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Stochastic Stability Analysis of the Power System with Losses

Author

Listed:
  • Hongyu Li

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Ping Ju

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Chun Gan

    (Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA)

  • Feng Wu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Yichen Zhou

    (Electrical and Electronic Department, North China Electric Power University, Baoding 071003, China)

  • Zhe Dong

    (Electrical and Electronic Department, North China Electric Power University, Beijing 102206, China)

Abstract

Renewable energy and electric vehicles have become involved in power systems, which has attracted researchers to stochastic continuous disturbances (SDEs). This paper addresses stochastic analysis issues for the stability of a power system with losses under SDEs. Firstly, the quasi-Hamiltonian models of power systems with losses under SDEs are given. Secondly, a novel analytical method is proposed to analyze the stability of the power system with losses under SDEs based on the stochastic averaging method. Thirdly, comparisons of stability probability under different parameters are performed, from which insights to improve the stability probability of power systems with losses under SDEs can be obtained. Even though it is challenging to assess the stability of a power system with losses under SDEs, the proposed method in this paper could serve well in this regard.

Suggested Citation

  • Hongyu Li & Ping Ju & Chun Gan & Feng Wu & Yichen Zhou & Zhe Dong, 2018. "Stochastic Stability Analysis of the Power System with Losses," Energies, MDPI, vol. 11(3), pages 1-11, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:678-:d:136753
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    References listed on IDEAS

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    1. María Pérez-Ortiz & Silvia Jiménez-Fernández & Pedro A. Gutiérrez & Enrique Alexandre & César Hervás-Martínez & Sancho Salcedo-Sanz, 2016. "A Review of Classification Problems and Algorithms in Renewable Energy Applications," Energies, MDPI, vol. 9(8), pages 1-27, August.
    2. Zárate-Miñano, Rafael & Anghel, Marian & Milano, Federico, 2013. "Continuous wind speed models based on stochastic differential equations," Applied Energy, Elsevier, vol. 104(C), pages 42-49.
    3. Qiaomu Zhu & Jinfu Chen & Lin Zhu & Xianzhong Duan & Yilu Liu, 2018. "Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach," Energies, MDPI, vol. 11(4), pages 1-18, March.
    4. Calif, Rudy & Schmitt, François G. & Huang, Yongxiang, 2013. "Multifractal description of wind power fluctuations using arbitrary order Hilbert spectral analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4106-4120.
    5. Mehrdad Ehsani & Milad Falahi & Saeed Lotfifard, 2012. "Vehicle to Grid Services: Potential and Applications," Energies, MDPI, vol. 5(10), pages 1-15, October.
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