Low-bandwidth communication channels are used to support the information exchange between a microgrid centralized controller and local controllers in the secondary frequency control of an islanded microgrid. However, the impact of the inherent time delay in these communication channels on the microgrid performance has not been taken into account when the secondary frequency controller is designed. This paper investigates the effect of the communication delays on the secondary frequency control of an islanded microgrid with multiple distributed generators. A small-signal model-based method is introduced for the microgrid to find delay margins below which the microgrid can remain stable. By performing a series of trial studies, the relationships between secondary frequency control gains and delay margins are obtained. A gain scheduling approach is also proposed to compensate the effect of the communication delay on the secondary frequency control. Results from the Canadian urban distribution system have verified that communication delays can adversely affect the microgrid secondary frequency control, and the proposed gain scheduling approach can improve the robustness of the microgrid secondary frequency controller to communication delays.

In this article, a novel distributed fuzzy load frequency control (LFC) approach is investigated for multiarea power systems under cross-layer attacks. The nonlinear factors existing in turbine dynamics and governor dynamics as well as the uncertain parameters therein are modeled and analyzed under the interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy framework. The cross-layer attacks threatening the stability of power systems are considered and modeled as an independent Bernoulli process, including denial-of-service (DoS) attacks in the cyber layer and phasor measurement unit (PMU) attacks in the physical layer. By using the Lyapunov theory, an area-dependent Lyapunov function is proposed and the sufficient conditions guaranteeing the system’s asymptotically stability with the area control error (ACE) signals satisfying $\mathcal {H}_{\infty }$ performance are deduced. In simulations, we adopt a four-area power system to verify the resiliency enhancement of the presented distributed fuzzy control strategy against random cross-layer DoS attacks. Results show that the designed resilient controller can effectively regulate the load frequency under different cross-layer DoS attack probabilities.

While open communication infrastructures are embedded into smart grids to support vast amounts of data exchange, it makes smart grids vulnerable to cyber attacks. In this paper, we investigate the effects of Denial-of-Service (DoS) attacks on load frequency control (LFC) of smart grids. In contrast with existing works, we consider the problem that how DoS attacks affect the dynamic performance of a power system. The state space model of power systems under DoS attacks is formulated as a switched system. By applying switched system theories, the existence of DoS attacks that make the dynamics of a power system unstable is proved. A two-area power system is used to conduct case studies. The dynamic performance of the power system, such as convergence and steady-state errors, is compared under different DoS attack scenarios. It is shown that the dynamic performance of the power system is affected strongly if the adversaries launch DoS attacks before the dynamics of the power system converge.

While distributed secondary controllers have been studied for multiple energy storage systems in islanded microgrids, information infrastructure has to be added for the extensive information transmission among these secondary controllers and the additional communication among distributed controllers is costly and increases the vulnerability surface to cyberattacks. In this work, a data-driven decentralized secondary control scheme is proposed for multiple heterogeneous battery energy storage systems (BESSs). The proposed secondary control scheme can achieve frequency regulation and the state-of-charge (SoC) balancing simultaneously for BESSs without requiring accurate BESS models. This scheme leverages an asynchronous advantage actor-critic (A3C) based multi-agent deep reinforcement learning (MA-DRL) algorithm where the centralized off-line learning with shared convolutional neural networks (CNN) is designed to maximize global rewards and ensure the performance of the entire system and a decentralized online execution mechanism is applied to each BESS. Furthermore, in view of possible denial-of-service (DoS) attack on local communication networks used for signal transfer between secondary controllers and remote sensors, a signal-to-interference-plus-noise ratio (SINR)-based dynamic and proactive event-triggered communication mechanism is proposed to alleviate the impact of DoS attacks and reduce the occupation of communication resources. Simulation results on a four-bus multiple BESS system show that the proposed decentralized secondary controller can achieve simultaneous frequency regulation and SoC balancing. Comparison results with other event-triggered mechanisms and MA-DRL algorithms show the A3C based MA-DRL algorithm with CNN can obtain a comparatively optimal policy through training and the designed event-triggered strategy can dynamically adapt the release frequency based on real-time SINR and significantly reduce the occupied network bandwidth and packet loss rate (PER) induced by DoS attacks.