  This paper demonstrates the scalability of open-source GPU-accelerated
nonlinear programming (NLP) frameworks -- ExaModels.jl and MadNLP.jl -- for
solving multi-period alternating current (AC) optimal power flow (OPF) problems
on GPUs with high memory capacities (e.g., NVIDIA GH200 with 480 GB of unified
memory). There has been a growing interest in solving multi-period AC OPF
problems, as the increasingly fluctuating electricity market requires operation
planning over multiple periods. These problems, formerly deemed intractable,
are now becoming technologically feasible to solve thanks to the advent of
high-memory GPU hardware and accelerated NLP tools. This study evaluates the
capability of these tools to tackle previously unsolvable multi-period AC OPF
instances. Our numerical experiments, run on an NVIDIA GH200, demonstrate that
we can solve a multi-period OPF instance with more than 10 million variables up
to $10^{-4}$ precision in less than 10 minutes. These results demonstrate the
efficacy of the GPU-accelerated NLP frameworks for the solution of
extreme-scale multi-period OPF. We provide ExaModelsPower.jl, an open-source
modeling tool for multi-period AC OPF models for GPUs.
