On Graph Theory vs. Time-Domain Discrete Event Simulation for Topology-Informed Assessment of Power Grid Cyber Risk

TL;DR

This paper combines graph theory and discrete-event simulation to evaluate power grid cyber risk, providing a topology-informed security assessment that identifies critical nodes and their vulnerabilities.

Contribution

It introduces a methodology integrating graph metrics with simulation to assess power grid cyber risk, enhancing validation of graph-based models against real-world scenarios.

Findings

Graph theory effectively identifies critical nodes in power grids.

Simulation results validate the use of graph metrics for security assessment.

Combining both methods improves understanding of system vulnerabilities.

Abstract

The shift toward more renewable energy sources and distributed generation in smart grids has underscored the significance of modeling and analyzing modern power systems as cyber-physical systems (CPS). This transformation has highlighted the importance of cyber and cyber-physical properties of modern power systems for their reliable operation. Graph theory emerges as a pivotal tool for understanding the complex interactions within these systems, providing a framework for representation and analysis. The challenge is vetting these graph theoretic methods and other estimates of system behavior from mathematical models against reality. High-fidelity emulation and/or simulation can help answer this question, but the comparisons have been understudied. This paper employs graph-theoretic metrics to assess node risk and criticality in three distinct case studies, using a Python-based discrete-event simulation called SimPy. Results for each case study show that combining graph theory and simulation provides a topology-informed security assessment. These tools allow us to identify critical network nodes and evaluate their performance and reliability under a cyber threat such as denial of service threats.