Optimization of an MRT Train Schedule: Reducing Maximum Traction Power by Using Genetic Algorithms

Chen, Jiann-Fuh; Lin, Ray-Lee; Liu, Yow-Chyi

doi:10.1109/tpwrs.2005.851939

Cited by 64 publications

(36 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Studies have been carried out on the utilization of regenerative energy by energy efficient control of multiple trains. In order to increase the utilization of regenerative energy, optimization models for controlling the trains' running time and schedule has been studied in [17][18][19]. Relevant studies employ the overlapping time of accelerating trains and braking trains to access the utilization rate of regenerative energy [20].…”

Section: Groundmentioning

confidence: 99%

Maximum Safety Regenerative Power Tracking for DC Traction Power Systems

Zhang

et al. 2017

Energies

View full text Add to dashboard Cite

Direct current (DC) traction power systems are widely used in metro transport systems, with running rails usually being used as return conductors. When traction current flows through the running rails, a potential voltage known as "rail potential" is generated between the rails and ground. Currently, abnormal rises of rail potential exist in many railway lines during the operation of railway systems. Excessively high rail potentials pose a threat to human life and to devices connected to the rails. In this paper, the effect of regenerative power distribution on rail potential is analyzed. Maximum safety regenerative power tracking is proposed for the control of maximum absolute rail potential and energy consumption during the operation of DC traction power systems. The dwell time of multiple trains at each station and the trigger voltage of the regenerative energy absorbing device (READ) are optimized based on an improved particle swarm optimization (PSO) algorithm to manage the distribution of regenerative power. In this way, the maximum absolute rail potential and energy consumption of DC traction power systems can be reduced. The operation data of Guangzhou Metro Line 2 are used in the simulations, and the results show that the scheme can reduce the maximum absolute rail potential and energy consumption effectively and guarantee the safety in energy saving of DC traction power systems.

show abstract

Section: Groundmentioning

confidence: 99%

Maximum Safety Regenerative Power Tracking for DC Traction Power Systems

Zhang

et al. 2017

Energies

View full text Add to dashboard Cite

show abstract

“…In a number of studies, schedule optimization of trains was performed so that it reduces energy consumption or energy loss. They include proposing an algorithm which distributes travel times of the trains for the most efficient energy consumption (Su et al, 2013), developing a cooperative scheduling model to increase simultaneous accelerates and brakes of the consecutive trains (Nasri et al, 2010;Yang et al, 2013) and applying the genetic algorithm to decrease the simultaneous acceleration of trains in order to avoid maximum traction power of the system (Chen et al, 2005).…”

Section: Related Workmentioning

confidence: 99%

Modelling Temporal Schedule of Urban Trains Using Agent-Based Simulation and Nsga2-Based Multiobjective Optimization Approaches

Sahelgozin

Alimohammadi

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Increasing distances between locations of residence and services leads to a large number of daily commutes in urban areas. Developing subway systems has been taken into consideration of transportation managers as a response to this huge amount of travel demands. In developments of subway infrastructures, representing a temporal schedule for trains is an important task; because an appropriately designed timetable decreases Total passenger travel times, Total Operation Costs and Energy Consumption of trains. Since these variables are not positively correlated, subway scheduling is considered as a multi-criteria optimization problem. Therefore, proposing a proper solution for subway scheduling has been always a controversial issue. On the other hand, research on a phenomenon requires a summarized representation of the real world that is known as Model. In this study, it is attempted to model temporal schedule of urban trains that can be applied in Multi-Criteria Subway Schedule Optimization (MCSSO) problems. At first, a conceptual framework is represented for MCSSO. Then, an agent-based simulation environment is implemented to perform Sensitivity Analysis (SA) that is used to extract the interrelations between the framework components. These interrelations is then taken into account in order to construct the proposed model. In order to evaluate performance of the model in MCSSO problems, Tehran subway line no. 1 is considered as the case study. Results of the study show that the model was able to generate an acceptable distribution of Pareto-optimal solutions which are applicable in the real situations while solving a MCSSO is the goal. Also, the accuracy of the model in representing the operation of subway systems was significant.

show abstract

“…This technology has been studied by many researchers. For example, Chen [33] optimized the timetable to reduce the maximum traction power. Nasri [34] got an optimal timetable for the test system and analyzed the effect of headway and reserve time.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One

Gong

Zhang

et al. 2014

Energies

View full text Add to dashboard Cite

Metro systems are one of the most important transportation systems in people's lives. Due to the huge amount of energy it consumes every day, highly-efficient operation of a metro system will lead to significant energy savings. In this paper, a new integrated Energy-efficient Operation Methodology (EOM) for metro systems is proposed and validated. Compared with other energy saving methods, EOM does not incur additional cost. In addition, it provides solutions to the frequent disturbance problems in the metro systems. EOM can be divided into two parts: Timetable Optimization (TO) and Compensational Driving Strategy Algorithm (CDSA). First, to get a basic energy-saving effect, a genetic algorithm is used to modify the dwell time of each stop to obtain the most optimal energy-efficient timetable. Then, in order to save additional energy when disturbances happen, a novel CDSA algorithm is formulated and proposed based on the foregoing method. To validate the correctness and effectiveness of the energy-savings possible with EOM, a real case of Shanghai Metro Line One (SMLO) is studied, where EOM was applied. The result shows that a significant amount of energy can be saved by using EOM. OPEN ACCESSEnergies 2014, 7 7306

show abstract

Optimization of an MRT Train Schedule: Reducing Maximum Traction Power by Using Genetic Algorithms

Cited by 64 publications

References 16 publications

Maximum Safety Regenerative Power Tracking for DC Traction Power Systems

Maximum Safety Regenerative Power Tracking for DC Traction Power Systems

Modelling Temporal Schedule of Urban Trains Using Agent-Based Simulation and Nsga2-Based Multiobjective Optimization Approaches

An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One

Contact Info

Product

Resources

About