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  5. Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland
 
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Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland

Author(s)
Meibom, Peter  
Barth, Rudiger  
Hasche, Bernhard  
et al.  
Uri
http://hdl.handle.net/10197/4736
Date Issued
2011-08
Date Available
2013-10-11T16:08:26Z
Abstract
A stochastic mixed integer linear optimization scheduling model minimizing system operation costs and treating load and wind power production as stochastic inputs is presented. The schedules are updated in a rolling manner as more up-to-date information becomes available. This is a fundamental change relative to day-ahead unit commitment approaches. The need for reserves dependent on forecast horizon and share of wind power has been estimated with a statistical model combining load and wind power forecast errors with scenarios of forced outages. The model is used to study operational impacts of future high wind penetrations for the island of Ireland. Results show that at least 6000 MW of wind (34% of energy demand) can be integrated into the island of Ireland without significant curtailment and reliability problems.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Institute of Electrical and Electronics Engineers
Journal
IEEE Transactions on Power Systems
Volume
26
Issue
3
Start Page
1367
End Page
1379
Copyright (Published Version)
2011 Institute of Electrical and Electronics Engineers
Subjects

Wind power

unit commitment and d...

reserves

forecast errors

energy policy

DOI
10.1109/TPWRS.2010.2070848
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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Meibom et al Stochastic optimization model.pdf

Size

694.6 KB

Format

Adobe PDF

Checksum (MD5)

9777f34cceb6f6aeeb260db121dae624

Owning collection
ERC Research Collection
Mapped collections
Electrical and Electronic Engineering Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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