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Title: Adjoint Sensitivity of FARMS to the Forecasting Variables of WRF-Solar: Preprint

Abstract

This study presents the development and application of an adjoint model for investigating the sensitivity of solar radiation to forecasted variables from WRF-Solar. The first part of this study focuses on developing an adjoint model for the Fast All-sky Model for Solar Applications (FARMS) to investigate the input variables having the highest sensitivity to global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI), which are the output variables. The applicability and usefulness of the adjoint sensitivity approach are demonstrated by conducting a sensitivity analysis under various scenarios defined by low, medium, and high values for the input variables. This preliminary study uses elasticity values to understand the sensitivity of solar radiation to the input variables (e.g. solar zenith angle, Angstrom turbidity coefficient, and cloud optical depth) of FARMS. This presentation will illustrate the implemented methodology and the obtained sensitivity results for FARMS, as well as future research steps that will lead to the development of high-quality probabilistic solar forecasts.

Authors:
 [1]; ORCiD logo [1];  [1];  [2];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. National Center for Atmospheric Research
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1566040
Report Number(s):
NREL/CP-5D00-74756
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2019 European Photovoltaic Specialists Conference (EU PVSEC), 9-13 September 2019, Marseille, France
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; WRF-Solar; sensitivity analysis; FARMS; adjoint/tangent linear model

Citation Formats

Yang, Jaemo, Sengupta, Manajit, Xie, Yu, Jimenez, Pedro A., and Kim, Ju-Hye. Adjoint Sensitivity of FARMS to the Forecasting Variables of WRF-Solar: Preprint. United States: N. p., 2019. Web.
Yang, Jaemo, Sengupta, Manajit, Xie, Yu, Jimenez, Pedro A., & Kim, Ju-Hye. Adjoint Sensitivity of FARMS to the Forecasting Variables of WRF-Solar: Preprint. United States.
Yang, Jaemo, Sengupta, Manajit, Xie, Yu, Jimenez, Pedro A., and Kim, Ju-Hye. Tue . "Adjoint Sensitivity of FARMS to the Forecasting Variables of WRF-Solar: Preprint". United States. https://www.osti.gov/servlets/purl/1566040.
@article{osti_1566040,
title = {Adjoint Sensitivity of FARMS to the Forecasting Variables of WRF-Solar: Preprint},
author = {Yang, Jaemo and Sengupta, Manajit and Xie, Yu and Jimenez, Pedro A. and Kim, Ju-Hye},
abstractNote = {This study presents the development and application of an adjoint model for investigating the sensitivity of solar radiation to forecasted variables from WRF-Solar. The first part of this study focuses on developing an adjoint model for the Fast All-sky Model for Solar Applications (FARMS) to investigate the input variables having the highest sensitivity to global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI), which are the output variables. The applicability and usefulness of the adjoint sensitivity approach are demonstrated by conducting a sensitivity analysis under various scenarios defined by low, medium, and high values for the input variables. This preliminary study uses elasticity values to understand the sensitivity of solar radiation to the input variables (e.g. solar zenith angle, Angstrom turbidity coefficient, and cloud optical depth) of FARMS. This presentation will illustrate the implemented methodology and the obtained sensitivity results for FARMS, as well as future research steps that will lead to the development of high-quality probabilistic solar forecasts.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

Conference:
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