Variability in large-scale wind power generation

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 loa...

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Published inWind energy (Chichester, England) Vol. 19; no. 9; pp. 1649 - 1665
Main Authors 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
Format Journal Article
LanguageEnglish
Published Bognor Regis Blackwell Publishing Ltd 01.09.2016
John Wiley & Sons, Inc
Subjects
Electric power generation
Electrical Engineering
Elektro- och systemteknik
net load
power systems
Probability distribution
Variability
variable generation
Wind power
ANE, Norway
ANE, Portugal
Canada, Quebec
ANE, Eire
ASW, USA, Texas
ANE, Sweden
ANE, Finland
China, People's Rep
Denmark
ANE, Spain
Germany
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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. Copyright © 2015 John Wiley & Sons, Ltd.
AbstractList 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. Copyright © 2015 John Wiley & Sons, Ltd.
The article 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. In some characteristics the power systems are different, but in others they are significantly similar. Somewhat surprisingly there seems to be no straightforward correlation between wind power penetration level and variability. As long as there are several wind power plants with enough geographical spread, most of the smoothing impact is captured. 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 auto correlated and dependent on the operating output level. In most cases wind power did not have strong diurnal or seasonal variations in the variability. However, there can be exceptions depending on the latitude and on the local characteristics of the wind resource.
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 1h 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. Copyright © 2015 John Wiley & Sons, Ltd.
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 1h 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.
Author Scharff, Richard
Gomez-Lazaro, Emilio
Söder, Lennart
Weir, David
Lannoye, Eamonn
Bai, Jianhua
Wan, Yih-Huei
Zhang, Qin
Holttinen, Hannele
Estanqueiro, Ana
Kiviluoma, Juha
Cutululis, Nicolaos A.
Milligan, Michael
Danti Lopez, Irene
Menemenlis, Nickie
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  organization: VTT Technical Research Centre of Finland, Espoo, Finland
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  organization: Energy Department, Norwegian Water Resources and Energy Directorate, Oslo, Norway
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  organization: KTH Royal Institute of Technology, Electric Power Systems, Stockholm, Sweden
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  organization: Electricity Research Centre, University College Dublin, Dublin, Ireland
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  organization: Electric Power Research Institute, California, Palo Alto, USA
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  organization: State Grid Corporation of China, Beijing, China
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  organization: National Renewable Energy Laboratory, Transmission and Grid Integration Group, Colorado, Golden, USA
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  surname: Milligan
  fullname: Milligan, Michael
  organization: National Renewable Energy Laboratory, Transmission and Grid Integration Group, Colorado, Golden, USA
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Snippet The paper demonstrates the characteristics of wind power variability and net load variability in multiple power systems based on real data from multiple years....
The article demonstrates the characteristics of wind power variability and net load variability in multiple power systems based on real data from multiple...
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SubjectTerms Electric power generation
Electrical Engineering
Elektro- och systemteknik
net load
power systems
Probability distribution
Variability
variable generation
Wind power
Title Variability in large-scale wind power generation
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