Clustering-based wind power scenario reduction technique

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Sumaili, J (Author) Other; Keko, H (Author) Other; Vladimiro Miranda (Author) FEUP; Botterud, A (Author) Other; Wang, J (Author) Other

Conference proceedings International

Title: 17th Power Systems Computation Conference, PSCC 2011

17th Power Systems Computation Conference, PSCC 2011

22 August 2011 through 26 August 2011

Indexing

Scopus - 19 Citations

Other information

Authenticus ID: P-00G-QPC

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific