Assessing wind curtailment under different wind capacity considering the possibilistic uncertainty of wind resources

Abstract For power system decision makers, they should decide a reasonable wind capacity or target penetration level considering the balance of wind curtailment, investment, system reliability and social benefit. Assessing wind curtailment under different wind capacity, considering the wind resource uncertainty, is therefore necessary for utilizing wind power effectively. In this paper, fuzzy wind capacity factor is proposed to analyze and propagate the uncertainty of wind resources based on the wind speed distribution. By investigating the maximal wind power output that can be accepted in candidate system nodes, a fuzzy linear programming problem is proposed to decide the required wind capacity considering the fuzzy wind capacity factor. Based on the cumulative distribution function of wind capacity resulting from large number of simulations, the relationship between wind capacity and wind curtailment is determined. The effectiveness of the proposed model is validated by its application in two power grids. The results demonstrate that the proposed model and method are capable of describing the possibilistic uncertainty of wind resources and assessing wind curtailment under different wind capacity. Compared with previous research, this method can aid power system planners to allocate wind capacity considering the possibilistic uncertainty of wind resources.

[1]  Yingzhong Gu,et al.  Fast Sensitivity Analysis Approach to Assessing Congestion Induced Wind Curtailment , 2015, IEEE Transactions on Power Systems.

[2]  Mohammad Shahidehpour,et al.  Communication and Control in Electric Power Systems: Applications of Parallel and Distributed Processing , 2003 .

[3]  Muhammad Ali,et al.  Probabilistic assessment of wind farm annual energy production , 2012 .

[4]  Zheng Yan,et al.  Estimating wind speed probability distribution by diffusion-based kernel density method , 2015 .

[5]  Saeid Abbasbandy,et al.  Weighted trapezoidal approximation-preserving cores of a fuzzy number , 2010, Comput. Math. Appl..

[6]  S. Faias,et al.  Assessment and Optimization of Wind Energy Integration Into the Power Systems: Application to the Portuguese System , 2012, IEEE Transactions on Sustainable Energy.

[7]  H. Saleh,et al.  Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt , 2012 .

[8]  Peihua Qiu,et al.  Fuzzy Modeling and Fuzzy Control , 2006, Technometrics.

[9]  R. Castro,et al.  A Comparison Between Chronological and Probabilistic Methods to Estimate Wind Power Capacity Credit , 2001, IEEE Power Engineering Review.

[10]  D J Burke,et al.  A Study of Optimal Nonfirm Wind Capacity Connection to Congested Transmission Systems , 2011, IEEE Transactions on Sustainable Energy.

[11]  E.F. El-Saadany,et al.  Wind Turbines Capacity Factor Modeling—A Novel Approach , 2009, IEEE Transactions on Power Systems.

[12]  T. Chang,et al.  Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: A case study of Taiwan , 2007 .

[13]  V. Miranda,et al.  Probabilistic Analysis for Maximizing the Grid Integration of Wind Power Generation , 2012, IEEE Transactions on Power Systems.

[14]  A. Yahalom,et al.  A Generalized Approach to Estimating Capacity Factor of Fixed Speed Wind Turbines , 2012, IEEE Transactions on Sustainable Energy.

[15]  Sung-Hyuk Cha Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions , 2007 .

[16]  Hassan Ghasemi,et al.  A stochastic security constrained unit commitment model for reconfigurable networks with high wind power penetration , 2015 .

[17]  Tsang-Jung Chang,et al.  Artificial neural networks in the estimation of monthly capacity factors of WECS in Taiwan , 2010 .

[18]  Shu-Ping Wan,et al.  Possibility linear programming with trapezoidal fuzzy numbers , 2014 .

[19]  D J Burke,et al.  Factors Influencing Wind Energy Curtailment , 2011, IEEE Transactions on Sustainable Energy.

[20]  Enrico Zio,et al.  Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System , 2012, ArXiv.

[21]  Min Xie,et al.  Effects of wind speed probabilistic and possibilistic uncertainties on generation system adequacy , 2015 .

[22]  Peng Zhang,et al.  Reliability evaluation of active distribution systems including microgrids , 2012, 2013 IEEE Power & Energy Society General Meeting.

[23]  Seyed Hamid Hosseini,et al.  Wind farm optimal connection to transmission systems considering network reinforcement using cost-reliability analysis , 2013 .

[24]  Zhijian Liu,et al.  Evaluation of the capability of accepting large-scale wind power in China , 2013 .

[25]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[26]  Stefanos V. Papaefthymiou,et al.  A Wind-Hydro-Pumped Storage Station Leading to High RES Penetration in the Autonomous Island System of Ikaria , 2010, IEEE Transactions on Sustainable Energy.

[27]  Tsang-Jung Chang,et al.  Estimation of monthly wind power outputs of WECS with limited record period using artificial neural networks , 2012 .

[28]  Seyit Ahmet Akdağ,et al.  A new method to estimate Weibull parameters for wind energy applications , 2009 .

[29]  Murray Thomson,et al.  Going with the wind: temporal characteristics of potential wind curtailment in Ireland in 2020 and opportunities for demand response , 2015 .