Cyber-physical systems (CPSs) are a new generation of engineering systems that deeply integrate embedded computing, communication, and control technology with physical systems. They have the characteristics of real-time capability, adaptability, and intelligence. Therefore, they have been widely applied in fields such as smart healthcare, smart energy, and smart manufacturing, effectively improving the production efficiency and quality of human life. The proliferation of open communication networks within CPSs significantly amplifies their susceptibility to malicious attacks. A successful intrusion by an adversary can lead to severe consequences, underscoring the critical need for robust cyber-security measures. This issue has garnered substantial research interest. The main purpose of this review is to comprehensively summarize the recent progress of cyber-security research in CPSs from the perspectives of both attackers and defenders. First, the principles and mathematical models of classical attacks are presented based on the cyber physical attack space. Second, various attack strategies are introduced, that is, how attackers exploit less energy and information to achieve maximum destruction while avoiding detection. Third, protection mechanisms such as attack detection, secure estimation, and control are discussed. Finally, the remaining challenges and potential research directions regarding the security problems of CPSs are provided for future research.

False data injection (FDI) attacks on battery energy storage system (BESS) can tamper with the battery measurement information collected by sensors. The state of charge (SOC) estimation of BESS is affected, thus threatening the safe and stable operation of BESS. To address FDI attacks on SOC estimation in BESS, an equivalent circuit model of the battery is developed. SOC estimation is performed using the extended kalman filter (EKF) algorithm, and FDI attacks of varying intensities are constructed. Further, an attack detection and defense scheme based on chaotic time/frequency hopping (TH/FH) signal coding is proposed. The scheme generates chaotic TH/FH signals by quantizing hyperchaotic sequences. These unpredictable chaotic TH/FH signals are then used to modulate and demodulate the measurement data. Detection of attacks is accomplished by analyzing the demodulated signals, and defense against attacks is achieved by filtering the attack signals. Finally, an experimental platform is set up to validate the effectiveness of the proposed approach, and the results indicate that SOC estimation performance is maintained without degradation. Compared with the chi-square detector, the proposed method can detect FDI attacks with smaller amplitude and can defend and reproduce the attack signals in real time.

Full-spectrum solar spectral splitting enhances renewable energy utilization, offering a novel approach to ensuring reliable island energy supply. However, significant solar irradiance uncertainty, coupled with differing dynamic characteristics across the island system's electricity, heat, hydrogen and freshwater subsystems, poses challenges for stable operation. To address these challenges, this study proposes a two-stage, bi-level optimization strategy. In the first stage, we formulate a distributionally robust optimization (DRO) model by constructing a Wasserstein distance-based uncertainty set to identify the worst-case scenario. This yields a minimumcost operational schedule designed to mitigate fluctuations induced by renewable variability. In the second stage, we develop a bi-level rolling optimization framework. The upper level performs long-term rolling optimization for heat energy supply and water storage, while the lower level executes short-term power dispatch by treating seawater desalination as a flexible load. Shared state variables facilitate interaction between levels to accommodate dynamic time-scale mismatches among heterogeneous energy carriers. Simulation results for a representative summer day demonstrate that, compared to conventional single-level optimization, the proposed twostage, bi-level method reduces the system's generation-load imbalance by 60.11% and decreases operational costs by 4.4% relative to traditional multi-time-scale dispatch methods.

Seismic hazards are sudden and destructive events that can cause failures in the electricity, natural gas, and heating subsystems of island integrated energy systems (IIES). Such failures severely hinder the stable supply of energy to critical loads and pose threats to island energy security. However, quantitative assessments of the impacts of seismic hazards on the energy supply capability of IIES remain limited, thus strategies for improving system resilience require further development. To address these issues, this study proposes a resilience enhancement strategy guided by energy supply satisfaction. First, models of the IIES and seismic hazards are established to quantify the failure probabilities of electricity, gas, and heat supply paths under seismic impacts. Second, a resilience evaluation framework is developed by introducing differentiated load weights to reflect the supply priority of critical loads. On this basis, a two-stage strategy combining pre-disaster reinforcement and post-disaster response is designed. Through topology reconfiguration and multi-energy coordination, the complementary characteristics of electricity, natural gas, and heating are utilized to enhance system resilience. Case studies on the E33-G14-H7 test system show that, compared with the post-disaster coordination-only scheme with an energy supply satisfaction of 0.69, the proposed strategy increases energy supply satisfaction by 0.24 to 0.93 and achieves a critical load restoration ratio of 95.62%, verifying its effectiveness under seismic conditions.

