Power Loss Mitigation and Voltage Profile Improvement with Distributed Generation Using Grid-based Multi-Objective Harmony Search Algorithm

Power distribution systems are challenged with rapid growth in load demand. Most times, it is observed that under certain critical loading conditions, distribution systems have high power loss and poor voltage profile which result in the collapse of certain areas in the network. To overcome these ch...

Full description

Bibliographic Details
Main Authors: AGAJIE Takele Ferede, SALAU Ayodeji Olalekan, HAILU Engidaw Abel, AWOKE Yayehyirad Ayalew
Format: Article
Language:English
Published: Editura Universităţii din Oradea 2020-10-01
Series:Journal of Electrical and Electronics Engineering
Subjects:
Online Access:https://electroinf.uoradea.ro/images/articles/CERCETARE/Reviste/JEEE/JEEE_V13_N2_OCT_2020/01%20paper%201623%20SALAU.pdf
Description
Summary:Power distribution systems are challenged with rapid growth in load demand. Most times, it is observed that under certain critical loading conditions, distribution systems have high power loss and poor voltage profile which result in the collapse of certain areas in the network. To overcome these challenges, a number of power distribution systems incorporate distributed generation (DG) on the grid near to the load center. However, for DG to serve its purpose, its location and size has to be determined optimally. In this paper, Grid-Based Multi-Objective Harmony Search Algorithm (GrMHSA) has been utilized to determine the size and location of DG in the Debre Markos town distribution system. An algorithm was developed to optimally place the distributed generators (DGs). This was with the aim to reduce power losses in the distribution network and enhance the voltage profile of the system under consideration. A matlab program was developed to mitigate power losses and improve the voltage profile by the optimal sizing and placing of DGs in the distribution network. After sizing and placing the DG in the network, the total voltage deviation, active and reactive power losses were reduced by 85.20%, 84.94% and 85.73%, respectively. In addition, the performance of the proposed algorithm (GrMHSA) was compared with Multi-objective Particle Swarm Optimization (MOPSO) and was found outperform the MOPSO.
ISSN:1844-6035
2067-2128