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In this paper, the consensus problem for a class of interconnected systems with different cyber‐physical topologies is investigated. A two‐layer control framework is proposed where two different connections exist among the systems in physical and cyber layers, respectively. The former directly reflects the physical coupling among systems, while the latter uses the states' information transferred by communication channels to generate control. The physical and cyber topologies are undirected and not required to be connected. An event‐triggered protocol based on cyber local sampled information is designed with no need to solve any matrix equation or inequality. Theoretical sufficient conditions for consensus are derived in terms of algebraic inequality and Zeno behavior is excluded. A novel expandable construction scheme is proposed to construct switching topologies for cyber layer. Numerical simulation and comparison show the effectiveness of the proposed control method and the advantage on saving communication network constructions. [ABSTRACT FROM AUTHOR]

Time-based pricing for residents is regarded as a critical component to facilitate integration of renewable energy, but it remains one of the significant barriers to target high potential customers. Randomly recruiting customers into time-based pricing may lead to customers' bad experiences and utility companies' economic losses. Focusing on this issue, this paper proposes a new customer selection framework for time-based pricing using the smart meter data. To utilize smart meter data effectively, a novel feature engineering procedure is designed, covering both of statistical characteristics and load patterns. Then, a Bayesian neural network (BNN) based prediction model is developed by integrating the Bayesian theory into neural networks for identifying high-potential customers. Finally, the prediction uncertainty is quantified to help utility companies to identify when to trust the prediction results. Case studies on an actual time-based pricing pilot demonstrate the effectiveness of the proposed framework. As for the comprehensive metric F1-score, BNN is in the lead, which is 9.0 % higher than neural network, 6.0 % higher than convolutional neural network (CNN), and 3.3 % higher than long short-term memory (LSTM). In addition, by leveraging the quantified uncertainties, the percentage of low-potential households among selected customers drops by 8.92 %, compared to blindly trusting the prediction results. Furthermore, it's found that load pattern features contribute more to model performance than statistical features, revealing that it's the shape, rather than the amplitude, of daily load curves that determines residential price responsiveness. © 2017 Elsevier Inc. All rights reserved. • A novel feature engineering approach is proposed to dig out information from smart meter data. • A Bayesian neural network-based prediction model is developed to identify customers with great price responsiveness. • The prediction uncertainty is quantified to determine when to trust the prediction results. • The effectiveness of the proposed customer selection framework is validated on an actual time-based pricing dataset. [ABSTRACT FROM AUTHOR]

In this paper, we consider event‐triggered control for linear singularly perturbed systems. A composite event‐triggered controller is proposed based on the decoupled slow and fast subsystems which are triggered simultaneously. Two internal dynamic variables are introduced in the event‐triggered mechanism. The results indicate that the systems can achieve asymptotic stability with the proposed event‐triggered control. In addition, the triggering time intervals have a finite lower bound independent of the initial states, thus Zeno phenomenon is excluded. The efficiency of the approach is illustrated through simulation examples. [ABSTRACT FROM AUTHOR]

The rapid increasing of data centers calls for an efficient method to reduce high operation costs and carbon emissions. This paper proposes a cooperative online schedule framework for local interconnected data centers considering shared energy storage. A time-average optimization problem is built to reduce the overall operation cost with essential operational constraints. Multiple randomness and the temporal coupling constraints of energy system levels make it challenging to solve the stochastic problem. Adapting the Lyapunov optimization theory with relaxation and perturbation technique, an online algorithm is proposed to find an acceptable solution to the problem to achieve both good service coordination and efficient energy coordination. The algorithm does not require prediction of random parameters, and has a fast convergence rate. Especially, the theoretical conditions for convergence and the distance between the acceptable and real optimal solution are derived in a closed form. Simulation studies show that the algorithm can effectively reduce operation cost of data centers, carbon emissions, and dependence on the main grid. • A new microgrid consisting of local data centers and shared ESS is investigated. • A cooperative framework to realize service and energy coordination is provided. • The proposed online algorithm finds an acceptable solution without predictions. • The algorithm is compared with forward-looking algorithm and rolling algorithm. [ABSTRACT FROM AUTHOR]

