Developing automated methods to estimate spectrally resolved direct normal irradiance for solar energy applications
File(s)
Accepted version
Author(s)
Choi, Tsz Hei
Brindley, Helen
Ekins-Daukes, Nicholas
Escobar, Rodrigo
Type
Journal Article
Abstract
We describe four schemes designed to estimate spectrally resolved direct normal irradiance (DNI) for
multi-junction concentrator photovoltaic systems applications. The schemes have increasing levels of
complexity in terms of aerosol and circumsolar irradiance (CSI) treatment, ranging from a climatological
aerosol classification with no account of CSI, to an approach which includes explicit aerosol typing and
type dependent CSI contribution. When tested against ground-based broadband and spectral measurements at five sites spanning a range of aerosol conditions, the most sophisticated scheme yields an
average bias of þ 0:068%, well within photometer calibration uncertainties. The average spread of error
is 2:5%. These statistics are markedly better than the climatological approach, which carries an average
bias of 1:76% and a spread of 4%. They also improve on an intermediate approach which uses Angstrom€
exponents to estimate the spectral variation in aerosol optical depth across the solar energy relevant
wavelength domain. This approach results in systematic under and over-estimations of DNI at short and
long wavelengths respectively. Incorporating spectral CSI particularly benefits sites which experience a
significant amount of coarse aerosol. All approaches we describe use freely available reanalyses and
software tools, and can be easily applied to alternative aerosol measurements, including those from
satellite.
multi-junction concentrator photovoltaic systems applications. The schemes have increasing levels of
complexity in terms of aerosol and circumsolar irradiance (CSI) treatment, ranging from a climatological
aerosol classification with no account of CSI, to an approach which includes explicit aerosol typing and
type dependent CSI contribution. When tested against ground-based broadband and spectral measurements at five sites spanning a range of aerosol conditions, the most sophisticated scheme yields an
average bias of þ 0:068%, well within photometer calibration uncertainties. The average spread of error
is 2:5%. These statistics are markedly better than the climatological approach, which carries an average
bias of 1:76% and a spread of 4%. They also improve on an intermediate approach which uses Angstrom€
exponents to estimate the spectral variation in aerosol optical depth across the solar energy relevant
wavelength domain. This approach results in systematic under and over-estimations of DNI at short and
long wavelengths respectively. Incorporating spectral CSI particularly benefits sites which experience a
significant amount of coarse aerosol. All approaches we describe use freely available reanalyses and
software tools, and can be easily applied to alternative aerosol measurements, including those from
satellite.
Date Issued
2021-08
Date Acceptance
2021-03-25
Citation
Renewable Energy, 2021, 173, pp.1070-1086
ISSN
0960-1481
Publisher
Elsevier
Start Page
1070
End Page
1086
Journal / Book Title
Renewable Energy
Volume
173
Copyright Statement
© 2021 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Natural Environment Research Council (NERC)
Identifier
https://www.sciencedirect.com/science/article/pii/S0960148121004894?via%3Dihub
Grant Number
JJR/NCEO/ContFP1
Subjects
0906 Electrical and Electronic Engineering
0913 Mechanical Engineering
0915 Interdisciplinary Engineering
Energy
Publication Status
Published
Date Publish Online
2021-03-30