To address the cost-effective voltage regulation in active distribution network (ADN), this paper proposes a multiagent deep reinforcement learning (MADRL) based photovoltaic (PV) and battery storage (BS) aggregators coordinated operation framework. The proposed framework treats PV and BS under each bus as an aggregator and enables model-free separated operation between multiple PV-BS aggregators and ADN by training the control agent of each PV-BS aggregator using MADRL. The coordinated operation only requires incomplete information from the internal information of each PV-BS aggregator and overall ADN information, thereby reducing communication overhead. A test system evaluates the performance of three model-free MADRL algorithms on the proposed PV-BS aggregators coordinated operation in ADN. It also validates the effectiveness of the MADRL for cost-effective voltage regulation in ADN with multiple PV-BS aggregators.

The information exchange among distributed energy resources (DERs) in microgrids (MGs) is through sensing and communication systems, which are prone to expose cyber-attack threats. This article investigates the stability issue of MG systems with distributed secondary control under latency attacks and random denial-of-service (DoS) attacks. Considering these two kinds of attack modes, the corresponding attack consequences including network jamming and time-varying latency in the communication network are simultaneously studied. First, a new metric is defined to quantify the DoS attacks by considering different network jamming choices. Then, the time-domain stability study is conducted considering both attack consequences. Next, a cyber-resilient control strategy is proposed with two control modes: 1) An adaptive-gain resilient controller to sustain the fast stabilization of MG systems under nonuniform time-varying latency attacks, which is proved by the stochastic stability analysis using Lyapunov–Krasovskii functional method. 2) An event-trigger topology reconfiguration controller against excessive latency and damaged cyber connectivity caused by DoS attacks. A switching mechanism for coordinating the above control modes is also designed to guarantee the secondary control functions of MG systems. A modified IEEE 13-bus MG system with five DERs is tested and the effectiveness of the proposed controller under different attack scenarios is verified by OPAL-RT real-time tests.

The optimal frequency control of autonomous microgrids (MGs), i.e., to achieve fast frequency recovery and dynamic power adjustment of the distributed generators in proportion to predefined participation factors, can be achieved in a fully distributed way based on the subgradient consensus protocol. However, such a distributively controlled MG is susceptible to different types of cyber attacks infiltrated from different locations. In this article, a robust and resilient distributed optimal frequency control scheme is proposed to address the threat of cyber attacks. It is facilitated by introducing an auxiliary networked system interconnecting with the original cooperative control system. On condition that the cyber attacks are within certain ranges, the robust design can maintain the functionalities by significantly attenuating the impact. Otherwise, the cyber attacks can be easily detected, and resilient reactions can be taken to mitigate their influences via isolation. Simulation results in a modified IEEE 34-bus MG validate the effectiveness of the proposed approach.

This article investigates the problem of attack detection of false data injection attacks for a class of large-scale smart grid systems in the context of cyber–physical systems. First, by exploiting the graph theory to decompose the considered system into multiple interconnected subsystems, a bank of dynamic reduced-order observers are delicately constructed to generate residual signals for the attack detection task. Then, a novel decentralized attack detection scheme is proposed based on the adaptive detection thresholds with prescribed performance. Compared with the existing results, the proposed detection scheme has less conservative thresholds and enhanced robustness against process disturbance and measurement noise, such that the detectability is improved. Finally, the effectiveness and availability of the proposed scheme are verified by two simulation examples and the experimental results from IEEE 30-bus system built in the OPAL-RT real-time simulator.

As a cyber-physical system (CPS), the security of microgrids (MGs) is threatened by unknown faults and cyberattacks. Most existing distributed control methods for MGs are proposed based on the assumption that secondary controllers of distributed generation units operate in normal conditions. However, the faults and attacks of the distributed control system could lead to a significant impact and consequently influence the security and stability of MGs. In this article, a distributed resilient control strategy for multiple energy storage systems (ESSs) in islanded MGs is proposed to deal with these hidden but lethal issues. By introducing an adaptive technique, a distributed resilient control method is proposed for frequency/voltage restoration, fair real power sharing, and state-of-charge balancing in MGs with multiple ESSs in abnormal condition. The stability of the proposed method is rigorously proved by Lyapunov methods. The proposed method is validated on test systems developed in OPAL-RT simulator under various cases.