Existing renewable power generation forecasting methods mainly focus on a single energy source and fail to effectively capture the spatio-temporal correlation between different power generation resources. Furthermore, the current single-site power forecasting no longer fulfills the demands of grid dispatch. This paper introduces an innovative framework for multi-task learning and uses it to achieve regional wind-photovoltaic combined power generation forecasting. First, this paper employs Maximum Information Coefficient (MIC) to identify the crucial meteorological features affecting power generation and analyze the complementarity and correlation between wind and photovoltaic power generation. Then, an innovative multi-task learning framework is proposed that separates task-specific components and shared components, allowing each task to select adaptive information that benefits itself. Besides, this paper proposes a loss optimization strategy to balance the loss magnitude and training velocity of different tasks. In order to effectively share the coupling information among the two kinds of power generation, the proposed framework is adopted to construct the regional wind-photovoltaic combined power generation forecasting model based on Temporal Pattern Attention LSTM (TPA-LSTM) algorithm. Finally, the efficiency and superiority of the proposed method are validated through several verification and comparison case studies. • A combined power forecasting model is proposed based on multi-task learning. • A novel multi-task learning framework is constructed. • A comprehensive joint loss optimization strategy is proposed. • Extensive experiments demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper investigates the cluster synchronisation problem for multi-agent non-zero sum differential game with partially unknown dynamics. The objective is to design a controller to achieve the cluster synchronisation and to ensure local optimality of the performance index. With the definition of cluster tracking error and the concept of Nash equilibrium in the multi-agent system (MAS), the previous problem can be transformed into the problem of solving the coupled Hamilton–Jacobi–Bellman (HJB) equations. To solve these HJB equations, a data-based policy iteration algorithm is proposed with an actor–critic neural network (NN) structure in the case of the MAS with partially unknown dynamics; the weights of NNs are updated with the system data rather than the complete knowledge of system dynamics and the residual errors are minimised using the least-square approach. A simulation example is provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

• A wrapper method of feature selection considering both relevance and redundancy is proposed for similar-day choosing. • A novel similar-day choosing method is used to excavate the uncertainties in residential loads. • An efficient and accurate probability density forecasting framework is proposed for residential loads. • Superior performances in both point forecasting and probabilistic forecasting are obtained. Probabilistic load forecasting can provide much richer information about uncertainties in residential loads than point load forecasting, and is gaining increasing attention in building efficient and economical demand response programs. However, obtaining satisfactory forecasting results for residential loads is challenging due to their intrinsic nonlinearity and volatility. Furthermore, existing probabilistic forecasting methods are limited in their ability to directly generate probability density forecasting in an efficient and accurate way. To address these issues, a new probability density forecasting framework for residential loads is proposed in this paper. Firstly, a similar-day choosing method is proposed by combining weighted external features and internal load curves to build a similar-day dataset. Then, point forecasting is achieved by training an exclusive predictor on the corresponding similar-day dataset for each forecasting target. Finally, probability density forecasting is realized by conducting kernel density estimation on the similar-day dataset and calibrating it according to the point forecasting result. Comparisons with other state-of-the-art methods carried out on the HUE dataset demonstrate the efficiency and superiority of the proposed framework in both point and probability density forecasting. [ABSTRACT FROM AUTHOR]

Consumer characteristics can help energy utilities implement efficient demand response programs and personalized services. However, there are two problems in obtaining consumer characteristics from smart meter data: label scarcity and class imbalance. The two problems greatly reduce the effectiveness of the existing supervised learning classifiers. Therefore, this paper proposes a self-training convolutional autoencoder (STCAE) framework for consumer characteristics identification with imbalance datasets. STCAE has three modules including encoder, decoder, and classifier. For the label scarcity problem, a seasonal pretext is designed to enable learning unlabeled samples through encoder–decoder structure of STCAE. For the class imbalance problem, a piecewise synthetic minority over-sampling technique (PSMOTE) method is designed for class rebalancing. Case studies and comparison studies demonstrate the validity of each major component and the superiority of STCAE over existing methods. In comparison studies of twelve consumer characteristics identification, the average improvements of accuracy and Matthews correlation coefficient are higher than 6.32% and 35.18%, respectively. Furthermore, in the ablation studies, STCAE performs better than the framework with different components in most consumer characteristics. [ABSTRACT FROM AUTHOR]

