Search

In this paper, a consensus based fully distributed optimization algorithm is proposed for solving economic dispatch problem (EDP) in smart grid. Since the incremental cost of all buses reach consensus when the optimal solution is achieved, it is selected as a consensus variable. An additional variable at each bus, called "surplus" is added to record the local power mismatch, which is used as a feedback variable to purse the balance between power supply and demand. Different from most of the existing distributed methods which require the communication network to be balanced, the algorithm uses a row random matrix and a column random matrix to precisely steer all the agents to asymptotically converge to a global optimal solution over a time‐varying directed communication network. Due to the use of a fixed step size, the proposed algorithm also outperforms other algorithms in terms of convergence speed. The graph and eigenvalue perturbation theories are employed for the algorithm convergence analysis, and the upper bound of the parameters required for convergence is given theoretically. Finally, the performance and scalability of the proposed distributed algorithm are verified by several case studies conducted on the IEEE 14‐bus power system and a 200‐node test system. [ABSTRACT FROM AUTHOR]

Alternating direction method of multipliers (ADMM) has been widely used for solving the distributed optimisation problems. This paper proposes a novel distributed ADMM algorithm to solve the distributed optimisation problems consisting of convex cost functions under an undirected connected graph. The proposed algorithm adopts the concepts of predecessors and successors in the distributed sequential ADMM algorithm, but changes the sequential updating manner to a parallel one, which allows the agents to update their local states and dual variables in a completely distributed and parallel manner. This brings some benefits when solving large‐scale optimisation problems. Variational inequality is applied to analyse the convergence of agents' states. It is proved that the states of all the agents converge to the optimal point, and the global cost function converge to the optimal value at a rate of O(1/k)$O(1/k)$. Numerical experiments are given to show the effectiveness and suitability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent studies, the power scheduling in more electric aircraft (MEA) has been formulated as a mixed-integer quadratic programming problem. Many model-driven methods, such as branch-and-bound algorithms, are advocated to solve it. However, these methods are often prone to considerably high complexities, which makes real-time processing a problem. In this article, a two-stage hybrid learning-based optimization approach is proposed to address this challenging issue. In the first stage, the task of optimizing integer variables is considered as a multilabel classification problem, which is solved by a data-driven method, i.e., ensemble deep neural networks (EDNNs). In the second stage, with the obtained integer solutions, the problem is transformed into a quadratic programming problem that can be quickly solved by model-driven numerical optimization methods. Compared to the state-of-the-art power scheduling algorithms, the model-driven and data-driven methods can compensate each other so that this two-stage hybrid learning-based approach can achieve orders of magnitude speedup in computational time, while guaranteeing the optimal scheduling performance. The structures of EDNNs are well designed so that the proposed approach can adapt to varying operating conditions in MEA. An offline simulation test and an online hardware-in-the-loop test validate the above advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

This article presents a novel networked rapid control prototyping (NRCP) system, which can significantly facilitate the theoretical analysis and product design of networked control systems (NCSs). The NRCP system consists of a PC controller equipped with MATLAB/Simulink software and an embedded target designed with open-source hardware, e.g., Raspberry Pi and STM32. Therefore, it benefits from the following advantages, such as simple structure, low cost, convenient deployment, and robust scalability. In order to verify the effectiveness and superiority of the proposed NRCP, a networked magnetic levitation control system (NMLCS) is developed on the basis of this NRCP system. Furthermore, a discrete-time equivalent-input-disturbance (DEID)-based model predictive control approach is proposed for NMLCS to deal with the common practical issues in NCSs, such as time delay and disturbance. Specifically, the time delay is modeled as delay-induced disturbance (DID), whose influence is effectively estimated and eliminated by the designed controller. Several experimental case studies are performed to verify the effectiveness of the NRCP, the DEID-based control algorithm, and the analytical method of DID. [ABSTRACT FROM AUTHOR]

