Operational Optimization Model Of Subway Trains Based On The Usage Of Regenerated Braking Energy

With the rapid growth of urban rail transit system and passenger traffic, reducing energy consumption has become an increasingly important issue for operators. Since the train traction power consumption accounts for the maximum proportion of the total power consumption in urban rail transit system, the optimization of train operation process is an important approach to save energy.Train energy-saving operational optimization mainly includes two aspects:the first is the strategic optimization for single train operation between stations and the second is multi-train coordination operation optimization. Based on the above perspectives, this paper has established a single train operational optimization model to minimize energy consumption, and a multi-train coordination optimization model to maximize the overlapping time for regenerative braking energy utilization. The case study due to the actual operation data from a certain line in Beijing subway verifies the optimization results. A global optimization scheme for the minimum energy consumption is then proposed. The main work in this thesis includes the following aspects.Firstly, it has analyzed the traction energy consumption calculation during the operation process according to ’Four stages’ control strategy and established a single train operational optimization model to achieve the optimal energy-saving operation strategy. The results of case study show that optimization ratio has reached 8.99%. The further study on the relationship between energy consumption and influence factors which include capacity, station distance and average slope indicates that train energy consumption is proportional to the three influencing factors and the effect can be superimposed.Secondly, considering the mutual utilization of regenerative braking energy among multi trains, it has constructed the collaborative optimization which takes traction stage time, dwell time and headway as independent variables. Using genetic algorithm to solve this model and the actual data of Beijing subway line to verify the model’s validity, the results of case study show that optimal overlapping time has increased 44.6% in light of initial value. In addition, through the sensitivity analysis on the three independent variables, we can draw the following conclusions. The overlapping time increases with train headways if their headways are less than 210 seconds while their headways are varying within the range between 210 and 300 seconds, on the overlapping time nearly keeps a constant value. The overlapping time decreases if the train headways are gradually increasing. Moreover, the increase of average traction time and dwell time both can lead to the increase of overlapping time.Finally, combining the single train operation optimization model with the multi-train collaborative optimization model,we proposed a global optimization strategy. The comparison results for the whole line energy consumption show that the collaborative optimization strategy is the most energy-saving strategy and the optimization effect reaches 12.6% among three optimization strategies (optimizing the single train inter-station operation, multi-train operation and collaboratively the both above).