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

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

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

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

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

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

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

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

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

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

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

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

• Considerable clusters are mutually interdependent to stay alive in cascading failure. • Heterogeneity of nodes in physical power grid, distributed generator and substation. • In physical power grid, the cluster with one distributed generator can stay alive. • The positive correlation between average size of clusters and robustness of systems. • The simulation analysis can guide rational allocation of inter-links in systems. In cyber-physical power systems, tiny failure generating in unilateral network may cause cascading failure iterative between physical power grid and communication network, and finally makes systems break into considerable clusters. In this paper, we find that considerable clusters respectively in physical power grid and communication network are mutually interdependent to survive in cascading failure. Taking account of survival clusters, we study robustness of cyber-physical power systems in various attack scenes. We employ IEEE Standard Bus test case and random network to model cyber-physical power systems. The numerical simulation illustrates that targeted attack disintegrates systems into more clusters than random attack, but with less nodes remained than random attack, which validates the positive correlation between average size of clusters and robustness of systems. Further, the simulation has proved the negative correlation between number of clusters and defined threshold of cluster size. Moreover, the simulation analysis on coupling interdependency and correspondence relation can guide rational allocation of inter-links in systems. [ABSTRACT FROM AUTHOR]

• A voltage regulation optimization problem is formulated considering source-load-storage collaboration. • An attention-based multi-agent deep reinforcement learning approach is designed for voltage control. • A multi-stage parallel training framework and an evolutionary selection mechanism is adopted to improve the scalability of voltage control. With the increasing penetration of renewable energy in active distribution networks (ADNs), voltage regulation problem is becoming more and more challenging. In this article, we focus on providing a scalable data-driven approach to ensure the voltage security of ADNs with high penetration of PVs. To this end, we first formulate an optimization problem for real-time voltage control considering source-load-storage collaboration while minimizing the total active power curtailment of PVs. Then, we reformulate the above problem as a Markov game and propose a novel voltage regulation algorithm based on evolutionary curriculum-based multi-agent deep reinforcement learning (EC-MADRL) to solve it. The key idea of the proposed algorithm is to adopt a multi-stage parallel training framework based on attention multi-agent deep deterministic policy gradient algorithm (Attention-MADDPG) and use an evolutionary mechanism to select voltage regulation models with high fitnesses in the previous stage, which means that good agents with best adaptability could be utilized for learning in the environment with a larger number of PVs. Simulation results show the effectiveness and scalability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the integration of wind power and photovoltaic power, optimal operation of hydrothermal power system becomes great challenge due to its non-convex, stochastic and complex-coupled constrained characteristics. This paper extends short-term hydrothermal system optimal model into short-term hydrothermal optimal scheduling of economic emission while considering integrated intermittent energy resources (SHOSEE-IIER). For properly solving SHOSEE-IIER problem, a gradient decent based multi-objective cultural differential evolution (GD-MOCDE) is proposed to improve the optimal efficiency of SHOSEE-IIER combined with three designed knowledge structures, which mainly enhances search ability of differential evolution in the shortest way. With considering those complex-coupled and stochastic constraints, a heuristic constraint-handling measurement is utilized to tackle with them both in coarse and fine tuning way, and probability constraint-handling procedures are taken to properly handle those stochastic constraints combined with their probability density functions. Ultimately, those approaches are implemented on five test systems, which testify the optimization efficiency of proposed GD-MOCDE and constraint-handling efficiency for system load balance, water balance and stochastic constraint-handling measurements, those obtained results reveal that the proposed GD-MOCDE can properly solve the SHOSEE-IIER problem combined with those constraint-handling approaches. [ABSTRACT FROM AUTHOR]

