Electric road systems (ERSs) are anticipated to be major energy consumers. The energy efficiency of an ERS can be significantly improved by implementing the practice of driving electric vehicles (EVs) in closely spaced platoons. This driving configuration effectively reduces the drag coefficient of all vehicles within the platoon, resulting in a substantial decrease in the power demanded from the grid. Moreover, it enables the collective recuperation of regenerative energy from braking EVs rather than feeding the individual braking energy into each vehicle battery. Recuperating energy is well understood from trains. To safeguard the network from overvoltage, braking resistors are commonly utilised in conjunction with a nearby energy storage system (ESS) or feeding power upstream into the AC grid via bidirectional substations. This paper utilises Simulink to model an ERS featuring two EV platoons (EVPs), simulating power flow within the system and assessing various technologies for regenerative energy recuperation. A control technique for efficient management of regenerative energy is introduced and validated through experiments by using dedicated software designed for emulating regenerative braking energy in DC railway applications.

The accurate estimation of the probability distribution of traction current harmonics in EMUs is crucial for preventing electromagnetic interference (EMI), managing high‐speed railway signaling systems, and analyzing traction system power quality. Although kernel density estimation (KDE) has been a longstanding flexible, nonparametric estimation method, it faces significant challenges, including high computational demands and difficulties in selecting appropriate bandwidths. This article introduces the Gaussian mixture model (GMM) as a novel approach for the probabilistic estimation of traction current harmonics, thereby providing a scientific foundation for harmonic analysis. To improve the precision and efficiency of harmonic interference assessments, we initialize the GMM parameters and the expectation‐maximization (EM) algorithm with the density‐based spatial clustering of applications with noise (DBSCAN). The effectiveness of our method is confirmed through the Kolmogorov–Smirnov (K–S) goodness‐of‐fit test, root mean square error (RMSE), and R 2 statistics, demonstrating that our approach provides greater stability and faster computation than existing methods.

This article introduces simultaneous control of oscillations in voltage and frequency within a single‐area power system that includes hydrogen energy and electrical vehicles as source. The study focuses on the critical roles played by Automatic Voltage Regulator (AVR) and Automatic Generation Control (AGC) loops in maintaining frequency and voltage stability. The article incorporates renewable energy sources (RESs) in this investigation, like photovoltaic (PV) systems, fuel cells (FCs), and aqua electrolyzers (AEs) into the power grid. Energy storage and electric vehicle integration have also been included in the research to see how they affect the reduction of frequency and voltage oscillations. This study also examined the impact of communication time delays (Tds), which may be the cause of system instability in real‐power systems. The proportional integral derivative (PID) controller is selected as a subsidiary controller for the combined study of AGC and AVR, and its efficacy in terms of operation is contrasted with classical I and PI controllers and other control techniques from the literature. A recently developed Secretary Bird Optimization (SBO) algorithm is selected for obtaining the parameters of the controller. This article contributes valuable insights into power system stability enhancement.

Rail breaks, which are crucial maintenance issues for the railways, require immediate inspection and maintenance as it can cause severe railway accidents. Though, it is still difficult to promptly detect rail breaks that occur during train operation even with the advances in maintenance and inspection technologies. In this research, as a preliminary study on rail break detection system, a deep learning‐based discontinuous rail position classification method, which is using vibration data obtained from distributed acoustic sensing (DAS) system during train operation, is proposed. To analyze the vibration data, a preprocessing algorithm for determining train occupancy is applied first. After that, the data in the space–time domain occupied by the train is converted to the spectrogram which is in the frequency domain by using short‐time Fourier transformation (STFT). In the third step, the spectrogram images are applied to the proposed 2D convolutional neural network (2D CNN) model and the network detects discontinuous rail positions along the track, which are geometrically distinct from continuous welded rails, such as rail breaks. In order to evaluate the superiority of the proposed network model, performance comparison tests with other existing models were conducted with data collected from an actual railway line. From the results, the proposed model could achieve 99.17%, 93.33%, 87.5%, 90.32% for accuracy, precision, recall, and F1 score, respectively, and the results show overwhelming detection performance compared to other models.

Reversible substations (RSs) permitting bidirectional power flows can recover the regenerative braking energy of trains in DC traction power supply systems (TPSSs), increasing the energy efficiency of railway systems. To predict their effects on system dynamics and energy savings, the paper develops multiresolution models (MRMs) to simulate the RS roles with different fidelities. A high‐resolution model for the transient simulation replicates a particular topology where a three‐level voltage source inverter is connected to the secondary winding of an existing 12‐pulse rectifier transformer and regulated to keep a constant DC voltage in the inverting mode. Furthermore, it can model the transient effects of pantograph‐to‐line arcing by inserting arc voltage profiles at the train’s input stage. To increase the computation speed in the long‐term energy flow simulation, a low‐resolution model simplifies the rectifiers into a series connection of a diode and a controlled voltage source depicting their nonlinear output characteristics and then places a DC voltage source in parallel to form a reverse path for braking power recovery. In addition, nonlinear conversion efficiencies are introduced to calculate energy flows across substations. The MRMs are tested based on a 1.5 kV DC TPSS and discussed alongside system dynamics under normal operation or pantograph arcing and the consistencies between different models. The RS using bidirectional voltage source converters only is additionally modelled to compare the technical performance of the two topologies in terms of system dynamics and energy efficiencies.

