Energy-Efficient Train Operation

Railway, as one of the most energy-efficient transport, plays an essential role in improving the world’s energy and environmental sustainability. Statistics about rail share of transport activities and the corresponding energy consumption will demonstrate the energy efficiency of railway and indicate the potential of developing railway transport. Therefore, this chapter will provide an overview of the railway's energy consumption and traffic volume shares. Statistics presented in this chapter show that railway energy consumption decreased in these decades while its transport volume kept stable, and the traffic volume share of the railway is extremely large in urban transport. To achieve the goal of carbon neutralization, the European Union and many countries have conducted research projects on railway energy conservation. The technologies developed in these projects include energy-efficient train driving, integrated timetabling, using regenerative braking energy, etc. A summary of these technologies is also given, along with their potential energy savings, which range from 1 to 25%. This book will analyse and illustrate the whole systems processes of train operation with optimisation solutions. The structure of the following chapters will be presented at the end.

The energy consumption of rail transport system is closely related to the train operation. This chapter will introduce the relationship between train operation and energy consumption under different energy-efficient strategies. This relationship will serve as the basis for the energy-efficient optimisation methods in the subsequent chapters. Specifically, the train-based energy-saving strategy aims to minimise the net energy consumption, for which four methods can be distinguished, including energy-efficient train control, energy-efficient train timetabling, optimisation of train timetables for regenerative braking, and energy-efficient driving considering energy storage systems. Second, the substation-based energy-saving strategy aiming to increase the efficiency of the power supply system is briefly presented.

There are infinitely many speed profiles that a train could follow between one station and the next, even if the total journey duration is fixed. Finding the most energy-efficient way to drive a train between stops can be formulated, and solved, as an optimal control problem. The motion of the train is described by a pair of differential equations, with parameters including the tractive and braking effort curves of the train, resistance forces, track gradient forces and track curvature forces. Speed limits and timing requirements impose constraints on the motion. The optimal driving strategy has only five driving modes: power, hold, coast, regen and brake. Furthermore, the necessary conditions for an optimal control can be used to determine the optimal sequence of control modes for a journey, and when the control should be switched between modes. Optimal journey profiles can be calculated quickly enough to provide driving advice that adapts in real-time to changing conditions on a rail network.

Running time calculation is an essential ingredient in train timetabling. Traditionally, the technical minimum running times are computed in detail after which a running time supplement is added to obtain the scheduled running times. This running time supplement must be translated into lower cruising speeds or coasting regimes to cover the entire scheduled running time for on-time running. How this is done determines the exact time-distance train paths and the energy consumption of the trains. This chapter explains how train trajectory optimization can be used to compute energy-efficient train trajectories between two stops, over multiple stops including the optimal allocation of running time supplements between the stops, and over corridors considering the track occupation of multiple trains. It is argued that microscopic train timetabling based on energy-efficient train trajectories and blocking time theory is required to design robust conflict-free timetables that enable energy-efficient train operation. The theory is illustrated with many examples under realistic conditions, such as varying gradients and speed restrictions.

Reduction of the traction energy and increasing the reused regenerative energy are two main ways for saving energy in rail systems, which are related to the driving strategy as well as the train timetable. To minimise the net energy consumption (i.e., the difference between the traction energy and the reused regenerative energy), an integrated optimisation approach is an efficient way, in which the driving strategy and the train timetable are jointly optimised. This chapter will focus on this optimisation in the metro system, where homogeneous trains are operated on a double-track line with up and down directions. Firstly, the mathematical models are formulated to calculate the amount of traction energy and reused regenerative braking energy. Then, two approaches to reduce the net energy consumption will be introduced for metro line. In the first approach, the departure times of trains are synchronized to the arrival time of regenerative braking trains to reuse the regenerative braking energy efficiently. The second approach jointly optimises the station arrival/departure times and the traction/braking regimes between the stations, to improve the energy-saving. Both approaches are verified by examples based on real world data of metro lines.

Regenerated energy has the potential to produce great energy saving figures in railway operation. However, in DC systems, regenerated energy cannot be harnessed completely. The presence of rectifier filters does not allow returning energy from the railway system to the utility grid. Therefore, the regenerated energy that cannot be consumed by other trains instantaneously must be wasted in on-board resistors. Energy storage systems, on-board the train or in the track-side, can be implemented to avoid this situation and maximise regenerated energy usage. The main technologies that have been applied in railways, the modelling of these energy storage systems and its control are discussed in this chapter. Besides, a case study is presented where different scenarios of energy storage and receptivity to regenerated energy are analysed based on the characteristics of a real line of the Madrid Underground. These scenarios allow to evaluate the influence in the energy consumption reduction because of the installation of energy storage devices and the influence in the optimal design of ATO speed profiles. The results indicate that energy storage systems would provide energy savings in scenarios with low density traffic, but low benefits are obtained in dense traffic scenarios where the regenerated energy can be exchanged between trains easily.

The railway traction power network supplies the electricity to electric trains. The traction power network modelling plays a significant role in evaluating the railway system energy flow and validating the energy-efficient train operations. This chapter presents the development of an energy evaluation simulation of electric railway systems. The train movement model and railway power network model are integrated into the simulator. This energy simulator is able to calculate the energy flow of the whole system according to multiple-train driving controls and timetables. Based on the power network model, this chapter analyses the energy consumption of railway systems with regenerating trains, including the energy supplied by substations, used in power transmission networks, consumed by monitoring trains, and regenerated by braking trains. A case study of Beijing Yizhuang Subway Line has been conducted which indicates that the available regenerative braking energy and total substation energy consumption vary with timetables. The difference in energy consumption between the different headways is up to 35%, suggesting the importance of timetable optimisation on energy consumption. The comparison case study indicates that the substation energy is reduced by around 38.6% with system optimised operations. The efficiency of using regenerative braking energy is improved from 80.6 to 95.5%.

This chapter gives the basic conclusions about energy-efficient train operation covering energy-efficient train driving, energy-efficient train timetabling, regenerative braking, energy storage systems and power supply networks. Future work that will develop energy-efficient train operation further include the interaction of connected driver advisory systems (C-DAS) and automatic train operation (ATO) with railway traffic management systems, cooperative train control in platoons of virtually coupled trains, digital twin technology and particularly its application to power supply systems, and the interaction between the railway network with the electrical power grid and renewable energy generation.