Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology.
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...
- Autores:
-
Torres Franco, Sebastian
Durán Tovar, Ivan Camilo
Suárez Pradilla, Mónica Marcela
Marulanda Guerra, Agustin
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2021
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/1944
- Acceso en línea:
- https://repositorio.escuelaing.edu.co/handle/001/1944
- Palabra clave:
- Vehículos eléctricos
Algoritmos heurísticos
Estaciones de carga de batería (Vehículos eléctricos)
Electric vehicles
Heuristic algorithms
Battery charging stations (Electric vehicles)
- Rights
- openAccess
- License
- © 2021 The Authors
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| dc.title.eng.fl_str_mv |
Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology. |
| title |
Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology. |
| spellingShingle |
Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology. Vehículos eléctricos Algoritmos heurísticos Estaciones de carga de batería (Vehículos eléctricos) Electric vehicles Heuristic algorithms Battery charging stations (Electric vehicles) |
| title_short |
Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology. |
| title_full |
Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology. |
| title_fullStr |
Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology. |
| title_full_unstemmed |
Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology. |
| title_sort |
Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology. |
| dc.creator.fl_str_mv |
Torres Franco, Sebastian Durán Tovar, Ivan Camilo Suárez Pradilla, Mónica Marcela Marulanda Guerra, Agustin |
| dc.contributor.author.none.fl_str_mv |
Torres Franco, Sebastian Durán Tovar, Ivan Camilo Suárez Pradilla, Mónica Marcela Marulanda Guerra, Agustin |
| dc.contributor.researchgroup.spa.fl_str_mv |
Manufactura y Servicios |
| dc.subject.armarc.spa.fl_str_mv |
Vehículos eléctricos Algoritmos heurísticos Estaciones de carga de batería (Vehículos eléctricos) |
| topic |
Vehículos eléctricos Algoritmos heurísticos Estaciones de carga de batería (Vehículos eléctricos) Electric vehicles Heuristic algorithms Battery charging stations (Electric vehicles) |
| dc.subject.armarc.eng.fl_str_mv |
Electric vehicles Heuristic algorithms Battery charging stations (Electric vehicles) |
| description |
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. |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2022-01-12T21:42:02Z |
| dc.date.available.none.fl_str_mv |
2022-01-12T21:42:02Z |
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Artículo de revista |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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http://purl.org/redcol/resource_type/ART |
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20429746 |
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https://repositorio.escuelaing.edu.co/handle/001/1944 |
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eng |
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eng |
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147 |
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134 |
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11 |
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N/A |
| dc.relation.ispartofjournal.eng.fl_str_mv |
Electrical Systems in Transportation |
| dc.relation.references.spa.fl_str_mv |
International Energy Agency: Global EV outlook 2018 (2018). https://www.iea.org/reports/global-ev-outlook-2018 Landi, M.M., Mohammadi, M., Rastegar, M.: Simultaneous determination of optimal capacity and charging profile of plug-in electric vehicle parking lots in distribution systems. Energy. 158, 504– 511 (2018) Alhazmi, Y.A., Mostafa, H.A., Salama, M.M.: Optimal allocation for electric vehicle charging stations using trip success ratio. Int. J. Electr. Power Energy Syst. 91, 101– 116 (2017) Noel, L., de Rubens, G.Z., Sovacool, B.K., Kester, J.: Fear and loathing of electric vehicles: the reactionary rhetoric of range anxiety. Energy Res. Social Sci. 48, 96– 107 (2019) Bonges, H.A., Lusk, A.C.: Addressing electric vehicle (EV) sales and range anxiety through parking layout, policy and regulation. Transport. Res. Pol. Pract. 