Affordability and Resilience in Infrastructure Systems

Community-impact centered resilience planning

Power outages do not impact all communities equally. Resilience planning must account for who is affected, not just aggregated load that is lost. Planning studies evaluate many hazard scenarios and investment portfolios, so grid models are often reduced to maintain computational tractability. But aggregation comes at a cost: averaging over households can hide the most vulnerable communities and lead to inequitable resource allocation. We developed a community impact-centered network reduction method that preserves both the power physics of the grid and the spatial distribution of community data. Our method embeds a household-level scarce resource score within an optimization-based Kron reduction, and limits how dissimilar the households grouped into a cluster can be. On a realistic distribution feeder of Galveston Island, Texas, we achieve an 86% reduction in network size with voltage errors below 0.01 p.u., while keeping vulnerable households visible in the reduced model. Next, we are integrating these reduced networks into resilience planning models, to understand how network representation shapes planning outcomes for communities.

Centering affordability in grid models

Access to affordable electricity is a central tenet of power systems. Energy burden, the share of household income spent on home energy bills, is a key measure of energy affordability. In the U.S., households spending more than 6% of their income on energy are considered to have a high energy burden. Energy burden is typically assessed using census and billing data, addressing the "income" part of the energy burden equation. In this project we explore the "electricity price" part of the equation, and try to understand how grid operations and infrastructure drive affordability. We introduce the locational marginal burden (LMB), which measures how energy burden at each node changes with demand across the network. By differentiating through the optimal power flow problem, the LMB is computed analytically from the same solution that sets electricity prices, creating a direct link between power system operations and energy equity. Applied to a synthetic Hawaii network with real census income data, we find that the LMB is inversely related to income, and that less densely populated areas impose higher burden on the rest of the network, pointing to the role of rural grid infrastructure in pricing equity. The LMB can support regulators and utilities in targeting infrastructure investments and evaluating equity under performance-based regulation. Our ongoing work uses the LMB for grid investment planning; exploring new residential retail rate structures; and extending the LMB analysis to distribution systems.

Left: a map of energy burden in the US. Anything that isn't shaded a pale yellow is above the energy burden threshhold of 6%. Energy poverty is experienced throughout the US, across all states, and in both urban and rural systems. Right: the power grid is the physical infrastructure that supplies electricity, moving it from generation to load centers. To better understand whether and how infrastructure can be improved to lower energy burden needs new tools. This is what we are developing.