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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. [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 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. [ABSTRACT FROM AUTHOR]

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. [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]

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 power-sharing accuracy is degraded. In this article, a virtual complex impedance based $P-\dot{V}$ 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 — ̇V 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 ̇V to zero. Compared with existing virtual impedance and Q — ̇V droop methods, the proposed method combines the advantages of both. Besides, a modified P — ̇V 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. [ABSTRACT FROM AUTHOR]

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. [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]

Resilient dynamic event‐triggered control design problem is concerned for frequency stabilisation of multi‐area power systems with renewable energy penetration under transmission delay and denial‐of‐service (DoS) attacks. The main works are presented as followings: (1) A novel state‐space model is established to describe multi‐area power systems with solar and wind generation disturbance under load frequency control and virtual inertia control (LFC‐VIC). (2) The influence of DoS attacks is considered and first modelled by model‐based average dwell time (MDADT). A criterion of preserving desired exponential H∞ performance is obtained about the required lower bound of average dwell time (ADT) of sleep intervals, namely τF>ln⁡λ0/α1 and the tolerable upper bound of ADT of attack intervals, namely τD<−(ln⁡λ1+(α0+α1)dM)/α0. (3) To reduce communication load, a dynamic event‐triggered mechanism (DETM) is proposed and designed jointly with resilient LFC‐VIC scheme by using piecewise Lyapunov–Krasovskii functional method and LMIs technique. (4) To defence DoS attacks actively, based on the resulting MDADT criterion, a dynamic prediction strategy is proposed to compensate the packet drops with unknown bound subjected to attack strategy. Finally, a numerical example for a two‐area power system is carried out to verify the designed resilient LFC‐VIC scheme and the DETM. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This brief studies an energy management (EM) problem with unknown dynamics of consumer appliances. A two-level optimization model is established between the utility company and the consumers. In this model, the utility company maximizes its profit by setting the electricity price, and the consumers respond to the price by regulating power usage to minimize their costs. The aforementioned process is performed in multiple stages. In each stage, the consumer response is formulated as a constrained optimization problem, which can be transformed into an unconstrained optimization problem using the penalty function method, and then an extremum seeking control (ESC) algorithm is developed to search for the quasi-optimal power consumption of the consumers. The ESC algorithm has noncontinuous first and second derivatives with respect to the variables. We propose an approximation method to make the ESC algorithm continuous and prove that the algorithm is semiglobally practically asymptotically (SPA) stable. After the consumer response in the same stage, the utility company updates the electricity price by the particle swarm optimization (PSO) algorithm. Then, we give an EM algorithm that integrates the ESC with PSO. In simulations, the algorithm is applied to achieve EM of heating, ventilation, and air conditioning (HVAC) systems, and the results show that the algorithm can converge to a neighborhood of the optimal solution and reduce the peak load and daily load. [ABSTRACT FROM AUTHOR]

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. [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]

It is necessary to design an economic dispatch mechanism to ensure the stability of the electricity market and the equilibrium of pricing strategy in a smart grid with real‐time pricing. In this study, the pricing strategy in the smart grid is analysed by modelling the economic dispatch problem as a bi‐level game in the electricity market, including the wholesale market and the retail market. In the wholesale market, the negotiation of the wholesale price between the generation company and multiple utility companies is formulated as a Nash bargaining problem. In order to guarantee the profit of the generation company and the social welfare of the utility companies simultaneously, the Nash bargaining solution is adopted to find an optimal wholesale price. In the retail market, the Shapley value is utilised to achieve profit distribution among the utility companies, and then a distributed gradient algorithm is proposed to search for the optimal retail price. Numerical results demonstrate the effectiveness of the proposed algorithm and the superiority of the economic dispatch strategy. [ABSTRACT FROM AUTHOR]