Short-term load forecasting for residential buildings is of great significance in ensuring the safe and economic operation of the power grid. However, most existing prediction methods focus solely on the temporal characteristics of individual buildings and ignore the spatial correlation between different buildings in a neighborhood. To tackle this problem and realize joint prediction for multiple buildings, a novel multi-task learning model with a selected-shared-private mechanism is proposed in this paper. Firstly, the temporal and spatial characteristics hidden in electricity consumption patterns are analyzed and represented by auto-correlation and cross-correlation, respectively. Then, a correlation-oriented combination strategy is proposed to build input feature set for the prediction model, and the temporal convolutional network is adopted to extract features. Furthermore, a novel selected-shared-private mechanism is designed for multi-task learning to improve the prediction accuracy, which can selectively utilize information from related tasks while learning private features. The proposed model is compared with other methods on the public dataset, and the results demonstrate that the proposed model can make satisfactory joint predictions for multiple buildings with more than 35% accuracy improvement over the support vector machine. • A correlation-oriented combination strategy is proposed to build the optimal input set. • A novel selected-shared-private mechanism is designed for multi-task learning. • The proposed model realizes superior joint load prediction for multiple residential buildings. [ABSTRACT FROM AUTHOR]

• A new intermittent electricity theft attack is presented by repeating normal and fraudulent behaviors alternately. • Electricity feature engineering is conducted on the automatically divided time intervals. • A machine learning-based detection framework is proposed to detecting the intermittent electricity theft attack. • VBGMM is used to overcome the difficulty of parameter selection of detection threshold. The widespread installation of advanced metering infrastructure (AMI) brings convenience to applications including but not limited to load management and demand response. However, AMI is also at risk of electricity theft and non-technical loss. Using the smart meter data provided by AMI to dig out user electricity consumption behavior is an effective way to construct electricity theft detectors. In this paper, a new intermittent electricity theft attack behavior is presented which switches between committing electricity theft and honestly consuming electricity alternately to skillfully evade the existing detectors. Based on the assumption that the labels of intermittent adversaries are unavailable, a new machine learning-based detection framework is proposed to detect this attack. Initially electricity features are constructed based on time intervals divided by a numerical iteration method. Then light gradient boosting method (LightGBM) is used to classify the normal users and adversaries. Further, the disperse degree of users is designed for capturing the differences between the intermittent adversaries and others. A new 2D label set is then constructed by combining the predicted labels of LightGBM and the disperse degree. Finally, a variational Bayesian Gaussian mixture model is employed based on the 2D label set to sort the users into the normal users and adversaries visually. Results of the case studies show that the presented attack can evade state-of-the-art detectors but still gain high profits. In addition, the proposed machine learning-based detector outperforms state-of-the-art detectors on both persistent attacks and intermittent attacks. [ABSTRACT FROM AUTHOR]

