Electric Vehicles (EVs) are projected to be one of the major contributors to energy transition in global transportation due to their rapid expansion. High-level of EVs integration into the electricity grid will introduce many challenges for the power grid planning, operation, stability, standards, and safety. Therefore, the wide-scale adoption of EVs imposes research and development of charging systems and EV supply equipment (EVSE) to achieve expected charging solutions for EV batteries as well as to improve ancillary services. Analysis of the status of EV charging technologies is important to accelerate EV adoption with advanced control strategies to discover a remedial solution for negative impacts and to enhance desired charging efficiency and grid support. This paper presents a comprehensive review of EV charging technologies, international standards, the architecture of EV charging stations, and the power converter configurations of EV charging systems. The charging systems require a dedicated converter topology, a control strategy, compatibility with standards, and grid codes for charging and discharging to ensure optimum operation and enhance grid support. An overview of different charging systems in terms of onboard and off-board chargers, AC-DC and DC-DC converter configuration, and AC and DC-based charging station architectures are evaluated. In addition, recent charging systems which are integrated with renewable energy sources are presented to identify the power train of modern charging stations. Finally, future trends and challenges in EV charging and grid integration issues are summarized as the future direction of the research.

This paper critically evaluates a number of sensitivity analysis (SA) techniques to identify the most influential parameters affecting power system small-disturbance stability. SA of uncertain parameters has attracted increased attention with the adoption of deregulated market structure, intermittent energy resources, and new types of loads. Identification of the most influential parameters affecting system stability using SA techniques will facilitate better operation and control with reduced monitoring (only of the parameters of interest) by system operators and stakeholders. In total, nine SA techniques have been described, implemented, and compared in this paper. These can be categorized into three different types: local, screening, and global SA. This comparative analysis highlights their computational complexity and simulation time. The methods have been illustrated using a two-area power system and 68 bus NETS-NYPS test system. The priority ranking of all uncertain parameters has been evaluated, identifying the most critical parameters with respect to the small-signal stability of the test systems. It is shown that for many applications, the Morris screening approach is most suitable, providing a good balance between accuracy and efficiency.

Energy consumption is increasing rapidly; hence, the energy demand cannot be fulfilled using traditional power resources only. Power systems based on renewable energy, including solar and wind, are effective and friendly for the environment. Islanded hybrid microgrid systems (IHMS) are relatively new in this industry and combine two or more sustainable sources, such as wind turbines, solar photovoltaic (PV), and other renewable alternatives, ocean, wave, and geothermal energy, etc. While sustainable, long-lasting power sources are the best choice to satisfy the growing energy demands, they are still not yet ready to be used on a large scale due to their stochastic characteristics. Furthermore, integrating these sources into the existing energy system can cause high technical difficulties, due to the stochastic nature of solar and wind in the conventional grid system and common stand-alone framework. A review of research and applications of the effective hybridization of renewable energy sources is therefore essential to address those technical and economic issues and ensure system stability, reliability, and cost-effectiveness. This article discusses the challenges that might arise when a PV plant and a wind power station are combined to produce power for the conventional main grid or in a stand-alone system. In addition, this analysis provides light on optimization approaches for improving power quality and cost-effectiveness in a solar and wind integrated IHMS. Voltage fluctuation, frequency deviation, and the uncertain nature of solar irradiation and wind sources are significant challenges for both grid-connected and standalone hybrid systems. This study then provides an overview of the control strategies which might help enhance the integration of the IHMS in producing electricity for distribution to the grid-connected load and the islanded load. In this study, the possible issues that can hinder the smooth integration of these renewable sources have been discussed. Finally, this study discusses the recent platforms being used in IHMS as well as the potential of dispatch strategies on solar and wind-integrated IHMS.

The renewable energy source based generating technologies and flexible demand and storage devices exhibit significant temporal and spatial uncertainties in generating and loading profiles and introduce additional level of uncertainty in network operation. The dynamic behaviors of such a network can be affected and the stable operation may be compromised. This paper proposes a probabilistic analysis approach for the evaluation of the effect of uncertain parameters on power system voltage and angular stability. Load margin, the damping of critical eigenvalues and the transient stability index have been chosen as the relevant stability indices for voltage stability, small-disturbance stability, and transient stability analysis, respectively. The Morris screening sensitivity analysis method coupled with a multivariate Gaussian copula to account for parameter correlations is used for the priority ranking of uncertain parameters. The approach is illustrated on a number of case studies using modified IEEE 68-bus NETS-NYPS test system. The results obtained in this paper reveal that the critical parameters appear as groups if the input dataset is correlated, and, hence even a parameter (which may be uninfluential individually) can have a significant impact on system dynamic behavior due to its correlation with other influential parameters.

It is increasingly difficult to identify complex cyberattacks in a wide range of industries, such as the Internet of Vehicles (IoV). The IoV is a network of vehicles that consists of sensors, actuators, network layers, and communication systems between vehicles. Communication plays an important role as an essential part of the IoV. Vehicles in a network share and deliver information based on several protocols. Due to wireless communication between vehicles, the whole network can be sensitive towards cyber-attacks.In these attacks, sensitive information can be shared with a malicious network or a bogus user, resulting in malicious attacks on the IoV. For the last few years, detecting attacks in the IoV has been a challenging task. It is becoming increasingly difficult for traditional Intrusion Detection Systems (IDS) to detect these newer, more sophisticated attacks, which employ unusual patterns. Attackers disguise themselves as typical users to evade detection. These problems can be solved using deep learning. Many machine-learning and deep-learning (DL) models have been implemented to detect malicious attacks; however, feature selection remains a core issue. Through the use of training empirical data, DL independently defines intrusion features. We built a DL-based intrusion model that focuses on Denial of Service (DoS) assaults in particular. We used K-Means clustering for feature scoring and ranking. After extracting the best features for anomaly detection, we applied a novel model, i.e., an Explainable Neural Network (xNN), to classify attacks in the CICIDS2019 dataset and UNSW-NB15 dataset separately. The model performed well regarding the precision, recall, F1 score, and accuracy. Comparatively, it can be seen that our proposed model xNN performed well after the feature-scoring technique. In dataset 1 (UNSW-NB15), xNN performed well, with the highest accuracy of 99.7%, while CNN scored 87%, LSTM scored 90%, and the Deep Neural Network (DNN) scored 92%. xNN achieved the highest accuracy of 99.3% while classifying attacks in the second dataset (CICIDS2019); the Convolutional Neural Network (CNN) achieved 87%, Long Short-Term Memory (LSTM) achieved 89%, and the DNN achieved 82%. The suggested solution outperformed the existing systems in terms of the detection and classification accuracy.