• Energy Research

Over the last decade, the number of cyber attacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, false data injection attacks (FDIAs) are a class of cyber‐attacks against power grid monitoring systems. Adversaries can successfully perform FDIAs to manipulate the power system state estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the energy management system towards estimating unknown state variables based on system redundant measurements and network topology. SE routines include bad data detection algorithms to eliminate errors from the acquired measurements, e.g. in case of sensor failures. FDIAs can bypass BDD modules to inject malicious data vectors into a subset of measurements without being detected, and thus manipulate the results of the SE process. To overcome the limitations of traditional residual‐based BDD approaches, data‐driven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor data due to their fast execution times and accurate results. This study provides a comprehensive review of the most up‐to‐date machine learning methods for detecting FDIAs against power system SE algorithms.

The digitization and decentralization of the electric power grid are key thrusts for an economically and environmentally sustainable future. Towards this goal, distributed energy resources (DER), including rooftop solar panels, battery storage, electric vehicles, etc., are becoming ubiquitous in power systems. Power utilities benefit from DERs as they minimize operational costs; at the same time, DERs grant users and aggregators control over the power they produce and consume. DERs are interconnected, interoperable, and support remotely controllable features, thus, their cybersecurity is of cardinal importance. DER communication dependencies and the diversity of DER architectures widen the threat surface and aggravate the cybersecurity posture of power systems. In this work, we focus on security oversights that reside in the cyber and physical layers of DERs and can jeopardize grid operations. Existing works have underlined the impact of cyberattacks targeting DER assets, however, they either focus on specific system components (e.g., communication protocols), do not consider the mission-critical objectives of DERs, or neglect the adversarial perspective (e.g., adversary/attack models) altogether. To address these omissions, we comprehensively analyze adversarial capabilities and objectives when manipulating DER assets, and then present how protocol and device-level vulnerabilities can materialize into cyberattacks impacting power system operations. Finally, we provide mitigation strategies to thwart adversaries and directions for future DER cybersecurity research. IEEE Systems Journal (2023)

Real-time simulation enables the understanding of system operating conditions by evaluating simulation models of physical components running synchronized at the real-time wall clock. Leveraging the real-time measurements of comprehensive system models, faster than real-time (FTRT) simulation allows the evaluation of system architectures at speeds faster than real-time. FTRT simulation can assist in predicting the system's behavior efficiently, thus assisting the operation of system processes. Namely, the provided acceleration can be used for improving system scheduling, assessing system vulnerabilities, and predicting system disruptions in real-time systems. The acceleration of simulation times can be achieved by utilizing digital real-time simulators (RTS) and high-performance computing (HPC) architectures. FTRT simulation has been widely used, among others, for the operation, design, and investigation of power system events, building emergency management plans, wildfire prediction, etc. In this paper, we review the existing literature on FTRT simulation and its applications in different disciplines, with a particular focus on power systems. We present existing system modeling approaches, simulation tools and computing frameworks, and stress the importance of FTRT accuracy. 9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES'2021)

Cyber-physical systems (CPS) are interconnected architectures that employ analog, digital, and communication resources for their interaction with the physical environment. CPS are the backbone of enterprise, industrial, and critical infrastructure. Thus, their vital importance makes them prominent targets for malicious attacks aiming to disrupt their operations. Attacks targeting cyber-physical energy systems (CPES), given their mission-critical nature, can have disastrous consequences. The security of CPES can be enhanced leveraging testbed capabilities to replicate power system operations, discover vulnerabilities, develop security countermeasures, and evaluate grid operation under fault-induced or maliciously constructed scenarios. In this paper, we provide a comprehensive overview of the CPS security landscape with emphasis on CPES. Specifically, we demonstrate a threat modeling methodology to accurately represent the CPS elements, their interdependencies, as well as the possible attack entry points and system vulnerabilities. Leveraging the threat model formulation, we present a CPS framework designed to delineate the hardware, software, and modeling resources required to simulate the CPS and construct high-fidelity models which can be used to evaluate the system's performance under adverse scenarios. The system performance is assessed using scenario-specific metrics, while risk assessment enables system vulnerability prioritization factoring the impact on the system operation. The overarching framework for modeling, simulating, assessing, and mitigating attacks in a CPS is illustrated using four representative attack scenarios targeting CPES. The key objective of this paper is to demonstrate a step-by-step process that can be used to enact in-depth cybersecurity analyses, thus leading to more resilient and secure CPS.

Over the past decades, power systems have experienced drastic transformations in order to address the growth in energy demand, reduce carbon emissions, and enhance power quality and energy efficiency. This shift to the smart grid concept involves, among others, the utilization of distributed energy resources (DERs) such as rooftop solar panels and storage systems, contributing towards grid decentralization while improving control over power generation. In order to seamlessly integrate DERs into power systems, embedded devices are used to support the communication and control functions of DERs. As a result, vulnerabilities of such components can be ported to the industrial environment. Insecure control networks and protocols further exacerbate the problem. Towards reducing the attack surface, we present an authentication scheme for DERs, DERauth, which leverages the inherent entropy of the DER battery energy storage system (BESS) as a root-of-trust. The DER authentication is achieved using a challenge-reply mechanism that relies on the corresponding DER's BESS state-of-charge (SoC) and voltage measurements. A dynamically updating process ensures that the BESS state is up-to-date. We evaluate our proof-of-concept in a prototype development that uses lithium-ion (li-ion) batteries for the BESS. The robustness of our design is assessed against modeling attacks performed by neural networks.

This paper summarizes the technical endeavors undertaken by the Task Force (TF) on Cyber-Physical Interdependence for Power System Operation and Control. The TF was established to investigate the cyber-physical interdependence of critical power system elements and their influence on the operation and control of energy systems. State-of-the-art analysis techniques, including co-simulation and digital twin technologies, are employed to address various layers of interdependence between cyber and physical systems, facilitating the identification of potential threats and vulnerabilities. The paper examines prospective trajectories for resilient cyber-physical systems and outlines the educational and workforce training imperatives for addressing cybersecurity threats in contemporary power systems. Furthermore, concluding remarks and future recommendations are provided to mitigate the inherent vulnerabilities within the extensively interoperable grid infrastructure.