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In this paper, a new control architecture of virtual power plants (VPP) is proposed for the frequency regulation (FR) in main grid. Firstly, to address the power prediction of aggregation nodes (AN) with spatio-temporal correlation under VPP, a novel combined model based on graph convolutional networks and bi-directional long and short-term memory is established. Further, considering the effect of prediction uncertainty, a power shortage measurement method based on interval estimation is proposed to determine the effective power of AN. Secondly, a dispatch-based control strategy is introduced to enable VPP to respond to regulation commands from the main grid. Additionally, the influence of transmission line power loss on FR is considered, which can be eliminated by applying the developed power compensation design scheme based on proportional allocation. Finally, numerical simulations illustrate that the proposed method enhances prediction performance by 68.65%, taking mean absolute error as an example, while ensuring the accuracy and reliability of power prediction. Also, the adopted control strategy can effectively utilize VPP to provide FR services for the main grid. Moreover, the application results in a real scene are better than those of benchmark methods, which also demonstrates that the proposed approach has a high practical application potential in power system. • Predict node power using a combined model based on the GCN-BiLSTM. • Propose a method for power shortage measurement for the forecast uncertainty. • Design a power compensation scheme for eliminating the effect of network loss. • The combined method enhances the prediction performance than existing methods. • The frequency problem of system improves through utilizing virtual power plants. [ABSTRACT FROM AUTHOR]

• The SPFTS scheme is applied to deal with the consensus issue for nonstrict nonlinear MASs. With the help of the proposed strategy, an adaptive NN fixed-time controller is devised to ensure the consensus of MASs. Compared with the finite-time control, the convergence time of the fixed-time consensus control doesn't depend on the initial values. • Full state constrains and input delay are first considered in nonstrict uncertain MASs. By using the backstepping technique, the barrier Lyapunov functions are created to overcome the difficulties caused by full state constraints and the method of Pade approximation is adopted to eliminate the influence of input delay. Then, the output trajectories of followers can track with the signal of the leader in fixed time. • A novel stability analysis method is developed to solve fixed-time consensus problem for for nonlinear nonstrict uncertain MASs in this note. This paper concentrates on a fixed-time consensus issue for nonstrict nonlinear uncertain multi-agent systems (MASs) with state constraints and input delay. In comparison with previous works, the topic of full state constraints and input delay is first embodied in nonstrict MASs in a fixed time. A semi-global practical fixed-time stability (SPFTS) is employed to handle the consensus problem in this note. The radial basis function neural networks (RBFNNs) are developed to counteract unknown items in each agent. Pade approximation approach is introduced to cope with input delay. By using the backstepping technique, adaptive virtual controllers, adaption laws and the actual consensus controller are devised. And the rest followers can converge to a specified trajectory built by the leader in fixed time. Finally, a practical example is employed to test the correctness for the proposed control protocol. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a distributed secondary control strategy based on finite‐time consensus protocol for islanded microgrids to ensure the stability of frequency and voltage. Simple droop control cannot achieve high‐precision adjustment due to its inherent shortcomings. Although traditional secondary control can solve this problem, the required conditions are harsh and the system has poor robustness. Based on finite‐time consensus protocol, the proposed distributed secondary control is completely distributed, and agents of network layer only communicate through peer‐to‐peer sparse networks without a central node, so the frequency and voltage remain stable and reach the nominal value within the set time that then improves the system reliability and robustness. Moreover, the improved extreme learning machine, where the empirical risk and structural risk are involved, is used to predict the missing part to deal with the problem of packet loss and communication interruptions. The effectiveness of the intended methods is verified by the simulation of an islanded microgrid. [ABSTRACT FROM AUTHOR]

