Many different types of electric vehicle (EV) charging technologies are described in literature and implemented in practical applications. This paper presents an overview of the existing and proposed EV charging technologies in terms of converter topologies, power levels, power flow directions and charging control strategies. An overview of the main charging methods is presented as well, particularly the goal is to highlight an effective and fast charging technique for lithium ions batteries concerning prolonging cell cycle life and retaining high charging efficiency. Once presented the main important aspects of charging technologies and strategies, in the last part of this paper, through the use of genetic algorithm, the optimal size of the charging systems is estimated and, on the base of a sensitive analysis, the possible future trends in this field are finally valued.

The widespread use of distributed generation (DG), which is installed in medium-voltage distribution networks, impacts the future development of modern electrical systems that must evolve towards smart grids. A fundamental topic for smart grids is automatic distributed voltage control (ADVC). The voltage is now regulated at the MV busbar acting on the on-load tap changer of the HV/MV transformer. This method does not guarantee the correct voltage value in the network nodes when the distributed generators deliver their power. In contrast, the ADVC allows control of the voltage acting on a single generator; therefore, a better voltage profile can be obtained. In this paper, an approach based on sensitivity theory is shown to control the node voltages regulating the reactive power injected by the generators. After the theoretical analysis, a numerical example is presented to validate the theory. The proposed voltage regulation method has been developed in collaboration with Enel Distribuzione S.p.A. (the major Italian DSO), and it will be applied in the Smart Grids POI-P3 pilot project, which is financed by the Italian Economic Development Ministry. Before the real field application in the pilot project, a real-time digital simulation has been used to validate the algorithm presented. Moving in this direction, Enel Distribuzione S.p.A. built a new test center in Milan equipped with a real-time digital simulator (from RTDS Technologies).

An energy integration between photovoltaic (PV) systems and electric vehicles (EVs) can help to overcome problems related to the feasibility of a more sustainable mobility, most of all in urban context. This paper aims to examine the potential and the technical benefits of using PV systems as energy supplier for charging EVs. For this purpose, an urban scale integrated system is presented through a mathematical model that considers the power flows related to the PV generator, the charging station, and electric grid. The most significant result is the evaluation of the self-consumption in order to optimize the interaction between the PV system and charging station for EVs. This analysis has been conducted for different vehicles typologies and different PV installations, giving rise to more than 9000 different cases and allowing to consider the wideness of the self-consumption range for different months.

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

Electric railway power systems (ERPS) as one of the most critical and high-power end-user loads of utility grids are characterized by outlandish power quality (PQ) problems all over the world. The extension and evolution of different supply topologies for these systems has resulted in significant and various forms of distortions in network voltage and current in all ERPS, the connected power system, and adjacent consumers. During the last years, numerous studies have been offered to investigate various aspects of PQs in a specific supplying topology. Variation in the supply structure of the ERPS and different types of locomotives has propelled the observation of different PQ phenomena. This versatility and development have led to confront considerable types of two-way interactive interfaces as well as reliability and PQ problems in ERPS. In addition, the lack of standards explicitly dedicated to ERPS has added to the ambiguity and complexity of this issue. In this paper, an extensive review of PQ distortions and phenomena in different configurations of ERPS is proposed and a systematic classification is presented. More than 140 scientific papers and publications are studied and categorized which can provide a fast review and a perfect perspective on the status of PQ indexes for researchers and experts.

Climate change and global warming drive many governments and scientists to investigate new renewable and green energy sources. Special attention is on solar panel technology, since solar energy is considered one of the primary renewable sources and solar panels can be installed in domestic neighborhoods. Photovoltaic (PV) power prediction is essential to match supply and demand and ensure grid stability. However, the PV system has assertive stochastic behavior, requiring advanced forecasting methods, such as machine learning and deep learning, to predict day-ahead PV power accurately. Machine learning models need a rich historical dataset that includes years of PV power outputs to capture hidden patterns between essential variables to predict day-ahead PV power production accurately. Therefore, this study presents a framework based on the transfer learning method to use reliable trained deep learning models of old PV plants in newly installed PV plants in the same neighborhoods. The numerical results show the effectiveness of transfer learning in day-ahead PV prediction in newly established PV plants where a sizable historical dataset of them is unavailable. Among all nine models presented in this study, the LSTM models have better performance in PV power prediction. The new LSTM model using the inadequate dataset has 0.55 mean square error (MSE) and 47.07% weighted mean absolute percentage error (wMAPE), while the transferred LSTM model improves prediction accuracy to 0.168 MSE and 32.04% wMAPE.