An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers
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 syst...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/13/3486 |
| _version_ | 1797563222383919104 |
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| author | Teguh Kurniawan Craig A. Baguley Udaya K. Madawala Suwarno Nanang Hariyanto Yuana Adianto |
| author_facet | Teguh Kurniawan Craig A. Baguley Udaya K. Madawala Suwarno Nanang Hariyanto Yuana Adianto |
| author_sort | Teguh Kurniawan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:39:27Z |
| format | Article |
| id | doaj.art-60c54a90802641f3a4cf1131ade5eb3d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T18:39:27Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-60c54a90802641f3a4cf1131ade5eb3d2023-11-20T05:57:23ZengMDPI AGEnergies1996-10732020-07-011313348610.3390/en13133486An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution TransformersTeguh Kurniawan0Craig A. Baguley1Udaya K. Madawala2Suwarno3Nanang Hariyanto4Yuana Adianto5School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, IndonesiaDepartment 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 ZealandDepartment of Electrical, Computer and Software Engineering, Faculty of Engineering, The University of Auckland, Auckland 1023, New ZealandSchool of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, IndonesiaSchool of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, IndonesiaSchool of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, IndonesiaDistribution 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.https://www.mdpi.com/1996-1073/13/13/3486distribution transformercharge schedulingfuzzy logicMonte Carlo simulation |
| spellingShingle | Teguh Kurniawan Craig A. Baguley Udaya K. Madawala Suwarno Nanang Hariyanto Yuana Adianto An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers Energies distribution transformer charge scheduling fuzzy logic Monte Carlo simulation |
| title | An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers |
| title_full | An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers |
| title_fullStr | An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers |
| title_full_unstemmed | An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers |
| title_short | An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers |
| title_sort | electric vehicle charge scheduling approach suited to local and supplying distribution transformers |
| topic | distribution transformer charge scheduling fuzzy logic Monte Carlo simulation |
| url | https://www.mdpi.com/1996-1073/13/13/3486 |
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