# EV Charging Scheduling Optimization Using PSO Algorithm for Cost Efficiency and Network Load Management
> Karim S.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105037624301
Jurnal / Konferensi: 2025 5th International Symposium on Materials and Electrical Engineering Ismee 2025
Tahun terbit: 2025
DOI: https://doi.org/10.1109/ISMEE68179.2025.11473152
Citations: 0

## Authors
- Karim S.

## Abstract
The rapid rise of electric vehicles (EVs) is transforming the transportation sector, driving innovation and sustainability efforts worldwide. However, this growth also places considerable pressure on existing power systems, particularly during periods of high demand. A major challenge lies in managing the surge in peak electricity demand and the resulting increase in charging costs, which can strain both utilities and consumers. To address these challenges, effective scheduling strategies are required to balance grid stability while ensuring efficient and cost-effective energy utilization. In this study, Particle Swarm Optimization (PSO) is employed as a computational method to optimize EV charging schedules. The approach focuses on identifying cost-saving patterns and improving charging behavior by distributing the charging load more evenly across time. The proposed model is designed to lower operational and charging expenses. Simulation results indicate that applying PSO can reduce costs by approximately <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$11-18 \%$</tex> compared to conventional scheduling methods, while also improving energy efficiency and enhancing overall load distribution. These outcomes highlight the potential of PSO as a practical and effective tool for supporting the sustainable integration of EVs into modern power systems and promoting a more reliable, resilient, and economically efficient energy infrastructure.

## Keywords
- Computer science
- Particle swarm optimization
- Scheduling (production processes)
- Mathematical optimization
- Load management
- Job shop scheduling
- Artificial neural network
- Efficient algorithm
- Optimization algorithm
- Efficient energy use
- Real-time computing
- Energy management
- Minification
- Load profile
- Algorithm design
- Load balancing (electrical power)
- Economic efficiency
- Engineering
- Cost efficiency

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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