With the continuous integration of more and more distributed photovoltaic (PV), distribution networks are facing serious voltage issues. While centralized battery energy storage (BES) can improve bus voltage profile through power compensation, it often suffers from economic operation problems and low flexibility. Hence, this paper proposes a flexible voltage regulation (VR) method that integrates planning and control of distributed BES. A collaborative planning scheme for BES is first designed to minimize its total capacity configuration. Then, a capacity-based proportional compensation mechanism is used to ensure uniform control state of PV or BES units. Afterwards, a VR algorithm is designed for the crucial bus with the most severe violations, which derives power control references for PV and BES based on sensitivity analysis. By integrating planning and control of BES, the VR constraint is considered in planning to achieve sufficient regulation effects, while the proportional compensation control prevents overuse issues. Finally, the effectiveness is verified by a case study in a real grid scene.

Although information interaction is essential for achieving group goals of distributed systems, the information interaction consumes lots of communication resources, and also leads to the privacy disclosure of individual information. Therefore, how to design an efficient and secure distributed information interaction algorithm naturally becomes a crucial issue. In this article, we propose a differential privacy consensus algorithm based on an event-triggered communication mechanism for current sharing and voltage regulation in DC Microgrids. The main idea is to, at each event-triggered instant, first release some sporadic current data, and then mask the data with additive Laplace noises with an invariant variance. By virtue of a stochastic approximation technique, time-varying control gains are designed to compensate for the adverse effect of the stationary noises. As a result, not only the execution efficiency of the network is improved, but the privacy of initial currents can be well protected. The advantage of the proposed algorithm lies in its ability to prevent the gradual leakage of privacy caused by noise attenuation. That is, it provides stronger privacy protection than the commonly adopted approach using exponentially decaying noise. Furthermore, we carry out rigorous convergence analysis for current sharing and average bus voltage regulation, and also evaluate the level of differential privacy achieved. Finally, the effectiveness of the proposed algorithms is validated by simulation results from a detailed switch-level microgrid model.

The carbon-factor accounting method is widely used for carbon emissions (CEs) evaluation and carbon quotas (CQs) allocation in integrated energy systems (IESs). However, its linear mapping model cannot capture the real-time influence of external and environmental factors, which weakens the constraint effect of CQs on CEs and limits the overall energy–carbon optimization capability. To address this issue, this article proposes a large language model (LLM)-assisted deep reinforcement learning (DRL) optimization method to enhance the constraint effect of CQs on CEs in IESs. First, a nonlinear CQ modeling method based on LLM semantic reasoning is proposed, breaking the dependence of the linear carbon-factor method on expert experience. Second, considering information including energy structure, market changes, policy orientation, and environmental constraints, an interpretable nonlinear CQ accounting method is designed based on LLM to enhance the constraint effect of CQs on CEs. Finally, a trigger mechanism is designed to achieve collaborative optimization through automatic interaction between LLM and DRL. Simulation results indicate that the optimized CQ mechanism enforces a more effective constraint on CE behaviors, enabling timelier response and enhanced energy–carbon optimization performance.

In order to improve bus voltage profile in active distribution networks with high penetration of photovoltaics (PV) and electric vehicle (EV), this paper proposes an event-triggered multi-time-scale regulation method. Under a designable triggered mechanism driven by changes in power flow, a sensitivity-based centralized hybrid power compensation of PV and EV is used to alleviate long-term voltage offset throughout trigger intervals. Then, a dispersed local power compensation of PV or EV is additionally used to reduce short-term voltage fluctuation at involved nontrigger instants. Besides, its comprehensive performance is analyzed by establishing quantitative index, which can in turn be used to design trigger parameter. Finally, the effectiveness of this method is verified through case study in a real scene.

To achieve low-carbon operation of a distribution network, new energy resources like photovoltaics (PVs) have been extensively integrated into it. However, this integration poses significant challenges to the supply-demand balance. Specifically, the generation of PVs is stochastic, causing power fluctuations. Additionally, the increase in power data and the open nature of the network will cause network congestion and communication disturbances. To address these issues, an active power support (APS) strategy is developed with the following innovations. First, an adaptive mutation-based generation prediction algorithm incorporating a multi-extreme learning mechanism (ELM) is proposed to optimize the prediction model and provide reliable predicted generation data for regulation. Second, a demand-driven path optimization method is proposed to prioritize critical data transmission, ensuring that regulatory service demands are met while mitigating congestion. Third, a hierarchical control strategy utilizing multifactor matching and a sliding mode controller (SMC)-based virtual leader-following consensus algorithm is designed to generate optimal control commands for PVs and suppress disturbances. Finally, adequate simulations demonstrate that the proposed method reduces the prediction error by at least 10.1% compared to existing methods, adjusts transmission paths based on data importance and service needs to mitigate congestion, and suppresses communication disturbances within 1s, thereby enabling effective APS.

Uncertainty aggregation characterization of multi spatial–temporal distributed energy resources (DERs) is crucial for effective decision-making and control in power systems. In this article, we propose a cloud-edge-end collaboration approach to quantify the aggregated uncertainty of power generation from multi spatial–temporal DERs. First, considering the temporal dynamic and electrical topology correlation of DERs, a local uncertainty aggregation model based on a spatial-temporal graph neural network (STGNN) is developed. This model can effectively extract the spatial-temporal characteristics of data. Second, addressing the data silo problem caused by the unwillingness of various stakeholders managing the DERs to share data due to privacy concerns, an uncertainty aggregation model training mechanism based on an adaptive secure federated learning is proposed. This mechanism enables collaborative modeling of uncertainty aggregation models across stakeholders while preserving user privacy. In addition, it improves the quality of local model training by adaptively extracting parameter information from the global model for local model initialization. Moreover, since the probability distribution of the aggregated uncertainty is unknown, this article combines STGNN with the weighted quantile regression model to characterize the aggregated uncertainty without prior assumptions about the distribution, and by assigning differentiated weights to aggregation results under different confidence levels based on their importance, the proposed method can better meet the diverse needs of power grid. Finally, simulations conducted on the IEEE 33-bus system and IEEE 69-bus system validate the effectiveness of the proposed method.

The intermittency of solar irradiance is the main cause of rapid fluctuations in the power output of photovoltaic (PV) systems. These fluctuations hinder the large-scale integration of solar power generation equipment into the grid, which in turn hinders the process of utilizing solar energy resources to reduce carbon emissions. The main way to solve this dilemma is to achieve high-precision forecasting of solar irradiance. Although various methods exist to forecast the variations of solar irradiance, few focus on fully utilizing multimodal data information and fuzzy method to improve the forecasting performance. Therefore, a forecasting method combining multimodal data fusion and fuzzification is proposed to forecast ultra short-term global horizontal irradiance (GHI). First, a modal conversion method is designed to convert temporal modal data to spatial modal data. Then, the fused data are formed by fusing the converted data with normal and under exposure all-sky images. Subsequently, the fuzzy method is used to generate fuzzy GHI data with low nonlinear features. Last, we utilize deep neural networks to learn potential patterns between fused data and fuzzy GHI data in an end-to-end manner. Our method has been comprehensively validated on data provided by the National Renewable Energy Laboratory, demonstrating its effectiveness, and achieving the highest forecasting accuracy compared to state-of-the-art methods.

