Paper published in a book (Scientific congresses and symposiums)
Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives
Glavic, Mevludin; Fonteneau, Raphaël; Ernst, Damien
2017In The 20th World Congress of the International Federation of Automatic Control, Toulouse 9-14 July 2017
Peer reviewed
 

Files


Full Text
IFAC_2017_2257.pdf
Author preprint (345.35 kB)
Download

All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
Electric power system; reinforcement learning; control, decision
Abstract :
[en] In this paper, we review past (including very recent) research considerations in using reinforcement learning (RL) to solve electric power system decision and control problems. The RL considerations are reviewed in terms of speci c electric power system problems, type of control and RL method used. We also provide observations about past considerations based on a comprehensive review of available publications. The review reveals the RL is considered as viable solutions to many decision and control problems across di erent time scales and electric power system states. Furthermore, we analyse the perspectives of RL approaches in light of the emergence of new-generation, communications, and instrumentation technologies currently in use, or available for future use, in power systems. The perspectives are also analysed in terms of recent breakthroughs in RL algorithms (Safe RL, Deep RL and path integral control for RL) and other, not previously considered, problems for RL considerations (most notably restorative, emergency controls together with so-called system integrity protection schemes, fusion with existing robust controls, and combining preventive and emergency control).
Disciplines :
Electrical & electronics engineering
Author, co-author :
Glavic, Mevludin ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète
Fonteneau, Raphaël ;  Université de Liège > 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
Language :
English
Title :
Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives
Publication date :
July 2017
Event name :
20th IFAC World Congress
Event organizer :
IFAC
Event place :
Toulouse, France
Event date :
from 09-07-2017 to 14-07-2017
Audience :
International
Main work title :
The 20th World Congress of the International Federation of Automatic Control, Toulouse 9-14 July 2017
Pages :
1-10
Peer reviewed :
Peer reviewed
Available on ORBi :
since 03 March 2017

Statistics


Number of views
1102 (42 by ULiège)
Number of downloads
4211 (46 by ULiège)

Scopus citations®
 
146
Scopus citations®
without self-citations
142
OpenCitations
 
102

Bibliography


Similar publications



Contact ORBi