83, 63– 73 (2016) Matteo, M.: Impact of uncoordinated plug-in electric vehicle charging on residential power demand. Nat. Energy. 3, 193– 201 (2018) Zhao, H., Li, N.: Optimal siting of charging stations for electric vehicles based on fuzzy delphi and hybrid multi-criteria decision making approaches from an extended sustainability perspective. Energies. 9(4), 1– 22 (2016) He, S.Y., Kuo, Y., Wua, D.: Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: a case study of Beijing, China. Transport. Res. Part C. 67, 131– 148 (2016) Wirges, J., Linder, S., Kessler, A.: Modelling the development of a regional charging infrastructure for electric vehicles in time and space. EJTIR. 12(4), 391– 416 (2012) Sechilariu, M., Molines, N., Richard, G., Martell-Flores, H., Locment, F., Baert, J.: Electromobility framework study: infrastructure and urban planning for EV charging station empowered by PV-based microgrid. IET Electr. Syst. Transp. 9(4), 176– 185 (2019) Budde, T., Wells, P., Cipcigan, L.: Can innovative business models overcome resistance to electric vehicles? Better place and battery electric cars in Denmark. Energy Policy. 48, 498– 505 (2012) Zhang, H., Hu, Z., Xu, Z., Song, Y.: An integrated planning framework for different types of PEV charging facilities in urban area. IEEE Trans. Smart Grid. 7(5), 2273– 2284 (2016) Lam, A.Y.S., Leung, Y., Chu, X.: Electric vehicle charging station placement: formulation, complexity, and solutions. IEEE Trans. Smart Grid. 5(6), 2846– 2856 (2014) Deb, S., Tammi, K., Kalita, K., Mahanta, P.: Review of recent trends in charging infrastructure planning for electric vehicles. WIREs Energy Environ. 7(6), 1– 26 (2018) Shukla, A., Verma, K., Kumar, R.: Multi-objective synergistic planning of EV fast-charging stations in the distribution system coupled with the transportation network,IET Generat. Transm. Distrib. 13(15), 3421– 3432 (2019) Rajabi-Ghahnavieh, A., Sadeghi-Barzani, P.: Optimal zonal fast-charging station placement considering urban traffic circulation. IEEE Trans. Veh. Technol. 66(1), 45– 56 (2017) Censo Nacional de PoblaciónVivienda y Explorador de datos (2018). https://sitios.dane.gov.co/cnpv/#!/ November 2019. Colombia. Xiong Gan, Y.J., An, B., Miao, C., Bazzan, A.L.C.: Optimal electric vehicle fast charging station placement based on game theoretical framework. IEEE Trans. Intell. Transport. Syst. 19(8), 2493– 2504 (2018) Zhang, Y., Liu, X., Zhang, T., Gu, Z.: Review of the electric vehicle charging station location problem, Dependability Sens. Cloud Big Data Syst. Appl. DependSys. 1123, 435– 445 (2019) Ko, J., Kim, D., Nam, D., Lee, T.: Determining locations of charging stations for electric taxis using taxi operation data. Transport. Plan. Technol. 40, 420– 433 (2017) Daskin, M.S., Maass, K.L.: The p-Median Problem. In: Location Science, pp. 21– 45. Springer (2015). Lin, C., Lin, C.: The p-center flow-refueling facility location problem. Transport. Res. Part B. 118, 124—142 (2018) Daskin, M.S.: A maximum expected covering location model: formulation, properties and heuristic solution. Transport. Sci. 17(1), 712– 716 (1983) Davidov, S., Pantoš, M.: Planning of electric vehicle infrastructure based on charging reliability and quality of service. Energy. 118(1), 1156– 1167 (2017) Liu, Y., Xiang, Y., Tan, Y., Wang, B., Liu, J., Yang, Z.: Optimal allocation model for EV charging stations coordinating investor and user benefits. IEEE Access. 