Integrated energy system (IES) facilitates efficient energy conversion and utilization. However, the joint optimization of energy use and carbon emissions (CEs) remains a significant and widely recognized challenge in this field. In this article, to solve the problem, a novel decision-making framework is proposed with leveraging a large language model (LLM) to assist deep reinforcement learning (DRL). First, a dynamic priority trading strategy is designed based on real-time supply and demand, which is adjusted dynamically through a trading matrix. Furthermore, a bidirectional equilibrium pricing mechanism is designed to determine reasonable prices that balance the interests of trading parties. Finally, the powerful inference and analysis capabilities of the LLM are leveraged to optimize DRL algorithms through interactive iterations and feedback loops, thereby enhancing decision-making performance. The experimental results demonstrate that the improved algorithm outperforms the baseline algorithm in terms of cost control, CE limitation.

This article presents a cryptography-based event-triggered resilient distributed charging scheduling method for plug-in electric vehicles (PEVs), addressing non-ideal factors such as privacy leakage, DoS attacks, and constraints on communication and computational resources. The proposed approach addresses two key challenges: first, integrating an edge-event-triggered mechanism (EETM) into the scheduling algorithm to reduce the communication and computational overhead caused by the cryptography-based confidential communication protocol (CCP), whose execution significantly increases transmitted data size and requires substantial computational resources; second, ensuring that the EETM-based regulation algorithm meets the following functional requirements in charging scheduling task: 1) CCP-compatibility, 2) sampled-data-based implementation, 3) resilience to DoS attack. Theoretical analysis demonstrates that the proposed method optimizes the charging strategy for each PEV while effectively handling the above non-ideal factors. Simulation results further confirm that under the constraints of minimizing system cost and maintaining an acceptable convergence rate, the execution count of CCP is reduced by 68% and 78% compared to the existing node-event-triggered mechanism and periodic sampling mechanism, respectively, which implies the superior communication and computational-resource efficiency of the proposed EETM framework.

This article introduces a distributed privacy-preserving economic dispatch (ED) algorithm for islanded microgrid (MG), addressing the challenge of balancing power generation and demand with minimal system cost while ensuring data privacy. The proposed algorithm utilizes homomorphic encryption to establish a confidential interaction protocol (CIP), safeguarding sensitive information during the ED process. To mitigate the computational and communication overheads of the CIP, the CIP-compatible static and dynamic event-triggered mechanisms (ETMs) are, respectively, developed, whose implementations only rely on the information obtained through the proposed CIP. The ETMs reduce the frequency of CIP execution and make the algorithm suitable for resource-constrained environments. The convergence and privacy preservation of algorithms are theoretically proved, and the simulation results validate the algorithm’s effectiveness, highlighting its advantages in privacy protection and resource efficiency.

The increasing number of devices and frequent interactions of agents from networked multiagent systems (MASs) exacerbate the risks of potential cyber attacks, especially the different point attacks and multiple pattern attacks. This article considers the output formation-containment problem for MASs under multipoint multipattern false data injection (FDI) attacks. The multipoint describes the attacks simultaneously occurring on the sensors, actuators, and communication channels; the multipattern captures that sensor and actuator attack signals are both continuous deterministic variables, and the communication channel attack signals are intermittent random variables, obeying the Bernoulli distribution. For such compromised MASs, a novel hybrid protocol is proposed, which integrates a state observer, an attack estimator, an impulsive interactor and a compensation controller. Thereinto, the state observer and the attack estimator are constructed to recover the unmeasured system states and the unknown FDI attack signals, respectively; the impulsive interactor is designed to guarantee that the neighbor's signals are transmitted only at impulsive instants, and meanwhile the channel attacks are randomly launched; using the recovered signals, the compensation controller is devised to alleviate the effect of attacks. A sufficient condition is identified, under which the output formation containment is achieved with cooperative uniform ultimate boundedness (UUB). Finally, simulation results are carried out to validate the effectiveness and advantages of the proposed approach.

This article focuses on an adaptive dynamic surface tracking control issue of nonlinear multiagent systems (MASs) with unmodeled dynamics and input quantization under predefined accuracy. Radial basis function neural networks (RBFNNs) are employed to estimate unknown nonlinear items. A dynamic signal is established to handle the trouble introduced by the unmodeled dynamics. Moreover, the predefined precision control is realized with the aid of two key functions. Unlike the existing works on nonlinear MASs with unmodeled dynamics, to avoid the issue of "explosion of complexity," the dynamic surface control (DSC) method is applied with the nonlinear filter. By using the designed controller, the consensus errors can gather to a precision assigned a priori. Finally, the simulation results are given to demonstrate the effectiveness of the proposed strategy.

Due to cyber-physical fusion and nonsmooth characteristics of energy management, this article proposes a security event-trigger-based distributed approach to address these issues with developed smoothing technique. To tackle with nonconvex and nondifferentiable issue, a randomized gradient-free-based successive convex approximation is developed to smooth economic objective function. Due to resilience ability against security issue, a security event-triggered mechanism-based distributed energy management is proposed to optimize social welfare, which coordinately controls both power generators and load demand. The security event-triggered mechanism is designed to reduce power system security risks, and relieve communication burden caused by smoothing calculation, the convergence of proposed distributed algorithm is also properly proved. According to those obtained results on both IEEE 9-bus and IEEE 39-bus systems, it reveals that the proposed approach can achieve good convergence performance and have less security risks than other alternatives, which also proves that the proposed approach can be a viable and promising way for tackling with energy management issue of cyber-physical isolated power system.

When there is a sudden load disturbance in an islanded microgrid, the peer-to-peer control model requires the energy resource to maintain a margin of generation, resulting in a relatively limited regulation range, that is, voltage/frequency sometimes requires additional control to maintain stability. A "source-storage-load" coordinated master-slave control strategy is proposed in this study to address the aforementioned issues. The system voltage and frequency will be stable as long as the output frequency and voltage of the master resource are stable. Furthermore, it can fully utilize the power supply capacity of resources to support the supply-demand balance. The following tasks are included in the proposed strategy: 1) to improve the operational security in the face of load disruption, a source-storage-load coordinated control method based on the "ramping speed" ratio is proposed, which can quickly restore the balance of supply and demand; 2) to improve the communication reliability in the face of interruption, a channel planning method is proposed, which can address the communication interruption problem by constructing an internal network among source-storage-load; and 3) to improve the mode switching stability of resources subjected to external disturbance, the external disturbance suppression and stability analysis involved in the regulation process are completed using sliding-mode control and small signal model methods. Related case studies are carried out to verify the effectiveness of the proposed strategies.

