PNNL

Abstract

Recent climate change is affecting the operation of the power systems. Extreme temperature events such as heatwaves and cold snaps can lead to generation scarcity scenarios due to increased demand for electricity. This paper uses the integration of advanced metering infrastructure (AMI) data with an outage management system to develop a rolling outage scheme. The proposed data-driven rolling outage scheme aims to identify a group of customers that would be selectively served in a rolling fashion based on the curtailment requirements. Restoring all the customers at once at the end of the rolling outage period leads to a cold load pickup phenomenon. To address this, a cold load pick-up management scheme that restores customers in blocks is also proposed. The proposed approach is tested using a realistic co-simulation platform utilizing the extreme event scenario of the Texas Winter Storm Uri. Results demonstrate that introducing data-driven AMI-based analytics in rolling outages substantially enhances power utilization (by 41.8%), individual customer thermal comfort, and overall system operational resilience as compared to the feeder-based outages. The proposed CLPU management scheme restores the customers in the block without increasing the restoration time beyond the CLPU settling time.