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dc.contributor.advisorAnuradha M. Annaswamy.en_US
dc.contributor.authorD'Achiardi Pascualy, David Humberto.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.date.accessioned2019-07-19T19:51:51Z
dc.date.available2019-07-19T19:51:51Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/121859
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 63-66).en_US
dc.description.abstractThis thesis proposes a transactive control mechanism that minimizes the operational costs of individual trains and integrates rail-side distributed energy resources (DERs) within an electric railway system. The operation of each individual train is posed as an energy cost minimization problem that is constrained by both the dynamics of the train and the specific schedule it must meet. The solution of this problem yields an optimal power profile for the train to follow. For each of the railway's segments, the proposed transactive controller determines the minimum average cost of electricity and the dispatch of the DERs based on the energy usage of the trains across space and time. By iterating between the transactive controller and the cost minimization scheme of the individual trains, the proposed methodology yields optimal power profiles of all the trains in the system and the dispatch of the generation assets. This methodology is tested through numerical simulation of the Amtrak Northeast Corridor service between University Park Station in the outskirts of Boston, Massachusetts and New Haven Station in Connecticut. Simulations of the southbound service over the course of a year demonstrate that the minimum cost operation reduces retail energy supply and delivery charges by 10% when compared to minimum work operation. We test the addition of solar generation across the system as an example of DER integration. The price signal of the transactive controller converges to within 1% after just 3 iterations for a single PV array and one train and within 4 iterations for two PV arrays and two trains.en_US
dc.description.sponsorshipSupported by award 1644877 from National Science Foundation, Cyber-Physical Systems programen_US
dc.description.statementofresponsibilityby David Humberto D'Achiardi Pascualy.en_US
dc.format.extent86 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleTransactive control of electric railway networksen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.identifier.oclc1102320475en_US
dc.description.collectionS.M. Massachusetts Institute of Technology, Department of Mechanical Engineeringen_US
dspace.imported2019-07-19T19:51:40Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentMechEen_US


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