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Title: Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

Abstract

Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind-energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to 12h of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80m wind speed observations from towers in Boulder, Colorado, and near the Columbia River gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of rampmore » events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method with regard to predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake shuffle method yields the highest skill at predicting ramp events for these datasets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO, site using any of the multivariate methods because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [4]
  1. Univ. of Colorado, Boulder, CO (United States). Dept. of Atmospheric and Oceanic Sciences
  2. Univ. of Colorado, Boulder, CO (United States). Cooperative Inst. for Research in the Environmental Sciences; NOAA Earth System Research Lab. (ESRL), Boulder, CO (United States). Physical Sciences Division
  3. NOAA Earth System Research Lab. (ESRL), Boulder, CO (United States). Physical Sciences Division
  4. Univ. of Colorado, Boulder, CO (United States). Dept. of Atmospheric and Oceanic Sciences; National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE; National Oceanic and Atmospheric Administration (NOAA) (United States)
OSTI Identifier:
1475521
Report Number(s):
NREL/JA-5000-72520
Journal ID: ISSN 2366-7451
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Wind Energy Science (Online)
Additional Journal Information:
Journal Volume: 3; Journal Issue: 1; Journal ID: ISSN 2366-7451
Publisher:
European Wind Energy Association - Copernicus
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind power forecasting; ramp events

Citation Formats

Worsnop, Rochelle P., Scheuerer, Michael, Hamill, Thomas M., and Lundquist, Julie K. Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing. United States: N. p., 2018. Web. doi:10.5194/wes-3-371-2018.
Worsnop, Rochelle P., Scheuerer, Michael, Hamill, Thomas M., & Lundquist, Julie K. Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing. United States. https://doi.org/10.5194/wes-3-371-2018
Worsnop, Rochelle P., Scheuerer, Michael, Hamill, Thomas M., and Lundquist, Julie K. Thu . "Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing". United States. https://doi.org/10.5194/wes-3-371-2018. https://www.osti.gov/servlets/purl/1475521.
@article{osti_1475521,
title = {Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing},
author = {Worsnop, Rochelle P. and Scheuerer, Michael and Hamill, Thomas M. and Lundquist, Julie K.},
abstractNote = {Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind-energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to 12h of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80m wind speed observations from towers in Boulder, Colorado, and near the Columbia River gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method with regard to predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake shuffle method yields the highest skill at predicting ramp events for these datasets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO, site using any of the multivariate methods because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.},
doi = {10.5194/wes-3-371-2018},
url = {https://www.osti.gov/biblio/1475521}, journal = {Wind Energy Science (Online)},
issn = {2366-7451},
number = 1,
volume = 3,
place = {United States},
year = {2018},
month = {6}
}

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