Rapid Concurrent GPU-CPU Solvers for Scalable Unit Commitment in Large Power Grids

Abstract

This paper presents an accelerated solver for the unit commitment problem in large-scale power systems. The approach is based on the concurrent execution of GPU- and CPU-based optimization solvers on a single machine, with the solver that converges first terminating the other to minimize overall runtime. This strategy effectively harnesses the complementary strengths of different solvers. Convergence is further accelerated through a systematic and aggressive presolve approach. Numerical experiments on a 6,049-bus system with millions of decision variables and constraints demonstrate speedups ranging from 2.14x to 5.61x, reducing the maximum runtime from 42.12 minutes to 5.77 minutes across 45 test cases. These results highlight the scalability and computational efficiency of the proposed GPU-CPU concurrent solver framework.