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.

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities.

Energy shortage is one of the major concerns in today’s world. As a consumer of electrical energy, the electric railway system (ERS), due to trains, stations, and commercial users, intakes an enormous amount of electricity. Increasing greenhouse gases (GHG) and CO₂ emissions, in addition, have drawn the regard of world leaders as among the most dangerous threats at present; based on research in this field, the transportation sector contributes significantly to this pollution. Railway Energy Management Systems (REMS) are a modern green solution that not only tackle these problems but also, by implementing REMS, electricity can be sold to the grid market. Researchers have been trying to reduce the daily operational costs of smart railway stations, mitigating power quality issues, considering the traction uncertainties and stochastic behavior of Renewable Energy Resources (RERs) and Energy Storage Systems (ESSs), which has a significant impact on total operational cost. In this context, the first main objective of this article is to take a comprehensive review of the literature on REMS and examine closely all the works that have been carried out in this area, and also the REMS architecture and configurations are clarified as well. The secondary objective of this article is to analyze both traditional and modern methods utilized in REMS and conduct a thorough comparison of them. In order to provide a comprehensive analysis in this field, over 120 publications have been compiled, listed, and categorized. The study highlights the potential of leveraging RERs for cost reduction and sustainability. Evaluating factors including speed, simplicity, efficiency, accuracy, and ability to handle stochastic behavior and constraints, the strengths and limitations of each optimization method are elucidated.

Low carbon emission transportation is attracting global attention where electric railway power systems (ERPS) and electric vehicles (EVs) act as a load. Besides the main utility grid, renewable energy sources (RES) including photovoltaic (PV) panels and wind turbines are implemented to supply the loads fully or partially. In this paper, a novel smart DC catenary system is proposed in which renewable sources, storage systems, and DC fast-charging stations are connected to the overhead DC catenary line of the high-speed railway power system. The generated power from renewable sources and consumed power by charging stations are processed by their dedicated DC-DC power electronics converters. Furthermore, a storage system is used as a backup system not only for the case of blackouts but also because of the intermittent nature of renewable energy sources to supply the loads continuously. The paper presents an optimal power control for various parts and a power management system (PMS) that manages the power flow from wind-PV-storage system to EV-ERPS system. The proposed system has been investigated using a real Italian Rome-Florence 3 kV high-speed line as a case study with real data of ERPS load. The EV fast-charging station power demand, wind speed, solar irradiance, and temperature were recorded for 24 h in order to provide us with realistic output data. The simulation results obtained by MATLAB/Simulink are presented to validate the effectiveness of the proposed system.

In recent years, the development of electric vehicle (EV) infrastructure and efforts to achieve more sustainable transportation systems have increased. The enhanced utilization of EVs will require fast charging systems, which will overload utility power grids and require high-cost additional installations and equipment. However, a significant amount of regenerative braking energy (RBE) is available in electric railway systems (ERSs), which needs to be properly stored and accommodated. The integration of ERSs and EV charging stations (EVCS) at strategic points, such as parking areas close to ERS stations or rail freight intermodal terminals where EVs are parked most hours of the day, can be a promising solution to save RBE in the dedicated internal EV batteries. In this paper, a new method is proposed to charge EVs by transferring RBE from ERSs to EVCSs, taking advantage of a combined power management scheme based on stationary hybrid energy storage systems (HESSs) integrated with railway power flow controllers (RPFCs). A coordinated hierarchical control (CHC) method collaborating with an internal model predictive control (IMPC) strategy is presented to manage an optimal power flow paradigm for various operating system modes. The effectiveness of the proposed strategy is confirmed through simulations executed with the desired precision representing real system behavior.

Rapid growth in the electrification of bus fleets, driven by substantial environmental benefits, is facing challenges such as range anxiety, prolonged charging durations, and reduced flexibility compared to combustion engine buses. This study first conducts a comprehensive bibliometric analysis of diverse publications to identify key research trends in electric buses (E-buses). It then offers a thorough comparison of charging technologies, encompassing topologies, power flow capabilities, costs, grid impacts, and efficiency, along with an examination of existing standards, norms, and challenges. With a classification of nearly 150 references, the study aims to illuminate the strengths and weaknesses of each charging technology, providing a solid background for selecting optimal topologies and strategies for specific applications. Emphasizing the importance of a nuanced trade-off between the quantity and type of chargers and E-bus battery capacity in each scenario, the research goes beyond technical considerations to explore potential future trends in the field. The information gathered in this review is a helpful guide for policymakers, industry experts, and researchers dealing with the complexities of E-bus charging infrastructure.