Visible to the public AWR: Anticipate, Withstand, and Recover Resilience Metric for Operational and Planning Decision Support in Electric Distribution System

TitleAWR: Anticipate, Withstand, and Recover Resilience Metric for Operational and Planning Decision Support in Electric Distribution System
Publication TypeJournal Article
Year of Publication2022
AuthorsKandaperumal, Gowtham, Pandey, Shikhar, Srivastava, Anurag
JournalIEEE Transactions on Smart Grid
Volume13
Pagination179—190
ISSN1949-3061
KeywordsAnalytical models, Data models, Distribution system, graph theory, Investment, Measurement, Planning, Predictive models, pubcrawl, resilience, Resilience Metrics, resilience planning, Resiliency, Scalability, work factor metrics
Abstract

With the increasing number of catastrophic weather events and resulting disruption in the energy supply to essential loads, the distribution grid operators' focus has shifted from reliability to resiliency against high impact, low-frequency events. Given the enhanced automation to enable the smarter grid, there are several assets/resources at the disposal of electric utilities to enhances resiliency. However, with a lack of comprehensive resilience tools for informed operational decisions and planning, utilities face a challenge in investing and prioritizing operational control actions for resiliency. The distribution system resilience is also highly dependent on system attributes, including network, control, generating resources, location of loads and resources, as well as the progression of an extreme event. In this work, we present a novel multi-stage resilience measure called the Anticipate-Withstand-Recover (AWR) metrics. The AWR metrics are based on integrating relevant 'system characteristics based factors', before, during, and after the extreme event. The developed methodology utilizes a pragmatic and flexible approach by adopting concepts from the national emergency preparedness paradigm, proactive and reactive controls of grid assets, graph theory with system and component constraints, and multi-criteria decision-making process. The proposed metrics are applied to provide decision support for a) the operational resilience and b) planning investments, and validated for a real system in Alaska during the entirety of the event progression.

DOI10.1109/TSG.2021.3119508
Citation Keykandaperumal_awr_2022