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Damping performance of the large scale Queensland transmission network with significant wind penetration

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  • Modi, Nilesh
  • Saha, Tapan K.
  • Anderson, Tom

Abstract

The Australian energy market operator is expecting a significant increase in wind power generation to meet the Australian renewable energy target of generating 20% of renewable energy by 2020. Along with other states, Queensland is expecting a large penetration of wind power, particularly in its northern region. In the presence of such large scale wind generation, it is important to examine its effect on stability of the Queensland network. In this paper, small-signal stability of the Queensland network has been investigated considering future wind power penetration. The wind farm is simulated by an aggregated doubly fed induction generation model. The work mainly focuses on inter-area modes. Wind power is accommodated by considering load growth and generator displacement individually for getting a useful insight into its impact on damping performance of the grid. For completeness of the study, generators equipped with stabilizers have also been displaced. The influence of wind power on system damping performance is studied and presented through eigenvalue analysis. It has been found that during the peak load condition, the inter-area Central Queensland – North Queensland (CQ–NQ) mode is largely influenced when system load is scaled up to accommodate wind power. It has also been observed that displacement of synchronous generator with stabilizers from CQ region does not have much influence on CQ–NQ inter-area modes while keeping Callide-C unit in service.

Suggested Citation

  • Modi, Nilesh & Saha, Tapan K. & Anderson, Tom, 2013. "Damping performance of the large scale Queensland transmission network with significant wind penetration," Applied Energy, Elsevier, vol. 111(C), pages 225-233.
    • Handle: RePEc:eee:appene:v:111:y:2013:i:c:p:225-233
    DOI: 10.1016/j.apenergy.2013.05.015
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    References listed on IDEAS

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    Cited by:

    1. Jin, Tao & Liu, Siyi & Flesch, Rodolfo C.C. & Su, Wencong, 2017. "A method for the identification of low frequency oscillation modes in power systems subjected to noise," Applied Energy, Elsevier, vol. 206(C), pages 1379-1392.
    2. Ping, Zuowei & Li, Xiuting & He, Wei & Yang, Tao & Yuan, Ye, 2020. "Sparse learning of network-reduced models for locating low frequency oscillations in power systems," Applied Energy, Elsevier, vol. 262(C).
    3. Xia, S.W. & Bu, S.Q. & Zhang, X. & Xu, Y. & Zhou, B. & Zhu, J.B., 2018. "Model reduction strategy of doubly-fed induction generator-based wind farms for power system small-signal rotor angle stability analysis," Applied Energy, Elsevier, vol. 222(C), pages 608-620.

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