Quantum-inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in Australia

Fang Yao, Zhao Yang Dong, Ke Meng, Zhao Xu, Herbert Ho Ching Iu, Kit Po Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

126 Citations (Scopus)

Abstract

In this paper, a computational framework for integrating wind power uncertainty and carbon tax in economic dispatch (ED) model is developed. The probability of stochastic wind power based on nonlinear wind power curve and Weibull distribution is included in the model. In order to solve the revised dispatch strategy, quantum-inspired particle swarm optimization (QPSO) is also adopted, which shows stronger search ability and quicker convergence speed. The dispatch model is tested on a modified IEEE benchmark system involving six thermal units and two wind farms using the real wind speed data obtained from two meteorological stations in Australia.
Original languageEnglish
Article number6249753
Pages (from-to)880-888
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume8
Issue number4
DOIs
Publication statusPublished - 1 Nov 2012

Keywords

  • Carbon tax
  • economic load dispatch
  • particle swarm optimization
  • stochastic optimization
  • wind power

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this