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基于混合整数规划的高速列车多区间节能优化研究
引用本文:金波,孙鹏飞,王青元,冯晓云. 基于混合整数规划的高速列车多区间节能优化研究[J]. 铁道学报, 2020, 0(2): 11-17
作者姓名:金波  孙鹏飞  王青元  冯晓云
作者单位:西南交通大学电气工程学院
基金项目:国家重点研发计划(2016YFB1200502)
摘    要:当今巨大的能源消耗成为高速铁路发展不可忽视的问题,节能优化显得格外重要。从列车优化操纵与区间运行时分分配两方面出发,在总运行时分约束下,探讨全程最节能的运行时分分配方案及所对应的列车操纵策略。在列车运动方程基础上,引入经停站约束,建立列车运行能耗和区间运行时分分配同步优化的非线性连续节能优化模型。通过离散化与线性变换,将节能优化模型重构为混合整数规划模型,并利用Cplex求解。以高速铁路线路为背景设计算例,验证模型的有效性。仿真结果证明在列车优化操纵策略的基础上,优化区间运行时分分配方案可以进一步降低列车总运行能耗。在不改变总运行时间的情况下,运行总能耗可降低1.03%。

关 键 词:节能  区间运行时分  列车操纵策略  混合整数规划

Energy-saving Optimization of Multi-interstation High-speed Train with Mixed Integer Linear Programming
JIN Bo,SUN Pengfei,WANG Qingyuan,FENG Xiaoyun. Energy-saving Optimization of Multi-interstation High-speed Train with Mixed Integer Linear Programming[J]. Journal of the China railway Society, 2020, 0(2): 11-17
Authors:JIN Bo  SUN Pengfei  WANG Qingyuan  FENG Xiaoyun
Affiliation:(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 611756,China)
Abstract:Nowadays,huge energy consumption has become a big challenge to the development of high-speed railway system,thus entailing the importance of energy-saving optimization.Considering both optimal train control and timetabling,this paper mainly discussesed an energy-efficient train timetabling and its corresponding control strategy under the total travel time limit.First a nonlinear continuous model was given to describe both the energy-efficient train control and the train timetabling,based on the train motion state equation and under scheduled stop constrains.Then by discretizing and linearizing,the former model was reformulated into a mixed integer linear programming(MILP)model,which was solved by Cplex.Case studies of high-speed train were given to demonstrate the performance of this approach.The result shows that total train energy consumption can be further reduced by constructing the train timetabling that allows optimal train control most effectively.The total energy consumption can be reduced by 1.03%without changing the total travel time.
Keywords:energy-saving  timetabling  optimal train control  MILP
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