At present, more and more communication-based methods are proposed for reactive power adjustment in islanded microgrid (IMG). However, the use of communication data will inevitably cause communication problems, for e.g., delay, packet loss, and communication failure. Among them, the random communication failure (RCF) is common and harmful. To realize the stable operation of IMG and the rational sharing of reactive power under the consideration of this problem, a cyber-physical cooperative hierarchical control strategy is proposed in this article, which include cyber and physical layers in control structure. In these two layers, the main designs are as follows. In the cyber layer, to solve the RCF problem, we have designed the corresponding observer, controller, and prediction compensation part; and to improve the frequency stability, a secondary control method is designed. In the physical layer, to share reactive power reasonably, a reactive power control method based on synchronous voltage regulation is proposed; to suppress the possible disturbance in the data used in the physical layer, a novel sliding-mode controller is designed; and to improve the voltage stability during adjusting power, a voltage compensation strategy is proposed. The effectiveness of proposed methods is finally verified by experiments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

When several microgrids are interconnected into microgrid cluster (MGC), the network topology of MGC system would be more complex. Traditional hierarchical control based on droop control tends to make full use of all networks so that the costs of energy exchange could be high. Considering the voltage stability and power sharing of MGC system, a distributed cooperative control method based on network topology optimisation for MGC is proposed to control the power flow of multi‐microgrid system. Specially, preliminary Q‐leaning algorithm is utilised with establishment of new adjacency matrices for the distributed cooperative control to decrease the costs. Moreover, multi‐level controls based on consensus protocol are proposed in the distributed cooperative MGC system to eliminate the necessity of a central controller. The adaptive regulation based on the state of charge among the battery groups will be taken as the first‐level control. The second‐level control including the current controller, voltage controller and power controller is implemented among the DGs and a novel third‐level control based on consensus is applied in eliminating the voltage deviation caused by the first‐level control and the second‐level control. The effectiveness of the proposed method is verified through the simulation by simulink. [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]

A layered management and hybrid control strategy based on hybrid automata and random forest for the microgrid is proposed in this study. It is used to realise energy management and multi‐modal coordinated switching of microgrid system to ensure high security, stability and high efficiency. Management and control strategies are implemented through a three‐layer microgrid architecture based on multi‐agent system theory. An energy management strategy is designed for the microgrid system in the upper layer agent structure. A coordinated control strategy is designed in the middle layer agent structure. A local control strategy is designed into the bottom layer agent structure. In the middle layer coordinated control strategy, the hybrid automata is used to characterise the complex logical features of each underlying unit and trigger the switching of working modes according to the condition change. The random forest algorithm is used to analyse the running state and reduce the number of switching. Finally, the simulation results verify the effectiveness of the proposed control strategy in different scenarios. [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]

Summary: This paper considers optimal consensus control problem for unknown nonlinear multiagent systems (MASs) subjected to control constraints by utilizing event‐triggered adaptive dynamic programming (ETADP) technique. To deal with the control constraints, we introduce nonquadratic energy consumption functions into performance indices and formulate the Hamilton‐Jacobi‐Bellman (HJB) equations. Then, based on the Bellman's optimality principle, constrained optimal consensus control policies are designed from the HJB equations. In order to implement the ETADP algorithm, the critic networks and action networks are developed to approximate the value functions and consensus control policies respectively based on the measurable system data. Under the event‐triggered control framework, the weights of the critic networks and action networks are only updated at the triggering instants which are decided by the designed adaptive triggered conditions. The Lyapunov method is used to prove that the local neighbor consensus errors and the weight estimation errors of the critic networks and action networks are ultimately bounded. Finally, a numerical example is provided to show the effectiveness of the proposed ETADP method. [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 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. [ABSTRACT FROM AUTHOR]

Frequency deviation and powers sharing error exist in a microgrid with multiple droop-controlled distributed generations. To solve the above problems, we propose an event-triggered multistage secondary control. First, to improve the synchronization accuracy, we divide secondary control into multiple stages based on frequency/voltage droop characteristics. The inputs of proportion-integration compensator for these stages are different. The corresponding stage will be activated when the value of frequency/voltage meets a certain “event” (given ranges). Then, we consider the network delay, use a discrete communication scheme to reduce the bandwidth utilization, and further avoid the oversized delay. To stabilize the interloop control with input delay, we derive the robustness criteria. Finally, simulations in RT-LAB confirm the effectiveness of the proposed control strategy. [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]