In this article, a cooperative resilient control method for dc microgrid (MG) is proposed to dispel the adverse influences of both communication delays and denial-of-service (DoS) attacks. To avoid that the sampling period is captured by intelligent attackers, a new time-varying sampling period, and an improved communication mechanism are first introduced under the sampling control framework. Based on the designed sampling period and communication mechanism, a resilient secondary controller is designed. It is theoretically shown that the developed method can achieve the goals of bus voltage restoration and current sharing even in the presence of both DoS attacks and heterogeneous communication delays. Finally, a dc MG test system is built in a controller-hardware-in-the-loop testing platform to illustrate and verify the effectiveness of our developed method against both communication delays and DoS attacks. [ABSTRACT FROM AUTHOR]

Localizing previously visited places during long‐term localization and mapping, that is, loop closure detection (LCD), is a crucial technique to correct accumulated inconsistencies. In common bag‐of‐words (BoW) model, a visual vocabulary is built to associate features for detecting loops. Currently, methods that build vocabularies off‐line determine scales of the vocabulary by trial‐and‐error, which results in unreasonable feature association. Moreover, the detection precision of the algorithm declines due to perceptual aliasing given that the BoW‐based method ignores the positions of visual features. To build the optimal vocabulary automatically and eliminate human heuristics, we propose a natural convergence criterion based on the comparison between the radii of nodes and the drifts of feature descriptors in vocabulary construction. Furthermore, a novel topological graph verification method is proposed for validating loop candidates, which can effectively distinguish visual ambiguities by involving geometrical position of features and thus improve the precision of LCD. Experiments on various public datasets verify the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

In recent years, renewable energy has received extensive attention due to its advantages of sustainability, economy, and environmental protection. However, with the rapid development of renewable energy, the problem of curtailment is becoming increasingly serious. Studying the calculation method and establishing a quantitative evaluation system of renewable energy accommodation capacity are important means to solve this problem. This paper comprehensively considers the factors affecting the accommodation of renewable energy, establishes a accommodation calculation model with the maximum accommodation of renewable energy as the optimization target based on the time series production simulation method, and uses the hybrid particle swarm optimization (PSO) algorithm to solve it. The model is verified with historical data such as load, photovoltaic (PV), and wind power in a certain region throughout the year. The experimental results verify the rationality of the renewable-energy accommodation-capacity model proposed in this paper and the correctness of the theoretical analysis. The calculation results have important reference and guiding significance for the operation and control of power-grid planning and dispatching. [ABSTRACT FROM AUTHOR]

The increasing number and functional complexity of power electronics in more electric aircraft (MEA) power systems have led to a high degree of complexity in modelling and computation, making real-time energy management a formidable challenge, and the discrete-continuous action space of the MEA system under consideration also poses a challenge to existing DRL algorithms. Therefore, this paper proposes an optimisation strategy for real-time energy management based on hybrid deep reinforcement learning (HDRL). An energy management model of the MEA power system is constructed for the analysis of generators, buses, loads and energy storage system (ESS) characteristics, and the problem is described as a multi-objective optimisation problem with integer and continuous variables. The problem is solved by combining a duelling double deep Q network (D3QN) algorithm with a deep deterministic policy gradient (DDPG) algorithm, where the D3QN algorithm deals with the discrete action space and the DDPG algorithm with the continuous action space. These two algorithms are alternately trained and interact with each other to maximize the long-term payoff of MEA. Finally, the simulation results show that the effectiveness of the method is verified under different generator operating conditions. For different time lengths T, the method always obtains smaller objective function values compared to previous DRL algorithms, is several orders of magnitude faster than commercial solvers, and is always less than 0.2 s, despite a slight shortfall in solution accuracy. In addition, the method has been validated on a hardware-in-the-loop simulation platform. [ABSTRACT FROM AUTHOR]

This article investigates the distributed state estimation problem for large-scale power systems with the appearance of bad data. The power system is decomposed into several nonoverlapping agents and these agents interact with each other through transmission lines to form an interconnected multiagent power system (IMAPS). The measurement at each agent is local measurement, and the measurement in transmission line is edge measurement. To obtain an accurate state estimation of each agent in a distributed manner when the measurements are coupled with bad data, a bad data detection process should be designed. The difficulty is how to detect the bad data in edge measurement in a distributed scheme. To solve this problem, the characteristics of the edge measurement residual is analyzed, and a distributed bad data detection strategy is presented based on a novel iterative distributed Kalman-like filter (IDKF). It is proved that the IDKF algorithm can converge in finite steps when the communication graph of the IMAPS is acyclic, and the estimation accuracy is similar to that of the centralized Kalman filter. In addition, the IDKF algorithm shows excellent performance even when bad data appears. Simulation tests conducted on the IEEE 118-bus power system verify the theoretical findings. [ABSTRACT FROM AUTHOR]

