ABC algorithm for multi-objective problem of DG unit insertion

M’dioud, Meriem, Er-Rays, Youssef, Bannari, Rachid, Kafazi, Ismailel, Bossoufi, Badre, Almalki, Mishari Metab, Alghamdi, Thamer A.H. and Alenezi, Mohammed 2025. ABC algorithm for multi-objective problem of DG unit insertion. Computers and Electrical Engineering 124 , 110398. 10.1016/j.compeleceng.2025.110398

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Abstract

Optimal DG insertion is a suitable method for satisfying the customer's requirements with minimum power loss and voltage dips, even during peak demand. Nonetheless, distributed generator insertion (DG) incurs special expenses, including investment, operating, and maintenance expenditures. This insertion is only economically effective if the expenses do not outweigh the energy loss's cost. This research investigates the best placement of DG units in electricity distribution systems. It provides a novel concept that aims to minimize overall energy costs, total active and reactive power loss, and overall voltage variation. In this research, we recommend employing a Novel Artificial Bee colony (NABC) algorithm to address this multi-objective problem. This novel technique employs the inverse of the initial solution, essentially doubling the population search space at the beginning and increasing the diversity of the result. On the other hand, this proposed technique uses the cosine of the chaotic map's formula to explore new solutions close to the best global solution. This strategy helps prevent local solutions and enhances the convergence speed of the basic ABC algorithm. We evaluate the proposed algorithm's performance against current algorithms. To study load flows in IEEE 33 and 69-bus distribution grids, we used the Backward Forward Sweep (BFS) approach. The results demonstrate that the proposed ABC algorithm outperforms other contemporary algorithms.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Elsevier
ISSN:	0045-7906
Date of Acceptance:	22 April 2025
Last Modified:	16 Dec 2025 15:00
URI:	https://orca.cardiff.ac.uk/id/eprint/183293

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