An Energy-Efficient Train Operation Approach by Integrating the Metro Timetabling and Eco-Driving
Energy-efficient train operation is regarded as an effective way to reduce the operational cost and carbon emissions in metro systems. Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable...
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Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2020-10, Vol.21 (10), p.4252-4268 |
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description | Energy-efficient train operation is regarded as an effective way to reduce the operational cost and carbon emissions in metro systems. Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable and driving strategy. To minimize the systematic net energy consumption, i.e., the difference between the traction energy consumption and the reused regenerative energy, this paper proposes an integrated train operation approach by jointly optimizing the train timetable and driving strategy. A precise train driving strategy is presented and the timetable model considers the headway between successive trains, the distribution of the trip time, and passenger demand in this paper. In addition, a distributed regenerative braking energy model is proposed, based on which the integrated optimization model is formulated. Then, a two-level approach is proposed to solve the problem. At the driving strategy level, the train control problem is transferred into a multi-step decision problem and the Dynamic Programming method is introduced to calculate the energy-efficient driving strategy with the given trip time. As for the timetable level, the trip times and headway of trains are optimized by using the Simulated Annealing algorithm based on the results of dynamic programming method. The timetable optimization level balances the mechanical traction energy of multi-interstations and the amount of the reused regenerative energy such that the net mechanical energy consumption of the metro system is minimized. Furthermore, two numerical examples are conducted for train operations in the peak and off-peak hours separately based on the real-world data of a metro line. The simulation results illustrate that the proposed approach can produce a good performance on energy-saving. |
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Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable and driving strategy. To minimize the systematic net energy consumption, i.e., the difference between the traction energy consumption and the reused regenerative energy, this paper proposes an integrated train operation approach by jointly optimizing the train timetable and driving strategy. A precise train driving strategy is presented and the timetable model considers the headway between successive trains, the distribution of the trip time, and passenger demand in this paper. In addition, a distributed regenerative braking energy model is proposed, based on which the integrated optimization model is formulated. Then, a two-level approach is proposed to solve the problem. At the driving strategy level, the train control problem is transferred into a multi-step decision problem and the Dynamic Programming method is introduced to calculate the energy-efficient driving strategy with the given trip time. As for the timetable level, the trip times and headway of trains are optimized by using the Simulated Annealing algorithm based on the results of dynamic programming method. The timetable optimization level balances the mechanical traction energy of multi-interstations and the amount of the reused regenerative energy such that the net mechanical energy consumption of the metro system is minimized. Furthermore, two numerical examples are conducted for train operations in the peak and off-peak hours separately based on the real-world data of a metro line. The simulation results illustrate that the proposed approach can produce a good performance on energy-saving.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2019.2939358</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Acceleration ; Algorithms ; Computer simulation ; Dynamic programming ; Energy conservation ; Energy consumption ; Mathematical model ; Optimization ; Rails ; Regenerative braking ; regenerative energy ; scheduling ; Simulated annealing ; Strategy ; Switches ; timetable ; Timetables ; Traction ; train control ; Travel demand</subject><ispartof>IEEE transactions on intelligent transportation systems, 2020-10, Vol.21 (10), p.4252-4268</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-f711fd41538a6927c5215653ba91e2b4985eaff912a7b1cedcb7e0c2ef4094433</citedby><cites>FETCH-LOGICAL-c293t-f711fd41538a6927c5215653ba91e2b4985eaff912a7b1cedcb7e0c2ef4094433</cites><orcidid>0000-0002-7329-2345 ; 0000-0001-8412-9853 ; 0000-0001-6631-4908 ; 0000-0002-2725-3919</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8846576$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8846576$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Su, Shuai</creatorcontrib><creatorcontrib>Wang, Xuekai</creatorcontrib><creatorcontrib>Cao, Yuan</creatorcontrib><creatorcontrib>Yin, Jiateng</creatorcontrib><title>An Energy-Efficient Train Operation Approach by Integrating the Metro Timetabling and Eco-Driving</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>Energy-efficient train operation is regarded as an effective way to reduce the operational cost and carbon emissions in metro systems. Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable and driving strategy. To minimize the systematic net energy consumption, i.e., the difference between the traction energy consumption and the reused regenerative energy, this paper proposes an integrated train operation approach by jointly optimizing the train timetable and driving strategy. A precise train driving strategy is presented and the timetable model considers the headway between successive trains, the distribution of the trip time, and passenger demand in this paper. In addition, a distributed regenerative braking energy model is proposed, based on which the integrated optimization model is formulated. Then, a two-level approach is proposed to solve the problem. At the driving strategy level, the train control problem is transferred into a multi-step decision problem and the Dynamic Programming method is introduced to calculate the energy-efficient driving strategy with the given trip time. As for the timetable level, the trip times and headway of trains are optimized by using the Simulated Annealing algorithm based on the results of dynamic programming method. The timetable optimization level balances the mechanical traction energy of multi-interstations and the amount of the reused regenerative energy such that the net mechanical energy consumption of the metro system is minimized. Furthermore, two numerical examples are conducted for train operations in the peak and off-peak hours separately based on the real-world data of a metro line. The simulation results illustrate that the proposed approach can produce a good performance on energy-saving.