Currently, a nonconvex economic dispatch problem is one of the research focuses in the field of smart grid (SG). A variety of algorithms are developed to solve it. However, these algorithms are prone to suffering from high computation cost and slow convergence rate, which creates an inevitable gap between theoretical analysis and practical real-time operations. In this article, we aim at providing an ensemble deep-learning-based approach to tackle such a challenging issue. First, a novel ensemble method is presented to explore the ground truth of nonconvex economic dispatch problems. Second, considering the time-varying total load demand, cost coefficients, and dispatchability of all generation units in a practical SG system as the features, a new deep neural network structure is proposed to learn the complex mapping from instant features to an optimal nonconvex economic dispatch solution. If such a mapping is well approximated by the designed deep neural network, no significant effort is required to solve a new economic dispatch problem, and the solution is obtained on the scale of milliseconds. Third, analyzing that a single deep neural network may be weak to a small part of the mapping space of the nonconvex economic dispatch problem, we further present an ensemble of multiple parallel deep neural networks trained sequentially with a simplified Adaboost.R2 algorithm. Finally, case studies reveal that the proposed approach achieves orders of magnitude speedup in computational time while guaranteeing similar or better performance on minimizing the overall generation cost compared to the state-of-the-art nonconvex economic dispatch algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing alternating direction method of multipliers (ADMM) cannot realize totally distributed computation, a totally distributed improved ADMM algorithm that combines logarithmic barrier function and virtual agent is proposed. We also investigate economic dispatch for microgrids considering demand response based on day-ahead real-time pricing (RTP), which forms a source-load-storage collaborative optimization scheme. First, three general distributed energy sources (DERs), renewable energy resources (RESs), conventional DERs and energy storage systems (ESSs), are considered in the method. Second, the goal of economic dispatch is to minimize the sum of three energy generation costs and implement the optimal power allocation of dispatchable DERs. Specifically, the approach not only inherits the fast computational speed of ADMM but also uses barrier function and virtual agent to handle inequality and equality, respectively. Moreover, the approach requires no coordination center and only the communication between current agent and adjacent agent to achieve totally distributed solution for every iteration, which can preserve information privacy well. Finally, a 30-node microgrid system is used for case analysis, and the simulation results demonstrate the feasibility and effectiveness of the proposed approach. It can be found that, the proposed approach converges to the optima when p = 0.01, v = 100, t 0 = 0.01 and μ = 2. [ABSTRACT FROM AUTHOR]

Keypoints matching is a pivotal component for many image-relevant applications such as image stitching, visual simultaneous localization and mapping (SLAM), and so on. Both handcrafted-based and recently emerged deep learning-based keypoints matching methods merely rely on keypoints and local features, while losing sight of other available sensors such as inertial measurement unit (IMU) in the above applications. In this paper, we demonstrate that the motion estimation from IMU integration can be used to exploit the spatial distribution prior of keypoints between images. To this end, a probabilistic perspective of attention formulation is proposed to integrate the spatial distribution prior into the attentional graph neural network naturally. With the assistance of spatial distribution prior, the effort of the network for modeling the hidden features can be reduced. Furthermore, we present a projection loss for the proposed keypoints matching network, which gives a smooth edge between matching and un-matching keypoints. Image matching experiments on visual SLAM datasets indicate the effectiveness and efficiency of the presented method. [ABSTRACT FROM AUTHOR]

