The distributed energy management (DEM) problem of smart grids is devoted to achieving optimal energy dispatch and allocation to ensure social welfare maximization. It can be modelled as a distributed optimization problem with physical constraints, whose solution depends on data sharing between smart devices. The exchange of information creates a potential risk for eavesdroppers to intercept and access private data. To avoid privacy leakages, a privacy-preserving optimization strategy via output masks in a consensus framework is proposed to realize social welfare maximization of energy management, where power demand relies on the parameters of the demand sides. Theoretical analysis discloses that eavesdroppers are unable to accurately infer private information of the generators/demand loads. Furthermore, both the convergence and optimality of the proposed strategy are discussed by means of the matrix perturbation theory and the famous KKT optimality condition. Finally, the effectiveness of the proposed strategy for solving the DEM problem is confirmed by the simulation experiments.

In this paper, the dynamic state estimation problem is investigated for integrated electricity-gas systems (IEGSs) subject to measurement outliers, where measurement signals are obtained from sensors with specified resolution. The consideration of sensor resolution is a crucial aspect that reflects real-world engineering practices, as it directly influences the reliability and accuracy of measurements. To address the challenges posed by measurement outliers, an innovation saturation mechanism is employed to constrain the innovation terms affected by these outliers. The main objective is to develop a recursive estimation algorithm tailored for the IEGS, which considers nonlinear measurements, sensor resolution effects, and measurement outliers. This approach ensures that an upper bound on the estimation error covariance is established, and subsequently minimized by designing the suitable estimator gain. The effectiveness of the proposed outlier-resistant recursive estimation algorithm is validated through extensive simulation experiments conducted on an IEGS consisting of a 14-node electrical power system and three 7-node natural gas systems.

This paper investigates the state estimation problem for power distribution networks subject to dynamic quantization effects and hybrid cyber-attacks, where measurement signals are transmitted from sensors to a remote filter via open digital communication networks. To enhance bandwidth utilization and ensure reliable data transmission, a dynamic quantization mechanism is introduced, which effectively accommodates the dynamic characteristics of power signals. Furthermore, the system is vulnerable to hybrid cyber-attacks that may occur simultaneously in a random manner, including denial-of-service attacks and false data injection attacks, characterized by Bernoulli distributed random variables. The primary objective of this work is to develop a recursive unscented Kalman filter capable of addressing the combined challenges of measurement nonlinearities, dynamic quantization effects, and hybrid cyber-attack scenarios. By solving Riccati-like difference equations, an upper bound on the filtering error covariance is derived, and subsequently minimized through the design of time-varying filter gains. Extensive simulations on the IEEE 69 distribution test system demonstrate the effectiveness of the proposed filtering algorithm.

In order to achieve energy consumption optimization and decarbonization in the Industrial Internet of Things (IIoT), this article establishes a novel framework for optimal scheduling of park-level integrated energy systems (PIESs). This framework incorporates carbon capture technologies alongside a multienergy joint supply subsystem model featuring hydrogen storage, complemented by a structured stepped carbon trading mechanism. Furthermore, a novel optimized federation model is developed to balance low carbon emissions and economic performance by federating the carbon capture system into the tiered carbon trading mechanism. The primary objective of this model in the context of the IIoT is to minimize energy procurement costs, wind abandonment costs, and carbon trading expenses while maximizing revenues from carbon dioxide sales. The CPLEX optimization tool is utilized to address this complex challenge. Finally, several scenarios are simulated to demonstrate the effectiveness of the optimized PIESs in reducing the operational costs and minimizing carbon footprint.

