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A new wind speed scenario generation method based on spatiotemporal dependency structure

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  • Deng, Jingchuan
  • Li, Hongru
  • Hu, Jinxing
  • Liu, Zhenyu

Abstract

The accuracy of wind speed scenario generation is critical to the energy dispatching and planning of the wind-integrated power system. The accuracy of the modeling of wind speed can be improved with a comprehensive consideration of the dependency structure between wind speeds, and then the accuracy of wind speed scenario generation can be improved. Therefore, spatial and temporal dependence have been widely studied. However, linear correlation does not fully reflect the nonlinear nature of wind speed. Tail dependence, as a kind of non-linear dependency structure of wind speed in space and time, is studied in this paper. This paper proposes a wind speed scenario generation method based on the C-vine copula that considers spatiotemporal tail dependency structure. With the consideration of spatiotemporal dependency structure, the number of modeled random variables increase significantly. Therefore, a two-step wind speed scenario generation method is proposed to avoid the dimensional disaster. The wind speed data of two wind farms provided by the National Renewable Energy Laboratory are used in simulation analysis, the results demonstrate that reasonable consideration of the spatial and temporal tail dependence of wind speed can improve the accuracy of the spatial and temporal models, and further improve the accuracy of scenarios.

Suggested Citation

  • Deng, Jingchuan & Li, Hongru & Hu, Jinxing & Liu, Zhenyu, 2021. "A new wind speed scenario generation method based on spatiotemporal dependency structure," Renewable Energy, Elsevier, vol. 163(C), pages 1951-1962.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:1951-1962
    DOI: 10.1016/j.renene.2020.10.132
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    Cited by:

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    4. Hui Hwang Goh & Gumeng Peng & Dongdong Zhang & Wei Dai & Tonni Agustiono Kurniawan & Kai Chen Goh & Chin Leei Cham, 2022. "A New Wind Speed Scenario Generation Method Based on Principal Component and R-Vine Copula Theories," Energies, MDPI, vol. 15(7), pages 1-21, April.
    5. Mu, Yunfei & Wang, Congshan & Cao, Yan & Jia, Hongjie & Zhang, Qingzhu & Yu, Xiaodan, 2022. "A CVaR-based risk assessment method for park-level integrated energy system considering the uncertainties and correlation of energy prices," Energy, Elsevier, vol. 247(C).
    6. Li, Yanting & Peng, Xinghao & Zhang, Yu, 2022. "Forecasting methods for wind power scenarios of multiple wind farms based on spatio-temporal dependency structure," Renewable Energy, Elsevier, vol. 201(P1), pages 950-960.
    7. Jiang, Sufan & Wu, Chuanshen & Gao, Shan & Pan, Guangsheng & Liu, Yu & Zhao, Xin & Wang, Sicheng, 2022. "Robust frequency risk-constrained unit commitment model for AC-DC system considering wind uncertainty," Renewable Energy, Elsevier, vol. 195(C), pages 395-406.
    8. Zhang, Dongdong & Li, Chunjiao & Goh, Hui Hwang & Ahmad, Tanveer & Zhu, Hongyu & Liu, Hui & Wu, Thomas, 2022. "A comprehensive overview of modeling approaches and optimal control strategies for cyber-physical resilience in power systems," Renewable Energy, Elsevier, vol. 189(C), pages 1383-1406.
    9. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "A novel spatial–temporal generative autoencoder for wind speed uncertainty forecasting," Energy, Elsevier, vol. 282(C).
    10. Chen, Xianqing & Dong, Wei & Yang, Lingfang & Yang, Qiang, 2023. "Scenario-based robust capacity planning of regional integrated energy systems considering carbon emissions," Renewable Energy, Elsevier, vol. 207(C), pages 359-375.

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