Research On Real-time Adjustment Optimization Method Of Urban Rail Train Energy Saving Operation Based On Model Predictive Control And Improved Heuristic Algorithm

With the continuous increase of urban population in China,a series of problems such as the imbalance between supply and demand of urban public transport and traffic congestion are becoming increasingly prominent,which seriously restrict the development of society.Urban rail transit is gradually becoming the primary mode of transportation to relieve the pressure of urban public transportation due to its merits of large volume of traffic,high frequency,small interval,punctuality and the energy-saving.However,when disturbed by uncertain factors such as large passenger flows,train operations are prone to delays,and if delays cannot be adjusted and restored in time,the further spread of delays will not only affect the normal operation of the system,but also lead to the increase of energy consumption.Therefore,it is of great practical significance to study the real-time adjustment and optimization method of urban rail train energy-saving operation considering dynamic passenger flow.In this paper,in view of the existing domestic and foreign related researches,comprehensive consideration of the coupling relationship between train and passenger flow changes,and the influence of running speed curve of energy consumption,are established based on the train speed curve to choose energy-saving running real-time adjustment optimization model,the train operation adjustment and speed curve integration model of real-time computing,and designs the corresponding algorithm,which alleviates train delay in time,improves the efficiency of operation and service,and ensures lower energy consumption of train operation under the condition of making up for delay.The main research contents of this paper are as follows:(1)Considering the coupling relationship between dynamic passenger flow changes and train operation,a real-time adjustment and optimization model of train energy saving operation based on speed curve selection is established.According to the train delay,the train stop time is adjusted in real time and the best speed curve is selected from the pre-stored speed curve in real time.Taking the minimum departure time deviation,departure interval deviation and train operation energy consumption as the objective function,the nonlinear optimization model was established with departure time,stop time,on-board passengers,waiting passengers on the platform,and on-board passengers as the constraint conditions.Furthermore,a Model Predictive Control(MPC)and adaptive genetic algorithm are designed to solve the problem in order to meet the real-time adjustment requirements.(2)In order to improve operation efficiency,service quality and energy utilization rate,an integrated model of train operation adjustment and real-time calculation of speed curve is established on the basis of the above research.On the basis of considering the dynamic change of passenger flow,the real-time calculation of train stop time and running speed curve and the combined dynamic adjustment and optimization of total energy consumption considering the braking regenerative energy can achieve the purpose of integrated adjustment.To minimize deviation,deviation of departure intervals and departure time considering the regenerative braking can train the practical energy consumption as objective function,and take the train departure time,real,actual operation time,stop time and arrival interval,running speed,the station waiting for passengers,vehicle passenger volume as constraint conditions,such as nonlinear optimization model is established.Furthermore,in order to meet the requirements of real-time integration adjustment,a solution method combining MPC and adaptive tabu algorithm was designed to solve the problem.(3)Based on the operation data of Chang Ping Line of Beijing rail transit,the rationality of the real-time adjustment and optimization model of train energy saving operation based on speed curve selection and the effectiveness of the algorithm are verified.Numerical experiments show that the proposed model and algorithm can effectively reduce departure time deviation in real time,improve the balance of headway,and ensure lower train energy consumption in the case of making up for delay,so as to achieve the purpose of energy saving.Based on the operation data of urban rail transit Yi Zhuang line,the effectiveness of the integrated model and algorithm for real-time calculation of train operation adjustment and speed curve is verified.Numerical experiments show that the proposed model and algorithm can realize the timely and obvious recovery of train delays,improve the regularity of departure intervals,and maintain the balance between train service efficiency and energy consumption.