6, 36039– 36049 (2018) Luo, C., Huang, Y., Gupta, V.: ‘Placement of EV charging stations—balancing benefits among multiple entities. IEEE Trans. Smart Grid. 8(2), 759– 768 (2018) Li, S., Huang, Y.: Heuristic approaches for the flow-based set covering problem with deviation paths. Transport. Res. E Logist. Transport. Rev. 72, 144– 158 (2014) Liu, H., Jin, C., Yang, B., Zhou, A.: Finding top-k shortest paths with diversity. IEEE Trans. Knowl. Data Eng. 30(3), 488– 502 (2018) Zhang, H., Moura, S.J., Hu, Z., Song, Y.: PEV fast-charging station siting and sizing on coupled transportation and power networks. IEEE Trans. Smart Grid. 9(4), 2595– 2605 (2018) Aljaidi, M., Aslam, N., Chen, X., Kaiwartya, O., Khalid, M.: Proc. 11th International Conference on Information and Communication Systems (ICICS), pp. 161– 166 (2020). An energy efficient strategy for assignment of electric vehicles to charging stations in urban environments Mao, D., Wang, J., Tan, J., Liu, G., Xu, Y., Li, J.: Location planning of fast charging station considering its impact on the power grid assets. Proc. IEEE Transportation Electrification Conference and Expo (ITEC), 1– 5 (2019) Bi, R., Xiao, J., Pelzer, D., Ciechanowicz, D., Eckhoff, D., Knoll, A., pp. 1– 7. Proc. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (2017). A simulation-based heuristic for city-scale electric vehicle charging station placement Andrade, J., Ochoa, L.F., Freitas, W.: Regional-scale allocation of fast charging stations: travel times and distribution system reinforcements. IET Gen. Transm. Distrib. 14(19), 4225– 4233 (2020) Giménez-Gaydou, D.A., Ribeiro, A.S.N., Gutiérrez, J., Antunes, A.P.: Optimal location of battery electric vehicle charging stations in urban areas: a new approach. Int. J. Sustainable Transport. 10(5), 393– 405 (2016) Deb, S., Tammi, K., Gao, X., Kalita, K., Mahanta, P.: A hybrid multi-objective chicken swarm optimization and teaching learning based algorithm for charging station placement problem. IEEE Access. 8, 92573– 92590 (2020) Zhang, Y., Liu, X., Zhang, T., Gu, Z.: ‘Review of the electric vehicle charging station location problem. Proc. 5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications. DependSys. 1123, 435– 445 (2019) Melaina, M., Bremson, J.: Refueling availability for alternative fuel vehicle markets: sufficient urban station coverage. Energy Pol. 36, 3233– 3241 (2008) Ruiz Estupiñán, N.H.: Estudio de la estructura urbana e identificación y análisis del impacto de la localización de la actividad económica sobre las dinámicas territoriales: El caso de Bogotá, Colombia, Ph.D. thesis, Universitat Politecnica de Catalunya (2016) ‘Zonas de Análisis de Transporte 2016, https://bogota-laburbano.opendatasoft.com/explore/dataset/zat/information/?location=10,4.5997,-74.02519&basemap=jawg.streets Avendaño, J.: Three essays on urban spatial structure in Bogotá D.C. Ph.D. thesis, Universitat Autonoma de Barcelona (2012) Yen, J.Y.: Finding the K shortest loopless paths in a network. Manag. Sci. 17(11), 712– 716 (2019) |
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© 2021 The Authors |
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John Wiley & Sons Ltd |
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Torres Franco, Sebastianccfc55b57a38e3e4c564e5c5675ff520600Durán Tovar, Ivan Camilo5908811a1b91cd2e6cd24297e7f9faba600Suárez Pradilla, Mónica Marcela6a96af13e672eb33b3d88359d4ec5864600Marulanda Guerra, Agustina1b471d3d1ddb9aac54e4d957b8182ab600Manufactura y Servicios2022-01-12T21:42:02Z2022-01-12T21:42:02Z202120429746https://repositorio.escuelaing.edu.co/handle/001/1944The 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.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.14 páginas.application/pdfengJohn Wiley & Sons Ltd© 2021 The Authorsinfo:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/els2.12011Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology.Artículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a8514713411N/AElectrical Systems in TransportationInternational Energy Agency: Global EV outlook 2018 (2018). https://www.iea.org/reports/global-ev-outlook-2018Landi, M.M., Mohammadi, M., Rastegar, M.: Simultaneous determination of optimal capacity and charging profile of plug-in electric vehicle parking lots in distribution systems. Energy. 158, 504– 511 (2018)Alhazmi, Y.A., Mostafa, H.A., Salama, M.M.: Optimal allocation for electric vehicle charging stations using trip success ratio. Int. J. Electr. Power Energy Syst. 91, 101– 116 (2017)Noel, L., de Rubens, G.Z., Sovacool, B.K., Kester, J.: Fear and loathing of electric vehicles: the reactionary rhetoric of range anxiety. Energy Res. Social Sci. 48, 96– 107 (2019)Bonges, H.A., Lusk, A.C.: Addressing electric vehicle (EV) sales and range anxiety through parking layout, policy and regulation. Transport. Res. Pol. Pract. 