This paper is concerned with the set-membership filtering problem for active power distribution systems that are influenced by unknown but bounded noises. Both the phenomena of fading channels and limited communication capacity are taken into account. In consideration of the integration of photovoltaic generation systems, an active power distribution system model is formulated which encompasses the conventional power distribution networks and distributed power sources. Network data are transmitted to a remote filter through fading channels, where a component-based dynamic event-triggered mechanism is introduced, by which the transmission frequency is reduced while sustaining the filtering performance, thereby mitigating the transmission load of the communication network. The purpose of this paper is to design a dynamic event-triggered filter such that, when faced with unknown but bounded noises, a set of zonotopes is devised to confine the system states. By minimizing the$F$-radius of these zonotopes, the time-varying filter gain is determined recursively at each time step. Additionally, simulation experiments on the IEEE 34 distribution test system are carried out, through which the efficiency of the proposed filtering methodology is validated.Note to Practitioners— The rapid increase in energy demand, coupled with the fast progression of energy technologies, has resulted in the widespread merging of traditional distribution networks with various other distributed power systems Consequently, these have transformed into active power distribution systems (APDSs), undergoing notable alterations in their distribution network configurations. Challenges like power back-flow and overvoltage, which might seriously compromise the stability, operation, and control of the APDS, can be introduced by this transformation. For addressing these challenges, having precise information about the system state is deemed crucial for the facilitation of real-time monitoring, improved control, and reliable protection of the APDS, and therefore the state estimation problem has been attracting an increasing research interest for ensuring the system’s safe operation and economical dispatch. In this paper, the problem of zonotopic set-membership filtering is examined for the APDS with unknown-but-bounded noises in fading channels. Initially, a component-based dynamic event-triggered mechanism is used in the APDS, which eases the load on communication networks with limited resources, and the fading coefficients are represented as uncertain variables within a specified range. Subsequently, by harnessing mathematical induction and set theory, the state estimation algorithm is introduced, ensuring the real system states are strictly encompassed within zonotopic sets and system states are estimated accurately. In conclusion, simulation experiments on the IEEE 34 distribution test system demonstrate the effectiveness of the introduced filtering algorithm.

This artcle investigates the fusion estimation problem for renewable energy microgrids subject to relay transmissions and cyber-attacks. A filter-and-forward (FaF) relay strategy is adopted to ensure the reliable transmission of power signals from distributed sensors to the remote estimator. This strategy jointly accounts for both sensor-relay and relay-estimator channels when determining signal transmission, and relays with embedded filtering capabilities are considered essential for extracting relay signals from corrupted measurements. In addition, four stochastic sequences governed by Bernoulli distributions are employed to characterize denial-of-service and false data injection attacks, which occur randomly throughout the communication process. The principal objective is to improve estimation accuracy by constructing an estimation scheme capable of operating under the presence of FaF relays and hybrid attacks. Specifically, the proposed scheme consists of two main components: first, the design of a recursive filtering algorithm at each FaF relay for generating the relay signal, and second, the development of a sequential fusion estimation algorithm to enable accurate state estimation. Sufficient conditions are derived to guarantee the mean-square boundedness of both the filtering error and the estimation error. The effectiveness of the proposed estimation methodology is demonstrated through simulation experiments conducted on renewable energy microgrids in a wireless multisensor system.

To address the challenge of inaccurate detection of the health status of roller tank lugs in the case of occlusion, this article proposes a nonlearning-based image inpainting method for roller tank lugs. The approach employs an unordered image stitching algorithm to effectively recover the occluded roller tank lugs and facilitate accurate detection of their health status. In terms of occluded region extraction, this article proposes an extraction algorithm based on a binary tree model, which effectively identifies and extracts the occluded regions by estimating the nonoverlapping areas in the reference image and subsequent multiframe images, thereby accurately extracting the images of the polyurethane wheels on roller tank lugs. This article presents an unordered image stitching technique that does not require image sorting and can directly perform stitching, effectively addressing the image distortion issues caused by cumulative stitching errors in traditional unordered stitching methods. The experimental results show that the proposed algorithm outperforms the traditional algorithm in both qualitative analysis and quantitative metrics in terms of occluded region extraction and image stitching. Compared with traditional image inpainting algorithms, the proposed method can recover the occluded portions of roller tank lugs more efficiently, with an average error rate of just 1.57% in the calculation of wear of its completed images. These results indicate that our approach meets the practical needs of engineering applications.

