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Profiling the regional wind power fluctuation in China

Author

Listed:
  • Yu, Dayang
  • Liang, Jun
  • Han, Xueshan
  • Zhao, Jianguo

Abstract

As China starts to build 6 10-GW wind zones in 5 provinces by 2020, accommodating the wind electricity generated from these large wind zones will be a great challenge for the regional grids. Inadequate wind observing data hinders profiling the wind power fluctuations at the regional grid level. This paper proposed a method to assess the seasonal and diurnal wind power patterns based on the wind speed data from the NASA GEOS-5 DAS system, which provides data to the study of climate processes including the long-term estimates of meteorological quantities. The wind power fluctuations for the 6 largest wind zones in China are presented with both the capacity factor and the megawatt wind power output. The measured hourly wind output in a regional grid is compared to the calculating result to test the analyzing model. To investigate the offsetting effect of dispersed wind farms over large regions, the regional correlations of hourly wind power fluctuations are calculated. The result illustrates the different offsetting effects of minute and hourly fluctuations.

Suggested Citation

  • Yu, Dayang & Liang, Jun & Han, Xueshan & Zhao, Jianguo, 2011. "Profiling the regional wind power fluctuation in China," Energy Policy, Elsevier, vol. 39(1), pages 299-306, January.
    • Handle: RePEc:eee:enepol:v:39:y:2011:i:1:p:299-306
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    Citations

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    Cited by:

    1. Yu, Dayang & Zhang, Bo & Liang, Jun & Han, Xueshan, 2011. "The influence of generation mix on the wind integrating capability of North China power grids: A modeling interpretation and potential solutions," Energy Policy, Elsevier, vol. 39(11), pages 7455-7463.
    2. Yang, Yulong & Wu, Kai & Long, Hongyu & Gao, Jianchao & Yan, Xu & Kato, Takeyoshi & Suzuoki, Yasuo, 2014. "Integrated electricity and heating demand-side management for wind power integration in China," Energy, Elsevier, vol. 78(C), pages 235-246.
    3. Zhang, Chongyu & Lu, Xi & Ren, Guo & Chen, Shi & Hu, Chengyu & Kong, Zhaoyang & Zhang, Ning & Foley, Aoife M., 2021. "Optimal allocation of onshore wind power in China based on cluster analysis," Applied Energy, Elsevier, vol. 285(C).
    4. Hongyu Long & Kunyao Xu & Ruilin Xu & Jianjun He, 2012. "More Wind Power Integration with Adjusted Energy Carriers for Space Heating in Northern China," Energies, MDPI, vol. 5(9), pages 1-16, August.
    5. Ding, Yi & Yang, Hongliang, 2013. "Promoting energy-saving and environmentally friendly generation dispatching model in China: Phase development and case studies," Energy Policy, Elsevier, vol. 57(C), pages 109-118.
    6. Zhao, Xiaoli & Wang, Feng & Wang, Mei, 2012. "Large-scale utilization of wind power in China: Obstacles of conflict between market and planning," Energy Policy, Elsevier, vol. 48(C), pages 222-232.
    7. Fang, Yong & Li, Jing & Wang, Mingming, 2012. "Development policy for non-grid-connected wind power in China: An analysis based on institutional change," Energy Policy, Elsevier, vol. 45(C), pages 350-358.
    8. Hou, Wenjuan & Zhang, Xueliang & Wu, Maowei & Yuxin Feng, & Yang, Linsheng, 2022. "Integrating stability and complementarity to assess the accommodable generation potential of multiscale solar and wind resources: A case study in a resource-based area in China," Energy, Elsevier, vol. 261(PB).
    9. Maria Jesus Herrerias and Eric Girardin, 2013. "Seasonal Patterns of Energy in China," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    10. Wang, Jinda & Zhou, Zhigang & Zhao, Jianing & Zheng, Jinfu, 2018. "Improving wind power integration by a novel short-term dispatch model based on free heat storage and exhaust heat recycling," Energy, Elsevier, vol. 160(C), pages 940-953.
    11. Liang, Zhengtang & Liang, Jun & Zhang, Li & Wang, Chengfu & Yun, Zhihao & Zhang, Xu, 2015. "Analysis of multi-scale chaotic characteristics of wind power based on Hilbert–Huang transform and Hurst analysis," Applied Energy, Elsevier, vol. 159(C), pages 51-61.
    12. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2020. "Spatial and temporal correlation analysis of wind power between different provinces in China," Energy, Elsevier, vol. 191(C).
    13. Xin-Rui Liu & Si-Luo Sun & Qiu-Ye Sun & Wei-Yang Zhong, 2020. "Time-Scale Economic Dispatch of Electricity-Heat Integrated System Based on Users’ Thermal Comfort," Energies, MDPI, vol. 13(20), pages 1-27, October.
    14. Caralis, George & Diakoulaki, Danae & Yang, Peijin & Gao, Zhiqiu & Zervos, Arthouros & Rados, Kostas, 2014. "Profitability of wind energy investments in China using a Monte Carlo approach for the treatment of uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 224-236.
    15. Chiyori T. Urabe & Tetsuo Saitou & Kazuto Kataoka & Takashi Ikegami & Kazuhiko Ogimoto, 2021. "Positive Correlations between Short-Term and Average Long-Term Fluctuations in Wind Power Output," Energies, MDPI, vol. 14(7), pages 1-15, March.
    16. Shi, Jie & Wang, Luhao & Lee, Wei-Jen & Cheng, Xingong & Zong, Xiju, 2019. "Hybrid Energy Storage System (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction," Applied Energy, Elsevier, vol. 256(C).
    17. Wu, Zhongqun & Sun, Hongxia & Du, Yihang, 2014. "A large amount of idle capacity under rapid expansion: Policy analysis on the dilemma of wind power utilization in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 271-277.
    18. Li, Cun-bin & Li, Peng & Feng, Xia, 2014. "Analysis of wind power generation operation management risk in China," Renewable Energy, Elsevier, vol. 64(C), pages 266-275.

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    Keywords

    Wind power Fluctuation Correlation;

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