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This paper studies load frequency control (LFC) for networked power systems with limited communications and probabilistic cyber attacks. Some restrictions exist during the information transmission, which can impair behavior and lead to instability of power systems. Throughout this paper, we consider such power systems that involve multi-path missing measurements and input–output time-varying delays as well as cyber attacks in the communication channels. A feedback controller is presented, which is based on the observer to implement H∞ LFC for power systems with disturbance rejection level γ. By Lyapunov stability theory, adequate criteria are given to ensure the stable operation of power systems. Finally, the validity of theoretical analysis is demonstrated and illustrated by numerical simulations.

This study explores the multi-rate sampling-based H∞ load frequency control (LFC) problem for networked power systems by using an area-information-fusion method. This problem is addressed for two reasons: (1) most of networked control methods for LFC are focused on the one-rate sampling scheme and (2) the previous looped function cannot be directly applied within the multi-rate sampling scheme. Here, the multi-rate sampling scheme involves each area sampling rate being reliant on its own sensor. Namely, all area sampling rates are different from each other. In the presence of a multi-rate sampling scheme, a new sampling instants sequence is established by using an area-information-fusion method. It contributes to constructing a corresponding closed-loop model by adding virtual state variables. In addition, a new looped-function approach is devised to capture the sampling information from diverse area sensors. Based on Lyapunov stability theory, less conservative LMI conditions are derived to guarantee the H∞ performance of the multi-rate LFC system. Additionally, a co-designed method for determining the control gain and maximum sampling frequency is established. Finally, simulation studies are conducted to validate the efficacy and features of the proposed control strategy.

In well-managed coordinated networked microgrids (MGs) besides electricity interchange between MGs, global optimisation is fulfilled. Here the authors studied a networked MG architecture, in which the control centre of microgrids communicates with a distribution network operator (DNO) to fulfil their local requirements. However, communication signals are always vulnerable to cyberattacks. While the surplus/deficit powers are reported by one MG to DNO, other MGs can act as potential cyber attackers aimed at decreasing their own costs. This action may also lead to threat the global optimisation of networked MGs. When an attacker manipulates the signal sent from the attacked MG to DNO, it will result in a false power interchange schedule produced by DNO. The attacker MG in the next step, maliciously accesses and changes the signal sent from the DNO to the attacked MG. In case a successful attack executed, the operation cost of the attacker MG will be decreased. Furthermore, a game-theoretic model of attacker–defender interaction is proposed, while different behaviours of players are addressed. The optimal scheduling scheme of MGs is formulated as a mixed-integer linear programming problem and solved by CPLEX. Simulation results show the impacts of the attacks and importance of the defend strategies.

The increasing impact of climate change and rising occurrences of natural disasters pose substantial threats to power systems. Strengthening resilience against these low-probability, high-impact events is crucial. The proposition of reconfiguring traditional power systems into advanced networked microgrids (NMGs) emerges as a promising solution. Consequently, a growing body of research has focused on NMG-based techniques to achieve a more resilient power system. This paper provides an updated, comprehensive review of the literature, particularly emphasizing two main categories: networked microgrids’ configuration and networked microgrids’ control. The study explores key facets of NMG configurations, covering formation, power distribution, and operational considerations. Additionally, it delves into NMG control features, examining their architecture, modes, and schemes. Each aspect is reviewed based on problem modeling/formulation, constraints, and objectives. The review examines findings and highlights the research gaps, focusing on key elements such as frequency and voltage stability, reliability, costs associated with remote switches and communication technologies, and the overall resilience of the network. On that basis, a unified problem-solving approach addressing both the configuration and control aspects of stable and reliable NMGs is proposed. The article concludes by outlining potential future trends, offering valuable insights for researchers in the field.

The electric power system is intrinsically a cyber-physical system (CPS) with power flowing in the physical system and information flowing in the cyber-network. Testbeds are crucial for understanding the cyber-physical interactions and provide environments for prototyping novel applications. This study proposes a four-layer architecture for CPS testbeds with emphases on communication network emulation and networked physical components. A configurable software-defined network is employed to bridge physical components with wide-area applications for closed-loop control. In order to distribute physically coupled devices into multiple software simulations, this study proposes a data broker setup based on a distributed messaging environment to achieve low-latency data streaming. The decoupled design with data streaming allows for building testbed components as modules and running them in a distributed manner. Case studies verify the data broker setup for low-latency sensing and actuation, as well as the communication emulation setup for the desired network latency. Also illustrated is a replay attack scenario using synchrophasors in the Western Electricity Coordinating Council (WECC) 181-bus system for demonstrating the closed-loop cyber-physical simulation capability of the testbed.

