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Title: Variability in large-scale wind power generation: Variability in large-scale wind power generation

Abstract

The paper demonstrates the characteristics of wind power variability and net load variability in multiple power systems based on real data from multiple years. Demonstrated characteristics include probability distribution for different ramp durations, seasonal and diurnal variability and low net load events. The comparison shows regions with low variability (Sweden, Spain and Germany), medium variability (Portugal, Ireland, Finland and Denmark) and regions with higher variability (Quebec, Bonneville Power Administration and Electric Reliability Council of Texas in North America; Gansu, Jilin and Liaoning in China; and Norway and offshore wind power in Denmark). For regions with low variability, the maximum 1 h wind ramps are below 10% of nominal capacity, and for regions with high variability, they may be close to 30%. Wind power variability is mainly explained by the extent of geographical spread, but also higher capacity factor causes higher variability. It was also shown how wind power ramps are autocorrelated and dependent on the operating output level. When wind power was concentrated in smaller area, there were outliers with high changes in wind output, which were not present in large areas with well-dispersed wind power.

Authors:
 [1];  [1];  [2]; ORCiD logo [3];  [4];  [5]; ORCiD logo [6];  [7];  [8];  [9];  [10];  [11];  [12];  [13];  [13]
  1. VTT Technical Research Centre of Finland, Espoo Finland
  2. Energy Department, Norwegian Water Resources and Energy Directorate, Oslo Norway
  3. KTH Royal Institute of Technology, Electric Power Systems, Stockholm Sweden
  4. Royal Institute of Technology, Electric Power Systems, Stockholm Sweden
  5. Institut de recherche Hydro-Québec, Montreal Canada
  6. DTU, Wind Energy, Roskilde Denmark
  7. Electricity Research Centre, University College Dublin, Dublin Ireland
  8. Electric Power Research Institute, Palo Alto California USA
  9. LNEG, Laboratorio Nacional de Energia e Geologia, UESEO, Lisbon Spain
  10. Renewable Energy Research Institute and DIEEAC/EDII-AB, Castilla-La Mancha University, Albacete Spain
  11. State Grid Corporation of China, Beijing China
  12. State Grid Energy Research Institute Beijing, Beijing China
  13. National Renewable Energy Laboratory, Transmission and Grid Integration Group, Golden Colorado USA
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:
1290783
Report Number(s):
NREL/JA-5000-65786
Journal ID: ISSN 1095-4244
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: Wind Energy; Journal Volume: 19; Journal Issue: 9
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; wind power; variability; net load; variable generation; power systems

Citation Formats

Kiviluoma, Juha, Holttinen, Hannele, Weir, David, Scharff, Richard, Söder, Lennart, Menemenlis, Nickie, Cutululis, Nicolaos A., Danti Lopez, Irene, Lannoye, Eamonn, Estanqueiro, Ana, Gomez-Lazaro, Emilio, Zhang, Qin, Bai, Jianhua, Wan, Yih-Huei, and Milligan, Michael. Variability in large-scale wind power generation: Variability in large-scale wind power generation. United States: N. p., 2015. Web. doi:10.1002/we.1942.
Kiviluoma, Juha, Holttinen, Hannele, Weir, David, Scharff, Richard, Söder, Lennart, Menemenlis, Nickie, Cutululis, Nicolaos A., Danti Lopez, Irene, Lannoye, Eamonn, Estanqueiro, Ana, Gomez-Lazaro, Emilio, Zhang, Qin, Bai, Jianhua, Wan, Yih-Huei, & Milligan, Michael. Variability in large-scale wind power generation: Variability in large-scale wind power generation. United States. doi:10.1002/we.1942.
Kiviluoma, Juha, Holttinen, Hannele, Weir, David, Scharff, Richard, Söder, Lennart, Menemenlis, Nickie, Cutululis, Nicolaos A., Danti Lopez, Irene, Lannoye, Eamonn, Estanqueiro, Ana, Gomez-Lazaro, Emilio, Zhang, Qin, Bai, Jianhua, Wan, Yih-Huei, and Milligan, Michael. Sun . "Variability in large-scale wind power generation: Variability in large-scale wind power generation". United States. doi:10.1002/we.1942.
@article{osti_1290783,
title = {Variability in large-scale wind power generation: Variability in large-scale wind power generation},
author = {Kiviluoma, Juha and Holttinen, Hannele and Weir, David and Scharff, Richard and Söder, Lennart and Menemenlis, Nickie and Cutululis, Nicolaos A. and Danti Lopez, Irene and Lannoye, Eamonn and Estanqueiro, Ana and Gomez-Lazaro, Emilio and Zhang, Qin and Bai, Jianhua and Wan, Yih-Huei and Milligan, Michael},
abstractNote = {The paper demonstrates the characteristics of wind power variability and net load variability in multiple power systems based on real data from multiple years. Demonstrated characteristics include probability distribution for different ramp durations, seasonal and diurnal variability and low net load events. The comparison shows regions with low variability (Sweden, Spain and Germany), medium variability (Portugal, Ireland, Finland and Denmark) and regions with higher variability (Quebec, Bonneville Power Administration and Electric Reliability Council of Texas in North America; Gansu, Jilin and Liaoning in China; and Norway and offshore wind power in Denmark). For regions with low variability, the maximum 1 h wind ramps are below 10% of nominal capacity, and for regions with high variability, they may be close to 30%. Wind power variability is mainly explained by the extent of geographical spread, but also higher capacity factor causes higher variability. It was also shown how wind power ramps are autocorrelated and dependent on the operating output level. When wind power was concentrated in smaller area, there were outliers with high changes in wind output, which were not present in large areas with well-dispersed wind power.},
doi = {10.1002/we.1942},
journal = {Wind Energy},
number = 9,
volume = 19,
place = {United States},
year = {Sun Oct 25 00:00:00 EDT 2015},
month = {Sun Oct 25 00:00:00 EDT 2015}
}