A Resilience Evaluation Framework for Electric Distribution Systems: Historical Weather Conditioning, Sensitivity Analysis, and a Flooding-Aware Extension

TL;DR

This paper extends a graph-based resilience evaluation framework for electric distribution systems by incorporating historical weather data, sensitivity analysis, and a flooding extension, enabling more realistic and comprehensive resilience assessments.

Contribution

It introduces a novel coupled power-flooding extension and analyzes resilience conditioned on real weather events, advancing the realism and scope of distribution system resilience modeling.

Findings

Wind-event resilience metrics stabilize at about 256 episodes.

Outage peak, duration, and intensity vary systematically with model assumptions.

Flood occurrence in joint simulations is 1.9%, increasing with outage severity.

Abstract

Evaluating resilience in electric distribution systems under severe weather requires models that can connect network topology, hazard simulation, fragility modeling, restoration assumptions, repair strategy, and downstream consequences. This paper extends our prior graph-based resilience evaluation framework for power distribution systems in three ways: it adds analysis conditioned on historical events with real outage and weather data, introduces sensitivity studies for key modeling assumptions, and includes a coupled power-flooding extension for sewage-backup assessment. Historical wind events drive Monte Carlo simulations conditioned on real weather, and the observed outage trajectories are treated as realized historical samples for comparison. Wind-event resilience metrics stabilize at approximately 256 episodes, and outage peak, duration, and outage intensity change systematically with fragility parameters, network topology, restoration assumptions, and repair strategies. In a separate 1000-episode joint power-flooding simulation, episodes with at least one flooded customer occur in 1.9% of episodes overall, and both flood occurrence and flood intensity increase with outage intensity, showing a selective power-to-flood consequence pathway. Overall, the framework provides a practical basis for resilience assessment, comparative scenario analysis, and coupled power-flooding studies in a limited public-data setting, while also suggesting that more detailed utility data could further improve simulation realism.