ANN-based scenario generation methodology for stochastic variables of electric power systems

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Vagropoulos, SI (Author) Other; Kardakos, EG (Author) Other; Simoglou, CK (Author) Other; Bakirtzis, AG (Author) Other; Catalao, JPS (Author) FEUP

Journal

Title: Electric Power Systems Research

Vol. 134

Pages: 9-18

ISSN: 0378-7796

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 76 Citations

Scopus - 82 Citations

Other information

Authenticus ID: P-00K-3C8

DOI: 10.1016/j.epsr.2015.12.020

Abstract (EN): In this paper a novel scenario generation methodology based on artificial neural networks (ANNs) is proposed. The methodology is flexible and able to generate scenarios for various stochastic variables that are used as input parameters in the stochastic short-term scheduling models. Appropriate techniques for modeling the cross-correlation of the involved stochastic processes and scenario reduction techniques are also incorporated into the proposed approach. The applicability of the methodology is investigated through the creation of electric load, photovoltaic (PV) and wind production scenarios and the performance of the proposed ANN-based methodology is compared to time series-based scenario generation models. Test results on the real-world insular power system of Crete and mainland Greece present the effectiveness of the proposed ANN-based scenario generation methodology.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10