Study on Optimal Power Flow with Large Scale Wind Power Integration Based on PSO

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This article is based on the idea that considers the influences of uncertainty, constraint programming model based on stochastic programming opportunities, expressed by means of probability constraints, the establishment of a constraint programming model for the optimal opportunity of wind power uncertainty, stochastic simulation technology development program and the particle swarm algorithm is used to solve this model based on IEEE30, at the end of the node optimization and simulation, to verify the feasibility of the model and algorithm. The whole procedure lays a good foundation for improving the situation of large-scale wind power grid dispatching and operation level access.

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3332-3335

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November 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Jincheng Shang, Jieying Zhou, Man Cheng. Coordination theory of electric power system optimal dispatch considering safety and economy[J]. Power System Automation, 2007, 06: 28-33.

Google Scholar

[2] Chenghong Gu, Qian Ai. Calculation of power systems containing wind farms optimal power flow based on the improved interior method [J]. China Power, 2007, 01: 89-93.

Google Scholar

[3] Yuewen Jiang, Chong Chen, Buying Wen. Economic dispatch in wind power integrated system simulation based on particle swarm optimization algorithm [J]. Advanced Technology of Electrical Engineering and Energy, 2007, 03: 37-41.

Google Scholar

[4] Jiageng Qiao, Fei Xu, Zongxiang Lu, Yong Min. Dependent chance programming of grid connected wind power capacity optimization analysis based on [J]. Power System Automation, 2008, 10: 84-87+103.

Google Scholar

[5] Yazhou Lei, Weisheng Wang, Yonghua Yin, Huizhu Dai. The active power optimal power flow of power system with wind farm[J]. Power System Technology, 2002, 06: 18-21.

DOI: 10.1109/icpst.1998.729318

Google Scholar

[6] Wei Zhou. Study on dynamic economic dispatch with wind farms [D]. Dalian University of Technology, (2010).

Google Scholar

[7] Suihua Wang, Naiqin Feng, Aiguo Li. A novel particle swarm optimization algorithm [J]. Computer engineering and Applications, 2003, 13: 109-110+134.

Google Scholar

[8] Ruiming Zhang, Xinyan Zhang. Study on the static voltage stability of induction generators and doubly fed wind power generator based on static models [J]. Power System Technology, 2011, 01: 175-179.

Google Scholar

[9] HODGE B M, MILLIGAN M. Wind power forecasting error distributions over multiple timescales[C]. Proceeding of Power & Energy Society General Meeting, (2011).

DOI: 10.1109/pes.2011.6039388

Google Scholar

[10] Fabbri A, Gomes T, Roman S. Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market[J]. IEEE Trans on Power System, 2005, 20(3): 1440-1446.

DOI: 10.1109/tpwrs.2005.852148

Google Scholar