PNNL

Abstract

Contingency analysis is a key function to assess the impact of various combinations of power system component failures. It involves combinatorial numbers of contingencies which exceed the capability of even very large supercomputing platforms. Therefore, it is critical to select contingency cases within the constraint of computing power. This paper presents a contingency selection method employing graph theory (edge betweenness centrality) to power grid weighted graphs to remove low-impact components. The parallel implementation of the method was successfully carried out on the Cray XMT machine. The implementation takes advantage of the superior capabilities of the Cray XMT for graph-based problems and its programming features. This paper presents the performance scalability of Cray XMT and comparison with a cache-based, shared-memory machine.