Optimal power scheduling for economic dispatch using moth flame optimizer
This paper proposes the optimal generator allocation to solve economic dispatch (ED) problem in power system using moth flame optimizer (MFO). With this approach, the optimum power for each unit generating in the system will be searched based on the power constraints per unit and the amount of power...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2020-10-01.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 02238 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | ijeecs22453_14224 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a M. Kamari, N. A. |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a A. Zulkifley, M. |e author |
| 700 | 1 | 0 | |a F. Ramli, N. |e author |
| 700 | 1 | 0 | |a Musirin, I. |e author |
| 245 | 0 | 0 | |a Optimal power scheduling for economic dispatch using moth flame optimizer |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-10-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/22453 | ||
| 520 | |a This paper proposes the optimal generator allocation to solve economic dispatch (ED) problem in power system using moth flame optimizer (MFO). With this approach, the optimum power for each unit generating in the system will be searched based on the power constraints per unit and the amount of power demand. The objective function of this study is to minimize the total cost of generation. The amount of power loss is measured to determine the effectiveness of the proposed technique. The performance of the MFO technique is also compared to the evolutionary programming (EP) and particle swarm optimization (PSO) methods. Five- and thirty-bus power system networks are selected as test systems and simulated using MATLAB. Based on simulation results, MFO provides better results in regulating the optimum power generation with minimum generation cost and power loss, compared to EP and PSO. | ||
| 540 | |a Copyright (c) 2020 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Economic dispatch; Evolutionary programming; Moth flame optimizer; Particle swarm optimization | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 20, No 1: October 2020; 379-384 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v20.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/22453/14224 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/22453/14224 |z Get fulltext |