The PyPSA meets Earth initiative has launched PyPSA-Earth as an open data-based open source global energy system model.
PyPSA-Earth, an extension of the PyPSA-Eur model for Europe, is aimed to enable large-scale collaboration by providing a tool that can model with data in high spatial and temporal resolution the world energy system or any subset of it.
The model is stated suitable for operational as well as combined generation, storage and transmission expansion studies.
The data includes electricity demand, generation and medium to high voltage networks from open sources, although additional data can be further integrated, while a range of clustering and grid meshing strategies help adapt the model to computational and practical needs.
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PyPSA (Python for Power Systems Analysis) is an open source toolbox for simulating and optimising modern power systems with features such as conventional generators, links with variable wind and solar generation, storage units, coupling to other energy sectors and mixed alternating and direct current networks.
The application, which emerged out of the German Federal Ministry for Education and Research (BMBF) funded CoNDyNet project to model networks for the transition to 100% renewable supply and is maintained by the Department of Digital Transformation in Energy Systems at the Technical University of Berlin, is designed to scale well with large networks and long time series.
PyPSA-Eur is an open model dataset of the European energy system at the transmission network level that covers demand and supply for all energy sectors across the full ENTSO-E area.
The PyPSA-Earth model adds new data and features, with the main features customisable data extraction and preparation scripts with global coverage and PyPSA energy modelling framework integration.
Model validation
As a demonstration of the model data validation has been performed for the entire African continent with the optimisation features tested with a 2060 net zero planning study for Nigeria.
For Africa transparent and open data sources are limited but for example the demand prediction from the PyPSA-Earth model was found to be within the range of others such as those from ‘Our World in Data’ and IRENA.
The Nigeria 2060 study is based on a brownfield capacity expansion optimisation, i.e. building new renewable and transmission capacity on top of existing infrastructure, and a dispatch optimisation, which finds that an optimised renewable electricity future could be cheaper than today.
While the reliance on open source data may be a limiting factor, there are image recognition procedures to improve the data situation, the developers comment, saying that demonstration shows that the presented developments can build a highly detailed power system model for energy planning studies to support policy and technical decision-making.
In particular it makes high resolution modelling accessible to countries which so far have not had detailed energy planning scenarios developed.
“We anticipate that PyPSA-Earth can represent an open reference model for system planning, and we welcome joining forces to address the challenges of the energy transition together.”
