Towards Efficient N - x Contingency Selection Using Group Betweenness Centrality

Abstract

The goal of N -x contingency selection is to pick a subset of critical cases to assess their potential for causing a severe crippling of an electric power grid. Even for a moderate-sized system there can be an overwhelming number of contingency cases that need to be studied. The number grows exponentially with x. This combinatorial explosion renders any exhaustive search strategy computationally infeasible, even for small to medium sized systems. We propose a novel method for N- x selection for x >1 using group betweenness centrality and show that the amount of computation can be decoupled from the problem size, thus making the contingency analysis for large systems with x > 1 computationally feasible. Consequently, it may be that N - x (for x > 1) contingency selection can be effectively deployed despite the combinatorial explosion of the number of possible N - x contingencies. Our strategy replaces computation with storage of intermediate results and therefore relies on high performance computing resources.