Quantum-behaved Particle Swarm Optimization for Multiple-fuel-constrained Generation Scheduling of Power System

DOI

10.2991/icitme-18.2018.37How to use a DOI?

Keywords

quantum-behaved particle swarm optimization; generation scheduling; multiple-fuel-constrained; power system

Abstract

This research proposes a quantum-behaved particle swarm optimization with a multiplier updating technique (QPSO-MU) for the multiple-fuel-constrained generation scheduling of power system. The quantum-behaved particle swarm optimization (QPSO) equips with a migration can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function and resulting in difficulty of solution searching. The proposed algorithm integrates the QPSO and the MU that has merits of automatically adjusting the randomly given penalty to a proper value and requiring only a small-size population for the power economic dispatch problem of the multiple-fuel-constrained generation scheduling. Numerical results of two test systems indicate that the proposed algorithm is more suitable than previous approaches in the practical economic dispatch for the multiple-fuel-constrained generation scheduling of power system.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Chao-Lung Chiang
PY  - 2018/08
DA  - 2018/08
TI  - Quantum-behaved Particle Swarm Optimization for Multiple-fuel-constrained Generation Scheduling of Power System
BT  - Proceedings of the 2018 International Conference on Information Technology and Management Engineering (ICITME 2018)
PB  - Atlantis Press
SP  - 185
EP  - 188
SN  - 1951-6851
UR  - https://doi.org/10.2991/icitme-18.2018.37
DO  - 10.2991/icitme-18.2018.37
ID  - Chiang2018/08
ER  -