https://github.com/pypsa/pypsa-za
PyPSA Model of the South African Energy System
https://github.com/pypsa/pypsa-za
Last synced: 10 months ago
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PyPSA Model of the South African Energy System
- Host: GitHub
- URL: https://github.com/pypsa/pypsa-za
- Owner: PyPSA
- Created: 2017-09-29T19:38:31.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-09-22T14:43:07.000Z (over 2 years ago)
- Last Synced: 2025-04-15T07:07:34.759Z (10 months ago)
- Language: Python
- Size: 648 KB
- Stars: 19
- Watchers: 10
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# PyPSA-ZA
[PyPSA](https://pypsa.org/) model of the South African electricity system at the level of ESKOM's supply regions.
The model is described and evaluated in the paper [PyPSA-ZA: Investment and operation co-optimization of integrating wind and solar in South Africa at high spatial and temporal detail](https://arxiv.org/abs/1710.11199), 2017, [arXiv:1710.11199](https://arxiv.org/abs/1710.11199).
This repository contains the scripts to automatically reproduce the analysis.
## Instructions
To build and solve the model, a computer with about 20GB of memory with a strong
interior-point solver supported by the modelling library
[PYOMO](https://github.com/Pyomo/pyomo) like Gurobi or CPLEX are required.
We recommend as preparatory steps (the path before the `%` sign denotes the
directory in which the commands following the `%` should be entered):
1. cloning the repository using `git` (**to a directory without any spaces in the path**)
```shell
/some/other/path % cd /some/path/without/spaces
/some/path/without/spaces % git clone https://github.com/FRESNA/pypsa-za.git
```
2. installing the necessary python dependencies using conda (from within the `pypsa-za` directory)
```shell
.../pypsa-za % conda env create -f environment.yaml
.../pypsa-za % source activate pypsa-za # or conda activate pypsa-za on windows
```
3. getting the separate [data bundle](https://vfs.fias.science/d/f204668ef2/files/?p=/pypsa-za-bundle.7z&dl=1) (see also [Data dependencies] below) and unpacking it in `data`
```shell
.../data % wget "https://vfs.fias.science/d/f204668ef2/files/?dl=1&p=/pypsa-za-bundle.7z"
.../data % 7z x pypsa-za-bundle.7z
```
All results and scenario comparisons are reproduced using the workflow
management system `snakemake`
```shell
.../pypsa-za % snakemake
[... will take about a week on a recent computer with all scenarios ...]
```
`snakemake` will first compute several intermediate data files in the directory
`resources`, then prepare unsolved networks in `networks`, solve them and save
the resulting networks in `results/version-0.x/networks` and finally render the
main plots into `results/version-0.5/plots`.
Instead of computing all scenarios (defined by the product of all wildcards in
the `scenario` config section), `snakemake` also allows to compute only a
specific scenario like `csir-aggressive_redz_E_LC`:
```shell
.../pypsa-za % snakemake results/version-0.5/plots/network_csir-aggressive_redz_E_LC_p_nom
```
## Data dependencies
For ease of installation and reproduction we provide a bundle
[`pypsa-za-bundle.7z`](https://vfs.fias.science/d/f204668ef2/files/?p=/pypsa-za-bundle.7z&dl=1)
with the necessary data files:
| File | Citation |
|----------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| South_Africa_100m_Population | WorldPop, South Africa 100m Population (2013). [doi:10.5258/soton/wp00246](https://doi.org/10.5258/soton/wp00246) |
| Supply area normalised power feed-in for PV.xlsx | D. S. Bofinger, B. Zimmermann, A.-K. Gerlach, D. T. Bischof-Niemz, C. Mushwana, [Wind and Solar PV Resource Aggregation Study for South Africa](https://www.csir.co.za/csir-energy-centre-documents). (2016). |
| Supply area normalised power feed-in for Wind.xlsx | same as above |
| EIA_hydro_generation_2011_2014.csv | U.S. EIA, [Hydroelectricity Net Generation ZA and MZ 2011-2014](http://tinyurl.com/EIA-hydro-gen-ZA-MZ-2011-2014) (2017). |
| Existing Power Stations SA.xlsx | Compiled by CSIR from [Eskom Holdings](https://www.eskom.co.za/) (Jan 2017) and RSA DOE, [IRP2016](http://www.energy.gov.za/IRP/2016/Draft-IRP-2016-Assumptions-Base-Case-and-Observations-Revision1.pdf) |
| Power_corridors | RSA DEA, [REDZs Strategic Transmission Corridors](https://egis.environment.gov.za/) (Apr 2017) |
| REDZ_DEA_Unpublished_Draft_2015 | RSA DEA, [Wind and Solar PV Energy Strategic Environmental Assessment REDZ Database](https://egis.environment.gov.za/) (Mar 2017) |
| SACAD_OR_2017_Q2 | RSA DEA, [South Africa Conservation Areas Database (SACAD)](https://egis.environment.gov.za/) (Jun 2017) |
| SAPAD_OR_2017_Q2 | RSA DEA, [South Africa Protected Areas Database (SAPAD)](https://egis.environment.gov.za/) (Jun 2017) |
| SystemEnergy2009_13.csv | Eskom, System Energy 2009-13 Hourly, available from Eskom on request |
| SALandCover_OriginalUTM35North_2013_GTI_72Classes | GEOTERRAIMAGE (South Africa), [2013-14 South African National Land-Cover Dataset](https://egis.environment.gov.za/data_egis/node/109) (2017) |