The rapid growth of electric vehicles (EVs) imposes great challenges to the flexible management and economic dispatch of virtual power plants. To overcome these obstacles, this paper proposes a bi-level economic dispatch strategy based on EVs aggregation. First, an aggregation method based on improved artificial bee colony Kmeans clustering algorithm is presented to aggregate the EVs with different dynamic characteristics in the upper level. Aiming at the issue that multi-parameter weights and aggregation effects are difficult to determine in the clustering process, information entropy and Silhouette metric are proposed to enhance the accuracy of clustering. Furthermore, pre-dispatch is conducted based on aggregation information and electricity market prices. Next, in the lower level, the charging demands information is utilized to formulate specific dispatch strategies for EVs users, and the pre-dispatch deviation in the upper level is modified. Finally, the simulation results show that the proposed aggregation method improves the clustering effect by 22.4% and guarantees the quality of aggregation and the flexibility of system management. Besides, the proposed strategy can reduce the total system operation cost and the computation time by 5.83% and 55.95% respectively and ensure the economy of the system operation and the computational efficiency of the dispatch. • Aggregate electric vehicles using a method based on improved Kmeans algorithm. • Formulate an allocation model considering the charging needs of users. • Construct a bi-level optimal dispatch model for developing effective dispatch plans. • The aggregation method enhances the aggregation quality than existing methods. • The flexibility of system dispatch improves through aggregating electric vehicles. [ABSTRACT FROM AUTHOR]

Background: The smart grid systems with advanced cyber technologies are vulnerable to the false data injection attacks. The attacks will bypass the online state estimation monitoring, steal energy by a false dispatch in the electricity markets, and gain financial benefits. In this paper, the FDI attack case in the electricity markets of a smart grid power system is considered with micro‐grids connected. The estimated transmitted power is modified, leading to the change of electricity prices which will bring the benefit to the attacker. Further, influence on the optimization output of energy management for a Micro‐grid has been discussed. By trading online electricity with the main grid, the micro‐grids are able to maintain power balance and reduce generation cost of the whole smart grid system power system. Then different optimal generation outputs of Micro‐grid and total generation cost of the power system are also shown and discussed. Aims: The focus of this paper is to investigate the FDI attack with limited information of the system topology and dynamics, and to discuss the economic impact of such limited attacks, where the estimated transmitted power is modified and the electricity prices are correspondingly changed for benefits. Different optimal generation outputs of Micro‐grid and total generation cost of the power system are also shown and discussed. Materials & Methods: MG‐based electricity markets model of a SG power system is considered for FDI attacks. Strategy with partial information is proposed from the attack's view.In real‐time market, optimization output changes of energy management for a MG when considering the electricity trading are discussed.The quick time detection method is developed with the feature extraction technique for an accurate real‐time estimation of the fault detection, when the operator is with partial information of the system topology.Digital signal processing approaches of the discrete wavelet transform (DWT) are adopted to better extract the time‐frequency characteristics of the input transient signals and improve the detection. Results: By considering of the distributed characteristics of MGs and the optimal operation requirements of system, we transformed the problem into a standard stochastic convex program, then solved the problem by the scenario generation method. We used a modified‐IEEE standard 14‐bus system and an IEEE standard 118‐bus system to show the effect of false data injection attacks in electricity prices and the influence of the optimization output of energy management for a MG. We employed modified‐online CUSUM algorithm to establish the quickest detection of the attacks. Numerical simulation results have shown that the proposed scheme is robust to the time‐varying dynamic system, and is efficient in terms of detection accuracy for the random and structured attacks. Discussion Modified‐online CUSUM algorithm is proposed with wavelet transform and feature extraction method for the quickest detection of the attacks. Numerical simulation results have shown that the proposed scheme is robust to the time‐varying dynamic system, and is efficient in terms of detection accuracy for the random and structured attacks. The proposed detectors are also proved to have quick and reliable responses to small attack magnitudes, which are difficult to be detected by traditional CUSUM algorithm. Further, the detection method is employed on IEEE standard 118‐bus system to be compared. The simulation results have shown that the proposed model is also adapted to complex system. Conclusion: In this paper, the impact of false data injection attacks on real‐time electricity markets of the SG power system with MGs connected is investigated. Attack strategies are designed of worst‐case robustness about grid dynamics when the attacker has partial information about the grid, where the attacker can still manipulate nodal prices of the real‐time markets and maximize the profit under different degrees of uncertainties. The optimal generation outputs of MGs are discussed for the ability of maintaining power balance of the whole SG power system by trading online electricity. Besides, the detection problem with partial information of the grid topology is also investigated from grid operator's view, and a new modified detection method is proposed to defend FDI attack when considering the existence of the unknown. Modified‐online CUSUM algorithm is proposed with wavelet transform and feature extraction method for the quickest detection of the attacks. Numerical simulation results have shown that the proposed scheme is robust to the time‐varying dynamic system, and is efficient in terms of detection accuracy for the random and structured attacks. [ABSTRACT FROM AUTHOR]

