This document discusses modelling photovoltaic power output. It examines factors that affect power production such as cell temperature, angle of incidence, solar spectrum, low irradiance performance, maximum power point tracking accuracy, soiling, ohmic losses, and inverters. Graphs are shown comparing modeled versus observed power for experiments in Cocoa, Florida and Eugene, Oregon, with Gaussian distributions of residuals. Further analysis of different module types and residual patterns is suggested.

1. MODELLING PHOTOVOLTAIC
POWER
Mavromatakis Fotis
Vignola Frank
Franghiadakis Yannis
T.E.I. of Crete & University of Oregon
Technological Educational Institute of Crete
Laboratory of Energy and Photovoltaic Systems

2. Estimating photovoltaic power
Technological Educational Institute of Crete
Laboratory of Energy and Photovoltaic Systems
STC
tpm
G
G
PP  
 Cell Temperature
 Angle of Incidence
 Solar spectrum
 Low irradiance performance
 Accuracy in MPPT
 Soiling
 Ohmic losses
 Inverter

3. Spectral effects
Spectra acquired by a LI1800 spectroradiometer at Heraklion, Crete. Typical cell spectral responses.
Technological Educational Institute of Crete
Laboratory of Energy and Photovoltaic Systems

4. Low irradiance effects
Technological Educational Institute of Crete
Laboratory of Energy and Photovoltaic Systems

5. Soiling
Technological Educational Institute of Crete
Laboratory of Energy and Photovoltaic Systems
 A fixed term can be used or a function related to the months in the year

6. Validating the model
Technological Educational Institute of Crete
Laboratory of Energy and Photovoltaic Systems
Difference between modeled and observed AC power

7. Validating the model
Technological Educational Institute of Crete
Laboratory of Energy and Photovoltaic Systems
NREL experiment
Site: Cocoa, FL
Altitude: Sea level
26980 measurements

8. Validating the model
Technological Educational Institute of Crete
Laboratory of Energy and Photovoltaic Systems
NREL experiment
Site: Eugene, OR
Altitude: 130 m
23970 measurements

9. Current Results
Technological Educational Institute of Crete
Laboratory of Energy and Photovoltaic Systems
Gaussian distributions
σ : 0.02 or 2% probability 68%
FWHM=2.355 · σ ≈ 0.05 = 5% probability 98%
To be done
Analyze all module types
Detailed study of the residuals
Efficiency data from manufacturers (?)