PyPSA Model of the South African Energy System
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README.md

PyPSA-ZA

PyPSA model of the South African electricity system at the level of ESKOM's supply regions.

Visualisation of optimal capacities and costs in the least cost scenario

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, 2017, arXiv: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 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)

    /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)

    .../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 (see also [Data dependencies] below) and unpacking it in data

    .../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

.../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:

.../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 with the necessary data files:

File Citation
South_Africa_100m_Population WorldPop, South Africa 100m Population (2013). doi: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. (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 (2017).
Existing Power Stations SA.xlsx Compiled by CSIR from Eskom Holdings (Jan 2017) and RSA DOE, IRP2016
Power_corridors RSA DEA, REDZs Strategic Transmission Corridors (Apr 2017)
REDZ_DEA_Unpublished_Draft_2015 RSA DEA, Wind and Solar PV Energy Strategic Environmental Assessment REDZ Database (Mar 2017)
SACAD_OR_2017_Q2 RSA DEA, South Africa Conservation Areas Database (SACAD) (Jun 2017)
SAPAD_OR_2017_Q2 RSA DEA, South Africa Protected Areas Database (SAPAD) (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 (2017)