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Title: Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production.

Abstract

Abstract not provided.

Authors:
; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1398364
Report Number(s):
SAND2016-9815C
647916
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the UVIG held September 27-29, 2016 in Denver, CO.
Country of Publication:
United States
Language:
English

Citation Formats

Silva-Monroy, Cesar Augusto, Watson, Jean-Paul, Staid, Andrea, and Woodruff, David L. Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production.. United States: N. p., 2016. Web.
Silva-Monroy, Cesar Augusto, Watson, Jean-Paul, Staid, Andrea, & Woodruff, David L. Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production.. United States.
Silva-Monroy, Cesar Augusto, Watson, Jean-Paul, Staid, Andrea, and Woodruff, David L. 2016. "Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production.". United States. doi:. https://www.osti.gov/servlets/purl/1398364.
@article{osti_1398364,
title = {Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production.},
author = {Silva-Monroy, Cesar Augusto and Watson, Jean-Paul and Staid, Andrea and Woodruff, David L.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 9
}

Conference:
Other availability
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  • Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less
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