This article investigates the event-triggered composite control problem for switched two-time-scale systems (TTSSs). First, the stabilization problem of switched TTSSs under switching composite control is addressed. Unlike existing results that require the singular perturbation parameter (SPP) to be sufficiently small or to satisfy specific linear matrix inequality conditions, an explicit upper bound of the SPP is derived. Then, both SPP-dependent and SPP-independent dynamic event-triggered mechanisms are developed to reduce the computational burden associated with control signal updates in switched TTSSs. These mechanisms are novel in that they incorporate the boundary layer system and explicitly balance the fast and slow time-scale dynamics. Furthermore, two sufficient stability conditions are established: one concerning the upper bound of the SPP and the other concerning the mode-dependent average dwell time, ensuring the stability of switched TTSSs under the respective mechanisms. In addition, Zeno's behavior is excluded in both cases. Finally, three numerical examples, including a comparative study, are provided to demonstrate the effectiveness and advantages of the proposed results.

This article studies an energy transaction management of multienergy prosumers (MEPs) in integrated energy community (IEC). A novel MEPs coalitional game model with transferable utility is proposed to coordinate the electricity–heat transactions and enhance the local energy consumption among MEPs in IEC. The superadditivity of the proposed coalitional game is rigorously proven to ensure coalition incentives. A superadditivity-directed coalition formation algorithm is developed to achieve a stable and efficient coalition partition with significantly reduced computational burden. Furthermore, the nonemptiness of the core for the proposed coalitional game is rigorously proven, and a Shapley value-based payoff allocation mechanism is designed and proven to align with the core, ensuring both fairness and stability in the proposed coalitional game. Simulation results show that the proposed model and method achieve the highest payoff across all cases, ensure the fair and stable payoff allocation, achieve reductions of 99.2% in iterations and 98.98% in solving time, and confirm their feasibility for large-scale applications.

Time-series forecasting with missing values remains a critical challenge across diverse domains. While Transformer-based models excel with complete data, they suffer from “attention sink” where attention mechanisms disproportionately focus on missing data points, creating a self-reinforcing feedback loop that degrades performance. To address this, we propose meta-enhanced attention (MeaTS), a novel end-to-end Transformer-based framework specifically designed for robust multivariate time-series forecasting with missing values. Our approach introduces two key innovations, first, an MeaTS mechanism that dynamically adjusts attention weights by suppressing focus on missing values while amplifying attention to observed data, effectively mitigating the attention sink problem; second, an extracting latent value module that transforms data with missing values into informative features through frequency-domain representations, enhancing data representation without explicit imputation. Extensive experiments demonstrate that MeaTS achieves a 19.86% improvement in mean absolute error compared to state-of-the-art models, such as SDformer, GinAR+, and BiTGraph.

Accurate electricity forecasting is crucial for ensuring grid stability, optimizing resource allocation, and enabling efficient energy markets. To enhance prediction accuracy, we challenge the paradigms of extracting temporal dependence patterns in traditional time series forecasting approaches and introduce a novel concept: time-element entanglement (TimeEE). This concept asserts that complex nonlinear coupling interactions between multiple past time steps significantly influence the current time step. Building on this idea, we present TimeEE, an innovative model for electricity forecasting through time-element entanglement. Its key component, the element entangled block, explicitly models the entangled information between time points, capturing essential time-element interactions for highly accurate predictions. Our model demonstrates state-of-the-art performance in both prediction accuracy and efficiency across multiple datasets, providing a new and efficient solution for electricity forecasting.

Privacy preservation is a challenging problem in decentralized nonconvex optimization containing sensitive data. Prior approaches to decentralized nonconvex optimization are either not strong enough to protect privacy or exhibit low utility under a high privacy guarantee. To address these issues, we propose a differentially private linearized alternating direction method of multipliers (DP-LADMM), which achieves fast convergence property for nonconvex objective functions while achieving saddle/maximum avoidance under differential privacy guarantee. We also apply the Analytic Gaussian Mechanism to track the cumulative privacy loss and provide a tight global differential privacy guarantee for DP-LADMM. The theoretical analysis offers an explicit convergence rate for our algorithm. To the best of our knowledge, this is the first paper to provide explicit convergence for decentralized nonconvex optimization with differential privacy and saddle/maximum avoidance. Numerical simulations and comparison studies on decentralized estimation confirm the superiority of the algorithm and the effectiveness of global privacy preservation.

