Low power factor (PF), high voltage unbalance (VU), and weak regenerative braking energy utilization are the three major challenges of traditional traction power supply system (TPSS) at present. To solve the problems above, an advanced TPSS adopting modular multilevel converter (MMC) based power flow controller (PFC) integrated asymmetric energy storage (AES) is proposed. Among them, the energy storage is only accessed in the traction side of MMC to suppress the phase circulating current, while the battery and supercapacitor are divided into the upper arms and lower arms respectively to reduce the submodule capacitor voltage ripple. Moreover, a hierarchical control strategy is proposed, the first stage of the upper layer establishes an operation optimization model to minimize the electricity cost, for making the energy storage charging and discharging strategy and PFC regulation schedule available. The second stage arranges the power flow distribution of PFC determined by the first stage, which is based on a partial compensation model of power quality, to satisfy the PF and VU thresholds and decrease the MMC capacity. Meanwhile, the reference power flow of PFC drives lower layer controllers, where the passive-based controller is employed to track the MMC port current, and the circulating current suppression controller is designed to decouple the AES power. Finally, the effectiveness of the topology and control strategy is verified by hardware-in-loop experiments. As a result, RMS current can be reduced by 10% and voltage ripple by 30%. According to field-test data, the substation can save 19% in operating electricity costs.

In order to improve the utilization rate of regenerative braking energy (RBE) and reduce the operation cost of railway system, this paper proposed an urban railway smart grids (URSG) with efficient and flexible energy management strategy is proposed, which integrates a traction power supply system (TPSS), electric vehicle charging station (EVCS), PV and battery storage. In this study, a bi-level stochastic optimization (BLSO) model is employed to determine the sizing of battery storage and the energy scheduling of URSG. The upper level aims to determine the optimal size of battery storage and minimize the comprehensive cost, considering the degradation of the battery capacity. In the lower level, based on piecewise linearization method, the power transmission loss of catenary is formulated as a linear mathematical model, and a stochastic mixed-integer linear programming model of URSG is established to schedule the power flow to minimize the electrical cost. Furthermore, regarding to the stochastic nature of EV charging behavior and PV output, the constraints with uncertain parameters are posed as chance constraints, for which Sample Average Approximation (SAA) method is introduced to transform chance constraints into deterministic constraints. Based on the actual urban rail transit line and traction load data, the simulation results show that the above system can reduce the cost by 18.16 %.

In order to solve the problem of the serious stray current existing in DC urban rail transit, and to improve the utilization rate of the regenerative braking energy (RBE) generated by locomotive, an AC traction power supply system (TPSS) for long-distance urban rail transit is proposed, in which the network power flow distribution is affected by the train trajectory, the parameters of network and hybrid energy storage system (HESS) configuration. For achieving the minimum comprehensive cost including the electricity cost and the life cycle cost (LCC) of HESS, this paper aims to develop a Train-Network-HESS integrated model to optimize network power flow distribution. The train trajectory is illustrated based on time-space conversion method and HESS control strategy is discussed to increase the utilization of the RBE. Furthermore, the optimized configuration of the AC TPSS for urban rail transit is obtained by the hybrid GA-PSO algorithm. Finally, the effectiveness of the proposed model is verified by detailed case studies and the comprehensive cost can be reduced up to 39.08% compared with conventional TPSS.

A dual mode traction power supply system (TPSS), as a high-efficiency transportation approach, is composed of a mainline railway (AC traction power supply system) and an urban railway (DC traction power supply system). However, due to the neutral sections, the power from the two systems has been isolated, resulting in a low utilization rate of regenerative braking energy (RBE). The power flow from the two systems can not be managed cooperatively, causing high electricity bills. Therefore, this paper proposes a dual mode based energy router (DMER), which provides a path for energy interaction between two systems through multi-terminal converters. The proposed DMER integrates a hybrid energy storage system (HESS) and an energy feedback system (EFS) to recycle RBE. Moreover, a hierarchical control strategy is presented. The upper control layer is an optimized operation model, which takes the state of charge (SoC), energy conservation, and the other parameters as constraints to utilize more RBE and achieve the minimum daily operation cost of substations. The lower control layer identifies the control strategy for multiple converters to ensure the stable operation and dynamic response of DMER. Finally, the effectiveness of the topology and control strategy is verified using field load data and experiments.

The cophase traction power supply system (CTPSS) is a promising solution in the smart electrified railways to effectively eliminate the neutral section, improve regeneration braking energy (RBE) utilization and mitigate voltage unbalance (VU). It works by coordinating the power flow distribution (PFD) among the energy storage (ES), power flow controller, and traction transformer. However, the conventional dispatch considering VU mitigation may result in increasing extra operation costs and accelerating ES aging. Therefore, driven by the day-ahead forecasted information, a dynamic VU regulation strategy is proposed to draw up the VU compensation schedule (VU-CS), which can satisfy the probabilistic limitations of power quality standards. Consequently, a Copula-based VU dependence model is developed to transform the VU-CS into the PFD constraint set of CTPSS. Meanwhile, an ES degradation model is introduced to quantify the system operation cost within different scenarios. Taking the PFD as a VU regulation constraint, an economic dispatch scheme of CTPSS is established to obtain a minimum operation cost and maximum RBE utilization. Finally, case study tests verify the efficiency of the proposed approach. The results show that scheme can improve ES cycle life and decrease cost under the probabilistic limitations of VU.

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.