Electric charging demand forecast and capture for infrastructure placement using gravity modelling: a case study

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Rodrigues, G (Autor) Outra; Barbosa, F (Autor) FEUP; Schuller, P (Autor) Outra; Silva, D (Autor) Outra; Pereira, J (Autor) Outra; Azevedo, R (Autor) Outra; Luis Guimarães (Autor) FEUP

Ata de Conferência Internacional

Título: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Páginas: 2112-2117

26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023

Bilbao, 24 September 2023 through 28 September 2023

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-010-32Z

DOI: 10.1109/itsc57777.2023.10421918

Abstract (EN): As the demand for electric charging accelerates, so does the stress on the relatively insufficient public charging infrastructure. To appropriately manage and scale charging infrastructure, there is a need for support tools capable of predicting the utilization and sales of charging stations, as well as the traffic flow of users from their original location to the charging stations. Therefore, this article proposes a generic methodology for infrastructure placement, namely forecasting demand and predicting its flow to the supply points. The methodology is applied in a case study to the electric charging grid of Portugal with real data, in the context of the needs of a particular charging point operator (CPO). Demand is first forecasted at a high-granularity level with a demand disaggregation model, followed by its capture by the grid of chargers using a parameterized gravity model. Validation is performed by comparing actual with predicted sales per charging station. Adequate visualizations to support decision-making are presented.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6