LAPSE:2023.4117v1
Published Article

LAPSE:2023.4117v1
Local Energy Storage and Stochastic Modeling for Ultrafast Charging Stations
February 22, 2023
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.
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.
Record ID
Keywords
electric vehicle (EV), Energy Storage, fast charging station, stochastic charging demand
Subject
Suggested Citation
Ligen Y, Vrubel H, Girault H. Local Energy Storage and Stochastic Modeling for Ultrafast Charging Stations. (2023). LAPSE:2023.4117v1
Author Affiliations
Ligen Y: Ecole Polytechnique Federale de Lausanne (EPFL), Laboratoire d’Electrochimie Physique et Analytique (LEPA), Rue de l’Industrie 17, CH-1951 Sion, Switzerland
Vrubel H: Ecole Polytechnique Federale de Lausanne (EPFL), Laboratoire d’Electrochimie Physique et Analytique (LEPA), Rue de l’Industrie 17, CH-1951 Sion, Switzerland
Girault H: Ecole Polytechnique Federale de Lausanne (EPFL), Laboratoire d’Electrochimie Physique et Analytique (LEPA), Rue de l’Industrie 17, CH-1951 Sion, Switzerland
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Vrubel H: Ecole Polytechnique Federale de Lausanne (EPFL), Laboratoire d’Electrochimie Physique et Analytique (LEPA), Rue de l’Industrie 17, CH-1951 Sion, Switzerland
Girault H: Ecole Polytechnique Federale de Lausanne (EPFL), Laboratoire d’Electrochimie Physique et Analytique (LEPA), Rue de l’Industrie 17, CH-1951 Sion, Switzerland
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Journal Name
Energies
Volume
12
Issue
10
Article Number
E1986
Year
2019
Publication Date
2019-05-23
ISSN
1996-1073
Version Comments
Original Submission
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PII: en12101986, Publication Type: Journal Article
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LAPSE:2023.4117v1
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https://doi.org/10.3390/en12101986
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Feb 22, 2023
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