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

TL;DR

This paper introduces a hazard resistance-based method for more accurate spatiotemporal risk analysis of power outages during hurricanes, addressing limitations of existing simulation techniques by incorporating high-resolution meteorological data.

Contribution

A novel HRSRA method that converts time-varying failure probabilities into hazard resistance, improving accuracy in outage risk assessment during hurricanes.

Findings

HRSRA reduces errors compared to traditional SMC methods.

The method effectively integrates high-resolution meteorological data.

Validation with Hurricane Fiona data demonstrates its practical applicability.

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.