False data injection attack (FDIA) can pose a severe threat to the distribution networks (DN), and the accurate detection of FDIA plays a key role in the safe and reliable operation of the DN. In this article, an end-edge-cloud collaboration-based detection framework is proposed to detect FDIA in the DN. First, in order to effectively preserve the privacy of different stakeholders in the DN and solve the problem of data island, a federated-learning-based edge-cloud collaboration mechanism is designed according to the proposed end-edge-cloud collaboration framework to jointly train the local FDIA detection models and eventually build a comprehensive FDIA detection model. Then, considering the temporal–spatial correlation of measurement data, a local data-driven FDIA detection model is proposed based on a novel temporal–spatial graph convolutional network, which can extract temporal–spatial features of the measurement data and improve the FDIA detection performance. In general, compared with the traditional centralized FDIA detection methods, the proposed method can make full use of the computational capacity of distributed edge devices and reduce the pressure of computation on the control center. Finally, simulation results based on the modified IEEE 14-bus and IEEE 118-bus distribution systems indicate that the proposed method can effectively improve the accuracy of FDIA detection compared with other methods.

Voltage is an imperative index for the successful operation of distribution networks. With a high penetration of photovoltaic, the intermittence can cause voltage violations that become severer especially during midday and nightfall periods. Coordinated power compensation has shown promising results in rapid voltage regulation while depending on communications. This article proposes a centralized event-triggered voltage regulation method incorporating the impacts of time delay. In order to makes all bus voltages acceptable, a sequential proportional power compensation is first proposed for invariable crucial bus. Then, an event-triggered mechanism is designed by considering multivariate changes under power fluctuations, thus reducing communication burden. Using a quantitative upper boundary obtained here, the impacts of time delays on steady-state regulation performance are fully analyzed. Finally, simulation on MATLAB is conducted to indicate the effectiveness of this method.

In order to improve the voltage quality of buses in active distribution networks with high photovoltaic penetration, power compensation of controllable resources is widely used. How to develop a method to reduce communication burden while facilitating their optimal coordination is rarely discussed. This article designs an optimal voltage regulation method via hybrid power compensation. First of all, an event-triggered mechanism based on multiconstraint of changes in power flow is designed, thereby dividing all instants into trigger and nontrigger types. Both voltage offset at trigger instants and voltage fluctuation at nontrigger instants are mitigated by using a hybrid coordinated power compensation. By establishing a quantitative index, the optimal tradeoff performance for hybrid power compensation is analyzed under the event-triggered way. Finally, the case study verifies the effectiveness of the proposed method.

The integration of large-scale distributed generators into active distribution networks (ADNs) will aggravate voltage fluctuations, which can affect the secure operation of power grids seriously. In this article, we investigate a cooperated voltage regulation problem of ADNs. Specifically, we first formulate a two-timescale voltage regulation problem considering the coordination of various hybrid devices while reducing the power loss of the whole ADNs. Given that the aforementioned problem is challenging to solve directly, we reformulate it as bilevel Markov games. Then, we propose a hierarchical multi-agent attention-based deep reinforcement learning algorithm to solve them. To be specific, the upper level Markov game is solved by a discrete multi-actor-attention-critic (MAAC) algorithm, and the lower level Markov game is solved by a continuous MAAC algorithm. In addition, the two-timescale coordination between upper level and lower level agents is implemented through the information exchange of rewards during the training process. Simulation results show that the proposed algorithm has good effectiveness, robustness, and scalability in voltage regulation.

The volatility and randomness of the source-load sides increase the difficulty for system frequency regulation, while flexible loads like thermostatically controlled loads (TCLs) and hydrogen energy storage system (HESS) can be used to provide frequency auxiliary services. In this article, a hierarchical frequency control framework is established, with TCLs and HESS interacting in a distributed cooperative way to track unmatched power. Then, power allocation principles are proposed, which realizes the rational utilization of various resources and ensures the close tracking of the upper-level power targets. Further, a leader–follower distributed dynamic event-triggered control strategy is designed to guarantee fast convergence speed and synchronization accuracy, where power transmission on demand is realized to save limited network resources. Moreover, sufficient conditions are derived to ensure the stability of the distributed TCLs and HESS system by combining the Lyapunov theory. Finally, case studies assess that the proposed strategy guarantees the effectiveness and rapidity of the frequency control, meanwhile reducing the communication burden.

The ever-increasing false data injection (FDI) attack on the demand side brings great challenges to the energy management of interconnected microgrids. To address those aspects, this article proposes a resilient optimal defensive strategy with the distributed deep reinforcement learning (DRL) approach. To evaluate the FDI attack on demand response (DR), an online evaluation approach with the recursive least-square (RLS) method is proposed to evaluate the extent of supply security or voltage stability of the microgrids system is affected by the FDI attack. On the basis of evaluated security confidence, a distributed actor network learning approach is proposed to deduce optimal network weight, which can generate an optimal defensive scheme to ensure the economic and security issue of the microgrids system. From the methodology's view, it can also enhance the autonomy of each microgrid as well as accelerate DRL efficiency. According to those simulation results, it can reveal that the proposed method can evaluate FDI attack impact well and an improved distributed DRL approach can be a viable and promising way for the optimal defense of microgrids against the FDI attack on the demand side.

The ever-increasing requirements of demand response dynamics, competition among different stakeholders, and information privacy protection intensify the challenge of the optimal operation of microgrids. To tackle the above problems, this article proposes a three-stage optimization strategy with a deep reinforcement learning (DRL)-based distributed privacy optimization. In the upper layer of the model, the rule-based deep deterministic policy gradient (DDPG) algorithm is proposed to optimize the load migration problem with demand response, which enhances dynamic characteristics with the interaction between electricity prices and consumer behavior. Due to the competition among different stakeholders and the information privacy requirement in the middle layer of the model, a potential game-based distributed privacy optimization algorithm is improved to seek Nash equilibriums (NEs) with encoded exchange information by a distributed privacy-preserving optimization algorithm, which can ensure the convergence as well as protect privacy information of each stakeholder. In the lower layer of the model of each stakeholder, economic cost and emission rate are both taken as operation objectives, and a gradient descent-based multiobjective optimization method is employed to approach this objective. The simulation results confirm that the proposed three-stage optimization strategy can be a viable and efficient way for the optimal operation of microgrids.

The proportion of distributed resources connected to the distribution network is gradually increasing. But in most scenarios, resources operate in passive response mode and cannot give full play to their active regulation potential. To awaken the regulation capability of distributed resources for active support of system frequency stability, a transmission and decision-making co-designed architecture is studied in this paper when a local supply-demand imbalance in the distribution network causes frequency instability. The architecture contains “data module”, “transmission module”, and “decision-making module”. Firstly, in the “data module”, based on the theory of multi-extreme learning machines and power flow calculation, the generation prediction of energy sources is performed to provide the data basis for generating regulation commands. Secondly, in the “transmission module”, through the nodal current equation-based sliding mode control and fast path reconstruction, the response strategy of the communication disturbance problem is proposed to provide the transmission support for generating regulation commands. Finally, based on the theory of “multiple factors matching” and “source-load interaction”, the source and load-side regulation commands are generated in the “decision-making module” by combining the transmitted data. Related case studies are carried out to verify the effectiveness of the proposed strategies.

