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PyPSA-Earth. A new global open energy system optimization model demonstrated in Africa
Parzen, M.; Abdel-Khalek, H.; Fedorov, E.; Mahmood, M.; Frysztacki, M.M.; Hampp, J.; Franken, L.; Schumm, L.; Neuman, F.; Poli, D.; Kiprakis, A.; Fioriti, D. (2022). PyPSA-Earth. A new global open energy system optimization model demonstrated in Africa. arXiv (Archive) arXiv:2209.04663v1: 36 pp. https://dx.doi.org/10.48550/arXiv.2209.04663
In: arXiv (Archive). Cornell University.
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| Beschikbaar in | Auteurs |
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Documenttype: Preprint
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| Trefwoord |
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| Author keywords |
Macro-Energy Systems, Optimization, OpenStreetMap, PyPSA-Earth, PyPSA-Africa, PyPSA meets Earth |
| Auteurs | | Top |
- Parzen, M.
- Abdel-Khalek, H.
- Fedorov, E.
- Mahmood, M.
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- Frysztacki, M.M.
- Hampp, J.
- Franken, L.
- Schumm, L.
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- Neuman, F.
- Poli, D.
- Kiprakis, A.
- Fioriti, D.
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| Abstract |
Macro-energy system modelling is used by decision-makers to steer the global energy transition toward an affordable, sustainable and reliable future. Closed-source models are the current standard for most policy and industry decisions. However, open models have proven to be competitive alternatives that promote science, robust technical analysis, collaboration and transparent policy decision-making. Yet, two issues slow the adoption: open models are often designed with limited geographic scope, hindering synergies from collaboration, or are based on low spatially resolved data, limiting their use. Here we introduce PyPSA-Earth, the first open-source global energy system model with data in high spatial and temporal resolution. It enables large-scale collaboration by providing a tool that can model the world energy system or any subset of it. This work is derived from the European PyPSA-Eur model using new data and functions. It is suitable for operational as well as combined generation, storage and transmission expansion studies. The model provides two main features: (1) customizable data extraction and preparation scripts with global coverage and (2) a PyPSA energy modelling framework integration. The data includes electricity demand, generation and medium to high-voltage networks from open sources, yet additional data can be further integrated. A broad range of clustering and grid meshing strategies help adapt the model to computational and practical needs. A data validation for the entire African continent is performed and the optimization features are tested with a 2060 net-zero planning study for Nigeria. The demonstration shows that the presented developments can build a highly detailed energy system model for energy planning studies to support policy and technical decision-making. We welcome joining forces to address the challenges of the energy transition together. |
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