A Physics-Based DNI Model Assessing All-Sky Circumsolar Radiation
By investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI)...
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| Published in: | iScience Vol. 23; no. 3; p. 100893 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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27.03.2020
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| ISSN: | 2589-0042, 2589-0042 |
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| Abstract | By investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region. In sharp contrast with the other DNI models, this method uses a finite-surface integration algorithm that computes solar radiation in differential solid angles and efficiently infers its contribution to a surface perpendicular to the sun direction. The new model substantially reduces the uncertainty in DNI by a factor of 2–7.
[Display omitted]
•A dramatic and unexpected bias is found in the routine computation of DNI•A physics-based model is developed to integrate circumsolar radiances•DNI computation comprises scattered radiation in circumsolar region•This new model leads to a remarkable improvement in assessing DNI
Environmental Science; Energy Resources; Radiation Measurement |
|---|---|
| AbstractList | By investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region. In sharp contrast with the other DNI models, this method uses a finite-surface integration algorithm that computes solar radiation in differential solid angles and efficiently infers its contribution to a surface perpendicular to the sun direction. The new model substantially reduces the uncertainty in DNI by a factor of 2–7.
•
A dramatic and unexpected bias is found in the routine computation of DNI
•
A physics-based model is developed to integrate circumsolar radiances
•
DNI computation comprises scattered radiation in circumsolar region
•
This new model leads to a remarkable improvement in assessing DNI
Environmental Science; Energy Resources; Radiation Measurement By investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region. In sharp contrast with the other DNI models, this method uses a finite-surface integration algorithm that computes solar radiation in differential solid angles and efficiently infers its contribution to a surface perpendicular to the sun direction. The new model substantially reduces the uncertainty in DNI by a factor of 2-7.By investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region. In sharp contrast with the other DNI models, this method uses a finite-surface integration algorithm that computes solar radiation in differential solid angles and efficiently infers its contribution to a surface perpendicular to the sun direction. The new model substantially reduces the uncertainty in DNI by a factor of 2-7. By investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region. In sharp contrast with the other DNI models, this method uses a finite-surface integration algorithm that computes solar radiation in differential solid angles and efficiently infers its contribution to a surface perpendicular to the sun direction. The new model substantially reduces the uncertainty in DNI by a factor of 2-7. By investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region. In sharp contrast with the other DNI models, this method uses a finite-surface integration algorithm that computes solar radiation in differential solid angles and efficiently infers its contribution to a surface perpendicular to the sun direction. The new model substantially reduces the uncertainty in DNI by a factor of 2–7. [Display omitted] •A dramatic and unexpected bias is found in the routine computation of DNI•A physics-based model is developed to integrate circumsolar radiances•DNI computation comprises scattered radiation in circumsolar region•This new model leads to a remarkable improvement in assessing DNI Environmental Science; Energy Resources; Radiation Measurement By investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region. In sharp contrast with the other DNI models, this method uses a finite-surface integration algorithm that computes solar radiation in differential solid angles and efficiently infers its contribution to a surface perpendicular to the sun direction. The new model substantially reduces the uncertainty in DNI by a factor of 2–7. : Environmental Science; Energy Resources; Radiation Measurement Subject Areas: Environmental Science, Energy Resources, Radiation Measurement |
| ArticleNumber | 100893 |
| Author | Xie, Yu Habte, Aron Min, Qilong Sengupta, Manajit Liu, Yangang Long, Hai Liu, Weijia |
| AuthorAffiliation | 1 Power Systems Engineering Center, National Renewable Energy Laboratory, Golden, CO 80401, USA 5 School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China 2 Environmental and Climate Sciences Department, Brookhaven National Laboratory, 99 Rochester St., Upton, NY 11973, USA 4 Atmospheric Sciences Research Center, State University of New York, 251 Fuller Road, Albany, NY 12203, USA 3 Computational Science Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA |
| AuthorAffiliation_xml | – name: 1 Power Systems Engineering Center, National Renewable Energy Laboratory, Golden, CO 80401, USA – name: 3 Computational Science Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA – name: 5 School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China – name: 4 Atmospheric Sciences Research Center, State University of New York, 251 Fuller Road, Albany, NY 12203, USA – name: 2 Environmental and Climate Sciences Department, Brookhaven National Laboratory, 99 Rochester St., Upton, NY 11973, USA |
| Author_xml | – sequence: 1 givenname: Yu surname: Xie fullname: Xie, Yu email: yu.xie@nrel.gov organization: Power Systems Engineering Center, National Renewable Energy Laboratory, Golden, CO 80401, USA – sequence: 2 givenname: Manajit surname: Sengupta fullname: Sengupta, Manajit organization: Power Systems Engineering Center, National Renewable Energy Laboratory, Golden, CO 80401, USA – sequence: 3 givenname: Yangang surname: Liu fullname: Liu, Yangang email: lyg@bnl.gov organization: Environmental and Climate Sciences Department, Brookhaven National Laboratory, 99 Rochester St., Upton, NY 11973, USA – sequence: 4 givenname: Hai surname: Long fullname: Long, Hai organization: Computational Science Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA – sequence: 5 givenname: Qilong surname: Min fullname: Min, Qilong organization: Atmospheric Sciences Research Center, State University of New York, 251 Fuller Road, Albany, NY 12203, USA – sequence: 6 givenname: Weijia surname: Liu fullname: Liu, Weijia organization: Environmental and Climate Sciences Department, Brookhaven National Laboratory, 99 Rochester St., Upton, NY 11973, USA – sequence: 7 givenname: Aron surname: Habte fullname: Habte, Aron organization: Power Systems Engineering Center, National Renewable Energy Laboratory, Golden, CO 80401, USA |
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