Finding representative wind power scenarios and their probabilities for stochastic models

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

Authors

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

Conference proceedings International

Title: 2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011

2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011

Hersonisos, Crete, 25 September 2011 through 28 September 2011

Indexing

Scopus - 26 Citations

Other information

Authenticus ID: P-008-1HJ

DOI: 10.1109/isap.2011.6082195

Abstract (EN): 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. © 2011 IEEE.

Language: English

Type (Professor's evaluation): Scientific