Reference : Probabilistic reliability management approach and criteria for power system real-time...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/2268/193403
Probabilistic reliability management approach and criteria for power system real-time operation
English
Karangelos, Efthymios mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Wehenkel, Louis mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Jun-2016
Power Systems Computation Conference
[en] PSCC
Yes
International
19th Power Systems Computation Conference (PSCC)
20-24 June, 2016
Genoa
Italy
[en] Power System Operation ; Reliability Criteria ; Reliability Management ; Electric energy systems ; Optimization
[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.
Researchers ; Professionals
http://hdl.handle.net/2268/193403
FP7 ; 608540 - GARPUR - Generally Accepted Reliability Principle with Uncertainty modelling and through probabilistic Risk assessment

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