| In recent years,owing to the accelerated implementation of urban rail transit network development projects,the energy consumption of it has experienced a significant surge.According to the statistics,in the year 2021,China’s urban rail transit system recorded a notable increase in its overall electrical energy consumption,as the figure surged to 21.31 billion kilowatt-hours,reflecting a substantial increment of 23.6% as compared to the consumption level recorded in the preceding year of 2020.The issue of energy consumption has emerged as a critical concern requiring immediate redressal in China’s urban rail transit system.This phenomenon is primarily attributed to the operation of trains,which accounts for the main energy consumption in urban rail transit systems,including air conditioning,ventilation,elevators,and lighting.Among them,the energy consumption of train operation accounts for nearly half amount of the whole system.Therefore,how to optimize the operation of urban rail transit to meet energy-saving needs is of important practical significance.The underlying objective of this article is to conduct an in-depth analysis of the distinctive effects of varying line conditions on the traction energy consumption exhibited by urban rail trains based on the existing operating lines in Xi’an.Based on the dynamic model of rail trains,a mathematical model for optimizing energy-saving operation of trains is established,and the multi-interval energy-saving operation optimization method is discussed.At the same time,a special scenario with small disturbances was added,and a train energy-saving optimization model under disturbances was constructed,and to address this problem,a refined particle swarm optimization algorithm was employed as the solution methodology.The specific work content is as follows:(1)Summarized the current research status of energy-saving operation optimization for urban rail trains at home and abroad,elaborated on the research focus of energy-saving optimization operation strategies for trains,and combined with existing research methods and results,proposed the target and significance of this paper,so determining the research content of optimizing train operation strategies and operation curves as a whole.(2)The fundamental operational mechanism of railway transit has been examined in this study.By analyzing the unique force distribution during the operational phase of urban rail trains,a dynamic model elucidating the train’s operational procedure has been developed.Subsequently,energy consumption during the operation of the train has been analyzed.Through case analysis and simulation using the train system simulation software Dynamis,the effect of line conditions on train traction energy consumption is quantitatively analyzed to confirm the influencing factors of train operation traction energy consumption.(3)In this study,optimization of the velocity profile of a single train has been conducted,with multiple objectives being considered.such as energy conservation,and reasonably allocating the running time of the entire line section based on the energy consumption time curve.Building upon the train operational dynamic model,limiting factors have been incorporated to develop a comprehensive optimization framework,such as energy conservation,punctuality,comfort,and parking error were established,and the constraints were transformed into evaluation functions for optimization indicators.An optimization model with multiple objectives was formulated and an enhanced multi-objective particle swarm optimization algorithm was used to attain its solution,obtaining the train interval running speed curve.Using the data of the optimization the speed curve result of urban rail trains,establish a model for allocating the operating time of the entire line,and the steepest descent method was employed to address the problem of optimizing the allocation of operation time for the entire line interval.(4)Building upon the previously developed energy-saving optimization model for the entire line,this study incorporates a small perturbation scenario.The proposed objective function is used to minimize the whole energy consumption of the entire train line while simultaneously minimizing the deviation of train operations.Considering the constraints of multiple train operations such as departure interval,dwell time,and minimum inter station running time.This study proposes a delayed rail transit train operation adjustment model,accounting for the impact of disturbance scenarios.To address the formulated problem,an enhanced multi-objective particle swarm optimization algorithm is utilized,capitalizing on the inherent attributes of the developed model.(5)Based on the line data and train data of Xi’an Urban Rail Transit Line 4,and using the characteristics of the optimization method,in this study,an optimization approach was utilized to enhance the energy efficiency of the train operation curve under both normal operating conditions as well as in the presence of small disturbances.The findings clearly demonstrate the efficacy of the proposed train operation optimization method,which successfully achieves significant reductions in the traction energy consumption of urban rail trains and enable trains to resume punctual operation as soon as possible,proved the effectiveness of the model and algorithm built in this paper. |
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