The imbalance between faulty and healthy samples of electric vehicle (EV) charging piles makes it difficult for both algorithm-level and data-level diagnostic methods to identify fault categories with limited data. In this article, a fault diagnosis scheme for EV DC charging piles based on a nonparametric balanced diffusion (BalDiff) model under imbalanced samples is proposed by the data-level method. First, the nonparametric BalDiff model is proposed to augment the imbalanced samples. Unlike existing generative data augmentation methods, it does not require the assumption of a specific data distribution. In addition, BalDiff uses attenuated noise instead of the constant noise intensity during the reverse process of the diffusion model to enhance the ability to capture the sample distribution. Second, an online fault diagnosis strategy applicable to different data sources is designed, which guarantees the diagnosis time while ensuring the diagnosis accuracy. Case studies on different data sources show that the proposed scheme achieves at least 4.2% and 6.7% improvement in F1-score and accuracy compared to the comparison methods, and the single diagnosis time is only 0.021 s.

This article proposes the design of an event-triggered control strategy for consensus of interconnected two-time scales systems with structured uncertainty. The control design under consideration ensures also that consensus is achieved with an overall guaranteed cost. Since each system involves processes evolving on both fast and slow time scales, two Zeno-free event-triggered mechanisms are designed to independently decide the sampling and transmission instants for the slow and fast states, respectively. As the first step, we design an event-triggering consensus protocol in the ideal/nominal case when the interconnected systems are not affected by uncertainties and the interactions happen over a fixed interaction network. Next, the results are extended in order to take into account structured uncertainties affecting the systems' dynamics. At this step, we go further and we provide sufficient conditions for event-triggering consensus with a guaranteed overall cost. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

This article proposes a new cooperation framework of energy storage sharing that comprises prosumers, energy storage providers (ESPs), and a middle agent to achieve social energy optimality. In this framework, the prosumers share multiple energy storages of the ESPs via the agent. An energy sharing optimization problem minimizing the total energy cost is formulated involving the energy storage operation, the shiftable load schedule, and the energy trading with the utility. To solve the problem, a fast alternating direction multiplier method (ADMM) with restart which converges faster than traditional ADMM is adopted. Furthermore, a clearing scheme, named as an ideal profit realization ratio distribution model is introduced to distribute the participants’ net profits fairly. Simulation results show that the energy costs can be substantially reduced, and the net profit distribution is relatively fairer compared with the widely used Nash bargaining scheme, which ensures the feasibility of our framework in real applications.

In this article, we propose an economic storage sharing framework for prosumers and energy storage providers (ESPs) to promote renewable energy utilization cooperatively. The optimal shared capacities of ESPs and the energy sharing profiles of prosumers are first derived via minimizing social energy costs. Then the storage sharing profits of ESPs and the energy sharing payments of prosumers are successively determined by the asymmetric bargaining-based benefits sharing model. Specifically, the prosumer group bargains with the ESPs with the nominal required capacity and the shared capacities as their bargaining power to share the storage sharing benefits. Then prosumers bargain with each other to share the energy sharing benefits with their bargaining power quantified by a nonlinear energy sharing mapping method. Therefore, the benefits sharing model based on the contributions of prosumers and ESPs is fair enough for the participants. Numerical simulation tests verify the efficiency of the proposed framework.

This paper introduces a distributed compensation approach for the global optimization with separable objective functions and coupled constraints. By employing compensation variables, the global optimization problem can be solved without the information exchange of coupled constraints. The convergence analysis of the proposed algorithm is presented with the convergence condition through which a diminishing step-size with an upper bound can be determined. The convergence rate can be achieved at O(lnT/√T). Moreover, the equilibrium of this algorithm is proved to converge at the optimal solution of the global optimization problem. The effectiveness and the practicability of the proposed algorithm is demonstrated by the parameter optimization problem in smart building.

This article presents an efficient peer-to-peer energy-sharing framework for numerous community prosumers to reduce energy costs and to promote renewable energy utilization. Specifically, for day-ahead and real-time energy management of prosumers, an intercommunity energy-sharing strategy and an intracommunity energy-sharing strategy are proposed, respectively. In the former strategy, prosumers can share energy with any community peers, and community aggregators represent their own prosumers to coordinate energy sharing. A two-phase model is designed. In the first phase, the optimal energy-sharing profiles of prosumers are derived to minimize the global energy costs, and in the second phase, equilibrium-based energy-sharing prices are induced considering the individual interests of prosumers. In the latter strategy, prosumers share energy only with its community peers for time saving to handle real-time uncertainties collaboratively to reduce real-time costs. The framework efficiency is verified by the simulation cases on a typical distribution network.

