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In recent years, the Electric Vehicles (EVs) industry has experienced rapid growth, driven by advancements in battery technology, environmental awareness, and government incentives. However, traditional charging infrastructure’s limited availability and long charging times pose significant challenges, especially for long-distance travel and public service vehicles like taxis, buses, and law enforcement vehicles. This work explores the innovative concept of Battery Swap Stations (BSSs), an emerging technology poised to transform the EV charging landscape. It specifically focuses on electric taxis operating in Chicago’s urban environment, highlighting the substantial benefits this technology can offer. BSSs demonstrated to dramatically reduce charging times, improving taxi service efficiency and increasing revenue potential. Instead, conductive charging impacts the working time of taxis across all case studies (as observed in the Level 2 charger scenario) While BSS technology has its drawbacks, such as optimal location challenges and battery management complexities, it has the potential to significantly enhance service quality. Additionally, these stations hold the promise of not only increasing urban transportation system efficiency but also contributing to their sustainability.

Climate disruptions have prompted institutions to invest in zero-emissions technologies, in recent years. As a result, the transportation sector has witnessed a shift from internal combustion engines to electric. Several public transport companies have initiated the “Zero-emissions” project to introduce alternatives to diesel in their bus service. This paper delves into the impacts of transitioning from diesel-powered to electric buses. It starts by estimating emissions produced by buses and comparing them. Subsequently, the analysis evaluates the implications of renewing the fleet on the service, considering the change in bus capacity. Additionally, a profitability analysis assesses the Total Cost of Ownership, factoring in helpful life and average distance (kilometres) driven annually. Overall, the findings indicate that switching to electric buses is a promising approach towards achieving environmental objectives. The study shows that investing in electric buses, particularly those measuring 9-m, offers significant economic benefits while aligning with sustainability goals. The research demonstrates that electric buses yield a substantial reduction in global and local emissions when compared to their diesel counterparts. Adopting a comprehensive “well-to-wheel” perspective, electric buses achieve an impressive 68 % reduction in emissions. However, concerning local emissions, certain specific lines recorded values exceeded legal limits. While the initial investment costs for electric buses may surpass diesel buses, the total cost of ownership analysis conducted over 15 years indicates that electric buses can become more cost-effective over time. This cost-effectiveness and their environmental advantages strengthen the case for adopting electric buses to pursue sustainable transportation systems.

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.

As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.

The progressive conversion of conventional bus fleets into full-electric fleets have gained focus in recent years, instilled by awareness about the environment and significant trends of urbanization. Public transport operators in major cities worldwide have put efforts into fulfilling this change. However, an efficient electrification process is still a challenge for most operators. This paper aims to propose an E-Bus vehicle model that estimates the actual energy consumption. The proposed model is implemented on the case study of a real bus line for Local Public Transport (LPT) and considers all technical characteristics of the vehicle. Real-time input data are represented by real driving cycles of the actual bus fleet and slope profile of the line. Real-time input data allow to establish directly the influence on the energy consumed during real operations. As simulation results, the global energy consumption and battery State of Charge (SOC) are then computed for the whole daily service operations. The simulation results are validated with the real data available and several scenarios are then considered within the simulations. Based on the results obtained, further improvements are proposed and discussed aiming to optimize the utilization of bus fleet, regarding both vehicles scheduling and new charging solutions.

