# 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.

Download#### 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