research-article

Optimal allocation of distribution generation sources with sustainable energy management in radial distribution networks using metaheuristic algorithm

Authors:

Amit Chakraborty,

Saheli RayAuthors Info & Claims

Volume 116, Issue C

https://doi.org/10.1016/j.compeleceng.2024.109142

Published: 24 July 2024 Publication History

Abstract

The optimal allocation and distribution of power from various distribution sources, supported by effective energy management, pose significant research challenges in distribution networks. This article aims to minimize the daily operational cost of distributed generators, reduce average daily active power loss, and improve the average daily voltage profile. Focusing on 33-bus and 69-bus systems, the article proposes a unified platform that optimally allocates and manages power distribution to achieve these objectives. The article focuses on three stages. The first stage aims to reduce power loss, followed by the second stage which focuses on enhancing the voltage profile while minimizing loss. In the final stage, it aims to tackle three objectives simultaneously: optimizing energy management for multiple distribution generators to lower operational cost, minimizing average daily active power loss, and enhancing the average daily voltage profile. The paper emphasizes the importance of optimizing all three objectives together as a comprehensive solution. To accomplish this intricate optimization, the article utilized a metaheuristic optimization technique called the slime mold algorithm, combined with the weighted sum technique and fuzzy clustering. The proposed method is evaluated using a 24-hour test period, considering industrial, commercial, and residential loads. The effectiveness of the proposed method is demonstrated by comparing its outcomes with those obtained from the sine cosine algorithm and grasshopper optimization algorithm.

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Published In

cover image Computers and Electrical Engineering

Computers and Electrical Engineering Volume 116, Issue C

May 2024

1385 pages

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Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 24 July 2024

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