The paper proposes a two-stage energy sharing framework for a new prosumer microgrid with renewable energy generation, multiple storage units, and load shifting. In the first stage, a robust bilevel energy sharing model is formulated to provide a robust energy sharing schedule for prosumers and retailer overcoming the impact of the uncertainties of market prices and renewable energy. Through proper linearization techniques, the bilevel optimization problem is transformed into a single-level mixed integer linear programming problem that is practically solvable. In the second stage, an online optimization model is formulated for each prosumer to continually optimize its energy schedule at each hour according to the latest system state, and the proposed punishment mechanism is embedded for prosumers adjusting their previous energy sharing schedules. The simulation cases show the benefits of the energy sharing management framework.

This study addresses the stability and L1-gain analysis for impulsive positive systems (IPSs). By constructing a discretized copositive Lyapunov function, a sufficient condition ensuring the asymptotic stability is established for IPSs under the range dwell time constraint. Based on the stability result, the L1-gain characterization is further analyzed. The obtained criteria can be checked by the linear programming method. The theoretical results obtained are demonstrated by a numerical example.

This paper studies the synchronization problem of coupled delayed multistable neural networks (NNs) with directed topology. To begin with, several sufficient conditions are developed in terms of algebraic inequalities such that every subnetwork has multiple locally exponentially stable periodic orbits or equilibrium points. Then two new concepts named dynamical multisynchronization (DMS) and static multisynchronization (SMS) are introduced to describe the two novel kinds of synchronization manifolds. Using the impulsive control strategy and the Razumikhin-type technique, some sufficient conditions for both the DMS and the SMS of the controlled coupled delayed multistable NNs with fixed and switching topologies are derived, respectively. Simulation examples are presented to illustrate the effectiveness of the proposed results.

The bipartite consensus problem of multiple two-time scales agents over the signed digraph, where both cooperative and competitive interactions exist among the agents, is considered with a new distributed protocol. Sufficient conditions for bipartite consensus is presented in terms of easily checkable algebraic Riccati equation (ARE). Compared with the existing result on consensus of multiple two-time scales agents, the communication topology here is more generic. Moreover, the upper bound of the singular perturbation parameter is also presented. Simulation examples are given to illustrate the effectiveness of the proposed results.

This paper studies the global synchronization of complex dynamical network (CDN) under digital communication with limited bandwidth. To realize the digital communication, the so-called uniform-quantizer-sets are introduced to quantize the states of nodes, which are then encoded and decoded by newly designed encoders and decoders. To meet the requirement of the bandwidth constraint, a scaling function is utilized to guarantee the quantizers having bounded inputs and thus achieving bounded real-time quantization levels. Moreover, a new type of vector norm is introduced to simplify the expression of the bandwidth limit. Through mathematical induction, a sufficient condition is derived to ensure global synchronization of the CDNs. The lower bound on the sum of the real-time quantization levels is analyzed for different cases. Optimization method is employed to relax the requirements on the network topology and to determine the minimum of such lower bound for each case, respectively. Simulation examples are also presented to illustrate the established results.

This paper is concerned with the consensus problem of discrete-time multi-agent systems (MASs) over lossy communication channels. The communication connection between agents at initial time is assumed to be accessible, which ensures that the system can reach consensus over ideal communication channels. However, the communication channel can be fading in practice, packet loss occurs inevitably. In this paper by modeling the packet losses as a Markov process with defective transition probabilities, a new description of the scenario of packet losses is addressed. Since the transition probabilities considered here comprise three types: known, uncertain within given intervals, and unknown, our model is more general and practical than previous results. A distributed protocol is introduced to achieve dynamic consensus. Sufficient condition for the mean-square consensus of discrete-time MASs is derived in the form of linear matrix inequalities. Numerical example is also given to illustrate the effectiveness of the theoretical results.

In this paper, synchronization of continuous dynamical networks with discrete-time communications is studied. Though the dynamical behavior of each node is continuous-time, the communications between every two different nodes are discrete-time, i.e., they are active only at some discrete time instants. Moreover, the communication intervals between every two communication instants can be uncertain and variable. By choosing a piecewise Lyapunov-Krasovskii functional to govern the characteristics of the discrete communication instants and by utilizing a convex combination technique, a synchronization criterion is derived in terms of linear matrix inequalities with an upper bound for the communication intervals obtained. The results extend and improve upon earlier work. Simulation results show the effectiveness of the proposed communication scheme. Some relationships between the allowable upper bound of communication intervals and the coupling strength of the network are illustrated through simulations on a fully connected network, a star-like network, and a nearest neighbor network.