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

11 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
Pages (from-to)594-615
Number of pages22
JournalEnvironment Systems and Decisions
Volume41
Issue number4
DOIs
StatePublished - Dec 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Funding

Funding was provided by the NSF Harnessing the Data Revolution (HDR) program, “Collaborative Research: Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis” (Awards #1940160, 2023755, 1940176, 1940190, 1940208, 1940223, 1940276, 1940291, and 1940696). R. McGranaghan was partially supported under the NSF Convergence Accelerator Award to the Convergence Hub for the Exploration of Space Science (CHESS) team (NSF Award Number: 1937152). We would like to thank Suoan Gao (UMASS Amherst) for research assistance. Funding was provided by the NSF Harnessing the Data Revolution (HDR) program, “Collaborative Research: Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis” (Awards #1940160, 2023755, 1940176, 1940190, 1940208, 1940223, 1940276, 1940291, and 1940696). R. McGranaghan was partially supported under the NSF Convergence Accelerator Award to the Convergence Hub for the Exploration of Space Science (CHESS) team (NSF Award Number: 1937152). We would like to thank Suoan Gao (UMASS Amherst) for research assistance.

FundersFunder number
Andrew Chess1937152
Los Alamos Natl. Lab., Center for Space Science and Exploration
Suoan Gao
National Science Foundation Arctic Social Science Program2023755, 1940176, 1940276, 1940223, 1940696, 1940190, 1940208, 1940291, 1940160
National Science Foundation Arctic Social Science Program

    Keywords

    • Critical risk indicator
    • Electric power grid
    • Multi-disciplinary
    • Risk
    • Systemic risk
    • Uncertainty

    ASJC Scopus subject areas

    • General Environmental Science

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