Research On Model And Algorithm Of Energy Saving Operation For Urban Rail Transit

Urban rail transit(URT)is playing an important role in the urban public transportation with its large capacity,fast speed,safety,reliability and its less pollution.According to statistics,there are 112 operation lines built in 25 cities in China until the end of December 2015,and the total operation mileage reaches 3286 kilometers.With the rapid development of URT,the energy consumption problem becomes increasingly prominent.Therefore,reducing energy consumption is of great significance to maintain the sustainable development of URT.The dynamic adaptive technique is used to adjust the crossover operator and the mutation operator on the basis of the traditional genetic algorithm,which improves the searching ability and reduces the occurrence of premature phenomenon.Then,this thesis utilizes the improved genetic algorithm as a core algorithm,taking the energy consumption and supply into consideration,to perform the research about the analysis and the approach of phase-in and integrative solving of the energy-saving problem for single train and multiple trains.The main work in this thesis is as follows.Firstly,the kinematics model of train is described based on analysis of train operation process.The main factors affecting the energy consumption are analyzed,and the calculation model of traction energy consumption is set up.Secondly,the necessary conditions of train energy saving control strategies are deduced by the maximum principle.Combined with the characteristics of URT,a three-stage mode of "traction-coasting-braking" is adopted for train energy-saving control.A model of single train at a single section with the objective of minimizing traction energy consumption in the fixed time is established.We use the method of controlling a single variable to study the relationship between energy consumption and influence factors including station distance,speed limit and gradient.The simulation results indicate that the three factors have proportional relationship with train energy consumption.Thirdly,the curve of train energy consumption and running time can be obtained according to the inverse relationship between traction energy consumption and running time.As a result,a model of single train at multiple sections with the objective of minimizing the traction energy consumption in the fixed time is established,and the running time of each section is reallocated.Fourthly,the running times of sections which belong to the same power center is redistributed by using the method of the second-stage,and then the first-stage control method to obtain the traction and braking time of the train.Considering the energy utilization of regenerative braking,a model of multiple trains at multiple sections with the objective of maximizing the overlap time of traction and braking in the fixed time is established.The improved genetic algorithm is applied to solve the optimized departure interval and stop time.At last,the actual data from Beijing subway line is employed to simulate the above-mentioned three-stage optimization algorithm.The results indicate that the total energy consumption of the new timetable has been reduced by 2.8%compared to the optimized schedule of the planned timetable and the optimal overlapping time has increased 43.1%in light of initial value.Therefore,the effectiveness is verified about the established phase-in and integrative systematic optimization approach for URT train energy-saving operation.