An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers

Teguh Kurniawan, Craig A. Baguley, Udaya K. Madawala, Suwarno, Nanang Hariyanto and Yuana Adianto
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Teguh Kurniawan: School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia
Craig A. Baguley: Department of Electrical and Electronic Engineering, School of Engineering, Computer and Mathematical Sciences, Faculty of Design and Creative Technologies, Auckland University of Technology, Auckland 1142, New Zealand
Udaya K. Madawala: Department of Electrical, Computer and Software Engineering, Faculty of Engineering, The University of Auckland, Auckland 1023, New Zealand
Suwarno: School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia
Nanang Hariyanto: School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia
Yuana Adianto: School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia

Energies, 2020, vol. 13, issue 13, 1-13

Abstract: Distribution networks with high electric vehicle (EV) penetration levels can experience transformer overloading and voltage instability issues. A charge scheduling approach is proposed to mitigate against these issues that suits smart home settings in residential areas. It comprises measurement systems located at distribution transformers that communicate directly with fuzzy logic controller (FLC) systems embedded within EV supply equipment (EVSE). This realizes a reduction in data processing requirements compared to more centralized control approaches, which is advantageous for distribution networks with large numbers of transformers and EV scheduling requests. A case study employing the proposed approach is presented. Realistic driver behavior patterns, EV types, and multivariate probabilistic modeling were used to estimate EV charging demands, daily travel mileage, and plug-in times. A Monte Carlo simulation approach was developed to obtain EV charging loads. The effectiveness of mitigation in terms of reducing distribution transformer peak load levels and losses, as well as improving voltage stability is demonstrated for a distribution network in Jakarta, Indonesia.

Keywords: distribution transformer; charge scheduling; fuzzy logic; Monte Carlo simulation (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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