scholarly journals Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm

Energies ◽  
2016 ◽  
Vol 9 (3) ◽  
pp. 208 ◽  
Author(s):  
Fei Lin ◽  
Shihui Liu ◽  
Zhihong Yang ◽  
Yingying Zhao ◽  
Zhongping Yang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Saving ◽  
Dwell Time ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Time Optimization ◽  
Urban Rail ◽  
Regenerative Energy

Thinking of Comprehensive Energy-Saving System Construction for Urban Rail Transit

2014 ◽  
Vol 505-506 ◽  
pp. 405-409
Author(s):  
Jun Ke Liang ◽  
Zhi Gang Liu ◽  
Yuan Chun Huang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Optimization Design ◽  
High Efficiency ◽  
High Energy ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
System Construction ◽  
Planning Period ◽  
Urban Rail

The High Energy Consumption of the Current Urban Rail Transit Industry, High Efficiency Energy Saving Measures must be Taken. this Paper Entity from the Traction Energy Consumption, Building Structure and Operating Equipment Aspects of the Current Situation, Described the Energy Saving Strategies. Aiming at the Present Problems Existing in Energy Saving Practice, this Article Puts Forward the Comprehensive Energy Saving System which Contains Optimization Design in Planning Period, Low Resource Consumption in Construction Period, Energy Saving Work in Operation Period. above all, Implement Energy Saving Practice at Every Concrete Work of Reaching.

scholarly journals Application of Regenerative Braking with Optimized Speed Profiles for Sustainable Train Operation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Leon Allen ◽  
Steven Chien
Keyword(s):  
Energy Saving ◽  
Travel Time ◽  
Transmission Lines ◽  
Urban Rail Transit ◽  
Regenerative Braking ◽  
Rail Transit ◽  
Time Step ◽  
Urban Rail ◽  
Train Operation ◽  
Storage Limit

This paper presents a method for synergizing the energy-saving strategies of integrated coasting and regenerative braking in urban rail transit operations. Coasting saves energy by maintaining motion with propulsion disabled, but it induces longer travel time. Regenerative braking captures and reuses the braking energy of the train and could shorten travel time but reduces the time available for coasting, indicating a tradeoff between the two strategies. A simulation model was developed based on fundamental kinematic equations for assessing sustainable train operation with Wayside Energy-Saving Systems (WESSs). The objective of this study is to optimize speed profiles that minimize energy consumption, considering the train schedule and specifications, track alignment, speed limit, and the WESS parameters such as storage limit and energy losses in the transmission lines. The decision variables are the acceleration at each time step of the respective motion regimes. Since the study optimization problem is combinatorial, a Genetic Algorithm was developed to search for the solution. A case study was conducted which examined various scenarios with and without WESS on a segment of an urban rail transit line to test the applicability of the proposed model and to provide a platform for the application of ideas developed in this study. It was determined that synergizing the energy-saving strategies of coasting and regenerative braking yielded the greatest efficiency of the scenarios examined.

scholarly journals Dwell Time Modelling and Optimized Simulations for Crowded Rail Transit Lines Based on Train Capacity

2015 ◽  
Vol 27 (2) ◽  
pp. 125-135 ◽  
Author(s):  
Zhibin Jiang ◽  
Chao Xie ◽  
Tingting Ji ◽  
Xiaolei Zou
Keyword(s):  
Dwell Time ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Systems ◽  
Time Simulation ◽  
Urban Rail ◽  
Micro Simulation ◽  
Potential Benefits ◽  
Passenger Trains ◽  
First Time

Understanding the nature of rail transit dwell time has potential benefits for both the users and the operators. Crowded passenger trains cause longer dwell times and may prevent some passengers from boarding the first available train that arrives. Actual dwell time and the process of passenger alighting and boarding are interdependent through the sequence of train stops and propagated delays. A comprehensive and feasible dwell time simulation model was developed and optimized to address the problems associated with scheduled timetables. The paper introduces the factors that affect dwell time in urban rail transit systems, including train headway, the process and number of passengers alighting and boarding the train, and the inability of train doors to properly close the first time because of overcrowded vehicles. Finally, based on a time-driven micro-simulation system, Shanghai rail transit Line 8 is used as an example to quantify the feasibility of scheduled dwell times for different stations, directions of travel and time periods, and a proposed dwell time during peak hours in several crowded stations is presented according to the simulation results.

Operation optimisation of urban rail transit train base on energy saving

2018 ◽  
Vol 12 (1/2/3) ◽  
pp. 1
Author(s):  
Fuyun Liu ◽  
Jianwei Wu ◽  
Zhicong Zhang ◽  
Hao Lu ◽  
Kuan Li
Keyword(s):  
Energy Saving ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Urban Rail

Modeling Capacity of Urban Rail Transit Network Based on Bi-Level Programming

2013 ◽  
Author(s):  
Jianqiang Hu ◽  
Haiying Li ◽  
Lingyun Meng ◽  
Xinyue Xu
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Urban Rail Transit ◽  
System Capacity ◽  
Rail Transit ◽  
Problem Model ◽  
Route Choice Behavior ◽  
Mutation Operators ◽  
Urban Rail ◽  
Level Model

Capacity index of Urban Rail Transit (URT) Network plays an improtant role in rational utilization of system capacity and operation management. A definition and calculating method of the capacity of URT Network was first proposed according to the features of URT network and route choice behavior of rail passengers in this paper. Several aspects of influencing factors of URT capacity were analyzed. A bi-level programming model was presented to optimize the URT capacity besides the system utility. Upper level of the model aims at maximizing the total OD flow through the URT network, and the lower level model is one kind of Fisk Equilibrium model. A new kind of impendence function relevant to the lower level model was put forward in consideration of practical traveler behavior. Genetic algorithm technique was applied to solve the bi-level programming model on the premise that the bi-level programming problem be converted into a single-level programming which was achieved by reformulating the lower-level problem model to its equivalent Karush-Kuhn-Tucker conditions. Effective crossover and mutation operators were proposed to enhance the convergence of the Genetic algorithm. A simplified network of Beijing URT was designed and numerical examples were conducted to prove that the proposed model and algorithm are feasible and valid in calculating the capacity of such network.

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