Though full-order electrochemical models provide precise descriptions of the reactions occurring within batteries, their complexity cannot be afforded by real-time embedded applications. This paper constructs a coupled electrochemical-thermal model to estimate the state of charge (SoC) and state of temperature (SoT) of li-ion batteries (LiBs). Firstly, an extended single particle model (eSPM) is elaborated and enhanced with a thermal part, thereby facilitating an effective interplay between electrochemical behavior and thermal state. Furthermore, the calibration of battery aging state is accomplished by identifying aging-related parameters utilizing a particle swarm optimization. Subsequently, the electrochemical process within the LiB is articulated in a statespace formulation, and the distribution of Li+ concentrations at different locations is estimated via the unscented Kalman filter (UKF). Eventually, the SoC and entropy change are obtained using the estimated Li+ concentrations, while the SoT is deduced from the thermal model. Experimental verifications, utilizing 18650 LiB cells under two dynamic loads and a temperature range of 0-50 degrees C, showcase the remarkable accuracy and superior robustness of the developed model across diverse operating conditions. Notably, a maximum voltage simulation RMSE of 0.055V, a commendable SoC estimation RMSE of 0.016 %, and an exceptionally low SoT estimation RMSE of 0.18 degrees C, are achieved.

The probabilistic prediction of energy generation by a wind farm quantifies the volatility of wind power. Thus, accurate probabilistic predictions can provide valuable information for grid dispatching and a basis for reliability assessment for safe operation. However, the inherent stochasticity and instability of wind power generation and the quantile crossover problem of traditional quantile regression neural networks pose challenges for prediction. Therefore, this study proposes a wind power probabilistic prediction model using a non-crossing quantile regression neural network (NCQRNN). A data preprocessing method using time-varying filtering empirical mode decomposition (TVFEMD) is introduced to reduce the volatility and sophistication of the wind power series. The NCQRNN model is designed to incorporate the monotonicity constraints and predict the results of multiple quantiles simultaneously. Furthermore, the predicted conditional quantiles are mathematically proven to not exhibit any crossover phenomena. The wind power data from Elia Grid, Belgium, is used to verify the prediction effectiveness of the proposed method. The obtained results indicate that the proposed probabilistic prediction model addresses the quantile crossover issue while adequately extracting the nonlinear and temporal features of the wind power series. This method accurately quantifies the uncertainty of wind power with high prediction efficiency and accuracy.

Covert attacks can gradually destroy the pressure regulation function of the pressurized water reactor (PWR) pressurizer, thus seriously threatening the safe operation of nuclear power plants. However, since convert attack has the ability of launching an attack without changing the residuals, some traditional attack detection methods lose their effects. To address this problem, a detection method is proposed based on quantum-chaos time/frequency hopping (Q-chaos TH/FH) signals. The proposed method is based on chaotic systems’ randomness and initial value sensitivity properties and adopts a quantum random sequence as the initial value of chaotic systems. In this way, a Q-chaos TH/FH-based detection signals with two-dimensional stochastic properties of duration and frequency are designed. Further, modulation and demodulation algorithms are designed to prevent attackers from accessing sensitive information and launching attacks. In addition, the frequency hopping rate constraint that maintains the system’s stability is derived through theoretical analysis. This constraint ensures the system maintains stable control performance in both frequency hopping rate synchronous and transient asynchronous states. Finally, hardware-in-the-loop simulation experiments are conducted to verify the effectiveness of the proposed method.

This article investigates energy trading management involving users, suppliers, and the utility company, focusing on a periodic energy trading mechanism that incorporates time-varying delays in the information transmission process. Compared with previous studies, the time-varying delays considered in this paper are different among participants. The time-varying delays for each participant are distributed according to an independent probability. A novel model for energy trading with time-varying delays is proposed using networked evolutionary game theory. Based on the algebraic state space representation, a criterion is provided for determining the convergence of the networked evolutionary game-based energy trading model. In order to converge all strategies of the networked evolutionary game-based energy trading model to the target game equilibrium set, a networked evolutionary game-based energy trading model with strategy feedback control is proposed. Then, an algorithm is presented for designing the strategy feedback control gain, which enables the strategies of all users and suppliers to converge to the target game equilibrium set, thereby regulating energy trading prices to the desired level. Finally, the effectiveness of the proposed approach is verified through an illustrative example.

This article investigates covert attack methods for lithium-ion batteries. Effective execution of a covert attack requires both an accurate system model and the capability to restore the system to its normal state after the attack is concluded. To address these challenges, a maximum likelihood form of dual-mode H2 optimal unbiased finite impulse response (MLDMH2OUFIR) is proposed to estimate the system state. This approach mitigates the decline in attack model accuracy caused by variations in battery parameters. Furthermore, an attack exit process is incorporated to ensure that the system state reverts to its expected condition after the attack concludes, allowing the attack to be withdrawn from the target system without leaving any trace. Finally, attack simulation experiments are conducted on lithium-ion batteries. The concealment and effectiveness of the proposed scheme is verified by overcharging the SOC of the experimental batteries to 110%, 115% at different ambient temperatures without being detected.

