Index for wind power variability

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

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientific

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 languageEnglish
Title of host publication13th Wind Integration Workshop proceedings
PublisherEnergynautics GmbH
ISBN (Print)978-3-98 13870-9-4
Publication statusPublished - 2014
MoE publication typeB3 Non-refereed article in conference proceedings
Event13th 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 201413 Nov 2014

Conference

Conference13th 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 titleWIW 2014
CountryGermany
CityBerlin
Period11/11/1413/11/14

Fingerprint

wind power
power generation
probability density function
power plant
weather
index

Keywords

  • wind power integration
  • variations
  • indices for variability
  • variable renewable energy sources

Cite this

Kiviluoma, J., Holttinen, H., Scharff, R., Weir, D. E., Cutululis, N., Litong-Palima, M., & Milligan, M. (2014). Index for wind power variability. In 13th Wind Integration Workshop proceedings Energynautics GmbH.
Kiviluoma, Juha ; Holttinen, Hannele ; Scharff, richard ; Weir, David Edward ; Cutululis, Nicolaos ; Litong-Palima, Marisciel ; Milligan, Michael. / Index for wind power variability. 13th Wind Integration Workshop proceedings. Energynautics GmbH, 2014.
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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.",
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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, Germany, 11/11/14.

Index for wind power variability. / Kiviluoma, Juha; Holttinen, Hannele; Scharff, richard; Weir, David Edward; Cutululis, Nicolaos; Litong-Palima, Marisciel; Milligan, Michael.

13th Wind Integration Workshop proceedings. Energynautics GmbH, 2014.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientific

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

ER -

Kiviluoma J, Holttinen H, Scharff R, Weir DE, Cutululis N, Litong-Palima M et al. Index for wind power variability. In 13th Wind Integration Workshop proceedings. Energynautics GmbH. 2014