ORBi: Cornélusse Bertrand - Supervised learning of intra-daily recourse strategies for generation management under uncertainties

Reference : Supervised learning of intra-daily recourse strategies for generation management under u...


	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	To cite this reference:	http://hdl.handle.net/2268/25949

Title :	Supervised learning of intra-daily recourse strategies for generation management under uncertainties
Language :	English
Author, co-author :	Cornélusse, Bertrand [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
	Vignal, Gerald [> > > >]
	Defourny, Boris [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore) >]
	Wehenkel, Louis [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Publication date :	2009
Main document title :	PowerTech, 2009 IEEE Bucharest
Pages :	1-8
On invitation :	No
Audience :	International
Event name :	IEEE Power Tech 2009
Event date :	29/06/2009-02/07/2009
Event place (city) :	Bucarest
Event country :	Roumanie
Abstract :	[en] The aim of this work is to design intra-daily recourse strategies which may be used by operators to decide in real-time the modifications to bring to planned generation schedules of a set of units in order to respond to deviations from the forecasted operating scenario. Our aim is to design strategies that are interpretable by human operators, that comply with real-time constraints and that cover the major disturbances that may appear during the next day. To this end we propose a new framework using supervised learning to infer such recourse strategies from simulations of the system under a sample of conditions representing possible deviations from the forecast. This framework is validated on a realistic generation system of medium size.
Funders :	Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/2268/25949
DOI :	10.1109/PTC.2009.5282226

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