Trip Pattern Impact of Electric Vehicles in Optimized Power Production using Orca Algorithm

Arif Nur Afandi; Shamsul Aizam Zulkifli; Petr Korba; Felix Rafael Segundo Sevilla; Anik Nur Handayani; Aripriharta Aripriharta; Aji Presetya Wibawa; Farrel Candra Winata Afandi

doi:10.5614/j.eng.technol.sci.2024.56.4.3

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Arif Nur Afandi
an.afandi@um.ac.id
Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Jalan Semarang 5, Malang 65145, Jawa Timur, Indonesia , Indonesia
Shamsul Aizam Zulkifli Department of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Persiaran Tun Dr. Ismail, 86400 Parit Raja, Johor, Malaysia, Malaysia
Petr Korba School of Engineering, Zurich University of Applied Sciences, Gertrudstrasse 15, 8400 Winterthur, Switzerland, Switzerland
Felix Rafael Segundo Sevilla School of Engineering, Zurich University of Applied Sciences, Gertrudstrasse 15, 8400 Winterthur, Switzerland, Switzerland
Anik Nur Handayani Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Jalan Semarang 5, Malang 65145, Jawa Timur, Indonesia , Indonesia
Aripriharta Aripriharta Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Jalan Semarang 5, Malang 65145, Jawa Timur, Indonesia , Indonesia
Aji Presetya Wibawa Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Jalan Semarang 5, Malang 65145, Jawa Timur, Indonesia , Indonesia
Farrel Candra Winata Afandi Smart Power and Advanced Energy Systems Research Center, Wastuasri 33, Junrejo, Batu 65321, Jawa Timur, Indonesia, Indonesia

Vol. 56 No. 4 (2024)

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Published August 6, 2024

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Power systems are run by combining different energy producers while the demand serves as the system’s energy user and covers all of the non-flexible and flexible loads, including electric vehicles (EVs). This study investigated the trip pattern impact of EVs, utilizing the Orca Algorithm (OA), in optimizing power production, applied to the IEEE-62 bus system as a model. Considering one-way and two-way trips over several categories of typical roads, the mobility of 14,504 EVs, divided into four driving patterns (Mobility 1-4), was estimated. Approximately 2,933 EVs traveled for working/business/study purposes, 3,862 EVs traveled for service/shopping purposes, approximately 5,376 EVs traveled for leisure purposes, while 2,334 EVs traveled for other reasons. The system had a total demand of 18,234.9 MVA, including 3,352.8 MW for electric vehicles and 14,151.5 MW for non-flexible loads. The EVs traveled a total of 119,018 km in Mobility 1, 141,799 km in Mobility 2, 184,614 km in Mobility 3, and 82,637 km in Mobility 4. The power produced was also used to charge the EVs during trips.

Afandi, A. N., Zulkifli, S. A., Korba, P., Sevilla, F. R. S., Handayani, A. N., Aripriharta, A., Wibawa, A. P., & Afandi, F. C. W. (2024). Trip Pattern Impact of Electric Vehicles in Optimized Power Production using Orca Algorithm. Journal of Engineering and Technological Sciences, 56(4), 463–473. https://doi.org/10.5614/j.eng.technol.sci.2024.56.4.3

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