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A Review of Uncertainty Modelling Techniques for Probabilistic Stability Analysis of Renewable-Rich Power Systems

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  • Ali M. Hakami

    (School of Engineering, RMIT University, Melbourne 3001, Australia
    Electrical Engineering, College of Engineering, King Khalid University, Asir-Abha 61421, Saudi Arabia)

  • Kazi N. Hasan

    (School of Engineering, RMIT University, Melbourne 3001, Australia)

  • Mohammed Alzubaidi

    (School of Engineering, RMIT University, Melbourne 3001, Australia
    College of Engineering, Umm Al-Qura University, Al-Qunfudhah 21912, Saudi Arabia)

  • Manoj Datta

    (School of Engineering, RMIT University, Melbourne 3001, Australia)

Abstract

In pursuit of identifying the most accurate and efficient uncertainty modelling (UM) techniques, this paper provides an extensive review and classification of the available UM techniques for probabilistic power system stability analysis. The increased penetration of system uncertainties related to renewable energy sources, new types of loads and their fluctuations, and deregulation of the electricity markets necessitates probabilistic power system analysis. The abovementioned factors significantly affect the power system stability, which requires computationally intensive simulation, including frequency, voltage, transient, and small disturbance stability. Altogether 40 UM techniques are collated with their characteristics, advantages, disadvantages, and application areas, particularly highlighting their accuracy and efficiency (as both are crucial for power system stability applications). This review recommends the most accurate and efficient UM techniques that could be used for probabilistic stability analysis of renewable-rich power systems.

Suggested Citation

  • Ali M. Hakami & Kazi N. Hasan & Mohammed Alzubaidi & Manoj Datta, 2022. "A Review of Uncertainty Modelling Techniques for Probabilistic Stability Analysis of Renewable-Rich Power Systems," Energies, MDPI, vol. 16(1), pages 1-26, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:112-:d:1011319
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