This paper presents the development of SmartDrive package to achieve the application of energy-efficient driving strategy. The results are from collaboration between Ricardo Rail and the Birmingham Centre for Railway Research and Education (BCRRE). Advanced tram and train trajectory optimization techniques developed by BCRRE as part of the UKTRAM More Energy Efficiency Tram project have been now incorporated in Ricardo's SmartDrive product offering. The train trajectory optimization method, associated driver training and awareness package (SmartDrive) has been developed for use on tram, metro, and some heavy rail systems. A simulator was designed that can simulate the movement of railway vehicles and calculate the detailed power system energy consumption with different train trajectories when implemented on a typical AC or DC powered route. The energy evaluation results from the simulator will provide several potential energy-saving solutions for the existing route. An enhanced Brute Force algorithm was developed to achieve the optimization quickly and efficiently. Analysis of the results showed that by implementing an optimal speed trajectory, the energy usage in the network can be significantly reduced. A driver practical training system and the optimized lineside driving control signage, based on the optimized trajectory were developed for testing. This system instructed drivers to maximize coasting in segregated sections of the network and to match optimal speed limits in busier street sections. The field trials and real daily operations in the Edinburgh Tram Line, U.K., have shown that energy savings of 10%-20% are achievable.

The flexible traction power supply system (FTPSS) integrates back-to-back converter, hybrid energy storage system (HESS) and PV generation system will be an important part of the smart railway. The FTPSS not only cancels the neutral zone, but also facilitates the utilization of regenerative braking (RB) and renewable energy, but the random fluctuation of PV and the sudden change of traction load will exert influence on the safe and efficient operation of the FTPSS. To improve the benefits of FTPSS, and compensate the imbalance between supply and demand in short-term operation, a multi-time scale optimal dispatch method is proposed for flexible railway energy management (FREM), which integrate day-ahead dispatch and intra-day feedback correction. During the day-ahead dispatch, the minimizing operating costs problem is formulated as a mixed linear programming model by coordination between HESS, RB and PV output. For intra-day energy adjustment dispatch, a rolling optimization based on model predictive control combined with feedback correction method is proposed, with aim of minimum operation deviation of FTPSS for thanks to adjusting the HESS dispatch plan drew up at day-ahead. Finally, the effectiveness of the proposed FREM control strategy is verified by the detailed real case study of a railway line in China.

Regenerating trains are now in common use on many DC fed railway systems, and train operating companies are able to get a discount on their energy costs if regeneration is active. The electrical energy consumption in a DC system is significant, and a comprehensive understanding of how regeneration affects the overall system energy consumption has not been developed. This study presents a simulation method in which a multi‐train analysis is used to determine the system energy consumption with and without regeneration in operation, as well as the impact on the system energy consumption of different headways. The results are used to determine a full ‘energy audit’ of the system based on the data of the Beijing Yizhuang subway line. This includes the energy supplied by the substations, the energy wasted in the power transmission network, the energy used by the train in traction and regenerated by braking trains. The initial results show that regenerating trains have a significantly lower substation demand, but slightly more energy is lost within the network. The results also indicate that, the available regenerative energy and total substation demand vary with different timetables, and there is a 27% difference between the best and worst headways.

The optimization of train trajectory and timetable offer two effective methods of energy-efficient operations. Most traditional research optimizes them separately and thus the global optimality cannot be achieved. Previous work has mainly concentrated on minimizing the mechanical energy consumption of trains, not the electrical energy. This paper aims to develop an integrated model to optimize the integrated timetable, which includes both the timetable and the train trajectory, achieving the minimum electrical energy consumption. The AC power supply system model is first proposed to calculate the parameters of the AC power supply network. Then, a model is developed to calculate various energy consumptions using power flow calculation. Furthermore, an integrated timetable optimization model for minimum total energy consumption of AC railway system is proposed and the optimal integrated timetable is obtained by the proposed hybrid GA-PSO algorithm. Finally, 5 real-world case studies based on the Lanzhou Xinjiang high-speed railway are presented. The results show that the proposed integrated model can achieve a reduction in total energy consumption for the entire line up to 14.3% compared with the previous optimization model. The hybrid GA-PSO algorithm achieves the best results compared with the results achieved by the GA and PSO algorithms applied along.