A spatio-temporal analysis for power grid resilience to extreme weather

In recent years, extreme weather events frequently cause large-scale power outages, affecting millions of customers for extended duration. Resilience, the capability of withstanding, adapting to, and recovering from a large-scale disruption, has becomes a top priority for power sector, in addition to economics and sustainability. However, a good understanding on the power grid resilience is still lacking, as most approaches still either stay on the conceptual level, yielding no actionable results, or focus on a particular technical issue, revealing little insights on the system level. In this study, we take a quantitative approach to understanding power system resilience by directly exploring real power outage data. We first give a qualitative analysis on power system resilience and large-scale power outage process, identifying key elements and developing conceptual models to describe grid resilience. Then we propose a spatio-temporal random process model, with parameters representing the identified resilience capabilities and interdependence between service areas. We perform analyse using our model on a set of large-scale customer-level quarter-hourly historical power outage data and corresponding weather records from three major service territories on the east-coast of the United States under normal daily operations and three major extreme weather events. It has shown that weather only directly cause a small portion of power outages, and the peak of power outages usually lag the weather events. Planning vulnerability and excessively accumulation of weather effects play a key role in causing sustained local outages to the power system in a short time. The local outages caused by weather events will later propagate to broader regions through the power grid, which subsequently lead to a much larger number of non-local power outages.