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Title: Constructing probabilistic scenarios for wide-area solar power generation

Abstract

Optimizing thermal generation commitments and dispatch in the presence of high penetrations of renewable resources such as solar energy requires a characterization of their stochastic properties. In this study, we describe novel methods designed to create day-ahead, wide-area probabilistic solar power scenarios based only on historical forecasts and associated observations of solar power production. Each scenario represents a possible trajectory for solar power in next-day operations with an associated probability computed by algorithms that use historical forecast errors. Scenarios are created by segmentation of historic data, fitting non-parametric error distributions using epi-splines, and then computing specific quantiles from these distributions. Additionally, we address the challenge of establishing an upper bound on solar power output. Our specific application driver is for use in stochastic variants of core power systems operations optimization problems, e.g., unit commitment and economic dispatch. These problems require as input a range of possible future realizations of renewables production. However, the utility of such probabilistic scenarios extends to other contexts, e.g., operator and trader situational awareness. Finally, we compare the performance of our approach to a recently proposed method based on quantile regression, and demonstrate that our method performs comparably to this approach in terms of two widelymore » used methods for assessing the quality of probabilistic scenarios: the Energy score and the Variogram score.« less

Authors:
 [1];  [2];  [2];  [2];  [3];  [4]
  1. Univ. of California, Davis, CA (United States). Graduate School of Management
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Discrete Math and Optimization Dept.
  3. Univ. of Duisburg-Essen (Germany). Dept. of Mathematics
  4. Demand Energy Networks, Liberty Lake, WA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S); USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1421637
Report Number(s):
SAND2017-13497J
Journal ID: ISSN 0038-092X; 659503
Grant/Contract Number:  
NA0003525
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Solar Energy
Additional Journal Information:
Journal Volume: 160; Journal ID: ISSN 0038-092X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 97 MATHEMATICS AND COMPUTING

Citation Formats

Woodruff, David L., Deride, Julio, Staid, Andrea, Watson, Jean-Paul, Slevogt, Gerrit, and Silva-Monroy, César. Constructing probabilistic scenarios for wide-area solar power generation. United States: N. p., 2017. Web. doi:10.1016/j.solener.2017.11.067.
Woodruff, David L., Deride, Julio, Staid, Andrea, Watson, Jean-Paul, Slevogt, Gerrit, & Silva-Monroy, César. Constructing probabilistic scenarios for wide-area solar power generation. United States. doi:10.1016/j.solener.2017.11.067.
Woodruff, David L., Deride, Julio, Staid, Andrea, Watson, Jean-Paul, Slevogt, Gerrit, and Silva-Monroy, César. Fri . "Constructing probabilistic scenarios for wide-area solar power generation". United States. doi:10.1016/j.solener.2017.11.067.
@article{osti_1421637,
title = {Constructing probabilistic scenarios for wide-area solar power generation},
author = {Woodruff, David L. and Deride, Julio and Staid, Andrea and Watson, Jean-Paul and Slevogt, Gerrit and Silva-Monroy, César},
abstractNote = {Optimizing thermal generation commitments and dispatch in the presence of high penetrations of renewable resources such as solar energy requires a characterization of their stochastic properties. In this study, we describe novel methods designed to create day-ahead, wide-area probabilistic solar power scenarios based only on historical forecasts and associated observations of solar power production. Each scenario represents a possible trajectory for solar power in next-day operations with an associated probability computed by algorithms that use historical forecast errors. Scenarios are created by segmentation of historic data, fitting non-parametric error distributions using epi-splines, and then computing specific quantiles from these distributions. Additionally, we address the challenge of establishing an upper bound on solar power output. Our specific application driver is for use in stochastic variants of core power systems operations optimization problems, e.g., unit commitment and economic dispatch. These problems require as input a range of possible future realizations of renewables production. However, the utility of such probabilistic scenarios extends to other contexts, e.g., operator and trader situational awareness. Finally, we compare the performance of our approach to a recently proposed method based on quantile regression, and demonstrate that our method performs comparably to this approach in terms of two widely used methods for assessing the quality of probabilistic scenarios: the Energy score and the Variogram score.},
doi = {10.1016/j.solener.2017.11.067},
journal = {Solar Energy},
number = ,
volume = 160,
place = {United States},
year = {Fri Dec 22 00:00:00 EST 2017},
month = {Fri Dec 22 00:00:00 EST 2017}
}

Journal Article:
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