Fuzzy power flow solution considering wind power variability and uncertainty

Summary With the rapid development of wind power, the variability and uncertainty of wind power have brought new problems and challenges to the secure and stable operation of the power systems. In this paper, based on the deterministic power flow solution, the probability theory and fuzzy sets theory are employed to implement systematic and thorough studies on power system fuzzy power flow solution incorporating uncertainties of wind power from different types of wind turbines squirrel cage induction generator, doubly fed induction generator and permanent magnet synchronous generator. The Weibull distribution with specific parameters is adopted as wind speed probability density model. With the possibility/probability consistency principle, the probability distribution of wind speed can be transformed into the possibility distribution of wind speed. In accordance with the simplified power vs wind speed curve of wind turbine, and with consideration of wake effect of wind farm, the fuzzy modeling for power output of wind farm is then presented. The proposed fuzzy power flow model incorporating wind power variability and uncertainty is linearized further for simplicity. A sensitivity analysis based method is employed to solve the fuzzy power flow involving different power characteristics of wind turbines. Finally, simulations are carried out on a real-sized China's Jiangxi power grid, and some meaningful and important conclusions and comments are drawn based on the simulation results. Copyright © 2014 John Wiley & Sons, Ltd.

[1]  R. Miceli,et al.  Efficiency Control for Permanent Magnet Synchronous Generators , 2006, 2006 IEEE International Conference on Industrial Technology.

[2]  C. R. Fuerte-Esquivel,et al.  Solution of Power Flow With Automatic Load-Frequency Control Devices Including Wind Farms , 2012, IEEE Transactions on Power Systems.

[3]  Y. X. Ni,et al.  Effects of wind power variability and intermittency on power flow , 2012, 2012 IEEE Power and Energy Society General Meeting.

[4]  Suresh H. Jangamshetti,et al.  Optimum siting of wind turbine generators , 2001 .

[5]  R.N. Allan,et al.  Evaluation Methods and Accuracy in Probabilistic Load Flow Solutions , 1981, IEEE Transactions on Power Apparatus and Systems.

[6]  Barbara Borkowska,et al.  Probabilistic Load Flow , 1974 .

[7]  O. Alsac,et al.  Fast Decoupled Load Flow , 1974 .

[8]  R.N. Allan,et al.  Probabilistic Load Flow Considering Dependence Between Input Nodal Powers , 1984, IEEE Transactions on Power Apparatus and Systems.

[9]  F. Gonzalez-Longatt,et al.  Evaluation of power flow variability on the Paraguaná transmission system due to integration of the first venezuelan wind farm , 2012, 2012 IEEE Power and Energy Society General Meeting.

[10]  Didier Dubois,et al.  Probability-Possibility Transformations, Triangular Fuzzy Sets, and Probabilistic Inequalities , 2004, Reliab. Comput..

[11]  J. Cidras,et al.  Modeling of wind farms in the load flow analysis , 2000 .

[12]  Nouredine Hadjsaid,et al.  Probabilistic load flow for voltage assessment in radial systems with wind power , 2012 .

[13]  Nikos D. Hatziargyriou,et al.  Probabilistic load flow in distribution systems containing dispersed wind power generation , 1993 .

[14]  Shenghu Li Power Flow Modeling to Doubly-Fed Induction Generators (DFIGs) Under Power Regulation , 2013, IEEE Transactions on Power Systems.

[15]  Kashem M. Muttaqi,et al.  Probabilistic load flow incorporating correlation between time-varying electricity demand and renewable power generation , 2013 .

[16]  Luis M. Fernández,et al.  Operating capability as a PQ/PV node of a direct-drive wind turbine based on a permanent magnet synchronous generator , 2010 .