This article presents a distributed consensus-based successive convex approximation (DSCA) algorithm to solve nonconvex nondifferentiable economic dispatch (ED) problems. The ED model formulated incorporates generation constraints, valve-point effects, and multiple fuel types. A perturbation technique enables the proposed DSCA to tackle such a nondifferentiable and nonconvex optimization, which paves the way to solving more complicated optimization problems that occur in practical applications. The local generation constraint is taken care by a local surrogate convex optimization directly. The global equality constraint is handled based on a consensus protocol, where the local generation–demand mismatch among all dispatchable generators (DGs) is shared in a distributed manner. As a result, the power distribution of DGs is updated, and the generation cost is minimized. Several case studies show that the proposed DSCA algorithm can achieve superior ED solutions and computational efficiency over existing nonconvex optimization algorithms. [ABSTRACT FROM AUTHOR]

In this article, the secondary frequency restoration as well as active power allocation problem in an ac microgrid (MG) system subject to bounded varying-time delays are addressed. For each distributed generator, a distributed dynamic event-triggered control law is proposed. Besides, benefiting from using dynamic event-triggered mechanisms, the communication burdens can be measurably reduced. By analyzing the resulting system through a Lyapunov function, a sufficient condition is established to ensure stability and achieve asymptotic frequency restoration and active power sharing. Based on the sufficient condition, an explicit tolerable upper bound of all time delays is obtained. The upper bound can be used for the MG system design guideline in the planning phase, which would enhance real time operating safety. Beisides, no Zeno behavior will exist. To test the proposed control method, the experiments are conducted on the real-time simulator OPAL-RT with DSP controllers. The results demonstrate the effectiveness and performance of the proposed controller. [ABSTRACT FROM AUTHOR]

For DC microgrids (MGs), cyber attacks of distributed secondary control system result in communication faults and thus, cause serious stability and cyber-security issues. In this paper, the cyber-security control problems for current sharing and voltage restoration of an islanded direct-current (DC) MG under denial-of-service (DoS) attacks are addressed. To ensure the stable system operation under DoS attacks, a distributed resilient control method is proposed in the secondary control layer. Subject to DoS attacks, the proposed control method does not only achieve bus voltage restoration but also realize the optimal current sharing optimization calculated by the tertiary layer all the time. Besides, different from most existing secondary control methods with a fixed sampling rate, a new resilient sampling mechanism is designed in the secondary layer to improve the security of the whole system against DoS attacks. The theoretical analysis proves that the proposed distributed resilient controller, which is easy for implementation, can still ensure the stability of the overall DC MG system under DoS attacks. Based on our theoretical analysis, a guideline for controller parameter selection can be used for the MG system design in the planning phase, which could enhance the safety of real-time operation. To test the proposed control method, a DC MG test system is built in a controller-hardware-in-the-loop (CHIL) testing platform. The effectiveness and robustness of our proposed control strategy is validated by the CHIL results. [ABSTRACT FROM AUTHOR]

The distributed control of DC microgrid is becoming increasingly important in modern power systems. One important control objective is to ensure DC bus voltage stability and proper current sharing with a reduced communication burden. This paper presents a new event-triggered distributed secondary control strategy for single-bus DC microgrid. Through this strategy, both current sharing and bus voltage regulation can be guaranteed. Moreover, through the event-triggering mechanism, each converter can decide locally when to transmit signals to its neighbours. In this way, the communication burden among converters is significantly reduced. Compared to existing results, the proposed strategy also enables various types of loads, including both linear and nonlinear loads, to be connected to the DC microgrid. Simulation and experiment results illustrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper is dedicated to solving the economic dispatch problem in smart grids, which aims at minimizing the total generation cost while satisfying the power supply-demand balance and generation capacity constraints. A distributed algorithm is proposed to solve the economic dispatch problem over directed communication topologies that are time-varying unbalanced. Its features are that the proposed algorithm is push-based, and the model of line losses is integrated into the problem of our interest to reduce the power losses. Resorting to geometric graph theory and convex analysis, it is proved that the incremental costs achieve consensus at a convergence rate O(1/√t) with t being the number of iterations. Furthermore, robustness of the proposed algorithm is investigated solving the EDP when the communication information undergoes arbitrary large but bounded constant delays and the local gradients involve observation noises with zero-mean and bounded variance. Finally, three case studies implemented on an islanded microgrid, the IEEE 30-bus system and the IEEE 118-bus system are tested to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In modern manufacturing processes, requirements for automatic fault diagnosis have been growing increasingly as it plays a vitally important role in the reliability and safety of industrial facilities. Rolling bearing systems represent a critical part in most of the industrial applications. In view of the strong environmental noise in the working environment of rolling bearing, its vibration signals have nonstationary and nonlinear characteristics, and those features are difficult to be extracted. In this article, we proposed a new intelligent fault diagnosis method for rolling bearing with unlabeled data by using the convolutional neural network (CNN) and fuzzy $C$ -means (FCM) clustering algorithm. CNN is first utilized to automatically extract features from rolling bearing vibration signals. Then, the principal component analysis (PCA) technique is used to reduce the dimension of the extracted features, and the first two principal components are selected as the fault feature vectors. Finally, the FCM algorithm is introduced to cluster those rolling bearing data in the derived feature space and identify the different fault types of rolling bearing. The results indicate that the newly proposed fault diagnosis method can achieve higher accuracy than other existing results in the literature. [ABSTRACT FROM AUTHOR]

