Optimal Scheduling of Movable Electric Vehicle Loads Using Generation of Charging Event Matrices, Queuing Management, and Genetic Algorithm

Nattavit Piamvilai and Somporn Sirisumrannukul
Additional contact information
Nattavit Piamvilai: Department of Electrical and Computer Engineering, Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
Somporn Sirisumrannukul: Department of Electrical and Computer Engineering, Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand

Energies, 2022, vol. 15, issue 10, 1-26

Abstract: The extensive adoption of electric vehicles (EVs) can introduce negative impacts on electric infrastructure in the form of sporadic and excessive charging demands, line overload, and voltage quality. Because EV loads can be movable around the system and time-dependent due to human daily activities, it is therefore proposed in this research to investigate the spatial effects of EV loads and their impacts on a power system. We developed a behavior-based charging profile simulation for daily load profiles of uncontrolled and controlled charging simulations. To mitigate the impact of increased peak demand, we proposed an optimal scheduling method by genetic algorithm (GA) using charging event matrices and EV queuing management. The charging event matrices are generated by capturing charging events and serve as an input of the GA-based scheduling, which optimally defines available charging slots while maximizing the system load factor while maintaining user satisfaction, depending on the weight coefficients prioritized by the system operator. The EV queuing management strategically selects EVs to be filled in the available slots based on two qualification indicators: previous charging duration and remaining state of charge (SoC). The proposed methodology was tested on a modified IEEE-14 bus system with 3 generators and 20 transmission lines. The simulation results show that the developed methodology can efficiently manage the peak demand while respecting the system’s operational constraints and the user satisfaction level.

Keywords: electric vehicle; behavior-based simulation; smart charging; genetic algorithm; demand management (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/10/3827/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/10/3827/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:10:p:3827-:d:821796

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().