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Title: A stochastic downscaling approach for generating high-frequency solar irradiance scenarios

Abstract

Solar power is increasingly cost viable with solar photovoltaic (PV) installations becoming commonplace. PV planning and operational studies, however, require high-frequency solar irradiance scenarios to understand potential electric grid impacts due to the variability and uncertainty of the underlying solar resource. Existing remote sensing solar data products are often available over large spatial domains, but are limited in temporal resolution. For example, the global horizontal irradiance (GHI) component contained within the National Solar Radiation Database (NSRDB) is available at time resolution of 30 min on an approximately four-kilometer grid. In contrast, substantial solar variability is present at finer time scales and this article describes an algorithm to stochastically generate one-minute GHI from widely available sub-hourly NSRDB. A generalized linear modeling (GLM) framework is proposed, which includes non-Gaussian mixtures, and extends the literature involving the synthesis of GHI data. The model is trained on a set of sample locations around Oregon, USA, and validated across the USA using both the Surface Radiation Budget Network (SURFRAD) dataset and Solar Radiation Monitoring Laboratory (SRML) network. Simulated ensembles show good coverage properties and temporal correlation structure. The resulting downscaled ensembles allow for understanding the unpredictable variability inherent in GHI at locations without direct measurements.more » Here, future work can leverage the algorithm as part of a stochastic optimization of electric grid operations with high-penetration PV systems.« less

Authors:
ORCiD logo [1];  [1]; ORCiD logo [2];  [2];  [2]
  1. Univ. of Colorado at Boulder, Boulder, CO (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
OSTI Identifier:
1481844
Report Number(s):
NREL/JA-5D00-72667
Journal ID: ISSN 0038-092X
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Solar Energy
Additional Journal Information:
Journal Volume: 176; Journal Issue: C; Journal ID: ISSN 0038-092X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 47 OTHER INSTRUMENTATION; Irradiance modeling; high-resolution; stochastic; non-Gaussian

Citation Formats

Zhang, Wenqi, Kleiber, William, Florita, Anthony R., Hodge, Bri -Mathias, and Mather, Barry A. A stochastic downscaling approach for generating high-frequency solar irradiance scenarios. United States: N. p., 2018. Web. doi:10.1016/j.solener.2018.10.019.
Zhang, Wenqi, Kleiber, William, Florita, Anthony R., Hodge, Bri -Mathias, & Mather, Barry A. A stochastic downscaling approach for generating high-frequency solar irradiance scenarios. United States. https://doi.org/10.1016/j.solener.2018.10.019
Zhang, Wenqi, Kleiber, William, Florita, Anthony R., Hodge, Bri -Mathias, and Mather, Barry A. Mon . "A stochastic downscaling approach for generating high-frequency solar irradiance scenarios". United States. https://doi.org/10.1016/j.solener.2018.10.019. https://www.osti.gov/servlets/purl/1481844.
@article{osti_1481844,
title = {A stochastic downscaling approach for generating high-frequency solar irradiance scenarios},
author = {Zhang, Wenqi and Kleiber, William and Florita, Anthony R. and Hodge, Bri -Mathias and Mather, Barry A.},
abstractNote = {Solar power is increasingly cost viable with solar photovoltaic (PV) installations becoming commonplace. PV planning and operational studies, however, require high-frequency solar irradiance scenarios to understand potential electric grid impacts due to the variability and uncertainty of the underlying solar resource. Existing remote sensing solar data products are often available over large spatial domains, but are limited in temporal resolution. For example, the global horizontal irradiance (GHI) component contained within the National Solar Radiation Database (NSRDB) is available at time resolution of 30 min on an approximately four-kilometer grid. In contrast, substantial solar variability is present at finer time scales and this article describes an algorithm to stochastically generate one-minute GHI from widely available sub-hourly NSRDB. A generalized linear modeling (GLM) framework is proposed, which includes non-Gaussian mixtures, and extends the literature involving the synthesis of GHI data. The model is trained on a set of sample locations around Oregon, USA, and validated across the USA using both the Surface Radiation Budget Network (SURFRAD) dataset and Solar Radiation Monitoring Laboratory (SRML) network. Simulated ensembles show good coverage properties and temporal correlation structure. The resulting downscaled ensembles allow for understanding the unpredictable variability inherent in GHI at locations without direct measurements. Here, future work can leverage the algorithm as part of a stochastic optimization of electric grid operations with high-penetration PV systems.},
doi = {10.1016/j.solener.2018.10.019},
journal = {Solar Energy},
number = C,
volume = 176,
place = {United States},
year = {Mon Oct 22 00:00:00 EDT 2018},
month = {Mon Oct 22 00:00:00 EDT 2018}
}

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Cited by: 13 works
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Figures / Tables:

Figure 1 Figure 1: Interpolated NSRDB data and in situ data for Eugene, OR, January 6, 2013.

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