Nowadays, the stationary energy storage systems (ESSs) are widely introduced to recover the regenerative braking energy in urban rail systems. And the multiple ESSs along the line, substations, traction, and braking trains in the traction power system make up a multienergy coupling system, whose energy efficiency is expected to be improved. With the aim of power flow optimization, a cooperative control strategy for multiple ESSs based on multiagent deep reinforcement learning is proposed in this article. Under the distributed control structure, the decision process of multiple ESS agents is formulated as a fully cooperative Markov game, in which each ESS makes independent decision and cooperates to improve the overall energy saving effect. And the value decomposition network, which decomposes the joint state-action value into value functions across agents, is adopted to stabilize the multiagent learning process. Three train operation scenarios are presented in simulation, and the power flow distributions are analyzed quantitatively to better evaluate the performance of the proposed cooperative strategy. A power hardware-in-the-loop (PHIL) experimental platform, which integrates the RT-LAB simulator and physical supercapacitor-based energy storage system (SCESS) is developed in this article to emulate the dc traction power system with multitrain operation. Based on the PHIL platform, the proposed cooperative control strategy is implemented experimentally. Both simulation and experimental results show that in comparison with genetic algorithm (GA), the proposed strategy optimizes the energy distribution between different SCESSs and trains, and improves the overall energy saving effect of the multi-SCESSs.

The installation of stationary super-capacitor energy storage system (ESS) in metro systems can recycle the vehicle braking energy and improve the pantograph voltage profile. This paper aims to optimize the energy management, location, and size of stationary super-capacitor ESSes simultaneously and obtain the best economic efficiency and voltage profile of metro systems. Firstly, the simulation platform of an urban rail power supply system, which includes trains and super-capacitor energy storage systems, is established. Then, two evaluation functions from the perspectives of economic efficiency and voltage drop compensation are put forward. Ultimately, a novel optimization method that combines genetic algorithms and a simulation platform of urban rail power supply system is proposed, which can obtain the best energy management strategy, location, and size for ESSes simultaneously. With actual parameters of a Chinese metro line applied in the simulation comparison, certain optimal scheme of ESSes’ energy management strategy, location, and size obtained by a novel optimization method can achieve much better performance of metro systems from the perspectives of two evaluation functions. The simulation result shows that with the increase of weight coefficient, the optimal energy management strategy, locations and size of ESSes appear certain regularities, and the best compromise between economic efficiency and voltage drop compensation can be obtained by a novel optimization method, which can provide a valuable reference to subway company.

In order to further improve the energy-saving and voltage stabilizing effect of the stationary energy storage system (ESS), this article tries to adopt the battery-supercapacitor (SC) hybrid ESS (HESS) as stationary ESS. The advantages of stationary HESS are verified by an example. An adaptive energy management framework for stationary HESS is proposed. A power distribution strategy is proposed to solve the adaptability of two energy storage media to the load variation of rail transportation power system, and to a certain extent, the battery degradation is delayed. The proposed strategy can not only realize the reasonable power-sharing between battery and SC considering the influence of headway on regenerative braking energy but also can keep the battery’s charge and discharge capacity for a long time by using fuzzy logic control (FLC). A simulation is carried out to illustrate the advantages of the proposed energy management strategy (PEMS). Finally, the PEMS is compared with two conventional strategies by the hardware-in-the-loop experiment platform, where it is shown to provide better performance (inferred through increases of 1.1% and 5.7% in energy conservation rate, 0.82% and 6.4% in voltage stabilization rate, and reduction of 2.26 and 0.36 A in the root-mean-square value of battery current at different headway).

In order to reduce the peak power of traction substation as much as possible and make better use of the configu-ration capacity of battery energy storage system (BESS) in urban rail transit, a BESS control strategy based on energy transfer is proposed. Based on the actual subway line data, the load characteristics of urban rail transit with different departure intervals are analyzed by using the simulation platform of urban rail transit traction power supply system. Then, based on the characteristics of urban rail traffic load and the high energy density of the battery, the SOC dynamic adjustment module and discharge threshold dynamic adjustment module were added. The discharge threshold is dynamically adjusted according to the SOC of the battery, and part of regenerative braking energy absorbed by the energy storage device is transferred from off-peak/flat peak period to peak period for release. The simulation model and 90kW physical platform are used to verify the proposed control strategy, and the results show that the control strategy can effectively realize energy transfer. Not only can achieve energy saving and voltage stability, but also can effectively reduce the peak power of traction substation, bring more economic benefits.

In the application of rail transit vehicles, when using typical wireless power transfer (WPT) systems with series–series (SS) compensation supply power for supercapacitors, the output current is in an approximately inverse relationship with the duty cycle in a wide range. This renders the typical buck circuit control inappropriate. In order to help resolve the above issues, this paper designs inductor/capacitor/capacitor (LCC) compensation with new compensation parameters, which can achieve an adjustable quasi-constant voltage from the input of the inverter to the output of the rectifier. In addition, the two-port network method is used to analyze the resonant compensation circuit. The analysis shows that LCC compensation is more suitable for the WPT system using the supercapacitor as the energy storage device. In the case of LCC compensation topology combined with the charging characteristics of the supercapacitor, an efficient charging strategy is designed, namely first constant current charging, followed by constant power charging. Based on the analysis of LCC compensation, the system has an optimal load, by which the system works at the maximum efficiency point. Combined with the characteristics of the constant voltage output, the system can maintain high efficiency in the constant power stage by making constant output power the same as the optimal power point. Finally, the above design is verified through experiments.

The application of a stationary ultra-capacitor energy storage system (ESS) in urban rail transit allows for the recuperation of vehicle braking energy for increasing energy savings as well as for a better vehicle voltage profile. This paper aims to obtain the best energy savings and voltage profile by optimizing the location and size of ultra-capacitors. This paper firstly raises the optimization objective functions from the perspectives of energy savings, regenerative braking cancellation and installation cost, respectively. Then, proper mathematical models of the DC (direct current) traction power supply system are established to simulate the electrical load-flow of the traction supply network, and the optimization objections are evaluated in the example of a Chinese metro line. Ultimately, a methodology for optimal ultra-capacitor energy storage system locating and sizing is put forward based on the improved genetic algorithm. The optimized result shows that certain preferable and compromised schemes of ESSs’ location and size can be obtained, acting as a compromise between satisfying better energy savings, voltage profile and lower installation cost.