The global energy system model PyPSA-Earth was introduced as part of the PyPSA meets Earth programme.
PyPSA-Earth, an expansion of the PyPSA-Eur model for Europe, aims to facilitate large-scale collaboration by offering a tool that can model the global energy system or any component of it using data at high geographical and temporal resolution.
According to the model’s description, expansion studies for generation, storage, and transmission can be conducted both operationally and simultaneously. Although more data can be further incorporated, the data covers energy demand, generation, and medium to high voltage networks from open sources. A variety of clustering and grid meshing algorithms assist adjust the model to computational and practical needs.
An open source toolbox called PyPSA (Python for Power Systems Analysis) is used to simulate and optimise contemporary power systems that include traditional generators, links with variable wind and solar generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks.
The CoNDyNet application, which was developed as part of the 100% renewable supply transition network modelling project funded by the German Federal Ministry for Education and Research (BMBF) and is maintained by the Department of Digital Transformation in Energy Systems at the Technical University of Berlin, is built to scale well with large networks and long time series.
The PyPSA-Eur open model dataset, which encompasses demand and supply for all energy sectors over the whole ENTSO-E region, represents the European energy system at the transmission network level. The primary characteristics of the PyPSA-Earth model are integrated PyPSA energy modelling framework and configurable data extraction and preparation scripts with worldwide coverage.
A 2060 net zero planning study for Nigeria was used to evaluate the optimisation features and undertake data validation for the entire African continent as a demonstration of the model. Although there are few transparent and accessible data sources for Africa, it was discovered that the demand projection from the PyPSA-Earth model was comparable to predictions from organisations like IRENA and “Our World in Data.”
The Nigeria 2060 study is based on a brownfield capacity expansion optimisation, which involves adding additional renewable and transmission capacity on top of existing infrastructure, and a dispatch optimisation, which discovers that an optimum renewable electricity future could be less expensive than the present.
The developers note that while the reliance on open source data may be a constraint, there are image recognition techniques to improve the data situation. They also note that a demonstration demonstrates how the presented developments can be used to construct a highly detailed power system model for energy planning studies to support technical and policy decision-making. In particular, it opens up high resolution modelling to nations who haven’t yet built intricate energy planning scenarios.
