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：将可再生能源大规模整合到输电网中给经济调度带来了重大挑战，因为它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——这类求解通常占总计算时间的一半以上。本文介绍了我们利用图形处理单元（GPU）等硬件加速器开发的稀疏线性求解器，这些求解器可在经济调度计算中提升整体性能。我们将问题视为稀疏问题，这虽然能加快执行速度，但也使数值方法的实现更具挑战性。我们提出了首个可同时在AMD和NVIDIA GPU上运行的GPU原生稀疏直接求解器。我们演示了在交流最优潮流（ACOPF）分析中使用高性能线性求解器时获得的显著性能提升。此外，我们还证明了在基于GPU的硬件上执行完整计算以实现显著性能改进的可行性。最后，我们指出了在更好利用异构系统（包括配备GPU的系统）方面尚待解决的研究问题与机遇。