In Italy, the availability of service areas (SAs) equipped with charging stations (CSs) for electric vehicles (EVs) on highways is limited in comparison to the total number of service areas. The scope of this work is to create a prototype and show a different approach to assessing the number of inlets required on highways. The proposed method estimates the energy requirements for the future electric fleet on highways. It is based on an energy conversion that starts with the fuel sold in the highway network and ends with the number of charging inlets. A proposed benchmark method estimates energy requirements for the electric fleet using consolidated values and statistics about refueling attitudes, with factors for range correction and winter conditions. The results depend on assumptions about future car distribution, with varying numbers of required inlets. The analysis revealed that vehicle traffic is a critical factor in determining the number of required charging inlets, with significant variance between different SAs. This study highlights the necessity of incorporating factors like weather, car charging power, and the future EV range into these estimations. The findings are useful for planning EV charging infrastructure, especially along major traffic routes and in urban areas with high-range vehicles relying on High-Power DC (HPDC) charging. The model’s applicability to urban scenarios can be improved by considering the proportion of energy recharged at the destination. A key limitation is the lack of detailed origin–destination (OD) highway data, leading to some uncertainty in the calculated range ratio coefficient and underscoring the need for future research to refine this model.

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.

The ubiquitous influence of E-mobility, especially electrical vehicles (EVs), in recent years has been considered in the electrical power system in which CO2 reduction is the primary concern. Having an accurate and timely estimation of the total energy demand of EVs defines the interaction between customers and the electrical power grid, considering the traffic flow, power demand, and available charging infrastructures around a city. The existing EV energy prediction methods mainly focus on a single electric vehicle energy demand; to the best of our knowledge, none of them address the total energy that all EVs consume in a city. This situation motivated us to develop a novel estimation model in the big data regime to calculate EVs’ total energy consumption for any desired time interval. The main contribution of this article is to learn the generic demand patterns in order to adjust the schedules of power generation and prevent any electrical disturbances. The proposed model successfully handled 100 million records of real-world taxi routes and weather condition datasets, demonstrating that energy consumptions are highly correlated to the weekdays’ traffic flow. Moreover, the pattern identifies Thursdays and Fridays as the days of peak energy usage, while weekend days and holidays present the lowest range.

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.

This study evaluates the impact of energy on the distribution network at the point of connection of an electric plant of a railway car parking facility in which charging points for electric vehicles (EVs) were installed. The objective is to identify a possible load curve of the simulated car park and, based on the principle of vehicle-to-grid (V2G) technology, to develop an appropriate algorithm. Such an algorithm explores the possibility of a two-way energy flow between the connected vehicles and the electricity grid, and performs a peak shaving of the load curve of the plant under examination in order to avoid absorption peaks, which are usually difficult to manage when using the distribution system operator (DSO). The work also presents the coupling with a photovoltaic system designed specifically for the car park. The study results are presented after a summary of the current state of development of electric mobility, describing the various types of EVs, the charging infrastructure, and the possible applications in smart grids (SGs).

The present paper is a review on efficiency issues related to three important sectors of the transportation systems: railways, electrical vehicles, and marine. For the three sectors, the authors, in reference of their knowledge and research area, show the results of a wide literature analysis, in order to highlight which are the measures, in terms of technological solutions and management techniques, which are recently investigated and implemented, for improving the three transportation systems, from the point of view of efficiency. In particular, for the railway transportation sector, a wide analysis is presented, detecting which are the main measures adopted for improving the efficiency, related to the power systems for supplying trains and to the train traffic control, with a focus on the storage system integration. For electric road vehicles the analysis is focused on the plug-in electrical vehicles and on the infrastructure for their recharge, with an emphasis on how these vehicles can support the grid, e.g., through Vehicle to Grid (V2G) applications. Finally, for the marine transport service the review is related to the propulsion systems and on how the different solutions can meet the objective of efficiency.

Electric vehicles (EVs), which have become a fundamental part of the automotive industry, were developed as part of concerted worldwide efforts to reduce dependency on fossil fuels due to their devastating effects on the environment. The aim of this study was to analyse a complete trip using an EV from Toronto to Ottawa (Canada) along Ontario’s Highway 401, considering that use of conventional vehicles powered by petrol or diesel allow one to make this trip without stops; using EVs, it is necessary to recharge the vehicle. For this purpose, an algorithm was developed for optimizing recharging stops during a complete trip. In particular, the simulations analysed the number of stops and specifically where it is possible to recharge taking into account the actual charging stations (CSs) located along the trip and the time of recharge during the stops as a function of the state of charge (SoC) of the vehicle. Using this approach, it was possible to evaluate the suitable coverage of the CSs on the stretch considered as well as to assess the main parameters that influence performance on the route.

