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|Abstract=This paper shows the results of an empirical analysis of electric vehicle (EV) charging behaviour at a cluster of 10 AC charging points at a commercial office site in Garching, Germany. It quantifies the potential of controlled charging to lower the charging cluster’s peak load. By utilizing charging meter data we were able to simulatively evaluate different control strategies, and to model their power profiles and peak loads. Backed by empirical charging data, our analysis shows that around 80% of charge events are characterized by a certain flexibility that allows to adjust their power profiles. Furthermore, the peak load can be reduced by 44% without affecting the EV’s mobility and by 69% if less than 20% of charging is controlled. | |Abstract=This paper shows the results of an empirical analysis of electric vehicle (EV) charging behaviour at a cluster of 10 AC charging points at a commercial office site in Garching, Germany. It quantifies the potential of controlled charging to lower the charging cluster’s peak load. By utilizing charging meter data we were able to simulatively evaluate different control strategies, and to model their power profiles and peak loads. Backed by empirical charging data, our analysis shows that around 80% of charge events are characterized by a certain flexibility that allows to adjust their power profiles. Furthermore, the peak load can be reduced by 44% without affecting the EV’s mobility and by 69% if less than 20% of charging is controlled. | ||
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Aktuelle Version vom 29. Juni 2022, 09:09 Uhr
Utilization of Electric Vehicle Charging Flexibility to Lower Peak Load by Controlled Charging (G2V and V2G)
Utilization of Electric Vehicle Charging Flexibility to Lower Peak Load by Controlled Charging (G2V and V2G)
Veröffentlicht: 2019
Journal: International Journal of Control, Automation and Systems
Referierte Veröffentlichung
Kurzfassung
This paper shows the results of an empirical analysis of electric vehicle (EV) charging behaviour at a cluster of 10 AC charging points at a commercial office site in Garching, Germany. It quantifies the potential of controlled charging to lower the charging cluster’s peak load. By utilizing charging meter data we were able to simulatively evaluate different control strategies, and to model their power profiles and peak loads. Backed by empirical charging data, our analysis shows that around 80% of charge events are characterized by a certain flexibility that allows to adjust their power profiles. Furthermore, the peak load can be reduced by 44% without affecting the EV’s mobility and by 69% if less than 20% of charging is controlled.