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 490 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.

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.

The location of Electric Vehicle Charging Stations (EVCSs) is gaining significant importance as part of the conversion to a full-electric vehicle fleet. Positive or negative impacts can be generated mainly based on the quality of service offered to customers and operational efficiency, also potentially involving the electrical grid to which the EVCSs are connected. The EVCS location problem requires an in-depth and comprehensive analysis of geographical, market, urban planning, and operational aspects that can lead to several potential alternatives to be evaluated with respect to a defined number of features. This paper discusses the possible use of a multi-criteria decision-making approach, considering the differences between multi-objective decision making (MODM) and multi-attribute decision-making (MADM), to address the EVCS location problem. The conceptual evaluation leads to the conclusion that the MADM approach is more suitable than MODM for the specific problem. The identification of suitable attributes and related features is then carried out based on a systematic literature review. For each attribute, the relative importance of the features is obtained by considering the occurrence and the dedicated weights. The results provide the identification of the most used attributes and the categorization of the selected features to shape the proposed MADM framework for the location of the electric vehicle charging infrastructure.

The goal of this work is to apply the idea of the city in 15 min to railway stations that can become the starting point of the analysis as they represent the “gateway”, where users start their last mile of travel after getting off the train. Within the research, 11 railway stations located in the Lombardy Region in Italy were identified and analyzed. To perform the analysis, an analytical index was implemented and determined for each station: this index summarizes the main features of the station itself in relation to the territory in which it is located. The adopted approach is comparative: it is not important the absolute value of the index of each station, but the comparison between the different indices. In this way it is possible on the one hand to classify the stations and on the other hand to identify and propose possible interventions to improve the role of a railway station in a territory. The proposed model is expandable and replicable: it is possible to add other useful indicators for the calculation of the index of each station and it is also possible to perform the analysis in different territorial contexts. In fact, it is a decision support tool able to provide indications and information for the planning and programming of the railway system and of the city; among the potential users of the proposed model there are railway station managers and administrations.

Environmental issues have reached global attention from both political and social perspectives. Many countries and companies around the world are adopting measures to help change current trends. Awareness of decarbonization in the transportation sector has led to an increasing development of energy storage systems in recent years, especially for ground vehicles. Batteries, due to their high efficiency, are one of the most attractive energy storage systems for vehicle propulsion. As for road vehicles, the growing interest in Electric Vehicles (EVs) is motivated by the fact that they reduce local emissions compared to traditional Internal Combustion Engine (ICE) vehicles. The purpose of the paper is to present a study on how to plan and implement vehicle charging infrastructure on motorways. In particular, a specific road in Italy is analyzed: the motorway A1 from Milan to Naples with a length of about 800 km. This motorway can be considered representative because it passes through some of Italy’s most important cities and regions and may represent the backbone of Italy. A useful model for defining the optimal location of electric vehicle charging stations is presented within the paper. Starting with the data on the average daily traffic flows passing through the main nodes of the motorways section, the demand for the potential vehicles needed to define the number and dimension of charging stations and provide an adequate supply is estimated. The analysis was performed considering five-time horizons (year 2022 to year 2025) and four Scenarios involving the installation of 4, 8, 16, and 32 Charging Stations (CSs) in each service area, respectively.

Wireless Power Transfer (WPT) for electric vehicles is transitioning from laboratory prototypes to deployable charging infrastructure, driven by the demand for safer, automated, and weather-robust charging in residential parking, depots, and public bays, and more recently by pilot electric-road concepts. This review focuses on near-field resonant inductive WPT and explicitly frames the discussion around standardization and deployment readiness, with SAE J2954 and related international frameworks as reference points for interoperability, alignment, conformance testing, and certification planning across static, quasi-dynamic, and dynamic solutions. Recent surveys and representative demonstrators are synthesized to consolidate dominant research and engineering themes, including magnetic coupler and shielding design, compensation-network and control co-design, segment architecture and handover strategies for dynamic tracks, safety functions, electromagnetic exposure verification, electromagnetic compatibility constraints, bidirectional operation, and data-driven methods supporting design and field adaptation. For light-duty static charging, interoperable pad families, alignment procedures, and mature compensation topologies enable repeatable high-efficiency operation and increasingly standardized validation workflows, supporting early commercial availability. Heavy-duty depot charging appears technically attractive where duty cycles favor opportunity charging and packaging constraints are manageable. Dynamic WPT has reached pilot readiness via segmented selective-energization tracks and coordinated localization and handover, but corridor-scale rollout remains limited by maintainability, seasonal reliability, cost per kilometer, and route and site-specific verification of safety, exposure, and EMC margins.