• Energy Research

Large-scale Load-Altering Attacks (LAAs) against Internet-of-Things (IoT) enabled high-wattage electrical appliances (e.g., wifi-enabled air-conditioners, electric vehicles, etc.) pose a serious threat to power systems' security and stability. In this work, a Cyber-Resilient Economic Dispatch (CRED) framework is presented to mitigate the destabilizing effect of LAAs while minimizing the overall operational cost by dynamically optimizing the frequency droop control gains of Inverter-Based Resources (IBRs). The system frequency dynamics incorporating both LAAs and the IBR droop control are modeled. The system stability constraints are explicitly derived based on parametric sensitivities. To incorporate them into the CRED model and minimize the error of the sensitivity analysis, a recursive linearization method is further proposed. A distributionally robust approach is applied to account for the uncertainty associated with the LAA detection/parameter estimation. The overall performance of the proposed CRED model is demonstrated through simulations in a modified IEEE reliability test system.

This work proposes a moving target defense (MTD) strategy to detect coordinated cyber-physical attacks (CCPAs) against power grids. The main idea of the proposed approach is to invalidate the knowledge that the attackers use to mask the effects of their physical attack by actively perturbing the grid's transmission line reactances via distributed flexible AC transmission system (D-FACTS) devices. The proposed MTD design consists of two parts. First, we identify the subset of links for D-FACTS device deployment that enables the defender to detect CCPAs against any link in the system. Then, in order to minimize the defense cost during the system's operational time, we formulate a zero-sum game to identify the best subset of links to perturb (which will provide adequate protection) against a strategic attacker. The Nash equilibrium robust solution is computed via exponential weights, which does not require complete knowledge of the game but only the observed payoff at each iteration. Extensive simulations performed using the MATPOWER simulator on IEEE bus systems verify the effectiveness of our approach in detecting CCPAs and reducing the operator's defense cost. arXiv admin note: substantial text overlap with arXiv:1908.02392

The COVID-19 pandemic has impacted our society by forcing shutdowns and shifting the way people interacted worldwide. In relation to the impacts on the electric grid, it created a significant decrease in energy demands across the globe. Recent studies have shown that the low demand conditions caused by COVID-19 lockdowns combined with large renewable generation have resulted in extremely low-inertia grid conditions. In this work, we examine how an attacker could exploit these {scenarios} to cause unsafe grid operating conditions by executing load-altering attacks (LAAs) targeted at compromising hundreds of thousands of IoT-connected high-wattage loads in low-inertia power systems. Our study focuses on analyzing the impact of the COVID-19 mitigation measures on U.S. regional transmission operators (RTOs), formulating a plausible and realistic least-effort LAA targeted at transmission systems with low-inertia conditions, and evaluating the probability of these large-scale LAAs. Theoretical and simulation results are presented based on the WSCC 9-bus {and IEEE 118-bus} test systems. Results demonstrate how adversaries could provoke major frequency disturbances by targeting vulnerable load buses in low-inertia systems and offer insights into how the temporal fluctuations of renewable energy sources, considering generation scheduling, impact the grid's vulnerability to LAAs.

We study moving-target defense (MTD) that actively perturbs transmission line reactances to thwart stealthy false data injection (FDI) attacks against state estimation in a power grid. Prior work on this topic has proposed MTD based on randomly selected reactance perturbations, but these perturbations cannot guarantee effective attack detection. To address the issue, we present formal design criteria to select MTD reactance perturbations that are truly effective. However, based on a key optimal power flow (OPF) formulation, we find that the effective MTD may incur a non-trivial operational cost that has not hitherto received attention. Accordingly, we characterize important tradeoffs between the MTD's detection capability and its associated required cost. Extensive simulations, using the MATPOWER simulator and benchmark IEEE bus systems, verify and illustrate the proposed design approach that for the first time addresses both key aspects of cost and effectiveness of the MTD. IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) - 2018

Load-altering attacks targetting a large number of IoT-based high-wattage devices (e.g., smart electric vehicle charging stations) can lead to serious disruptions of power grid operations. In this work, we aim to uncover spatiotemporal characteristics of LAAs that can lead to serious impact. The problem is challenging since existing protection measures such as $N-1$ security ensures that the power grid is naturally resilient to load changes. Thus, strategically injected load perturbations that lead to network failure can be regarded as \emph{rare events}. To this end, we adopt a rare-event sampling approach to uncover LAAs distributed temporally and spatially across the power network. The key advantage of this sampling method is the ability of sampling efficiently from multi-modal conditional distributions with disconnected support. Furthermore, we systematically compare the impacts of static (one-time manipulation of demand) and dynamic (attack over multiple time periods) LAAs. We perform extensive simulations using benchmark IEEE test simulations. The results show (i) the superiority and the need for rare-event sampling in the context of uncovering LAAs as compared to other sampling methodologies, (ii) statistical analysis of attack characteristics and impacts of static and dynamic LAAs, and (iii) cascade sizes (due to LAA) for different network sizes and load conditions.

The widespread deployment of "smart" electric vehicle charging stations (EVCSs) will be a key step toward achieving green transportation. The connectivity features of smart EVCSs can be utilized to schedule EV charging operations while respecting user preferences, thus avoiding synchronous charging from a large number of customers and relieving grid congestion. However, the communication and connectivity requirements involved in smart charging raise cybersecurity concerns. In this work, we investigate charge manipulation attacks (CMAs) against EV charging, in which an attacker manipulates the information exchanged during smart charging operations. The objective of CMAs is to shift the EV aggregator's demand across different times of the day. The proposed CMAs can bypass existing protection mechanisms in EV communication protocols. We quantify the impact of CMAs on the EV aggregator's economic profit by modeling their participation in the day-ahead (DA) and real-time (RT) electricity markets. Finally, we propose an unsupervised deep learning-based mechanism to detect CMAs by monitoring the parameters involved in EV charging. We extensively analyze the attack impact and the efficiency of the proposed detection on real-world EV charging datasets. The results highlight the vulnerabilities of smart charging operations and the need for a monitoring mechanism to detect malicious CMAs.

AbstractAdvances in edge computing are powering the development and deployment of Internet of Things (IoT) systems to provide advanced services and resource efficiency. However, large‐scale IoT‐based load‐altering attacks (LAAs) can seriously impact power grid operations, such as destabilising the grid's control loops. Timely detection and identification of any compromised nodes are essential to minimise the adverse effects of these attacks on power grid operations. In this work, two data‐driven algorithms are proposed to detect and identify compromised nodes and the attack parameters of the LAAs. The first method, based on the Sparse Identification of Nonlinear Dynamics approach, adopts a sparse regression framework to identify attack parameters that best describe the observed dynamics. The second method, based on physics‐informed neural networks, employs neural networks to infer the attack parameters from the measurements. Both algorithms are presented utilising edge computing for deployment over decentralised architectures. Extensive simulations are performed on IEEE 6‐, 14‐, and 39‐bus systems to verify the effectiveness of the proposed methods. Numerical results confirm that the proposed algorithms outperform existing approaches, such as those based on unscented Kalman filter, support vector machines, and neural networks (NN), and effectively detect and identify locations of attack in a timely manner.