|Résumé :||In our society, private and industrial activities increasingly rest on the implicit assumption that electricity is available at any time and at an affordable price. Even if operational data and feedback from the electrical sector is very positive, a residual risk of blackout or undesired load shedding in critical zones remains. The occurrence of such a situation is likely to entail major direct and indirect economical consequences, as observed in recent blackouts. Assessing this residual risk and identifying scenarios likely to lead to these feared situations is crucial to control and optimally reduce this risk of blackout or major system disturbance. The objective of this PhD thesis is to develop a methodology able to reveal scenarios leading to a blackout or a major system disturbance and to estimate their frequencies and their consequences with a satisfactory accuracy.
A blackout is a collapse of the electrical grid on a large area, leading to a power cutoff, and is due to a cascading failure. Such a cascade is composed of two phases: a slow cascade, starting with the occurrence of an initiating event and displaying characteristic times between successive events from minutes to hours, and a fast cascade, displaying characteristic times between successive events from milliseconds to tens of seconds. In cascading failures, there is a strong coupling between events: the loss of an element increases the stress on other elements and, hence, the probability to have another failure. It appears that probabilistic methods proposed previously do not consider correctly these dependencies between failures, mainly because the two very different phases are analyzed with the same model. Thus, there is a need to develop a conceptually satisfying probabilistic approach, able to take into account all kinds of dependencies, by using different models for the slow and the fast cascades. This is the aim of this PhD thesis.
This work first focuses on the level-I which is the analysis of the slow cascade progression up to the transition to the fast cascade. We propose to adapt dynamic reliability, an integrated approach of Probabilistic Risk Analysis (PRA) developed initially for the nuclear sector, to the case of transmission power systems. This methodology will account for the double interaction between power system dynamics and state transitions of the grid elements. This PhD thesis also introduces the development of the level-II to analyze the fast cascade, up to the transition towards an operational state with load shedding or a blackout. The proposed method is applied to two test systems. Results show that thermal effects can play an important role in cascading failures, during the first phase. They also show that the level-II analysis after the level-I is necessary to have an estimation of the loss of supplied power that a scenario can lead to: two types of level-I scenarios with a similar frequency can induce very different risks (in terms of loss of supplied power) and blackout frequencies. The level-III, i.e. the restoration process analysis, is however needed to have an estimation of the risk in terms of loss of supplied energy. This PhD thesis also presents several perspectives to improve the approach in order to scale up applications to real grids.