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A post-contingency power flow emulator for generalized probabilistic risks assessment of power grids

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  • Rocchetta, Roberto
  • Patelli, Edoardo

Abstract

Risk-based power dispatch has been proposed as a viable alternative to Security-Constrained Dispatch to reduce power grid costs and help to better understand of prominent hazards. In contrast to classical approaches, risk-based frameworks assign different weights to different contingencies, quantifying both their likelihood occurrence and severity. This leads to an economically profitable operational schedule by exploiting the trade-off between grid risks and costs. However, relevant sources of uncertainty are often neglected due to issues related to the computational cost of the analysis. In this work, we present an efficient risk assessment frameworks for power grids. The approach is based on the Line-Outage Distribution Factors for the severity assessment of post-contingency scenarios. The proposed emulator is embedded within a generalized uncertainty quantification framework to quantify: (1) The effect of imprecision on the estimation of the risk index; (2) The effect of inherent variability, aleatory uncertainty, in environmental-operational variables. The computational cost and accuracy of the proposed risk model are discussed in comparison to traditional approaches. The applicability of the proposed framework to real size grids is exemplified by several case studies.

Suggested Citation

  • Rocchetta, Roberto & Patelli, Edoardo, 2020. "A post-contingency power flow emulator for generalized probabilistic risks assessment of power grids," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
  • Handle: RePEc:eee:reensy:v:197:y:2020:i:c:s0951832019303023
    DOI: 10.1016/j.ress.2020.106817
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    References listed on IDEAS

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    1. Hall, Jim W., 2006. "Uncertainty-based sensitivity indices for imprecise probability distributions," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1443-1451.
    2. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2017. "AC power flow importance measures considering multi-element failures," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 89-97.
    3. Rocchetta, R. & Li, Y.F. & Zio, E., 2015. "Risk assessment and risk-cost optimization of distributed power generation systems considering extreme weather conditions," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 47-61.
    4. Simon, Christophe & Bicking, Frédérique, 2017. "Hybrid computation of uncertainty in reliability analysis with p-box and evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 629-638.
    5. Rocchetta, Roberto & Zio, Enrico & Patelli, Edoardo, 2018. "A power-flow emulator approach for resilience assessment of repairable power grids subject to weather-induced failures and data deficiency," Applied Energy, Elsevier, vol. 210(C), pages 339-350.
    6. Ferson, Scott & Troy Tucker, W., 2006. "Sensitivity analysis using probability bounding," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1435-1442.
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    Citations

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    Cited by:

    1. Stover, Oliver & Karve, Pranav & Mahadevan, Sankaran, 2023. "Reliability and risk metrics to assess operational adequacy and flexibility of power grids," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Rocchetta, Roberto & Zhan, Zhouzhao & van Driel, Willem Dirk & Di Bucchianico, Alessandro, 2024. "Uncertainty analysis and interval prediction of LEDs lifetimes," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Rocchetta, Roberto, 2022. "Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. Dariusz Gołȩbiewski & Tomasz Barszcz & Wioletta Skrodzka & Igor Wojnicki & Andrzej Bielecki, 2022. "A New Approach to Risk Management in the Power Industry Based on Systems Theory," Energies, MDPI, vol. 15(23), pages 1-19, November.
    6. Salomon, Julian & Winnewisser, Niklas & Wei, Pengfei & Broggi, Matteo & Beer, Michael, 2021. "Efficient reliability analysis of complex systems in consideration of imprecision," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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