A Scalable Automatic Model Generation Tool for Cyber-Physical Network Topologies and Data Flows for Large-Scale Synthetic Power Grid Models

TL;DR

The paper addresses the need for realistic, cyber-physical representations of power grids by automating the generation of CPPS overlays on synthetic power networks with SAM-GT. It couples a scalable data-driven pipeline (PowerWorld inputs) with an object-oriented cyber-physical model that includes data flows and protocols across star, radial, and statistics-based WAN topologies, producing JSON outputs and NetworkX visualizations. Key contributions include an end-to-end generation workflow, geographically informed placement of UCCs/BAs via $k$-means, a detailed five-class CPPS architecture, and comprehensive metrics (such as $L_{ave}$ and $diameter$) validated on 500-, 2,000-, and 10,000-bus cases, plus a comparison with prior approaches like europe_topology and the statistics-based topology. The work enables realistic cyber-physical test cases for cybersecurity analysis, situational awareness, and defense planning in large-scale power systems, with practical impact for utilities and operators.

Abstract

Power grids and their cyber infrastructure are classified as Critical Energy Infrastructure/Information (CEII) and are not publicly accessible. While realistic synthetic test cases for power systems have been developed in recent years, they often lack corresponding cyber network models. This work extends synthetic grid models by incorporating cyber-physical representations. To address the growing need for realistic and scalable models that integrate both cyber and physical layers in electric power systems, this paper presents the Scalable Automatic Model Generation Tool (SAM-GT). This tool enables the creation of large-scale cyber-physical topologies for power system models. The resulting cyber-physical network models include power system switches, routers, and firewalls while accounting for data flows and industrial communication protocols. Case studies demonstrate the tool's application to synthetic grid models of 500, 2,000, and 10,000 buses, considering three distinct network topologies. Results from these case studies include network metrics on critical nodes, hops, and generation times, showcasing effectiveness, adaptability, and scalability of SAM-GT.

Figures (23)

• Figure 1: Large-scale synthetic electric grid models: (left) 500-Bus (footprint of South Carolina); (center) 2,000-Bus (footprint of Texas); (right) 10,000-Bus (footprint of Western U.S.)
• Figure 2: Network topology based on Texas2000. This shows two different topologies, star, and radial, and how the different substations would connect to the UCC.
• Figure 3: Comparison between star, radial, and statistics-based topologies illustrates how the substations are connected to the UCC.
• Figure 4: Data flows used between nodes.
• Figure 5: Process for creating the cyber-physical JSON model.