A study of optimal non-firm wind capacity connection to congested transmission systems

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Title: A study of optimal non-firm wind capacity connection to congested transmission systems
Authors: Burke, Daniel J.
O'Malley, Mark
Permanent link: http://hdl.handle.net/10197/3584
Date: Apr-2011
Online since: 2012-04-24T14:48:02Z
Abstract: As wind is a low capacity factor source of power generation, a non-physically-firm connection strategy is key to its cost-effective and timely integration to presently constrained transmission networks. This paper therefore outlines the design and study of an optimal non-firm wind capacity allocation model. While a precise statistical representation of wind power variations and geographical inter-dependency requires a significant number of data samples, the structured very-large-scale linear programming problem that results is shown to be exploitable by the Benders’ decomposition scheme. Various wind capacity target levels are considered, and important sensitivity analyses performed for multiple load profiles, wind profiles, and fuel price parameter values. Interestingly, the optimal wind capacity allocation is found to be reasonably robust to sizeable load and fuel price deviations, and while the effect of a limited historical wind data profile is more influential, the associated cost-function penalty is not significantly critical. The economic value of combining wind connection with advanced post-contingency network remedial action schemes is also highlighted.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: IEEE
Journal: IEEE Transactions on Sustainable Energy
Volume: 2
Issue: 2
Start page: 167
End page: 176
Copyright (published version): 2011 IEEE
Keywords: Power generation planningPower transmissionWind energy
Subject LCSH: Electric power production--Planning
Electric power transmission
Wind power
DOI: 10.1109/TSTE.2010.2094214
Other versions: http://dx.doi.org/10.1109/TSTE.2010.2094214
Language: en
Status of Item: Peer reviewed
Appears in Collections:ERC Research Collection
Electrical and Electronic Engineering Research Collection

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