A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system
Maximizing regenerative energy utilization in subway systems has become a hot research topic in recent years. By coordinating traction and braking trains in a substation, regenerative energy is optimally utilized and thus energy consumption from the substation can be reduced. This article proposes a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2018-09-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/1687814018797034 |
| _version_ | 1819127533968293888 |
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| author | Hongjie Liu Tao Tang Xiwang Guo Xisheng Xia |
| author_facet | Hongjie Liu Tao Tang Xiwang Guo Xisheng Xia |
| author_sort | Hongjie Liu |
| collection | DOAJ |
| description | Maximizing regenerative energy utilization in subway systems has become a hot research topic in recent years. By coordinating traction and braking trains in a substation, regenerative energy is optimally utilized and thus energy consumption from the substation can be reduced. This article proposes a timetable optimization problem to maximize regenerative energy utilization in a subway system with headway and dwell time control. We formulate its mathematical model, and some required constraints are considered in the model. To keep the operation time duration constant, the headway time between different trains can be different. An improved artificial bee colony algorithm is designed to solve the problem. Its main procedure and some related tasks are presented. Numerical experiments based on the data from a subway line in China are conducted, and improved artificial bee colony is compared with a genetic algorithm. Experimental results prove the correctness of the mathematical model and the effectiveness of improved artificial bee colony, which improves regenerative energy utilization for the experimental line and performs better than genetic algorithm. |
| first_indexed | 2024-12-22T08:13:27Z |
| format | Article |
| id | doaj.art-195fe83ace624c84bcb3ecf16ddd53ba |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-12-22T08:13:27Z |
| publishDate | 2018-09-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-195fe83ace624c84bcb3ecf16ddd53ba2022-12-21T18:32:58ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402018-09-011010.1177/1687814018797034A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway systemHongjie Liu0Tao Tang1Xiwang Guo2Xisheng Xia3Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USASchool of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, ChinaComputer and Communication Engineering College, Liaoning Shihua University, Fushun, ChinaResearch and Development Center, Traffic Control Technology Co., Ltd., Beijing, ChinaMaximizing regenerative energy utilization in subway systems has become a hot research topic in recent years. By coordinating traction and braking trains in a substation, regenerative energy is optimally utilized and thus energy consumption from the substation can be reduced. This article proposes a timetable optimization problem to maximize regenerative energy utilization in a subway system with headway and dwell time control. We formulate its mathematical model, and some required constraints are considered in the model. To keep the operation time duration constant, the headway time between different trains can be different. An improved artificial bee colony algorithm is designed to solve the problem. Its main procedure and some related tasks are presented. Numerical experiments based on the data from a subway line in China are conducted, and improved artificial bee colony is compared with a genetic algorithm. Experimental results prove the correctness of the mathematical model and the effectiveness of improved artificial bee colony, which improves regenerative energy utilization for the experimental line and performs better than genetic algorithm.https://doi.org/10.1177/1687814018797034 |
| spellingShingle | Hongjie Liu Tao Tang Xiwang Guo Xisheng Xia A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system Advances in Mechanical Engineering |
| title | A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system |
| title_full | A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system |
| title_fullStr | A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system |
| title_full_unstemmed | A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system |
| title_short | A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system |
| title_sort | timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system |
| url | https://doi.org/10.1177/1687814018797034 |
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