Masafumi Miyatake

Access to reliable and sustainable electricity remains a critical challenge in Timor-Leste. This island developing nation relies on imported diesel for over 99% of its electricity generation, with the government subsidizing more than 80% of energy costs. This study investigates the techno-economic feasibility of a grid-connected hybrid renewable energy system for rural electrification in Liquiça Municipality. Using HOMER Pro, four configurations were modeled: (i) hybrid system with Combined Heat and Power (CHP) and battery, (ii) hybrid system with CHP only, (iii) hybrid system with battery only, and (iv) hybrid system without CHP and battery. Results indicate that the configuration without CHP and battery yields the lowest Net Present Cost (USD 1.67 million) and Levelized Cost of Energy (USD 0.0669/kWh). However, systems with CHP integration demonstrate superior environmental performance. The hybrid system with CHP and without battery achieved the highest renewable fraction (92.7%), while the configuration with CHP and battery resulted in the most significant reduction in CO2 emissions (701,634 kg/ year). This study highlights the trade-offs between economic cost, sustainability, and grid dependence. Its novelty is comparing CHP and battery integration under Timor-Leste’s unique energy landscape, offering actionable insights for small island developing states transitioning toward cleaner energy systems.

This paper is focused on solving the existing practices for charging a new Lead-acid battery set before actually using it for signalling related operations and power circuits of diesel locomotives in Railways. The primary objective is to use renewable energy sources during the charging cycle and also utilize the energy waste during the discharging cycle. Instead of wasting that energy, we can transfer it to the grid to generate revenue from it or utilize it to meet the demand of DC load of a railway station. For the charge cycle, a PV panel is used during the day and a single-phase grid is used during the night to charge the battery set in this proposed Simulink model. For the discharge cycle, grid and DC loads are used to discharge the battery set. To replace the existing manual process, a state flow control logic is designed to work according to the standard process of initial charging a battery set. It is observed that the proposed method can save around 4910.902 MWh of energy compared to the existing practice taking place in Indian Railways.

In DC electric railway systems, energy-saving can be expected by using regenerative brake. However, since the regenerative brake force decreases at high-speed range, the use of a mechanical brake to obtain a constant brake force results in losses and reduces the energy-saving effect. The “regenerative brake notch”, brake method proposed in a previous study, suggests a way of informing the brake start point for stopping only with the regenerative brake force. However, when this method is applied to an actual train running, it is not always possible to stop at the stopping position due to disturbances such as the running resistance and the gradient of railways. In this study, we proposed a driver advisory system and a control method to solve this problem. We verified the responsivity of this control method in actual motors drive system and the energy-saving effects on applying the proposed method by using a numerical simulation assuming of an actual railway running.

The transition towards environmentally friendly transportation solutions has prompted a focused exploration of energy-saving technologies within railway transit systems. Energy Storage Systems (ESS) in railway transit for Regenerative Braking Energy (RBE) recovery has gained prominence in pursuing sustainable transportation solutions. To achieve the dual-objective optimization of energy saving and investment, this paper proposes the collaborative operation of Onboard Energy-Storage Systems (OESS) and Stationary Energy-Storage Systems (SESS). In the meantime, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied to optimize the ESS capacity and reduce its redundancy. The simulation is programmed in MATLAB. The results show that the corporation of OESS and SESS offers superior benefits (70 kWh energy saving within 30 min operation) compared to using SESS alone. Moreover, the OESS plays a significant role, emphasizing its significance in saving energy and investment, therefore presenting a win–win scenario. It is recommended that the capacity of OESS be designed to be two to three times that of SESS. The findings contribute to the ongoing efforts in developing more sustainable and energy-efficient transportation solutions, with implications for the railway industry’s investment and broader initiatives in energy saving for sustainable urban mobility.

This paper is focussed to solve the optimal train control problem using Pseudospectral method and application of Dynamic Programming. The primary objective in Energy-Efficient Train Control is to generate optimum speed profile under the given speed and gradient constraints so that the train reaches the destination within the allotted time. In this proposed methodology, the state and control variables are approximated using a global polynomial and collocation is performed at Legendre-Gauss-Lobatto points. Further, the transformed problem is solved using Dynamic Programming. The advantage of obtaining global optimum with Dynamic Programming is combined with possibility of reducing the calculation time by employing the Pseudospectral method is investigated. The choice of number of collocation points and its effect on the quality of optimum speed trajectory and computation times are provided in the simulation results. It is observed that the proposed method converges faster when compared with compared to Dynamic Programming performed in time domain.

This paper addresses the challenge of optimizing the capacity and energy management of energy storage system (ESS) in rail transit systems, with a focus on the utilization of regenerative braking energy (RBE) generated by train braking. The system utilizes a hybrid energy storage system (HESS) consisting of superconducting magnetic energy storage (SMES) and battery to exploit the advantages of both ESS. Traditional methods have limitations in solving capacity redundancy problem. To address this issue, a cooperative optimization approach based on non-dominated sorting genetic algorithm-II (NSGAII) and mixed-integer linear programming (MILP) is proposed in this paper. By optimizing the capacity of the HESS via NSGA-II, the dual-objective optimization targeting expenditure and RBE utilization rate is achieved. MILP optimizes the input and output power of the HESS to meet various energy balance requirements. Simulation results demonstrate that the proposed approach effectively balanced system operating expenditure and RBE utilization, exploring the optimal configuration of HESS and ensuring power system stability and reliability at the same time.

