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Using Machine Learning to Enable Probabilistic Reliability Assessment in Operation Planning
Duchesne, Laurine; Karangelos, Efthymios; Wehenkel, Louis
2018In Power Systems Computation Conference 2018 Proceedings
Peer reviewed
 

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Keywords :
Reliability assessment; Operation planning; Machine learning; Monte Carlo simulation; Variance reduction; Control variates; Security constrained optimal power flow
Abstract :
[en] In the context of operation planning, probabilistic reliability assessment essentially boils down to predicting, efficiently and with sufficient accuracy, various economic and reliability indicators reflecting the expected performance of the system over a certain look-ahead horizon, so as to guide the operation planner in his decision-making. In order to speed-up the crude Monte Carlo approach, which would entail a very large number of heavy computations, we propose in this paper an approach combining Monte Carlo simulation, machine learning and variance reduction techniques such as control variates. We provide an extensive case study testing this approach on the three-area IEEE-RTS96 benchmark, in the context of day-ahead operation planning while using a security constrained optimal power flow model to simulate real-time operation according to the N-1 criterion. From this case study, we can conclude that the proposed approach allows to reduce the number of heavy computations by about an order of magnitude, without sacrificing accuracy.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Duchesne, Laurine ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore)
Karangelos, Efthymios ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Using Machine Learning to Enable Probabilistic Reliability Assessment in Operation Planning
Publication date :
2018
Event name :
20th Power Systems Computation Conference
Event place :
Dublin, Ireland
Event date :
from 11-06-2018 to 15-06-2018
Audience :
International
Main work title :
Power Systems Computation Conference 2018 Proceedings
Publisher :
PSCC
Peer reviewed :
Peer reviewed
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
European Projects :
FP7 - 608540 - GARPUR - Generally Accepted Reliability Principle with Uncertainty modelling and through probabilistic Risk assessment
Funders :
CE - Commission Européenne [BE]
Union Européenne [BE]
Available on ORBi :
since 24 April 2018

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