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# This CITATION.cff file was generated with cffinit. | ||
# Visit https://bit.ly/cffinit to generate yours today! | ||
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cff-version: 1.2.0 | ||
title: OPF-Gym | ||
message: >- | ||
If you use this software, please cite it using the | ||
metadata from this file. Additionally, please check out the publication | ||
"Learning the optimal power flow: Environment design matters", where the | ||
library was first introduced. | ||
(https://www.sciencedirect.com/science/article/pii/S2666546824000764) | ||
type: software | ||
authors: | ||
- given-names: Thomas | ||
family-names: Wolgast | ||
email: thomas.wolgast@uni-oldenburg.de | ||
affiliation: Carl von Ossietzky Universität Oldenburg | ||
orcid: 'https://orcid.org/0000-0002-9042-9964' | ||
repository-code: 'https://github.com/Digitalized-Energy-Systems/opfgym' | ||
abstract: >- | ||
The OPF-Gym library allows for easy creation of | ||
reinforcement learning (RL) environments for solving the | ||
optimal power flow (OPF) problem. OPF-Gym also provides | ||
five benchmark environments to ensure comparability of | ||
future RL-OPF research. Various kinds of OPF problems are | ||
supported, for example, multi-stage OPF, discrete actions, | ||
stochastic OPF, etc. Further, it is possible to generate | ||
labeled training data for supervised learning, which again | ||
improves comparability of research advances. | ||
keywords: | ||
- Optimal Power Flow | ||
- Reinforcement Learning | ||
- Gymnasium | ||
- Supervised Learning | ||
- Voltage Control | ||
- Economic Dispatch | ||
- Reactive Power Market | ||
- Load Shedding | ||
- Benchmark | ||
license: MIT | ||
version: 0.1.0 |
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warnings.simplefilter('once') | ||
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class PowerFlowNotAvailable(Exception): | ||
pass | ||
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