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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]

• 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]

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]

• 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]