The availability of DC Fast Charging Stations (DCFCs) is considered a fundamental step for the widespread adoption of electric vehicles (EVs). To mitigate the impact of high-power charging events on the grid, DCFCs are often equipped with stationary energy storage and renewable energy resources. In literature, many methods have been proposed to design, control, and optimize the performance of multi-sources DCFCs. Many of the research contributions use the averaged EV charging power consumption as input, not the real-time event-based power request. This paper aims at comparing the effects of average-based and event-based EV charging power profiles on the design and control of multi-sources DCFCs. An algorithm that generates event-based EV charging power profiles has been developed based on the data from the California Energy Commission (CEC) report and NREL's EVI-Pro I tool. Multiple scenarios can be generated based on different weekday and weekend energy consumptions, EV penetrations, and the number or power level of chargers. The case scenario in this paper considers a 150kW charging station with second life energy storage and photovoltaic (PV) plant with a low power grid connection (<50kW). The utilization of local distributed energy resources is fundamental for enabling EV charging power delivery. Event-based and average-based EV charging power profiles, having the same energy requirements, are used to assess the impact on the design and control of the stations.