In this article, we consider a convex optimization problem which minimizes the sum of local agents’ cost functions subject to certain local constraints. Besides, both the local cost function and local constraints are only known by the local agent itself. To solve this problem, a new accelerated distributed gradient-based algorithm is proposed, which is inspired by the “momentum” phenomena in nature and aims to accelerate the convergence speed of conventional distributed gradient algorithms. Sufficient conditions for the stepsizes and the acceleration gains are derived to ensure the convergence of the proposed algorithm. Furthermore, based on this proposed fast distributed algorithm, a new decentralized approach is proposed to solve economic dispatch problem, especially for a large-scale power system. Based on the idea of virtual agent, it is proved that this decentralized algorithm is equivalent to the original fast distributed gradient method. Several case studies implemented on IEEE 30-bus, IEEE 118-bus power systems, and a large-scale power system consisting of 1000 generators are conducted to validate the proposed method. [ABSTRACT FROM AUTHOR]

As a key part of modern power systems, DC microgrid is becoming increasingly important. Among different control methods for DC microgrid, secondary control has been widely investigated since it can guarantee both current sharing and DC bus voltage restoration. However, the existing secondary control results only consider fixed current sharing ratio among DC converters, and thus they cannot be applied to the case where an adjustable current sharing ratio is desired. Motivated by this observation, this paper presents a new distributed secondary control strategy. By imposing a time-varying droop gain and specifying the “virtual voltage drop,” this strategy is able to ensure adjustable current sharing ratio among DC converters. Moreover, the effects of time delay on the control performance is also analyzed. Three case studies and two hardware-in-the-loop (HIL) tests are provided to verify the efficacy of the presented strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a novel and applicable approach to calculate the reactive power loss of converter transformer under dc bias. By using this proposed method, only general information of the converter transformer and the network is needed for the calculation within a certain dc current range. Specifically, a three-dimensional model of ± 800 kV converter transformer model for simulation test is established by the finite element method. Through simulation test, the electromagnetic characteristics and reactive loss characteristics of converter transformer under dc bias can be obtained, and the relationship between the bias current and the reactive loss of ± 800 kV converter transformer is quantified. Simulation results validate the feasibility of proposed algorithm. [ABSTRACT FROM AUTHOR]

The interleave boost converter (IBC) has been used as the interface converter between the low-voltage electrical energy sources, such as lithium-ion battery banks, solar panels, and fuel cells, and the dc bus of dc microgrids. With increasing penetration of tightly regulated power electronic loads, which behave as constant power loads, the stability of microgrid dc-bus voltage that is fed by the IBC is threatened by the loads’ negative incremental impedance feature. To ensure the stability of the bus voltage, this article proposes a nonlinear disturbance observer (NDO)-based sliding-mode control algorithm. The proposed algorithm has excellent robustness, low computational burden, and no extra hardware cost. A generalized average state-space model is proposed to facilitate the control design. In addition, an NDO is employed to estimate the output power of IBC rapidly and accurately. To verify the effectiveness of the proposed algorithm, simulation and experimental results are presented. [ABSTRACT FROM AUTHOR]

