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Optimal Location and Sizing of Distributed Generation Using Artificial Bee Colony and JAYA Algorithms

Year 2025, Issue: Erken Görünüm, 1 - 12

Abstract

The rapid increase in energy demand, environmental concerns, and the need for efficient energy resource utilization have highlighted the importance of distributed generation (DG) systems. The optimal placement and sizing of DG units are critical for reducing power losses, improving voltage profiles, and enhancing system reliability. This study utilizes Artificial Bee Colony (ABC) and JAYA algorithms to solve the problem of optimal DG allocation and sizing in the IEEE 33 bus distribution system. The ABC algorithm is inspired by the natural foraging behavior of honeybees and is recognized for its ability to efficiently explore complex solution spaces. The JAYA algorithm is a new optimization approach with its simple, parameter-free structure and fast convergence properties. The performance of both algorithms was evaluated based on convergence rate, power loss reduction, voltage improvement, and computational time. Results indicate that the JAYA algorithm achieves superior convergence and lower power losses compared to the ABC algorithm, while the ABC algorithm provides better voltage profile improvements. The findings of this study aim to contribute to the efficient planning and implementation of DG systems in modern power grids.

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There are 40 citations in total.

Details

Primary Language English
Subjects Electrical Energy Transmission, Networks and Systems, Electrical Energy Generation (Incl. Renewables, Excl. Photovoltaics)
Journal Section Research Articles
Authors

Salih Ermiş 0000-0002-1053-9160

Oğuz Taşdemir 0000-0003-1782-0024

Early Pub Date April 14, 2025
Publication Date
Submission Date December 30, 2024
Acceptance Date February 3, 2025
Published in Issue Year 2025 Issue: Erken Görünüm

Cite

IEEE S. Ermiş and O. Taşdemir, “Optimal Location and Sizing of Distributed Generation Using Artificial Bee Colony and JAYA Algorithms”, GJES, no. Erken Görünüm, pp. 1–12, April 2025.

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