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

Author

Listed:
  • Judy P. Che-Castaldo

    (Lincoln Park Zoo)

  • Rémi Cousin

    (Earth Institute/Columbia University)

  • Stefani Daryanto

    (University of Kentucky)

  • Grace Deng

    (Cornell University)

  • Mei-Ling E. Feng

    (Lincoln Park Zoo)

  • Rajesh K. Gupta

    (University of California)

  • Dezhi Hong

    (University of California)

  • Ryan M. McGranaghan

    (Atmospheric and Space Technology Research Associates)

  • Olukunle O. Owolabi

    (Tufts University)

  • Tianyi Qu

    (Isenberg School of Management, UMASS Amherst)

  • Wei Ren

    (University of Kentucky)

  • Toryn L. J. Schafer

    (Cornell University)

  • Ashutosh Sharma

    (Pennsylvania State University
    Indian Institute of Technology Roorkee)

  • Chaopeng Shen

    (Pennsylvania State University)

  • Mila Getmansky Sherman

    (Isenberg School of Management, UMASS Amherst)

  • Deborah A. Sunter

    (Tufts University
    Tufts University
    Tufts University
    Tufts University)

  • Bo Tao

    (University of Kentucky)

  • Lan Wang

    (University of Miami)

  • David S. Matteson

    (Cornell University)

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.

Suggested Citation

  • 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. Sch, 2021. "Critical Risk Indicators (CRIs) for the electric power grid: a survey and discussion of interconnected effects," Environment Systems and Decisions, Springer, vol. 41(4), pages 594-615, December.
  • Handle: RePEc:spr:envsyd:v:41:y:2021:i:4:d:10.1007_s10669-021-09822-2
    DOI: 10.1007/s10669-021-09822-2
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    Cited by:

    1. Olukunle O. Owolabi & Deborah A. Sunter, 2022. "Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages," Energies, MDPI, vol. 15(6), pages 1-22, March.

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