In this article, we propose a planning-operation coordinated mitigation scheme for load redistribution (LR) attacks to overcome the deficiencies of separately designed phase shifting transformer-based mitigation strategies. Specifically, the interactions amongst the defender, attacker, and system are formulated as a trilevel optimization, where the deployment of defense devices and phase shift angles can be optimized according to possible operation state. Based on the proposed load similarity metric, a clustering-based approximate solution is designed to reduce the computational complexity caused by the integration of planning and operation stages. Simulation results on the IEEE 14-bus and 30-bus test systems verify the performance of the proposed mitigation scheme and the clustering-based approximate solution method.

Modern power systems benefit from enhanced reliability and efficiency due to advanced information technologies, but face increased cyber vulnerabilities, particularly false data injection attacks (FDIAs) that threaten grid stability. We propose a Transformer-Gated Recurrent Unit (TGRU) framework, integrating Transformer encoders’ feature extraction with GRUs’ temporal modeling. A novel Euclidean distance-based threshold selection method distinguishes legitimate from malicious data, and an FDILocator module accurately identifies attack locations by analyzing discrepancies between TGRU predictions and actual measurements. Experiments on the IEEE 14-bus system validate the approach’s effectiveness and accuracy.

Cyber-Physical Systems (CPS) are closely connected with human social production and daily life, and ensuring their security is of vital importance. Anomaly detection in CPS has therefore become an important research area for safeguarding their security. However, existing approaches struggle to effectively capture nonlinear spatio-temporal interactions, dynamically model spatio-temporal relationships among variables, and enforce temporal causality, which ultimately result in inaccurate anomaly detection, reduced robustness, and limited applicability in real-world CPS scenarios. To overcome these limitations, we propose FST-AD, a Frequency-Spatio-Temporal Graph Neural Network framework for anomaly detection. FST-AD employs multiscale convolutions with an alternating padding strategy and Fast Fourier Transform (FFT) to jointly extract time-frequency features. The resulting time-frequency features are modeled through an adaptive graph structure learning module to capture evolving spatio-temporal dependencies and complex variable interactions. A message passing neural network (MPNN) combining multi-order graph convolutions and attention mechanisms further enables deep fusion of spatio-temporal features, and leveraging Principal Component Analysis (PCA) driven dimensionality reduction and reconstruction enhances noise suppression and stability in anomaly recognition. Experiments on real-world industrial datasets show that FST-AD achieves significant gains in accuracy, robustness, and generalization; on the SWaT dataset, it surpasses the best baseline by 8.57 and 6.3 percentage points in ROC and PRC, respectively, offering a reliable and scalable solution for CPS anomaly detection.

In load redistribution (LR) attacks, attackers compromise measurement devices at the cyber layer to inject false data, resulting in misoperations in the physical power system. However, most existing methods trace the source of attacks in cy-ber or physical layers separately, where the effective coordination between cyber and physical layers is neither modeled nor utilized. To address this issue, this paper proposes a cross-layer attribution method. Specifically, at the physical layer, a comprehensive evaluation metric is proposed to accurately locate high-risk branches. At the cyber layer, a labeled subgraph isomorphism matching algorithm based on a traceability graph is developed to reconstruct attack paths from log data. To enable cross-layer attribution, a time-topology coupling mechanism is introduced, which can significantly enhance cyber-physical correlation and attribution efficiency. Simulations using a publicly available real-world dataset on the IEEE 39-bus system verify the effectiveness of the proposed method in cross-layer attack attribution.

State estimation methods in smart grids are vulnerable to false data injection attacks (FDIAs). To address this threat, recent research has adopted deep neural networks (DNNs) to detect such attacks. However, DNNs exhibit inherent security flaws, making their decisions susceptible to adversarial perturbations. Exploiting this vulnerability, adversarial FDIAs have been designed to evade DNN-based detection. In this paper, we propose a black-box adversarial false data injection attack leveraging autoencoders. The attack uses an autoencoder to learn the underlying physical model of grid data without prior knowledge of the network topology or detection mechanisms. This is jointly optimized with a surrogate model to generate adversarial perturbations. Experimental results demonstrate that the proposed attack successfully evades both conventional bad data detectors and DNN-based detectors, achieving high success rates in black-box settings. This vulnerability poses a significant security threat to smart grids.

The vulnerabilities of power system state estimation have been widely analyzed recently. However, most of the existing attack model can only pass the bad data detector (BDD) in power system state estimation, where the state-of-the-art neural attack detectors (NADs) are not fully considered. To generate stealthy attack in power system with high penetrated renewable energy sources(RESs), the principle of constructing spatiotemporally stealthy attack is analyzed, where a spatio-temporality principle is proposed to ensure the stealthiness of the attack. A spatiotemporal correlation generation framework is proposed in the improved WGAN-GP framework, which can generate spatiotemporally stealthy false data injection (FDI) attack in power system state estimation deployed with the state-of-the-art NADs. Simulations in the IEEE 14-bus, the IEEE 57-bus and the IEEE 118-bus test systems verify the stealthiness and effectiveness of the proposed spatiotemporally stealthy FDI attack.

