The Coordinated Operation of Vertically Structured Power Systems for Electric Vehicle Charge Scheduling

Yuana Adianto, Craig Baguley, Udaya Madawala, Nanang Hariyanto, Suwarno Suwarno and Teguh Kurniawan
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Yuana Adianto: School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia
Craig 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 Madawala: Department of Electrical, Computer and Software Engineering, Faculty of Engineering, The University of Auckland, Auckland 1023, New Zealand
Nanang Hariyanto: School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia
Suwarno Suwarno: School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia
Teguh Kurniawan: School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia

Energies, 2021, vol. 15, issue 1, 1-15

Abstract: Charge scheduling can mitigate against issues arising from excessive electric vehicle (EV) charging loads and is commonly implemented using time-of-use pricing. A charge scheduling strategy to suit vertically structured power systems without relying on time-of-use pricing has not yet been reported, despite being needed by industry. Therefore, a novel charge scheduling strategy to meet this need is proposed in this paper. Key aspects include the provision of a decision-making framework that accommodates for the considerations of transmission and distribution network operators, and the allowance for dynamically changing charging loads through timely forecast updates with reduced communication requirements. A case study based on the Indonesian Java-Bali power system is undertaken to demonstrate the strategy’s effectiveness. Different and realistic EV uptake scenarios are considered, using probabilistic modeling, survey work, and a Monte Carlo modeling approach. Even under slow EV charging conditions case study results show assets are overloaded and high electricity production costs are incurred. These are alleviated through adopting the proposed strategy.

Keywords: electric vehicle; Monte Carlo; smart scheduling; particle swarm optimization (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: 2021
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