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

TL;DR

This paper surveys models and methods for assessing critical risk indicators across interconnected domains affecting the electric power grid, emphasizing systemic risk and data-driven predictive analysis in complex, coupled systems.

Contribution

It introduces a comprehensive survey of models and methods for interconnected risk analysis and proposes a compositional approach to systemic risk assessment involving multiple domains.

Findings

Identification of diverse critical risk indicators across domains

Discussion of convergence of indicators for systemic risk

Proposal of a data-driven, compositional risk assessment framework

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 solar weather, climate, hydrology, and ecology. The emerging interconnected and complex network dependencies make such interactions increasingly dynamic causing potentially large swings, thus 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 influence electric power grid risks, 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-domains interconnections. Our study provides an important first step towards data-driven analysis and predictive modeling of risks in the coupled interconnected systems. 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.