LAPSE:2023.12166
Published Article

LAPSE:2023.12166
Method for Planning, Optimizing, and Regulating EV Charging Infrastructure
February 28, 2023
Abstract
The paper presents and solves the problems of modeling and designing the required EV charging service capacity for systems with a slow dynamic component. This includes possible bursts within a peak hour interval. A simulation tool with a newly implemented capacity planning method has been developed and implemented for these needs. The method can be used for different system simulations and simultaneously for systems with high, medium, and low service dynamics. The proposed method is based on a normal distribution, a primary mechanism that describes events within a daily interval (24 h) or a peak hour interval (rush hour). The goal of the presented approach, including the proposed method, is to increase the level and quality of the EV charging service system. The near-optimal solution with the presented method can be found manually by changing the service capacity parameter concerning the criterion function. Manual settings limit the number of rejected events, the time spent in the queue, and other service system performance parameters. In addition to manual search for near-optimal solutions, the method also provides automatic search by using the automation procedure of simulation runs and increasing/decreasing the service capacity parameter by a specifically calculated amount.
The paper presents and solves the problems of modeling and designing the required EV charging service capacity for systems with a slow dynamic component. This includes possible bursts within a peak hour interval. A simulation tool with a newly implemented capacity planning method has been developed and implemented for these needs. The method can be used for different system simulations and simultaneously for systems with high, medium, and low service dynamics. The proposed method is based on a normal distribution, a primary mechanism that describes events within a daily interval (24 h) or a peak hour interval (rush hour). The goal of the presented approach, including the proposed method, is to increase the level and quality of the EV charging service system. The near-optimal solution with the presented method can be found manually by changing the service capacity parameter concerning the criterion function. Manual settings limit the number of rejected events, the time spent in the queue, and other service system performance parameters. In addition to manual search for near-optimal solutions, the method also provides automatic search by using the automation procedure of simulation runs and increasing/decreasing the service capacity parameter by a specifically calculated amount.
Record ID
Keywords
bursts, capacity planning, normal distribution, rush-hour, service system, stochastic process
Subject
Suggested Citation
Chowdhury A, Klampfer S, Sredenšek K, Seme S, Hadžiselimović M, Štumberger B. Method for Planning, Optimizing, and Regulating EV Charging Infrastructure. (2023). LAPSE:2023.12166
Author Affiliations
Chowdhury A: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia; Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva cesta 6, 1000 Ljubljana, Slovenia [ORCID]
Klampfer S: Margento R&D d.o.o., Gosposvetska 84, 2000 Maribor, Slovenia
Sredenšek K: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia [ORCID]
Seme S: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia; Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia [ORCID]
Hadžiselimović M: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia; Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Štumberger B: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia; Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Klampfer S: Margento R&D d.o.o., Gosposvetska 84, 2000 Maribor, Slovenia
Sredenšek K: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia [ORCID]
Seme S: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia; Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia [ORCID]
Hadžiselimović M: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia; Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Štumberger B: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia; Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Journal Name
Energies
Volume
15
Issue
13
First Page
4756
Year
2022
Publication Date
2022-06-28
ISSN
1996-1073
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Original Submission
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PII: en15134756, Publication Type: Journal Article
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LAPSE:2023.12166
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https://doi.org/10.3390/en15134756
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