Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91946
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dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorOuyang, Xen_US
dc.creatorXu, Men_US
dc.creatorZhou, Ben_US
dc.date.accessioned2022-01-25T08:58:54Z-
dc.date.available2022-01-25T08:58:54Z-
dc.identifier.issn1566-113Xen_US
dc.identifier.urihttp://hdl.handle.net/10397/91946-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021en_US
dc.rightsThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11067-021-09546-5.en_US
dc.subjectCharging station locationen_US
dc.subjectPartial chargingen_US
dc.subjectNonlinear elastic demanden_US
dc.subjectPiecewise linear approximationen_US
dc.subjectGray codeen_US
dc.titleAn elastic demand model for locating electric vehicle charging stationsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage31en_US
dc.identifier.volume22en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1007/s11067-021-09546-5en_US
dcterms.abstractIn this study, we aim to optimally locate multiple types of charging stations, e.g., fast-charging stations and slow-charging stations, for maximizing the covered flows under a limited budget while taking drivers’ partial charging behavior and nonlinear demand elasticity into account. This problem is first formulated as a mixed-integer nonlinear programming model. Instead of generating paths and charging patterns, we develop a compact formulation to model the partial charging logic. The proposed model is then approximated and reformulated by a mixed-integer linear programming model by piecewise linear approximation. To improve the computational efficiency, we employ a refined formulation using an efficient Gray code method, which reduces the number of constraints and binary auxiliary variables in the formulation of the piecewise linear approximate function effectively. The ε-optimal solution to the proposed problem can be therefore obtained by state-of-the-art MIP solvers. Finally, a case study based on the highway network of Zhejiang Province of China is conducted to assess the model performance and analyze the impact of the budget on flow coverage and optimal station selection.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationNetworks and spatial economics, Mar. 2022, v. 22, no. 1, p. 1-31en_US
dcterms.isPartOfNetworks and spatial economicsen_US
dcterms.issued2022-03-
dc.identifier.isiWOS:000738417200001-
dc.identifier.scopus2-s2.0-85122319215-
dc.identifier.eissn1572-9427en_US
dc.description.validate202201 bcrcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0958-n01-
dc.identifier.SubFormID2205-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingText25207319, P0030389; P0000250en_US
dc.description.pubStatusPublisheden_US
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