A scenario generation method for wind power ramp events forecasting

Wind power ramp events (WPREs) have received increasing attention in recent years due to their significant impact on the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual data from a wind power plant in the Bonneville Power Administration (BPA) was selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.

[1]  Chandrika Kamath,et al.  Understanding wind ramp events through analysis of historical data , 2009, IEEE PES T&D 2010.

[2]  Jean-Francois Cardoso,et al.  Source separation using higher order moments , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[3]  P. Sorensen,et al.  Power Fluctuations From Large Wind Farms , 2007, IEEE Transactions on Power Systems.

[4]  R. Rajagopal,et al.  Wind power ramps: Detection and statistics , 2012, 2012 IEEE Power and Energy Society General Meeting.

[5]  Robin Girard,et al.  Forecasting Uncertainty Related to Ramps of Wind Power Production , 2010 .

[6]  Hamidreza Zareipour,et al.  Wind power ramp events classification and forecasting: A data mining approach , 2011, 2011 IEEE Power and Energy Society General Meeting.

[7]  Nivad Navid,et al.  Market Solutions for Managing Ramp Flexibility With High Penetration of Renewable Resource , 2012, IEEE Transactions on Sustainable Energy.

[8]  A. Feijoo,et al.  Simulation of Correlated Wind Speed Data for Economic Dispatch Evaluation , 2012, IEEE Transactions on Sustainable Energy.

[9]  N. D. Hatziargyriou,et al.  Probabilistic Wind Power Forecasting Using Radial Basis Function Neural Networks , 2012, IEEE Transactions on Power Systems.

[10]  Andrew Kusiak,et al.  Prediction of Wind Farm Power Ramp Rates: A Data-Mining , 2009 .

[11]  Ram Rajagopal,et al.  Detection and Statistics of Wind Power Ramps , 2013, IEEE Transactions on Power Systems.

[12]  Torben Skov Nielsen,et al.  Detecting, categorizing and forecasting large ramps in wind farm power output using meteorological observations and WPPT , 2007 .