Power System Operation; Reliability Criteria; Reliability Management; Electric energy systems; Optimization
Résumé :
[en] This paper develops a probabilistic approach for power system reliability management in real-time operation where risk is a product of i) the potential occurrence of contingencies, ii) the possible failure of corrective (i.e., post-contingency) control and, iii) the socio-economic impact of service interruptions to end-users. Stressing the spatiotemporal variability of these factors, we argue for reliability criteria assuring a high enough probability of avoiding service interruptions of severe socio-economic impact by dynamically identifying events of nonnegligible
implied risk. We formalise the corresponding decision making problem as a chance-constrained two-stage stochastic programming problem, and study its main features on the single area IEEE RTS-96 system. We also discuss how to leverage this proposal for the construction of a globally coherent reliability management framework for long-term system development, midterm asset management, and short-term operation planning.
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
Karangelos, Efthymios ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Langue du document :
Anglais
Titre :
Probabilistic reliability management approach and criteria for power system real-time operation
Date de publication/diffusion :
juin 2016
Nom de la manifestation :
19th Power Systems Computation Conference (PSCC)
Lieu de la manifestation :
Genoa, Italie
Date de la manifestation :
20-24 June, 2016
Manifestation à portée :
International
Titre de l'ouvrage principal :
Power Systems Computation Conference
Titre traduit de l'ouvrage principal :
[en] PSCC
Peer reviewed :
Peer reviewed
Projet européen :
FP7 - 608540 - GARPUR - Generally Accepted Reliability Principle with Uncertainty modelling and through probabilistic Risk assessment
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