Index for wind power variability

Juha Kiviluoma; Hannele Holttinen; richard Scharff; David Edward Weir; Nicolaos Cutululis; Marisciel Litong-Palima; Michael Milligan

Index for wind power variability

Juha Kiviluoma, Hannele Holttinen, richard Scharff, David Edward Weir, Nicolaos Cutululis, Marisciel Litong-Palima, Michael Milligan

DOES - Design and Operation of Energy Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific

Abstract

Variability of large scale wind power generation is dependent on several factors: characteristics of installed wind power plants, size of the area where the plants are installed, geographic dispersion within that area and its weather regime(s). Variability can be described by ramps in power generation, i.e. changes from time period to time period. Given enough data points, it can be described with a probability density function. This approach focuses on two dimensions of variability: duration of the ramp and probability distribution. This paper proposes an index based on these two dimensions to enable comparisons and characterizations of variability under different conditions. The index is tested with real, large scale wind power generation data from several countries. Considerations while forming an index are discussed, as well as the main results regarding what the drivers of variability experienced for different data.

Original language	English
Title of host publication	13th Wind Integration Workshop proceedings
Publisher	Energynautics GmbH
ISBN (Print)	978-3-98 13870-9-4
Publication status	Published - 2014
MoE publication type	B3 Non-refereed article in conference proceedings
Event	13th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, WIW 2014 - Berlin, Germany Duration: 11 Nov 2014 → 13 Nov 2014

Conference

Conference	13th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, WIW 2014
Abbreviated title	WIW 2014
Country/Territory	Germany
City	Berlin
Period	11/11/14 → 13/11/14

Keywords

wind power integration
variations
indices for variability
variable renewable energy sources

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@inproceedings{70737f92f5e44dd794e1be51ba270202,

title = "Index for wind power variability",

abstract = "Variability of large scale wind power generation is dependent on several factors: characteristics of installed wind power plants, size of the area where the plants are installed, geographic dispersion within that area and its weather regime(s). Variability can be described by ramps in power generation, i.e. changes from time period to time period. Given enough data points, it can be described with a probability density function. This approach focuses on two dimensions of variability: duration of the ramp and probability distribution. This paper proposes an index based on these two dimensions to enable comparisons and characterizations of variability under different conditions. The index is tested with real, large scale wind power generation data from several countries. Considerations while forming an index are discussed, as well as the main results regarding what the drivers of variability experienced for different data.",

keywords = "wind power integration, variations, indices for variability, variable renewable energy sources",

author = "Juha Kiviluoma and Hannele Holttinen and richard Scharff and Weir, {David Edward} and Nicolaos Cutululis and Marisciel Litong-Palima and Michael Milligan",

note = "Project code: 104775 ; 13th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, WIW 2014, WIW 2014 ; Conference date: 11-11-2014 Through 13-11-2014",

year = "2014",

language = "English",

isbn = "978-3-98 13870-9-4",

booktitle = "13th Wind Integration Workshop proceedings",

publisher = "Energynautics GmbH",

address = "Germany",

}

Kiviluoma, J, Holttinen, H, Scharff, R, Weir, DE, Cutululis, N, Litong-Palima, M & Milligan, M 2014, Index for wind power variability. in 13th Wind Integration Workshop proceedings. Energynautics GmbH, 13th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, WIW 2014, Berlin, Berlin, Germany, 11/11/14.

TY - GEN

T1 - Index for wind power variability

AU - Kiviluoma, Juha

AU - Holttinen, Hannele

AU - Scharff, richard

AU - Weir, David Edward

AU - Cutululis, Nicolaos

AU - Litong-Palima, Marisciel

AU - Milligan, Michael

N1 - Project code: 104775

PY - 2014

Y1 - 2014

N2 - Variability of large scale wind power generation is dependent on several factors: characteristics of installed wind power plants, size of the area where the plants are installed, geographic dispersion within that area and its weather regime(s). Variability can be described by ramps in power generation, i.e. changes from time period to time period. Given enough data points, it can be described with a probability density function. This approach focuses on two dimensions of variability: duration of the ramp and probability distribution. This paper proposes an index based on these two dimensions to enable comparisons and characterizations of variability under different conditions. The index is tested with real, large scale wind power generation data from several countries. Considerations while forming an index are discussed, as well as the main results regarding what the drivers of variability experienced for different data.

AB - Variability of large scale wind power generation is dependent on several factors: characteristics of installed wind power plants, size of the area where the plants are installed, geographic dispersion within that area and its weather regime(s). Variability can be described by ramps in power generation, i.e. changes from time period to time period. Given enough data points, it can be described with a probability density function. This approach focuses on two dimensions of variability: duration of the ramp and probability distribution. This paper proposes an index based on these two dimensions to enable comparisons and characterizations of variability under different conditions. The index is tested with real, large scale wind power generation data from several countries. Considerations while forming an index are discussed, as well as the main results regarding what the drivers of variability experienced for different data.

KW - wind power integration

KW - variations

KW - indices for variability

KW - variable renewable energy sources

M3 - Conference article in proceedings

SN - 978-3-98 13870-9-4

BT - 13th Wind Integration Workshop proceedings

PB - Energynautics GmbH

T2 - 13th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, WIW 2014

Y2 - 11 November 2014 through 13 November 2014

ER -

Index for wind power variability

Abstract

Conference

Keywords

UN SDGs

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