Open AccessOptimal Sitting and Sizing of Distributed Generators using Pareto-Based Multi-Objective Particle Swarm Optimization for Improving Power System Operation
Author(s) -
Saifulnizam Abd. Khalid,
Meenuja Arumugam Arumugam,
Hussain Shareef
Publication year - 2022
Publication title -
jurnal elektrika
Language(s) - English
Resource type - Journals
ISSN - 0128-4428
DOI - 10.11113/elektrika.v21n1.346
Subject(s) - sizing , particle swarm optimization , pareto principle , distributed generation , mathematical optimization , power (physics) , computer science , voltage , metaheuristic , multi objective optimization , reliability engineering , engineering , mathematics , electrical engineering , art , physics , quantum mechanics , visual arts
Utilization of distributed generation (DG) in the distribution network has become trending ever since it has been introduced with proven benefits. DG plays a significant role in improving the quality and quantity as well as the efficiency of the power transmission and distribution system. By allowing smaller generating units to operate in parallel with the main grid, a continuous reliable power supply with lower power loss and higher power output can be achieved. However, improper placement and inappropriate sizing of DG leads to a moderate level performance in terms of power loss and voltage profile. Limited studies have been conducted on mitigating these problems in order to maximize the benefits from DG’s application. To solve this problem, a research is proposed which mainly aims in determining the location and size of DG as well as improving the voltage profile and efficiency of the distribution system significantly. A metaheuristic algorithm called Multi-Objective Particle Swarm Optimization (MOPSO) method is used to simultaneously determine the optimal size and location of DG. To assist the proposed method, Pareto analysis is incorporated to handle conflicting objectives. This method is then tested on the IEEE 14-bus and 33-bus distribution systems under two different conditions which is before and after optimization. The percentage of power loss reduction is calculated and the voltage profile is drawn to compare the output of both conditions. Evaluations from the tests have proven that by using the Pareto-Based MOPSO method, the most optimal size and location of DG in producing an improved voltage profile with lower power loss is identified.