Local Energy Storage and Stochastic Modeling for Ultrafast Charging Stations

Abstract

Multi-stall fast charging stations are often thought to require megawatt-range grid connections. The power consumption profile of such stations results in high cost penalties due to monthly power peaks and expensive linkage fees. A local energy storage system (ESS) can be used to address peak power demands. However, no appropriate sizing method is available to match specific constraints, such as the contracted power available from the grid and the projected recharging demand. A stochastic distribution of charging events was used in this paper to model power demand profiles at the station, with a one minute resolution. Based on 100 simulated months, we propose an optimum number of charging points, and we developed an algorithm to return the required local ESS capacity as a function of the available grid connection. The role of ESSs in the range of 100 kWh to 1 MWh was studied for all stations with up to 2000 charging events per week. The relevance of ESS implementation was assessed along three parameters: the number of charging points, the available grid connection, and the ESS capacity. This work opens new possibilities for multi-stall charging station deployment in locations with limited access to the medium voltage grid, and provides sizing guidelines for effective ESSs implementation. In addition, it helps build business cases for charging station operators in regions with high demand charges.

Stochastic Load Modeling of High-Power Electric Vehicle Charging - A Norwegian Case Study

“…The state-of-the-art literature uses different approaches to model the arrival rate at the FCS. Predefined arrival rates are used in [4]- [6]. Both [4] and [5] use a predefined arrival rate based on arrival time distribution of internal combustion engine (ICE) vehicles at gas stations.…”

“…Predefined arrival rates are used in [4]- [6]. Both [4] and [5] use a predefined arrival rate based on arrival time distribution of internal combustion engine (ICE) vehicles at gas stations. The daily expected number of EVs visiting the FCS is then used with the hourly arrival rate, to determine the hourly expected number of EVs.…”

Stationary Energy Storage System for Fast EV Charging Stations: Simultaneous Sizing of Battery and Converter

“…The constraints related to the battery and the converter are discussed in this section. The SOC of the battery is limited to a certain range to enhance the lifetime of the battery, as given by (12). The maximum and minimum SOC bounds can be determined in terms of the battery capacity by using (13).…”

“…Several studies are available in the literature on optimal sizing of ESS for fast charging stations with different objectives. Rule-based methods for optimal sizing of ESS in fast charging stations have been proposed in [11,12]. In [11], the objective of the optimization is to minimize the waiting time of the users and determine the size of the ESS to assure an acceptable waiting time.…”

“…In [11], the objective of the optimization is to minimize the waiting time of the users and determine the size of the ESS to assure an acceptable waiting time. Charging time is modeled as a stochastic event in [12], and the number of charging slots is determined in the first stage. In the second stage, the information of the number of charging stations determined in the first stage is utilized to determine the optimal size of ESS for fast charging stations.…”