Reference : Supervised learning of intra-daily recourse strategies for generation management unde...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/2268/25949
Supervised learning of intra-daily recourse strategies for generation management under uncertainties
English
Cornélusse, Bertrand mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Vignal, Gerald mailto [> > > >]
Defourny, Boris [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore) >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2009
PowerTech, 2009 IEEE Bucharest
1-8
Yes
No
International
IEEE Power Tech 2009
29/06/2009-02/07/2009
Bucarest
Roumanie
[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.
Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/25949
10.1109/PTC.2009.5282226

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