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Title: Simulating wind power forecast error distributions for spatially aggregated wind power plants

Abstract

Dispersion and aggregation of wind power plants lower the uncertainty of wind power by reducing wind power forecasting errors. Using quantitative methods, this paper studies the dispersion's impact on the uncertainty of the aggregated wind power production. A method to simulate day-ahead forecast error distributions at different dispersion and forecasting skill scenarios is presented. The proposed method models the uncertainty of wind power forecasting on an annual basis and at different levels of production. As a result, the uncertainty in the forecasting of spatially dispersed wind power plants is modelled using two continuous distributions: Laplace and beta distribution. The analysis shows that even the production level uncertainty can be modelled in various dispersion and forecasting skill scenarios. The model for aggregated forecast error distributions requires only four variables: capacity-weighted distance of the total wind power plant fleet, mean of the site-specific mean absolute errors (MAEs), number of aggregated wind power plants, and the mean variability of the elevations from the proximities of the aggregated wind power plants. Here, the results are especially promising when the number of aggregated wind power plants exceeds five, the terrain complexity is low or moderate, and the aggregation region is large. This is demonstrated throughmore » a case study for Texas, United States.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]
  1. Technical Research Centre of Finland, Espoo (Finland). Design and Operation of Energy Systems VTT
  2. National Renewable Energy Lab. (NREL), Denver, CO (United States). Power System Design and Studies; Univ. of Colorado, Boulder, CO (United States). Computer and Energy Engineering
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1571902
Report Number(s):
NREL/JA-5D00-75244
Journal ID: ISSN 1095-4244
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Wind Energy
Additional Journal Information:
Journal Name: Wind Energy; Journal ID: ISSN 1095-4244
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind power forecasting; wind power; aggregated wind power production

Citation Formats

Miettinen, Jari, Holttinen, Hannele, and Hodge, Bri‐Mathias S. Simulating wind power forecast error distributions for spatially aggregated wind power plants. United States: N. p., 2019. Web. doi:10.1002/we.2410.
Miettinen, Jari, Holttinen, Hannele, & Hodge, Bri‐Mathias S. Simulating wind power forecast error distributions for spatially aggregated wind power plants. United States. doi:10.1002/we.2410.
Miettinen, Jari, Holttinen, Hannele, and Hodge, Bri‐Mathias S. Wed . "Simulating wind power forecast error distributions for spatially aggregated wind power plants". United States. doi:10.1002/we.2410.
@article{osti_1571902,
title = {Simulating wind power forecast error distributions for spatially aggregated wind power plants},
author = {Miettinen, Jari and Holttinen, Hannele and Hodge, Bri‐Mathias S.},
abstractNote = {Dispersion and aggregation of wind power plants lower the uncertainty of wind power by reducing wind power forecasting errors. Using quantitative methods, this paper studies the dispersion's impact on the uncertainty of the aggregated wind power production. A method to simulate day-ahead forecast error distributions at different dispersion and forecasting skill scenarios is presented. The proposed method models the uncertainty of wind power forecasting on an annual basis and at different levels of production. As a result, the uncertainty in the forecasting of spatially dispersed wind power plants is modelled using two continuous distributions: Laplace and beta distribution. The analysis shows that even the production level uncertainty can be modelled in various dispersion and forecasting skill scenarios. The model for aggregated forecast error distributions requires only four variables: capacity-weighted distance of the total wind power plant fleet, mean of the site-specific mean absolute errors (MAEs), number of aggregated wind power plants, and the mean variability of the elevations from the proximities of the aggregated wind power plants. Here, the results are especially promising when the number of aggregated wind power plants exceeds five, the terrain complexity is low or moderate, and the aggregation region is large. This is demonstrated through a case study for Texas, United States.},
doi = {10.1002/we.2410},
journal = {Wind Energy},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

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