This paper focuses on a class of nonlinear switching multi-agent systems about event-triggered adaptive tracking control. Compared with existing results, the event-triggered mechanism is first employed in nonlinear switched multi-agent systems with unknown parameters, and it can reduce the amount of computation and the frequency of the controller updates. By utilizing the backstepping technology, adaptive laws are designed to analyze the unknown parameters. In the sequel, all signals of multi-agent systems are bounded, the tracking errors converge to a small neighborhood of the origin. Finally, simulation results verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

At present, the traditional islanded microgrid is changing to a cyber-physical system. And the data in its cyber layer is often used to solve practical problems in the physical layer through sensors and communicators. But the use of communication data also brings a lot of problems. Among them, the upload interruption problem (i.e., communicator or sensor failure) will make a heavy impact on the system. To improve the voltage and frequency stability of microgrid through secondary control considering data uploading interruption, an improved growing and pruning-radial basis function neural network-based secondary control strategy is designed in this study. The main designs can be summarized as below: (1) When the communicator cannot receive data, a prediction compensation part is designed directly in the corresponding communicator, i.e., the predicted data is used to complete the follow-up works; (2) When the sensor cannot send data, a nonlinear cyber-physical vulnerability assessment part and a neighbor transmission part are designed. In this way, a neighbor sensor is selected to send the interrupted data for the follow-up control process; (3) Based on these designs above, a novel secondary control is designed for microgrid. Finally, the simulation results show the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Aiming at the economy of microgrid operation and the uncertainty of renewable energy, a coordinated operation schedule of a multienergy microgrid based on a flexible load is proposed in this paper. Considering the difference in price and demand characteristics of multienergy, a multienergy interaction model based on a demand side response is established in day-ahead scheduling. In the intraday rolling optimization phase, according to the state of charge of battery and the demand of load, different battery function models are established at different time intervals, cooperating with the flexible load to obtain the optimal value of each unit's output. The main purpose of day-ahead scheduling and intraday scheduling is to achieve economic optimal scheduling and real-time scheduling is to guarantee the stability of the system. Finally, the simulation results verify the effectiveness of the proposed strategy. The proposed coordinated operation schedule of the multienergy microgrid achieves an integrated optimization for multienergy-type supply. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The event‐triggered hybrid control is proposed in this study to realise multi‐model switching when the microgrid system is operating in the island mode. The hybrid control includes decentralised control strategy and coordinated control strategy. Firstly, control commands in the hybrid control strategy are triggered by the event triggering mechanism including trigger events and trigger conditions. When a trigger condition is met, the corresponding event is triggered to execute the corresponding control command. Secondly, the microgrid system is divided into an upper control layer and a lower execution layer by using the multi‐agent system theory. For the upper control layer, a coordinated control strategy based on decision tree algorithm is designed, which can not only issue switching control commands to the lower execution layer, but also reduce the number of switching, improve system stability, and enrich the control strategy. For the lower execution layer, a decentralised control strategy is designed to realise distributed operation of the underlying units. Finally, the model of hybrid automata of each unit is established to ensure the smooth switching of working modes of each unit. The simulation results verify the effectiveness of the event‐triggered hybrid control strategy for controlling the microgrid. [ABSTRACT FROM AUTHOR]

This paper focuses on multi-agent-system-based bi-level bidding strategy of microgrid to make all power producers participate in electricity allocation as rational agents under electricity market transaction. Firstly, the multi-agent-system-based bidding system is constructed as bi-level MAS. Then, the bidding model is formulated as a bi-level optimization problem, which makes the best of renewable energy source, and subjects it to the object of obtaining maximum profits for all power producers. According to this model, electricity market is divided into two subsidiary markets, which involve the upper and the lower electricity market. The lower electricity market is composed of microgrids, which ally together in the upper electricity market to offset their shortages compared to traditional power producers, such as efficient market influence, the reliability of the energy supplying. Additionally, game theory is introduced for modeling and Nash equilibrium as the solution of bidding optimization problem is found through a series of iterative process based on the bi-level MAS. Finally, both the upper market and the lower market are illustrated in the case studies and corresponding proper conclusions are received. [ABSTRACT FROM AUTHOR]

