Hazard resistance-based spatiotemporal risk analysis for distribution network outages during hurricanes

TL;DR

The paper tackles the challenge of accurately quantifying spatiotemporal distribution-network outages during evolving hurricanes by moving beyond time-varying fragility-based samplings. It introduces hazard-resistance spatiotemporal risk analysis (HRSRA), which replaces time-dependent failure probabilities with time-invariant hazard resistances $R_i^W$ sampled from region-specific fragility distributions and integrated with a physics-based hurricane wind field. The approach is validated against real outage data from Puerto Rico during Hurricane Fiona 2022, showing closer alignment with observations and substantially lower RMSE than the traditional Sequential Monte Carlo (SMC) method, especially at higher temporal resolutions. The method enables high-spatiotemporal-resolution risk assessments for distribution networks under persistent hazards, enhancing resilience planning and emergency response capabilities.

Abstract

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.

Figures (10)

• Figure 1: Overview of the proposed framework for quantifying spatiotemporal power outage in distribution networks during hurricane events.
• Figure 2: High-resolution geographic information system data of the Puerto Rico distribution network: An illustration of the Luquillo Substation at 8kV with three distribution feeders, each highlighted in white at the bottom.
• Figure 3: Seven regions of the Puerto Rico power grid and power supply rate. (a) Puerto Rico power grid operational divisions. (b) Regional and total power outage data.
• Figure 4: Diagram of a distribution network.
• Figure 5: Comparison of uncertainties considered in the Sequential Monte Carlo (SMC) method (Left) and in the proposed HRSRA (Right). Both methods are based on the same fragility curve. SMC samples the system state based on a fragility curve with uncertainty in the time-varying failure probability. We redefine the uncertainty into the time-invariant hazard resistance distribution of all components.