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Title: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

Abstract

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 time 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 contextmore » 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

Authors:
ORCiD logo [1];  [1];  [2];  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Univ. of California, Davis, CA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1369306
Report Number(s):
SAND-2017-6908J
Journal ID: ISSN 1095-4244; 654918
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Wind Energy
Additional Journal Information:
Journal Volume: 20; Journal Issue: 12; Journal ID: ISSN 1095-4244
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind power; forecasting; uncertainty; probabilistic scenarios; stochastic unit commitment and economic dispatch

Citation Formats

Staid, Andrea, Watson, Jean -Paul, Wets, Roger J. -B., and Woodruff, David L. Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators. United States: N. p., 2017. Web. doi:10.1002/we.2129.
Staid, Andrea, Watson, Jean -Paul, Wets, Roger J. -B., & Woodruff, David L. Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators. United States. doi:10.1002/we.2129.
Staid, Andrea, Watson, Jean -Paul, Wets, Roger J. -B., and Woodruff, David L. Tue . "Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators". United States. doi:10.1002/we.2129. https://www.osti.gov/servlets/purl/1369306.
@article{osti_1369306,
title = {Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators},
author = {Staid, Andrea and Watson, Jean -Paul and Wets, Roger J. -B. and Woodruff, David L.},
abstractNote = {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 time 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.},
doi = {10.1002/we.2129},
journal = {Wind Energy},
number = 12,
volume = 20,
place = {United States},
year = {2017},
month = {7}
}

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Works referenced in this record:

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Generation and evaluation of space–time trajectories of photovoltaic power
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journal, April 2012


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journal, July 2008


Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms
journal, January 2016

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    Works referencing / citing this record:

    Multi-period forecasting and scenario generation with limited data
    journal, March 2015

    • Rios, Ignacio; Wets, Roger J-B; Woodruff, David L.
    • Computational Management Science, Vol. 12, Issue 2
    • DOI: 10.1007/s10287-015-0230-5

    A methodology to generate statistically dependent wind speed scenarios
    journal, March 2010


    Spatio-temporal analysis and modeling of short-term wind power forecast errors
    journal, January 2011

    • Tastu, Julija; Pinson, Pierre; Kotwa, Ewelina
    • Wind Energy, Vol. 14, Issue 1
    • DOI: 10.1002/we.401

    Toward scalable stochastic unit commitment. Part 1: load scenario generation
    journal, April 2015


    Toward scalable stochastic unit commitment: Part 2: solver configuration and performance assessment
    journal, April 2015


    Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms
    journal, January 2016

    • Thorarinsdottir, Thordis L.; Scheuerer, Michael; Heinz, Christopher
    • Journal of Computational and Graphical Statistics, Vol. 25, Issue 1
    • DOI: 10.1080/10618600.2014.977447

    From probabilistic forecasts to statistical scenarios of short-term wind power production
    journal, January 2009

    • Pinson, Pierre; Madsen, Henrik; Nielsen, Henrik Aa.
    • Wind Energy, Vol. 12, Issue 1
    • DOI: 10.1002/we.284

    Generation and evaluation of space–time trajectories of photovoltaic power
    journal, August 2016


    Strictly Proper Scoring Rules, Prediction, and Estimation
    journal, March 2007

    • Gneiting, Tilmann; Raftery, Adrian E.
    • Journal of the American Statistical Association, Vol. 102, Issue 477
    • DOI: 10.1198/016214506000001437

    Time-adaptive quantile-copula for wind power probabilistic forecasting
    journal, April 2012


    Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds
    journal, July 2008


    Evaluating the quality of scenarios of short-term wind power generation
    journal, August 2012


    A stochastic model for the unit commitment problem
    journal, January 1996

    • Takriti, S.; Birge, J. R.; Long, E.
    • IEEE Transactions on Power Systems, Vol. 11, Issue 3
    • DOI: 10.1109/59.535691

    Fusion of hard and soft information in nonparametric density estimation
    journal, December 2015

    • Royset, Johannes O.; Wets, Roger J-B
    • European Journal of Operational Research, Vol. 247, Issue 2
    • DOI: 10.1016/j.ejor.2015.06.034