A probabilistic model for the dynamics of cascading failures and blackouts in power grids

Current power grids suffer periodic disturbances that may trigger cascades of component failures, which, in turn, can result in blackouts of different scales. Understanding cascading failures and the ability to predict the risk of blackouts are therefore of great value in designing control and outage management systems for future smart grids. In this paper, a novel probabilistic approach is proposed to model the dynamics of cascading failures in power grids. The model characterizes the probability of reaching a blackout of an arbitrary size in any time interval starting from an initial grid state. The model exploits two power grid parameters: the maximum capacity of the set of failed lines and the grid loading ratio. Simulations have shown that these parameters largely affect the cascading failures. In addition, the number of failed lines and the maximum capacity of the failed lines are utilized to reduce the space of all possible grid configurations into a much smaller set of equivalence classes, thereby making the model scalable to large power grids. The results of the analytical model are in agreement with Monte Carlo simulations performed with the IEEE 118-bus system.