Timetabling with Regenerative Energy Maximization

Author(s):  
Xiang Li ◽  
Xin Yang
2016 ◽  
Vol 27 (5) ◽  
pp. 1552-1566 ◽  
Author(s):  
N.M. Thometz ◽  
M.M. Staedler ◽  
J.A. Tomoleoni ◽  
J.L. Bodkin ◽  
G.B. Bentall ◽  
...  

Author(s):  
Juanjuan Cai ◽  
Jing Xun ◽  
Xiangyu Ji ◽  
Yue Lei

Urban rail transit (URT) develops rapidly in modern cities, and its energy efficiency attracts extensive attention. The utilization of regenerative energy (URE) is an important method for energy-efficient operation of URT. Regenerative braking is an energy recovery mechanism that slows down a moving train by converting its kinetic energy into electric energy. The electric energy can be utilized for other trains to accelerate in a cooperative way. To take full advantage of the regenerative energy, an energy calculation method which considers regenerative braking power to optimize the timetable is proposed in this paper. First, four operating modes of URE are defined and an integer programming model is formulated. Second, a branch and bound algorithm is designed to solve the optimal timetable in different scenarios. Third, the model is evaluated based on the operation data from the Yanfang Line, Beijing Metro, China. For peak hours, the results illustrate that the proposed method can significantly improve URE by 73.7% compared with the original timetable. Also, URE can be improved by 46.3% for off-peak hours. Finally, the comparison between the proposed method and the method based on the kinetic energy theorem is given. The simulation results illustrate that the proposed method could increase URE by 29.7% and 9.9% for peak and off-peak hours scenarios, respectively, in comparison with the method based on the kinetic energy theorem.


2018 ◽  
Vol 180 ◽  
pp. 02005 ◽  
Author(s):  
Włodzimierz Jefimowski ◽  
Anatolii Nikitenko

The paper presents the results of economic study of energy storage system (ESS) implemented in 3 kV DC power supply system. Two conceptions of ESS have been investigated: ESS with supercapacitor (SC) and hybrid ESS (HESS) with SC and LFP battery. The investigated locations of energy storage systems are considered among existing traction substations in two railway lines with different density of train operation. The considered aims of energy storage system implementation are decreasing of energy consumption by maximum regenerative energy utilization and reduction of peak 15- min power demand of traction substation. The paper presents a method of regenerative power estimation depending on the location of the considered ESS implementation point. Also the method of optimal location selection of ESS in terms of minimization of Simple Payback Time (SPBT) of investment is presented. Besides the influence of initial cost value as well as energy price on the SPBT value are investigated. The results are compared between two railway lines with different number of trains operating.


2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ziyan Luo ◽  
Xiaoyu Li ◽  
Naihua Xiu

In this paper, we propose a sparse optimization approach to maximize the utilization of regenerative energy produced by braking trains for energy-efficient timetabling in metro railway systems. By introducing the cardinality function and the square of the Euclidean norm function as the objective function, the resulting sparse optimization model can characterize the utilization of the regenerative energy appropriately. A two-stage alternating direction method of multipliers is designed to efficiently solve the convex relaxation counterpart of the original NP-hard problem and then to produce an energy-efficient timetable of trains. The resulting approach is applied to Beijing Metro Yizhuang Line with different instances of service for case study. Comparison with the existing two-step linear program approach is also conducted which illustrates the effectiveness of our proposed sparse optimization model in terms of the energy saving rate and the efficiency of our numerical optimization algorithm in terms of computational time.


2019 ◽  
Vol 20 (9) ◽  
pp. 3247-3257 ◽  
Author(s):  
Hongjie Liu ◽  
MengChu Zhou ◽  
Xiwang Guo ◽  
Zizhen Zhang ◽  
Bin Ning ◽  
...  

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