Research On Energy-efficient Train Timetable Optimization Based On Regenerative Braking Energy In Urban Rail Transit

With the progress of China’s urbanization,traffic demand has a sharp increase,and traffic congestion has become increasingly serious,which has brought tremendous pressure on urban rail transit.With the advantages of large volume,fast speed,safety,punctuality,environmental protection and land conservation,urban rail transit has gradually become the backbone of the urban transportation system.However,with the increase of operation mileage,the continuous expansion of the scale and the surge in passenger flow,the problem of excessive energy consumption in urban rail transit operations has also emerged.Therefore,how to reduce the total energy consumption to achieve energy-saving operation of urban rail transit has become an important research topic at present.Due to the short distance between urban rail transit station sections,frequent braking will generate considerable regenerative braking energy,and this part of the regenerative braking energy can be fed back to the contact line for traction trains to use immediately.At the same time,the regenerated braking energy that is not used immediately can be absorbed by the energy storage device.Therefore,it is an efficient way to reduce energy consumption by utilizing the regenerative braking energy in urban rail transit systems.In view of this,this study aims to reduce the total energy consumption by using the wayside supercapacitor energy storage device.Specifically,the main research work of this paper includes the following four aspects:(1)Analyze the internal mechanism of the transmission,storage and utilization of the regenerative braking energy.When a train is braking,the kinetic energy of the braking train can be converted into electrical energy,and then the electrical energy can be fed back to the contact line for further reuse by other accelerating trains.By using the energy storage device,the regenerative braking energy which is not used immediately will be absorbed,and then released to realize the delayed utilization of regenerative braking energy.In view of this,this study analyzes the internal mechanism of the transmission,storage and utilization of the regenerative braking energy.At the same time,considering that the energy store time in the energy storage device is finit and the limited capacity,an attenuation function is proposed to calculate the dissipated energy through the attenuation process within a certain time.This section is the foundation of the full text,providing a theoretical basis for the energy-efficent train timetable optimization in later chapters.(2)Research on the energy-efficient train timetable optimization based on the energy storage device.With the consideration of the direct and indirect use of regenerative braking energy,this study optimizes the headway between adjacent trains to further optimize the coupling relationship between trains.Based on this,a multi-train cooperative optimization model is constructed to maximize the utilization of regenerative braking energy.A tabu search algorithm based on simulation is designed to obtain the optimal train timetable.The results of numerical experiments based on the Beijing Yizhuang Line show that by optimizing the timetable,the utilization ratio of regenerative braking energy can be further increased by 5.2%.(3)Research on the robust and energy-efficient train timetable based on random delay scenarios.Considering the dynamics of passenger demand in urban rail transit,the train will face delays due to the passenger crowding in the daily operation,and the delay is easy to propagate to subsequent trains,resulting in joint train delays.Based on this,this study aims to construct a robust and energy-efficient train timetable to deal with train delays which are caused by intermittent passenger crowding in peak hours.First of all,assuming that the initial delay mostly occurs at busy stations during peak hours,the train delay time is regarded as a random variable,and a series of random delay scenarios are generated.Secondly,formulating the adjustment strategies for the arrival and release trains,and then loading the random delay scenarios to obtain the adjusted timetables.After that,taking the headway between adjacent trains and the selection of speed profiles as the decision variables,and with the goal of the minimizing the deviation among train timetables and the total energy consumption,a multi-train collaborative mathematical optimization model is constructed.Finally,a variable neighborhood search algorithm based on simulation is designed to obtain the feasible train timetable.The results of numerical experiments show that by optimizing the headway and choosing an appropriate speed profile,it can effectively reduce the propagation of train delay and reduce the energy consumption of the system,thereby increase the robustness and the energy efficiency of the train timetable.(4)Research on the energy-efficient train timetable based on dynamic passenger demand.During the operation of urban rail transit trains,a smaller headway is used during peak hours and a larger headway is used during off-peak hours.However,during the transition from the end of peak hours to the beginning of off-peak hours,the passenger demand shows a steady downward trend.Therefore,using a smaller headway at the end of peak hours may waste transportation capacity,and a larger headway at the beginning of off-peak hours may increase passenger’s waiting time.In view of this,this study takes the transition period as the research object,and aims to achieve the best match between passenger demand and transportation capacity by optimizing the existing train timetable.Specifically,taking the headway,the selection of speed profiles and service frequency as decision variables,we construct a mathematical optimization model with the goal of minimizing the total waiting time of passengers,the total energy consumption,and the total operating cost.Then,a simulation-based genetic algorithm is designed to solve the model.The results of numerical experiments show that on the basis of meeting the demand of dynamic passenger flow,optimizing the existing train timetable can effectively reduce the system energy consumption and operation costs.