论文标题
Exascale网格优化（EXAGO）工具包：用于解决大规模网格优化问题的开源高性能套件
Exascale Grid Optimization (ExaGO) toolkit: An open-source high-performance package for solving large-scale grid optimization problems
论文作者
论文摘要
本文介绍了Exascale网格优化（EXAGO）工具包，该工具包，用于解决大规模交替的当前最佳功率流（ACOPF）问题，包括随机效应，安全约束和多周期约束。 Exago可以在并行分布式内存平台上运行，包括大量并行硬件加速器，例如图形处理单元（GPU）。我们介绍了Exago库的详细信息，包括其体系结构，配方，建模细节及其在多种优化应用程序中的性能。
This paper introduces the Exascale Grid Optimization (ExaGO) toolkit, a library for solving large-scale alternating current optimal power flow (ACOPF) problems including stochastic effects, security constraints and multi-period constraints. ExaGO can run on parallel distributed memory platforms, including massively parallel hardware accelerators such as graphical processing units (GPUs). We present the details of the ExaGO library including its architecture, formulations, modeling details, and its performance for several optimization applications.
