Method for Planning, Optimizing, and Regulating EV Charging Infrastructure

Amor Chowdhury, Saša Klampfer, Klemen Sredenšek, Sebastijan Seme, Miralem Hadžiselimović and Bojan Štumberger
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Amor Chowdhury: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia
Saša Klampfer: Margento R&D d.o.o., Gosposvetska 84, 2000 Maribor, Slovenia
Klemen Sredenšek: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia
Sebastijan Seme: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia
Miralem Hadžiselimović: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia
Bojan Štumberger: Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia

Energies, 2022, vol. 15, issue 13, 1-32

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.

Keywords: service system; capacity planning; bursts; rush-hour; normal distribution; stochastic process (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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