Distributed cooperative control has been used as a preferred secondary control strategy for maintaining frequency synchronization and voltage restoration in cyber-physical AC microgrids due to its flexibility, scalability and better computational performance. However, such a control system is susceptible to potential cyber attacks, e.i., false data injection (FDI) attacks. To this end, this paper introduces a hidden layer based attack-resilient distributed cooperative control algorithm to solve the problem of the secondary control of islanded microgrids under FDI attacks. In comparison to the existing attack-resilient distributed control methods, the proposed controller with sufficient large α can mitigate the adverse effects of time dependent FDI attacks on actuators, sensors and communication links of the control system, and is also robust to state dependent FDI attacks. Furthermore, the algorithm is applicable even when all DGs and communications are compromised. Finally, the efficiency of the proposed controller is evaluated for a test microgrid with 4 DGs under different types of attack.

The intermittency introduced by the increasing integration of distributed renewable energy sources is challenging the efficient operation of residential distribution systems. A promising solution to tackle this challenge is the implementation of residential demand response through responsive household appliances such as heat pumps, refrigeration devices, and energy storage units. In this paper, a peer-to-peer energy trading platform among residential houses is proposed to coordinate demand response schemes and level off potential generation/consumption disturbances in the hour-ahead intraday context. First, the day-ahead and intraday energy management models for residential houses are established considering the characteristics of responsive household appliances and energy storages. The discomfort and possible economic losses for performing demand responses are quantified with respect to the risk preferences of residential customers. The peer-to-peer energy trading platform is developed and a double auction mechanism employed to promote the collaborative demand response schemes in the face of disturbances. An optimal bidding strategy of residential houses is also proposed. The feasibility of the proposed models and bidding strategy are verified through case studies. It is also illustrated that the residential demand response schemes and intraday peer-to-peer energy trading are effective in managing the uncertainties of load demand and renewable generation.

Mental health problems are an increasingly common social issue severely affecting health and well-being. Multimedia processing technologies via facial expression show appealing prospects in the consumer field for mental health monitoring, while still suffer from intensive computation and low energy efficiency. This paper proposes an energy-efficiency memristive sequencer network (EMSN) for human emotion classification, which offers an environmentally friendly approach for consumers with low cost and easily deployable hardware. Firstly, two-dimensional (2D) materials are employed to construct an eco-friendly memristor, the efficacy and reliability of which are confirmed through performance testing. Then, a sequencer block is proposed using memristive circuits. Notably, it is a core component of the EMSN, consisting of a bidirectional long short-term memory circuit, normalisation circuit module, and multi-layer perception module. After combining some necessary function modules, the EMSN can be achieved. Furthermore, the proposed EMSN is applied for human emotion classification. The experimental results demonstrate that the proposed EMSN has advantages in computational efficiency and classification accuracy compared to existing mainstream methods, indicating an advancement in consumer health monitoring.

To enable smart home applications, embedding home monitoring systems with different sensors can be a feasible remedy to capture the multimodal sensory information from daily life. However, the previously developed monitoring systems have high implementation cost, large power consumption, and complicated device configuration, which are not conducive to achieving carbon neutrality and carbon peak goals. Here, we demonstrate a multimodal neuromorphic sensory-processing system with memristor circuits for smart homes, offering a more environmentally friendly approach with low cost and easily deployable hardware. We used three components to facilitate the multimodal neuromorphic sensory-processing system design. First, a memristor crossbar array is fabricated using low-cost, reliable and eco-friendly two-dimensional (2D) materials; enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. Second, a neuromorphic multimodal sensory-processing module is designed for collecting and processing multiple sensory cues efficiently; to establish accurate depiction of the environment and realize timely response. Third, the biomimetic hierarchical learning module is realized for smart home monitoring and relative applications (e.g., indoor human behavior recognition). The developed multimodal neuromorphic sensory-processing system effectively combines the nanotechnology, intelligent sensing, and deep learning; indicating an advancement in smart home applications.

The conventional distributed secondary control of islanded microgrids (MGs) often requires each distributed generator (DG) to communicate with its neighboring DGs successively with a small fixed sampling period, which is neither economical nor efficient for MGs due to its redundant communications. To this end, this paper proposes a distributed event-triggered control strategy for the secondary control in MGs to reduce the requirement for communications. By designing the trigger function and the trigger condition for each DG with Lyapunov method, the secondary control aim is achieved with an exponential convergence rate only when each DG communicating with its neighbors at some particular events. In addition, a proper trigger condition checking period is provided based on the proposed event-triggered control scheme, with which the computation requirement can be reduced. The proposed distribute event-triggered secondary control scheme has been tested on a cyber-physical MG, and its effectiveness is validated by the simulation results.

Distributed event-triggered secondary control in microgrids has been widely investigated to improve system efficiency. But most of them are based on consecutive triggering condition monitors, which would in turn increase the computation burden of the system. To this end, this article presents distributed self-triggered algorithmic solutions to the frequency restoration control and active power sharing control of islanded microgrids. Different from event-triggered control schemes, in our self-triggered solutions, each distributed generator is equipped with a local algorithm that enables it to pre-compute the next triggering time instant according to the states at the previous one. Our starting point is to design a triggering condition with a novel estimate error. Then, the next triggering time instant is determined by solving a quadratic equation established based on the triggering condition, rather than monitoring the triggering condition consecutively. Theoretical analysis and simulation results show that the proposed distributed self-triggered secondary controllers can highly reduce the communication and computation costs simultaneously.