A hybrid multi-objective Evolutionary Programming-Firefly Algorithm for different type of Distributed Generation in distribution system

With the rise in electricity demand, various additional sources of generation, known as Distributed Generation (DG), have been introduced to boost the performance of power systems. A hybrid multi-objective Evolutionary Programming-Firefly Algorithm (MOEPFA) technique is presented in this study for s...

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Bibliographic Details
Published in:	Energy reports Vol. 8; pp. 169 - 174
Main Authors:	Husni, Noor Najwa Husnaini Mohammad, Rahim, Siti Rafidah Abdul, Adzman, Mohd Rafi, Hussain, Muhamad Hatta, Musirin, Ismail, Azmi, Syahrul Ashikin
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01-12-2022 Elsevier
Subjects:	Distributed Generation Evolutionary Programming Loss minimization Multi-objective optimization Loss minimization Multi-objective optimization Evolutionary Programming Distributed Generation
Online Access:	Get full text
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Summary:	With the rise in electricity demand, various additional sources of generation, known as Distributed Generation (DG), have been introduced to boost the performance of power systems. A hybrid multi-objective Evolutionary Programming-Firefly Algorithm (MOEPFA) technique is presented in this study for solving multi-objective power system problems which are minimizing total active and reactive power losses and improving voltage profile while considering the cost of energy losses. This MOEPFA is developed by embedding Firefly Algorithm (FA) features into the conventional EP method. The analysis in this study considered DG with 4 different scenarios. Scenario 1 is the base case or without DG, scenario 2 is for DG with injected active power, scenario 3 is for DG injected with reactive power only and scenario 4 is for DG injected with both active and reactive power. The IEEE 69-bus test system is applied to validate the suggested technique.
ISSN:	2352-4847 2352-4847
DOI:	10.1016/j.egyr.2022.10.192