Electric vehicle charging stations emplacement using genetic algorithms and agent-based simulation
[EN] The increasingly evident incorporation of the electric vehicle in urban environments is an already undeniable change. Electric vehicles are appearing on the market with more autonomy and lower prices, which is facilitating the progressive change of the vehicle fleet. However, the electric vehic...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/193710 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/193710 |
| Access Level: | acceso abierto |
| Palabra clave: | Genetic algorithm Electric vehicle Charging station Mobility Agent-based simulation LENGUAJES Y SISTEMAS INFORMATICOS 07.- Asegurar el acceso a energías asequibles, fiables, sostenibles y modernas para todos 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación 11.- Conseguir que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles 13.- Tomar medidas urgentes para combatir el cambio climático y sus efectos |
| Sumario: | [EN] The increasingly evident incorporation of the electric vehicle in urban environments is an already undeniable change. Electric vehicles are appearing on the market with more autonomy and lower prices, which is facilitating the progressive change of the vehicle fleet. However, the electric vehicle brings with it the need to provide enough charging stations distributed throughout the city, so that the autonomy of the vehicle is not a problem. This work presents how a genetic algorithm that analyzes the open data sources of a city is used to propose the most suitable locations for these stations. This proposal is the input for a series of experiments that simulate the impact that has the placement of these stations along the city, in order to measure the benefits of the solution proposed by the genetic algorithm. To do this, an agent-based simulation infrastructure was built around a fleet simulator. |
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