  Integrating renewable resources within the transmission grid at a wide scale
poses significant challenges for economic dispatch as it requires analysis with
more optimization parameters, constraints, and sources of uncertainty. This
motivates the investigation of more efficient computational methods, especially
those for solving the underlying linear systems, which typically take more than
half of the overall computation time. In this paper, we present our work on
sparse linear solvers that take advantage of hardware accelerators, such as
graphical processing units (GPUs), and improve the overall performance when
used within economic dispatch computations. We treat the problems as sparse,
which allows for faster execution but also makes the implementation of
numerical methods more challenging. We present the first GPU-native sparse
direct solver that can execute on both AMD and NVIDIA GPUs. We demonstrate
significant performance improvements when using high-performance linear solvers
within alternating current optimal power flow (ACOPF) analysis. Furthermore, we
demonstrate the feasibility of getting significant performance improvements by
executing the entire computation on GPU-based hardware. Finally, we identify
outstanding research issues and opportunities for even better utilization of
heterogeneous systems, including those equipped with GPUs.
