Finding Representative Wind Power Scenarios and their Probabilities for Stochastic Models
Finding Representative Wind Power Scenarios and their Probabilities for Stochastic Models
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Date
2011
Authors
Jean Sumaili
Hrvoje Keko
Vladimiro Miranda
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Abstract
This paper analyzes the application of clustering
techniques for wind power scenario reduction. The results have
shown the unimodal structure of the scenario generated under a
Monte Carlo process. The unimodal structure has been
confirmed by the modes found by the information theoretic
learning mean shift algorithm. The paper also presents a new
technique able to represent the wind power forecasting
uncertainty by a set of representative scenarios capable of
characterizing the probability density function of the wind power
forecast. From an initial large set of sampled scenarios, a reduced
discrete set of representative scenarios associated with a
probability of occurrence can be created finding the areas of high
probability density. This will allow the reduction of the
computational burden in stochastic models that require scenario
representation.