Flexibility management of electric vehicles based on user profiles: The Arnhem case study
The ever-increasing global adoption of electric vehicles has created both challenges and opportunities for electrical grids and power systems as well as the market itself. Smart charging is broadly presented as a relevant opportunity to provide demand-side flexibility, benefiting both the user and t...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/19649 |
| Acceso en línea: | http://hdl.handle.net/10256/19649 |
| Access Level: | acceso abierto |
| Palabra clave: | Vehicles elèctrics Electric vehicles |
| Sumario: | The ever-increasing global adoption of electric vehicles has created both challenges and opportunities for electrical grids and power systems as well as the market itself. Smart charging is broadly presented as a relevant opportunity to provide demand-side flexibility, benefiting both the user and the power system through flexibility aggregators. However, coordinating all sessions for the same optimization objective could be inefficient when the flexibility potential mismatches the flexibility demand. Instead, this paper proposes the user profile concept as a tool to group sessions into similar flexibility levels and then schedule the charging sessions of each user profile according to its most convenient optimization objective. Therefore, a clustering methodology based on a bivariate Gaussian Mixture Models is presented and validated with a real-world data set, resulting in seven different user profiles. The simulation of two smart charging scenarios, first coordinating all flexible sessions and second coordinating two selected user profiles, resulted in a more efficient scheduling in the latter case, obtaining similar results with a 35% fewer sessions shifted and the corresponding reduction in exploitation costs |
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