False data injection attacks (FDIAs) pose a significant security threat to power system state estimation. To detect such attacks, recent studies have proposed machine learning (ML) techniques, particularly deep neural networks (DNNs). However, most of these methods fail to account for the risk posed by adversarial measurements, which can compromise the reliability of DNNs in various ML applications. In this paper, we present a DNN-based FDIA detection approach that is resilient to adversarial attacks. We first analyze several adversarial defense mechanisms used in computer vision and show their inherent limitations in FDIA detection. We then propose an adversarial-resilient DNN detection framework for FDIA that incorporates random input padding in both the training and inference phases. Our simulations, based on an IEEE standard power system, demonstrate that this framework significantly reduces the effectiveness of adversarial attacks while having a negligible impact on the DNNs' detection performance. Index Terms-False Data Injection Attack, Smart Grid Communication, Deep Learning, Adversarial Attacks

High penetration of converter-interfaced renewable energy resources will significantly change the swing dynamics between synchronous generators (SGs) in future power systems. This article examines the impact of high converter penetration on wave-like disturbance propagation arising from sudden generator and load losses in radial (1-D) and meshed (2-D) power systems. To keep the uniformity assumption as converters are introduced, the rating of each SG is decreased with a converter resource making up for the reduction. Numerical simulations demonstrate that as the penetration level of constant-power grid-following (GFL) converters increases, the speed of disturbance propagation increases due to the reduced system inertia. Naturally, converters with the capabilities to positively respond to disturbances would in turn reduce the propagation speed. Analytical studies based on continuum models are presented for the 2-D system with SGs and constant-power GFL converters in order to visualize the disturbance propagation and validate numerical simulations based on differential-algebraic equations. In addition, fast active power control of converters can slow down the electromechanical wave (EMW) propagation and even contain it. These concepts are illustrated on the idealized radial and meshed systems and a reduced model of the U.S. eastern interconnection.

Recent research demonstrated that the superficially well-trained machine learning (ML) models are highly vulnerable to adversarial examples. As ML techniques are becoming a popular solution for cyber-physical systems (CPSs) applications in research literatures, the security of these applications is of concern. However, current studies on adversarial machine learning (AML) mainly focus on pure cyberspace domains. The risks the adversarial examples can bring to the CPS applications have not been well investigated. In particular, due to the distributed property of data sources and the inherent physical constraints imposed by CPSs, the widely-used threat models and the state-of-the-art AML algorithms in previous cyberspace research become infeasible.

The rapid evolution of sensor technologies and communication networks is tightly coupling the cyber and physical layers of power systems. Because a few power grids have fallen victim to cyber intrusions causing unexpected device failure and large-scale power outages, enhancing power system cybersecurity is the utmost focus of power grid development today. Financially, the deregulation of the electricity market opens the gate to profit-oriented cyberattacks. Real-time market auctions rely heavily on the accuracy of state estimation, which is susceptible to cyberattacks. Extensive reviews have been conducted on modern power system cybersecurity. However, the lack of a comprehensive and in-depth review of electricity market cyberattacks prevents independent system operators from systematically analyzing the financial consequences of cyberattacks and limits public awareness of the significant monetary loss. This article briefly summarizes previous review works and analyzes the two-settlement market design from a cybersecurity perspective. Then the current achievements of electricity market cyberattacks are discussed, and state-of-the-art works are analyzed based on their contributions. Additionally, a few possible improvements and future directions are presented.

With growing attention to sustainability and recognition of the impact of global warming problems, energy supply and consumption have become critically important. This paper presents the construction of a modeling platform accommodating cooperative energy management systems (EMSs), which virtually produces the model of a smart city with a distribution network (DN) by using a wide range of data obtained from the real world. The platform involves models of various EMSs, governing the operation of a power system or controlling consumer-installed devices, and simulating the power flow, electrical losses, and voltage in the DN. In addition, indices measuring the sustainability of the model city, such as CO2emission, are estimated from scenarios, for example, photovoltaic system installation, electric vehicle penetration, etc. The results can be visually displayed and the platform is highly versatile and applicable to various types of issues associated with smart cities. Two case studies are presented in detail.

The power grid is not only a network interconnecting generators and loads through a transmission and distribution system, but is overlaid with a communication and control system that enables economic and secure operation. This multilayered infrastructure has evolved over many decades utilizing new technologies as they have appeared. This evolution has been slow and incremental, as the operation of the power system consisting of vertically integrated utilities has, until recently, changed very little. The monitoring of the grid is still done by a hierarchical design with polling for data at scanning rates in seconds that reflects the conceptual design of the 1960s. This design was adequate for vertically integrated utilities with limited feedback and wide-area controls; however, the thesis of this paper is that the changing environment, in both policy and technology, requires a new look at the operation of the power grid and a complete redesign of the control, communication and computation infrastructure. We provide several example novel control and communication regimes for such a new infrastructure.