In this article, the challenges and a future vision of the cyber–physical security of photovoltaic (PV) systems are discussed from a firmware, network, PV converter controls, and grid security perspective. The vulnerabilities of PV systems are investigated under a variety of cyberattacks, ranging from data integrity attacks to software-based attacks. A success rate metric is designed to evaluate the impact and facilitate decision-making. Model-based and data-driven methods for threat detection and mitigation are summarized. In addition, the blockchain technology that addresses cyberattacks in software and cybernetworks is described. Simulation and experimental results that show the impact of cyberattacks at the converter (device) and grid (system) levels are presented. Finally, potential research opportunities are discussed for next-generation, cybersecure power electronics systems. These opportunities include multiscale controllability, self-/event-triggering control, artificial intelligence/machine learning, hot patching, and online security. As of today, this study will be one of the few comprehensive studies in this emerging and fast-growing area.

With the ever-increasing number of blackouts in distribution systems arising from a variety of natural and manmade disasters, the frequent and necessary isolation/reconnection of loads without power deviations/fluctuations has become an important issue. Grid of microgrids (MG)s is a promising solution towards a highly resilient and efficient power grid operation. To facilitate this implementation, seamless transition with the utility grid is a key feature the today's MG control needs to possess. This transition ability is of great prominence, especially after the accelerated adaptation of distributed renewable energy sources (RES) in MGs. This ability of the MGs should ensure uninterrupted energy services to critical loads and infrastructures. Thus, the implementation of MG control strategies to enable smooth transition between grid-connected (GC) and islanded (IS) operation modes is mandatory. The control scheme implemented should therefore be capable of mitigating the stirring voltage/current deviations due to frequency/phase misalignment during the transition process. This paper provides an overview of the various MG control schemes that enable seamless transition between GC and IS modes of operation. The main purpose of this paper is to provide a generic overview of the challenges and existing techniques available in literature to mitigate the voltage and frequency (V-f) fluctuations at the MG's point of common coupling (PCC) and that of the utility grid; during the transition process. It aims at motivating the development of advanced control schemes in this area of MG related research.

The energy paradigm is making the modern power grid more difficult to study, design, and control. Precisely speaking, the pace of the new energy paradigm involves the high penetration of power electronics systems in the power grid, which becomes a challenge from stability, vulnerability, and power-quality points of views. Therefore, various literature have focused on defining the modern power grid as the power electronics-dominated grid (PEDG).

This paper presents an auto-tuning technique for online selection of the cost function weight factors in model predictive control (MPC). The weight factors in the cost function with multiple control objectives directly affect the performance and robustness of the MPC. The proposed method in this paper determines the optimum weight factors of the cost function for each sampling time; the optimization of the weight factors is done based on the prediction of the absolute tracking error of the control objectives and the corresponding constraints. The proposed method eliminates the need of trial-and-error approach to determine a fix weight factor in the cost function. The application considered is a capacitor-less static synchronous compensator (STATCOM) based on MPC of a direct matrix converter (DMC). This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors (e-caps). The result demonstrates that the proposed auto-tuning approach of cost function weights makes the control algorithm robust to parameter variation and other uncertainties in the system. The proposed capacitor-less reactive power compensator based on auto-tuned MPC cost function weight factor is verified experimentally.

In this paper, an operational framework is proposed for peer-to-peer (P2P) energy trading between an electric vehicle (EV) charging station and a business entity equipped with solar generation that will significantly improve the benefit of both parties compared to having sole agreements with the utility. A dynamic pricing mechanism for available EVs in the charging station is developed based on the price of the stored energy that not only improves the profit of the owners, but also promotes the contribution of charging stations in P2P energy markets. To evaluate the proposed framework, the performance of the system under P2P energy sharing is compared with peer-to-grid (P2G) energy trading. The results show a 23.24% reduction in total cost of prosumers and an improvement of 10% in self-consumption of PV generation as well as 100% participation willingness of prosumers. The results also suggest that due to the availability of solar energy during the daylight hours and the possibility to trade the stored energy in EVs outside of home, P2P energy transactions under the proposed control framework will bring economic benefits for EV owners.

Real-time anomaly detection system (ADS) and anomaly classification system (ACS) techniques are becoming a crucial need for future power electronic dominated grid (PEDG). Artificial intelligence techniques such as recurrent neural networks, specifically long short-term memory (LSTM) provide a promising solution to detect anomalies in power grids. The main challenge is the implementation of these methods for real-time detection and classification for preventing catastrophic failure in PEDG. This article is addressing the challenge for detection and classification of anomalies in real-time in PEDG. The proposed approach is based on integration of model predictive control (MPC) and LSTM for realizing real-time ADS and ACS. The LSTM detection network can utilize the same time-series input data as the MPC, allowing for anomaly classification and correction. The proposed integrated LSTM-MPC approach has features of power electronics internal failure detection and corrective actions, which is an important aspect in future PEDG to differentiate inverters internal failures versus anomalies. Such internal failures include open circuit fault that needs to be detected and classified from a potential cyber-attack, allowing resilient operation of PEDG. The proposed integrated LSTM-MPC scheme for real-time ADS and ACS scheme is tested on a realistic 14-bus system dominated with inverters forming PEDG.