Energy-efficient Operation And Timetable Optimization For Urban Rail Transit System

The rapid development of urban rail transit alleviates the road traffic congestion and improves public transit efficiency. However, along with the increasing length of urban rail transit, a constantly growth in energy consumption reveals that the key measurement to maintain sustainable development is lowering the energy consumption and operation cost. In terms of energy requirement and supplement, an analysis concerning influencing factors of traction energy consumption and the low regenerative braking energy utilization at present is conducted. An energy-saving control model and a timetable optimization model based on regenerative braking energy are established to lower traction energy consumption, improve the utilization of regenerative braking energy and save the train operation energy consumption. The main contents of this paper are given as followings.Firstly, in perspective of energy-saving requirement, this paper establishes energy-saving control model considering the gradients, curves and speed limits of lines, which helps trains to reach the lowest traction energy consumption through optimizing the line condition sequence, operation handle grades and corresponding transition points. Meanwhile, a genetic algorithm is proposed and modified based on practical scenarios, where a gene modification and orientation variation is processed to chromosome those fail to meet conditions of time and speed limits for realizing a quick search. Energy-saving driving strategies are designed for different line conditions between two successive stations, the impacts on traction energy consumption due to different loading and travel time are analyzed. An example is given to verify the validity and feasibility of the proposed model.Secondly, as for increasing energy supplement, the timetable optimization model based on regenerative braking energy application is established on basis of energy-saving control model. It searches an ideal travel time between successive stations and dwell time by minimizing the absolute value of the difference between traction and regenerative braking energy. The model is solved by a genetic method of changing operators. The example analysis proves the optimization model is able to take a step further on lowering traction energy and improving regenerative braking energy utilization, as well as maintaining the service level.Finally, using actual data from a certain line in Beijing subway, the energy-saving control model designs energy-saving driving strategies on different travel time between successive stations and the timetable is optimized based on it. Four schemes, travel time optimization, dwell time optimization, integrated optimization and the maximizing regenerative braking energy optimization, are compared in terms of their energy-saving effects. It turns out the integrated optimization scheme has the highest energy-saving rate that attains 11.59% of the train operation energy consumption under original timetable.