Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22319
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dc.contributor.authorWu, X-
dc.contributor.authorFeng, Q-
dc.contributor.authorBai, C-
dc.contributor.authorLai, CS-
dc.contributor.authorJia, Y-
dc.contributor.authorLai, LL-
dc.date.accessioned2021-02-25T03:03:05Z-
dc.date.available2021-02-25T03:03:05Z-
dc.date.issued2021-02-18-
dc.identifierORCID iD: Chun Sing Lai https://orcid.org/0000-0002-4169-4438-
dc.identifier120106-
dc.identifier.citationWu X, et al.(2021) 'A novel fast-charging stations locational planning model for electric bus transit system', Energy. 224,:120106, pp. 1 - 14. doi: 10.1016/j.energy.2021.120106.en_US
dc.identifier.issn0360-5442-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/22319-
dc.description.abstractWith more electric buses, the optimal location of charging station plays an important role for bus electrification. This paper proposes a location planning model of electric bus fast-charging stations for the electric bus transit system, that takes the bus operation network and the distribution network into account. The model 1) simulates the operation network of electric buses thoroughly to obtain the charging demand of electric buses and 2) takes into account of the absorption capacity of distribution network and other constraints in the siting and capacity determination stage. The objective of the model is to minimize the sum of the construction cost, operation and maintenance costs, travel cost to charging stations, and the cost of power loss for charging stations at established bus terminus. The Affinity Propagation method is adopted to cluster the bus terminuses in order to obtain a preliminary number of charging stations. Subsequently, the Binary Particle Swarm Optimization algorithm is used to optimize the site selection and capacity. Finally, the model is applied to simulate and analyze the bus operation network of a coastal city in South China. The case study shows that the model can effectively optimize the layout of bus charging stations for the city.-
dc.description.sponsorshipChina Southern Power Grid; Department of Finance and Education of Guangdong Province 2016; Key Discipline Construction Program, China; Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group; Brunel University London BRIEF Funding, UK.en_US
dc.format.extent1 - 14-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rightsCopyright © 2021 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.energy.2021.120106, made available on this repository under a Creative Commons CC BY-NC-ND attribution licence (https://creativecommons.org/licenses/by-nc-nd/4.0/)-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.subjectfast-charging stationen_US
dc.subjectlocation planningen_US
dc.subjectafinity propagationen_US
dc.subjectbinary particle swarm optimizationen_US
dc.titleA novel fast-charging stations locational planning model for electric bus transit systemen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1016/j.energy.2021.120106-
dc.relation.isPartOfEnergy-
pubs.publication-statusPublished-
pubs.volume224-
dc.identifier.eissn1873-6785-
dc.rights.holderElsevier Ltd.-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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