Optimal Allocation of Distributed Generation Using Modified Grey Wolf Optimizer

In this paper a case study is performed to optimally allocate real power injections at unity power factor as DG on the IEEE 33 bus radial distribution system using a modified Grey Wolf Optimization method. The study is performed in MATLAB. The main objective of this study is to explore the effective...

Full description

Saved in:
Bibliographic Details
Published in2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) pp. 884 - 889
Main Authors Altayara, Ibrahim Alkhaleel, Al-Ammar, Essam A., Ghazi, Ghazi A., AL Katheri, Ahmed A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.12.2022
Subjects
Distributed Generation
Grey Wolf Optimizer
IEEE 33 bus system
Load Flow Analysis
Metaheuristic Optimization
Optimal Allocation
Power demand
Power distribution
Power Distribution System
Radial Distribution System
Reactive power
Renewable energy sources
System performance
Uncertainty
Voltage
Online AccessGet full text

Cover

More Information
Summary:In this paper a case study is performed to optimally allocate real power injections at unity power factor as DG on the IEEE 33 bus radial distribution system using a modified Grey Wolf Optimization method. The study is performed in MATLAB. The main objective of this study is to explore the effectiveness of using the modified algorithm in improving the system voltage profile and in reducing the total system line losses. The objective function is modeled as the total system line losses which shall be subject to equality constraints in power-flow and inequality constraints in voltage level. The obtained results are then compared to the outputs from using other optimization techniques including the classical grey wolf optimizer method as well as other metaheuristic methods such as Particle Swarm Optimization, and Bat algorithms. Furthermore, two runs are performed for each of the modified algorithm as well as the classical algorithm to investigate the effect of tightening the voltage constraint has on the algorithms' performances.
DOI:10.1109/ICACRS55517.2022.10029048