Wind power integration studies using a multi-stage stochastic electricity system model

Peter Meibom; R. Barth; H. Brand; C. Weber

doi:10.1109/PES.2007.385950

Wind power integration studies using a multi-stage stochastic electricity system model

Peter Meibom, R. Barth, H. Brand, C. Weber

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

Abstract

A large share of integrated wind power causes technical and financial impacts on the operation of the existing electricity system due to the fluctuating behaviour and unpredictability of wind power. The presented stochastic electricity market model optimises the unit commitment considering four kinds of electricity markets (e.g. a spot and balancing market) and taking into account the stochastic behaviour of the wind power generation and of the prediction error. It can be used for the evaluation of varying electricity prices and system costs due to wind power integration and for the investigation of integration measures.

Original language	English
Title of host publication	2007 IEEE POWER ENGINEERING SOCIETY GENERAL MEETING
Place of Publication	Tampa
Publisher	IEEE
Publication date	2007
Pages	2984-2987
ISBN (Print)	978-1-4244-1296-9
DOIs	https://doi.org/10.1109/PES.2007.385950
Publication status	Published - 2007
Event	2007 IEEE Power Engineering Society General Meeting - Tampa, FL, United States Duration: 24 Jun 2007 → 28 Jun 2007 http://ewh.ieee.org/r3/floridawc/pesias/

Conference

Conference	2007 IEEE Power Engineering Society General Meeting
Country/Territory	United States
City	Tampa, FL
Period	24/06/2007 → 28/06/2007
Internet address	http://ewh.ieee.org/r3/floridawc/pesias/

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http://www.risoe.dk/rispubl/art/2007_222_paper.pdf

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title = "Wind power integration studies using a multi-stage stochastic electricity system model",

abstract = "A large share of integrated wind power causes technical and financial impacts on the operation of the existing electricity system due to the fluctuating behaviour and unpredictability of wind power. The presented stochastic electricity market model optimises the unit commitment considering four kinds of electricity markets (e.g. a spot and balancing market) and taking into account the stochastic behaviour of the wind power generation and of the prediction error. It can be used for the evaluation of varying electricity prices and system costs due to wind power integration and for the investigation of integration measures.",

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AU - Barth, R.

AU - Brand, H.

AU - Weber, C.

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Y1 - 2007

N2 - A large share of integrated wind power causes technical and financial impacts on the operation of the existing electricity system due to the fluctuating behaviour and unpredictability of wind power. The presented stochastic electricity market model optimises the unit commitment considering four kinds of electricity markets (e.g. a spot and balancing market) and taking into account the stochastic behaviour of the wind power generation and of the prediction error. It can be used for the evaluation of varying electricity prices and system costs due to wind power integration and for the investigation of integration measures.

AB - A large share of integrated wind power causes technical and financial impacts on the operation of the existing electricity system due to the fluctuating behaviour and unpredictability of wind power. The presented stochastic electricity market model optimises the unit commitment considering four kinds of electricity markets (e.g. a spot and balancing market) and taking into account the stochastic behaviour of the wind power generation and of the prediction error. It can be used for the evaluation of varying electricity prices and system costs due to wind power integration and for the investigation of integration measures.

KW - Samfund og systemer

U2 - 10.1109/PES.2007.385950

DO - 10.1109/PES.2007.385950

M3 - Article in proceedings

SN - 978-1-4244-1296-9

SP - 2984

EP - 2987

BT - 2007 IEEE POWER ENGINEERING SOCIETY GENERAL MEETING

PB - IEEE

CY - Tampa

T2 - 2007 IEEE Power Engineering Society General Meeting

Y2 - 24 June 2007 through 28 June 2007

ER -

Wind power integration studies using a multi-stage stochastic electricity system model

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