Abstract The energy consumption of a battery‐powered train in an interstation depends on the running time and state of energy (SOE) at departure. In this paper, we develop an optimization method of train timetables to minimize energy consumption in line with several stations. The variables in this proposed optimization model are running, dwell, and charging times as real numbers and places of charging facilities as binaries. Additionally, we conducted a case study using the real‐world light rail transit (LRT) route, vehicle, and onboard battery model to confirm the effectiveness. In the case study, the proposed method can optimize the timetable and placement of charging facilities by considering the track gradient and battery SOE.

The energy consumption of a battery-powered train in an interstation depends on the running time and state of energy (SOE) at departure. In this paper, we develop an optimization method of train timetables to minimize energy consumption in line with several stations. The variables in this proposed optimization model are running, dwell, and charging times as real numbers and places of charging facilities as binaries. Additionally, we conduct a case study using the real-world light rail transit (LRT) route, vehicle, and onboard battery model to confirm the effectiveness. In the case study, the proposed method can optimize the timetable and placement of charging facilities by considering the track gradient and battery SOE.

The timetable of urban rail greatly affects its daily energy consumption. To improve the utilization of renewable energy between trains using timetabling has become an effective way to reduce energy consumption. Previous studies ignore or simplify the modelling of traction power supply network, which failed to accurately describe the flow of energy between trains through the power network. This paper proposed an optimisation method of energy efficiency timetabling considering the power flow of traction power supply network. First, an urban rail transit DC traction network model is established, and the current-vector iterative method is used to characterize the energy consumption. Then, a train timetable optimisation model is proposed to minimize the total energy consumption of the traction network system by adjusting the dwell time and section running time. The genetic algorithm is used to solve the optimisation problem. Finally, simulation result shows that the proposed method can accurately characterize the energy flow and effectively reduce the total energy consumption of the urban rail transit.

Besides operation, railway operators are tending to focus on energy saving from construction recently, which can lead to cost saving. Among them, the most representative means are energy saving slope and section running time optimisation. This paper proposed an energy economy method that coordinating optimise the section running time and the energy saving slope of gradient. The double layer optimisation model, includes model of train energy efficient operation and the coordinating optimisation model, is be formulated. Dynamic programming and traverse calculation are used to solve the optimal solution, respectively. The result shows the energy saving slop not always effectively and the coordinating optimisation can significantly improve the energy saving effect.

Urban railways and electric vehicles will be critical in achieving city sustainability. EV shows great potential for improving railway energy efficiency. At the same time, the integration of a Photovoltaic (PV) system and an Electric Vehicle (EV) charging system is a common method of utilizing renewable resources on-the-spot. An energy storage system (ESS) can work as a shared infrastructure to combine railway, PV, and EV into a DC micro-grid. In this paper, an energy dispatch model based on the structure of the DC micro-grid and the function of each component is built and solved with Mixed-integer linear programming (MILP). To investigate the model’s rationality and the effects of each part on the overall cost and operation pattern, three specific cases are tested. The global cost minimization that can be obtained by using EV and ESS to absorb the energy from PV and railway has been assessed. The utilization of ESS capacity can be promoted by the integration of EV charging demand and renewable energy.

This paper presents automatic software for E-liked shaped contactless inductive power transfer (CIPT) device study and design that provides attended-free, multiple-case auto-generating and auto-deploying analysis in one go. It provides visualized and listed results in a design space or for optimizing solutions. To satisfy the demand for static and dynamic charging devices, the software provides specific cores, such as EE-, EI-, IE-, and II-shaped, with or without legs as optional core structures. The software contains three main parts: a user-friendly interface, analytic approaches providing grid analysis that represent the general performance in a designated parameter range, and optimal analysis for multi-objective optimization using a genetic algorithm (GA). The post-analysis processor converts the analysis results to easy-to-read outputs. Users can customize various parameters, such as core type, structural size, circuit configuration, materials, and analysis setting. Automatic functions, such as resistance and compensation calculation, are available for the convenience of the user. By applying one approach, or by combining them in a specific order, the software achieves designs that satisfy the user’s demands within the user-provided range. The software is built in Python and collaborates with a finite element method (FEM) solver, which is JMAG in this paper. Some examples are given to demonstrate the performance of the software.

Utilization of wireless power transfer in light rail transits is seen as one solution for electrification of lines. The main advantage of this supply system is the reduction of installation; moreover, the alignment between the transmitter coil in the track and the receiver coil in the train should be perfect in order not to affect the power transfer. To reduce the effects of misalignment on the input and output electric parameters of the system, a new planar core and coil design, called hybrid intercore coil, is proposed. The proposed design uses a magnetic material layer between the windings in the inner half of the coil to create a non-uniform magnetic field distribution, which makes the system more robust against the effects of coil misalignment on the system current and voltage. Simulations with finite element method software were conducted to compare designs. The results show that the proposed design is less susceptible to the effects of misalignment and is more efficient. Prototype cores were constructed to verify the simulation results. Measurements show a smaller input overcurrent and output overvoltage when operating in resonance mode. The proposed design reduced the effects of coil misalignment on electrical parameters.