Equitably allocating wildfire resilience investments for power grids: The curse of aggregation and vulnerability indices

Abstract

Social vulnerability indices have increased traction for guiding infrastructure investment decisions to prioritize communities that need these investments most. One such plan is the Biden-Harris Justice40 initiative, which aims to guide equitable infrastructure investments by ensuring that disadvantaged communities defined by the Climate & Economic Justice Screening Tool (CEJST) receive 40% of the total benefit realized by the investment. However, there is limited research on the practicality of applying vulnerability indices like the CEJST to real-world decision-making for policy outcomes. In this paper, we study this gap by examining the effectiveness of vulnerability indices in a case study focused on power shutoff and undergrounding decisions in wildfire-prone regions. Using a mixed-integer program and a high-fidelity synthetic transmission network in Texas, we model resource allocation policies inspired by Justice40 and evaluate their impact on reducing power outages and mitigating wildfire risk for vulnerable groups. Our analysis reveals that the Justice40 framework may fail to protect certain communities facing high wildfire risk. In our case study, we show that indigenous groups are particularly impacted. We posit that this outcome is likely due to information losses from data aggregation and the use of generalized vulnerability indices. Through the use of explicit group-level protections, we are able to bound the best possible outcome for population groups that are proportionally most affected.

Figures (16)

• Figure 1: This figure shows some vulnerability characteristics of the synthetic Texas7k network where the circled regions show the overlap in vulnerabilities between these three metrics (wildfire risk, high cost to underground, and racial minority status). These areas also have a lower likelihood of being selected for power line undergrounding due to lower population density (not pictured).
• Figure 2: Load shed (in the absence of undergrounding decisions and equity considerations, BL-M0), visualized with red bubbles on the map, occurs almost exclusively in the vulnerable areas highlighted in Figure \ref{['fig: Texas7k profile']}.
• Figure 3: Summary of all models considered in the results section of this paper. Appendix results show alternative budget outcomes. All models include the constraints \ref{['eq: dcots']}, and \ref{['const: Line high']}--\ref{['const: undergrounding threshold']}. Budgets are listed in millions USD.
• Figure 4: In the no-budget baseline case (BL-M0) we observe that Indigenous, Hispanic, and uninsured populations face above-average percentages of load shed.
• Figure 5: Trends in load shed, risk reduction, and budget allocation per person in each group under BL-M1 as a function of the total budget allocated for undergrounding.