Resilience of Energy Infrastructure and Services: Modeling, Data Analytics, and Metrics

Chuanyi Ji, Yun Wei, H. Vincent Poor

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

Large-scale power failures induced by severe weather have become frequent and damaging in recent years, causing millions of people to be without electricity service for days. Although the power industry has been battling weather-induced failures for years, it is largely unknown how resilient the energy infrastructure and services really are to severe weather disruptions. What fundamental issues govern the resilience? Can advanced approaches such as modeling and data analytics help industry to go beyond empirical methods? This paper discusses the research to date and open issues related to these questions. The focus is on identifying fundamental challenges and advanced approaches for quantifying resilience. In particular, the first aspect of this problem is how to model large-scale failures, recoveries, and impacts, involving the infrastructure, service providers, customers, and weather. The second aspect is how to identify generic vulnerability in the infrastructure and services through large-scale data analytics. The third aspect is to understand what resilience metrics are needed and how to develop them.

Original languageEnglish (US)
Article number7951075
Pages (from-to)1354-1366
Number of pages13
JournalProceedings of the IEEE
Volume105
Issue number7
DOIs
StatePublished - Jul 2017

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • Data analytics
  • failure
  • nonstationary spatiotemporal models
  • power distribution infrastructure
  • recovery
  • resilience metrics
  • services to customers

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