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Data-driven Parameterized Policies for Microgrid Control
Boukas, Ioannis; El Mekki, Sélim; Cornélusse, Bertrand
2019
 

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Keywords :
Microgrid control; Optimization; Reinforcement learning
Abstract :
[en] If we account for data uncertainty, centralized microgrid control can be decomposed in four tasks: estimating the parameters of the microgrid devices, forecasting the consumption and the renewable production, operational planning to anticipate weather effects and human activities, and real-time control to adapt planned decision to the reality of the moment. As the devices of a microgrid deteriorate over their lifetime and microgrids are by definition small systems, it is of paramount importance to automate the meta-parameterization of these stages to maximize the microgrid efficiency and decrease its maintenance costs. This paper studies how reinforcement learning can be used to address this problem. As reinforcement learning makes use of microgrid operation data (or of simulated data before the microgrid is actually operated) to learn an operation policy, it inherently merges the four stages above, and can in theory adapt to some types of changes without having to perform manual tuning.
Disciplines :
Energy
Author, co-author :
Boukas, Ioannis  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart-Microgrids
El Mekki, Sélim ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart-Microgrids
Cornélusse, Bertrand  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart-Microgrids
Language :
English
Title :
Data-driven Parameterized Policies for Microgrid Control
Publication date :
October 2019
Available on ORBi :
since 10 October 2019

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