For the droop-controlled distributed generation units (DGs) in microgrids (MGs), the power sharing error associated with voltage difference of DGs can be attributed to the mismatched line impedance. To improve the power sharing accuracy in islanded MGs, a neighbor-prediction based hierarchical control strategy is proposed. In secondary control, a consensus protocol is used to regulate the DGs voltages interacted by communication network to the state of tracking synchronization. Thus, the error-free power sharing is obtained. Network data-loss and delay problems are considered. To stabilize the DGs’ inner-loop control in which there is a delayed input, the H∞ robustness criteria of a dynamic model of DGs is derived. If the DG i ’s voltage in network transmission loses, the prediction system of a neighbor will be activated to forecast the lost voltage for secondary consensus control by extreme learning machine (ELM). Thus, the voltages regulation will be back to normalization. Simulations in MATLAB confirm the effectiveness of proposed control in MGs. [ABSTRACT FROM AUTHOR]

Communication data in the communication network of the islanded micro‐grid can be disturbed in many ways, such as data attack or packet loss. To improve the control effect on the voltages and frequencies obtained by droop control of distributed energy resources (DERs) facing communication data disturbance (CDD), a response hierarchical control strategy is proposed in this study. Based on the concept of the cyber physical system, the control structure is divided into two layers: the cyber layer and physical layer. In the cyber layer, firstly, the effect of the CDD on the micro‐grid system is analysed and then, an event‐triggered data compensation method combining the back‐propagation neural network and extreme learning machine is proposed in this layer to solve the problem of the CDD. In the physical layer, firstly, the droop control is used as the primary control to control the voltages and frequencies of DERs and then, a novel virtual leader‐following consensus control method considering time‐delay is proposed in this layer. Also, it is used to complete the secondary control of the voltage and frequency obtained by primary control. In the end, the simulation results confirm the effectiveness of the proposed hierarchical control strategy under CDD. [ABSTRACT FROM AUTHOR]

Photovoltaic power prediction for reducing the impact of the grid-connected photovoltaic power generation system on the power system is of great significance. Aiming at the power generation characteristics of the photovoltaic system, a method of Elman neural network based photovoltaic power forecasting using association rules and kernel principal component analysis (KPCA) is proposed in this paper. Gray relation analysis is a means of data mining and used for selecting several power days which are highly correlated with predicted days. In order to remove redundant information, the kernel principal component analysis (KPCA) is used to extract the feature of photovoltaic (PV) power time series. The Elman neural network is used for power prediction due to its dynamic recursive performance. In view of the fact that the prediction error of the Elman neural network prediction model at the peak of power fluctuation is large, the Markov method is proposed to revise and compensate the prediction value of the model to further improve the prediction accuracy. The model is validated by using real data from the National Renewable Energy Laboratory. The results show that the proposed method can effectively improve the prediction accuracy and enhance the generalization ability of the neural network model, which has a good feasibility. [ABSTRACT FROM AUTHOR]

Accurate renewable resource and load forecasting plays a key role in the progress of power grid planning schemes. In this study, a hybrid short‐term forecasting method based K ‐means clustering and variational mode decomposition (VMD) technique is proposed to deal with the problem of forecasting accuracy. K ‐means clustering is a means of data mining approach and used for classifying data into several clusters. A cluster selection method is adopted to extract similar features from historical days. To better analyse the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies. Self‐adaptive evolutionary extreme learning machine as a novel and fast regression tool is trained and used for predicting each component. Eventually, the forecasting result generated by reconstructing all the predicted components values. The performance of the proposed hybrid forecasting model is evaluated by using real data from National Renewable Energy Laboratory. The simulation results show that it can obtain better forecasting accuracy than some previously reported methods. [ABSTRACT FROM AUTHOR]

Droop control is a common method in the universal microgrid applications. Conventional droop control is unpractical for low‐voltage microgrid, where the line impedance among distributed generation units (DGs) is mainly resistive to generate the active and reactive power of DG is coupled. Besides, accurate reactive power sharing is not achieved due to the voltage deviation of DGs caused by disparate line impedance in microgrid. An improved droop control based on the virtual power source (VPS) and composite virtual impedance, which is constituted by a negative resistance and a negative inductance, is proposed for low‐voltage microgrid. The virtual negative resistance counteracts the line resistance to decouple power. The reactive power sharing accuracy based on the virtual negative inductance and VPSs control increases through analysing the line and virtual voltage drop. Finally, the fractional‐order PID controller is adopted to control the inverter voltage. The optimal controller parameters are obtained by the differential evolution algorithm. To verify the viability and availability of improved control strategy, simulations are carried out on MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

