(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives

[en] This paper reviews existing works on (deep) reinforcement learning considerations in electric power system control. The works are reviewed as they relate to electric power system operating states (normal, preventive, emergency, restorative) and control levels (local, household, microgrid, subsystem, wide-area). Due attention is paid to the control-related problems considerations (cyber-security, big data analysis, short-term load forecast, and composite load modelling). Observations from reviewed literature are drawn and perspectives discussed. In order to make the text compact and as easy as possible to read, the focus is only on the works published (or "in press") in journals and books while conference publications are not included. Exceptions are several work available in open repositories likely to become journal publications in near future. Hopefully this paper could serve as a good source of information for all those interested in solving similar problems.

Disciplines :

Electrical & electronics engineering

Author, co-author :

Glavic, Mevludin ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids

Language :

English

Title :

(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives

Publication date :

October 2019

Journal title :

Annual Reviews in Control

ISSN :

1367-5788

Publisher :

Elsevier, United Kingdom

Volume :

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 07 October 2019

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2977 (9 by ULiège)

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Scopus citations^®

112

Scopus citations^®
without self-citations

111

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114

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