Quantum-Inspired Particle Swarm Optimization for Power System Operations Considering Wind Power Uncertainty and Carbon Tax in Australia

Author

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

Author_Institution

Sch. of Electr., Electron. & Comput. Eng., Univ. of Western Australia, Perth, WA, Australia

Volume

8

Issue

4

fYear

2012

Firstpage

880

Lastpage

888

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.

Keywords

Weibull distribution; carbon; environmental economics; particle swarm optimisation; power generation dispatch; power generation economics; probability; stochastic processes; taxation; wind power plants; Australia; ED model; QPSO; Weibull distribution; carbon tax; economic dispatch model; meteorological stations; modified IEEE benchmark system; nonlinear wind power curve; power system operations; quantum-inspired particle swarm optimization; real wind speed data; revised dispatch strategy; stochastic wind power probability; thermal units; wind farms; wind power uncertainty; Carbon tax; Fuels; Wind farms; Wind forecasting; Wind power generation; Wind speed; Wind turbines; Carbon tax; economic load dispatch; particle swarm optimization; stochastic optimization; wind power;

fLanguage

English

Journal_Title

Industrial Informatics, IEEE Transactions on

Publisher

ieee

ISSN

1551-3203

Type

jour

DOI

10.1109/TII.2012.2210431

Filename

6249753