Electrical Power Network Modeling Framework for Wildfire Risk and Resilience Analysis

The increasing intensity and frequency of wildfires are causing significant economic and societal impacts on communities through direct effects on the built environment, particularly critical infrastructure. Electrical systems can both initiate wild-fires (grid-to-fire) and be damaged by wildfire exposure (fire-to-grid). Therefore, resilient electric systems can both limit ignitions and be hardened such that they are more robust to fire demands. Researchers have investigated wildfire mitigation strategies using traditional transmission and distribution electrical test-system models. However, these test cases may not accurately represent realistic electrical system configurations or fuel landscapes, nor capture community impacts, particularly the social and economic effects of mitigation strategies. A wildfire-aware modeling framework enables researchers to develop test cases that benchmark resilience and mitigation strategies while reducing reliance on overly simplistic assumptions about wildfire effects on electrical systems and communities. This study proposes a modeling framework for wildfire-electrical system research by analyzing recent literature and identifying key dimensions as well as gaps within these dimensions. In particular, the framework considers how fire in the wildland-urban interface propagates in space and time, how hazard-infrastructure interactions (e.g., wind and fire) cause system- and component-level damage, and how wildfire-related power outages affect communities.

💡 Research Summary

The paper addresses the growing challenge of wildfires intersecting with electric power infrastructure, proposing a comprehensive modeling framework that captures the bidirectional interactions between fire and grid—namely fire‑to‑grid (f2g) and grid‑to‑fire (g2f). The authors begin by reviewing the literature and identifying nine analytical dimensions that have been used to study wildfire‑related power resilience: operations/planning, system type, case‑study testbed, hazard representation, component exposure, component response modeling, disaster phase, and community resilience. By mapping existing studies onto these dimensions, they reveal that most research focuses on either transmission (Tx) or distribution (Dx) networks in isolation, relies on traditional IEEE test systems, and lacks realistic representation of wildland‑urban interface (WUI) fuel landscapes, fire spread dynamics, and socio‑economic impacts of outages.

To overcome these gaps, the framework integrates three core modules: (1) Hazard Input, which uses high‑resolution weather, topography, and vegetation data to drive fire‑spread simulators such as FARSITE, FlamMap, or semi‑empirical Rothermel‑based models; (2) Component Exposure and Response, which couples physical fire exposure (heat flux, ember loading) with detailed equipment vulnerability models (thermal degradation of conductors, pole failure thresholds, transformer fire‑resistance) and includes automatic protection device behavior; (3) Power System Dynamics, which links the Tx and Dx networks through dynamic AC/DC power‑flow calculations, allowing the assessment of cascading outages, voltage collapse, and load shedding triggered by fire‑induced faults or pre‑emptive public safety power shutoffs (PSPS). The framework also incorporates operational and planning strategies—PSPS, automated shutoffs, microgrid islanding, hardening investments, and DER placement—enabling scenario‑based evaluation of their effectiveness across both network layers.

A fourth, community‑centric module quantifies the socio‑economic consequences of power interruptions. By overlaying GIS‑based population density, critical facility locations (hospitals, fire stations), and vulnerability indices onto the outage maps, the model produces key performance indicators such as outage duration, restoration cost, loss of essential services, and equity‑adjusted impact metrics. This allows policymakers to weigh technical resilience measures against their broader societal outcomes.

The framework is designed to be modular: researchers can substitute alternative fire‑spread models, replace the power‑flow engine, or augment the social impact layer with more detailed economic loss functions. This flexibility supports a wide range of use cases—from evaluating the trade‑offs of aggressive PSPS policies under extreme wind events to testing the benefits of undergrounding lines in high‑fuel‑load zones.

In the concluding discussion, the authors argue that a standardized, WUI‑embedded testbed is essential for reproducible benchmarking of wildfire‑grid resilience strategies. By coupling realistic fire dynamics with integrated transmission‑distribution networks and community impact assessments, the proposed framework moves beyond the “single‑system, single‑hazard” paradigm that has dominated prior work. It offers a path toward more informed decision‑making that simultaneously reduces wildfire ignition risk, enhances grid robustness, and mitigates the downstream economic and social disruptions experienced by affected communities.