In this paper, two-level decentralized optimization power dispatch control strategies are proposed in an islanded microgrid without communication network to ensure frequency security and economic benefit. First, the secondary-level decentralized optimization control strategy is designed only by using local measurement regarding frequency deviation. It is able to implement the optimal power dispatch for all dispatchable distributed generation units in a coordinated way in order to completely restore the system frequency with minimum operation cost. Then, the first-level decentralized droop compensation control strategy is designed to implement real-time optimal power sharing among all energy storage units in order to quickly compensate large supply-demand imbalance and ensure frequency security. Moreover, the proposed control strategies are proved to be able to meet the requirements of the 'plug and play' operation in the absence of communication network. Simulation results verify the validity of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study proposes a hierarchical hybrid control strategy to ensure stabilisation performance during the micro‐grid operating mode conversion. Designing the hierarchical hybrid control strategy is divided into two stages. In the first stage, the research focuses on constructing a hybrid control scheme, where besides that the storage I unit needs to implement switching control strategy by means of an upper‐level switching controller, the storage II unit that acts as a master unit also needs to be switched between two kinds of control modes including voltage–frequency droop control and PQ control in response to different micro‐grid operating modes, and other slave distributed generation (DG) units operate in PQ control mode all the time. Moreover, during the operation mode conversion, according to the assessment indexes regarding voltage and frequency, the upper‐level switching controller determines whether and when to send switching commands to the master DG unit. In the second stage, the research focuses on designing the hierarchical control strategies in the PQ control mode and the voltage–frequency droop control mode, as well as designing the upper‐level switching controller to guarantee the stabilisation performance of the micro‐grid during operation mode conversion. In the PQ control mode, all controllers are designed as a fractional order proportional–integral–derivative (FOPID) controller. Finally, the validity of the proposed hybrid control strategy is demonstrated by means of simulation results. [ABSTRACT FROM AUTHOR]

In this Letter, a novel voltage regulation method is proposed for ensuring voltage security in photovoltaic (PV) distribution systems. It is a two‐level regulation to reduce overall voltage deviation (VDE) and voltage difference (VDI). Firstly, evaluation indexes for VDE and VDI are built. Then, in order to reduce VDE, primary regulation by sensitivities‐based power compensation is developed. Furthermore, in order to reduce VDI, secondary regulation by using PI coordinate compensator is built. Compared with other methods, the proposed method can achieve high regulation accuracy. The effectiveness is verified in a PV platform. [ABSTRACT FROM AUTHOR]

An advanced control scheme for managing a hybrid energy generation system (HEGS) is presented in this paper. A hierarchical management and control architecture based on multi-agent systems (MAS) is discussed. MAS will account for the complex behavior of a hybrid energy supply system. The management and control strategies are implemented through a system of agents based on three tiers. The upper level agents develop overall energy management strategies for a hybrid energy supply system. The middle-level agents integrate coordinated switching controllers. The lower level agents are responsible for dealing with local control strategies. Coordinated switching controllers within the middle-level agents are designed as event-triggered hybrid controllers based on differential hybrid Petri-net (DHPN) models. The operation modes of distributed energy resources (DERs) can smoothly transfer in a coordinated manner due to the coordinated action of the switching controllers according to variation in operating conditions. Finally, simulation results from different scenarios verifying the feasibility of the proposed scheme are offered. [ABSTRACT FROM PUBLISHER]

In this study, a two‐hierarchical decentralised coordinated control scheme based on the multi‐agent system (MAS) is proposed to improve the security and the stability of the microgrid. The coordinated switching control strategies are constructed in upper level agent to ensure the microgrid security. The making process of the coordinated switching control strategies is regarded as searching the optimal switching operation mode by means of designing constraint violation function and colour Petri‐net, so that the operation mode of the microgrid can be switched during larger power unbalance. The decentralised continuous control is designed by integrating the outer loop droop controller with the inner loop prescribed performance controller in the lower level unit agents to guarantee its unit system stability. Finally, the simulation results demonstrate the effectiveness of the decentralised coordination control based on MAS. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the robust exponential input-to-state stability (robust e-ISS) for impulsive systems. New notions of input-to-state exponent (IS-e) and e-property are proposed. Based on the established relation between IS-e and e-property, and the method of variation of constants formula, the equivalent conditions for robust e-ISS have been derived. Then the notion of robust event-e-ISS is defined. The sufficient conditions and the robust regions for robust e-ISS and robust event-e-ISS are also derived by using the IS-e of every subsystem. It shows the whole system may have robust event-e-ISS while every subsystem may have no ISS. It also shows the external disturbances may lead to relatively small robust regions. The results are then specialized to derive the equivalent conditions of interval e-ISS for interval impulsive systems. As an application, the result is used to test the ISS for a controlled micro-grid. [Copyright &y& Elsevier]

