The stationary supercapacitor energy storage system (SCESS) is one of effective approaches for the utilization of train's regenerative braking energy in urban rail systems. In this paper, the capacity configuration of SCESSs, the no-load voltage of substation, the control of onboard braking resistors and train operation diagrams are considered comprehensively. Based on the equivalent circuit model, the effects of traction power system parameters on the energy transmission between powering trains, braking trains and SCESSs are analyzed, and the relationships between the train operating parameters and system energy consumptions are revealed. A multi-variable synthetic optimization method is proposed to optimize the SCESS capacity, train operation diagrams and traction power system parameters collaboratively. In order to reduce the search space and improve the efficiency of the optimization algorithm, a hierarchical multi-objective optimization model is established, based on which the design variables and the control variables are optimized iteratively. And as the traffic density has a great impact on the regenerative braking energy distribution of the system, the optimization objective function fully considers the frequency distribution of headways throughout the day. Combined the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) with the traction power flow simulator, the flowchart of the two-stage optimization algorithm is designed, and the pareto set of the multi-objective problem is obtained. The advantages of reducing system energy consumptions and configuration cost under the proposed algorithm are verified based on case studies of Beijing Batong Line.

中文翻译：

---

城市轨道交通中基于超级电容器的储能系统、牵引功率参数和列车运行的两阶段综合优化

固定式超级电容器储能系统（SCESS）是城市轨道系统利用列车再生制动能量的有效途径之一。本文综合考虑了SCESS的容量配置、变电站空载电压、车载制动电阻控制和列车运行图。基于等效电路模型，分析了牵引动力系统参数对动力列车、制动列车和SCESS之间能量传输的影响，揭示了列车运行参数与系统能耗之间的关系。提出了一种多变量综合优化方法，协同优化SCESS容量、列车运行图和牵引电力系统参数。为了减小搜索空间，提高优化算法的效率，建立了分层多目标优化模型，在此基础上对设计变量和控制变量进行迭代优化。并且由于交通密度对系统再生制动能量分配有很大影响，优化目标函数充分考虑了全天车距频率分布。将精英非支配排序遗传算法（NSGA-II）与牵引潮流模拟器相结合，设计了两阶段优化算法的流程图，得到了多目标问题的帕累托集。