Internet-of-Things concepts are evolving the power systems to the Energy Internet paradigm. Microgrids (MGs), as the basic element in an Energy Internet, are expected to be controlled in a cooperative and flexible manner. This article proposes a novel distributed control scheme for multiagent systems (MASs) governed MGs in future Energy Internet. The control objectives are frequency/voltage restoration and proportional power sharing. The proposed control scheme considers both intra- and inter-MASs interactions, which offers group plug-and-play capability of distributed generators. The stability and communication delay issues in the control framework are analysed. A multisite implementation framework is presented to explain the agent architecture as well as data exchange in local area networks and the cloud server. Then a cyber hardware-in-the-loop experiment is conducted to validate the proposed control method with multisite implementation. The experimental results prove the effectiveness and application potentials of the proposed approach.

Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multiconstrained least costly formulation is proposed by augmenting the overtemperature penalty and multistress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.

This paper proposes a cyber-resilience enhancement method to detect and mitigate the denial of service (DoS) attacks in microgrids. It interprets the interactions between cyber and physical systems of microgrids by side-channel detector and dynamic priority scheduling at the first time. It is a cross-layer design scalable to realistic controller and device constrains and compatible with existing designs in both cyber and physical systems. At the same time, it can also guarantee the survival by moving target defense even against the infinite-energy DoS attack at the cost of tolerant performance loss. Numerical simulations verify the effectiveness of the proposed method.

This paper designs a novel distributed controller for the islanded DC microgrid. The proposed control method provides a data-driven multi-agent framework to solve the DC bus voltage regulation and current sharing. In order to accurately solve the control action, an online deep reinforcement learning (DRL) algorithm, called deep deterministic policy gradient (DDPG), is employed to secondary controllers in a DC microgrid. Based on the previous knowledge and current system state, DDPG algorithm generates the control action to compensate the voltage reference. In addition, the load reward function for each agent is designed to seek the optimal action of the system. Besides, the proposed control scheme is fully distributed, where each agent only exchange information with neighboring agents. Simulation results of a 4-DG DC microgrid demonstrate the effectiveness and satisfied performance of the proposed multi-agent DDPG-based control strategy.

In this paper, a fully distributed secondary-tertiary coordinated control framework for islanded ac microgrids has been proposed. First, the distributed secondary control for frequency restoration, voltage regulation and power sharing has been proposed. Therefore, the system frequency and bus voltages can be maintained at set points. Each DG can follow the tertiary control signals, while the rest load-generation mismatch is proportionally shared among DGs. Then in the tertiary control, optimal power flow of islanded ac microgrids is formulated as an optimization problem and solved by alternating direction method of multipliers. The entire control framework is achieved in a distributed way with sparse communication networks among each agent. A cyber-physical microgrid platform has been built to validate the proposed controller design in a real-time and hardware-in-the-loop condition. A six-bus three-DG microgrid is developed on the platform and the experimental results validate the effectiveness of the proposed method.

Fast and stochastic power fluctuations caused by renewable energy sources and flexible loads have significantly deteriorated the frequency performance of modern power systems. Power system frequency control aims to achieve real-time power balance between generations and loads. In practice, it is much more difficult to exactly acquire the values of unbalance power in both transmission and distribution systems, especially when there is a high penetration level of renewable energies. This paper explores a deep learning approach to identify active power fluctuations in real-time, which is based on a long short-term memory recurrent neural network. The developed method provides a more accurate and faster estimation of the value of power fluctuations from the real-time measured frequency signal. The identified power fluctuations can serve as control reference so that the system frequency can be better maintained by automatic generation control, as well as emerging frequency control elements, such as energy storage system. A detailed model of Singapore power system integrated with distributed energy storage systems is used to verify the proposed method and to compare with various classical methods. The simulation results clearly demonstrate the necessity for power fluctuation identification, and the advantages of the proposed method.