Spatial-Temporal Solar Power Forecasting for Smart Grids

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

Authors

Bessa, RJ (Author) Other; Trindade, A (Author) Other; Vladimiro Miranda (Author) FEUP

Journal

Title: IEEE Transactions on Industrial Informatics

Vol. 11

Pages: 232-241

ISSN: 1551-3203

Publisher: IEEE

Indexing

ISI Web of Knowledge - 73 Citations

Scopus - 105 Citations

Other information

Authenticus ID: P-00A-AWA

DOI: 10.1109/tii.2014.2365703

Abstract (EN): The solar power penetration in distribution grids is growing fast during the last years, particularly at the low-voltage (LV) level, which introduces new challenges when operating distribution grids. Across the world, distribution system operators (DSO) are developing the smart grid concept, and one key tool for this new paradigm is solar power forecasting. This paper presents a new spatial-temporal forecasting method based on the vector autoregression framework, which combines observations of solar generation collected by smart meters and distribution transformer controllers. The scope is 6-h-ahead forecasts at the residential solar photovoltaic and medium-voltage (MV)/LV substation levels. This framework has been tested in the smart grid pilot of vora, Portugal, and using data from 44 microgeneration units and 10 MV/LV substations. A benchmark comparison was made with the autoregressive forecasting model (AR-univariate model) leading to an improvement on average between 8% and 10%.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10