Despliegue Óptimo Georreferenciado de Estaciones de Carga Vehicular Pública Considerando Capacidad de Flujo y Distancias Máximas Habilitantes

Translated title of the contribution: Optimal Georeferenced Deployment of Public Vehicle Charging Stations Considering Flow Capacity and Maximum Enabling Distances

Miguel Angel Campaña Molina, Esteban Mauricio Inga Ortega

Research output: Contribution to journalArticle

Abstract

Modern challenges are aimed at significantly reducing greenhouse gases, which deteriorate air quality. The vehicle fleet, energy supplies (gas, steam, air conditioning, water for industrial use and electrical energy) are sources that are released into the atmosphere. For this reason, this article proposes a heuristic model aimed at significantly reducing dependence on internal combustion vehicles. The success or failure of the massive penetration of plug-in electric vehicles (PHEVs) will depend not only on the installed capacity of the electric distribution systems, but also on the autonomy and comfort that PHEVs can provide to the end user. Therefore, with the Minimum Emplacement of Geolocated Charging Stations (MEECG) algorithm, a model capable of locating public charging station infrastructure (IECP) considering vehicle flow and maximum enabling distances is proposed. In such a way that, the minimum number of public charging stations (PCE) is selected ensuring the possibility that a VEE can connect to a PCE reducing to the maximum the range anxiety on the part of the VEE operator.
Translated title of the contributionOptimal Georeferenced Deployment of Public Vehicle Charging Stations Considering Flow Capacity and Maximum Enabling Distances
Original languageSpanish (Ecuador)
Pages (from-to)68-78
Number of pages11
JournalRevista De I+D Tecnológico
Volume15
Issue number15
DOIs
StatePublished - 30 Jul 2019

Keywords

  • Balanced traffic flow
  • Graph theory
  • Heuristic model
  • Multiple charging facilities
  • Optimal location
  • Plug-in electric vehicles
  • Public charging station networks

CACES Knowledge Areas

  • 317A Electricity and Energy

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