Consensus control of multiagent systems (MASs) has applications in various domains. As MASs work in networked environments, their security control becomes critically desirable in response to various cyberattacks, such as denial of service (DoS). Efforts have been made in the development of both time- and event-triggered consensus control of MASs. However, there is a lack of precise calculation of control input during the attacking periods. To address this issue, a distributed secure consensus control with event triggering is developed for linear leader-following MASs under DoS attacks. It is designed with a dual-terminal event-triggered mechanism, which schedules information transmission through two triggered functions for each follower: one on the measurement channel (sensor-to-controller) and the other on the control channel (controller-to-actuator). To deal with DoS attacks, the combined states in the triggered functions are replaced by their estimations from an observer. Sufficient conditions are established for the duration and frequency of DoS attacks. To remove continuous monitoring of the measurement errors, a self-triggered secure control scheme is further developed, which combines the system states and other information at past triggered instants. Theoretical analysis shows that the followers in MASs under DoS attacks are able to track the leader and meanwhile the Zeno behavior is excluded. Case studies are conducted to demonstrate the effectiveness of our distributed secure consensus control of MASs.

In this article, a cloud-edge collaboration based control framework is proposed for the voltage regulation and economic operation in incremental distribution networks (IDN). The voltage regulation and economic operation, usually considered in separated aspects, can be integrated in a hierarchical control method by coordinating the active power and reactive power of distributed generators (DGs) and distributed storages (DSs) in an “active” mode. Promising the voltage security of the IDN, the upper level multiobjective optimization is formulated to maximize the consumption of the DGs, moreover, the lower level model predictive control (MPC) aims to regulate the dynamics of the DGs and DSs based on the established state space model. Time delay in the downstream channel is considered due to the open environment of the proposed control framework, which can be eliminated by using the PCM derived from the MPC considering model uncertainty. Finally, simulation results demonstrate the validity and robustness of the proposed method.

Multi-functional grid-tied inverters (MFGTIs) have been investigated recently for improving the power quality (PQ) of microgrids (MGs) by exploiting the residual capacity (RC) of distributed generators. Several centralized and decentralized methods have been proposed to coordinate the MFGTIs. However, with the increasing number of the MFGTIs, it demands a method with improved reliability and flexibility, which are characteristics of distributed framework that has not been introduced into the PQ improvement (PQI) field before. In this paper, we propose a distributed consensus method to undertake the PQI task. The task is proportionally shared among the MFGTIs according to their instant RCs. Besides, most of the existing methods assume that the RCs of the MFGTIs are sufficient for tackling the PQ problem (PQP), which is not always true. In the case of insufficient RC, the active power output of each MFGTI is scaled down by the same factor determined by a proposed leader-follower protocol to make room for the task. In summary, the PQP is dealt with in both cases of sufficient and insufficient RC under the distributed control framework. Finally, simulations and hardware-in-the-loop experiments of an MG consisting of three 10kVA MFGTIs are presented to verify the effectiveness of the proposed methods.

In the multi-microgrid system, once a microgrid is severely disturbed into the alert or emergency state, the effective multi-mode management method is necessary to make the system restore the balance of supply and demand rapidly. Therefore, a hierarchical multi-mode management strategy is proposed in this study, which includes three steps:1. The first step is to predict and fit the support capacity of the neighbor microgrids and to determine whether these microgrids need to participate in the support, which constitutes the upper layer. To this end, an event-triggered mode management strategy is proposed considering the multi-source fitting and line loss factor, etc. 2. When the support of neighbor microgrids is not required, the second step is to manage local source-storage-load to restore the balance of supply and demand rapidly, which constitutes the lower layer. To this end, another event-triggered management strategy is designed to generate management commands without pre-processing data; 3. To establish the mathematical models of a microgrid in hybrid mode consisting of continuous operation status and discrete management commands, the related small signal model is designed as the third step, which can analyze the system stability conveniently. Finally, the effectiveness of methods is verified by case studies.

The coordinated scheduling of plug-in electric vehicle (PEV) charging should be constructed in distributed architecture due to the growing population of PEVs. Since the information and communication technology makes the adversary more permeable, the distributed PEV charging coordination is vulnerable to cyber-attack which may degrade the performance of scheduling and even cause the failure of scheduler task. Considering the tradeoff between system-wide economic efficiency, distribution level limitations and PEV battery degration, this paper investigates the resilient distributed coordination of PEV charging to resist cyber-attack, where the steps of detection, isolation, updating and recovery are designed synthetically. Under the proposed scheduling scheme, the misbehaving PEVs suffering from cyber-attack are gradually marginalized and finally isolated, and the remaining well-behaving PEVs obtain their own optimal charging strategy to minimize the total system cost in distributed architecture. The simulation results verify the effectiveness of theoretical method.