• A new deep learning-based clustering method is proposed to deal with the large number of daily load curves. • A novel customer selection framework is proposed to identify the potential good candidates for demand response programs. • The proposed clustering method outperforms the classical K-means and two state-of-the-art deep clustering methods. • The customers selected by the proposed framework contribute to 59 % of total peak reduction with only accounting for 23 % of population. Demand response (DR) is regarded as a promising solution to the problem of renewable energy integration, while it remains one of the key barriers for DR to target the right customers, i.e. the customers with high potential in peak reduction. Exploring the electricity consumption behaviors of customers can help DR program practitioners evaluate customers' peak reduction potential. Clustering has been the most common approach to describe customers' consumption behaviors in the literature. However, the existing clustering methods suffer from either unscalability or poor performance when handling a large number of daily load curves. To overcome these issues, a new deep learning-based clustering method is proposed in this paper, which integrates both intra-cluster compactness and inter-cluster separation into the objective function of clustering for consideration. Based on the clustering results, a new customer selection framework is developed to identify the potential good candidates for DR programs, which takes the stability of consumption behaviors and the characteristics at peak hours into account. Case studies on a London dataset demonstrate the superiority of the proposed clustering method over classical K-means and two state-of-the-art deep clustering methods in terms of clustering validity indexes. Furthermore, the simulations performed on an actual price-based pilot show that the customers selected by the proposed customer selection framework contribute to 59 % of total peak reduction with only accounting for 23 % of population. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the event‐triggered output feedback control problem for the two‐time‐scale systems with structured uncertainty. Due to the non‐ignorable problem in applications such as equipment aging or parameter drifting, the robustness of the designed controller is worth discussing. A dynamic event‐triggered scheme is proposed to reduce the computational cost of the control signal. The results indicate that with the proposed event‐triggered controller, the system can achieve asymptotic stability without some common restrictions on the fast system rather than practical stability. Moreover, the Zeno behavior is excluded. Simulation examples with comparison studies illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Demand response is a promising technology to cope with the ever-increasing peak energy consumption in smart grid. For the smart home energy management system, it may choose to charge the battery when the electricity price is low, and discharge the battery to supply the home load when the electricity price is high. Under this circumstance, the temporally and spatially coupled constraints coexist in the energy demand, battery charging and discharging energy, and energy supply. The coupled constraints make the demand response problem more challenging than the existing ones and thus the existing distributed algorithms cannot be employed to find the optimal solution of the problem. In this paper, we focus on the real-time two-way communications between utility company and multiple residential users, and each user is regarded as a smart home energy management system. A distributed real-time algorithm is proposed to find the optimal energy management scheduling scheme for each user and utility company to maximize the social welfare. To overcome the obstacle brought by the temporally and spatially coupled constraints, dual decomposition technique is employed and the primal problem is decoupled into several independent subproblems which can be solved in a distributed way by each user and utility company without revealing or exchanging their private information. Simulation results are provided to demonstrate that the proposed distributed algorithm can bring potential benefits to the society. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed optimisation problem for the multi-agent systems (MASs) with the simultaneous presence of external disturbance and the communication delay. To solve this problem, a two-step design scheme is introduced. In the first step, based on the internal model principle, the internal model term is constructed to compensate the disturbance asymptotically. In the second step, a distributed optimisation algorithm is designed to solve the distributed optimisation problem based on the MASs with the simultaneous presence of disturbance and communication delay. Moreover, in the proposed algorithm, each agent interacts with its neighbours through the connected topology and the delay occurs during the information exchange. By utilising Lyapunov–Krasovskii functional, the delay-dependent conditions are derived for both slowly and fast time-varying delay, respectively, to ensure the convergence of the algorithm to the optimal solution of the optimisation problem. Several numerical simulation examples are provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design and the synthesis of the impulsive positive observer (IPO) for positive linear continuous systems. The IPO can estimate the states for positive systems even when the measured output is only available at discrete-time instants. In this paper, a time-varying weighted copositive Lyapunov function is constructed, and the upper and lower bounds of impulsive intervals method combined with the convex combination technique are used to establish sufficient conditions for the existence of the IPO. Furthermore, the design of the dynamic output feedback controller based on the IPO is addressed to positively stabilize positive linear systems. Algorithms are given to design the IPO and the controller, respectively. Finally, two numerical examples are provided to show the effectiveness of the theoretical results. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Multi-energy microgrids are facing a dilemma that realizing high local energy efficiency requires large-capacity ESS with hefty investment costs. To address the dilemma, an efficient and economic hybrid storage and energy sharing model for multiple microgrids is proposed. Specificly, a hybrid energy storage system (HESS) is introduced, which contains an electrical battery and a heat storage tank and is able to realize energy conversion. The multiple microgrids can share energy through the HESS in a collaborative way. An energy optimization problem is formulated to minimize the overall energy costs including energy purchase cost and HESS operating cost. ADMM algorithm is used to solve the problem in a distributed manner to avoid privacy concerns. The storage and energy sharing benefits of the microgrids and the HESS are determined by Nash bargaining solution. Simulation results show that the model can effectively improve the utilization of the renewable energy, and lead to considerable economic benefits for both the microgrids and the HESS. • An efficient and economical storage and energy sharing model is proposed. • Using the ADMM algorithm to solve the problem of the model in a distributed way. • The global energy efficiency and benefit of participates can be guaranteed. • Abundant simulation results prove the feasibility of the model. [ABSTRACT FROM AUTHOR]

