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 article presents the Scalable Automatic Model Generation Tool (SAM-GT). This tool creates 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 dataflows 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 SAM-GT ’s effectiveness, adaptability, and scalability.

Modern power systems are the backbone of our society, supplying electric energy for daily activities. With the integration of communication networks and high penetration of renewable energy sources (RESs), modern power systems have evolved into a cross-domain multilayer complex system of systems with improved efficiency, controllability, and sustainability. However, increasing numbers of unexpected events, including natural disasters, extreme weather, and cyberattacks, are compromising the functionality of modern power systems and causing tremendous societal and economic losses. Resilience, a desirable property, is needed in modern power systems to ensure their capability to withstand all kinds of hazards while maintaining their functions. This article presents a systematic review of recent power system resilience enhancement techniques and proposes new directions for enhancing modern power systems’ resilience considering their cross-domain multilayer features. We first answer the question, “what is power system resilience?” from the perspectives of its definition, constituents, and categorization. It is important to recognize that power system resilience depends on two interdependent factors: network design and system operation. Following that, we present a review of articles published since 2016 that have developed innovative methodologies to improve power system resilience and categorize them into infrastructural resilience enhancement and operational resilience enhancement. We discuss their problem formulations and proposed quantifiable resilience measures, as well as point out their merits and limitations. Finally, we argue that it is paramount to leverage higher order subgraph studies and scientific machine learning (SciML) for modern power systems to capture the interdependence and interactions across heterogeneous networks and data for holistically enhancing their infrastructural and operational resilience.

This paper explores how electricity systems may evolve in the 21st century. The paper focuses on some fundamental challenges facing the utilization of electricity today and for years to come. Paralleling the challenges, several directions of how new solutions may emerge are suggested. In this context, some new approaches to manage power system development and deployment are outlined.

This paper describes a project that integrates real devices used in the electric power grid with a simulation of electrical power generation and distribution, and a computer/communication simulator. The testbed is designed to evaluate the cyber-security of power grid control systems. Through a combination of simulation and emulation, the testbed seamlessly integrates virtual and real components, allowing for the evaluation of both high-level design descriptions on large-scale models, and precise measurements taken from production code executing on real hardware.