GeoHeat-GB is an open-source power systems planning model for heat electrification in Britain with high spatial resolution. This model is built on the electricity-only PyPSA-Eur open model dataset of the European power system.
GeoHeat-GB includes high spatial and temporal resolution electricity demand projections for residential heat pump adoption. The level of residential air- and ground-source heat pump adoption is exogenously determined and can be specified in the config.yaml file. This model also includes a high-resolution representation of the British power system and low-resolution resolution representation of interconnected grids.
GeoHeat-GB is distributed under the MIT license. Please note that some of the data used in this model have different licenses.
When you use GeoHeat-GB, please cite the following paper:
- Claire Halloran, Jesus Lizana, Filiberto Fele, Malcolm McCulloch, Data-based, high spatiotemporal resolution heat pump demand for power system planning, Applied Energy, Volume 355, 2024, 122331, https://doi.org/10.1016/j.apenergy.2023.122331.
Please use the following BibTex:
@article{Halloran2024,
title = {Data-based, high spatiotemporal resolution heat pump demand for power system planning},
volume = {355},
issn = {0306-2619},
url = {https://www.sciencedirect.com/science/article/pii/S0306261923016951},
doi = {https://doi.org/10.1016/j.apenergy.2023.122331},
journal = {Applied Energy},
author = {Halloran, Claire and Lizana, Jesus and Fele, Filiberto and McCulloch, Malcolm},
year = {2024},
pages = {122331},
}
GeoHeat-GB is based on the PyPSA-Eur open model dataset of the European power system. When using GeoHeat-GB, please also credit the authors of PyPSA-Eur following their guidelines. You should also note the licenses used in their databundle.
The model uses historical temperature data to project hourly residential heating at high spatial resolution using heating demand profiles based on the Renewable Heat Premium Payment trials. The development of these profiles is described in the paper How will heat pumps alter national half-hourly heat demands? Empirical modelling based on GB field trials. These profiles are used under CC BY 4.0 and have been modified from half-hourly to hourly to match the temporal resolution of other generation and demand data used in the model.
The model uses high spatial resolution population data that contains data supplied by Natural Environment Research Council. ©NERC (Centre for Ecology & Hydrology). Contains National Statistics data © Crown copyright and database right 2011. These data are used under the Open Government License. If you use this model, you must cite UK gridded population 2011 based on Census 2011 and Land Cover Map 2015.
Clone the GeoHeat-GB repository using the following command in your terminal:
/some/other/path % cd /some/path
/some/path % git clone https://github.com/clairehalloran/GeoHeat-GB.git
Install the python dependencies (which are the same as those of PyPSA-Eur) using the package manager of your choice. When using conda, enter the following commands in your terminal to install and activate the environment:
.../GeoHeat-GB % conda env create -f envs/environment.yaml
.../GeoHeat-GB % conda activate pypsa-eur
Install a solver of your choice that is compatible with PyPSA following these instructions.
The model can be configured in a similar way to PyPSA-Eur using the configuration file config.yaml. An example file with the heating options is included as config.heat.yaml. The configuration options added in the heating section are:
heating:
cutout: europe-2019-era5
single_GB_temperature: true
heat_sources: [air, ground]
air:
share: 0.75
ground:
share: 0.25
The heating cutout parameter provides name of the file used to create the Atlite cutout used to calulcate heating demand and COP values.
The single_GB_temperature parameter provides the option to use spatially uniform temperatures to calculate heating demand and COP values in Britain. See this paper for detailed discussion.
For both air- and ground-source heat pumps, the share of British households using the technology can be specified with a value between 0 and 1 for the share parameter. A value of 0 indicates that no households use the technology, and a value of 1 indicates that all households use the technology. Currently technology adoption is uniform across all parts of Britain.
For additional configuration options, refer to the PyPSA-Eur documentation on configuration.
Like the PyPSA-Eur model, this model is built through a snakemake workflow. Users are referred to the PyPSA-Eur documentation for detailed instructions on running the model.