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.

In various industries, cybersecurity regulations have been enacted in an effort to drive improvements to organizational security postures. Despite the prominent influence of these regulations, there has been limited prior investigation of how organizations engage with these regulations and the challenges that they face. Assessing these factors is vital for understanding the impact of cybersecurity regulations in practice and how to enhance them moving forward.

Transient instability poses a critical challenge to the reliable operation of modern power systems, often leading to large-scale blackouts. Despite the success of data-driven Transient Stability Assessment (TSA), its practical implementation remains limited by challenges in processing high-speed real-time data streams and preserving data privacy. To address these limitations, this article develops a novel Federated Adaptive Random Forest (FedARF) method that integrates federated learning with the Adaptive Random Forest (ARF) model. The proposed decentralized framework incorporates concept drift adaptation mechanisms to accommodate the stochastic and dynamic characteristics of modern power systems. FedARF facilitates distributed knowledge aggregation learned from various heterogeneous local data sensors (clients) to predict and evaluate the TSA status with minimal communication overhead. Comprehensive experiments on the New England 39-Bus system, the IEEE 68-Bus system, and the large-scale ACTIVIgs 25k-Bus system demonstrate the efficiency of the proposed method with an overall accuracy of 99.65%. Compared to traditional centralized forecasting methods, and state-of-the-art models, the proposed approach not only maintains high prediction accuracy but also enhances data privacy preservation while substantially reducing communication bandwidth requirements.

As a sustainable participant in the modernization of transportation systems, electric vehicles (EVs) call for a well-planned charging infrastructure. To meet the ever-increasing charging demands of EVs, an efficient dynamic spatio-temporal allocation strategy of charging stations (CSs) is necessary. With newly allocated CSs, additional distributed generators (DGs) are required to compensate for the load increase. Given a budget to be allocated over a certain time horizon, we formulate the joint spatio-temporal CSs and DGs planning problem as a multi-objective optimization problem. During each planning period, the allocation strategy aims at minimizing the total power generation costs and CSs/DGs installation costs while satisfying budgetary and power constraints and ensuring a minimum level for the charging requests satisfaction rate. In this regard, we first predict the future power demand of EVs using a graph convolutional neural network (GCNN). Then, using the power demand forecast, we obtain the optimal number and locations of CSs and DGs at each time stage using reinforcement learning. A case study of the proposed allocation strategy over 6 time stages for the 2000-bus power grid of Texas coupled with 720 initially existing CSs is presented to illustrate the performance of the planning strategy.

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.

For researchers studying cyber-physical system security, working with realistic datasets is essential. To produce the datasets, the existing methodology is to emulate the cyber network. A challenge is that the industrial control systems (ICS) network consists of not just computers and communication equipment, but also field devices that collect data and execute controls. These devices play a significant role in the operation and the security of the system. However, in comparison to the cyber network, the research reproducibility and realism of the cyber-physical system emulation and its data has received far less attention. This paper thus develops an approach to answer, ”How well can emulated devices replicate the behavior of physical intelligent electronics devices (IEDs) in a realistic cyber attack and defense environment?” To study this, we perform a comparison study based on an emulation experiment using theminimegatestbed environment that is entirely virtual and a hardware-in-the-loop experiment using the Resilient Energy Systems Lab (RESLab) cyber-physical testbed featuring real industrial controllers and communications devices. Results show that under different reconnaissance attack scenarios,RESLabgenerates realistic datasets that validate the emulation-based cybersecurity model inminimega. The approach is generalizable toward validating the realism of other types of ICS devices in security studies.

False data injection attacks (FDIAs) on smart power grids’ measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5−100% and 99 − 100%, respectively, presenting improvements of 5 − 30% compared to benchmarks.

Recent major attacks against unmanned aerial vehicles (UAV) and their controller software necessitate domain-specific cyber-physical security protection. Existing offline formal methods for (untrusted) controller code verification usually face state-explosion. On the other hand, runtime monitors for cyber-physical UAVs often lead to too-late notifications about unsafe states that makes timely safe operation recovery impossible.