In the multi-microgrid system, once a microgrid is severely disturbed into the alert or emergency state, the effective multi-mode management method is necessary to make the system restore the balance of supply and demand rapidly. Therefore, a hierarchical multi-mode management strategy is proposed in this study, which includes three steps:1. The first step is to predict and fit the support capacity of the neighbor microgrids and to determine whether these microgrids need to participate in the support, which constitutes the upper layer. To this end, an event-triggered mode management strategy is proposed considering the multi-source fitting and line loss factor, etc. 2. When the support of neighbor microgrids is not required, the second step is to manage local source-storage-load to restore the balance of supply and demand rapidly, which constitutes the lower layer. To this end, another event-triggered management strategy is designed to generate management commands without pre-processing data; 3. To establish the mathematical models of a microgrid in hybrid mode consisting of continuous operation status and discrete management commands, the related small signal model is designed as the third step, which can analyze the system stability conveniently. Finally, the effectiveness of methods is verified by case studies.

Due to the existence of different stakeholders, it makes competitive game characteristic in hybrid energy systems (HESs). Combined with the high-dimensional complexity and output uncertainty of distributed energy resources, the optimal operation of HESs can be a more challenging problem. Here, this article proposes a potential game-based two-layered hierarchical optimization strategy to deal with this problem. With consideration of its high-dimensional complexity, a two-layered hierarchical HES model is created, consisting of an upper-level and a lower-level model. For properly solving competitive relationships among different stakeholders in the upper-level model, a multiagent system for stakeholders is created and a potential game is employed with a distributed primal-dual perturbed algorithm, and its convergence and optimality have been both proved. Moreover, an uncertainty and robustness analysis is done with coordination between lower and upper models, which deduces a feasible robust uncertainty interval in the lower-level model. For better dealing with the lower-level model, a gradient descent-based multiobjective differential evolution (GD-MODE) algorithm is utilized to optimize the economic cost and emission issue simultaneously, producing a set of Pareto-optimal schemes. Combined with simulation results, it is proven that the proposed method can reduce computational complexity as well as properly deal with uncertainty problems for the optimal operation of HESs.

The distribution network reconfiguration (DNR) can realize the voltage preventive control of the distribution network (DN) under alert state, which solves the voltage security issues and maintains the safe operation of the DN. However, since the great scale and complexity of the DN would make the traditional centralized DN reconfiguration method have a heavy computing burden, a DNR method based on cloud-edge collaborative architecture for voltage preventive control is proposed in this article, which can reduce the huge computational pressure caused by the excessive concentration of computing tasks. In order to formulate the optimal topology reconfiguration strategy according to the specifics of voltage alerts, a differential hybrid Petri-net model with event-triggered strategy is constructed based on the cloud-edge collaborative architecture to characterize the logical relations of the solution process of the reconfiguration strategy. Considering the short time scale characteristic of preventive control, corresponding to the solution process described by the constructed model, an evaluation network based on the graph convolutional neural network (GCN) is proposed for the reachability discrimination of solutions to significantly reduce the number of candidate solutions, as well as a decision network based on multilayer perceptron is proposed for the selection of the optimal solution among the reachable solutions. Numerical tests are conducted on the modified IEEE 33-bus and IEEE 118-bus distribution systems to validate the effectiveness of the proposed method in dealing with voltage alert problems.

With consideration of false data injection (FDI) on the demand side, it brings a great challenge for the optimal defensive strategy with the security issue, voltage stability, power flow, and economic cost indexes. This article proposes a Takagi-Sugeuo-Kang (TSK) fuzzy system-based reinforcement learning approach for the resilient optimal defensive strategy of interconnected microgrids. Due to FDI uncertainty of the system load, TSK-based deep deterministic policy gradient (DDPG) is proposed to learn the actor network and the critic network, where multiple indexes' assessment occurs in the critic network, and the security switching control strategy is made in the actor network. Alternating direction method of multipliers (ADMM) method is improved for policy gradient with online coordination between the actor network and the critic network learning, and its convergence and optimality are proved properly. On the basis of security switching control strategy, the penalty-based boundary intersection (PBI)-based multiobjective optimization method is utilized to solve economic cost and emission issues simultaneously with considering voltage stability and rate-of-change of frequency (RoCoF) limits. According to simulation results, it reveals that the proposed resilient optimal defensive strategy can be a viable and promising alternative for tackling uncertain attack problems on interconnected microgrids.

This article considers the distributed resilient cooperative control for photovoltaic generators (PVs) suffered from two types of denial-of-service (DoS) attack, where the global DoS attack jams all the communication channels and the distributed one jams each of the channels independently. The proposed control method realizes the fair utilization of all PVs, and restore the active power flow across certain transmission line and the voltage of the critical bus to their reference values. The self-triggered mechanism is introduced to ensure the control performance under the DoS attack and reduce the data flow in the communication network simultaneously. In addition, the parameter selection guidance for the resilient control method with fluctuation range, triggering frequency, and stabilization time is provided. The effectiveness of the theoretical result is verified by simulation.

This article aims to a predefined tracking precision consensus control issue for nonlinear uncertain multiagent systems (MASs) with disturbance and input saturation. Unlike the existing results of prespecified accuracy for MASs, the phenomenon of unknown control gains is solved in this article. A saturation model based on the Gaussian error function is applied due to the appearance of input saturation. The unknown disturbance is considered which can be solved by a disturbance observer. Also, to handle the problem of an unknown coefficient for the controller, the Nussbaum function is employed. Moreover, the radial basis function neural networks (RBF NNs) are utilized to estimate unknown nonlinear functions. On account of the Lyapunov stability method and backstepping technique, adaptive laws are created and the desired distributed controller is designed which guarantees that the consensus errors can converge to prescribed values. Finally, several simulation examples demonstrate the valid of the proposed method.

Due to the uncertain output of intermittent energy resources and dynamic communication topology, it brings a great challenge for the optimal security control of isolated power system. To address this problem, this article proposes a two-level optimal control strategy with event-triggered switching mechanisms. For ensuring the security of the isolated power system, event-triggered switching mechanisms are proposed in the upper-level model to decrease potential risk of supply security and voltage stability, which can ensure system security as well as a low switching cost. In the lower-level model, a distributed optimization with switching topology is developed to minimize power generation cost under the above switching mechanisms, and the convergence ability of the proposed distributed optimization method is well proved with a uniformly globally exponentially stable condition. The obtained simulation results reveal that the proposed optimization approach can properly deal with the security issue of an isolated power system as well as dynamically minimize the economic cost.

