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Quantitative Evaluation Methods of Cluster Wind Power Output Volatility and Source-Load Timing Matching in Regional Power Grid

Author

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  • Yongqian Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (NCEPU), School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Yanhui Qiao

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (NCEPU), School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Shuang Han

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (NCEPU), School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Yanping Xu

    (State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute Co., Ltd., Beijing 100192, China)

  • Tianxiang Geng

    (State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750001, China)

  • Tiandong Ma

    (State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750001, China)

Abstract

The quantitative evaluation of cluster wind power output volatility and source-load timing matching is vital to the planning and operation of the future power system dominated by new energy. However, the existing volatility evaluation methods of cluster wind power output do not fully consider timing volatility, or are not suitable for small sample data scenarios. Meanwhile, the existing source-load timing matching evaluation indicator ignores the impact of wind power permeability on the timing matching degree between wind power output and load. Therefore, the authors propose quantitative evaluation methods of cluster wind power output volatility and source-load timing matching in regional power grid. Firstly, the volatility-based smoothing coefficient is defined to quantitatively evaluate the smoothing effect of wind-farm cluster power output. Then, the source-load timing matching coefficient considering wind power permeability is proposed to quantitatively evaluate the timing matching degree of regional wind power output and load, and the corresponding function model of volatility-based smoothing coefficient and source-load timing matching coefficient is established. Finally, the validity and applicability of the proposed methods are verified by MATLAB software based on the actual power output of 10 wind farms and actual grid load in a certain grid dispatching cross-section of northeast China. The results demonstrated that the proposed volatility-based smoothing coefficient can accurately represent the smoothing effect of wind farm cluster power output while maintaining the volatility continuity of wind power output time series and without affect from the data sample size. The source-load timing matching coefficient can accurately characterize the difference in the timing matching degree between wind power output and grid load under different wind power permeability and the influence degree on grid load.

Suggested Citation

  • Yongqian Liu & Yanhui Qiao & Shuang Han & Yanping Xu & Tianxiang Geng & Tiandong Ma, 2021. "Quantitative Evaluation Methods of Cluster Wind Power Output Volatility and Source-Load Timing Matching in Regional Power Grid," Energies, MDPI, vol. 14(16), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5214-:d:620240
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    References listed on IDEAS

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    1. Ping Jiang & Qiwei Chen, 2018. "An Optimal Source-Load Coordinated Restoration Method Considering Double Uncertainty," Energies, MDPI, vol. 11(3), pages 1-18, March.
    2. Tarroja, Brian & Mueller, Fabian & Eichman, Joshua D. & Brouwer, Jack & Samuelsen, Scott, 2011. "Spatial and temporal analysis of electric wind generation intermittency and dynamics," Renewable Energy, Elsevier, vol. 36(12), pages 3424-3432.
    3. Shahriari, Mehdi & Blumsack, Seth, 2017. "Scaling of wind energy variability over space and time," Applied Energy, Elsevier, vol. 195(C), pages 572-585.
    4. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    5. Commin, Andrew N. & Davidson, Magnus W.H. & Largey, Nicola & Gaffney, Paul P.J. & Braidwood, David W. & Gibb, Stuart W. & McClatchey, John, 2017. "Spatial smoothing of onshore wind: Implications for strategic development in Scotland," Energy Policy, Elsevier, vol. 109(C), pages 36-48.
    6. Han, Li & Jing, Huitian & Zhang, Rongchang & Gao, Zhiyu, 2019. "Wind power forecast based on improved Long Short Term Memory network," Energy, Elsevier, vol. 189(C).
    7. Huang, Junling & Lu, Xi & McElroy, Michael B., 2014. "Meteorologically defined limits to reduction in the variability of outputs from a coupled wind farm system in the Central US," Renewable Energy, Elsevier, vol. 62(C), pages 331-340.
    8. Hasan, Kazi Nazmul & Preece, Robin & Milanović, Jovica V., 2019. "Existing approaches and trends in uncertainty modelling and probabilistic stability analysis of power systems with renewable generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 168-180.
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    Cited by:

    1. Yanhui Qiao & Yongqian Liu & Yang Chen & Shuang Han & Luo Wang, 2022. "Power Generation Performance Indicators of Wind Farms Including the Influence of Wind Energy Resource Differences," Energies, MDPI, vol. 15(5), pages 1-25, February.
    2. Peizhao Hong & Zhijun Qin, 2022. "Distributed Active Power Optimal Dispatching of Wind Farm Cluster Considering Wind Power Uncertainty," Energies, MDPI, vol. 15(7), pages 1-16, April.

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