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New Hybrid Multi-Objective Optimization Technique for Multi-DG Installation in Bulk Distribution System

This article proposes a multi-objective Integrated Immune Moth Flame Evolutionary Programming (MO-IIMFEP) algorithm to identify the optimal sizing and placement of distribution generation (DG) in a radial distribution system (RDS). These objectives are simultaneously minimizing the total active powe...

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Main Authors: Abdullah A., Musirin I., Othman M.M., Rahim S.R.A., Kumar A.V.S.
Other Authors: 57197864035
Format: Conference paper
Published: American Institute of Physics 2025
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spelling my.uniten.dspace-362922025-03-03T15:41:49Z New Hybrid Multi-Objective Optimization Technique for Multi-DG Installation in Bulk Distribution System Abdullah A. Musirin I. Othman M.M. Rahim S.R.A. Kumar A.V.S. 57197864035 8620004100 35944613200 11639107900 56888921600 This article proposes a multi-objective Integrated Immune Moth Flame Evolutionary Programming (MO-IIMFEP) algorithm to identify the optimal sizing and placement of distribution generation (DG) in a radial distribution system (RDS). These objectives are simultaneously minimizing the total active power loss, reducing the total operating cost and reducing the cumulative voltage deviation (CVD) while considering the distribution system's operational constraints. With the aid of the fuzzy decision-making procedure, the non-dominant Pareto solutions are narrowed down to the optimal prospective compromise solution. The proposed efficacy is evaluated using a bulk distribution system, i.e. IEEE 118-bus RDS, and the outcomes are contrasted with multi-objective Evolutionary Programming (MO-EP) and multiobjective Moth Flame Optimization (MO-MFO) approaches. The outcomes demonstrate that the MO-IIMFEP algorithm is effective in obtaining the best compromise solutions for multi-objective problems. The study also shows that installing DG Type 1 into a distribution system with multi-objective optimization substantially reduces total power loss, enhances cumulative voltage deviation, and minimizes the total operating costs. ? 2024 American Institute of Physics Inc.. All rights reserved. Final 2025-03-03T07:41:49Z 2025-03-03T07:41:49Z 2024 Conference paper 10.1063/5.0207745 2-s2.0-85205486569 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205486569&doi=10.1063%2f5.0207745&partnerID=40&md5=4890623dc534d071709fe246ed51ba2d https://irepository.uniten.edu.my/handle/123456789/36292 3115 1 20001 American Institute of Physics Scopus
institution Universiti Tenaga Nasional
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content_provider Universiti Tenaga Nasional
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description This article proposes a multi-objective Integrated Immune Moth Flame Evolutionary Programming (MO-IIMFEP) algorithm to identify the optimal sizing and placement of distribution generation (DG) in a radial distribution system (RDS). These objectives are simultaneously minimizing the total active power loss, reducing the total operating cost and reducing the cumulative voltage deviation (CVD) while considering the distribution system's operational constraints. With the aid of the fuzzy decision-making procedure, the non-dominant Pareto solutions are narrowed down to the optimal prospective compromise solution. The proposed efficacy is evaluated using a bulk distribution system, i.e. IEEE 118-bus RDS, and the outcomes are contrasted with multi-objective Evolutionary Programming (MO-EP) and multiobjective Moth Flame Optimization (MO-MFO) approaches. The outcomes demonstrate that the MO-IIMFEP algorithm is effective in obtaining the best compromise solutions for multi-objective problems. The study also shows that installing DG Type 1 into a distribution system with multi-objective optimization substantially reduces total power loss, enhances cumulative voltage deviation, and minimizes the total operating costs. ? 2024 American Institute of Physics Inc.. All rights reserved.
author2 57197864035
author_facet 57197864035
Abdullah A.
Musirin I.
Othman M.M.
Rahim S.R.A.
Kumar A.V.S.
format Conference paper
author Abdullah A.
Musirin I.
Othman M.M.
Rahim S.R.A.
Kumar A.V.S.
spellingShingle Abdullah A.
Musirin I.
Othman M.M.
Rahim S.R.A.
Kumar A.V.S.
New Hybrid Multi-Objective Optimization Technique for Multi-DG Installation in Bulk Distribution System
author_sort Abdullah A.
title New Hybrid Multi-Objective Optimization Technique for Multi-DG Installation in Bulk Distribution System
title_short New Hybrid Multi-Objective Optimization Technique for Multi-DG Installation in Bulk Distribution System
title_full New Hybrid Multi-Objective Optimization Technique for Multi-DG Installation in Bulk Distribution System
title_fullStr New Hybrid Multi-Objective Optimization Technique for Multi-DG Installation in Bulk Distribution System
title_full_unstemmed New Hybrid Multi-Objective Optimization Technique for Multi-DG Installation in Bulk Distribution System
title_sort new hybrid multi-objective optimization technique for multi-dg installation in bulk distribution system
publisher American Institute of Physics
publishDate 2025
_version_ 1825816101739560960
score 13.244413

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