TY - JOUR
T1 - Critical Risk Indicators (CRIs) for the electric power grid
T2 - a survey and discussion of interconnected effects
AU - Che-Castaldo, Judy P.
AU - Cousin, Rémi
AU - Daryanto, Stefani
AU - Deng, Grace
AU - Feng, Mei Ling E.
AU - Gupta, Rajesh K.
AU - Hong, Dezhi
AU - McGranaghan, Ryan M.
AU - Owolabi, Olukunle O.
AU - Qu, Tianyi
AU - Ren, Wei
AU - Schafer, Toryn L.J.
AU - Sharma, Ashutosh
AU - Shen, Chaopeng
AU - Sherman, Mila Getmansky
AU - Sunter, Deborah A.
AU - Tao, Bo
AU - Wang, Lan
AU - Matteson, David S.
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Critical risk indicator
KW - Electric power grid
KW - Multi-disciplinary
KW - Risk
KW - Systemic risk
KW - Uncertainty
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U2 - 10.1007/s10669-021-09822-2
DO - 10.1007/s10669-021-09822-2
M3 - Article
AN - SCOPUS:85110707400
SN - 2194-5403
VL - 41
SP - 594
EP - 615
JO - Environment Systems and Decisions
JF - Environment Systems and Decisions
IS - 4
ER -