• A prescribed tracking accuracy containment control scheme for non-affine nonlinear uncertain multi-agent systems is proposed in this paper. In the existing results, the presented strategy just ensure that the containment errors gather to a small neighborhood around the origin. In this note, each containment error can converge to a assigned precision value by the proposed method, and the magnitude of each error can be confirmed precisely before the containment controller is designed. • Two important auxiliary functions are used to help deal with the precision tracking issue. With the aid of these two n-order differentiable functions, the Lyapunov functions can be created. And in combination with adaptive and the backstepping techniques, the technical difficulties of the desire controller design are overcome for non-affine nonlinear uncertain multi-agent systems. • A novel analysis method of containment errors is proposed in this note. By using this method, we can conclude that the convergence error for each agent is a given constant. This paper concentrates on a containment control issue for multi-agent systems in non-affine form with a given accuracy. In comparison with the existing studies on multi-agent systems, precision-based containment control idea is first formulated. With the aid of the proposed strategy, the main merit of this note is that the synchronization errors converge to arbitrary given positive number. Simultaneously, the particular Layapunov functions are constructed by feat of two auxiliary functions. By employing the backstepping and adaptive control technique, the key variables and the actual controller are designed. Unlike the traditional stability analysis, a novel method is used to analyse the convergence of containment errors. In the end, some simulation results demonstrate the correctness for the proposed protocol. [ABSTRACT FROM AUTHOR]

• Analyze the correlation between power load data and relevant search engine indices. • Take search engine indices as input variables to forecast electricity load. • Use Hodrick-Prescott filter to decompose the time series of search engine indices. • Propose a LSTM model based on quantum-behaved particle swarm optimization algorithm. Accurate electricity load forecast plays an important role in the operation of a power system. Many factors influence the electricity load data such as air temperature, humidity and holidays, and they are taken as the explanatory variables in load forecasting cases traditionally. Search engine indices, a variable may be related to load data which has been never considered before in load forecasting, is discussed and utilized to increase the accuracy of power load prediction in this paper. Spearman's correlation coefficients and Granger test results verify the correlation between Google Trends (GT) and electricity load data. A methodology for processing GT time series with Hodrick-Prescott filter is proposed. To forecast electricity load with an adaptive network model in such a novel situation, we propose a long short-term memory neural network model based on quantum particle swarm algorithm. The performance of load forecast for Long Island region taking GT and weather data as input variables is compared with that taking only weather data as input variables, which shows that the introduction of GT improves short-term forecasting effectiveness significantly. [ABSTRACT FROM AUTHOR]

• To reduce the power curtailment, a new PCA is used to realize coordinated RPC-APC in an optimal order. • To improve the voltage regulation ability, the PRN with the maximum voltage rise is always regulated. • To reduce the number of power control, the most sensitive PV is preferred for the RPC-APC for PRN. • To achieve an accurate RPC-APC, the real-time PRN update and exact power calculation are carried out. Increasing photovoltaic (PV) penetration has caused overvoltage problem in distribution systems. Power curtailment that includes power absorption of PV inverter and charge of battery energy storage is usually used to improve the voltage profile. Most methods prefer to use an excessive local power curtailment to regulate voltage. This inevitably reduces PV generation economy. Hence, a coordinated power compensation algorithm (PCA) is proposed in this paper to regulate bus voltage, aimed at reducing the total power curtailment while serving requisite load demand. Selecting primary control nodes and optimal PV compensation orders based on sensitivity sequence, our PCA achieves an effective voltage regulation with the minimal power curtailment. Final results on a tested PV system model verify the effectiveness. [ABSTRACT FROM AUTHOR]