Nowadays, with the development of communication technology and its application in islanded microgrid, the pure power grid has gradually become a kind of cyber-physical system. In this system, the communication data and consensus algorithm are widely used in the secondary control of energy resources. However, in the communication process, there are also risks of network attacks, such as denial of service attack. To deal with this kind of attack existing in the data uploading channel during secondary control, the evolutionary game-based defense mechanism is designed, and the main works are as follows: firstly, the caused influence by attack on the control effect is analyzed through constructing a small-signal model. Secondly, two defense strategies including “Adjacent prediction” and “Path reconstruction” are designed. Facing different attack situations, the most suitable strategy can be selected via the evolutionary game. Thirdly, a game-based active defense strategy is designed, which can simulate the attack probability in the near future so that the defense can be prepared in advance. Finally, based on Laplace transformation and$\text{H}_{\infty } $robust theories, the parameter design in defense strategies is completed. The effectiveness of the above strategies is shown in multiple case studies.

To improve the voltage and power sharing of distributed generations, the hierarchical controls are widely used in microgrids while the dependent communication network makes it difficult to ensure the performance under limited bandwidth. In this paper, a predictive voltage hierarchical controller is designed. With the delayed secondary PI compensation signals, an inner-loop robust control is designed in the primary controller, ensuring stable voltage tracking. Then, a predictive controller is designed to provide neighbor and local predictions, such that the data integrity is improved by an accurate predictive compensation under bidirectional data-loss. Final case study results verify the effectiveness of the proposed method.

Due to the high resistance/reactance (R/X) ratio of a low-voltage microgrid (LVMG), virtual complex impedance-based P-· V/Q-ω droop control is adopted in this article as the primary control (PC) technique for stabilizing the system. A distributed event-triggered restoration mechanism (ETSM) is proposed as the secondary control (SC) technique to restore the output-voltage frequency and improve power sharing accuracy. The proposed ETSM ensures that neighboring communication happens only at some discrete instants when a predefined event-triggering condition (ETC) is fulfilled. In general, the design of the ETC is the crucial challenge of an event-triggered mechanism (ETM). Thus, in this article, a static ETM (SETM) is proposed as the ETC at first, where two static parameters are utilized to reduce the triggering frequency. Bounded stability is ensured under the SETM, which means that the output-voltage frequency is restored to the vicinity of its nominal value, and close to fair utilization of the distributed generators (DGs) is achieved. To further improve the power sharing accuracy and accelerate the regulation process, a dynamic ETM (DETM) is then introduced. In the DETM, two dynamic parameters that converge to zero in the steady state are designed, which promises asymptotic stability of the system. Besides, Zeno behavior is excluded in both mechanisms. An LVMG consisting of four DGs is constructed in MATLAB/Simulink to illustrate the effectiveness of the proposed methods, and the simulations correspond with our theoretical analysis.

This article is concerned with attack-resilient event-triggered$H_{\infty }$filtering for a class of networked nonlinear systems described by an interval type-2 (IT2) fuzzy model. Suppose that data transmission from the plant to the filter is completed through a wireless sensor network subject to denial-of-service attacks (DoS). In order to save the limited network bandwidth and resist the effects of DoS attacks, a resilient event-triggered communication scheme is devised. Then, an attack-resilient IT2 filter model is introduced to estimate system states of the nonlinear plant. Based on a piecewise Lyapunov–Krasovskii functional, sufficient conditions are obtained to ensure that the filtering error system is exponentially stable and satisfies a certain$H_{\infty }$performance level. Moreover, explicit expressions for the attack-resilient filter gain parameters and event-triggering parameters can be derived if a set of linear matrix inequalities are feasible. Finally, a practical example is provided to demonstrate the effectiveness of the proposed theoretical results.

In this article, an event-triggered practical fixed-time containment control issue for stochastic nonlinear multiagent systems is addressed. The event-triggering mechanism is designed for the controller update which is reduced update frequency. In combination with the backstepping technique and fuzzy logic systems, an adaptive fuzzy containment control strategy is developed for stochastic uncertain multiagent systems. By adopting the protocol, an adaptive containment controller is devised, which can ensure that the containment errors gather to a small range in fixed time. Finally, simulation results for the practical example demonstrate the correctness of the proposed scheme.

In this article, a source–grid–load-collabora tion-based control framework is proposed to improve the power quality of active distribution networks (ADNs) with high penetration of low-carbon energy. First, hybrid dynamics of ADNs are characterized by addressing the voltage regulation and operation economics in each operation mode, and the mode switching control is designed in line with the operation principle of the on-load tap changer, where voltage security events are used to build the event-triggered functions. Second, multiobjective optimization is formulated with consideration of the system-wide operation cost and distribution circuit loss of the ADN in a relatively slow time scale, while in the fast time scale, all the inverter-based distributed generators, energy storages, and static var compensator devices are coordinated at the source–load side, through which multiple voltage issues, including voltage profile issue and voltage increment issue, can be addressed in a fully distributed manner. Finally, simulation results validate the effectiveness and robustness of the proposed method based on the modified IEEE 33-bus system.

In this article, a cloud-edge collaboration based control framework is proposed for the voltage regulation and economic operation in incremental distribution networks (IDN). The voltage regulation and economic operation, usually considered in separated aspects, can be integrated in a hierarchical control method by coordinating the active power and reactive power of distributed generators (DGs) and distributed storages (DSs) in an “active” mode. Promising the voltage security of the IDN, the upper level multiobjective optimization is formulated to maximize the consumption of the DGs, moreover, the lower level model predictive control (MPC) aims to regulate the dynamics of the DGs and DSs based on the established state space model. Time delay in the downstream channel is considered due to the open environment of the proposed control framework, which can be eliminated by using the PCM derived from the MPC considering model uncertainty. Finally, simulation results demonstrate the validity and robustness of the proposed method.

This article investigates the problem of event-triggered switched fault-detection filters for networked stochastic systems under Denial-of-Service (DoS) jamming attacks. The considered DoS attacks are imposed by a power-constrained pulse-width modulated (PWM) jammers. A new resilient event-triggered communication strategy is designed to save network resources while counteracting the periodic PWM DoS jamming attacks. A new event-based switched residual model for fault detection is established, which characterizes the effects of the event-triggering scheme and DoS attacks simultaneously. By employing a piecewise stochastic Lyapunov functional method, sufficient conditions for achieving the exponentially mean-square stability and the prescribed performance of the residual system under the DoS attacks are formulated in terms of linear matrix inequalities. Consequently, co-design of the desired fault-detection filter parameters and the triggering parameters is achieved if the previous presented conditions are feasible. At last, an F-18 aircraft model is provided to show the validity of the proposed theoretical results.

Resilient distributed coordination control is studied on multiarea power systems with low inertia under hybrid attacks, including denial-of-service (DoS) attack and deception attack. The communication among various areas under the DoS attack is deteriorated to switching residual topologies whose time characteristic is modeled by model-dependent average dwell time (MDADT). Deception attack with malicious strategy targeting at negative feedback control is modeled by a sign function. To obtain resilience performance of the power system under low inertia and hybrid attacks, resilient distributed scheme combining load-frequency control (LFC) with virtual inertia control (VIC) is proposed. Then, resilient frequency control problem of the studied power system is converted to$H_{\infty }$control of the switched nonlinear system. By employing the Lyapunov stability theory and switched system method, the resilient conditions are given by the lower bound of the average dwell time of each residual topology and the upper bound of deception attacks. Furthermore, a linear matrix inequality (LMI) technique is used to design the distributed resilient control gains of the LFC-VIC scheme. Finally, a simulation of four-area power systems is carried out to verify the validness of our theory.

