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Probabilistic small signal stability analysis with large scale integration of wind power considering dependence

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  • Xu, Jin
  • Kanyingi, Peter Kairu
  • Wang, Keyou
  • Li, Guojie
  • Han, Bei
  • Jiang, Xiuchen

Abstract

Modern power systems are experiencing an escalation in the penetration of wind power sources which have both positive and negative impact on small signal stability. Wind power being stochastic in nature introduces uncertainties which make it necessary to account for such uncertainties during power system modeling and analysis. Probabilistic methodologies are appropriate choices in accounting for such uncertainties. These methodologies have recently developed concerns with regard to the probabilistic small signal stability analysis of power system integrated with wind power generation. Accounting for the dependence existing between wind power sources is considered as an important option to aid in simulating the impact of the wind power integration towards small signal stability. Numerous dependence modeling methods exist, each with its strengths and weaknesses. This paper presents an overview of the probabilistic methods applied in small signal stability analysis and methodologies used to model dependence or correlation in the probabilistic stability analysis. A comparative analysis of different dependence models is performed. The model accuracy is compared under different scenarios and the efficiency is discussed.

Suggested Citation

  • Xu, Jin & Kanyingi, Peter Kairu & Wang, Keyou & Li, Guojie & Han, Bei & Jiang, Xiuchen, 2017. "Probabilistic small signal stability analysis with large scale integration of wind power considering dependence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1258-1270.
    • Handle: RePEc:eee:rensus:v:69:y:2017:i:c:p:1258-1270
    DOI: 10.1016/j.rser.2016.12.041
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    References listed on IDEAS

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    Cited by:

    1. Ji, Ling & Huang, Guohe & Xie, Yulei & Zhou, Yong & Zhou, Jifang, 2018. "Robust cost-risk tradeoff for day-ahead schedule optimization in residential microgrid system under worst-case conditional value-at-risk consideration," Energy, Elsevier, vol. 153(C), pages 324-337.

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