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Directional wind energy assessment of China based on nonparametric copula models

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  • Han, Qinkai
  • Chu, Fulei

Abstract

The joint probabilistic density functions (JPDF) of wind speed and direction are important prerequisites for directional wind energy assessment (DWEA). Based on the beta boundary kernel and the optimal bandwidth algorithm in the R programs, a nonparametric copula model (NP-copula) for the JPDF of wind vector data is proposed for DWEA in China. Eight parametric models, including five parametric copula models, two angle-linear (AL) models, and the anisotropic Gaussian model, are introduced for comparison. The three-year daily-average wind vector forecast data of mainland China (total 6019 nodes) is adopted for comparisons at the regional scale. The comprehensive metric value reaches 4.9669 (full score 5), indicating that the NP-copula model has the superior performance in fitting JPDF of wind vector data. Subsequently, the DWEA for China, including the estimations of direction-related wind power density (WPD) and wind turbine power output (WTPO), is carried out using the proposed NP-copula model. The estimated values of WPD and WTPO have good consistency with the reference values, indicating that the DWEA based on the NP-copula model is reliable. It is found that the regions with the most abundant wind resources are concentrated at the southeast coastal region, some western provinces, and the central and eastern regions of Inner Mongolia. The average values of WPD and WTPO could reach (or exceed) 240 and 5 GWh, respectively. Besides the average values, the direction-related WPD and WTPO are also identified based on the NP-copula model. For example, the wind resources at the southeast coastal region are concentrated at the S and SW directions. For the southern Xinjiang and western Gansu provinces, wind resources with SW and W directions are dominant. These results might be useful for the wind farm site selection, as well as the design and condition monitoring of wind turbine systems in China.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:164:y:2021:i:c:p:1334-1349
    DOI: 10.1016/j.renene.2020.10.149
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    References listed on IDEAS

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    1. Han, Xingxing & Liu, Deyou & Xu, Chang & Shen, Wen Zhong, 2018. "Atmospheric stability and topography effects on wind turbine performance and wake properties in complex terrain," Renewable Energy, Elsevier, vol. 126(C), pages 640-651.
    2. Han, Qinkai & Ma, Sai & Wang, Tianyang & Chu, Fulei, 2019. "Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    3. Zhang, Jie & Chowdhury, Souma & Messac, Achille & Castillo, Luciano, 2013. "A Multivariate and Multimodal Wind Distribution model," Renewable Energy, Elsevier, vol. 51(C), pages 436-447.
    4. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2013. "Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions," Renewable Energy, Elsevier, vol. 52(C), pages 273-282.
    5. Edward Frees & Emiliano Valdez, 1998. "Understanding Relationships Using Copulas," North American Actuarial Journal, Taylor & Francis Journals, vol. 2(1), pages 1-25.
    6. Gozgor, Giray & Tiwari, Aviral Kumar & Khraief, Naceur & Shahbaz, Muhammad, 2019. "Dependence structure between business cycles and CO2 emissions in the U.S.: Evidence from the time-varying Markov-Switching Copula models," Energy, Elsevier, vol. 188(C).
    7. Soukissian, Takvor H. & Karathanasi, Flora E., 2017. "On the selection of bivariate parametric models for wind data," Applied Energy, Elsevier, vol. 188(C), pages 280-304.
    8. Song, Mengxuan & Chen, Kai & Zhang, Xing & Wang, Jun, 2016. "Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction," Renewable Energy, Elsevier, vol. 85(C), pages 57-65.
    9. Han, Qinkai & Hao, Zhuolin & Hu, Tao & Chu, Fulei, 2018. "Non-parametric models for joint probabilistic distributions of wind speed and direction data," Renewable Energy, Elsevier, vol. 126(C), pages 1032-1042.
    10. Oliveira, María & Crujeiras, Rosa M. & Rodríguez-Casal, Alberto, 2014. "NPCirc: An R Package for Nonparametric Circular Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i09).
    11. 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.
    12. Miao, Shuwei & Yang, Hejun & Gu, Yingzhong, 2018. "A wind vector simulation model and its application to adequacy assessment," Energy, Elsevier, vol. 148(C), pages 324-340.
    13. 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.
    14. Lee, Yongwoong & Yang, Kisung, 2019. "Modeling diversification and spillovers of loan portfolios' losses by LHP approximation and copula," International Review of Financial Analysis, Elsevier, vol. 66(C).
    15. 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.
    16. Böhme, Gustavo S. & Fadigas, Eliane A. & Gimenes, André L.V. & Tassinari, Carlos E.M., 2018. "Wake effect measurement in complex terrain - A case study in Brazilian wind farms," Energy, Elsevier, vol. 161(C), pages 277-283.
    17. Adriano Z. Zambom & Ronaldo Dias, 2013. "A Review of Kernel Density Estimation with Applications to Econometrics," International Econometric Review (IER), Econometric Research Association, vol. 5(1), pages 20-42, April.
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