Quantifying Metrics for Wildfire Ignition Risk from Geographic Data in Power Shutoff Decision-Making

TL;DR

This paper evaluates six different metrics for quantifying wildfire ignition risks from geographic data to improve power shutoff decisions, demonstrating that optimization-based methods can reduce load shedding while maintaining risk reduction.

Contribution

It introduces and compares six risk metrics for wildfire ignition risk assessment in power shutoff planning using a large-scale, realistic test case with real fire potential data.

Findings

Optimization-based risk metrics reduce load shedding compared to threshold-based metrics.

Choice of risk metric significantly influences which power lines are de-energized.

First application of optimal power shutoff planning on a large, realistic grid model.

Abstract

Faults on power lines and other electric equipment are known to cause wildfire ignitions. To mitigate the threat of wildfire ignitions from electric power infrastructure, many utilities preemptively de-energize power lines, which may result in power shutoffs. Data regarding wildfire ignition risks are key inputs for effective planning of power line de-energizations. However, there are multiple ways to formulate risk metrics that spatially aggregate wildfire risk map data, and there are different ways of leveraging this data to make decisions. The key contribution of this paper is to define and compare the results of employing six metrics for quantifying the wildfire ignition risks of power lines from risk maps, considering both threshold- and optimization-based methods for planning power line de-energizations. The numeric results use the California Test System (CATS), a large-scale synthetic grid model with power line corridors accurately representing California infrastructure, in combination with real Wildland Fire Potential Index data for a full year. This is the first application of optimal power shutoff planning on such a large and realistic test case. Our results show that the choice of risk metric significantly impacts the lines that are de-energized and the resulting load shed. We find that the optimization-based method results in significantly less load shed than the threshold-based method while achieving the same risk reduction.