• A CPHVA is proposed to evaluate the vulnerability of nodes in islanded MG. • The interruption problem in communicators is solved by path reconstruction. • The interruption problem in sensors is solved by predictive compensation. • A novel secondary control method is proposed to improve the control effect on DERs. Recently, more and more consensus-based hierarchical control methods are proposed in micro-grid. The main components of these methods are primary and secondary controllers. And they are usually used to adjust the output frequency and voltages of distributed energy resources. But the implementation of these methods is highly dependent on the communication network which is consisted by sensors and communicators. Once the data transmit of some sensors or communicators is interrupted, it will directly affect the control effect of hierarchical control. To solve the above problems, a cyber-physical cooperative response strategy is proposed in this paper. The main designs are as follows: (1) To solve the interruption problem among communicators, there are two defense methods proposed in the cyber layer: path planning and path reconstruction. Both methods are combined with cyber-physical hybrid vulnerability assessment and improved genetic algorithm; (2) To solve the interruption problem in the output channels of sensors, a distributed predictive compensation method combined with extreme learning machine and model predictive control is proposed in physical layer; (3) Based on the designs in (1) and (2), a novel secondary controller is proposed in this paper. Finally, the simulation results confirm the effectiveness of the proposed cooperative response strategy. [ABSTRACT FROM AUTHOR]

Summary: Due to lack of support from the main grid, the problem of frequency over‐limitation is easily caused during heavy load disturbance occurs in the islanded microgrid (MG). For this reason, an improved curve‐shaped droop control strategy, which is composed by virtual synchronous generator (VSG) and an improved droop control, is designed to ensure the frequency stable of the MG system. In the contributed strategy, when the frequency of MG is changed within the normal range, the VSG control will be adopted to maintain the stability of the output power and frequency, this method can ensure that the frequency can be adjusted within the stable range rapidly; when the frequency deviation is serious, the proposed strategy will select the improved droop control strategy to ensure the output power equals to the load demand, and to ensure that the controlled frequency cannot lower than the minimum value. In addition, in order to suppress the voltage deviation and the system oscillation caused by the switching of the control methods, a voltage synchronization control strategy is also proposed, which can realize the smooth switching of the two control strategies and guarantees the voltage stable of the MG system. The effectiveness of the proposed methods is verified by simulation experiments via matlab/Simulink software. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a cyber‐physical cooperative control strategy considering communication interruption is proposed to improve the control effect on output voltages and frequency obtained by droop control of distributed energy resources (DERs). The control structure consists of the physical and cyber layers. And in the two layers, the interruption occurs mostly in sensors and data aggregators. To solve the problem of interruption among data aggregators, a path‐planning method combined with improved Dijkstra algorithm (IDA) and improved genetic algorithm (IGA) is proposed in the cyber layer. To solve the problem of interruption among sensors, a predictive compensation method combined with extreme learning machine (ELM) and model predictive control (MPC) is proposed in the physical layer. Meanwhile, so as to improve the control effect of DERs, a virtual leader‐following consensus control (VLFCC) method combined with double sliding mode control (SMC) is first proposed in cyber layer and then applied to the physical layer to accomplish the secondary control on DERs. Finally, the simulation results show that under the condition of communication interruption, the output voltage and frequency of each DER can be adjusted to their respective reference values through the proposed cooperative control strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

By means of advanced information and communication technology, cyber‐physical power systems can attain real‐time monitor and control but have to suffer the damage caused by a cyber attack. In this study, the authors' define attack efficiency to evaluate attack paths that attacker may adopt to intrude systems, to find the most efficient attack paths (i.e. the most vulnerable paths). Based on the actual power grid and security stability control system, the coupling model of cyber‐physical power systems is established to analyse the propagation of cascading failure within and across the physical power grid and communication network. In the attack model, the attack cost matrix is built to describe the cost between directly connected nodes in a communication network. Furthermore, considering the probability that control commands are successfully transmitted from router nodes to generator/substation nodes, the total attack cost from the communication network to the physical power grid is figured out. To search for the most vulnerable attack paths in systems, the attack efficiency is proposed to quantify the relation between attack cost and attack gain, wherein the attack gain is the damage on systems caused by the cyber attack. [ABSTRACT FROM AUTHOR]

In cyber‐physical power systems, tiny failure occurring in the communication network could propagate to affect the operation of the physical power grid through inter‐links and even cause catastrophic blackouts. The authors propose a partial random coupling systems model to study the vulnerability of nodes and connectivity of systems in a cascading failure. In terms of traffic loads generated in systems, they employ load spill index to analyse dynamic states of nodes in cascading failure. In cascading failure, considering the effect from a communication network, vulnerability matrices are established to study the vulnerability of nodes in the physical power grid. Furthermore, an efficiency index is defined to analyse connectivity of systems. In the numerical simulation, they verify the accuracy of the proposed evaluation approach and test its scalability and universality in different power systems. [ABSTRACT FROM AUTHOR]

