Title :
Supervised learning of intra-daily recourse strategies for generation management under uncertainties
Author :
Cornélusse, Bertrand ; Vignal, Gérald ; Defourny, Boris ; Wehenkel, Louis
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of Liege, Liege, Belgium
Abstract :
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.
Keywords :
learning (artificial intelligence); load forecasting; power engineering computing; power generation planning; power generation scheduling; power system management; forecasted operating scenario; generation management; generation planning; intra-daily recourse strategies; machine learning; mixed integer linear programming; planned generation schedules; supervised learning; uncertainty management; Conference management; Economic forecasting; Energy management; Humans; Power generation; Predictive models; Processor scheduling; Scheduling algorithm; Supervised learning; Uncertainty; Mixed integer linear programming; generation planning; machine learning; uncertainty management;
Conference_Titel :
PowerTech, 2009 IEEE Bucharest
Conference_Location :
Bucharest
Print_ISBN :
978-1-4244-2234-0
Electronic_ISBN :
978-1-4244-2235-7
DOI :
10.1109/PTC.2009.5282226
