Regenerative Braking Energy-saving Based Timetable Optimization Research Of Urban Rail Transit Systems

Urban rail transit is a fast,large capacity and high frequency traffic mode.Compared with other traffic modes,it has a higher level of operational efficiency,safety and energy efficiency.However,due to the operating characteristics of urban rail transit,it is still one of the world's high energy intensive industries.On the other hand,with the rapid development of the city scale,the urban rail transit lines will be widely used.At the same time,the high operating costs caused by high energy consumption will be more and more serious.Regenerative braking is a popular energy-efficient technology that converts the kinetic energy to electricity during braking.The recovered energy can be immediately used to provide energy for other trains belonging to the same substation.Better than mechanical braking,regenerative braking can sufficiently save energy and effectively control the temperature rise in the subway tunnels.Therefore,regenerative braking technology has been paid more and more attention by researchers.In order to improve the utilization efficiency of regenerative energy and avoid excessive voltage caused by the redundant recovered electricity,it is necessary to rational design the timetable of urban rail transit.Starting from the regenerative braking energy-saving point,considering more uncertain factors and the actual situation,taking the timetable as the research object,this paper makes a deep research and designs various algorithms.In addition,a series of numerical examples are given to demonstrate the effectiveness and significance of the study.Therefore it improves that our study can provide a new theory and decision analysis method for the operation of urban rail transit.The research contents of this paper are as follow:(1)Introduce the background and research status of energy-efficient timetabling problem.Based on the domestic and foreign research,this paper analyzes the advantages and disadvantages of previous studies.And then,we find the breakthroughs and innovation,and build the model to discuss it.(2)In this paper,the theory of chance constrained programming and mean-variance are introduced to provide a new quantitative tool for the optimization of urban rail transit timetable.In addition,the data-driven modeling method makes the assumption and background of the problem more realistic,and avoids the construction of model too idealistic.(3)On the basis of constructed model,various algorithms are designed to solve the problem.This paper designs a monkey algorithm for stochastic chance constrained programming model,and constructs a genetic-particle swarm combined algorithm to improve the performance of the mean-variance model and the robustness of the results.(4)In the process of using the above two algorithms to solve the model,we first verify the validity and convergence of each algorithm,and do the experiment of the parameters.After that,the optimal timetable and optimal solutions are given.Finally,through a series of comparative experiments,we proves the validity and advanced of our proposed models.