Summary: The distributed optimal frequency regulation of power grid containing plenty of distributed renewable energy resources is highly dependent on the communication network. However, the utilization of a communication network makes the power grid vulnerable to cyberattacks, which could deteriorate the control performance and even cause the failure of a control task. To address this problem, this paper investigates the secure distributed optimal frequency regulation of power grid under cyberattack. The stealthy deceptive cyberattack model is constructed, and its perniciousness on optimal frequency regulation is analyzed. A novel type of secure distributed optimal frequency regulation is developed by introducing a secure‐based communication network to improve the resilience against cyberattack. It is proved theoretically that the secure regulation can restore the frequency deviation of each area to zero and minimize the total generation cost to retain the economic efficiency even under cyberattack. Simulation result shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Considering the limitation of communication network, the event‐triggered mechanism based distributed optimal frequency regulation is proposed, which can restore the frequency and retain the economic efficiency of power grid simultaneously under certain load disturbances. Compared with the traditional distributed optimal frequency regulation based on continuous or periodic sampled‐data information transmission mechanism, the communication burden among the areas in power grid is reduced since two novel types of event‐triggered mechanisms, which possess the information demand‐transmission property, are constructed and introduced in the regulation method in this study. Moreover, Zeno behaviour is proved to be avoided in order to guarantee the availability of event‐triggered mechanisms in practical power grid. The effectiveness of theoretical result of this study is verified by the simulation study. [ABSTRACT FROM AUTHOR]

Cascading failure in interdependent networks is studied in recent decade, and considerable research has been done on systems modeling, vulnerability analysis and protection strategy, but partial work on vulnerability analysis and protection strategy still cannot completely utilize coupling relation between physical power grid and communication network into research, thus we introduce inter-relation of support nodes to perfect the strategies. Compared with traditional vulnerability analysis, we, through studying state of systems after attack, analyze robustness of systems. From the perspective of attacker, four attack strategies considering inter-relation have been proposed, and simulation results verify the serious destructiveness of attack strategies to steady of systems. Taking inter-relation and heterogeneity of networks into consideration, we propose two redundancy protection strategies on intra-links redundancy. Both protection strategies are proved to attain better performance than other strategies without considering inter-relation, meanwhile existence of saturation point can give guidance on rational allocation of limited intra-links. • Four optimizing attack strategies are proposed to detect critical nodes. • Cascading failure is simulated in four coupled symmetric networks. • Two optimizing intra-links addition strategies are raised. • The effectiveness of optimizing attack and intra-links addition strategies. • Discovery of saturation point has guide in rationally allocating limited resource. [ABSTRACT FROM AUTHOR]

In this brief, a type of novel fully distributed adaptive protocols is designed for achieving leader‐follower multi‐agents tracking. The design procedure constitutes three steps. First, a local observer is constructed for each agent (including the leader) in virtue of its own inputs and outputs, aiming to estimating of that agent's state. Second, to propagate the estimate of the leader from target followers to all the followers, a distributed adaptive estimator is then proposed, via embedding with time‐varying coupling weights. At last, an elegant controller is designed for each follower based on the estimate of its own state and the estimate of the leader's state. The rigorous convergence analysis is provided. Finally, the simulation results validate the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

The fluctuation of energy price and the expensive energy cost during the peak period will affect the living cost of users, and transmission congestion will also bring additional transmission costs. To solve this problem, this paper establishes the demand response model of energy users. Users can obtain a lower energy price by adjusting their replaceable load and price load. This paper uses cooperative game pricing method to solve the problem of congestion cost allocation. In order to ensure the privacy and independence of the energy system, the alternating direction method of multipliers is proposed to decouple the integrated energy system. The model is verified in the integrated energy system of IEEE 14-node electricity system and 14-node natural gas system. Simulation results show that the algorithm has convergence and can reduce the cost of user participation in demand response. • ADMM divides IES into two subsystems: electricity system and natural gas system. • Shapley value method is used to allocate congestion cost and improve social welfare. • Users participate in demand response to alleviate transmission congestion. • Consider the discomfort cost of energy use, making the user cost more accurate. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]