CLUSTERING-BASED WIND POWER SCENARIO REDUCTION TECHNIQUE

Abstract

This paper describes a new technique aimed at representing wind power forecasting uncertainty by a set of discrete scenarios able to characterize the probability density function of the wind power forecast. From an initial large set of sampled scenarios, a reduced discrete set of representative or focal scenarios associated with a probability of occurrence is created using clustering techniques. The advantage is that this allows reducing the computational burden in stochastic models that require scenario representation. The validity of the reduction methodology has been tested in a simplified Unit Commitment (UC) problem.