Probabilistic solar power forecasting in smart grids using distributed information

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Vladimiro Miranda (Author) FEUP; Ricardo Bessa (Author) Other; Artur Trindade (Author) Other; Cátia Silva (Author) Other

Journal

Title: International Journal of Electrical Power and Energy Systems

Vol. 72

Pages: 16-23

ISSN: 0142-0615

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 88 Citations

ISI Web of Science

Scopus - 97 Citations

Other information

Authenticus ID: P-00G-AR4

DOI: 10.1016/j.ijepes.2015.02.006

Abstract (EN): The deployment of Smart Grid technologies opens new opportunities to develop new forecasting and optimization techniques. The growth of solar power penetration in distribution grids imposes the use of solar power forecasts as inputs in advanced grid management functions. This paper proposes a new forecasting algorithm for 6 h ahead based on the vector autoregression framework, which combines distributed time series information collected by the Smart Grid infrastructure. Probabilistic forecasts are generated for the residential solar photovoltaic (PV) and secondary substation levels. The test case consists of 44 micro-generation units and 10 secondary substations from the Smart Grid pilot in Evora, Portugal. The benchmark model is the well-known autoregressive forecasting method (univariate approach). The average improvement in terms of root mean square error (point forecast evaluation) and continuous ranking probability score (probabilistic forecast evaluation) for the first 3 lead-times was between 8% and 12%, and between 1.4% and 5.9%, respectively. (C) 2015 Published by Elsevier Ltd.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8