Conventional fossil-fuel energy resources are being drastically depleted; thus, the current shift towards renewable energy (RE) resources has become imperative. However, there are many impediments to the adoption of renewable power generation. These impediments can be overcome by enacting policies to encourage the acceptance of sustainable energy resources. For instance, the net-metering policy can provide the necessary incentives to promote the development of local distributed energy sources, primarily solar photovoltaic and wind generators. While there has been significant advancement and development in net-metering in Asia with the increased penetration of RE, at present there is a lack of systematic review in this area. This paper aims to present an in-depth review on net-metering advances and challenges, current RE shares, and future RE targets in the Asian region. Additionally, a case study is performed and an economic analysis of net-metering regulations in an Asian country is carried out. In this study, the monetary benefits of net-metering policies for residential consumers are proved. It is envisaged that the information gathered in this paper will be a valuable one-stop source of information for Asian researchers working on this topic.

In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain.

Inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet the control objectives. The cyber anomalies targeting the communication network may distort normal operation, therefore, an effective cyber anomaly mitigation technique using an Artificial Neural Network (ANN) is proposed in this paper. The intelligent anomaly mitigation control is modeled using a dynamic neural network that employs a nonlinear autoregressive network with exogenous inputs. The effects of false data injection on the distributed cooperative controller at the secondary control level are considered. The training data for designing the neural network are generated by multiple simulations of the designed microgrid under various operating conditions using MATLAB/Simulink. An explainable framework is employed to interpret the output generated by the trained neural network-based controller after the neural network has been trained offline and validated online in the simulated microgrid. The proposed technique is applied as secondary voltage and frequency control of distributed cooperative control-based microgrid to regulate the voltage under various operating conditions. The performance of the proposed control technique is verified by injecting various types of false data injection-based cyber anomalies. The proposed ANN-based secondary controller maintained the normal operation of the microgrid under various cyber anomalies as demonstrated on a real-time digital simulator.

This paper presents the frequency regulation in the hybrid distributed generation system (HDGS). Being a critical problem it directly affects the reliability and quality of the HDGS. Therefore a roboust controller is designed for the frequency regulation of HDGS. The proposed hybrid system consists of conventional thermal source and distributed generation resources including wind turbine, fuel cell, aqua electrolyzer, diesel engine generator and super capacitor. H-infinity (H∞) controller for frequency regulation, is considered using linear matrix inequalities. The robustness of this controller is presented for the proposed hybrid power system with varying load and wind power conditions. The performance of the proposed controller is verified by performing simulations.