Research On Online Optimization Of Energy-saving Operation Strategy For Urban Rail Transit Trains

In recent years,urban rail transit has developed rapidly due to its characteristics of safety,comfort,efficiency and environmental protection.How to reduce the traction energy consumption of subway trains has become a research focus.But the structure of urban rail transit system is complex,the train control system needs to meet multiple objectives,and the train operation process is extremely vulnerable to external disturbances of varying degrees.Meanwhile,the energy consumption of each single train can act on each other in the multi-train operation environment.In view of the above problems,this thesis proposes a single train online energy consumption optimization method and a double train collaborative energy consumption optimization method based on regenerative braking energy under the constraints of ensuring safety,punctuality,comfort and accurate parking.The specific research content is as follows:1.In order to ensure that the train meets the constraints of safety,punctuality and accurate parking,a multi-objective speed adjustment model is designed,including the overspeed protection model combined with the actual signal control system,the punctuality speed adjustment model based on the remaining time allocation algorithm and the accurate parking model based on heuristic learning adjustment strategy.The experimental results show that the train operation safety,punctuality and stopping accuracy can meet the requirements under the constraints of this model.2.For the problem that the off-line energy consumption optimization algorithm cannot respond to the real disturbance online,a single train online energy consumption optimization algorithm is proposed based on the urban rail train operation strategy.This algorithm does not need the target speed curve but the actual running state information of the train,including its speed,position,running time,speed limit information,etc.It combines the multi-objective speed adjustment model to control and optimize the energy consumption of the train in real time.The experimental results show that this algorithm can select the energy saving operation strategy in real time under the constraints of train operation safety,punctuality,comfort and accurate stopping.The energy consumption is reduced by 35% compared to tracking the command level speed corresponding to the same scheduled run time.In addition,it can make online response to the disturbance in the actual operation process,which is real-time,flexible and adaptable.3.On the basis of single train online energy consumption optimization,considering the utilization of regenerative braking energy,a collaborative optimization algorithm aiming at the minimum total energy consumption of two trains is proposed.By analyzing the operation process of two adjacent trains in the same power supply section and establishing their overall dynamic model,the operation strategy of the two trains is coordinated to increase the running overlap time when one train is in traction condition and the other train is in braking condition so that to improve the utilization rate of regenerative braking energy and achieve the goal of collaborative optimization.The experimental results show that this method can inherit the applicability of the single-train on-line energy consumption optimization algorithm,and compared with the single-train on-line energy consumption optimization algorithm,the operating energy consumption optimization rate can reach 12.6%.