Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots

Gerwin Hoogsteen; Leoni Winschermann; Bart Nijenhuis; Nataly Bañol Arias; Johann L. Hurink

doi:10.1109/SmartGridComm57358.2023.10333900

Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots

Gerwin Hoogsteen, Leoni Winschermann, Bart Nijenhuis, Nataly Bañol Arias, Johann L. Hurink

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

Grid congestion forms an obstacle for the adoption of electric vehicles (EVs) by companies for their sustainability goals. This paper presents a robust approach to smart charging of large fleets of EVs that only utilizes currently available technologies and data, such that it can be applied in practice. The method relies only on available data at charge point operators. The presented approach uses offline aggregated planning to determine a robust fill-level. Subsequently, this fill-level is used in online control to maximise the quality of service to users while being able to provide flexibility to the energy system. Simulation results using real data show that the self-consumption of PV energy can be improved from 72.0% to 85.4%. Furthermore, the energy not served is reduced from 5.9% to 2.8%.

Original language	English
Title of host publication	2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781665455541
ISBN (Print)	978-1-6654-5557-2
DOIs	https://doi.org/10.1109/SmartGridComm57358.2023.10333900
Publication status	Published - 6 Dec 2023
Event	2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) - University of Strathclyde, Glasgow, United Kingdom Duration: 31 Oct 2023 → 3 Nov 2023

Conference

Conference	2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
Country/Territory	United Kingdom
City	Glasgow
Period	31/10/23 → 3/11/23

Keywords

Computers
Simulation
Quality of service
Companies
Electric vehicles
Smart grids
Planning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SmartGridComm57358.2023.10333900

RobustAggregateEVControl-final-authors-versionAccepted author version, 561 KB

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10333900

Cite this

Hoogsteen, G., Winschermann, L., Nijenhuis, B., Arias, N. B., & Hurink, J. L. (2023). Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots. In 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 1-6). Article 10333900 IEEE. https://doi.org/10.1109/SmartGridComm57358.2023.10333900

@inproceedings{c3eef005336c494a81039119edd5427b,

title = "Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots",

abstract = "Grid congestion forms an obstacle for the adoption of electric vehicles (EVs) by companies for their sustainability goals. This paper presents a robust approach to smart charging of large fleets of EVs that only utilizes currently available technologies and data, such that it can be applied in practice. The method relies only on available data at charge point operators. The presented approach uses offline aggregated planning to determine a robust fill-level. Subsequently, this fill-level is used in online control to maximise the quality of service to users while being able to provide flexibility to the energy system. Simulation results using real data show that the self-consumption of PV energy can be improved from 72.0% to 85.4%. Furthermore, the energy not served is reduced from 5.9% to 2.8%.",

keywords = "Computers, Simulation, Quality of service, Companies, Electric vehicles, Smart grids, Planning",

author = "Gerwin Hoogsteen and Leoni Winschermann and Bart Nijenhuis and Arias, {Nataly Ba{\~n}ol} and Hurink, {Johann L.}",

year = "2023",

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doi = "10.1109/SmartGridComm57358.2023.10333900",

language = "English",

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booktitle = "2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)",

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Hoogsteen, G , Winschermann, L , Nijenhuis, B , Arias, NB & Hurink, JL 2023, Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots. in 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)., 10333900, IEEE, Piscataway, NJ, pp. 1-6, 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Glasgow, United Kingdom, 31/10/23. https://doi.org/10.1109/SmartGridComm57358.2023.10333900

Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots. / Hoogsteen, Gerwin ; Winschermann, Leoni ; Nijenhuis, Bart et al.
2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). Piscataway, NJ: IEEE, 2023. p. 1-6 10333900.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots

AU - Hoogsteen, Gerwin

AU - Winschermann, Leoni

AU - Nijenhuis, Bart

AU - Arias, Nataly Bañol

AU - Hurink, Johann L.

PY - 2023/12/6

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N2 - Grid congestion forms an obstacle for the adoption of electric vehicles (EVs) by companies for their sustainability goals. This paper presents a robust approach to smart charging of large fleets of EVs that only utilizes currently available technologies and data, such that it can be applied in practice. The method relies only on available data at charge point operators. The presented approach uses offline aggregated planning to determine a robust fill-level. Subsequently, this fill-level is used in online control to maximise the quality of service to users while being able to provide flexibility to the energy system. Simulation results using real data show that the self-consumption of PV energy can be improved from 72.0% to 85.4%. Furthermore, the energy not served is reduced from 5.9% to 2.8%.

AB - Grid congestion forms an obstacle for the adoption of electric vehicles (EVs) by companies for their sustainability goals. This paper presents a robust approach to smart charging of large fleets of EVs that only utilizes currently available technologies and data, such that it can be applied in practice. The method relies only on available data at charge point operators. The presented approach uses offline aggregated planning to determine a robust fill-level. Subsequently, this fill-level is used in online control to maximise the quality of service to users while being able to provide flexibility to the energy system. Simulation results using real data show that the self-consumption of PV energy can be improved from 72.0% to 85.4%. Furthermore, the energy not served is reduced from 5.9% to 2.8%.

KW - Computers

KW - Simulation

KW - Quality of service

KW - Companies

KW - Electric vehicles

KW - Smart grids

KW - Planning

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Hoogsteen G , Winschermann L , Nijenhuis B , Arias NB , Hurink JL. Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots. In 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). Piscataway, NJ: IEEE. 2023. p. 1-6. 10333900 doi: 10.1109/SmartGridComm57358.2023.10333900

Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots

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Conference

Keywords

UN SDGs

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