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Towards an integrated probabilistic analysis of the blackout risk in transmission power systems

par Henneaux, Pierre ;Labeau, Pierre-Etienne ;Maun, Jean Claude
Référence PSA 2011 International Topical Meeting on Probabilistic Safety Assessment and Analysis(March 13-17, 2011: Wilmington, NC, USA), Proceedings of ANS PSA 2011 International Topical Meeting on Probabilistic Safety Assessment and Analysis, American Nuclear Society, LaGrange Park, IL, USA
Publication Publié, 2011-03

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Résumé :

In our modern society, the electrical grid has become one of the most critical infrastructures. Even if feedback from the electrical sector is very positive, electricity generation and transmission cannot be considered as totally reliable activities. A residual blackout risk remains, especially as new ways of generating electricity and operating the grid develop. To study the grid reliability, deterministic criteria are usually considered. Probabilistic risk assessment methods have also been developed, but they usually neglect the dependencies between failures and the dynamic evolution of the grid in the course of a transient: yet a blackout is due to cascading failures in the grid. There is a strong coupling between events, since the loss of an element increases the stress on others and, hence, their probability to fail. Our purpose is therefore to develop an integrated probabilistic approach to blackout analysis, capable of handling the dynamic response of the grid to stochastic initiating perturbations and the event sequences they possibly entail. This approach is adapted from dynamic reliability methodologies, by accounting for the different characteristic times and processes of different cascading phases leading to a blackout. This paper focuses on the modeling adopted for the first phase, ruled by thermal transients. The goal is to identify dangerous cascading scenarios (possibly leading to a blackout) and calculate their frequency. A Monte Carlo code derived from this methodology is validated on a test grid. Some dangerous scenarios are presented and their frequency calculated by this method is compared with the classical estimation.