This tutorial introduces the CommonPower library, designed to benchmark safe reinforcement learning (RL) algorithms on control problems for power systems. We highlight two crucial issues in RL for power system control: safeguarding RL decision-making and assessing the impact of forecast quality on control performance. Participants will learn how to use CommonPower to further invesitigate these topics.
Authors:
- Hannah Markgraf, Technical University of Munich, hannah.markgraf@tum.de
- Michael Eichelbeck, Technical University of Munich, michael.eichelbeck@tum.de
- Matthias Althoff, Technical University of Munich, althoff@in.tum.de
Originally presented at ICLR 2024
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: 25 minutes
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Usage of this tutorial is subject to the MIT License.
Markgraf, H., Eichelbeck, M., & Althoff, M. (2024). Empowering Safe Reinforcement Learning for Power System Control with CommonPower [Tutorial]. In International Conference on Learning Representations. Climate Change AI. https://doi.org/10.5281/zenodo.14611580
@misc{markgraf2024commonpower,
title={Empowering Safe Reinforcement Learning for Power System Control with CommonPower},
author={Markgraf, Hannah and Eichelbeck, Michael and Althoff, Matthias},
year={2024},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.14611580},
booktitle={International Conference on Learning Representations},
howpublished={\url{https://github.com/climatechange-ai-tutorials/commonpower-safe-rl}}
}