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Variogram time-series analysis of wind speed

Author

Listed:
  • Liu, Jinfu
  • Ren, Guorui
  • Wan, Jie
  • Guo, Yufeng
  • Yu, Daren

Abstract

Fluctuations of wind-power production are a significant hindrance to its high penetration in power systems. System operators have to provide complementary power and relevant control strategies to smooth out the fluctuations when large-scale wind power ones is injected into the grid. To better smooth the fluctuations, the change rate of the wind speed is a critical piece of information. In this study, the variogram function is introduced to measure the change rate of the wind speed. Based on the variogram time-series, some statistical analyses are conducted. These results contribute to a better understanding of the characteristics of the change rate of the wind speed, such as the chronological variation pattern of the change rate on a day, whether the future change rate can be forecasted, and whether there is a relationship between the change rate and wind speed.

Suggested Citation

  • Liu, Jinfu & Ren, Guorui & Wan, Jie & Guo, Yufeng & Yu, Daren, 2016. "Variogram time-series analysis of wind speed," Renewable Energy, Elsevier, vol. 99(C), pages 483-491.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:483-491
    DOI: 10.1016/j.renene.2016.07.013
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren & Liu, Jizhen, 2017. "Measurement and statistical analysis of wind speed intermittency," Energy, Elsevier, vol. 118(C), pages 632-643.
    2. 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.
    3. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
    4. Liu, Guangbiao & Zhou, Jianzhong & Jia, Benjun & He, Feifei & Yang, Yuqi & Sun, Na, 2019. "Advance short-term wind energy quality assessment based on instantaneous standard deviation and variogram of wind speed by a hybrid method," Applied Energy, Elsevier, vol. 238(C), pages 643-667.
    5. Ren, Guorui & Liu, Jinfu & Wan, Jie & Li, Fei & Guo, Yufeng & Yu, Daren, 2018. "The analysis of turbulence intensity based on wind speed data in onshore wind farms," Renewable Energy, Elsevier, vol. 123(C), pages 756-766.
    6. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Characterization of wind resource in China from a new perspective," Energy, Elsevier, vol. 167(C), pages 994-1010.
    7. Wang, Jianzhou & Dong, Yunxuan & Zhang, Kequan & Guo, Zhenhai, 2017. "A numerical model based on prior distribution fuzzy inference and neural networks," Renewable Energy, Elsevier, vol. 112(C), pages 486-497.
    8. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.

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