Optimization method for allocations of energy storage systems and tractions for metro systems
LIU Anbang1, CHEN Xi1, ZHAO Qianchuan1, LI Borui2
1. Beijing National Research Center for Information Science and Technology, Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University, Beijing 100084, China; 2. National Innovation Center of High Speed Train, Qingdao 266000, China
Abstract：[Objective] Urban metro systems are the most important public transport system in many countries. These systems are associated with high speed, large capacity, and punctuality. However, traction energy costs account for a high proportion of the total operating cost. Thus, energy storage systems (ESSs), such as supercapacitors, have been recently introduced to collect and utilize the renewable energy generated by trains. Specifically, renewable energy is generated when a train brakes, and this energy can be collected using an ESS. The collected energy can later be released and used to start a train. Thus, this approach reduces the energy consumed by the entire system. The generated renewable energy and the energy used for starting a train are different in different stations. Therefore, the allocations (i.e., positions and capacities) of the ESSs and the traction substations can significantly affect the efficiency of energy collection and utilization. Hence, it is essential to optimize such allocations, but this process is difficult due to the complicated features of the problem. Most of the existing studies have considered either ESSs or traction substations in their optimization model and have not considered joint optimization. In addition, these studies have used rule-based methods or metaheuristics as solution methodologies which cannot quantified the qualities of the solutions obtained.[Methods] In this paper, the allocations of the ESSs and the traction substations are jointly optimized. We first model the devices in the power supply network as ideal components and then build a simulation model that can provide the current and the voltage at each position of the network. Based on the simulation model, we develop a simulation optimization problem for the energy consumption of the line, with the upper limits of voltage fluctuation being considered as constraints. Due to the complexity of the simulation optimization model, evaluating the objective function once is computationally expensive. Thus, we develop a solution method based on ordinal optimization theory to efficiently solve the optimization problems. We establish a crude model, which has low computational requirements and use it to obtain a set of candidate solutions. Then, through the evaluation of the ordered performance curve and error bounds, a good enough solution is selected from the candidate solutions.[Results] The testing results obtained on a metro line in Qingdao demonstrate that the optimized configuration can reduce the traction energy consumption by 6.1% when compared to the empirical configuration. Moreover, our results are better than those obtained by only optimizing the allocation of the ESSs.[Conclusions] To reduce the energy consumption of urban metro systems, this paper proposes a model and a solution method for optimizing the allocations of ESSs and traction substations. The effectiveness of our method is proven based on the numerical results. Our proposed method can be used to optimize other problems associated with the urban metro system, such as the optimization of charging and discharging strategies for supercapacitors. Because the train timetable can affect the collection and utilization of the regenerative braking energy, the joint optimization of the timetable and allocations of ESSs requires further investigation.
刘安邦, 陈曦, 赵千川, 李博睿. 地铁线路储能装置与牵引装置联合优化配置方法[J]. 清华大学学报（自然科学版）, 2023, 63(9): 1408-1414.
LIU Anbang, CHEN Xi, ZHAO Qianchuan, LI Borui. Optimization method for allocations of energy storage systems and tractions for metro systems. Journal of Tsinghua University(Science and Technology), 2023, 63(9): 1408-1414.
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2023, Vol. 63 