Direct current (DC) microgrid is being increasingly investigated in modern power grid. An important issue in DC microgrid operation is to ensure proper current sharing among converters. While this has been addressed through droop control, the resulting voltage deviation in DC bus has to be compensated. To solve this problem, a new distributed secondary control scheme is presented in this paper for both current sharing and voltage restoration. A key part of the presented scheme is the integration of a new parameter ‘virtual voltage drop’ defined from droop gain and line resistance. Since the DC bus voltage is not required as a feedback signal, the proposed secondary control is simple and easy to design and implement. In addition, as the proposed scheme has no requirement for the loads, it can handle both resistance loads and constant power loads (CPLs). Simulations as well as experimental studies are carried out to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents a distributed unscented information filtering (UIF) method for state estimation of interconnected nonlinear dynamic systems. The UIF method is an information filter based on unscented transformation (UT), which is a nonlinear estimation method. Therefore, the estimation accuracy of UT-based distributed UIF method should be higher than that of linearization-based distributed maximum a posteriori (MAP) method. When implementing the distributed UIF algorithm, we first calculate the local estimate by UIF method based on the local observations, and then gradually integrate the neighboring information by iterative method to obtain a more accurate distributed estimate. The IEEE 118-bus system is adopted to conduct a series of simulations to evaluate the performance of the proposed distributed UIF method, the centralized UIF estimator, local UIF estimator and the distributed MAP estimator. Simulation results show that the proposed distributed UIF approach achieves a worse estimation accuracy than the centralized UIF, but is better than both the distributed MAP estimator and the local UIF estimator. [ABSTRACT FROM AUTHOR]

This paper presents a new distributed control scheme to achieve both accurate voltage restoration and precise current sharing for islanded dc microgrid (MG) system only with limited noncontinuous communication among the distributed generators (DGs). A two-layer multiagent framework is employed for this MG system, which consists of a physical layer and a cyber layer. A distributed voltage restoration control scheme is proposed in the cyber layer, where no overall system information is required and only dc bus voltage feedback is needed. Furthermore, by employing the idea of event-triggered communication, our proposed approach only relies on limited aperiodic communication, which greatly reduces the communication cost in the cyber layer. The stability of proposed method is analyzed through a Lyapunov function based approach and we also demonstrate that the Zeno behavior can be excluded if a proper event-triggered condition is established. Our proposed method is validated in an islanded dc MG test system built in the Simulink environment and an experimental prototype consisting of three DGs simultaneously. [ABSTRACT FROM AUTHOR]

A distributed cooperative control paradigm is proposed to handle the load sharing and transmission power loss optimisation‐based optimal power flow (OPF) problems in DC microgrids, which is based on a distributed finite‐time average consensus algorithm and a linear variable weighted summation algorithm. Firstly, an OPF problem is formulated to minimise the global transmission power loss, which is then solved by a novel distributed OPF regulator in secondary control. Furthermore, a distributed OPF considering load sharing controller is proposed in secondary control, which aims to guarantee that the load sharing deviation is limited to the assigned permissible range and the global transmission power loss is reduced to a minimum simultaneously. Compared to existing methods, these two control algorithms are developed in a completely distributed fashion, and the load distribution matrix and conductance matrix of DC microgrids are not needed. The effectiveness of the proposed control methods is verified by simulation results. [ABSTRACT FROM AUTHOR]

For a hybrid ac/dc microgrid (MG), bidirectional interlinking converters (BICs) enable flexible power interactions between ac and dc subgrids. In each subgrid, power sharing among diversified sources has been effectively realized by droop controllers. These power sharing concepts can also be extended to BIC applications. This paper proposes a distributed power management strategy (DPMS) for multi-paralleled BICs in the hybrid MG to avoid the overstress of a single BIC. In this strategy, each BIC is assigned with a well-devised localized distributed controller (LDC) which generates the respective power reference for the BIC. By using the LDC, BICs are allowed to exchange information with one another in the distributed communication graph. The power interactions between ac and dc subgrids can be proportionally allocated to BICs based on their different power ratings in a full distributed manner. Then the system reliability and scalability are significantly improved. Meanwhile, accurate global power sharing among all ac and dc sources in the MG would be accordingly attained. Considering the communication time delay involved in BICs, a small signal model is derived to predict the maximum tolerable delay of the studied system. The validities of the proposed DPMS and delay stability analyses are verified by a controller hardware-in-loop experimental platform. [ABSTRACT FROM AUTHOR]

