A Framework for Risk Assessment and Optimal Line Upgrade Selection to Mitigate Wildfire Risk

TL;DR

This paper presents a framework that uses risk metrics derived from wildfire data to optimize power line upgrades, aiming to minimize wildfire ignition risk in transmission networks, demonstrated on California's lines.

Contribution

It introduces a novel risk-based optimization model for prioritizing power line upgrades to reduce wildfire risk, incorporating multiple risk factors from historical wildfire data.

Findings

The model effectively identifies lines for undergrounding that significantly reduce wildfire risk.

Application to California's network shows substantial risk mitigation potential.

Risk metrics based on wildfire maps improve upgrade prioritization accuracy.

Abstract

As wildfires in the United States are becoming more frequent and severe, mitigating wildfire ignition risk from power line faults is an increasingly crucial effort. Long-term ignition prevention strategies, especially converting overhead lines to underground cables, are expensive. Thus, it is important to prioritize upgrades on lines that will reduce wildfire ignition risk the most. However, since so many factors contribute to ignition risk, it is difficult to quantify the wildfire risk associated with power lines. This paper examines how various risk definitions based on historical wildfire risk maps can be used to inform transmission upgrade planning. These risk metrics are evaluated using an optimization model that determines which overhead lines should be undergrounded such that the total wildfire risk in the network is minimized. The risk assignment and upgrade selection are tested on both a synthetic network and the actual transmission lines in California.