Collaborative optimization of energy-efficient train schedule and train circulation plan for urban rail
It is of great practical significance to save train traction energy for reducing the operation cost of urban rail transit. The energy-efficient train scheduling without combining with train circulation planning may inadvertently increase the other cost of rolling stocks, and finally lead to an incre...
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| Veröffentlicht in: | Energy (Oxford) Jg. 263; S. 125599 |
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| Sprache: | Englisch |
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| ISSN: | 0360-5442 |
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| Abstract | It is of great practical significance to save train traction energy for reducing the operation cost of urban rail transit. The energy-efficient train scheduling without combining with train circulation planning may inadvertently increase the other cost of rolling stocks, and finally lead to an increment of the total operation cost. This paper studies the integrated problem of energy-efficient train scheduling and train circulation planning for urban rail, and aims to reduce the total operation cost of rolling stocks including energy consumption. Its main challenge is to simultaneously solve three subproblems, namely the saving of train's traction energy in each rail section, the utilizing of regenerative braking energy and the optimizing of train circulation plan. We construct an optimization model to simultaneously optimize schedule and train circulation plan. Based on the designing of a strategy to create the train circulation plan for each train schedule, an efficient particle swarm algorithm is formed to solve our proposed model. The numerical experiments based on Guangzhou Metro Line 9 of China illustrate that the collaborative optimization can reduce the total operation cost of trains by 4.48% compared with the initial train schedule.
•Integrate train circulation planning with energy-efficient train scheduling.•Optimize trains' traction strategies to reduce traction energy consumption.•Regenerative braking energy can be utilized in the same substation to save energy.•The effectiveness is demonstrated in a real-word case. |
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| AbstractList | It is of great practical significance to save train traction energy for reducing the operation cost of urban rail transit. The energy-efficient train scheduling without combining with train circulation planning may inadvertently increase the other cost of rolling stocks, and finally lead to an increment of the total operation cost. This paper studies the integrated problem of energy-efficient train scheduling and train circulation planning for urban rail, and aims to reduce the total operation cost of rolling stocks including energy consumption. Its main challenge is to simultaneously solve three subproblems, namely the saving of train's traction energy in each rail section, the utilizing of regenerative braking energy and the optimizing of train circulation plan. We construct an optimization model to simultaneously optimize schedule and train circulation plan. Based on the designing of a strategy to create the train circulation plan for each train schedule, an efficient particle swarm algorithm is formed to solve our proposed model. The numerical experiments based on Guangzhou Metro Line 9 of China illustrate that the collaborative optimization can reduce the total operation cost of trains by 4.48% compared with the initial train schedule. It is of great practical significance to save train traction energy for reducing the operation cost of urban rail transit. The energy-efficient train scheduling without combining with train circulation planning may inadvertently increase the other cost of rolling stocks, and finally lead to an increment of the total operation cost. This paper studies the integrated problem of energy-efficient train scheduling and train circulation planning for urban rail, and aims to reduce the total operation cost of rolling stocks including energy consumption. Its main challenge is to simultaneously solve three subproblems, namely the saving of train's traction energy in each rail section, the utilizing of regenerative braking energy and the optimizing of train circulation plan. We construct an optimization model to simultaneously optimize schedule and train circulation plan. Based on the designing of a strategy to create the train circulation plan for each train schedule, an efficient particle swarm algorithm is formed to solve our proposed model. The numerical experiments based on Guangzhou Metro Line 9 of China illustrate that the collaborative optimization can reduce the total operation cost of trains by 4.48% compared with the initial train schedule. •Integrate train circulation planning with energy-efficient train scheduling.•Optimize trains' traction strategies to reduce traction energy consumption.•Regenerative braking energy can be utilized in the same substation to save energy.•The effectiveness is demonstrated in a real-word case. |
| ArticleNumber | 125599 |
| Author | Qin, Jin Zhou, Wenliang Huang, Yu Deng, Lianbo |
| Author_xml | – sequence: 1 givenname: Wenliang surname: Zhou fullname: Zhou, Wenliang – sequence: 2 givenname: Yu surname: Huang fullname: Huang, Yu – sequence: 3 givenname: Lianbo surname: Deng fullname: Deng, Lianbo email: lbdeng@csu.edu.cn – sequence: 4 givenname: Jin orcidid: 0000-0002-1864-6489 surname: Qin fullname: Qin, Jin |
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| Keywords | Train circulation planning Urban rail transit Particle swarm algorithm Train tracking strategy Regenerative braking energy Energy-efficient train scheduling |
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| SubjectTerms | algorithms China energy efficiency Energy-efficient train scheduling operating costs Particle swarm algorithm rail transportation Regenerative braking energy Train circulation planning Train tracking strategy Urban rail transit |
| Title | Collaborative optimization of energy-efficient train schedule and train circulation plan for urban rail |
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