An Unsupervised Neural Network Approach for Solving the Optimal Power Flow Problem

Alexander Marcial, Magnus Perninge

2023

Abstract

Optimal Power Flow is a central tool for power system operation and planning. Given the substantial rise in intermittent power and shorter time windows in electricity markets, there’s a need for fast and efficient solutions to the Optimal Power Flow problem. With this in consideration, this paper propose an unsupervised deep learning approach to approximate the optimal solution of Optimal Power Flow problems. Once trained, deep learning models benefit from being several orders of magnitude faster during inference compared to conventional non-linear solvers.

Paper Citation

in Harvard Style

Marcial A. and Perninge M. (2023). An Unsupervised Neural Network Approach for Solving the Optimal Power Flow Problem. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 214-220. DOI: 10.5220/0012187400003543

in Bibtex Style

@conference{icinco23,
author={Alexander Marcial and Magnus Perninge},
title={An Unsupervised Neural Network Approach for Solving the Optimal Power Flow Problem},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={214-220},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012187400003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - An Unsupervised Neural Network Approach for Solving the Optimal Power Flow Problem
SN - 978-989-758-670-5
AU - Marcial A.
AU - Perninge M.
PY - 2023
SP - 214
EP - 220
DO - 10.5220/0012187400003543
PB - SciTePress