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Research on the Multi-Period Small-Signal Stability Probability of a Power System with Wind Farms Based on the Markov Chain

Author

Listed:
  • Rundong Ge

    (Electrical and Electronic Engineering Institute, North China Electric Power University, Mailbox 435, No. 2 Beinong Road, Changping District, Beijing 102206, China
    These authors contributed equally to this work.)

  • Wenying Liu

    (Electrical and Electronic Engineering Institute, North China Electric Power University, Mailbox 435, No. 2 Beinong Road, Changping District, Beijing 102206, China
    These authors contributed equally to this work.)

  • Huiyong Li

    (Electrical and Electronic Engineering Institute, North China Electric Power University, Mailbox 435, No. 2 Beinong Road, Changping District, Beijing 102206, China)

  • Jianzong Zhuo

    (Electrical and Electronic Engineering Institute, North China Electric Power University, Mailbox 435, No. 2 Beinong Road, Changping District, Beijing 102206, China
    These authors contributed equally to this work.)

  • Weizhou Wang

    (Gansu Electrical Power Research Institute, No.8 Binhe East Road, Chengguan North District, Lanzhou 730030, China
    These authors contributed equally to this work.)

Abstract

In the traditional studies on small-signal stability probability of a power system with wind farms, the frequency of wind speed was often assumed to obey to some extent a particular probability distribution. The stability probability that is thus obtained, however, actually only reflects the power system stability characteristics on long time scales. In fact, there is a direct correlation between the change of wind speed and the current state of wind speed, resulting in the system stability characteristics in different time periods having a great difference compared with that of long time scales. However, the dispatchers are more concerned about the probability that the power system remains stable in the next period or after several periods, namely the stability characteristics of the power system in a short period or multi-period. Therefore, research on multi-period small-signal stability probability of a power system with wind farms has important theoretical value and practical significance. Based on the Markov chain, this paper conducted in-depth research on this subject. Firstly, the basic principle of the Markov chain was introduced, based on which we studied the uncertainty of wind power by adopting the transition matrix and the wind speed−power output transformation model and established the probability distribution model of multi-period wind power. Then the boundary-based small-signal stability probability evaluation method was used to establish an evaluation model of multi-period small-signal stability probability of power system with wind farms. Finally, taking the power system with two wind farms as an example, we analyzed its small-signal stability probability and studied the influence of the initial states of wind speed and different periods on the probability of stability. This study provides a new method and support for analyzing the small-signal stability probability of a power system with wind farms.

Suggested Citation

  • Rundong Ge & Wenying Liu & Huiyong Li & Jianzong Zhuo & Weizhou Wang, 2015. "Research on the Multi-Period Small-Signal Stability Probability of a Power System with Wind Farms Based on the Markov Chain," Sustainability, MDPI, vol. 7(4), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:4:p:4582-4599:d:48350
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    References listed on IDEAS

    as
    1. Wenying Liu & Rundong Ge & Quancheng Lv & Huiyong Li & Jiangbei Ge, 2015. "Research on a Small Signal Stability Region Boundary Model of the Interconnected Power System with Large-Scale Wind Power," Energies, MDPI, vol. 8(4), pages 1-25, March.
    2. Fernández, Luis M. & Jurado, Francisco & Saenz, José Ramón, 2008. "Aggregated dynamic model for wind farms with doubly fed induction generator wind turbines," Renewable Energy, Elsevier, vol. 33(1), pages 129-140.
    3. Hernández-Escobedo, Q. & Saldaña-Flores, R. & Rodríguez-García, E.R. & Manzano-Agugliaro, F., 2014. "Wind energy resource in Northern Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 890-914.
    4. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
    5. Wenying Liu & Rundong Ge & Huiyong Li & Jiangbei Ge, 2014. "Impact of Large-Scale Wind Power Integration on Small Signal Stability Based on Stability Region Boundary," Sustainability, MDPI, vol. 6(11), pages 1-24, November.
    6. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    7. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
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