A "Random Chemistry" Algorithm for Identifying Multiple Contingencies that Initiate Cascading Failure

This paper describes a stochastic "Random Chem- istry" (RC) algorithm to identify multiple (n − k) contingencies that initiate large cascading failures in a simulated power system. The method requires only O(log(n)) simulations per contingency identified, which is orders of magnitude faster than random search of this combinatorial space. We applied the method to a model of cascading failure in a power network with n =2 896 branches and identify 148,243 unique, minimal n − k branch contingencies (2 ≤ k ≤ 5) that cause large cascades, many of which would be missed by using pre-contingency flows, linearized line outage distribution factors, or performance indices as screening factors. Within each n− k collection, the frequency with which individual branches appear follows a power-law (or nearly so) distribution, indicating that a relatively small number of components contribute disproportionately to system vulner- ability. The paper discusses various ways that RC generated collections of dangerous contingencies could be used in power systems planning and operations.