</description><subject>Acceleration</subject><subject>Algorithms</subject><subject>Computer simulation</subject><subject>Dynamic programming</subject><subject>Energy conservation</subject><subject>Energy consumption</subject><subject>Mathematical model</subject><subject>Optimization</subject><subject>Rails</subject><subject>Regenerative braking</subject><subject>regenerative energy</subject><subject>scheduling</subject><subject>Simulated annealing</subject><subject>Strategy</subject><subject>Switches</subject><subject>timetable</subject><subject>Timetables</subject><subject>Traction</subject><subject>train control</subject><subject>Travel demand</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9UEFOwzAQtBBIlMIDEBdLnFO8jp3Ex6oEqFTUA-EcOe66ddU6xXGR-nsSteK0u6OZ2dEQ8ghsAsDUSzWvviacgZpwlapUFldkBFIWCWOQXQ87F4likt2Su67b9qiQACOip56WHsP6lJTWOuPQR1oF7TxdHjDo6FpPp4dDaLXZ0OZE5z7iesD9msYN0k-MoaWV22PUzW5AtV_R0rTJa3C__X1PbqzedfhwmWPy_VZWs49ksXyfz6aLxPSJY2JzALsSINNCZ4rnRnKQmUwbrQB5I1QhUVurgOu8AYMr0-TIDEcrmBIiTcfk-ezbZ_05YhfrbXsMvn9ZcyEKkUlRZD0LziwT2q4LaOtDcHsdTjWwemiyHpqshybrS5O95umscYj4zy8GyzxL_wD6dm9L</recordid><startdate>20201001</startdate><enddate>20201001</enddate><creator>Su, Shuai</creator><creator>Wang, Xuekai</creator><creator>Cao, Yuan</creator><creator>Yin, Jiateng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-7329-2345</orcidid><orcidid>https://orcid.org/0000-0001-8412-9853</orcidid><orcidid>https://orcid.org/0000-0001-6631-4908</orcidid><orcidid>https://orcid.org/0000-0002-2725-3919</orcidid></search><sort><creationdate>20201001</creationdate><title>An Energy-Efficient Train Operation Approach by Integrating the Metro Timetabling and Eco-Driving</title><author>Su, Shuai ; Wang, Xuekai ; Cao, Yuan ; Yin, Jiateng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-f711fd41538a6927c5215653ba91e2b4985eaff912a7b1cedcb7e0c2ef4094433</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Acceleration</topic><topic>Algorithms</topic><topic>Computer simulation</topic><topic>Dynamic programming</topic><topic>Energy conservation</topic><topic>Energy consumption</topic><topic>Mathematical model</topic><topic>Optimization</topic><topic>Rails</topic><topic>Regenerative braking</topic><topic>regenerative energy</topic><topic>scheduling</topic><topic>Simulated annealing</topic><topic>Strategy</topic><topic>Switches</topic><topic>timetable</topic><topic>Timetables</topic><topic>Traction</topic><topic>train control</topic><topic>Travel demand</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Su, Shuai</creatorcontrib><creatorcontrib>Wang, Xuekai</creatorcontrib><creatorcontrib>Cao, Yuan</creatorcontrib><creatorcontrib>Yin, Jiateng</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on intelligent transportation systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Su, Shuai</au><au>Wang, Xuekai</au><au>Cao, Yuan</au><au>Yin, Jiateng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Energy-Efficient Train Operation Approach by Integrating the Metro Timetabling and Eco-Driving</atitle><jtitle>IEEE transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2020-10-01</date><risdate>2020</risdate><volume>21</volume><issue>10</issue><spage>4252</spage><epage>4268</epage><pages>4252-4268</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><abstract>Energy-efficient train operation is regarded as an effective way to reduce the operational cost and carbon emissions in metro systems. Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable and driving strategy. To minimize the systematic net energy consumption, i.e., the difference between the traction energy consumption and the reused regenerative energy, this paper proposes an integrated train operation approach by jointly optimizing the train timetable and driving strategy. A precise train driving strategy is presented and the timetable model considers the headway between successive trains, the distribution of the trip time, and passenger demand in this paper. In addition, a distributed regenerative braking energy model is proposed, based on which the integrated optimization model is formulated. Then, a two-level approach is proposed to solve the problem. At the driving strategy level, the train control problem is transferred into a multi-step decision problem and the Dynamic Programming method is introduced to calculate the energy-efficient driving strategy with the given trip time. As for the timetable level, the trip times and headway of trains are optimized by using the Simulated Annealing algorithm based on the results of dynamic programming method. The timetable optimization level balances the mechanical traction energy of multi-interstations and the amount of the reused regenerative energy such that the net mechanical energy consumption of the metro system is minimized. Furthermore, two numerical examples are conducted for train operations in the peak and off-peak hours separately based on the real-world data of a metro line. 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subjects | Acceleration Algorithms Computer simulation Dynamic programming Energy conservation Energy consumption Mathematical model Optimization Rails Regenerative braking regenerative energy scheduling Simulated annealing Strategy Switches timetable Timetables Traction train control Travel demand |
title | An Energy-Efficient Train Operation Approach by Integrating the Metro Timetabling and Eco-Driving |
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