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Title: Short-term solar irradiance forecasting via satellite/model coupling

Abstract

Here, the short-term (0-3 h) prediction of solar insolation for renewable energy production is a problem well-suited to satellite-based techniques. The spatial, spectral, temporal and radiometric resolution of instrumentation hosted on the geostationary platform allows these satellites to describe the current cloud spatial distribution and optical properties. These properties relate directly to the transient properties of the downwelling solar irradiance at the surface, which come in the form of 'ramps' that pose a central challenge to energy load balancing in a spatially distributed network of solar farms. The short-term evolution of the cloud field may be approximated to first order simply as translational, but care must be taken in how the advection is handled and where the impacts are assigned. In this research, we describe how geostationary satellite observations are used with operational cloud masking and retrieval algorithms, wind field data from Numerical Weather Prediction (NWP), and radiative transfer calculations to produce short-term forecasts of solar insolation for applications in solar power generation. The scheme utilizes retrieved cloud properties to group pixels into contiguous cloud objects whose future positions are predicted using four-dimensional (space + time) model wind fields, selecting steering levels corresponding to the cloud height properties of eachmore » cloud group. The shadows associated with these clouds are adjusted for sensor viewing parallax displacement and combined with solar geometry and terrain height to determine the actual location of cloud shadows. For mid/high-level clouds at mid-latitudes and high solar zenith angles, the combined displacements from these geometric considerations are non-negligible. The cloud information is used to initialize a radiative transfer model that computes the direct and diffuse-sky solar insolation at both shadow locations and intervening clear-sky regions. Here, we describe the formulation of the algorithm and validate its performance against Surface Radiation (SURFRAD; Augustine et al., 2000, 2005) network observations. Typical errors range from 8.5% to 17.2% depending on the complexity of cloud regimes, and an operational demonstration outperformed persistence-based forecasting of Global Horizontal Irradiance (GHI) under all conditions by ~10 W/m2.« less

Authors:
 [1];  [1];  [1];  [2];  [3]
  1. Colorado State Univ., Fort Collins, CO (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  3. National Oceanic and Atmospheric Administration, Madison, WI (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)
OSTI Identifier:
1414069
Alternate Identifier(s):
OSTI ID: 1582884
Report Number(s):
NREL/JA-5D00-70674
Journal ID: ISSN 0038-092X
Grant/Contract Number:  
AC36-08GO28308; AGJ-040282-01
Resource Type:
Accepted Manuscript
Journal Name:
Solar Energy
Additional Journal Information:
Journal Volume: 168; Journal Issue: C; Journal ID: ISSN 0038-092X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 47 OTHER INSTRUMENTATION; geostationary satellite; cloud properties; parallax; shadows; advection; solar irradiance

Citation Formats

Miller, Steven D., Rogers, Matthew A., Haynes, John M., Sengupta, Manajit, and Heidinger, Andrew K. Short-term solar irradiance forecasting via satellite/model coupling. United States: N. p., 2017. Web. https://doi.org/10.1016/j.solener.2017.11.049.
Miller, Steven D., Rogers, Matthew A., Haynes, John M., Sengupta, Manajit, & Heidinger, Andrew K. Short-term solar irradiance forecasting via satellite/model coupling. United States. https://doi.org/10.1016/j.solener.2017.11.049
Miller, Steven D., Rogers, Matthew A., Haynes, John M., Sengupta, Manajit, and Heidinger, Andrew K. Fri . "Short-term solar irradiance forecasting via satellite/model coupling". United States. https://doi.org/10.1016/j.solener.2017.11.049. https://www.osti.gov/servlets/purl/1414069.
@article{osti_1414069,
title = {Short-term solar irradiance forecasting via satellite/model coupling},
author = {Miller, Steven D. and Rogers, Matthew A. and Haynes, John M. and Sengupta, Manajit and Heidinger, Andrew K.},
abstractNote = {Here, the short-term (0-3 h) prediction of solar insolation for renewable energy production is a problem well-suited to satellite-based techniques. The spatial, spectral, temporal and radiometric resolution of instrumentation hosted on the geostationary platform allows these satellites to describe the current cloud spatial distribution and optical properties. These properties relate directly to the transient properties of the downwelling solar irradiance at the surface, which come in the form of 'ramps' that pose a central challenge to energy load balancing in a spatially distributed network of solar farms. The short-term evolution of the cloud field may be approximated to first order simply as translational, but care must be taken in how the advection is handled and where the impacts are assigned. In this research, we describe how geostationary satellite observations are used with operational cloud masking and retrieval algorithms, wind field data from Numerical Weather Prediction (NWP), and radiative transfer calculations to produce short-term forecasts of solar insolation for applications in solar power generation. The scheme utilizes retrieved cloud properties to group pixels into contiguous cloud objects whose future positions are predicted using four-dimensional (space + time) model wind fields, selecting steering levels corresponding to the cloud height properties of each cloud group. The shadows associated with these clouds are adjusted for sensor viewing parallax displacement and combined with solar geometry and terrain height to determine the actual location of cloud shadows. For mid/high-level clouds at mid-latitudes and high solar zenith angles, the combined displacements from these geometric considerations are non-negligible. The cloud information is used to initialize a radiative transfer model that computes the direct and diffuse-sky solar insolation at both shadow locations and intervening clear-sky regions. Here, we describe the formulation of the algorithm and validate its performance against Surface Radiation (SURFRAD; Augustine et al., 2000, 2005) network observations. Typical errors range from 8.5% to 17.2% depending on the complexity of cloud regimes, and an operational demonstration outperformed persistence-based forecasting of Global Horizontal Irradiance (GHI) under all conditions by ~10 W/m2.},
doi = {10.1016/j.solener.2017.11.049},
journal = {Solar Energy},
number = C,
volume = 168,
place = {United States},
year = {2017},
month = {12}
}

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    Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study
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