Multi-energy microgrids are facing a dilemma that realizing high local energy efficiency requires large-capacity ESS with hefty investment costs. To address the dilemma, an efficient and economic hybrid storage and energy sharing model for multiple microgrids is proposed. Specificly, a hybrid energy storage system (HESS) is introduced, which contains an electrical battery and a heat storage tank and is able to realize energy conversion. The multiple microgrids can share energy through the HESS in a collaborative way. An energy optimization problem is formulated to minimize the overall energy costs including energy purchase cost and HESS operating cost. ADMM algorithm is used to solve the problem in a distributed manner to avoid privacy concerns. The storage and energy sharing benefits of the microgrids and the HESS are determined by Nash bargaining solution. Simulation results show that the model can effectively improve the utilization of the renewable energy, and lead to considerable economic benefits for both the microgrids and the HESS. • An efficient and economical storage and energy sharing model is proposed. • Using the ADMM algorithm to solve the problem of the model in a distributed way. • The global energy efficiency and benefit of participates can be guaranteed. • Abundant simulation results prove the feasibility of the model. [ABSTRACT FROM AUTHOR]

Energy storage systems have received widespread attention due to their advantages on rapid response, smooth fluctuations, and the reduction of temporal and spatial imbalance. At present, most researchers mainly consider the allocation of energy storage capacity while using an average allocation of the power capacity, which may lead to conflicts among users when executing the energy sharing strategies for the case with limited power capacity. In order to better improve energy efficiency and reduce electricity costs, this paper proposes an energy storage sharing framework considering both the storage capacity and the power capacity. Since each prosumer intends to minimize its costs which depend on the behavior of other prosumers, the relationship among prosumers forms a non-cooperative game. By using an algorithm that combines the alternating direction multiplier method and heavy ball method, a generalized Nash equilibrium is found to serve as an energy sharing strategy. Simulation studies and comparisons show that the proposed energy storage sharing framework driven by a dynamic electricity price mechanism can reduce prosumers' net demands, electricity costs and improve the efficiency of energy storage utilization. • A new community energy storage sharing framework is proposed. • The strategies with storage capacity and power capacity allocation are provided. • ADMM and the heavy ball method are presented to seek an equilibrium solution. • The efficiency is verified by several simulation cases from several aspects. [ABSTRACT FROM AUTHOR]

DC microgrid as a microscale power system has drawn growing attention for its various applications, which calls for good power quality, proper power sharing, and fast response property. However, the existing researches are not yet able to achieve the purpose in a predefined time. In this article, a new predefined-time secondary controller is proposed for DC microgrid to realize the objectives of both voltage regulation and current sharing among DGs within a predefined time. The controller is designed by employing a sign function and a $K^1$ function and by defining a new composite error, which does not need to sample the voltage of DC bus or the current of neighbors, but only the local current and voltage. The upper bound of the convergence time of both the voltage and current in the DC microgrid is just related to one parameter which can be predefined and is independent of initial error. The effectiveness of the proposed controller is verified by multiple simulations and experiments built on MATLAB/Simulink and a hardware-in-the-loop experimental platform. The conducted simulations and experiments include several typical scenarios together with a comparison, which illustrate the advantages of the proposed controller such as fast convergence rate and small overshoot. [ABSTRACT FROM AUTHOR]

Abstract: The problem about cluster synchronization of fractional-order CDNs is studied via a pinning adaptive approach in this paper. Based on the stability theory of fractional differential equations, some sufficient criteria for local and global cluster synchronization of fractional-order CDNs are derived. In this paper, the coupling configuration matrix can be asymmetric as well as reducible and the inner coupling matrix can also be asymmetric. Moreover, the number of pinning nodes in each cluster can be evaluated. Especially, when the coupling strength is large enough and the coupling configuration matrix is symmetric, cluster synchronization can be achieved via pinning a single node in each cluster. Finally, some typical examples are given to illustrate the correctness and effectiveness of our results, a surprising finding is that the synchronization performance will become better as the fractional order decreases in this simulation. [Copyright &y& Elsevier]

Abstract: This paper studies the projective synchronization of a drive-response dynamical network of partially linear systems. The coupling-configuration matrix of the network is not necessarily symmetric. Two adaptive pinning control strategies are derived based on the Barbalat’s Lemma. In the first control strategy, the pinning control gain is adaptively updated to realize the projective synchronization. In the second control strategy, both the pinning control gain and the coupling strength between nodes are adaptively updated. Numerical simulation results show that the coupling strength needed in the second control strategy is smaller than that in the first control strategy to achieve the projective synchronization of the network. [Copyright &y& Elsevier]

