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Dynamic Modeling of Power Outages Caused by Thunderstorms

Author

Listed:
  • Berk A. Alpay

    (Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • David Wanik

    (Department of Operations and Information Management, University of Connecticut, Storrs, CT 06269, USA)

  • Peter Watson

    (Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Diego Cerrai

    (Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Guannan Liang

    (Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Emmanouil Anagnostou

    (Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA)

Abstract

Thunderstorms are complex weather phenomena that cause substantial power outages in a short period. This makes thunderstorm outage prediction challenging using eventwise outage prediction models (OPMs), which summarize the storm dynamics over the entire course of the storm into a limited number of parameters. We developed a new, temporally sensitive outage prediction framework designed for models to learn the hourly dynamics of thunderstorm-caused outages directly from weather forecasts. Validation of several models built on this hour-by-hour prediction framework and comparison with a baseline model show abilities to accurately report temporal and storm-wide outage characteristics, which are vital for planning utility responses to storm-caused power grid damage.

Suggested Citation

  • Berk A. Alpay & David Wanik & Peter Watson & Diego Cerrai & Guannan Liang & Emmanouil Anagnostou, 2020. "Dynamic Modeling of Power Outages Caused by Thunderstorms," Forecasting, MDPI, vol. 2(2), pages 1-12, May.
    • Handle: RePEc:gam:jforec:v:2:y:2020:i:2:p:8-162:d:361802
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    References listed on IDEAS

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    7. Feifei Yang & David W. Wanik & Diego Cerrai & Md Abul Ehsan Bhuiyan & Emmanouil N. Anagnostou, 2020. "Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration," Sustainability, MDPI, vol. 12(4), pages 1-19, February.
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    Cited by:

    1. Gallaher, Adam & Graziano, Marcello & Fiaschetti, Maurizio, 2021. "Legacy and shockwaves: A spatial analysis of strengthening resilience of the power grid in Connecticut," Energy Policy, Elsevier, vol. 159(C).
    2. Hughes, William & Zhang, Wei & Bagtzoglou, Amvrossios C. & Wanik, David & Pensado, Osvaldo & Yuan, Hao & Zhang, Jintao, 2021. "Damage modeling framework for resilience hardening strategy for overhead power distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    3. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    4. Hughes, William & Watson, Peter L. & Cerrai, Diego & Zhang, Xinxuan & Bagtzoglou, Amvrossios & Zhang, Wei & Anagnostou, Emmanouil, 2024. "Assessing grid hardening strategies to improve power system performance during storms using a hybrid mechanistic-machine learning outage prediction model," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    5. Peter L. Watson & Marika Koukoula & Emmanouil Anagnostou, 2021. "Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System," Forecasting, MDPI, vol. 3(3), pages 1-20, August.
    6. Olukunle O. Owolabi & Deborah A. Sunter, 2022. "Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages," Energies, MDPI, vol. 15(6), pages 1-22, March.
    7. Tara C. Walsh & David W. Wanik & Emmanouil N. Anagnostou & Jonathan E. Mellor, 2020. "Estimated Time to Restoration of Hurricane Sandy in a Future Climate," Sustainability, MDPI, vol. 12(16), pages 1-27, August.

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