With the yearly increase of air conditioning (AC) usage in summer, the electric power system is facing great challenges. Meanwhile, electric vehicles (EVs), as an emerging power load, play an important role in alleviating the energy crisis. However, the disordered charging of large-scale EVs poses a significant threat to the safe and stable operation of the power system. To address this problem, a coordinated optimal scheduling method considering EV and AC loads is proposed. First, the EV charging load model and the AC load model are constructed; then, the coordinated optimal scheduling model is established with the objectives of minimizing the grid compensation cost and reducing the peak-to-valley difference of loads. Finally, the simulation results show that the method can effectively balance the grid load, reduce the pressure of electricity consumption during peak hours, and satisfy the electricity demand of users.

This paper investigates the ${H_{\infty}}$ dynamic composite nonlinear output feedback control of the inductive coupled power transfer (ICPT) system with load resistance variation, coil structural perturbation, and energy-bounded external disturbance under event-triggering mechanism. A small-signal model is first developed to characterize the dynamic behavior of the ICPT system under S/S resonance, wherein the system mismatch is considered as a type of stochastic process and described as a Markov jump model. In view of the difficulties in measuring system state, poor transient performance, limited controller computation and communication capabilities, an event-triggered dynamic composite nonlinear output feedback controller is designed, and sufficient conditions are presented to guarantee the mean square stability and ${H_{\infty}}$ performance of the ICPT system. The controller gains can be obtained by solving a set of linear matrix inequalities. Finally, the effectiveness and reliability of the proposed control method is verified through simulation examples.

The interconnection of Energy Hub can enhance the energy efficiency and reliability associated with the independent operation of energy systems. However, the traditional optimal scheduling methods are hard to tackle the situation with the incomplete load information and competitive constraints of a multi-Energy Hub. This paper proposes a dynamic Bayesian game optimization scheduling strategy considering incomplete information on users’ load-side demand. First, a load forecast error coefficient is introduced to handle the conditional probability problem caused by incomplete information through the Bayesian rule. The constraint relationship among multi-Energy Hub under the dynamic price mechanism is analyzed to meet the economic and environmental coordination goals under load uncertainty. Second, extending from Nash equilibrium to Bayes Nash equilibrium proves the existence and uniqueness of Nash equilibrium solutions in Bayesian game models. The decision game algorithm is used to optimize the scheduling of the multi-Energy Hub and subsequently ensure the autonomous scheduling of the system and solve the information barriers. Finally, an Integrated Energy System composed of three Energy Hubs was used to validate the effectiveness and superiority of the proposed optimization method. Results show that under the Bayesian game decision, the multi-Energy Hub game model has reduced the total cost of the Integrated Energy System by 0.78%, which can improve the economic benefit and address the environmental pollution problem.

Probability forecasting is a powerful tool for quantifying uncertainty in short-term load forecasting. However, its performance may be hampered by excessive feature redundancy and the quantile crossing phenomenon. To overcome these challenges, this study proposes a novel deep noncrossing quantile method with multi-information fusion for day-ahead load probabilistic density forecasting. This method extracts different types of input features through distinct neural networks, and can reduce the redundancy of feature information. Based on the positive differences among output values from neural networks, a novel quantile noncrossing strategy is introduced. This strategy, integrated within the neural network, eliminates quantile crossing phenomena and enhances the interpretability of model during the training process. Experimental results show that the proposed model reduces the quantile loss by 11% to 31%, produces prediction intervals with higher quality, precision, and no crossovers.

The traditional Vine-Copula method employs Vine structure to represent the dependent structure between wind speed of wind turbines in wind farms, which can fully capture the spatial correlation among them, but lacks the consideration of temporal correlation. Therefore, a wind speed forecast method utilizing an improved Vine-Copula model for multiple wind farms is proposed. In this method, the Copula joint distribution function samples points of the wind speed for the Vine turbine using a Markov process to reflect its change on the time scale. Then, the Vine-Copula model is constructed between wind turbines to solve the spatial correlation problem and achieve wind speed prediction. Using the actual measured data from wind turbines at a wind power plant in Gansu, China, the effectiveness of the proposed method is demonstrated.

