Calliope model, specific to a (illustrative) district in Bangalore, India.
For more information on the model structure and general use of Calliope, see the documentation.
This commit provides the model for the publication:
(Under Review) B. Pickering and R. Choudhary, ‘Out-of-Sample, Out of Mind: Quantifying Resilience in Energy Systems with Out-of-Sample Testing’, Energy, 2020
If you use this model or work derived from it in an academic publication, please cite the above publication!
This paper ran on a release candidate of Calliope 0.6.3. To install, clone this repository, navigate into the cloned directory and create the 'calliope_DMUU' conda environment:
$ conda env create -f requirements.yml
The notebook 'Building and running the model' will guide you through building the model.
- Native Calliope plotting will not work in most cases, due to the existence of the 'scenario' dimension.
Previous model versions
If you're looking for the model used in a previous study, please refer to the commit(s) given in the following link(s):
B. Pickering and R. Choudhary, ‘District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles’, Applied Energy, vol. 236, pp. 1138–1157, Feb. 2019. DOI: 10.1016/j.apenergy.2018.12.037
B. Pickering, R. Choudhary. Mitigating risk in district-level energy investment decisions by scenario optimisation, In: Proceedings of the 4th IBPSA-England Conference BSO 2018, Emmanuel College, Cambridge, 2018
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