An Optimized Method for the Energy-Saving of Multi-metro Trains at Peak Hours Based on Pareto Multi-objective Genetic Algorithm
Urban rail train starts and brakes frequently in it’s movement. It is important to improve the utilization efficiency of electric energy and reduce the traction energy consumption in the field of metro transit. At peak hours, the overlap time between two trains in the same power supply interval is longer and there is much more renewable energy generated by the train’s braking due to a large increasement in passenger flow and the number of departure. In this paper, a method based on pareto multi-objective genetic algorithm is proposed to optimize energy consumption. By optimizing the stopping time of trains in each station, train schedule is optimized and the regenerative braking energy can be used more efficiently.
KeywordsTrain energy-saving Multi-objective optimization Genetic algorithm Train timetable optimization
This work is supported by National Key R&D Program of China under Grant (2016YFB1200402) and Guang Zhou science and technology plan project (No. 201604030061).
- 1.Jian Y, Fayang L (2011) Review of the utilization of vehicular braking energy in urban railway transportation. J Railway 11:27–32 (in Chinese)Google Scholar
- 2.Shili L, Wenji S, Jingxian H (2014) Simulation on regenerative braking energy and utilization of rail transit vehicle. J Mass Trans 17(5):59–63,67 (in Chinese)Google Scholar
- 3.Bo L (2004) Research and implementation of braking-energy recovery system based on pure electric vehicle. School of Computer Science and Technology, TsingHua University, Beijing (in Chinese)Google Scholar
- 4.Qiurui Z, Daqian B (2012) Application of regenerative braking energy injected-grid device for subway. J Power Electron 9(46):61–64 (in Chinese)Google Scholar
- 8.Nasri A, Fekri Moghadam M, Mokhtari H (2010) Timetable optimization for maximum usage of regenerative energy of braking in electrical railway systems. In: SPEEDAM 2010. IEEEGoogle Scholar
- 9.Jia F (2014) Train behavior optimization of urban rail transit system considering energy saving. Beijing Jiaotong University, Beijing (in Chinese)Google Scholar
- 10.Haichuan T (2012) Energy-efficient multi-train control in metro transit system. Southwest Jiaotong University, Chengdu (in Chinese)Google Scholar
- 11.Fang W, Yunqing R (2016) Fast construction method of pareto non-dominated solution for multi-objective decision making problem. J Syst Eng Theor Pract 36(2):454–463 (in Chines)Google Scholar
- 12.Deb K, Jain H (2012) Handling many-objective problems using an improved NSGA-II procedure. Evol Comput 22(10):1–8Google Scholar