Assessment of the worthwhileness of efficient driving in railway systems with high-receptivity power supplies

Eco-driving is one of the most important strategies for significantly reducing the energy consumption of railways with low investments. It consists of designing a way of driving a train to fulfil a target running time, consuming the minimum amount of energy. Most eco-driving energy savings come from the substitution of some braking periods with coasting periods. Nowadays, modern trains can use regenerative braking to recover the kinetic energy during deceleration phases. Therefore, if the receptivity of the railway system to regenerate energy is high, a question arises: is it worth designing eco-driving speed profiles? This paper assesses the energy benefits that eco-driving can provide in different scenarios to answer this question. Eco-driving is obtained by means of a multi-objective particle swarm optimization algorithm, combined with a detailed train simulator, to obtain realistic results. Eco-driving speed profiles are compared with a standard driving that performs the same running time. Real data from Spanish high-speed lines have been used to analyze the results in two case studies. Stretches fed by 1 × 25 kV and 2 × 25 kV AC power supply systems have been considered, as they present high receptivity to regenerate energy. Furthermore, the variations of the two most important factors that affect the regenerative energy usage have been studied: train motors efficiency ratio and catenary resistance. Results indicate that the greater the catenary resistance, the more advantageous eco-driving is. Similarly, the lower the motor efficiency, the greater the energy savings provided by efficient driving. Despite the differences observed in energy savings, the main conclusion is that eco-driving always provides significant energy savings, even in the case of the most receptive power supply network. Therefore, this paper has demonstrated that efforts in improving regenerated energy usage must not neglect the role of eco-driving in railway efficiency

Optimal train control on various track alignments considering speed and schedule adherence constraints

The methodology discussed in this dissertation contributes to the field of transit operational control to reduce energy consumption. Due to the recent increase in gasoline cost, a significant number of travelers are shifting from highway modes to public transit, which also induces higher transit energy consumption expenses. This study presents an approach to optimize train motion regimes for various track alignments, which minimizes total energy consumption subject to allowable travel time, maximum operating speed, and maximum acceleration/deceleration rates. The research problem is structured into four cases which consist of the combinations of track alignments (e.g., single vertical alignment and mixed vertical alignment) and the variation of maximum operating speeds (e.g., constant and variable). The Simulated Annealing (SA) approach is employed to search for the optimal train control, called golden run . To accurately estimate energy consumption and travel time, a Train Performance Simulation (TPS) is developed, which replicates train movements determined by a set of dynamic variables (e,g., duration of acceleration and cruising, coasting position, braking position, etc.) as well as operational constraints (e.g., track alignment, speed limit, minimum travel time, etc.) The applicability of the developed methodology is demonstrated with geographic data of two real world rail line segments of The New Haven Line of the Metro North Railroad: Harrison to Rye Stations and East Norwalk to Westport Stations. The results of optimal solutions and sensitivity analyses are presented. The sensitivity analyses enable a transit operator to quantify the impact of the coasting position, travel time constraint, vertical dip of the track alignment, maximum operating speed, and the load and weight of the train to energy consumption. The developed models can assist future rail system with Automatic Train Control (ATC), Automatic Train Operation (ATO) and Positive Train Control (PTC), or conventional railroad systems to improve the planning and operation of signal systems. The optimal train speed profile derived in this study can be considered by the existing signal system for determining train operating speeds over a route

