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Wind speed probability distribution estimation and wind energy assessment

Author

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  • Wang, Jianzhou
  • Hu, Jianming
  • Ma, Kailiang

Abstract

The statistical characteristics of wind and the selection of suitable wind turbines are essential to effectively evaluate wind energy potential and design wind farms. Using four sites in central China as examples, this research reviews and compares the popular parametric and non-parametric models for wind speed probability distribution and the estimation methods for these models’ parameters (the widely used methods and stochastic heuristic optimization algorithm). The simulations reveal that the non-parametric model outperforms all of the selected parametric models in terms of the fitting accuracy and the operational simplicity, and the stochastic heuristic optimization algorithm is superior to the widely used estimation methods. This study also reviews and discusses six power curves proposed by the literature and the power loss caused by the mutual wake effect between turbines in the wind energy potential assessment process. The evaluation results demonstrate that choice of power curves influences the selection of wind turbines and that consideration of the mutual wake effect may help to optimize wind farm design in wind energy assessment.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:rensus:v:60:y:2016:i:c:p:881-899
    DOI: 10.1016/j.rser.2016.01.057
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    1. Ling, Yu & Cai, Xu, 2012. "Exploitation and utilization of the wind power and its perspective in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2111-2117.
    2. 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.
    3. Harvey, Andrew & Oryshchenko, Vitaliy, 2012. "Kernel density estimation for time series data," International Journal of Forecasting, Elsevier, vol. 28(1), pages 3-14.
    4. Zhang, Hua & Yu, Yong-Jing & Liu, Zhi-Yuan, 2014. "Study on the Maximum Entropy Principle applied to the annual wind speed probability distribution: A case study for observations of intertidal zone anemometer towers of Rudong in East China Sea," Applied Energy, Elsevier, vol. 114(C), pages 931-938.
    5. Ahmed Shata, A.S. & Hanitsch, R., 2006. "Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt," Renewable Energy, Elsevier, vol. 31(8), pages 1183-1202.
    6. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    7. Li, H. & Chen, Z., 2009. "Design optimization and site matching of direct-drive permanent magnet wind power generator systems," Renewable Energy, Elsevier, vol. 34(4), pages 1175-1184.
    8. 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.
    9. Dong, Yao & Wang, Jianzhou & Jiang, He & Shi, Xiaomeng, 2013. "Intelligent optimized wind resource assessment and wind turbines selection in Huitengxile of Inner Mongolia, China," Applied Energy, Elsevier, vol. 109(C), pages 239-253.
    10. Lo Brano, Valerio & Orioli, Aldo & Ciulla, Giuseppina & Culotta, Simona, 2011. "Quality of wind speed fitting distributions for the urban area of Palermo, Italy," Renewable Energy, Elsevier, vol. 36(3), pages 1026-1039.
    11. Li, Gong & Shi, Jing, 2010. "Application of Bayesian model averaging in modeling long-term wind speed distributions," Renewable Energy, Elsevier, vol. 35(6), pages 1192-1202.
    12. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    13. Wu, Jie & Wang, Jianzhou & Chi, Dezhong, 2013. "Wind energy potential assessment for the site of Inner Mongolia in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 215-228.
    14. Pishgar-Komleh, S.H. & Keyhani, A. & Sefeedpari, P., 2015. "Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 313-322.
    15. Keyhani, A. & Ghasemi-Varnamkhasti, M. & Khanali, M. & Abbaszadeh, R., 2010. "An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran," Energy, Elsevier, vol. 35(1), pages 188-201.
    16. Ahmed, Ahmed Shata, 2010. "Wind energy as a potential generation source at Ras Benas, Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(8), pages 2167-2173, October.
    17. Chang, Tsang-Jung & Tu, Yi-Long, 2007. "Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: A case study of Taiwan," Renewable Energy, Elsevier, vol. 32(12), pages 1999-2010.
    18. 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.
    19. Ucar, Aynur & Balo, Figen, 2009. "Evaluation of wind energy potential and electricity generation at six locations in Turkey," Applied Energy, Elsevier, vol. 86(10), pages 1864-1872, October.
    20. Marsaglia, George & Tsang, Wai Wan & Wang, Jingbo, 2003. "Evaluating Kolmogorov's Distribution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i18).
    21. EL-Shimy, M., 2010. "Optimal site matching of wind turbine generator: Case study of the Gulf of Suez region in Egypt," Renewable Energy, Elsevier, vol. 35(8), pages 1870-1878.
    22. Liu, Feng Jiao & Chang, Tian Pau, 2011. "Validity analysis of maximum entropy distribution based on different moment constraints for wind energy assessment," Energy, Elsevier, vol. 36(3), pages 1820-1826.
    23. Gökçek, Murat & Bayülken, Ahmet & Bekdemir, Şükrü, 2007. "Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey," Renewable Energy, Elsevier, vol. 32(10), pages 1739-1752.
    24. Soukissian, Takvor, 2013. "Use of multi-parameter distributions for offshore wind speed modeling: The Johnson SB distribution," Applied Energy, Elsevier, vol. 111(C), pages 982-1000.
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