83, 63– 73 (2016)Matteo, M.: Impact of uncoordinated plug-in electric vehicle charging on residential power demand. Nat. Energy. 3, 193– 201 (2018)Zhao, H., Li, N.: Optimal siting of charging stations for electric vehicles based on fuzzy delphi and hybrid multi-criteria decision making approaches from an extended sustainability perspective. Energies. 9(4), 1– 22 (2016)He, S.Y., Kuo, Y., Wua, D.: Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: a case study of Beijing, China. Transport. Res. Part C. 67, 131– 148 (2016)Wirges, J., Linder, S., Kessler, A.: Modelling the development of a regional charging infrastructure for electric vehicles in time and space. EJTIR. 12(4), 391– 416 (2012)Sechilariu, M., Molines, N., Richard, G., Martell-Flores, H., Locment, F., Baert, J.: Electromobility framework study: infrastructure and urban planning for EV charging station empowered by PV-based microgrid. IET Electr. Syst. Transp. 9(4), 176– 185 (2019)Budde, T., Wells, P., Cipcigan, L.: Can innovative business models overcome resistance to electric vehicles? Better place and battery electric cars in Denmark. Energy Policy. 48, 498– 505 (2012)Zhang, H., Hu, Z., Xu, Z., Song, Y.: An integrated planning framework for different types of PEV charging facilities in urban area. IEEE Trans. Smart Grid. 7(5), 2273– 2284 (2016)Lam, A.Y.S., Leung, Y., Chu, X.: Electric vehicle charging station placement: formulation, complexity, and solutions. IEEE Trans. Smart Grid. 5(6), 2846– 2856 (2014)Deb, S., Tammi, K., Kalita, K., Mahanta, P.: Review of recent trends in charging infrastructure planning for electric vehicles. WIREs Energy Environ. 7(6), 1– 26 (2018)Shukla, A., Verma, K., Kumar, R.: Multi-objective synergistic planning of EV fast-charging stations in the distribution system coupled with the transportation network,IET Generat. Transm. Distrib. 13(15), 3421– 3432 (2019)Rajabi-Ghahnavieh, A., Sadeghi-Barzani, P.: Optimal zonal fast-charging station placement considering urban traffic circulation. IEEE Trans. Veh. Technol. 66(1), 45– 56 (2017)Censo Nacional de PoblaciónVivienda y Explorador de datos (2018). https://sitios.dane.gov.co/cnpv/#!/ November 2019. Colombia.Xiong Gan, Y.J., An, B., Miao, C., Bazzan, A.L.C.: Optimal electric vehicle fast charging station placement based on game theoretical framework. IEEE Trans. Intell. Transport. Syst. 19(8), 2493– 2504 (2018)Zhang, Y., Liu, X., Zhang, T., Gu, Z.: Review of the electric vehicle charging station location problem, Dependability Sens. Cloud Big Data Syst. Appl. DependSys. 1123, 435– 445 (2019)Ko, J., Kim, D., Nam, D., Lee, T.: Determining locations of charging stations for electric taxis using taxi operation data. Transport. Plan. Technol. 40, 420– 433 (2017)Daskin, M.S., Maass, K.L.: The p-Median Problem. In: Location Science, pp. 21– 45. Springer (2015).Lin, C., Lin, C.: The p-center flow-refueling facility location problem. Transport. Res. Part B. 118, 124—142 (2018)Daskin, M.S.: A maximum expected covering location model: formulation, properties and heuristic solution. Transport. Sci. 17(1), 712– 716 (1983)Davidov, S., Pantoš, M.: Planning of electric vehicle infrastructure based on charging reliability and quality of service. Energy. 118(1), 1156– 1167 (2017)Liu, Y., Xiang, Y., Tan, Y., Wang, B., Liu, J., Yang, Z.: Optimal allocation model for EV charging stations coordinating investor and user benefits. IEEE Access. 