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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Teguh Kurniawan & Craig A. Baguley & Udaya K. Madawala & Suwarno & Nanang Hariyanto & Yuana Adianto, 2020. "An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers," Energies, MDPI, vol. 13(13), pages 1-13, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3486-:d:380872
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    References listed on IDEAS

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    1. Monica Alonso & Hortensia Amaris & Jean Gardy Germain & Juan Manuel Galan, 2014. "Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms," Energies, MDPI, vol. 7(4), pages 1-27, April.
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    Cited by:

    1. George Konstantinidis & Emmanuel Karapidakis & Alexandros Paspatis, 2022. "Mitigating the Impact of an Official PEV Charger Deployment Plan on an Urban Grid," Energies, MDPI, vol. 15(4), pages 1-18, February.
    2. Stavros Poniris & Anastasios I. Dounis, 2022. "Electric Vehicle Charging Schedules in Workplace Parking Lots Based on Evolutionary Optimization Algorithm," Energies, MDPI, vol. 16(1), pages 1-16, December.
    3. Héricles Eduardo Oliveira Farias & Camilo Alberto Sepulveda Rangel & Leonardo Weber Stringini & Luciane Neves Canha & Daniel Pegoraro Bertineti & Wagner da Silva Brignol & Zeno Iensen Nadal, 2021. "Combined Framework with Heuristic Programming and Rule-Based Strategies for Scheduling and Real Time Operation in Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(5), pages 1-27, March.

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