The ongoing advancement of industry and technology has led industrial control systems to evolve towards integration. System structures become increasingly complex, which renders them increasingly vulnerable to external attacks. Due to their clandestine and destructive character, covert attacks pose a significant threat to the secure operation of nuclear power unit control systems. In order to optimize the performance of control systems for nuclear power units, it is important to study the damage process caused by covert attacks on these systems. Facing the problem of obtaining high-precision estimation models of attack targets for covert attacks, this paper proposes a model estimation method based on long and short-term memory (LSTM) neural network and symbiotic organisms search (SOS) algorithm, which takes the feedback controller output and input signals of the attacking target as the dataset of the LSTM neural network, and optimizes the network parameters of the LSTM neural network using SOS algorithm to improve the accuracy of the model, and designs the covert attacker by obtaining the estimation model of the attacked area through training. The root mean square error of the estimation model for the primary loop of the nuclear power unit has been verified by comparative experiments to be reduced by at least 93.59%, 96.52%, and 91.11%, respectively, compared with the other methods. Loop experiment results concerning the covert attack for the primary loop of nuclear power unit illustrate that this attack method successfully meets the predefined objectives while maintaining high levels of stealthiness.

With the increasing global energy scarcity and environmental concerns, the wind-solar-hydrogen (WSH) coupled system has garnered widespread attention as an efficient and eco-friendly renewable energy solution. Addressing the impact of uncertainty on the source and load of the coupled system concerning its power balance, a parameter adaptive stochastic model predictive control (PAS-MPC) power regulation strategy based on the scenario analysis method is proposed herein. In order to characterize the probabilistic information about the uncertainty on both sides of the system, scenario generation and reduction techniques are used to obtain classical scenarios of wind and solar outputs as well as electrical loads as inputs to PAS-MPC. With the aim of optimizing the computational time constant of SMPC, the parameter adaptive method is proposed to change the prediction time domain and control time domain of the system. Simulation results demonstrate the effectiveness of PAS-MPC in addressing uncertainties on the source and load sides of the WSH coupled system. Optimization results reveal that PAS-MPC considerably improves the system power balance compared to SMPC. Compared to the conventional model predictive control (MPC), PAS-MPC effectively mitigates high-power state of the controllable equipment of the system, thereby enhancing its lifecycle.

The significant fluctuations and stochasticity of wind speed (WS) and the complex spatio-temporal coupling characteristics create challenges for harnessing wind power. To address this problem, a short-term WS prediction method based on time-varying filtering empirical modal decomposition (TVFEMD) with spatio-temporal correlation error correction is proposed. The TVFEMD method is introduced to mitigate the volatility in the original WS series, the temporal convolutional network (TCN) is applied to capture the long-term dependencies that exist within the time series, and the kernel density estimation (KDE) and the Copula function correction model are constructed respectively to explore the spatio-temporal characteristics of the error series, the error component is reconstructed, and the error correction is accomplished. The proposed method is evaluated for its effectiveness using operational data obtained from a north China wind farm, and the results indicate that the method adequately extracts temporal and spatial features with high prediction accuracy.

Virtual power plants (VPPs) aggregate a large number of distributed energy resources (DERs) through IoT technology to provide flexibility to the grid. It is an effective means to promote the utilization of renewable energy, and enable carbon neutrality for future power systems. This paper addresses the evaluation issue of DERs‘ low-carbon benefits, proposes a flexibility assessment model for self-organized VPP to quantify the low-carbon value of DERs’ response behavior in different time periods. Firstly, we introduce the definition of zero-carbon index based on the curve simultaneous rate of renewable energy and load demand. Then, we establish a multi-level self-organized aggregation method for virtual power plants, define the basic rules of DER, and characterize its self-organized aggregation as a Markov game process. Moreover, we use QMIX to achieve a bottom-up, hierarchical construction of VPP from simple to complex. Experimental results show that when users track the zero-carbon curve, they can achieve zero carbon emissions without reducing the overall load, significantly enhancing the grid’s regulation capabilities and the consumption of renewable energy. Additionally, self-organized algorithms can optimize the combinations of DERs to improve the coordination efficiency of VPPs in complex environments.

Covert attacks significantly threaten the safety of nuclear reactors’ liquid zone control systems (LZCSs). Successful covert attacks require an accurate model of the target system and the ability to exit without leaving traces after the attack. This paper proposes a four-stage covert attack method for LZCSs. First, in the attack preparation stage, a dual-mode H2 optimal unbiased finite impulse response (FIR) is offline designed via maximizing the likelihood estimation. This FIR is used to estimate the system states in the state estimation stage. In the attack execution stage, specific attack sequences are designed and injected into the system signal transmission channels to complete a covert attack, considering the anticipated goals and system states. Finally, in the attack exit stage, a constrained optimization problem is constructed based on the system state offsets to provide the optimal attack exit sequences, allowing for an unnoticed exit. The feasibility of the proposed covert attack method is simulated and verified in different situations, taking into account the system noise intensity and attack model accuracy. The experimental results demonstrate that the proposed attack method can covertly achieve its goals and exit without a trace, under the influence of the above factors individually or combined.