Scalable Multi-Period AC Optimal Power Flow Utilizing GPUs with High Memory Capacities

TL;DR

This paper showcases the ability of GPU-accelerated NLP frameworks to efficiently solve large-scale multi-period AC optimal power flow problems using high-memory GPUs, enabling solutions to previously intractable instances.

Contribution

It introduces the use of high-memory GPUs and open-source NLP frameworks for solving large-scale multi-period AC OPF problems, demonstrating significant scalability and efficiency.

Findings

Solved a multi-period OPF with over 10 million variables in under 10 minutes.

Demonstrated the effectiveness of GPU-accelerated NLP frameworks for extreme-scale power flow problems.

Provided an open-source tool, ExaModelsPower.jl, for modeling multi-period AC OPF on GPUs.

Abstract

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.