Multi-functional grid-tied inverters (MFGTIs) have been investigated recently for improving the power quality (PQ) of microgrids (MGs) by exploiting the residual capacity (RC) of distributed generators. Several centralized and decentralized methods have been proposed to coordinate the MFGTIs. However, with the increasing number of the MFGTIs, it demands a method with improved reliability and flexibility, which are characteristics of distributed framework that has not been introduced into the PQ improvement (PQI) field before. In this paper, we propose a distributed consensus method to undertake the PQI task. The task is proportionally shared among the MFGTIs according to their instant RCs. Besides, most of the existing methods assume that the RCs of the MFGTIs are sufficient for tackling the PQ problem (PQP), which is not always true. In the case of insufficient RC, the active power output of each MFGTI is scaled down by the same factor determined by a proposed leader-follower protocol to make room for the task. In summary, the PQP is dealt with in both cases of sufficient and insufficient RC under the distributed control framework. Finally, simulations and hardware-in-the-loop experiments of an MG consisting of three 10kVA MFGTIs are presented to verify the effectiveness of the proposed methods.

The relative state between neighbors represents the difference of two connected agents' states, and it possesses specific physical meanings in practice. Under this background, the saturation constraints in the relative state inevitably occur. This article studies the consensus problems under the relative state saturation constraints. Novel adaptive proportional-integral (PI) protocols are designed to solve the constrained consensus problem. Specifically, the adaptive coupling weights and the saturation functions are embedded into the proposed protocols, and the former can render the protocols independent of any global topology graph information, while the latter can confine the relative state to stay in its constrained set. Sufficient conditions are identified under which the constrained consensus can be achieved. Considering that the solution matrix is required to be diagonally dominant, an iterative learning-based heuristic algorithm is proposed to seek the diagonally dominant positive-definite solution matrix. For the special case that the input matrix is row full rank, more stringent saturation functions are constructed, and it not only achieves the constrained consensus but also realizes the nonovershoot and shorter settling time associated with edge states. Besides, this result can be applied to preserve connectivity of the communication network. The theoretical analyses are validated by a simulation example.

In this article, a cyber-physical cooperative control strategy is proposed for islanded microgrid (MG), which divides the MG into cyber and physical layers. And the main designs in these two layers are two event-triggered mechanisms, where one mechanism is used to improve the voltage and frequency stability of MG considering the packet loss problem, the other is used to reduce the communication burden in the control process. More specifically, the control process of the first mechanism can be understood as we use these event-triggered mechanisms to complete the secondary control in the physical layer based on the information in the cyber layer. In this mechanism, the packet loss situation in one communication channel is divided into three categories: 1) to handle the case where the loss rate is small, an adaptive virtual leader-following consensus controller (AVLFCC) is proposed in the cyber layer; 2) to handle the case where the loss rate is large and the forecasted data can be used, a hybrid forecast supplement method (HFSM) is proposed in the physical layer; and 3) to handle the case where the loss rate is large and the forecasted data cannot be used, a path reconstruction method combined with a novel sliding-mode control (SMC) is proposed in the cyber layer. In the second mechanism, an event-triggered protocol is designed for the consensus controller to reduce the communication burden based on the designs in 1)-3). Finally, based on these designs in the two mechanisms, a novel secondary controller is designed. And the experimental results have confirmed the validity of the contributed strategy.

Due to uncertainty and dynamic characteristics from intermittent energy and load demand response (DR), the optimal operation of the hybrid energy system is a great challenge. This article proposes an event-triggered multiagent coordinated optimization strategy with two-layered architecture. First, the price-bidding-based DR model is proposed with different stakeholders, and it also deduces the optimal bidding price with the Nash equilibrium theory. Then, four agents are designed to control different kinds of energy resources: agent 1 mainly analyzes the uncertainty or randomness caused by intermittent power, agent 2 takes charge of the dynamic economic dispatch (DED) within thermal units, agent 3 manages the optimal scheduling of energy storage, and agent 4 mainly undertakes the load-shifting strategy from consumers. In the upper-layer level, all agents coordinate together to ensure the stability of the hybrid energy system with an event-triggered mechanism, and the intelligent control approach mainly depends on switching ON/OFF power generators or curtailing system load, and the consensus algorithm is utilized to optimize the subsystem problem in the lower-layer level. Furthermore, the simulation results can further verify the efficiency of the proposed method, and it also reveals that the event-triggered multiagent optimization strategy can be a promising way to solve the hybrid energy system problem.

Due to the high R/X ratio and mismatched feeder impedance of low-voltage microgrids, conventional droop method is no longer able to decouple the active and reactive power of distributed generators and the powersharing accuracy is degraded. In this article, a virtual complex impedance based P - V̇ droop method is proposed to decouple the powers and improve the power-sharing accuracy among DGs. With the virtual impedance method, the equivalent impedance between virtual power source and point of common coupling is shaped to be purely resistive. Then, a P - V̇ strategy is adopted to alleviate the effect of mismatched line impedance, where the virtual powers rather than the ordinary P/Q are used in the droop equation. In case the output voltage violates the operation code, a restoration mechanism is proposed to reset V̇ to zero. Compared with existing virtual impedance and Q - V̇ droop methods, the proposed method combines the advantages of both. Besides, a modified P - V̇ strategy is also presented to accelerate the restoration process and improve the active power-sharing accuracy at the same time. Simulation results validate the effectiveness of the proposed method.

The fast-growing demand of refueling electric vehicles (EVs) blocks the application and popularization of EVs. Battery swapping provides the EV users with a quick and convenient refueling way. In this article, an aggregative shared battery station (SBS) model is proposed, which is composed of a control center and a group of SBSs. With the SBS, the customers can rent the battery and pay a corresponding fee based on the swapped energy and satisfaction level. In order to enhance the SBS system responsiveness and reconfiguration to meet the changeable customers' battery demand and peak shaving and valley filling task, a two-stage framework for the multi-SBS is designed based on a self-adaptive dispatching strategy. On behalf of the SBS operator, an optimization objective function is established to maximize the operating revenue by optimizing the charging, discharging, and sleeping process of the batteries. Using the genetic algorithm, we perform extensive simulations to validate the optimization model and demonstrate the efficiency of the self-adaptive dispatching strategy. The results suggest that the proposed dispatching strategy is effective for scheduling SBSs to satisfy the EV refueling demand, provide peak shaving and valley filling service, and achieve the revenue maximization.

