Electric vehicles (EVs) have been increasingly experiencing sales growth, and it is still not clear how to handle the associated impacts of a substantial integration of EVs against the power network performance and electricity deregulated market. Power networks development moves slowly compared to EVs, so it is hard to harmonize the two systems. Also, the associated cost required to modernize electric networks to accommodate the EVs additional loading is so huge that it makes it impractical. An electric network would suffer from many performances and quality-related problems if a significant number of EVs are not taken into account. Thus, it is vital to calculate at a reasonable accuracy the additional capacity to which the network would take. Also, the modeling of these EVs in terms of charge/discharge procedure (central or distributed) is essential to enable the optimization and compute potential impacts. The electricity market has to interface with the bidirectional supply nature of the EVs, so they become economically feasible. A business model that incorporates market structure and standards is selected in harmony with the charge/discharge model for best outcomes. Some approaches, algorithms, and schemes are deemed necessary to mitigate the effects of having EVs in the network. This paper presents a comprehensive review categorically on the recent advances and past research developments of EV paradigm over the last two decades. The main intent of this paper is to provide an application-focused survey, where every category and subcategory herein is thoroughly and independently investigated.

The emergence of power system digitalization initiatives is revolutionizing the way electricity grids are monitored and protected. However, the integration of cyber and physical electrical infrastructures leads to an increase in the risk of cyber intrusions. Attackers can gain access to the smart grid and inject falsified data, leading the protection schemes to activate unnecessary power outage actions. Such outages can be devastating to end users. In this paper, an intrusion detection and mitigation system (IDMS) is proposed using deep learning neural networks (DLNNs) to detect, classify, and locate intrusions in smart grids. Once the disturbance is detected, the IDMS is designed to diagnose the intrusion and classify the attack into a single point or coordinated intrusion. Afterward, the algorithm locates and isolates the contaminated intelligent electronic device (IED) and predicts its current waveform utilizing long short-term memory (LSTM) to maintain power system observability. The proposed IDMS performs the required diagnosis on the modified IEEE 13-bus system. Simulation results demonstrate high accuracy in the proposed detection, classification, location, and prediction approach.

The rising concerns over global climate change and depleting fossil fuel reserves are two of the main reasons for the ongoing efforts towards the electrification of the transportation sector. While greenhouse gases (GHGs) emissions from other sectors are generally falling, emissions from the road transport have increased over the past few decades, with both full electric vehicles (FEVs) and plug-in hybrid electric vehicles (PHEVs) being recognized as potential alternatives to combat climate change and reduce GHG emissions. However, wide-spread integration of FEVs and PHEVs will substantially increase the load on the power system which will eventually affect the reliability of existing power systems. In this paper, a probabilistic model for integrating FEVs and PHEVs with existing power grids is proposed that incorporates important FEV and PHEV characteristics, such as battery capacity, charge depleting distance, and charging rates. In addition, user behavior is taken into account through time of recharging, arrival and departure times, and daily miles driven. Furthermore, different charging strategies, i.e., opportunistic charging and controlled charging with and without vehicle-to-grid (V2G) scheme have been considered to evaluate the impact of FEVs and PHEVs on the composite power system. IEEE-RTS-79 system is used to examine the proposed probabilistic technique considering different FEV and PHEV penetration levels as well as charging strategies. Simulation results show that even a relatively low penetration level of FEVs or PHEVs might have a significant impact on the system reliability unless a proper charging and/or discharging schemes are utilized.

The shift toward electric vehicles (EVs) is inspired by the commitment to mitigate emissions and reliance on fossil fuels, with drive systems at the core of the transformation. EVs replace the traditionally internal combustion engines (ICE) by adopting electric motors powered by batteries. However, there are major issues that may complicate the adoption of EVs including battery limitations, energy density, charging speed, insufficient charging stations, cost, and reliance on critical materials for instance, lithium and cobalt. This paper provides a holistic review about powertrain technologies for EVs discussing the key elements to be considered to ensure efficient charging system, high output power to increase the charging speed, etc. These can be achieved by incorporating cutting-edge technologies in switching power electronic devices (i.e., utilizing silicon-carbide and gallium-nitride), broadly efficient power topologies, leveraging solid-state and silicon anode batteries, thermal management, etc. It also explores several critical technological challenges that impede the performance of EVs, including battery degradation, charging infrastructure, and other related issues along with solutions to address these challenges, such as advancements in battery chemistry, the development of higher energy-density materials, and the implementation of advanced thermal management systems to enhance overall efficiency and longevity. In top of that, this review clearly highlights the classification of the recently adopted power train technologies, broadly adopted converters, as well as future trends on breakthroughs in next-generation solid-state batteries with higher energy density and faster charging, and charging station accessibility improvement. It also offers a constructive insight on integrating some renewable energy sources to enhance EV accessibility and sustainability. Overall, while challenges remain, innovations in batteries, electronics, and infrastructure are paving the way for broader EV adoption.

Machine learning techniques are becoming popular for monitoring the health and faults of different components in power systems, including transformers, generators, and induction motors. Normally, fault monitoring is performed based on predetermined healthy and faulty data from the corresponding system. The main objective of this study was to recognize the start of a system fault using a Support Vector Machine (SVM) approach. This technique was applied to detect power system instability before entering an unstable condition. Bus voltages, generator angles, and corresponding times before and after faults were used as training data for the SVM to detect abnormal conditions in a system. Therefore, a trained SVM would be able to determine the fault status after providing similar test data once a disturbance has been resolved.

Power system reliability measures for residential distribution networks have gained a lot of attention in recent years. This is due to the introduction of smart grids where the quality of the delivered power becomes a crucial issue. In this paper, a new methodology to include cold load pickup (CLPU) events in the reliability assessment of power distribution systems is proposed. The developed methodology presents a platform to include CLPU events in the reliability assessment assuming an optimal systematic procedure is used to restore the affected load points. The methodology is applied on a test system using a state-of-the-art search technique named lightning search algorithm (LSA). The optimal plan takes into account electrical code protection system settings, system operation limits, and stochastic behavior of the load. The reliability indices for a base case without considering CLPU is compared with the case that considers CLPU assuming an optimal restoration. Both cases are compared further under fixed-restoration plans with different time intervals. The results demonstrate the effectiveness of the proposed methodology to improve the distribution network reliability by considering the CLPU events.