As wind power systems expand in size and complexity, the imperative for cyber security measures becomes increasingly critical, especially in light of the escalating threat posed by the zero-dynamics attack. Unfortunately, when the relative degree of a continuous-time system is greater than two and the sampling period is small, the sampled-data system will inevitably introduce unstable zeros, making it vulnerable to attackers. Meanwhile, it is difficult for traditional output-based system monitoring methods to detect such attacks. The current mainstream approach employs the generalized hold or generalized sampler to achieve zero shifting, but the implementation is challenging. In this article, we propose a defense method based on electronically tunable passive components to address this challenge. The method effectively defends against the zero-dynamics attack by introducing a gain-scheduling combined with the real-time parameter adjustment function of the electronically tunable passive components to move the unstable zeros of the system. Simulation results show that this defense method can improve the ability of the wind power system to resist zero-dynamics attack.

The precise localization of false data injection attacks (FDIAs) is vital to ensure the stable operation of smart grids. However, the intermittency and uncertainty of renewable energy (RE) can lead to confusion with unknown FDIA. As a result, previous works encountered difficulties in extracting distinguishable spatiotemporal features to construct accurate behavior models, thereby affecting the effectiveness of the localization task. To address this challenge, we establish a more practical data set for FDIA localization that takes RE into account. Subsequently, we propose a spatiotemporal sequence analysis framework for the task. Specifically, we propose a factorized module to mitigate the impact of temporal fluctuations, which processes data sequence with down sampling and feature aggregation. Additionally, we introduce a fine-tuning matrix to take regional correlations of RE into consideration, where the weights of spatial information aggregation are adjusted. We evaluate the effectiveness of our approach through comprehensive case studies on IEEE 14-bus, IEEE 57-bus, and IEEE 118-bus standard test systems. The experimental results indicate that our method outperforms the compared methods by an average of 2.52% and 3% in terms of recall and F1-score, respectively.

Enabled by the widely deployed distributed flexible alternating current transmission system (D-FACTS) devices in practical systems, moving target defense (MTD) has been considered as an effective way to detect stealthy false data injection (FDI) attacks by actively changing branch parameters. However, existing MTD methods heavily depend on the assumption that opponents can not timely obtain the newly changed branch parameters. In this paper, a parameter-estimate-first FDI (PEF-FDI) attack is proposed to reveal vulnerabilities of MTD methods in AC state estimation, which can bypass bad data detectors in the existence of MTD. Specifically, a PEF-FDI attack model is proposed to timely construct attack vector and stealthily misguide the results of alternating current (AC) state estimation in the presence of MTD. Requirements of constructing PEF-FDI attacks on eavesdropped measurements are deduced to reveal the limitation on capability of attackers. Simulations in the IEEE 118-bus system verify the performance of the proposed PEF-FDI attacks.

Energy storage systems (ESS) are pivotal component in the energy market, serving as both energy suppliers and consumers. ESS operators can reap benefits from energy arbitrage by optimizing operations of storage equipment. To further enhance ESS flexibility within the energy market and improve renewable energy utilization, a heterogeneous photovoltaic-ESS (PV-ESS) is proposed, which leverages the unique characteristics of battery energy storage (BES) and hydrogen energy storage (HES). For scheduling tasks of the heterogeneous PV-ESS, a practical cost function plays a crucial role in guiding operator’s strategies to maximize benefits. We develop a comprehensive cost function that takes into account degradation, capital, and operation/maintenance costs to reflect real-world scenarios. Moreover, while numerous methods excel in optimizing ESS energy arbitrage, they often rely on black-box models with opaque decision-making processes, limiting practical applicability. To overcome this limitation and enable explainable scheduling strategies, a prototype-based policy network with inherent interpretability is introduced. This network employs human-designed prototypes to guide decision-making by comparing similarities between prototypical situations and encountered situations, which allows for naturally explained scheduling strategies. Comparative results across four distinct cases demonstrate the effectiveness and practicality of our proposed pre-hoc interpretable optimization method when contrasted with black-box models.

Enabled by the widely existed distributed flexible alternating current transmission system devices in power systems, moving target defense (MTD) has been considered as an effective way to detect stealthy false data injection (FDI) attacks. However, due to the limitation of power system topology, not all stealthy FDI attacks can be detected in power system with MTD. In this article, the authors propose a joint meter coding (MC) and moving target defense (MC-MTD) method to cost-effectively improve the detection of stealthy FDI attacks through integrating MC with MTD. Detection conditions and requirements on MC-MTD are theoretically analyzed, which reveal the close coupling between MC and MTD in collaboratively detecting stealthy FDI attacks. The design of the coding matrix and the selection of encoded measurements are theoretically analyzed to integrate MC with MTD in a special case that the coding matrix is diagonal. An optimization of MC-MTD is formulated and approximately solved to improve detection effectiveness with a small defending cost. Finally, simulations are carried out on both direct and alternating current state estimations to validate the performance of MC-MTD.

