Extracting Resilience Metrics From Distribution Utility Data Using Outage and Restore Process Statistics
Year: 2021
Type: article
Abstract: Resilience curves track the accumulation and restoration of outages during an event on an electric distribution grid. We show that a resilience curve generated from utility data can always be decomposed into an outage process and a restore process and that these processes generally overlap in time. We use many events in real utility data to characterize the statistics of these processes, and derive formulas based on these statistics for resilience metrics such as restore duration, customer hours not served, and outage and restore rates. The formulas express the mean value of these metrics as a function of the number of outages in the event. We also give a formula for the variability of restore duration, which allows us to predict a maximum restore duration with 95% confidence. Overall, we give a simple and general way to decompose resilience curves into outage and restore processes and then show how to use these processes to extract resilience metrics from standard distribution system data.
Institution: Iowa State University
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,045
Awards: Data-driven modeling, monitoring and mitigation of cascading outages in transmission and distribution systems, AWARD NUMBER DE-OE0000875 FOR PROJECT ENTITLED, “ROBUST REAL-TIME MODELING OF DISTRIBUTION SYSTEMS WITH DATA-DRIVEN GRID-WISE OBSERVABILITY.”, CRISP Type 2/Collaborative Research: Understanding the Benefits and Mitigating the Risks of Interdependence in Critical Infrastructure Systems, EAGER: Renewables: Fundamental allometric scalings for distribution networks with renewables
OpenAlex ID: W3095359630