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

Abstract: In this paper, a general switched model of complex network with two types of delays is presented. The complex switched network (CSN) contains switching behaviors on both its nodes and topology configuration. Different from those CSNs studied in the existing literatures, the two switching signals dominating the nodes and topology are desynchronized. The two types of delays are the time-varying system delay in nodes and the time-varying coupling delay between nodes and they have different values. The inherent synchronization properties of the CSN have been studied not subject to any controllers. By defining a piecewise Lyapunov–Krasovskii function and by using the improved free-weighting matrix method, global exponential synchronizations are obtained. Illustrated example is presented to show the effectiveness of the proposed result. [Copyright &y& Elsevier]

Accurate wind power generation forecasting is of great significance to improve the operation of power system. Probabilistic forecasting has a higher application value in power grid because it can provide more abundant forecasting information than deterministic forecasting. In addition, multi-step forecasting can provide forecasting results in a longer time range, so that decision makers can make longer-term planning and strategic arrangements. In this paper, we propose a novel multi-step improved temporal convolutional network based on quadratic spline quantile function (MITCN-QSQF) for probabilistic wind power forecasting. First, we combine maximum information coefficient, Gaussian similarity and adaptive resample to propose an effective similar power generation feature extraction method (MGR) for power generation. Then the temporal convolutional network is improved to construct the multi-step time series forecasting model MITCN. By combining the proposed model and the powerful probabilistic forecasting method quadratic spline quantile function (QSQF), high-quality probabilistic forecasting of wind power is achieved. Through comprehensive simulations on an open-source dataset, the superiority and efficiency of the proposed method are verified. Compared with some advanced benchmarks, the proposed model can obtain more accurate deterministic and probabilistic forecasting results. • A similar power generation feature extraction method is proposed. • The novel Seq2Seq model MITCN is proposed for multi-step forecasting. • Combining MITCN with QSQF enables high-quality probabilistic forecasting. [ABSTRACT FROM AUTHOR]

• A united energy management for a community of energy buildings is investigated. • A game-based peer-to-peer energy sharing management model is proposed. • An efficient distributed algorithm is proposed to seek the game equilibrium. • The efficiency of the proposed model is verified by several simulation cases. Energy building techniques have gained significant attention for the potential to achieve local energy sustainability. Different from the research on individual energy management for zero energy buildings requiring sufficiently large renewable energy systems due to the mismatch of energy loads and generations, this paper investigates the coordinated energy management for a community of energy buildings with peer-to-peer energy sharing to promote community energy efficiency. Specifically, the energy buildings have controllable heating, ventilation and air conditioning (HVAC) units and renewable energy installations. In the coordinated energy management, in a peer-to-peer mode, the buildings determine their energy sharing profiles and related payments within the community while the sharing energy balance and payment balance are required to be satisfied. Since the buildings are self-interested, the problem with peer-to-peer energy sharing is modeled as a noncooperative game with global constraints. Then the existence of the game equilibrium is illustrated, and a distributed algorithm is proposed to seek the game equilibrium. A 6-building case and a 30-building case are carried out to verify the energy efficiency, economic benefits, and the scalability of the proposed peer-to-peer energy sharing management model. [ABSTRACT FROM AUTHOR]

The pelagic islands usually include resource islands and load islands with electricity and natural gas transactions among them. Since they are apart from the main land, the finiteness of energy resources results in crucial need of developing efficient energy management framework for the pelagic islanded microgrid clusters (PIMGCs). In this paper, we introduce a novel multi-energy management framework for a PIMGC, where the operators on resource islands sell energy resources, while the aggregators and users on load islands dispatch and consume energy resources, respectively. The multi-scale energy management strategy is proposed, where the operators determine their daily optimal energy supply in a distributed collaborative way by adopting the primal-dual subgradient method, each aggregator determines its daily optimal energy demand and hourly optimal energy usage, and each user determines its hourly optimal energy consumption. A hierarchical day-ahead distributed algorithm is proposed to obtain the Nash equilibrium strategy, where the operators minimize their aggregate operational cost, each aggregator maximizes its revenue and each user maximizes its payoff. Simulation results are provided to show the effectiveness and benefits of the proposed multi-energy management framework for the PIMGCs. • A pelagic islanded microgrid cluster multi-energy management framework is proposed. • A hierarchical multi-scale bidirectional interaction mechanism is designed. • A tri-level day-ahead distributed algorithm is proposed. • Simulation results show the effectiveness and benefits of the proposed framework. [ABSTRACT FROM AUTHOR]