Considering the popularity of electric vehicles and the flexibility of household appliances, it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents. In this paper, a novel home energy management (HEM) approach is proposed based on a data-driven deep reinforcement learning method. First, to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles (EVs), an improved mathematical model integrating driver’s experience, unexpected events, and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems. Second, a decoupled advantage actor-critic (DA2C) algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks. Furthermore, separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios. Finally, comprehensive experiments are carried out to compare the proposed approach with existing methods, and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.

In Home Energy Management System (HEMS), the scheduling of energy storage equipment and shiftable loads has been widely studied to reduce home energy costs. However, existing data-driven methods can hardly ensure the transferability amongst different tasks, such as customers with diverse preferences, appliances, and fluctuations of renewable energy in different seasons. This paper designs a transferable scheduling strategy for HEMS with different tasks utilizing a Meta-Reinforcement Learning (Meta-RL) framework, which can alleviate data dependence and massive training time for other data-driven methods. Specifically, a more practical and complete demand response scenario of HEMS is considered in the proposed Meta-RL framework, where customers with distinct electricity preferences, as well as fluctuating renewable energy in different seasons are taken into consideration. An inner level and an outer level are integrated in the proposed Meta-RL-based transferable scheduling strategy, where the inner and the outer level ensure the learning speed and appropriate initial model parameters, respectively. Moreover, Long Short-Term Memory (LSTM) is presented to extract the features from historical actions and rewards, which can overcome the challenges brought by the uncertainties of renewable energy and the customers’ loads, and enhance the robustness of scheduling strategies. A set of experiments conducted on practical data of Australia’s electricity network verify the performance of the transferable scheduling strategy.

Due to the close relevance to the reliability and efficiency of power systems, network parameters such as branch admittance have been the target of various cyberattacks. However, existing attack models are generally based on the impractical assumption that attackers can directly modify the data of network parameter stored in well-secured control centers. This article proposes a practical attack model and designs an optimal strategy to detect malicious modification of critical network parameters. Specifically, the vulnerability of network parameter error processing is discovered and exploited to indirectly modify the data of network parameter without accessing to the well-secured control center. A model of false-data-injection-enabled network parameter modification is proposed, which significantly reduces the requirements on attackers’ capability and system information. An optimal detection strategy is designed based on the analysis of the minimal protection set at a single branch, which can significantly reduce the number of protected measurements in detecting malicious modification of critical network parameters. Finally, numerical simulations are carried out on the PJM 5-bus and the IEEE 118-bus test systems to validate the theoretical results.

Load redistribution (LR) attacks have proven to be hard-detectable and damaging, which require effective corrective schemes to mitigate the impact on power grid operations. Traditional game-theoretic methods and corrective dispatches employing static line rating (SLR) have been studied for attack mitigation based on specific attack objectives but have high dispatch cost and limited performance of attack mitigation. This is because the power transfer capacity of the existing transmission network is underestimated with SLR, and in practical operations, the specific objective of the adversary is not available to the defender, which would introduce uncertainties to the design of corrective schemes. As such, this article incorporates the dynamic line rating (DLR) technology, which enhances the power transfer capability of the existing network, to develop the cost-effective corrective dispatch for mitigating LR attacks with unknown objectives. Specifically, a DLR-based robust corrective (DRC) dispatch model is presented, which guarantees the system security as well as the economic performance. A methodology utilizing the robust counterpart technique and column constraint generation (CCG) algorithm is proposed to solve the dispatch model in a decomposition framework. Case studies based on the IEEE 14- and 118-bus systems verify the performance of the proposed DRC dispatch in enhancing the cyber–physical security of power grids.

With the popularity of electric vehicles, numerous cities have adopted electric vehicles as a part of taxis system. Compared with traditional fuel taxis, electric taxis (ETs) have to rely on charging stations (CSs) to charge frequently, so that it is possible to use charging behavior to control the actions of ETs. This paper considers the problem of optimizing dispatch of electric taxis and charging stations' prices making. Specifically, based on the electricity price control strategy, electric taxis are guided to suitable charing stations deliberately to match a desired dispatch which could improve service quality or operating efficiency of taxis system. In this paper, a Stackelberg (leader-followers) game model is proposed to describe the optimal dispatch and price-making problems. The existence of Nash equilibrium of this game is analyzed, and a low computational complexity algorithm that is suitable for large scale problem is designed to solve this game. In addition, a practical situation is simulated and the impacts of several parameters are presented.