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

TL;DR

The paper addresses how to quantify wildfire ignition risk for transmission lines using WFPI-based risk maps and compares six risk aggregation metrics under two shutoff strategies: thresholding and Optimal Power Shutoff (OPS). Using the California Test System with year-long WFPI data, it demonstrates that metric choice materially changes which lines are de-energized and the resulting load shed, and that OPS can reduce load shed by about 80% while achieving comparable risk reduction to thresholding. The findings highlight the sensitivity of PSPS planning to how risk is aggregated over line corridors and suggest that optimization-based planning offers substantial resilience benefits. The work also underscores the need for careful selection of risk metrics and further research into high-fidelity fire dynamics and equity considerations in de-energization decisions.

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.

Figures (7)

• Figure 1: California's transmission line paths on a Wildland Fire Potential Index map for Oct. 26, 2020.
• Figure 2: A conceptual example of three transmission lines. Boxes represent pixels that lines intersect, with their risk values shown above. Red pixels represent high-risk pixels in the set $\mathcal{P}^{h}_{\ell,d}$.
• Figure 3: The number of lines de-energized daily by metric under the 95th-percentile thresholding method.
• Figure 4: Seven-day rolling average load shed from thresholded line de-energizations.
• Figure 5: The number of lines de-energized daily, by metric, under the optimal power shutoff method.