Multi Objective Ant Colony Optimisation to obtain efficient metro speed profiles

[EN] Obtaining efficient speed profiles for metro trains is a multi- objective optimisation problem where energy consumption and travel time must be balanced. Automatic Train Operation (ATO) systems may handle a great number of possible speed profiles; hence optimisation algorithms are required find efficient ones in a timely manner. This paper aims to assess the performance of a particular meta-heuristic optimisation algorithm, a variation of the traditional Ant Colony (ACO) modified to deal with multi-objective problems with continuous variables: MOACOr. This algorithm is used to obtain efficient speed profiles in up to 32 interstation sections in the metro network of Valencia (Spain), and the convergence and diversity of these solution sets is evaluated through metrics such as Inverse Generational Distance (GD) and Normalised Hypervolume (NH). The results are then compared to those obtained with a conventional genetic algorithm (NSGA-II), including a statistical analysis to identify significant differences. It has been found that MOACOr shows a better performance than NSGA-II in terms of convergence, regularity and diversity of the solution. These results indicate that MOACOr is a good alternative to the widely used genetic algorithm and could be a better tool for rail operation managers trying to improve energy efficiency.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and Competitiveness grant number TRA2011-26602.Martínez Fernández, P.; Font Torres, JB.; Villalba Sanchis, I.; Insa Franco, R. (2023). Multi Objective Ant Colony Optimisation to obtain efficient metro speed profiles. Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit. 237(2):232-242. https://doi.org/10.1177/09544097221103351232242237

Optimizing speed profiles for sustainable train operation with wayside energy storage systems

Large hauling capability and low rolling resistance has put rail transit at the forefront of mass transportation mode sustainability in terms of congestion mitigation and energy conservation. As such, rail vehicles are one of the least energy-intensive modes of transportation and least environmentally polluting. Despite, these positives, improper driving habits and wastage of the braking energy through dissipation in braking resistors result in unnecessary consumption, extra costs to the operator and increased atmospheric greenhouse gas emissions. This study presents an intelligent method for the optimization of the number and locations of wayside energy storage system (WESS) units that maximize the net benefits of the operation of a rail line. First, the optimized speed profiles with and without WESS is determined for a single alignment segment. Then, using the speed profiles obtained as an input, the number and locations of the WESS units that maximize the net benefit is determined for an entire rail line. The energy recovery methods used comprise optimal coasting, regenerative braking, and positioning of the energy storage devices to achieve maximum receptivity. Coasting saves energy by maintaining motion with propulsion disabled, but this increases the total travel time. Regenerative braking converts the kinetic energy of the train into electrical energy for the powering of subsequent acceleration cycles and although it does not affect travel time, it reduces the time available for coasting, indicative of a tradeoff. The study entails the design of a model that simulates the movement of the train over an existing alignment section while considering alignment topography, speed limits, and train schedule. Since on-time performance is the priority of railroad operations, the simulator instructs the driver to operate according to several motion regimes to optimize the energy consumption while maintaining schedule. The model consists of several time-varying inputs which add increased levels of complexity to the problem. This, in addition to its combinatorial nature, necessitates a heuristic algorithm to solve it, because traditional analytical solution methods are deficient. The optimization problem is solved by applying Genetic Algorithms (GA) because of their ability to search for a global solution in a complex multi-dimensional space. This strategy adds sustainability and reduces the carbon footprint of the operator. A case study is conducted on a single segment of a commuter rail line and yields a 34% energy reduction. The case study is extended to an entire line with multiple segments where the aim is to optimize the locations of wayside energy storage devices (WESS) for maximum economic benefit. It was found that out of the 10 alignment segments in the study, a maximized benefit of over $600,000 was achieved with WESS units installed on only three of those segments. The methods derived in this study can be used to generate speed profiles for planning purposes, to assist in recovery from service disruptions, to plan for infrastructural upgrades related to energy harvesting or to assist in the development of Driver Advisory Systems (DAS)

