Solar Power Forecasting in Smart Grids Using Distributed Information

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

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

Conference proceedings International

Title: Proceedings - 2014 Power Systems Computation Conference, PSCC 2014

Pages: 1-7

2014 Power Systems Computation Conference, PSCC 2014

18 August 2014 through 22 August 2014

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00A-8MD

DOI: 10.1109/PSCC.2014.7038462

Abstract (EN): The growing penetration of solar power technology at low voltage (LV) level introduces new challenges in the distribution grid operation. Across the world, Distribution System Operators (DSO) are implementing the Smart Grid concept and one key function, in this new paradigm, is solar power forecasting. This paper presents a new forecasting framework, based on vector autoregression theory, that combines spatial-temporal data collected by smart meters and distribution transformer controllers to produce six-hour-ahead forecasts at the residential solar photovoltaic (PV) and secondary substation (i.e., MV/LV substation) levels. This framework has been tested for 44 micro-generation units and 10 secondary substations from the Smart Grid pilot in Evora, Portugal (one demonstration site of the EU Project SuSTAINABLE). A comparison was made with the well-known Autoregressive forecasting Model (AR - univariate model) leading to an improvement between 8% and 12% for the first 3 lead-times.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7