This paper studies the current sharing problem of a dc microgrid using the hybrid dynamic control method. The hybrid dynamic controller framework is established including a continuous-time part and a discrete-time part, where the former part eliminates the voltage deviation of the dc bus and the latter part ensures the current sharing accuracy of the dc microgrid. The proposed distributed hybrid secondary controller not only guarantees a high accuracy of current sharing but also maintains the voltage regulation at the dc bus. Different from most existing methods, it only utilizes the sampling output current information of neighbors at the discrete time instants, which greatly reduces the communication burden. Under the framework of stability analysis on the closed-loop system, the proposed hybrid dynamic controller achieves both current sharing and voltage regulation if the average interacted interval of the discrete time interaction satisfies a bounded constraint. Besides, a detailed parameter design of the controller is provided. Finally, simulation and experimental tests are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a distributed secondary control scheme for accurate power allocation and voltage restoration in islanded dc microgrids. Conventionally the droop control function is employed in the primary control layer to realize power sharing among the distributed generators (DGs) in a decentralized fashion. However, the dc bus voltage may deviate from its nominal value due to different load profiles. In order to restore the dc bus voltage to its nominal value while maintaining the power sharing accuracy, a distributed control scheme is proposed in the secondary control layer. By using limited information of dc bus voltage and the secondary control inputs of their neighboring controllers, a distributed secondary control input can be designed and then sent to its corresponding primary controller. Besides, compared to most existing methods, no global information is required and only dc bus voltage feedback is needed. In addition, by employing the idea of pinning control, our proposed secondary control can be further simplified by only sending the dc bus voltage to one DG. An islanded dc microgrid test system is built in the MATLAB Simulink environment to validate our proposed method. An experimental prototype consisting of two DGs is designed to demonstrate the newly proposed approach and verify the obtained theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, a priority sorting approach based on simplified model predictive control (MPC) is proposed for modular multilevel converter (MMC). It aims at reducing the computational burden of conventional MPC method while maintaining the system performance, especially under high voltage levels. The proposed approach mainly consists of three parts, i.e., grid-side current control (GCC), circulating current control (CCC), and capacitor voltage balancing control (CVBC). The GCC and CCC are separately designed with simplified MPCs, avoiding the weight factor. Meanwhile, the redundant calculations are eliminated in GCC by considering the desired predicted output voltage of equivalent MMC model. To further minimize the optional combinations of the switching states, the CCC is constructed by utilizing the output of GCC and the arm current. Besides, a novel priority sorting approach is proposed for the CVBC to alleviate the sorting operation. The submodules are divided into three groups according to the detected capacitor voltages. Moreover, the groups are assigned with different priorities based on the arm current, and only one group needs the sorting process. Additionally, a reduced frequency approach is introduced to decrease the power loss in the steady state. The effectiveness of the proposed approach is validated by both simulation and experimental results. [ABSTRACT FROM PUBLISHER]

In this paper, a new hierarchical decentralized optimization architecture is proposed to solve the economic dispatch problem for a large-scale power system. Conventionally, such a problem is solved in a centralized way, which is usually inflexible and costly in computation. In contrast to centralized algorithms, in this paper we decompose the centralized problem into local problems. Each local generator only solves its own problem iteratively, based on its own cost function and generation constraint. An extra coordinator agent is employed to coordinate all the local generator agents. Besides, it also takes responsibility to handle the global demand supply constraint based on a newly proposed concept named virtual agent. In this way, different from existing distributed algorithms, the global demand supply constraint and local generation constraints are handled separately, which would greatly reduce the computational complexity. In addition, as only local individual estimate is exchanged between the local agent and the coordinator agent, the communication burden is reduced and the information privacy is also protected. It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion. Several case studies implemented on the IEEE 30-bus and the IEEE 118-bus are discussed and tested to validate the proposed method. [ABSTRACT FROM PUBLISHER]

