AC Optimal Power Flow (OPF) attempts to determine the setpoints of generators that would minimize the operating cost of a power system while meeting other operational constraints. In this tutorial, learn how to leverage PyTorch to train a neural network to approximate the optimal solutions.
Author(s):
- Jorge Montalvo, Climate Change AI, jorge@climatechange.ai
- Utkarsha Agwan, University of California, Berkeley, uagwan@berkeley.edu
- Panos Moutis, Climate Change AI, panay1ot1s@climatechange.ai
- Enming Liang, City University of Hong Kong, enming.cityu@gmail.com
Originally presented at Climate Change AI Summer School 2023
We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.
Estimated time to execute end-to-end: 10 minutes
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Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.
Usage of this tutorial is subject to the MIT License.
Montalvo, J., Agwan, U., Moutis, P., Liang, E. (2024). AI for Optimal Power Flow [Tutorial]. In Climate Change AI Summer School. Climate Change AI. https://doi.org/10.5281/zenodo.13119828
@misc{montalvo2024ai,
title={AI for Optimal Power Flow},
author={Montalvo, Jorge and Agwan, Utkarsha and Moutis, Panos and Liang, Enming},
year={2024},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.13119828},
booktitle={Climate Change AI Summer School},
howpublished={\url{https://github.com/climatechange-ai-tutorials/optimal-power-flow}}
}