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.

Co-phase traction power supply system provides the insights for solving the existing power quality and electrical sectioning issues in high-speed railways, and the flexible control of co-phase traction substation (CTSS) with the integration of photovoltaic (PV) and hybrid energy storage system (HESS) attracts widespread attention. However, the strong volatilities and uncertainties of PV and traction load introduce challenges to the reliable and economic operation of CTSS. In this study, the energy management of CTSS aims at minimizing the operating cost of CTSS by coordinating PV, HESS, power flow controller (PFC) and the energy transactions with the power grid, where the three-phase voltage unbalance constraint is taken into account in particular. Furthermore, to handle the uncertainties of PV and traction load, a two-stage robust optimization model is proposed. The first stage determines the operating status of HESS and energy transaction status with grid, and the second stage aims at finding the optimal scheduling power of CTSS and the worst-case scenario with maximized operating cost of CTSS. A column-and-constraint generation algorithm is employed to solve this robust optimization model, along with efficient linearization approaches for the formulation of mixed-integer linear programming. The proposed methodology is tested with a real high-speed railway line case in China.

An advanced combined co‐phase traction power supply system is proposed by combining a single‐phase traction transformer and an active power flow controller (PFC) in an electrified railway. In the new system, the power quality problems caused by single‐phase traction load are solved in the grid side and continuous power can be provided to electric trains without neutral sections in the traction side. The mathematic model of the new system is built by power transformation analysis. The compensation currents of the PFC are calculated based on the power balance principle. According to the power quality standard in China, the optimised partial compensation algorithm is presented to replace the existing full compensation one. Moreover, the capacity of the PFC adopting the proposed algorithm is much less than those in the previous studies with the same outcome. The validity of the compensation algorithm and the control method are demonstrated by the simulation results and the effectiveness of the proposed system is verified by the case studies.

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.

For improving the energy efficiency of railway systems, on-board energy storage devices (OESDs) have been applied to assist the traction and recover the regenerative energy. This paper aims to address the optimal sizing problem of OESDs to minimize the catenary energy consumption for practical train operations. By employing a mixed integer linear programming (MILP) model based on energy flow and the law of conservation of energy, three types of widely used OESD: supercapacitors, Li-ion batteries and flywheels have been studied in a real-world case of Beijing Changping line. Results show that without the constraints of capital cost and volume, the supercapacitor, Li-ion battery and flywheel with optimized capacity can save the catenary energy consumption by 23.6%, 22.9% and 23.7% compared to the cases without OESDs respectively. The minimum catenary energy consumption for each type of OESD has also been found with the constraints of capital cost and volume. The study shows that with a volume constraint less than 0.6 m3 and a higher allowed capital cost more than 20 k$, flywheel tends to achieve the least catenary energy consumption. When the volume is relaxed to go beyond 0.6 m3, supercapacitors always achieve the minimum catenary energy consumption disregarding the constraint of capital cost.

To improve the power quality (PQ) and eliminate the neutral zone (NZ), a flexible traction power supply system (FTPSS) was proposed to provide flexible interfaces for hybrid energy storage system (HESS) and photovoltaic (PV). However, in order to realize the optimal scheduling of FTPSS, it is necessary to further study the high cost of HESS and battery available capacity. In this study, a two-layer model with the lowest comprehensive cost as the goal is proposed, which includes the cost of investment, replacement, operation and maintenance (O&M), and electricity. In the upper layer, the HESS sizing and replacement strategy are performed to achieve the lowest daily comprehensive cost within the project period. In the lower layer, based on the piecewise linear method, the battery capacity degradation is formulated as a linear mathematical model concerning the depth of discharge (DOD). Then, with the aim to achieve the lowest electricity charge, a mixed-integer linear programming (MILP) model is formulated by associating PV, regenerative braking energy (RBE), and HESS. Sparrow search algorithm (SSA) with CPLEX solver embedded is utilized to solve this two-layer nonlinear model. Finally, the simulation results show that the proposed model can achieve a 13.55% cost reduction.