首页|Hazard Resistance-Based Spatiotemporal Risk Analysis for Distribution Network Outages During Hurricanes

Hazard Resistance-Based Spatiotemporal Risk Analysis for Distribution Network Outages During Hurricanes

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  • NETL
  • NSTL
  • IEEE
In recent decades, blackouts have shown an increasing prevalence of power outages due to extreme weather events such as hurricanes. Precisely assessing the spatiotemporal outages in distribution networks, the most vulnerable part of power systems, is critical to enhancing power system resilience. The Sequential Monte Carlo (SMC) simulation method is widely used for spatiotemporal risk analysis of power systems during extreme weather hazards. However, it is found here that the SMC method can lead to large errors as it repeatedly samples the failure probability from the time-invariant fragility functions of system components in time-series analysis, particularly overestimating damages under evolving hazards with high-frequency sampling. To address this issue, a novel hazard resistance-based spatiotemporal risk analysis (HRSRA) method is proposed. This method converts the failure probability of a component into a hazard resistance and uses it as a time-invariant value in time-series analysis. The proposed HRSRA provides an adaptive framework for incorporating high-spatiotemporal-resolution meteorology models into power outage simulations. By leveraging the geographic information system data of the power system and a physics-based hurricane wind field model, the superiority of the proposed method is validated using real-world time-series power outage data from Puerto Rico, including data collected during Hurricane Fiona in 2022.

HazardsSpatiotemporal phenomenaPower system reliabilityMeteorologyHurricanesDistribution networksResistanceRisk analysisGeographic information systemsPower system stability

Luo Xu、Ning Lin、Dazhi Xi、Kairui Feng、H. Vincent Poor

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Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA|Center for Policy Research on Energy and the Environment, Princeton University, Princeton, NJ, USA

Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA

2025

10.1109/TPWRS.2024.3469168

IEEE transactions on power systems: Institute of Electrical and Electronics Engineers transactions on power systems

IEEE transactions on power systems: Institute of Electrical and Electronics Engineers transactions on power systems

ISSN：
年,卷(期)：2025.40(3)
  • 38