This paper investigates the output formation-containment problem of interacted heterogeneous linear systems, where each heterogeneous system, whether the leader or the follower, has different dimensions and dynamics. Different from existing literature, discrete-time communication manner is deployed to reduce the communication consumption. By the impulsive control method, a distributed hybrid active controller is designed using the discrete-time information of neighbors. It achieves the output formation-containment of heterogeneous systems if two related conditions are satisfied, namely, local linear matrix inequalities (LMIs) and a bounded constraint on the average interacted interval. Moreover, the controller parameter design is further simplified by replacing the LMI condition with a Hurwitz condition, which can be easily guaranteed by solving a Riccati equation. Finally, a numerical example is provided to demonstrate the effectiveness of the theoretical result. [ABSTRACT FROM AUTHOR]

This paper addresses the modulus consensus in a network of singularly perturbed systems, all of which own multi-time scales characteristics. The interactions among the agents are collaborative and antagonistic interactions, and it can been modelled by a signed directed graph. Also, the effect of time-varying delay has been taken into account when modelling the agent dynamics. Based on singularly perturbed decomposition method, Kronecker product technique and the comparison principle, several sufficient conditions are presented under which networked singularly perturbed time-varying delay systems with collaborative and antagonistic interactions achieve modulus consensus. Moreover, the upper bound of singular perturbation parameter is deduced explicitly. Two simulation examples are presented to illustrate the validity of the proposed results. [ABSTRACT FROM PUBLISHER]

In this paper, the distributed hierarchical control design is investigated for cooperations of heterogeneous linear systems subjected to the switching networks, where each subgraph of switching networks is allowed to be disconnected. Each heterogeneous system contains uncontrollable states and evolves in different dimension with different dynamics. The hierarchical control framework proposed in this paper consists of distributed hierarchical controllers, which contain the upper-layers dealing with the communication topologies and the lower-layers dealing with the heterogeneity of the systems. Under the framework, the cooperation problem of heterogeneous linear systems is decoupled into a cooperation problem of homogeneous virtual systems in the upper-layer and tracking problems of each single systems in the lower-layer. These two layers are designed based on the optimal control method and the output regulation method, respectively. Under the hierarchical control framework, the leader-following consensus of heterogeneous linear systems is achieved and the result is extended to the formation control problem. Finally, numerical examples are provided to demonstrate the effectiveness of the theoretical results. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this letter, a class of complex dynamical networks with additive stochastic time-varying delays is investigated. Two kinds of delays in complex dynamical networks are taken into consideration, one is called the nodes-induced delay in this paper, and the other is the network-induced delay. Both of the delays are assumed to obey different stochastic distributions. By utilising a novel Lyapunov–Krasovskii functional and stochastic analysis techniques, a sufficient criterion is obtained in the form of linear matrix inequalities to ensure the synchronisation of the complex dynamical networks. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper addresses the robust reliable guaranteed cost control problem of positive interval systems with multiple time delays and actuator failure for a given quadratic cost function. Through constructing a Lyapunov–Krasovskii functional, a sufficient condition for the existence of robust reliable guaranteed cost controllers is established such that the closed-loop system is positive and asymptotically stable, and the cost function is guaranteed to be no more than a certain upper bound. Based on the linear matrix inequality method, a criterion for the design of robust reliable guaranteed cost controllers is derived which can tolerate all admissible uncertainties as well as actuator failure. Moreover, a convex optimisation problem with linear matrix inequality constraints is formulated to design the optimal robust reliable guaranteed cost controller which minimises the upper bound of the closed-loop system cost. A numerical example is given to show the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

This paper addresses the stability problem of switched positive linear systems with stable and unstable subsystems. Based on a multiple linear copositive Lyapunov function, and by using the average dwell time approach, some sufficient stability criteria of global uniform exponential stability are established in both the continuous-time and the discrete-time cases, respectively. Finally, some numerical examples are given to show the effectiveness of the proposed results. [ABSTRACT FROM PUBLISHER]

