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Enabling Methodologies for Predictive Power System Resilience Analysis in the Presence of Extreme Wind Gusts

Author

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  • Ennio Brugnetti

    (Department of Engineering (DING), University of Sannio, 82100 Benevento, Italy)

  • Guido Coletta

    (Department of Engineering (DING), University of Sannio, 82100 Benevento, Italy)

  • Fabrizio De Caro

    (Department of Engineering (DING), University of Sannio, 82100 Benevento, Italy)

  • Alfredo Vaccaro

    (Department of Engineering (DING), University of Sannio, 82100 Benevento, Italy)

  • Domenico Villacci

    (Department of Engineering (DING), University of Sannio, 82100 Benevento, Italy)

Abstract

Modern power system operation should comply with strictly reliability and security constraints, which aim at guarantee the correct system operation also in the presence of severe internal and external disturbances. Amongst the possible phenomena perturbing correct system operation, the predictive assessment of the impacts induced by extreme weather events has been considered as one of the most critical issues to address, since they can induce multiple, and large-scale system contingencies. In this context, the development of new computing paradigms for resilience analysis has been recognized as a very promising research direction. To address this issue, this paper proposes two methodologies, which are based on Time Varying Markov Chain and Dynamic Bayesian Network, for assessing the system resilience against extreme wind gusts. The main difference between the proposed methodologies and the traditional solution techniques is the improved capability in modelling the occurrence of multiple component faults and repairing, which cannot be neglected in the presence of extreme events, as experienced worldwide by several Transmission System Operators. Several cases studies and benchmark comparisons are presented and discussed in order to demonstrate the effectiveness of the proposed methods in the task of assessing the power system resilience in realistic operation scenarios.

Suggested Citation

  • Ennio Brugnetti & Guido Coletta & Fabrizio De Caro & Alfredo Vaccaro & Domenico Villacci, 2020. "Enabling Methodologies for Predictive Power System Resilience Analysis in the Presence of Extreme Wind Gusts," Energies, MDPI, vol. 13(13), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3501-:d:381241
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    References listed on IDEAS

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    1. Rachunok, Benjamin & Nateghi, Roshanak, 2020. "The sensitivity of electric power infrastructure resilience to the spatial distribution of disaster impacts," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
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    Cited by:

    1. Ziyi Wang & Zengqiao Chen & Cuiping Ma & Ronald Wennersten & Qie Sun, 2022. "Nationwide Evaluation of Urban Energy System Resilience in China Using a Comprehensive Index Method," Sustainability, MDPI, vol. 14(4), pages 1-36, February.
    2. Adel Mottahedi & Farhang Sereshki & Mohammad Ataei & Ali Nouri Qarahasanlou & Abbas Barabadi, 2021. "The Resilience of Critical Infrastructure Systems: A Systematic Literature Review," Energies, MDPI, vol. 14(6), pages 1-32, March.
    3. Antonio Pepiciello & Alfredo Vaccaro & Loi Lei Lai, 2020. "An Interval Mathematic-Based Methodology for Reliable Resilience Analysis of Power Systems in the Presence of Data Uncertainties," Energies, MDPI, vol. 13(24), pages 1-14, December.

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