In this paper a general model for the estimation of the uncoordinated charging costs of Electric Vehicles (EVs) in the presence of distributed and intermittent generation, and variable electricity tariffs is presented. The proposed method aims at estimating the monthly average cost of uncoordinated charging of a single EV depending on the hour at which the EV is plugged into the EV Supply Equipment (EVSE). The feasibility and relevance of the proposed model is verified by applying the considered cost estimation method to a suitable use case. A single EV charging service offered at a public building equipped with a Photovoltaic (PV) system has been considered as reference case. The proposed model has been applied to the PV production and loads consumption data collected during one year, and the results of the study compared with the Time-Of-Use (TOU) electricity tariff. The application of the proposed model identified noticeable deviations among the computed EV charging costs and the reference TOU profile, with differences up to 40%, depending on the considered month and on the time of charging during the day. It can be concluded that such model could be used to properly detect opportunities of energy savings, and to define dedicated EV price signals that could help to promote the optimal use of distributed energy resources.

The increasing penetration of renewable sources with power-electronic interfaces in power systems is raising technical problems and the overall efficiency of photovoltaic systems can decrease dramatically. In this context, the optimal layout for the photovoltaic system is required. The most adequate strategy to connect the renewable system to the electrical power grid or to supply the end users must be adopted. The present paper proposes a design layout of a PV plant using a DC bus system to improve the overall energy conversion efficiency. An analysis of steady-state system stability, voltage drop and DC cable conduction losses is conducted. The leakage currents to the ground are investigated through simulations. Experimental results are shown focused on the analysis of optimal layout of photovoltaic systems under particular operating conditions.

Lithium-ion batteries are a key technology for current and future energy storage in mobile and stationary application. In particular, they play an important role in the electrification of mobility and therefore the battery lifetime prediction is a fundamental aspect for successful market introduction. Numerous studies developed ageing models capable of predicting battery life span. Most of the previous works compared the effect of the ageing factors to a battery’s cycle life. These cycles are identical, which is not the case for electric vehicles applications. Indeed, most of the available information is based on results from laboratory testing, under very controlled environments, and using ageing protocols, which may not correctly reflect the actual utilization. For this reason, it is important to link the effect of duty cycles with the ageing of the batteries. This paper proposes a simple method to investigate the effect of the duty cycle on the batteries lifetime through tests performed on different cells for different kinds of cycle. In this way, a generic complex cycle can be seen as a composition of elemental cycles by means of Rainflow procedures. Consequently, the ageing due to any cycle can be estimated starting from the knowledge of simpler cycles.

This paper addresses the resonance phenomena that can occur in an isolated distribution system during transient events such as repeated energizations or power converter switching. In particular, the aim of this study is to analyze the energization of an onboard radial distribution system installed on an electric ship and to determine how the various leakage parameters that can cause resonance problems such as high peak overvoltages when the circuit breaker is closed are relevant. The paper presents a detailed model of whole distribution system, which is validated using infield measurements that refer to a real case in which these events damaged the ships transformers, causing it to be removed from duty.

Variable and non-programmable resources, such as solar and wind, have undergone a stunning growth in recent years and are likely to gain even more importance in the future. Their strong presence in the national electricity mix has created issues in many countries regarding the secure operation of the power system. In order to guarantee the stability of the system, several TSOs have resorted to wind energy curtailment, which represents a waste of clean energy and an economic loss. In order to analyze this issue, a model of the Italian power system was developed, a program able to simulate the electricity dispatching mechanism. The model was, then, used to evaluate possible solutions to reduce wind curtailment. In particular, a proposal for the construction of an HVDC line linking Southern and Northern Italy was studied.