openmodelica-microgrid-gym 0.3.0

Installation

Getting started

The environment is initialized and run like any other OpenAI Gym

import gym

if __name__ == '__main__':
env = gym.make('openmodelica_microgrid_gym:ModelicaEnv-v1',
               max_episode_steps=None,
               net='../net/net.yaml',
               model_path='../omg_grid/grid.network.fmu')

env.reset()
for _ in range(1000):
    env.render()
    env.step(env.action_space.sample())  # take a random action
env.close()

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.

Citation & white paper

Please find a white paper on the OMG toolbox including an exemplary usage scenario here:

• https://arxiv.org/abs/2005.04869

Please use the following BibTeX entry for citing us:

@article{OMG-code2020,
    title = {OMG: A Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control},
    author = {Stefan Heid and Daniel Weber and Henrik Bode and Eyke Hüllermeier and Oliver Wallscheid},
    year = {2020},
    doi = {10.21105/joss.02435},
    url = {https://doi.org/10.21105/joss.02435},
    publisher = {The Open Journal},
    volume = {5},
    number = {54},
    pages = {2435},
    journal = {Journal of Open Source Software}
}

@article{OMG-whitepaper2020,
    title={Towards a Scalable and Flexible Simulation and
           Testing Environment Toolbox for Intelligent Microgrid Control},
    author={Henrik Bode and Stefan Heid and Daniel Weber and Eyke Hüllermeier and Oliver Wallscheid},
    year={2020},
    eprint={http://arxiv.org/abs/2005.04869},
    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.

0.3.0 (2020-12-18)

0.2.0 (2020-05-27)

0.1.3 (2020-05-13)

• best parameter set output after termination of SafeOpt agent (#7)

• proper action and observation space (#14)

• resolved problem related to environment model_params (#21)

• documentation improvements (more examples, installation)

0.1.2 (2020-05-04)

• corrected pip install requirements

0.1.1 (2020-04-22)

• First release on PyPI.