This article investigates the issue of H∞ security output feedback control for a nonlinear networked cascade control system with deception attacks. First, to further reduce the amount of communication data, reasonably schedule network resources, and alleviate the impact of multi-channel deception attacks, an attack-dependent adaptive event-triggered mechanism is introduced into the primary network channel, and its adaptive triggered threshold can be adjusted according to the random attack probability. Secondly, the output dynamic quantization of the secondary network channel is considered. Then, a novel security cascade output feedback controller design framework based on the Takagi–Sugeno (T-S) fuzzy networked cascade control system under deception attacks is established. In addition, by introducing the Lyapunov–Krasovskii stability theory, the design conditions of the controller are given. Finally, the effectiveness and superiority of the proposed design strategies are verified by two simulation examples of power plant boiler–turbine system and power plant boiler power generation control system.

This paper provides a strategic solution for enhancing the cybersecurity of power distribution system operations when information and operation technologies converge in active distribution network (ADN). The paper first investigates the significance of Internet of Things (IoT) in enabling fine-grained observability and controllability of ADN in networked microgrids. Given severe cybersecurity vulnerabilities embedded in conventionally centralized energy management schemes, the paper then proposes a cyber-secure decentralized energy management framework that applies a distributed decision-making intelligence to networked microgrids while securing their individual mandates for optimal operation. In particular, the proposed framework takes advantage of software-defined networking technologies that can secure communications among IoT devices in individual microgrids, and exploits potentials for introducing blockchain technologies that can preserve the integrity of communications among networked microgrids in ADN. Furthermore, the paper presents the details of application scenarios where the proposed framework is employed to secure peer-to-peer transactive energy management based on a set of interoperable blockchains. It is finally concluded that the proposed framework can play a significant role in enhancing the efficiency, reliability, resilience, and sustainability of electricity services in ADN.

For political and economic reasons, renewable sources of energy have gained much importance in establishing a sustainable energy economy. By their very nature, however, their benefits depend on changeable weather conditions, and are unrelated to the generation and consumption patterns in industrial or home environments. This generation–dissipation disparity induces price fluctuations and threatens the stability of the supply system, yet can be alleviated by installing energy depots. While the classic methods of energy storage are hardly cost-effective, they may be supplemented, or replaced, by a distributed system of small-scale hydropower plants with ponds used as energy reservoirs. In this paper, following a rigorous mathematical argument, a dynamic model of a multi-cascade of hydropower plants is constructed, and a cost-optimal controller, with formally proven properties, is designed. On the one hand, it allows for an increase in the owners’ revenue by as much as 30% (compared to a free-flow state); on the other hand, it reduces the load fluctuation imposed on the grid and the legacy supply system. Moreover, the risk of floods and droughts downstream resulting from inappropriate use of the plants is averted.

Individual microgrids can improve the reliability of power systems during extreme events, and networked microgrids can further improve efficiency through resource sharing and increase the resilience of critical end-use loads. However, networked microgrid operations can be subject to large transients due to switching and end-use loads, which can cause dynamic instability and lead to system collapse. These transients are especially prevalent in microgrids with high penetrations of grid-following inverter-connected renewable energy resources, which do not provide the system inertia or fast frequency response needed to mitigate the transients. One potential mitigation is to engage the existing generator controls to reduce system voltage in response to a frequency deviation, thereby reducing load and improving primary frequency response. This study investigates the use of a reinforcement-learning-based controller trained over several switching transient scenarios to modify generator controls during large frequency deviations. Compared to previously used proportional–integral controllers, the proposed controller can improve primary frequency response while adapting to changes in system topologies and conditions.

Wind energy conversion systems (WECSs) based networked microgrids has been widely used in recent years. The mean square error (MSE) metric can yield imprecise outcomes if measurement data is polluted by non‐Gaussian disturbances or extreme values. To address this problem, we propose a new robust square root cubature Kalman filter (SRCKF) method called maximum correlation criterion (MCC)‐SRCKF, which incorporates MCC into the SRCKF framework of dynamic state estimation. In MCC, by considering the high‐order moments of the error distribution, it demonstrates anti‐interference ability against non‐Gaussian noise, thus serving as an ideal alternative in the MSE cost function field of SRCKF. Furthermore, within the framework of SRCKF, this study introduces statistical linear regression models and non‐moving point iteration strategies to solve the optimal state estimation under MCC conditions. Therefore, a historical measurement triggered DoS attack model is proposed from the attacker's perspective, aiming to destabilise the WECS‐based networked microgrids. The security conditions of the power system under such attacks are obtained. The proposed method is validated numerically using an IEEE 39‐bus system, and the results demonstrate its effectiveness and superiority. This work addresses the issue of rejection delay due to DoS attacks triggered by historical measurements during the transmission of a large amount of measurement data in WECS‐based networked microgrids. We propose a novel robust SRCKF method, designated as MCC‐SRCKF, which incorporates MCC into the SRCKF structure of DSE. The MCC with higher‐order error distribution moments demonstrated robustness in the presence of non‐Gaussian noise, rendering it an optimal alternative to the SRCKF cost function MSE.