🤖 AI Summary
Wildfire ignition by overhead transmission lines poses a critical public safety risk, necessitating targeted undergrounding investments to enhance grid resilience.
Method: This paper proposes a segment-level (span-wise) undergrounding investment prioritization framework that integrates probabilistic ignition modeling, weather-driven simulation, and IEEE-standard power system modeling. Leveraging Monte Carlo analysis across 43,712 weather–operating scenarios, it quantifies both ignition probability and potential wildfire losses per line segment, enabling the first fine-grained assessment coupling ignition risk with full-weather-scenario variability.
Contribution/Results: The method establishes a quantitative CapEx–risk-mitigation trade-off framework, yielding an economically optimal undergrounding priority list. Validated on the IEEE 30-bus system, it achieves significantly higher risk-reduction efficiency per unit investment compared to conventional approaches, providing a reusable, scalable decision-support tool for utility-scale grid hardening.
📝 Abstract
Grid-ignited wildfires are one of the most destructive catastrophic events, profoundly affecting the built and natural environments. Burying power lines is an effective solution for mitigating the risk of wildfire ignition. However, it is a costly capital expenditure (CapEx) requiring meticulous planning and investment prioritization. This paper proposes a systematic approach to estimate the potential wildfire ignition damage associated with each transmission line and accordingly offers a priority list for undergrounding. The proposed approach allows electric utilities to make risk-informed decisions for grid modernization and resiliency improvement against wildfires. As a case study, we examine the likelihood of wildfire ignition for each line segment, i.e., between two high-voltage towers, under diverse weather conditions throughout the year. The studies on the standard IEEE 30-bus test system, simulated on 43,712 scenarios, demonstrate the effectiveness of the proposed approach.