Research On Energy Saving Optimization Of Urban Rail Transit Train Operation

Urban rail transit is the lifeline of urban sustainable development.More and more cities plan urban rail transit as part of urban development.As urban rail transit is put into operation in large quantities,the generated urban electricity consumption is increasing day by day.Among them,Train traction energy consumption accounts for about half of the electricity consumption of the entire urban rail transit system.Therefore,the energy-saving optimization of trains has become the key to the energy conservation of urban rail transit systems.The ultimate goal of train energy-saving optimization is to optimize the train schedule,and Energy saving optimization of speed curve between single station and multi station is the basis and important component of optimizing train schedule.In this paper,the Coasting optimization is first selected as the optimization strategy to control the energy-saving operation of the train.Combined with the basic model of the train operation,a hyperbolic model is proposed to construct the energy-saving speed curve of the train.Then,by building a single-station and multi-station constraint model with the goal of minimizing energy consumption,the performance evaluation of the train energy-saving speed curve is performed,and the optimal energy-saving speed curve that meets the constraint model is optimized using a differential evolution algorithm.The specific work is as follows:(1)A hyperbolic model of coasting optimization based on the shortest running time between stations is proposed to construct the energy-saving speed curve.According to the time-saving strategy,the speed limit section is divided into several convex sections with the simulation step length as the simulation step length.By comparing the speed values on both sides of the convex section,the shortest running time speed curve between stations is generated;the idling optimization curve is calculated with the time step length,and the shortest running time speed curve between stations is tracked to generate the energy-saving speed curve.(2)Aiming at the energy saving optimization problem of speed curve between single stations,this paper designs a Coasting optimization scheme between single stations.The scheme first establishes a single-station constraint model based on the hyperbolic model according to the evaluation criteria of the ATO system;then builds an objective function based on the single-station constraint model to evaluate the performance of the energy-saving speed curve and iterates using differential evolution algorithm Optimization,to obtain the optimal energy-saving speed curve;Finally,through case simulation,the adaptability of the program and the energy-saving performance of the Coasting optimization scheme are verified.Among them,the single-station constraint model aims at minimizing energy consumption,combined with the characteristics of the hyperbolic model,to remove the train speed limit and precise parking constraints,only need to consider The train arrives on time,less switching times and minimum idling speed.(3)Aiming at the energy-saving optimization problem of the speed curve between multiple stations,this paper firstly proposes optimization scheme of timing energy saving coasting between multi stations.This solution uses the ability of the hyperbolic model to construct the energy-saving speed curve,and uses differential evolution algorithm to perform multiple " twice coasting" optimizations across the entire line between multiple stations to optimize the optimal energy-saving speed curve that meets the timing constraint model between multiple stations;then use This scheme successfully solved the problem of energy-saving optimization of the speed curve of the multi-convex interval and the problem of timetable planning.Finally,this scheme was used to re-optimize the operation time between stations in the 13 intervals of the Yizhuang Line,and achieved certain results.Among them,the multi-station timing constraint model aims at minimizing energy consumption and the structure is a double-layer constraint.The first layer mainly punishes each station for violating the minimum idling speed and the shortest working distance;the second layer punishes the operation time between stations and the operation time between stations to achieve the effect of multi station timing optimization.