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  4. A Model-Based Filtering Strategy to Reconstruct the Maximum Power Generation of Curtailed Photovoltaic Installations: application to forecasting
 
conference paper

A Model-Based Filtering Strategy to Reconstruct the Maximum Power Generation of Curtailed Photovoltaic Installations: application to forecasting

Scolari, Enrica  
•
Sossan, Fabrizio  
•
Paolone, Mario  
Milanovic, Jovica
2017
Proceedings of the 2017 IEEE PES PowerTech
2017 IEEE PES PowerTech

In this paper, we propose a model-based filtering strategy to reconstruct the maximum power production of a PV power plant thanks to integrating measurements of the PV cell temperature, system DC voltage and DC current. The filter relies on a reversed physical model of the targeted PV system and enables to, first, determine analytically the irradiance incident to the panels, and, second, estimate the DC power production as if the plant was operating in maximum power point tracking (MPPT) mode. We present how the approach can be used to reconstruct the maximum power value starting from a generic operating point. As an application, we show that the proposed strategy can improve time series-based solar power forecasting techniques, in particular when the production of the PV system is curtailed and thus the measured power does not correspond to the maximum available.

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Type
conference paper
DOI
10.1109/PTC.2017.7980794
Web of Science ID

WOS:000411142500008

Author(s)
Scolari, Enrica  
Sossan, Fabrizio  
Paolone, Mario  
Editors
Milanovic, Jovica
Date Issued

2017

Publisher

IEEE

Publisher place

New York

Published in
Proceedings of the 2017 IEEE PES PowerTech
ISBN of the book

978-1-5090-4237-1

Total of pages

6

Start page

1

End page

6

Subjects

Solar power estimation

•

Maximum power forecast

•

Photovoltaic (PV) systems

•

Stochastic generation

•

epfl-smartgrids

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
DESL  
Event nameEvent placeEvent date
2017 IEEE PES PowerTech

Manchester

June 18-22, 2017

Available on Infoscience
June 23, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/138598
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