Pricing function plays an important role in optimal energy scheduling problem in smart grid systems. The authors propose a novel real‐time pricing (RTP) strategy named proportional and derivative (PD) pricing. Different from conventional RTP strategies, which only depend on the current total energy consumption, their proposed pricing strategy also takes the historical energy consumption into consideration, which aims to further fill the valley load and shave the peak load. An optimal energy scheduling problem is then formulated to minimise the total social cost of the overall power system. Two different distributed optimisation algorithms with different communication strategies are proposed to solve the problem. Several case studies implemented on a heating ventilation and air conditioning system are tested and discussed to show the effectiveness of both the proposed pricing function and distributed optimisation algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider a constrained optimization problem for a large-scale multi-cluster agent system, in which a number of clusters already exist as a priori. The aim is to minimize a global objective function being the sum of multi-cluster local agents’ cost functions subject to certain global constraints. To solve this problem, a novel distributed hierarchical algorithm based on projected gradient method is proposed by using synchronous and sequential communication strategies. We firstly assign one agent as leader agent in each cluster, which can communicate with the leaders of its neighboring clusters. The agents in the same cluster conduct local optimization and communicate with their neighboring agents synchronously while the leader agents of different clusters exchange information in a sequential way. Then a scheme is proposed for each agent to iteratively estimate a solution of the optimization problem in a distributed manner. It is theoretically proved that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically when the chosen stepsizes are diminishing. Numerical examples are provided to validate the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present a distributed economic dispatch (ED) strategy based on projected gradient and finite-time average consensus algorithms for smart grid systems. Both conventional thermal generators and wind turbines are taken into account in the ED model. By decomposing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution of the optimization problem in a distributed manner with limited communication among neighbors. It is theoretically shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. This scheme also brings some advantages, such as plug-and-play property. Different from most existing distributed methods, the private confidential information, such as gradient or incremental cost of each generator, is not required for the information exchange, which makes more sense in real applications. Besides, the proposed method not only handles quadratic, but also nonquadratic convex cost functions with arbitrary initial values. Several case studies implemented on six-bus power system, as well as the IEEE 30-bus power system, are discussed and tested to validate the proposed method. [ABSTRACT FROM PUBLISHER]

This paper presents a distributed cooperative control scheme for voltage unbalance compensation (VUC) in an islanded microgrid (MG). By letting each distributed generator (DG) share the compensation effort cooperatively, unbalanced voltage in sensitive load bus (SLB) can be compensated. The concept of contribution level (CL) for compensation is first proposed for each local DG to indicate its compensation ability. A two-layer secondary compensation architecture consisting of a communication layer and a compensation layer is designed for each local DG. A totally distributed strategy involving information sharing and exchange is proposed, which is based on finite-time average consensus and newly developed graph discovery algorithm. This strategy does not require the whole system structure as a prior and can detect the structure automatically. The proposed scheme not only achieves similar VUC performance to the centralized one, but also brings some advantages, such as communication fault tolerance and plug-and-play property. Case studies including communication failure, CL variation, and DG plug-and-play are discussed and tested to validate the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, restorations for both voltage and frequency in the droop-controlled inverter-based islanded microgrid (MG) are addressed. A distributed finite-time control approach is used in the voltage restoration which enables the voltages at all the distributed generations (DGs) to converge to the reference value in finite time, and thus, the voltage and frequency control design can be separated. Then, a consensus-based distributed frequency control is proposed for frequency restoration, subject to certain control input constraints. Our control strategies are implemented on the local DGs, and thus, no central controller is required in contrast to existing control schemes proposed so far. By allowing these controllers to communicate with their neighboring controllers, the proposed control strategy can restore both voltage and frequency to their respective reference values while having accurate real power sharing, under a sufficient local stability condition established. An islanded MG test system consisting of four DGs is built in MATLAB to illustrate our design approach, and the results validate our proposed control strategy. [ABSTRACT FROM PUBLISHER]