Critical Risk Indicators (CRIs) for the electric power grid: a survey and discussion of interconnected effects

Judy P. Che-Castaldo, Rémi Cousin, Stefani Daryanto, Grace Deng, Mei Ling E. Feng, Rajesh K. Gupta, Dezhi Hong, Ryan M. McGranaghan, Olukunle O. Owolabi, Tianyi Qu, Wei Ren, Toryn L.J. Schafer, Ashutosh Sharma, Chaopeng Shen, Mila Getmansky Sherman, Deborah A. Sunter, Bo Tao, Lan Wang, David S. Matteson

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The electric power grid is a critical societal resource connecting multiple infrastructural domains such as agriculture, transportation, and manufacturing. The electrical grid as an infrastructure is shaped by human activity and public policy in terms of demand and supply requirements. Further, the grid is subject to changes and stresses due to diverse factors including solar weather, climate, hydrology, and ecology. The emerging interconnected and complex network dependencies make such interactions increasingly dynamic, posing novel risks, and presenting new challenges to manage the coupled human–natural system. This paper provides a survey of models and methods that seek to explore the significant interconnected impact of the electric power grid and interdependent domains. We also provide relevant critical risk indicators (CRIs) across diverse domains that may be used to assess risks to electric grid reliability, including climate, ecology, hydrology, finance, space weather, and agriculture. We discuss the convergence of indicators from individual domains to explore possible systemic risk, i.e., holistic risk arising from cross-domain interconnections. Further, we propose a compositional approach to risk assessment that incorporates diverse domain expertise and information, data science, and computer science to identify domain-specific CRIs and their union in systemic risk indicators. Our study provides an important first step towards data-driven analysis and predictive modeling of risks in interconnected human–natural systems.

Original languageEnglish (US)
JournalEnvironment Systems and Decisions
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)

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