Voltage regulation is imperative for the successful operation of electricity distribution networks, especially with a high penetration level of photovoltaic (PV) systems. Power compensation control (PCC) that uses both reactive power compensation and active power curtailment has shown promising results in alleviating voltage rise problems. It crucially relies on real-time communications among distributed PV systems. However, the transmission of state measurements and control signals in PCC is hampered by inevitable communication delays. Therefore, it is important to not only estimate the maximum tolerable communication delay (MTCD) but also develop an alternative technique for PCC under abnormal communication delay (ACD) conditions. This article presents a delay-tolerant predictive PCC for voltage regulation in distribution feeders. After estimating the MTCD based on voltage and power mutation, it uses normal PCC for effective operation when communication delay is within MTCD, or switches to predictive PCC under ACD conditions. An accurate prediction is achieved using a double neural network with online adjustment of weights and samples. Simulations on a sample distribution network demonstrate the effectiveness of our presented approach.

High penetration and fluctuation of renewable energy resources are threatening the voltage security of the integrated hybrid energy system (IHES). This article focuses on providing a fully distributed method to ensure the voltage security and information privacy of the IHES. First, a multiagent system based control scheme is presented, in which the upper level agent is responsible for the voltage security of the whole system, and the local dynamic performance of each distributed energy resource (DER) unit is formulated in the lower level unit agent. Second, a distributed model predictive control (DMPC) based coordinated voltage control method is proposed, by which only partial information exchange is needed in the interaction. Furthermore, the diverse requirements on voltage quality of each microgrid in the IHES are considered in this article and it provides a novel paradigm in this field compared with the traditional methods. To study the stability of the resultant DMPC, the Nash equilibrium is achieved and theoretically proved to ensure the convergence of the proposed method. Finally, the validity of the proposed method is verified by virtue of the simulation and experimental test results.

Relative states between neighbors are ubiquitous in large-scale systems, and the relative state saturations need to be considered when designing controllers based on this relative state information. This article investigates the consensus control of the delayed multiagent systems under the relative state saturations. By means of an incidence matrix, the consensus under the relative state saturations is transformed into the stability of edge dynamics operating on constrained sets. To enable the edge states to stay within the constrained sets, a novel nonlinear protocol is designed by embedding an elaborate saturation function. Sufficient conditions are identified, under which not only consensus is achieved but also relative state saturations do not occur. Moreover, this analytical result can address the consensus problem while preserving connectivity, within the limited communication range framework. Simulations illustrate the theoretical results.

This article investigates the consensus tracking of exponentially unstable multiagent systems with time-varying input delay. The truncated predictor feedback approach is utilized for designing delay-dependent state and output feedback protocols. And the explicit conditions that can realize consensus tracking are established in terms of parametric Lyapunov equations and scalar inequalities. Besides, the protocol design algorithms under the maximum allowable delay and the maximum convergence rate are, respectively, provided. Compared with the existing results, the salient characteristic of the current results is to reveal the quantitative relationship among the time delay, unstable plant, network topologies, and the convergence rate. Specifically, for achieving the consensus tracking, the synchronization force from network connectivity needs to dominate the anti-synchronization force from unstable open-loop poles and input delays; the maximum allowable input delay is inversely proportional to the sum of the unstable open-loop poles; the convergence rate becomes smaller as the input delay bounds or/and the sum of the unstable poles in plant increase. Numerical simulations confirm the effectiveness of the proposed theoretical design.

This note is concerned with bandwidth allocation-based switched dynamic triggering control under DoS attacks and time delay. To prevent DoS attacks from causing open-loop unstable operation of the system with single-channel transmission, we present a primary-redundancy (PR) communication structure on the basis of limited bandwidth allocation; the correlation of communication delay bound between PR channels is introduced. To save the limited bandwidth, we propose a dynamic the event-triggered mechanism (DETM). Then, a switched delay system model is established to describe system dynamic driven by primary channel control, redundancy channel control, and unstable operation under DoS attacks. Further, by using the convex combination method, a switching law for PR channel is designed with the aim of keeping the concealment of the redundancy channel. With the switching law, a criterion of exponential stability is obtained by using piecewise Lyapunov-Krasovskii functional method. A co-design method is proposed to obtain feedback control gains, DETM parameters and switching law parameters. Finally, two simulation examples are illustrated to verify the validness of our proposed method.

Hybrid and intermittent characteristics of the distributed energy resources (DERs) bring great challenges to the security control and economic dispatch (ED) of the microgrids. To bypass these hurdles, this article proposes a multiagent system-based integrated design of security control and ED to guarantee the effective and economical operation of the interconnected microgrids. First, a hierarchical control scheme is constructed by two-level unit agents, in which the switching control and dynamic regulation are fully implemented with the corresponding hybrid behaviors based on the differential hybrid Petri-net (DHPN) model. Based on the DHPN model, a novel dynamic ED integrated with security control is proposed to overcome the issues that cannot be solved in conventional models. Furthermore, to reduce the computational complexity and unified the mathematical model of the DERs, the inverter-based power control strategy is converted to a predictive control model which can be decomposed into several subsystems. In the optimization process, all the subsystems are implemented in a fully distributed, communication free, and rolling optimization manner based on the distributed model predictive control (DMPC). The validity of the proposed design is demonstrated according to the simulation results in case studies.

This paper is concerned with the observer-based event-triggered control for a continuous networked linear system subject to denial-of-service (DoS) attacks, where the attacks are launched periodically to block the data transmission in control channels. First, a new observer state-based resilient event-triggering scheme is developed in the presence of DoS attacks. Second, a novel event-based switched system model is established by considering the effect of the event-triggering scheme and DoS attacks simultaneously. By virtue of this new model combined with a piecewise Lyapunov-Krasovskii functional method, the sufficient conditions are derived to guarantee exponential stability of the resulting switched system. It is shown that the proposed results can establish a quantitative relationship among the launching/sleeping periods of the attacks, the event-triggering parameters, the sampling period, and the exponential decay rate. Third, criteria for designing a desired observer-based event-triggered controller are provided and expressed in terms of a set of linear matrix inequalities. Finally, an offshore structure model is presented to illustrate the efficiency of the developed control method.

Due to the uncertainty of intermittent energy and system load, it is a big challenge to optimally operate an isolated power system. This article proposes a two-stage optimal operation strategy with a Takagi–Sugeno–Kang (TSK) fuzzy system to address the supply security under uncertainty circumstance. For proper analysis of the uncertainty characteristics, adjustable uncertainty parameters of intermittent energy resource and system load are taken as fuzzy sets; with the consideration of the robustness of these uncertainty parameters on isolated power system, it creates a supply-security identification model with the TSK fuzzy approach under radial basis function (RBF) neural network, and deduces optimal weight values with a recursive least square method. For properly avoiding potential risks, security index is classified into several degrees, each degree of risk can switch a different operation model, which can ensure the supply security of an isolated power system. For properly solving the optimization model, gradient descent-based multiobjective cultural differential evolution is employed to minimize economic cost and emission rate simultaneously. With simulations on isolated regional network, the obtained results reveal that the proposed method can be a viable alternative for optimal operation in isolated power systems.