• A newly defined concept of predictive transmission security margin (PTSM) is presented accounting for the risk of uncertainty-induced congestion. • By incorporation of PTSM, the risk-averse UC and real-time thermal re-dispatch are formulated as a bi-level optimization model. • Quantification of PTSM can be exactly characterized given the impacts of thermal re-dispatch on the ex-post line flows in a probabilistic manner. • Transmission flexibility is gained economically by procuring PTSM for the alleviation of congestion and reserve undeliverability. Optimal scheduling of generation and transmission resources in the day-ahead market is vital for the assurance of operational reliability in the case of with high wind penetration. To mitigate the ramifications of wind power uncertainty, this paper presents a risk-averse approach for the unit commitment (UC) model in a two-settlement market. A newly defined concept of predictive transmission security margin (PTSM) is formulated in the UC, which provides a measure for the risk margin of transmission congestion while accounting for wind power forecast error and contingencies. These procured PTSMs on each line are dynamically characterized based on the statistics of ex-post line flows in the real-time re-dispatch. The proposed framework is formed as a bi-level optimization problem, and is transformed into a mixed integer programming formulation using Karush–Kuhn–Tucher (KKT) optimality conditions for computational tractability. The effectiveness of the proposed method is tested using various case scenarios on a modified IEEE RTS 24-bus and a 118-bus system in terms of key performance metrics, including the total operation cost, congestion hours, and required and undelivered generation reserves. The results show that the proposed method can effectively reduce the total operation costs by suppressing congestion risk and improving reserve deliverability. [ABSTRACT FROM AUTHOR]

The variability of renewable energy sources (RESs) introduces more frequency fluctuations, and the reserve capacity of generation side needs to be sharply enlarged to maintain frequency stability, which inevitably increases the cost. In this paper, massive inverter air conditioners (IACs) as flexible regulation resources are aggregated to provide capacity support in frequency regulation. This paper establishes the state-space dynamic model and then presents a coordinated optimal control strategy by using model predictive control (MPC). However, the communication delay during the control signal transmission to IACs is one of the main obstacles that degrades the system performance in frequency regulation. To handle this issue, a predictive compensation method (PCM) based on MPC is applied to the control loop of IACs to compensate for the communication delay. Moreover, the robustness of the proposed MPC method with PCM against variations of system delay and parameters in the frequency response process is investigated in comparison to the proportional–integral (PI) controller. The simulation results are conducted to validate the superiority of the proposed method to the PI control method in virtue of the dynamic response and the performance indices, which demonstrates faster response, robustness, fewer fluctuations. • The state-space model of new power system incorporating the IACs is firstly proposed. • Based on this model, advanced control methods can be used for frequency control. • MPC is utilized to control aggregated IACs to obtain an optimal control strategy. • A predictive compensation method is applied to handle the impact of transmission delay. • The proposed method is robust over PI controller in the presence of delay. [ABSTRACT FROM AUTHOR]

• The dynamic of multi-area LFC system with uncertainty and GRC under DoS attacks and time delay is described by a switched delay system. • To analyze the resilience of LFC system, we combine the piecewise Lyapunov-Krasovskii functional method and switched system method. • A criterion is derived to estimate the tolerable DoS attacks: attacks frequency, uniform bound of sleep intervals and attack intervals. Under the criterion, the design method of resilient LFC control gain is given by using LMIs techniques. Resilient load frequency control (LFC) design problem is concerned for a multi-area power system with uncertainty and physical constraint under denial-of-service (DoS) attacks and transmission delay. LFC scheme consists of a local PI control loop and additional control loop to be designed. PI control loop is subjected to constant transmission delay while additional control loop is subjected to DoS attacks. We characterize DoS attacks by attack frequency, uniform lower bound of sleep interval and uniform upper bound of attack interval. Considering DoS attacks and time delay, we describe LFC system dynamic by a switched delay system model. Further, a stability criterion is derived by using delay system method and switched system method. Under the criterion, a design method is presented to solve state feedback control gain. Finally, numerical simulations are given to verify the validness of our proposed design method. [ABSTRACT FROM AUTHOR]