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Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology.
dc.contributor.author | Torres Franco, Sebastian | |
dc.contributor.author | Durán Tovar, Ivan Camilo | |
dc.contributor.author | Suárez Pradilla, Mónica Marcela | |
dc.contributor.author | Marulanda Guerra, Agustin | |
dc.date.accessioned | 2022-01-12T21:42:02Z | |
dc.date.available | 2022-01-12T21:42:02Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 20429746 | |
dc.identifier.uri | https://repositorio.escuelaing.edu.co/handle/001/1944 | |
dc.description.abstract | The lack of public charging infrastructure has been one of the main barriers preventing the technological transition from traditional vehicles to electric vehicles. To accelerate this technological transition, it is necessary to elaborate optimal charging station location strategies to increase the user confidence, and maintain investment costs within acceptable levels. However, the existing works for this purpose are often based on multipath considerations or multi-objective functions, that result in taxing computational efforts for urban transportation networks. This article presents a heuristic methodology for urban transportation networks, that considers the deployment of the charging stations for coverage purposes, and the fulfilment of user preferences and constraints as two separated processes. In this methodology, a Reallocation Algorithm is formulated to prioritize the selection of Locations of Interest, and to reduce the number of stations with overlapping covering areas. The methodology results are compared to those drawn from a Greedy Algorithm based on a multipath consideration, in an extensive metropolitan transportation network. The results show that the proposed methodology significantly reduce the computational time required for solving the location problem, and furthermore, allows for similar results to those obtained when considering k = 2 and k = 3 deviation paths. | eng |
dc.description.abstract | La falta de infraestructura pública de recarga ha sido una de las principales barreras que ha impedido la transición tecnológica de los vehículos tradicionales a los eléctricos. Para acelerar esta transición tecnológica, es necesario elaborar estrategias óptimas de ubicación de estaciones de carga para aumentar la confianza del usuario y mantener los costos de inversión dentro de niveles aceptables. Sin embargo, los trabajos existentes para este propósito a menudo se basan en consideraciones de caminos múltiples o funciones de objetivos múltiples, que resultan en esfuerzos computacionales difíciles para las redes de transporte urbano. Este artículo presenta una metodología heurística para redes de transporte urbano, que considera el despliegue de las estaciones de carga con fines de cobertura y el cumplimiento de las preferencias y restricciones de los usuarios como dos procesos separados. En esta metodología, se formula un algoritmo de reasignación para priorizar la selección de ubicaciones de interés y reducir la cantidad de estaciones con áreas de cobertura superpuestas. Los resultados de la metodología se comparan con los extraídos de un Algoritmo Greedy basado en una consideración de trayectos múltiples, en una extensa red de transporte metropolitano. Los resultados muestran que la metodología propuesta reduce significativamente el tiempo computacional requerido para resolver el problema de ubicación y además, permite obtener resultados similares a los obtenidos al considerar k = 2 y k = 3 caminos de desviación. | spa |
dc.format.extent | 14 páginas. | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.publisher | John Wiley & Sons Ltd | spa |
dc.rights | © 2021 The Authors | eng |
dc.source | https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/els2.12011 | spa |
dc.title | Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology. | eng |
dc.type | Artículo de revista | spa |
dc.type.version | info:eu-repo/semantics/publishedVersion | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
dc.contributor.researchgroup | Manufactura y Servicios | spa |
dc.relation.citationendpage | 147 | spa |
dc.relation.citationstartpage | 134 | spa |
dc.relation.citationvolume | 11 | spa |
dc.relation.indexed | N/A | spa |
dc.relation.ispartofjournal | Electrical Systems in Transportation | eng |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.creativecommons | Atribución 4.0 Internacional (CC BY 4.0) | spa |
dc.subject.armarc | Vehículos eléctricos | spa |
dc.subject.armarc | Electric vehicles | eng |
dc.subject.armarc | Algoritmos heurísticos | spa |
dc.subject.armarc | Heuristic algorithms | eng |
dc.subject.armarc | Estaciones de carga de batería (Vehículos eléctricos) | spa |
dc.subject.armarc | Battery charging stations (Electric vehicles) | eng |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
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