Within the framework of multiagent systems, relative information can be directly acquired by vehicle-mounted sensors and the relative information constraints inevitably occur due to limited sensing capabilities. This paper investigates observer-based consensus of nonlinear multiagent systems subject to relative state estimate constraints. Each agent's state is constructed via a state observer, and the relative state estimate is assumed to be confined into a hypercube. In virtue of the edge Laplacian, the consensus problem of nonlinear multiagent systems under this constraint is converted into the stabilization problem of edge dynamics operating on the constrained set. Observer-based intermittent protocol and adaptive protocol are, respectively, designed for achieving consensus. A convergence analysis is provided with the help of state saturation theory, switched system theory and adaptive theory. Finally, the results on consensus with relate state estimate constraints are applied into the consensus problem while preserving the connectedness, and are validated by a simulation example.

This paper investigates leaderless consensus and leader-following consensus of multiagent systems with Lipschitz nonlinearity and input delay. For such systems, the observer-based truncated prediction feedback protocols are designed via dropping the distributed term and remaining the exponential term of the solution of the system equation over the delay period. Using Lyapunov-Krasovskii functional approach, two sufficient criteria are, respectively, established for achieving leaderless and leader-following consensus. The observer and controller gains are then obtained by means of an iterative linear matrix inequality procedure. A numerical simulation verifies the effectiveness of the proposed control design approach.

The distributed cooperative control for frequency and voltage stability and power sharing in microgrid considering the limitation of communication network is concerned in this paper. Two types of novel event-triggered mechanism with distributed architecture are first proposed, which can greatly reduce the communication burdens among power source inverters. Based on the event-triggered schemes, distributed restoration mechanism is constructed, which can restore the frequency and voltage magnitude of microgrid and realize the fair utilization of all power sources with comparative less requirements for the transmission data. Simulation is carried out to verify the effectiveness of the proposed method.

This paper is concerned with data-driven distributed optimal consensus control for unknown multiagent systems (MASs) with input delays. The input-delayed MAS model is first converted into a delay-free form using a model reduction method. By establishing an equivalent relationship on the predesigned performance indices of the two MASs, optimal consensus control of input-delayed MAS can be fully transformed to that of delay-free MAS. Based on the coupled Hamilton-Jacobi equations and Bellman's optimality principle, optimal consensus control policies are derived for the transformed delay-free MAS. Then a policy iteration algorithm based on distributed asynchronous update mechanism is proposed to learn the coupled Hamilton-Jacobi-Bellman equations online. To perform the proposed data-driven adaptive dynamic programming algorithm, we adopt the measured data-based critic-actor neural networks to approximate the value functions and the control policies, respectively. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

In this paper, we optimize power and spectrum allocation simultaneously to improve the demand-side communication quality in smart grid, to further reduce the cost of utility companies. The electricity cost is first modeled based on regulation errors caused by direct load control in the smart grid. Then the subbands are allocated to different data aggregator units according to the band confidence levels and the utility company's maximum cost. An algorithm is designed to optimize transmission power of the relay and refine the spectrum allocation to reduce the cost of utility companies. Simulation results demonstrate that the packet loss rate and cost of utility companies can be significantly reduced.

This paper proposes a switched system method (SSM) for the fusion state estimation (SE) under denial-of-service (DoS) attacks in power systems. One of the subsystems in the SSM employs the widely used dynamic model with unit state transition matrix and zero control input. The performance of this model is favorable when the system operates in normal conditions but tends to suffer while the system state dramatically changes. Moreover, its accuracy and convergence is sensitive to the value of the process noise covariance matrix. The other subsystem, with some opposite characteristics, utilizes a pseudo-dynamic model derived from the linearization of the power flow equation. A switching rule based on the innovations is established to make a tradeoff between the two subsystems with regard to estimation accuracy, converge speed, and computing time. Apart from the difference in the reporting rate of various measurements, cyber attacks, such as DoS attacks may result in the absence of measurements in the snapshot of SE at the finest time scale. To deal with this problem, the linear extrapolation and multistep prediction techniques are involved. Simulation studies are conducted on the IEEE-14 bus test system and a 33 bus distribution system under various scenarios, and the performance of the proposed method is discussed.

A voltage regulation scheme considering communication security is proposed for photovoltaic (PV) power system. The scheme is a two-level regulation to, respectively, reduce overall voltage deviation (VDE) and voltages difference (VDI). First, the evaluation indexes of VDE and VDI are built. Then, primary regulation through a powers compensation scheme is used. Considering communication topology change and delay under upper bound, secondary regulation through consensus protocol is developed. In addition, communication packet-loss and large delay are solved by predictive compensation. Finally, effectiveness of the proposed method is verified by simulation in MATLAB.

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

This paper proposes multiagent system-based event-triggered hybrid controls for guaranteeing energy supply of a hybrid energy generation system with high security. First, a multiagent system is constituted by an upper level central coordinated control agent combined with several lower level unit agents. Each lower level unit agent is responsible for dealing with internal switching control and distributed dynamic regulation for its unit system. The upper level agent implements coordinated switching control to guarantee the power supply of overall system with high security. The internal switching control, distributed dynamic regulation, and coordinated switching control are designed fully dependent on the hybrid behaviors of all distributed energy resources and the logical relationships between them, and interact with each other by means of the multiagent system to form hierarchical hybrid controls. Finally, the validity of the proposed hybrid controls is demonstrated by means of simulation results in different scenarios.

Since a microgrid consists of various distributed energy resources and local loads, integration of a distributed grid and multiple microgrids leads to an integrated hybrid energy system. This paper focuses on improving profit, economy, security, and dynamic performance of the integrated hybrid energy system by means of developing different areas of management and control strategies upon four-level hierarchical multiagent system. The level 1 agent implements optimal price bidding strategies for high profit of the overall system. The level 2 agent optimizes the energy management strategies for economic operation of each microgrid. The level 3 agent executes coordinated switching control for maintaining security of each microgrid. The level 4 agent facilitates local hybrid control of each distributed energy resource for guaranteeing dynamic performance. Finally, validity of the proposed scheme is tested by means of simulation study.

This paper is focused on an advanced control for autonomous microgrids. In order to improve the performance regarding security and stability, a hierarchical decentralized coordinated control scheme is proposed based on multi-agents structure. Moreover, corresponding to the multi-mode and the hybrid characteristics of microgrids, an event-triggered hybrid control, including three kinds of switching controls, is designed to intelligently reconstruct operation mode when the security stability assessment indexes or the constraint conditions are violated. The validity of proposed control scheme is demonstrated by means of simulation results.

This paper studies the smart control issue for large power systems to effectively solve the problems of security, stability, and economical efficiency. A control scheme called as hierarchical hybrid control is proposed versus the hierarchical structure and hybrid dynamics of large power systems. A novel hybrid model is first founded to clearly explain the interactive hybrid behaviors of the power systems during significant disturbances. On the basis of the model, the hybrid control is proposed, which consists of upper level control strategies (including logic control commands for discrete events and energy management strategies) and lower level continuous controls for component units. The upper level control strategies are responsible for switching operating modes for the purpose of security operation following a significant disturbance, as well as power assignment for the economical efficiency of operation, and the lower level continuous controllers are responsible for regulating dynamic stability of the systems. The effectiveness of the proposed hybrid control is demonstrated through simulation examples.