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.

翻译：在输电网中大规模集成可再生能源资源给经济调度带来了重大挑战，因为需要分析更多的优化参数、约束条件和不确定性来源。这促使我们研究更高效的计算方法，特别是用于求解底层线性系统的方法（通常占用总计算时间的一半以上）。本文介绍了利用硬件加速器（如图形处理单元GPU）的稀疏线性求解器，并展示了其在经济调度计算中显著提升整体性能的应用。我们将问题视为稀疏形式，这虽能加快执行速度，但也使数值方法的实现更具挑战性。我们提出了首个可在AMD和NVIDIA GPU上执行的GPU原生稀疏直接求解器，并在交流最优潮流（ACOPF）分析中展示了使用高性能线性求解器带来的显著性能提升。此外，我们证明了通过在基于GPU的硬件上执行完整计算来获得显著性能提升的可行性。最后，我们指出了当前亟待解决的研究问题与机遇，以更高效地利用包括配备GPU在内的异构系统。