Improved Black Widow for Optimal Distributed Generation Placement in Radial Distribution Networks

Sochima Egeruo; Oladepo Olatunde

doi:10.12928/biste.v7i2.12954

Authors

Sochima Egeruo Transmission Company of Nigeria
Oladepo Olatunde Osun State University

DOI:

https://doi.org/10.12928/biste.v7i2.12954

Keywords:

Distributed Generation, Metaheuristic Algorithms, Radial Distribution Networks, Voltage Profile Improvement, Non-Linear Inertia Weight, Optimization

Abstract

Mitigating active and reactive power losses and improving voltage profiles in radial distribution networks remain critical challenges for system operators. While the introduction of Distributed Generation offers a promising solution, determining their optimal placement and sizing is a complex problem. Metaheuristic algorithms, though effective, have seen limited application in addressing issues specific to radial feeders, where traditional analytical methods dominate. This paper presents an Improved Black Widow Optimization Algorithm to improve Distributed Generation location and proper sizing in radial distribution networks. The Improved Black Widow Optimization Algorithm incorporates a non-linear inertia weight adjustment to enhance the balance between diverse exploration and focused exploitation, addressing a key limitation of the standard Black Widow Optimization. A backward-forward sweep algorithm is used to calculate the initial losses and voltage profile of the test systems, while the Improved Black Widow Optimization Algorithm determines optimal Distributed Generation parameters. The proposed method is tested on the IEEE 33-bus system and validated on a Nigerian 32-bus 11kV distribution feeder using MATLAB. Results demonstrate that the Improved Black Widow Optimization Algorithm reduces power losses by 49.49% and improves voltage profiles by 85.64% on the IEEE system, outperforming the standard Black Widow Optimization Algorithm (44.81% loss reduction, 84.64% voltage improvement). On the Nigerian network, the Improved Black Widow Optimization Algorithm achieves a 52.86% loss reduction and 92.22% voltage improvement, compared to 25.98% and 79.04% with the Black Widow Optimization Algorithm. These improvements translate to enhanced energy efficiency, reduced technical losses, and better voltage stability, confirming the superior performance of the Improved Black Widow Optimization Algorithm in addressing radial distribution network challenges.

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