6, 36039– 36049 (2018)Luo, C., Huang, Y., Gupta, V.: ‘Placement of EV charging stations—balancing benefits among multiple entities. IEEE Trans. Smart Grid. 8(2), 759– 768 (2018)Li, S., Huang, Y.: Heuristic approaches for the flow-based set covering problem with deviation paths. Transport. Res. E Logist. Transport. Rev. 72, 144– 158 (2014)Liu, H., Jin, C., Yang, B., Zhou, A.: Finding top-k shortest paths with diversity. IEEE Trans. Knowl. Data Eng. 30(3), 488– 502 (2018)Zhang, H., Moura, S.J., Hu, Z., Song, Y.: PEV fast-charging station siting and sizing on coupled transportation and power networks. IEEE Trans. Smart Grid. 9(4), 2595– 2605 (2018)Aljaidi, M., Aslam, N., Chen, X., Kaiwartya, O., Khalid, M.: Proc. 11th International Conference on Information and Communication Systems (ICICS), pp. 161– 166 (2020). An energy efficient strategy for assignment of electric vehicles to charging stations in urban environmentsMao, D., Wang, J., Tan, J., Liu, G., Xu, Y., Li, J.: Location planning of fast charging station considering its impact on the power grid assets. Proc. IEEE Transportation Electrification Conference and Expo (ITEC), 1– 5 (2019)Bi, R., Xiao, J., Pelzer, D., Ciechanowicz, D., Eckhoff, D., Knoll, A., pp. 1– 7. Proc. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (2017). A simulation-based heuristic for city-scale electric vehicle charging station placementAndrade, J., Ochoa, L.F., Freitas, W.: Regional-scale allocation of fast charging stations: travel times and distribution system reinforcements. IET Gen. Transm. Distrib. 14(19), 4225– 4233 (2020)Giménez-Gaydou, D.A., Ribeiro, A.S.N., Gutiérrez, J., Antunes, A.P.: Optimal location of battery electric vehicle charging stations in urban areas: a new approach. Int. J. Sustainable Transport. 10(5), 393– 405 (2016)Deb, S., Tammi, K., Gao, X., Kalita, K., Mahanta, P.: A hybrid multi-objective chicken swarm optimization and teaching learning based algorithm for charging station placement problem. IEEE Access. 8, 92573– 92590 (2020)Zhang, Y., Liu, X., Zhang, T., Gu, Z.: ‘Review of the electric vehicle charging station location problem. Proc. 5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications. DependSys. 1123, 435– 445 (2019)Melaina, M., Bremson, J.: Refueling availability for alternative fuel vehicle markets: sufficient urban station coverage. Energy Pol. 36, 3233– 3241 (2008)Ruiz Estupiñán, N.H.: Estudio de la estructura urbana e identificación y análisis del impacto de la localización de la actividad económica sobre las dinámicas territoriales: El caso de Bogotá, Colombia, Ph.D. thesis, Universitat Politecnica de Catalunya (2016)‘Zonas de Análisis de Transporte 2016, https://bogota-laburbano.opendatasoft.com/explore/dataset/zat/information/?location=10,4.5997,-74.02519&basemap=jawg.streetsAvendaño, J.: Three essays on urban spatial structure in Bogotá D.C. Ph.D. thesis, Universitat Autonoma de Barcelona (2012)Yen, J.Y.: Finding the K shortest loopless paths in a network. Manag. Sci. 17(11), 712– 716 (2019)Vehículos eléctricosAlgoritmos heurísticosEstaciones de carga de batería (Vehículos eléctricos)Electric vehiclesHeuristic algorithmsBattery charging stations (Electric vehicles)ORIGINALElectric vehicle charging stations location in urban transportation.pdfElectric vehicle charging stations location in urban transportation.pdfArtículo principal.application/pdf2789522https://repositorio.escuelaing.edu.co/bitstream/001/1944/1/Electric%20vehicle%20charging%20stations%20%20location%20in%20urban%20transportation.pdf2309c1dfcb8ade32a57cffca3bab336cMD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/1944/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessTEXTElectric vehicle charging stations location in urban transportation.pdf.txtElectric vehicle charging stations location in urban transportation.pdf.txtExtracted texttext/plain49925https://repositorio.escuelaing.edu.co/bitstream/001/1944/3/Electric%20vehicle%20charging%20stations%20%20location%20in%20urban%20transportation.pdf.txt2468b8c06b1f2f65928219d131e70f38MD53open accessTHUMBNAILElectric vehicle charging stations location in urban transportation.pdf.jpgElectric vehicle charging stations location in urban transportation.pdf.jpgGenerated Thumbnailimage/jpeg15847https://repositorio.escuelaing.edu.co/bitstream/001/1944/4/Electric%20vehicle%20charging%20stations%20%20location%20in%20urban%20transportation.pdf.jpg54c5ff520b5fd870ebbdd4fe3305bddbMD54open access001/1944oai:repositorio.escuelaing.edu.co:001/19442022-09-16 17:22:06.022open accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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 |