Economic dispatch plays a significant role in intelligent microgrids, which offer a promising approach to facilitate the integration of distributed renewable energy. In the past few years, information sharing has promoted new development opportunities for distributed economic dispatch algorithms of microgrids. However, integrating communications into distributed algorithms gives rise to significant concerns about data security and privacy. As such, this paper investigates a privacy-preserving distributed economic dispatch problem within microgrids. The primary objective is to propose a privacy-preserving distributed zero-gradient-sum (DZGS) algorithm that realizes optimal power dispatch with the balance of supply and demand while ensuring sensitive information from potential leakage. To achieve this goal, the hybrid privacy strategies based on a variable decomposition rule and noise injection are implemented to enhance the security of sensitive data. Sufficient conditions, including a suitable range of step sizes, are established theoretically to reveal the linear convergence of the algorithm. Furthermore, the privacy of the algorithm is analyzed from the perspectives of both honest-but-curious nodes and external eavesdroppers. Finally, the effectiveness of the algorithm is validated through empirical analyses of the IEEE 39-bus system.

In this article, the state estimation problem is investigated for permanent magnet synchronous motors with sensor degradations under the encoding–decoding scheme. To reduce the network communication burden and enhance data transmission security, a uniform quantization-based encoding–decoding strategy is introduced in the sensor-to-estimator channel, which allows the transmitted power signals to be converted into digital format. Furthermore, the sensor degradation is represented by using a set of independent stochastic variables obeying uniform distributions. The primary objective of this article is to design a recursive state estimation algorithm such that, in the presence of sensor degradations and encoding–decoding strategy, a minimal upper bound on the estimation error covariance is derived by designing an appropriate estimator gain matrix. Simulation experiments for permanent magnet synchronous motors are conducted to validate the efficacy of the proposed recursive state estimation algorithm.

This paper is concerned with the set-membership state estimation problem for power harmonics under quantization effects by using the dynamic event-triggered mechanism. The underlying system is subject to unknown but bounded noises that are confined to a sequence of zonotopes. The data transmissions are realized over a digital communication channel, where the measurement signals are quantized by a logarithmic-uniform quantizer before being transmitted from the sensors to the remote estimator. Moreover, a dynamic event-triggered mechanism is introduced to reduce the number of unnecessary data transmissions, thereby relieving the communication burden. The objective of this paper is to design a zonotopic set-membership estimator for power harmonics with guaranteed estimation performance in the simultaneous presence of 1) unknown but bounded noises, 2) quantization effects and 3) dynamic event-triggered executions. By resorting to the mathematical induction method, a unified set-membership estimation framework is established, within which a family of zonotopic sets is first derived that contains the estimation errors and, subsequently, the estimator gain matrices are designed by minimizing the$F$-radii of these zonotopic sets. The effectiveness of the proposed estimation scheme is verified by a series of simulation experiments.

Accurate acquisition of real-time electromechanical dynamic states of synchronous generators plays an essential role in power systems. The phasor measurement units (PMUs) are widely used in data acquisition of synchronous generator operation parameters, which can capture the dynamic responses of generators. However, distortion of measurement results of synchronous generator operation parameters is inevitable due to various reasons, such as device failure and operating environment interference and so on. Meanwhile, it is hard to transmit gigantic volumes of data to the information center due to limited communication bandwidth. To tackle these challenges, this article proposes a dynamic state estimation method for synchronous generators with event-triggered scheme. The proposed method first establishes a non-linear model to describe the dynamics of generators. Then, a measure-based event-triggering scheme is adopted to schedule the data transmission from the sensor to estimator, thus reducing communication pressure and enhanced resource utilization. Finally, an improved regularized particle filter (IRPF) algorithm is designed to guarantee the estimation performance. To this end, the genetic algorithm is used to optimize the particles sampled by regularized particle filter algorithm, which can solve particle exhaustion problem. The CEPRI7 system is used to verify the performance of the proposed method.