential and economic analysis with a comparison of different methods for determining the optimal distribution parameters," Renewable Energy, Elsevier, vol. 161(C), pages 1092-1109.
    12. Dai, Juchuan & Yang, Xin & Wen, Li, 2018. "Development of wind power industry in China: A comprehensive assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 156-164.
    13. Fazelpour, Farivar & Soltani, Nima & Soltani, Sina & Rosen, Marc A., 2015. "Assessment of wind energy potential and economics in the north-western Iranian cities of Tabriz and Ardabil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 87-99.
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    3. Junejo, Allah Rakhio & Gilal, Nauman Ullah & Doh, Jaehyeok, 2023. "Physics-informed optimization of robust control system to enhance power efficiency of renewable energy: Application to wind turbine," Energy, Elsevier, vol. 263(PB).

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