  Blackouts in recent decades show 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 enhance 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 by directly applying the
fragility function or failure probability of system components in
time-sequential 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 time-varying failure probability of a component into a
hazard resistance as a time-invariant value during the simulation of evolving
hazards. 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 during Hurricane Fiona 2022.
