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

• Published in: AIP Conference Proceedings
• Main Author: Abdullah A.; Musirin I.; Othman M.M.; Rahim S.R.A.; Kumar A.V.S.
• Format: Conference paper
• Language: English
• Published: American Institute of Physics 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205486569&doi=10.1063%2f5.0207745&partnerID=40&md5=4890623dc534d071709fe246ed51ba2d

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.