Assessment of Electric Vehicles Charging Grid Impact via Predictive Indicator

In recent years, the integration of electric vehicles (EV) into urban fleets has seen a significant rise, leading to a considerable increase in the number of EV chargers and fast charging stations (FCS) connected to distribution networks. Depending on the characteristics of the electrical power syst...

ver descrição completa

Detalhes bibliográficos
Autores: Dias Vasconcelos, Samuel, Filho Da Costa Castro, Jose, Gouveia, Felipe, Venancio De Moura Lacerda Filho, Antonio, Fonseca Buzo, Ricardo [UNESP], Henrique Alves De Medeiros, Luiz, Rodrigues Limongi, Leonardo, Da Costa Marques, Davidson, Lopes Fernandes, Amanda, Chai, Jiyong, Kellyano Leite Dantas, Nicolau, Zhang, Chenxin, Rosas, Pedro, Medina, Nestor
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:Brasil
Recursos:Universidade Estadual Paulista (UNESP)
Repositorio:Repositório Institucional da UNESP
Idioma:inglés
OAI Identifier:oai:repositorio.unesp.br:11449/303431
Acesso em linha:http://dx.doi.org/10.1109/ACCESS.2024.3482095
https://hdl.handle.net/11449/303431
Access Level:acceso abierto
Palavra-chave:electric mobility
Electric vehicle charging stations
electric vehicles
electrical impact indicator
Descrição
Resumo:In recent years, the integration of electric vehicles (EV) into urban fleets has seen a significant rise, leading to a considerable increase in the number of EV chargers and fast charging stations (FCS) connected to distribution networks. Depending on the characteristics of the electrical power system, such as short-circuit power and voltage harmonic distortion, due to the dynamic operation during charging sessions, EV charging stations may impacts the quality of power in the connection point. As a result, it is crucial for utility companies and facility managers to perform preliminary assessments to identify potential exceedances of quality limits.In this context, this work describes the development of an electrical grid impact indicator that evaluates the parameters that influence the electrical network during charging of electric vehicles. A case study and a simulation model were used to identify and incorporate into the indice the main relevant factors, such as power demand, short-circuit power, harmonic distortion, and power factor. The simulation models were employed to evaluate critical operational points, and measurement data further validated the model's performance. The results highlighted the importance of considering these parameters to ensure effective and safe recharging of electric vehicles. The proposed electrical impact indicator offers an electrical network management tool, allowing a predictive assessment of the impact of EV charging and enabling the adoption of appropriate measures to ensure the quality of power of the distribution networks accessed by charging stations.