This section describes three examples. The first example deals with a microgrid system design problem and illustrates the basic features of GBOML.
This section is divided in two parts. The first part is dedicated to three examples. The first example deals with a microgrid system design problem and illustrates the basic features of GBOML.
The second example is more sophisticated and focuses on a remote carbon-neutral fuel supply chain planning problem.
Finally, the third example is based on a hypothetical problem and illustrates the Python API.
The second part provides a list of reference papers and models that use GBOML.
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* Berger et al. 2021, Remote Renewable Hubs for Carbon-Neutral Synthetic Fuel Production, https://www.frontiersin.org/articles/10.3389/fenrg.2021.671279/full . The model can be found at : https://gitlab.uliege.be/smart_grids/public/gboml/-/tree/master/examples/remote_energy_supply_chain .
* Cauz et al. 2023, Reinforcement Learning for Joint Design and Control of Battery-PV Systems, https://arxiv.org/pdf/2307.04244.pdf .
* Dachet et al. 2023, Towards CO2 valorization in a multi remote renewable energy hub framework, https://arxiv.org/pdf/2303.09454.pdf . The model can be found at : https://gitlab.uliege.be/smart_grids/public/gboml/-/tree/master/examples/towards_co2_valorization .
* Fonder et al. 2023, Synthetic methane for closing the carbon loop: Comparative study of three carbon sources for remote carbon-neutral fuel synthetization, https://arxiv.org/pdf/2310.01964.pdf . The model can be found at : https://gitlab.uliege.be/smart_grids/public/gboml/-/tree/master/examples/synthetic_methane_morocco/gboml_models .