OPTIMIZATION OF STATION LOCATIONS AND TRACK ALIGNMENTS FOR RAIL TRANSIT LINES

Designing urban rail transit systems is a complex problem, which involves the determination of station locations, track geometry, right-of-way type, and various other system characteristics. The existing studies overlook the complex interactions between railway alignments and station locations in a practical design process. This study proposes a comprehensive methodology that helps transit planners to concurrently optimize station locations and track alignments for an urban rail transit line. The modeling framework resolves the essential trade-off between an economically efficient system with low initial and operation cost and an effective system that provides convenient service for the public. The proposed method accounts for various geometric requirements and real-world design constraints for track alignment and stations plans. This method integrates a genetic algorithm (GA) for optimization with comprehensive evaluation of various important measures of effectiveness based on processing Geographical Information System (GIS) data. The base model designs the track alignment through a sequence of preset stations. Detailed assumptions and formulations are presented for geometric requirements, design constraints, and evaluation criteria. Three extensions of the base model are proposed. The first extension explicitly incorporates vehicle dynamics in the design of track alignments, with the objective of better balancing the initial construction cost with the operation and user costs recurring throughout the system's life cycle. In the second extension, an integrated optimization model of rail transit station locations and track alignment is formulated for situations in which the locations of major stations are not preset. The concurrent optimization model searches through additional decision variables for station locations and station types, estimate rail transit demand, and incorporates demand and station cost in the evaluation framework. The third extension considers the existing road network when selecting sections of the alignment. Special algorithms are developed to allow the optimized alignment to take advantage of links in an existing network for construction cost reduction, and to account for disturbances of roadway traffic at highway/rail crossings. Numerical results show that these extensions have significantly enhanced the applicability of the proposed optimization methodology in concurrently selecting rail transit station locations and generating track alignment

Bi-level Optimization of Sizing and Control Strategy of Hybrid Energy Storage System in Urban Rail Transit Considering Substation Operation Stability

The hybrid energy storage system (HESS) which consists of battery and ultracapacitor can efficiently reduce the substation energy cost from grid and achieve the peak shaving function, due to its characteristics of high-power density and high-energy density. The sizing of HESS affects the operation cost of whole system. Besides, operation stability (like substation peak power and voltage fluctuations) is rarely considered in urban rail transit (URT) when sizing optimization of HESS is considered. Thus, this research proposes a sizing and control strategy optimization of HESS in URT. First, the mathematic model of URT with HESS is established, which is used to simulate URT and HESS operation state by power flow analysis method. Then, based on the proposed HESS control principle, a bi-level optimization of HESS in URT is proposed. The master level aims to optimize the rated capacity and power of HESS, reducing total operational cost. Then, the HESS control strategy is optimized at slave level, reducing substation peak power and voltage fluctuations of URT. The case study is conducted based on the data of Merseyrail line in Liverpool. A comparison is also conducted, which shows that the proposed method can reduce daily operation cost by 12.68% of the substation, while the grid energy cost is decreased by 57.26%

Infrastructure Design, Signalling and Security in Railway

Railway transportation has become one of the main technological advances of our society. Since the first railway used to carry coal from a mine in Shropshire (England, 1600), a lot of efforts have been made to improve this transportation concept. One of its milestones was the invention and development of the steam locomotive, but commercial rail travels became practical two hundred years later. From these first attempts, railway infrastructures, signalling and security have evolved and become more complex than those performed in its earlier stages. This book will provide readers a comprehensive technical guide, covering these topics and presenting a brief overview of selected railway systems in the world. The objective of the book is to serve as a valuable reference for students, educators, scientists, faculty members, researchers, and engineers

System energy optimisation strategies for DC railway traction power networks

Energy and environmental sustainability in transportation are becoming ever more important. In Europe, the transportation sector is responsible for about 32% of the final energy consumption. Electrified railway systems play an important role in contributing to the reduction of energy usage and C0$_2$ emissions compared with other transport modes. Previous studies have investigated train driving strategies for traction energy saving. However, few of them consider the overall system energy optimisation. This thesis analyses the energy consumption of urban systems with regenerating trains, including the energy supplied by substations, used in power transmission networks, consumed by monitoring trains, and regenerated by braking trains. This thesis proposes an approach to searching energy-efficient driving strategies with coasting controls. A Driver Advisory System is designed and implemented in a field test on Beijing Yizhuang Subway Line. The driver guided by the DAS achieves 16% of traction energy savings, compared with normal driving. This thesis also proposes an approach to global system energy consumption optimisation, based on a Monte Carlo Algorithm. The case study indicates that the substation energy is reduced by around 38.6% with the system optimised operations. The efficiency of using regenerative braking energy is improved to from 80.6 to 95.5%