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. [ABSTRACT FROM AUTHOR]

In order to fully consume renewable energies and schedule demand‐side resources more hierarchically, a double‐deck optimal schedule model of micro‐grid, which connects to power grids and concludes battery energy storage system (BESS), is proposed. Unlike the original peak‐valley time‐of‐use (TOU) price, in the upper layer optimal schedule, the improved TOU price which takes account into the user's satisfaction can express the adjusted loads accurately and the resulting net loads can be treated as a link between upper and lower layer scheduling. For the reason of having no precise model of BESS, the lower model for the goal of minimising the operation cost is solved by action dependent heuristic dynamic programming algorithm that is not relying on the accurate controlled object model. This algorithm is used to obtain the most optimal performance index function and control strategy by the optimal iterative process, which is based on the back propagation neural network used for evaluating the optimal performance index. Analysis of examples and results has been presented to show the effectiveness of the proposed strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A multiagent system based distributed cooperative control is proposed for a dc microgrid. First, the multiagent system based control scheme is built, where each distributed energy resource unit is associated with a first-level unit control agent to deal with primary control and a secondary-level distributed cooperative control agents to implement secondary control. For the secondary control purposes of global voltage regulation and proportional current sharing, both voltage and current regulators are installed in each secondary-level agent. Their consensus secondary control protocols are designed by using the state information exchanged among only adjacent secondary-level control agents based on a switching topology communication network with time-vary delays. Then these protocols are applied into the associated first-level agent to structure an improved outer-loop droop controller. According to the reference set points corrected by the improved droop controller, an inner-loop voltage/current robust controller is designed in place of the conventional PI control to regulate the voltage and current of distributed energy resource unit. Finally, the validity of the proposed control is testified by means of simulation results. [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]

In order to improve the control effect on voltage and frequency of each distributed energy resource (DER) and improve the stability of the islanded micro‐grid, a novel hierarchical control strategy combined sliding mode control (SMC) and consensus control is proposed in this study. The control structure is divided into two layers: the physical layer and the cyber layer. In the physical layer, in order to improve the dynamic control effect, an improved droop control based on consensus theory with considering packet loss is proposed in this study. And the control can be completed in finite time by selecting the approximate parameters in the controller; in order to improve the stability of voltage loop and current loop, an improved double closed‐loop structure based on the SMC is proposed. Moreover, the control can also be completed in the finite time. In the cyber layer, an event‐triggered SMC‐virtual leader–following consensus protocol is proposed in this study to solve the problem of channel noise (disturbance). For improving the control effect, the protocol is used to complete the secondary control on voltage and frequency of each DER. Finally, the effectiveness of the novel control strategy is verified by simulation results. [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]

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. [ABSTRACT FROM PUBLISHER]

This paper focuses on a multiagent-based distributed coordinated control for radial dc microgrid considering transmission time delays. First, a two-level multiagent system is constructed, where local control is formulated based on local states and executed by means of the first-level agent, and distributed coordinated control law is formulated based on wide-area information and executed by means of the secondary-level agent in order to improve the voltage control performances. Afterward, the research mainly focuses on designing the local controller and the distributed coordinated controller. For the purpose of robust stability, the local control is designed as local state-feedback-based \textH\boldsymbol \infty robust controller. It is worth mentioning that the distributed coordinated control consists of local state feedback control and decoupling coordinated control law that only come from adjacent distributed energy resource units. Moreover, the distributed coordinated controller is designed by means of delay-dependent \textH\boldsymbol \infty robust control method taking into account transmission time delays. Finally, the validity of the proposed control scheme is demonstrated by means of simulation results. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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]

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. [ABSTRACT FROM PUBLISHER]