Real-time parameter estimation captures the timevarying behavior of line parameters, preventing attackers from maintaining an accurate system model. As attackers rely on outdated parameter information, the measurement effects of their injections no longer align with the attacker’s intended outcomes, rendering perfectly stealthy False Data Injection Attacks (FDIAs) ineffective. This paper introduces Confusion False Data Injection Attacks (CFDIAs), which exploit the mismatch between an attacker’s outdated model and the system’s time-varying reality. CFDIAs reshape the resulting measurement discrepancies so they fall within the statistical behavior of normal noise, enabling approximate stealthiness under residual-based detection. A bi-level optimization framework is established to quantify the economic impacts of CFDIAs. The upper level designs approximately stealthy attacks, while the lower level performs optimal power flow. A Conservative Chance-Constraint Linearization (CCCL) technique is incorporated to provide a geometric and tractable approximation of nonlinear chance constraints. The results show that real-time parameter updates can create a false sense of protection, as some lines that appear secure remain covertly exploitable by CFDIAs. Experiments further demonstrate that such attacks can raise dispatch costs by 33.16% in the IEEE 30-bus system and 27.21% in the 89-bus system.

False data injection attacks are commonly used to evade the bad data detector in cyber-physical power systems. This paper proposes an extended attack strategy and a deep reinforcement learning-based detection method. Traditional false data injection attacks aim to remain stealthy and avoid detection by conventional detection mechanisms. An extended load attack is introduced to increase the potential for damage. Directly adding an extended component directly to the measurement makes it easily detectable by bad data detector. Accordingly, the extended attack integrates the added component into the state variables to improve stealth. An optimization model for the extended components of the proposed attack is developed, along with a homologous matrix. Additionally, an online attack detection scheme is formulated as a partially observable Markov decision process problem. A deep reinforcement learning-based detection framework is proposed, featuring a compound reward designed to minimize false alarms and time delays. The proposed online detector extracts state features under varying operating conditions and generates a policy to determine whether the power grid is under attack. An extended Euclidean distance indicator and an adaptive weight matrix are also proposed in the dynamic state estimation to improve estimation or detection accuracy. Numerical experiments validate the effectiveness and robustness of the proposed deep reinforcement learning-based detection scheme in power systems. Note to Practitioners—This paper is motivated by the lack of research on modeling the destructive capabilities of cyber-attacks and the inaccuracy of anomaly detection methods for data integrity attacks. Existing approaches to modeling false data injection attacks primarily focus on the hidden features bypassing detection of bad data detection in cyber-physical power systems. This paper proposes a novel false data injection modeling approach, triggered by the spinning reserves of power grids. The proposed false data injection strategy mathematically characterizes extended stealth attack mechanisms and introduces an extended load attack to explore greater destructive potential. Considering the uncertain environments in perspective attackers and defenders, this study formulates the attack detection problem as a partially observable Markov decision process. This study then characterizes how such metrics of detectors can be efficiently computed, this can allow a defender to automatically learn or generate a detection threshold policy using the deep reinforcement learning, and distinguishes real false data injection attacks from system noises. To improve the detection performance, a novel indicator and an adaptive weight matrix are proposed to enhance learning efficiency of detectors. Numerical simulations suggest that the proposed detection scheme is feasible to both traditional and extended false data injection attacks, though it does not yet account for incomplete measurements. Future work will focus on designing attack detection strategies under conditions of incomplete knowledge of network topology and measurements.

Quantifying operational risks of power systems under multiple uncertainties and determining necessary reserves present complex challenges. This article introduces a risk dispatch approach based on uncertain intervals to address the conservatism of existing interval optimization methods. We use an uncertain model based on an interval random variable (IRV), substituting the unknown probability distributions of random variables. We establish a framework combining IRV and probabilistic random variable for risk quantification, leading to a risk evaluation model centered on uncertain interval–probabilistic conditional value-at-risk. We establish the joint probability distribution of wind power and load. Following this, we introduce reserve models based on the relative positions of actual and prediction intervals. Subsequently, we present an enhanced economic dispatch based on uncertain intervals to achieve more accurate results. Our proposed method breaks down the uncertain interval-based dispatch problem into two suboptimization models. We demonstrate the effectiveness of this method by applying it to IEEE-39 and IEEE-118 bus systems.

Due to the challenges in achieving accurate probabilities, representing uncertainty as intervals helps mitigate issues arising from the lack of distribution information. However, current interval-based methods for optimization modeling in power grids fail to fully capture the uncertainties of interval variables, leading to higher reserve costs. To overcome the conservatism of existing dispatch models, this work develops a novel uncertain interval variable (UIV) for risk assessment, where the radius of an interval is treated as a random variable. Inspired by the Affine algorithm, we propose a novel uncertain interval-based conditional value-at-risk (CVaR) metric, called UP-CVaR, for multiple random variables. The dispatch results for the New England 39-bus and 118-bus systems show that the proposed method can obtain tighter interval dispatch results compared to existing economic dispatch (ED) models. Moreover, as the stochastic level of wind power (mean and standard deviation) increases, the range of scheduling results expands.

State of health (SOH) estimation is essential for battery health monitoring, particularly in cross-domain scenarios where data variability and domain shifts present significant challenges. To address these issues, this study proposes the adversarial dynamic spatiotemporal attention network (ADSTAN), which integrates a graph attention network for spatial feature extraction, a gated recurrent unit for temporal dependency modeling, and a gradient reversal layer-based domain alignment module for unsupervised domain adaptation. Representing battery health data as dynamic graphs, with each cycle serving as a node, ADSTAN effectively captures spatiotemporal dependencies and dynamically aligns feature distributions between source and target domains. Experiments on cross-domain datasets, including the CALCE and NASA battery datasets, demonstrate the model’s effectiveness. Using data from 10 cycles to predict SOH for 5, 10, and 15 horizons, ADSTAN achieved RMSE values of 2.49%, 2.61%, and 2.94%, respectively. Ablation experiments validated the model’s design, highlighting the superiority of its spatial, temporal, and alignment modules. These results underscore ADSTAN’s robust performance and its suitability for accurate and generalizable SOH estimation in diverse cross-domain settings.

Electric power systems have been increasingly subjected to false data injection attacks (FDIAs) and adversarial examples, which inject well-designed disturbance signals into the measurements, and thereby generate erroneous state estimation (SE) results. The present work addresses this issue by proposing a robust data-driven attack detection algorithm. We apply a novel metric denoted as Euclidian distance similarity ratio for detecting stealthy attack during the SE process. Second, two different deep Q networks are, respectively, employed for detecting FDIAs and adversarial examples based on their respective inflection points (IPs). We also propose sufficient and necessary conditions for the successful detection of adversarial examples based on the corresponding analyses of IPs. Finally, two networks are trained using deep reinforcement learning. The effectiveness of the proposed robust detection method is demonstrated based on simulations involving IEEE 14, 57, and 118 bus power systems.

This paper presents a robust optimization model for scheduling of appliances in home energy local network while considering the user behavior uncertainty based on time-varying price. Mathematical models for the renewable energy resources, energy storage system, and electric vehicle are introduced, as well as the essential, shiftable and the throttleable appliances. In order to reduce loads congestion during operation periods based on robust optimization, a robust scatter index (RSI) method is proposed and integrated into the load scheduling in the form of additional constraints. A new user's satisfaction level with RSI is proposed in the objective function to reflect the comfort violation caused by the user behavior. These constraints and the objective function are reformulated to be a nonlinear problem, which is solved via the nonlinear programming method. Numerical case studies illustrate the effectiveness of the proposed method and demonstrate the application in load scheduling.