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  4. Photovoltaic Model-Based Solar Irradiance Estimators: Performance Comparison and Application to Maximum Power Forecasting
 
research article

Photovoltaic Model-Based Solar Irradiance Estimators: Performance Comparison and Application to Maximum Power Forecasting

Scolari, Enrica  
•
Sossan, Fabrizio  
•
Paolone, Mario  
January 1, 2018
IEEE Transactions on Sustainable Energy

Due to the increasing proportion of distributed photovoltaic (PV) production in the generation mix, the knowledge of the PV generation capacity has become a key factor. In this work, we propose to compute the PV plant maximum power starting from the indirectly-estimated irradiance. Three estimators are compared in terms of i) ability to compute the PV plant maximum power, ii) bandwidth and iii) robustness against measurements noise. The approaches rely on measurements of the DC voltage, current, and cell temperature and on a model of the PV array. We show that the considered methods can accurately reconstruct the PV maximum generation even during curtailment periods, i.e. when the measured PV power is not representative of the maximum potential of the PV array. Performance evaluation is carried out by using a dedicated experimental setup on a 14.3 kWp rooftop PV installation. Results also proved that the analyzed methods can outperform pyranometer-based estimations, with a simple sensing system. We show how the obtained PV maximum power values can be applied to train time series-based solar maximum power forecasting techniques. This is beneficial when the measured power values, commonly used as training, are not representative of the maximum PV potential.

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Type
research article
DOI
10.1109/TSTE.2017.2714690
Web of Science ID

WOS:000418644700004

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

2018-01-01

Published in
IEEE Transactions on Sustainable Energy
Volume

9

Issue

1

Start page

35

End page

44

Subjects

Photovoltaic power systems

•

Solar irradiance

•

Renewable generation

•

Kalman Filter

•

Maximum power forecast

•

epfl-smartgrids

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
DESL  
FunderGrant Number

CTI/Innosuisse

SCCER FURIES

Available on Infoscience
June 16, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/138485
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