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.