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.
@article{arxiv.2203.10587,
title = {Exascale Grid Optimization (ExaGO) toolkit: An open-source high-performance package for solving large-scale grid optimization problems},
author = {Shrirang Abhyankar and Slaven Peles and Tamara Becejac and Jesse Holzer and Asher Mancinelli and Cameron Rutherford},
journal= {arXiv preprint arXiv:2203.10587},
year = {2022}
}