Persistent Geospatial Outage Scenario Construction for Interdependent Infrastructure Simulation
Solongo Ganbold, Gelila Webster, Sohini Roy
Extreme weather events are producing persistent geographic patterns of power-grid disruption across the United States, yet these patterns are rarely incorporated into geospatial simulation of interdependent infrastructure failures. This paper presents a data-driven geospatial simulation framework that transforms persistent outage patterns into empirically grounded regional simulation scenarios for interdependent power-communication cascade simulation. Using a national outage dataset from 2015–2023, we introduce the Hotspot Persistence Index (HPI), a severity-ranked recurrence metric for identifying counties that repeatedly emerge as outage hotspots over time. We then apply a multi-scale density-based reLinement process to convert persistent county-level hotspots into geographically interpretable regional simulation scenarios characterized by persistence, severity, and spatial extent. These empirically derived scenarios are used to parameterize the ModiLied Implicative Interdependency Model (MIIM), enabling simulation of cascading behavior across coupled power and communication layers. Results show that three persistent regional scenarios account for 54.4% of total HPI-weighted cascade impact, while communication-layer entities fail at 2.5× the rate of power buses under high-impact persistent scenarios. HPI-guided hardening reprioritizes protection candidates relative to degree- and betweenness-centrality baselines, demonstrating that empirically derived outage scenarios lead to different infrastructure protection priorities than topology-only approaches. These results demonstrate how empirically derived geospatial outage scenarios can improve simulation-based infrastructure resilience analysis and hardening prioritization.