[en] This paper addresses the problem of efficiently operating the storage devices in an electricity microgrid featuring photovoltaic (PV) panels with both short- and long-term storage capacities. The problem of optimally activating the storage devices is formulated as a sequential decision making problem under uncertainty where, at every time-step, the uncertainty comes from the lack of knowledge about future electricity consumption and weather dependent PV production. This paper proposes to address this problem using deep reinforcement learning. To this purpose, a specific deep learning architecture has been designed in order to extract knowledge from past consumption and production time series as well as any available forecasts. The approach is empirically illustrated in the case of a residential customer located in Belgium.
Disciplines :
Computer science
Author, co-author :
François-Lavet, Vincent ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore)
Taralla, David; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore)
Ernst, Damien ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids
Fonteneau, Raphaël ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore)
Language :
English
Title :
Deep Reinforcement Learning Solutions for Energy Microgrids Management
Publication date :
December 2016
Event name :
European Workshop on Reinforcement Learning (EWRL 2016)
Event date :
3-4 December 2016
Audience :
International
Main work title :
European Workshop on Reinforcement Learning (EWRL 2016)
Peer reviewed :
Peer reviewed
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
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