Comparison of Sine-Cosine and Bat Algorithm for Distributed Generation Placement

Lindiasari Martha Yustika; Jangkung Raharjo; Rifki Rahman Nur Ikhsan; I Gede Putu Oka Indra Wijaya

doi:10.22146/jnteti.v14i3.19191

Lindiasari Martha Yustika Energy Systems Engineering Study Program, School of Electrical Engineering, Telkom University, Bandung, Jawa Barat 40257, Indonesia
Jangkung Raharjo Energy Systems Engineering Study Program, School of Electrical Engineering, Telkom University, Bandung, Jawa Barat 40257, Indonesia
Rifki Rahman Nur Ikhsan Energy Systems Engineering Study Program, School of Electrical Engineering, Telkom University, Bandung, Jawa Barat 40257, Indonesia
I Gede Putu Oka Indra Wijaya Energy Systems Engineering Study Program, School of Electrical Engineering, Telkom University, Bandung, Jawa Barat 40257, Indonesia

DOI: https://doi.org/10.22146/jnteti.v14i3.19191

Keywords: Sine-Cosine Algorithm, Bat Algorithm, Distributed Generator, IEEE 9 Bus, Metaheuristic Method

Abstract

The enhancement of electricity distribution is a crucial factor in supporting sustainable development and reducing energy access inequality. To ensure the reliability and stability of energy systems, the integration of distributed generation (DG) has a significant role. Numerous studies have explored optimal DG placement using metaheuristic methods. The study evaluated the performance of both algorithms based on key indicators, including voltage profile improvement and power loss reduction, under normal load conditions and under a 10% load increase to simulate future demand growth. The methods employed were the sine-cosine algorithm (SCA) and the bat algorithm (BA). By comparing these two methods, this study aims to optimize the placement and sizing of DG units, with a case study based on the IEEE 9 bus system configuration. Load flow analysis was performed using Electric Transient Analysis Program (ETAP) software to validate the effectiveness of optimized DG placement under various scenarios. Key performance indicators, namely losses reduction and improvement of voltage profile, were evaluated to determine the relative strengths of each algorithm. The results show that both SCA and BA are effective in optimizing DG implementation. Specifically, SCA achieved reductions in active power losses by up to 85% and reactive power losses by 93%, outperforming BA in certain scenarios. Both algorithms enhance system reliability and stability. These findings highlight the potential of metaheuristic algorithms to address the challenges of modern energy systems and contribute to the broader goal of developing sustainable power systems.

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