Two-Stage Integrated Optimization Design of Reversible Traction Power Supply System

Xiaodong Zhang, Wei Liu (), Qian Xu, Zhuoxin Yang, Dingxin Xia and Haonan Liu
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Xiaodong Zhang: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Wei Liu: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Qian Xu: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Zhuoxin Yang: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Dingxin Xia: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Haonan Liu: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Energies, 2025, vol. 18, issue 3, 1-17

Abstract: In a traction power supply system, the design of traction substations significantly influences both the system’s operational stability and investment costs, while the energy management strategy of the flexible substations affects the overall operational expenses. This study proposes a novel two-stage system optimization design method that addresses both the configuration of the system and the control parameters of traction substations. The first stage of the optimization focuses on the system configuration, including the optimal location and capacity of traction substations. In the second stage, the control parameters of the traction substations, particularly the droop rate of reversible converters, are optimized to improve regenerative braking energy utilization by applying a fuzzy logic-based adjustment strategy. The optimization process aims to minimize the total annual system cost, incorporating traction network parameters, power supply equipment costs, and electricity expenses. The parallel cheetah algorithm is employed to solve this complex optimization problem. Simulation results for Metro Line 9 show that the proposed method reduces the total annual project costs by 5.8%, demonstrating its effectiveness in both energy efficiency and cost reduction.

Keywords: power supply; urban rail transit; adaptive management; optimal design (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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