With increasing wind power penetration, the inherent uncertainty of wind power poses significant challenges to dispatch decisions in power systems. To address this issue, this paper proposes a two-stage distributionally robust chance-constrained (TDRC) model for the unit commitment problem with wind power uncertainty. In this model, an ambiguity confidence set is developed to characterise wind power uncertainty with unknown probability distributions, and wind power curtailment and load shedding levels are modelled as chance constraints to balance wind power uncertainty and system security of dispatch decisions. A hybrid parallel solution (HPS) is proposed for efficient computation by integrating Benders decomposition (BD) and column-and-constraint generation (C&CG) methods. Case studies on the IEEE 24- and 118-bus systems demonstrate the rationality of the proposed approach, while experiments on a practical 126-bus system using the cyber-physical power system (CPPS) dispatch platform further validate the effectiveness and practical applicability of the proposed TDRC model.

False data injection (FDI) attacks can mislead the system operator to conduct incorrect dispatch decisions, causing cyber-induced physical line overloads. However, traditional false data is constructed either further from normal data and easy to detect, or not effective to overload multiple lines. To improve both attack stealth and overload impacts, this paper proposes a bilevel cyber-induced overloads (CIO) mechanism that can cause a predefined number of multi-line overloads considering post-attack economic dispatch, where the injected false data is minimised to improve the attack stealth. Within this mechanism, a detailed CIO attack model is formulated that explicitly incorporates the post-attack economic dispatch, enabling it to design more practical attack strategies. One advanced feature of this CIO attack model is the optimal selection of overloaded lines for the bilevel optimization of cyberattack resources and post-attack impacts in terms of line overloads, operation costs, and load loss. To solve the proposed model, it is converted into a single-level nonlinear model by a strong duality theory, and then CIO attacks are discretised to convert this model into a mixed-integer linear programming (MILP) problem. Case studies conducted on an IEEE 14-bus power system and an industrial 126-bus equivalent power system validate the superiority and effectiveness of our proposed approach.

Line hardening is regarded as a critical defense measure against malicious attacks in power systems. However, this defense strategy often ignores the uncertainty and multistage nature of malicious attacks. Moreover, with high wind power penetration in power system operation, the inherent volatility of wind power may compromise the performance of the defense strategy against malicious attacks. To address such issues, this article proposes an adaptive robust planner-attacker-operator (AR-PAO) model that accounts for both volatile wind power and multistage uncertain malicious attacks within a line hardening framework. A novel solution algorithm is then developed to solve the AR-PAO model for the optimal defense strategy, in which it improves computational efficiency. Comparative simulation results on two IEEE Reliability Test Systems validate the effectiveness of the proposed approach.

Existing simulation methods for cyber-physical power systems (CPPS) often oversimplify cyber-physical interactions, particularly in time synchronisation and data exchange processes, limiting their accuracy to assess cascading failure risks. To address this gap, this article develops a cyberphysical simulation framework for CPPS cascading failure analysis, with its core in the detailed modelling of cyberphysical interactions through multitimescale synchronisation and multidata exchange. The framework employs queuing theory and a stack-based first-in-first-out algorithm to explicitly simulate the data exchange processes of measurement upload, control centre data analytics, and command download, thereby capturing end-to-end delay and packet loss arising from traffic fluctuations. Furthermore, a multitimescale synchronisation mechanism is constructed to coordinate physical system dynamics with the respective time cycles of these information processes. This framework is further integrated with a dynamic power flow model that incorporates frequency response to capture power-frequency dynamics, particularly during fault conditions. Case studies demonstrate the effectiveness of the proposed cyber-physical interactions for cascading failure simulation, revealing critical risks caused by queuing delays and multitimescale processes that are overlooked by conventional methods.

This letter develops a high-risk regional load attack mechanism in smart grids with incomplete network information. Different from previous research, this attack mechanism enables an attacker to launch a regional load attack with network information about an arbitrary attack region, while minimising the deviation in corrupted data to enhance the stealth of this attack. Such an attack only corrupts a limited number of loads while still overloading multiple lines within a targeted attack region, resulting in significant impacts on smart grids. Case studies conducted on two modified IEEE test systems validate the effectiveness of our proposed attack mechanism and pave the foundation for the future development of practical defensive strategies.

This letter proposes an island-maximising attack mechanism to maximise the number of power islands in cyber-physical power systems (CPPS) by disrupting lines. Specifically, a single-level mixed-integer programming (MIP) model is developed with a graph-theoretic approach, which is able to divide CPPS into power islands, while intra-island line overloads are induced in a high-stealth manner with increasing load shedding and economic loss. Case studies conducted on modified IEEE 14-bus and practical 36-zone Great Britain systems validate the effectiveness of our proposed island-maximising approach.

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the Internet of Things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this article, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. First, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Second, a new indicator is proposed to measure the effectiveness of MTDper. Third, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems.

The physics-aware watermarking-based detection method has shown great potential in detecting stealthy False Data Injection Attacks (FDIAs) by adding appropriate watermarks to control commands or sensor measurements, especially in industrial control systems and grid-tied Distributed Energy Resources (DERs). However, existing watermarking-based detection methods have limitations in either handling the intricate physical couplings among DERs or characterising the fast changing power electronics dynamics, and thus cannot be directly applied to microgrids. Inspired by the methodology of Unknown Input Observer (UIO), which can be employed for the distributed anomaly monitoring in cyber-physical microgrids but would be easily bypassed once the adversary has the knowledge of certain electrical parameters, this paper makes the first attempt to investigate the physics-aware watermarking embedded in UIOs such that the stealthy FDIAs would be intentionally disrupted by the watermarking scheme. Based on the theoretical analysis of the detection enhancement and performance degradation under watermarking-enhanced UIOs, the watermark strengths, UIO parameters, and control gains are optimally co-designed to significantly enhance the detection effectiveness while not degrading the control performance. The robustness of the watermarking-enhanced UIO to Time Synchronisation Errors (TSEs) is improved by employing a sliding time window with appropriate length. The performance of the proposed method is validated through Matlab/Simulink studies and cyber-physical co-simulation experiments, and the sensitivities of the detection latency and TSE robustness to watermark strength and detection window’s length are comprehensively studied.

EPSRC Supergen Energy Networks Hub (grant number: EP/S00078X/1): ‘GRid flexibility by Electrifying Energy Networks for Airport (GREEN Airport)’; UK Department for Transport Transport-Technology Research Innovation Grant (T-TRIG 2019): ‘Aircraft to Grid: Hybrid and Smart Charging for Electric Aircraft’.