Exascale Grid Optimization (ExaGO) toolkit: An open-source high-performance package for solving large-scale grid optimization problems

TL;DR

The paper presents ExaGO, an open-source high-performance toolkit designed for large-scale grid optimization, capable of leveraging parallel and GPU hardware to efficiently solve complex ACOPF problems with stochastic and multi-period features.

Contribution

It introduces ExaGO, a novel scalable library for large-scale grid optimization that supports advanced features and high-performance computing platforms.

Findings

ExaGO efficiently solves large-scale ACOPF problems.

It supports stochastic, security, and multi-period constraints.

Demonstrates high performance on parallel and GPU systems.

Abstract

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.