In this study, the consensus problem for a class of second‐order multi‐agent systems (MASs) with Markovian characterisations is investigated. The stochastic switching topology and the random communication delay are dominated by two mutually independent Markov chains. The communication delay exists in the switching signal as well as the position information exchanges in the authors' work. A novel consensus protocol is presented without using the neighbours' velocity information. By performing three steps of model transformation and introducing a mapping for the two independent Markov chains, the original system is converted into an expanded analogous error system with two Markovian jumping parameters. A necessary and sufficient criterion for the mean square consensus of the Markovian jump second‐order MASs with random communication delay is derived. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the theoretical result. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, a complex switched network (CSN) with parametric uncertainties and two types of time-varying delays is presented. The CSN contains switching behaviors on both its nodes and the network topology. Different from those CSNs studied in the literatures, the switching mode dominating the nodes systems is independent with that dominating the network topology. The two types of time-varying delays are the system delay in the node and the coupling delay between nodes and they have different values. The inherent synchronization properties of the CSN were investigated not subject to any controllers, and sufficient conditions for the global robust exponential synchronizations are obtained by defining a piecewise Lyapunov-Krasovskii function. Both the case with known derivative of delays and the case with unknown or nonexistent derivative of delays are investigated. Two special cases, i.e. the network without coupling delay and the network without switching behaviors and coupling delay, are studied. Illustrated examples are presented to show the effectiveness of the proposed methods. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a general impulsively-coupled complex switched network (ICCSN) model with parametric uncertainties and multiple Time-varying Delays in both the linear and nonlinear terms. The model is more general than those in the literature in that it contains switching behaviors on nodes and impulsive effects in the whole topology. Robust synchronization of ICCSNs with parametric uncertainties and time-varying delays is investigated. Based on the Lyapunov stability theory, delay-independent synchronization conditions for ICCSNs with uncertainties and delays are obtained. In addition, we consider five special synchronization cases: ICCSNs with delays in both the linear and nonlinear terms, ICCSNs with parametric uncertainties and delays either in the linear or in the nonlinear term, ICCSNs without switching behaviors but with parametric uncertainties and delays, and impulsively-switched-coupled complex switched network with uncertainties and delays. A systematic-design procedure is presented, and a numerical example is carried out to demonstrate the effectiveness of the proposed synchronization strategy. A comparative study of the maximum impulsive intervals for synchronization is presented for all special cases. [Copyright &y& Elsevier]

Abstract: In this paper, the synchronization of complex dynamical networks (CDNs) under ‘successful’ but recoverable attacks is studied by utilizing the framework of switching systems. ‘Successful’ attacks refer to a class of attacks that break a network into a group of isolated clusters. Recoverable attacks refer to a class of attacks that the network can recover from after a period of time. To facilitate the investigation of the synchronization of a CDN under ‘successful’ but recoverable attacks, the attack frequency and the average recovering time are introduced. By using a piecewise Lyapunov function, the upper bounds of the attack frequency and the average recovering time are obtained to ensure that the whole network can achieve global synchronization under attacks. [Copyright &y& Elsevier]

Shaping residential electricity consumption behaviors can achieve the effect of load shifting to emit less carbon. Electricity consumption pattern is a good way to describe electricity consumption behavior, and is basically determined by household characteristics (HCs). Effective mining of important factors in HCs can help to shape residential electricity consumption behaviors. This paper proposes a new mining framework for discovering the association between HCs and residential electricity consumption patterns. At first, HCs are classified into six categories by the theory of planned behavior based factor classification. Then, typical electricity consumption patterns (TECPs) of each individual resident in different periods divided by seasons and weekday are extracted by a proposed piecewise symbolic spatial clustering with noise algorithm which combines symbolic aggregate approximation and density-based spatial clustering of applications with noise algorithm. Afterward, TECPs are clustered by hierarchical clustering method. To determine the clustering number, six clustering validity indexes are applied to evaluate the clustering results. Finally, a feature-selection-based multifactor analysis method is designed to evaluate 95 factors in HCs impacts on TECPs. Results of the case study using 3117 records including smart meter data and survey data show that most factors of attitudes toward energy category and socio-demographic category are effective factors for predicting TECPs, while the factors in appliance and heating category are non-significant. The proposed mining framework and the findings can provide guidance of shaping grid-friendly electricity consumption behavior. • A mining framework is proposed for factor analysis of electricity consumption. • A symbolic clustering algorithm is proposed for electricity consumption patterns. • A numerical iterative method is designed for time interval division. • Feature selection is used in multifactor analysis for household characteristics. [ABSTRACT FROM AUTHOR]

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