A novel graphical processing unit method for power systems security analysis

Abstract

There is an increasing need for computational power to drive software tools used in power systems planning and operations, since the emergence of modern energy markets and recent renewable generation technology fundamentally alters how energy flows through the existing power grid. While special-purpose hardware, including supercomputers, has been explored for this purpose, inexpensive commodity hardware is another way of getting increased computational power within the power systems control centers. Adding General-Purpose Graphical Processing Units (GPGPUs) to the nodes in a control center's existing computational platform is a significantly lower expense than adding an equivalent number of new nodes and the infrastructure to support them. If accelerating computations with GPGPUs can halve the time needed for for a set of contingencies to run on a set of given computational nodes, freeing up crucial minutes for analysis of additional contingencies, the investment can be worth the costs. Yet this would be considered a quite modest speedup for GPGPU computing if the problem is conditioned in a way that maps well to the architecture and programming model of the GPGPU. The novel method for GPGPU contingency analysis and its variants presented in this thesis allows that process of speedup to be taken substantially further, since it re-maps as much of the computation as possible to be a series of dense vector operations based on simple arithmetic that is conservative with respect to data movement and flexible with respect to implementation details such as thread block size. Where sparse matrix operations cannot be avoided, this method, by slicing across contingencies, re-maps such operations to the much more efficient problem of a sparse matrix multiplied by a block of dense vectors larger than the matrix itself. The method applies to (N-1-1), (N-2), and (N-3) contingencies with little modification and little increase in computational burden or data movement per contingency. The method is designed to accommodate systems of thousands to tens of thousands of buses, if need be, with the large power systems resulting from control area consolidation in mind.