EARNEST: Resilience strategies for a net-zero neighborhood
Led by Stanford University, Equitable, Affordable & Resilient Nationwide Energy System Transition (EARNEST) is a US Department of Energy-funded consortium of universities, industry, and communities.
Click here for a video of the EARNEST kickoff
Project Summary
Under EARNEST, the pilot project the University of Michigan School for Environment and Sustainability will be working on is resilience strategies for a low-income net zero neighborhood. Bryant neighborhood in Ann Arbor, MI comprises about 260 homes where 75% of households are low-income and 50% are BIPOC. The City of Ann Arbor plans to decarbonize Bryant through home electrification, the deployment of distributed energy resources, and the creation of a Sustainable Energy Utility (SEU) that will operate in parallel to the existing investor-owned utility.
From 2000 - 2021, Michigan ranked second in the nation for the most reported weather-related power outages. Long-duration power outages are becoming more common. Much of the country's electrical infrastructure was constructed without accounting for changing climate conditions. The performance, resilience, and capacity of our electricity systems to meet demand may be further compromised, even as more loads electrify. We will build tools that help communities assess the trade-off between financial cost and resilience. Uniquely, we will assess how residents value services within their homes and in the community.
We will do this in three steps:
- Interview Bryant neighborhood residents to ask questions regarding previous outage experiences and coping strategies employed.
- After analyzing the results, we will conduct a card stacking activity to learn about how they prioritize different appliances and community services during an outage, given different budget constraints. This will allow us to quantify the trade-offs people are willing to make between cost and resilience.
- We will gather feedback on the estimates of trade-offs that our analytical tools produce, and refine those tools so that they produce outputs that help community decision-making. We will then codify what we learn in user-friendly tools.