Machine Learning for AC Optimal Power Flow
Machine Learning
2019-10-22 v1 Signal Processing
Machine Learning
Abstract
We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly predict the optimal generator settings, and 2) a constraint prediction task where we predict the set of active constraints in the optimal solution. We validate these approaches on two benchmark grids.
Keywords
Cite
@article{arxiv.1910.08842,
title = {Machine Learning for AC Optimal Power Flow},
author = {Neel Guha and Zhecheng Wang and Matt Wytock and Arun Majumdar},
journal= {arXiv preprint arXiv:1910.08842},
year = {2019}
}
Comments
3 pages, 2 tables. Presented at the Climate Change Workshop at ICML 2019