openmodelica-microgrid-gym 0.1.2

Installation

$ conda install -c conda-forge pyfmi

Getting started

OMG uses the FMI standard for the exchange of the model between OpenModelica and python.

An example network consisting out of two inverters, three filters and an inductive load.

You can either use one of the provided FMUs (Windows and Linux, 64-bit, both included in the grid.network.fmu) or create your own by running:

openmodelica_microgrid_gym\fmu> omc create_fmu.mos

Running the staticctrl.py starts a simulation with a manually tuned cascaded PIPI controller

A save Bayesian approach of a reinforcement learning agent is provided under examples/berkamkamp.py.

Every user defined settings can be directly done in the example program.

env = gym.make(environment-id, **kwargs)

Returns an instantiated grid environment. Provide any additional settings right here (see full documentation for all possibilities)

Citation

A whitepaper for this framework will be avaiable soon. Please use the following BibTeX entry for citing us:

@misc{LEA2020XXXXXXX,
    title={XXXXXXXXXX},
    author={Daniel Weber and Stefan Heid and Henrik Bode and Oliver Wallscheid},
    year={2020},
    eprint={XXXXX},
    archivePrefix={arXiv},
    primaryClass={eess.SY}
}

Contributing

Please refer to the contribution guide.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

0.1.2 (2020-05-04)

• corrected pip install requirements

0.1.1 (2020-04-22)

• First release on PyPI.