This paper addresses challenges related to the short service life and low efficiency of hybrid energy storage systems. A semiactive hybrid energy storage system with an ultracapacitor and a direct current (DC) bus directly connected in parallel is constructed first, and then related models are established for the lithium‐ion battery, system loss, and DC bus. Based on the multiobjective evaluation function, a hybrid energy storage system Model Predictive Control‐Differential Evolution (MPC‐DE) energy management method is proposed. Experiments were conducted under China Light‐Duty Vehicle Test Cycle‐Passenger Car (CLTC‐P) and Highway Fuel Economy Test (HWFET) driving cycles. The experimental results show that compared with other methods, this strategy can effectively reduce the loss of lithium‐ion battery and ultracapacitor hybrid energy storage system and improve the system efficiency.

Abstract Hybrid ships have recently garnered increasing attention as a promising solution for energy shortages and environmental pollution. However, changes in the navigation environment can greatly affect the energy efficiency of these vessels. Therefore, improving the energy efficiency of hybrid ships continues to be a key issue. Our study examined the energy efficiency of a diesel‐electric series hybrid ship operating on an inland river, and a method to optimise series hybrid ship energy efficiency was proposed. The energy consumption model, power demand model, and decision function with minimum fuel consumption as the evaluation criteria were established according to the ship's characteristics and hybrid system. By analysing the data collected from an actual ship, the operating conditions of the navigation environment were identified using the k‐means algorithm, and the total distance travelled by the ship was divided into several sub‐trajectories. Afterwards, the optimised speed of the ship and the optimised output power of each power source were determined using the grasshopper optimization algorithm. The ship's energy efficiency levels before and after using the energy efficiency enhancement method were compared and analysed, and the effects of this optimization method on the ship's energy efficiency in several typical voyages were analysed. Our findings demonstrated that this optimization method can distribute the output of the power source in a better way, thereby optimising the speed of the vessel and maintaining stable sailing. More importantly, the proposed method can reduce fuel consumption by 17.04%.

Abstract Traction power networks can significantly influence a country's national grid due to their significant power consumption and numerous coupling points. To modernise the ageing Dutch traction power networks and enhance their impact on the utility grid, this study explores practical and cost‐effective approaches for upgrading existing 1.5 kV DC traction substations (TS) in the Netherlands into 3 kV bi‐directional DC TS. After evaluating the benefits of a 3 kV bi‐directional DC, two novel topologies are proposed that re‐use the existing substation's components and reduce the need for higher investments. These topologies incorporate parallel voltage source converters (VSCs) to recuperate braking energy from the DC grid and transfer it back to the AC grid. Furthermore, the study investigates additional use cases for the VSCs, including improving DC TS's reliability during faults, reducing harmonics through active power filtering, compensating for reactive power, and supporting the integration of renewable energy sources into the DC grid. A comprehensive control strategy for the VSCs is also proposed based on a thorough analysis of their working methodology and functional modes. The feasibility and effectiveness of the proposed solutions are validated through scenario analysis relevant to the Netherlands' traction network, utilising both a Simulink model and an Opal‐RT real‐time simulator. This study serves as a starting point for the various stakeholders of the Dutch traction network in their journey towards modernising the current traction power supply. It has the potential to serve as a reference for reusing existing railway infrastructures to provide ancillary services and support the energy transition.

Due to the interaction of electric multiple units (EMUs), and the electric traction networks, low frequency oscillations (LFOs) appear that leads to traction blockade and overall stability related issues. For suppressing these LFOs, coronavirus herd immunity optimiser (CHIO), a recently developed meta-heuristic based on the herd immunity concept in tackling the coronavirus pandemic, has been applied for tuning controller parameters.

Abstract In order to calculate the dynamic distribution of rail potential and stray current accurately, a multi‐state unified calculation model of rail potential and stray current is proposed, and the characteristics of different protection devices and reflux system in multi‐state DC railway systems are analysed. In the proposed model, the multi‐state characteristics of the traction substation, train, protection devices and reflux system are uniformly simulated by unified node models. The mixed simulation parameters of the catenary and reflux system are unified to lumped parameter networks, and the multi‐node voltage equations are established based on the simulation model. Then, the power flow of DC railway systems is calculated using an iterative solution, and the dynamic distribution of rail potential and stray current are solved by differential equations. The multi‐state unified calculation model is built and the distribution of rail potential and stray current in different conditions are simulated based on the parameters of Wuxi Subway Line 2. In order to verify the multi‐state unified calculation model proposed, field tests and simulations are carried out. Results show that the multi‐state unified calculation model can unify the complex states of DC railway systems and calculate the distribution of rail potential and stray current accurately.