Tutorial

Before getting started with PyPSA-RSA it makes sense to be familiar with its general modelling framework PyPSA.

The tutorial uses fewer computing resources than the entire model, allowing the user to explore the majority of its features on a local computer.

If not yet completed, follow the Installation steps first.

The tutorial will cover examples on how to

configure and customise the PyPSA-RSA model and
step-by-step exucetion of the snakemake workflow, from network creation through solving the network to analysing the results.

The model_file.xlsx and config.yaml files are utilised to customise the PyPSA-RSA model. The tutorial’s configuration is contained in model_file_tutorial.xlsx and config.tutorial.yaml. Use the configuration and model setup files config.yaml and model_file.xlsx to run the tutorial

.../pypsa-rsa % cp config.tutorial.yaml config.yaml
.../pypsa-rsa % cp model_file_tutorial.xlsx model_file.xlsx

How to customise PyPSA-RSA?

Model setup: model_file.xlsx

The model_file.xlsx contains databases of existing conventional and renewable power stations owned by Eskom or by IPP’s.

existing Eskom stations
- scenario
  
  power station name
  
  carrier
  
  carrier type
  
  status
  
  capacity (MW)
  
  unit size (MW)
  
  number of units
  
  future commissioning date
  
  decommissioning date
  
  heat rate (GJ/MWh)
  
  fuel price (R/GJ)
  
  max ramp up (MW/min)
  
  max ramp down (MW/min)
  
  min stable level (%)
  
  variable O&M cost (R/MWh)
  
  fixed O&M cost (R/MWh)
  
  pump efficiency (%)
  
  pump units
  
  pump load per unit (MW)
  
  pumped storage - max storage (GWh)
  
  csp storage (hours)
  
  diesel storage (Ml)
  
  gas storage (MCM)
  
  GPS latitude
  
  GPS longitude
existing non-Eskom stations
- scenario
  
  same as above, in addition:
  
  grouping
new build limits
- scenario
  
  minimum installed limit
  
  maximum installed limit
projected parameters
- scenario
  
  demand
  
  coal fleet energy availability factor (EAF)
  
  spinning reserves
  
  total reserves
  
  reserve margin
  
  active reserve margin
technology costs
- scenario
  
  discount rate
  
  heat rate
  
  efficiency
  
  fixed O&M
  
  variable O&M
  
  investment
  
  lifetime
  
  fuel
  
  CO2 intensity
model setup
- wildcard
- simulation years
- scenario: existing Eskom stations
- scenario: existing non-Eskom stations
- scenario: new build limits
- scenario: projected parameters
- scenario: costs

Configuration: config.yaml

The model can be further adapted using the config.yaml to only include a select number of regions (e.g. 1-supply, 11-supply or 27-supply). The tutorial is setup to run the 1-supply which uses a single node for the entire country.

The model uses the regions selected to determine the network topology. When the option build_topology is enabled, the model constructs the network topology. It is necessary to enable this when running the model for the first time or when changing the regions tag.

PyPSA-RSA provides three methods for generating renewable resource data. The tag use_eskom_wind_solar uses the pu profiles for all wind and solar generators as obtained from Eskom. The tag use_excel_wind_solar utilises user specific hourly pu profiles provided in an excel spreadsheet. The tag build_renewables_profiles enables the model to calculate the temporal and spatial availability of renewables such as wind and solar energy using historical weather data.

For either three methods historical weather data is used and thus the year in which the data was obtained is specified for each carrier under the tag reference_weather_years.

If build_renewables_profiles is enabled then atlite is used to generate the renewable resource potential using reanalysis data which can be downloaded by enabling the build_cutout tag.

The cutout is is configured under the atlite tag. The options below can be adapted to download weather data for the required range of coordinates surrounding South Africa. For more details on atlite please follow the tutorials.

The spatial resolution of the downloaded ERA5 dataset is given on a 30km x 30km grid. For wind power generation, this spatial resolution is not enough to resolve the local dynamics. Enabling the apply_wind_correction tag, uses global wind atlas mean wind speed at 100m to correct the ERA5 data.

Once the historical weather data is downloaded, atlite is used to convert the weather data to power systems data. Atlite uses pre-defined or custom turbine properties which are specified under the resource tag.

Similarly, solar pv profiles are generated using pre-defined or custom panel properties which are specified under the resource tag.

The renewable potentials are calculated for eligible land, excluding the conservation and protected areas. When the natura tag is enabled, the SACAD and SAPAD shape files located in data/bundle are converted into tiff files. The conservation and protectected areas together with the areas of land with the grid_codes specified are excluded from calculation of renewable potential.

In addition, the expansion of renewable resources is limited to either the redz regions or areas close to the strategic transmission corridors.

The hydro power is obtained directly from Eskom data.

Finally, it is possible to pick a solver. For instance, this tutorial uses the open-source solvers CBC and Ipopt and does not rely on the commercial solvers Gurobi or CPLEX (for which free academic licenses are available).

Note

To run the tutorial, either install CBC and Ipopt (see instructions for Installation).

Alternatively, choose another installed solver in the config.yaml at solving: solver:.

Note, that we only note major changes to the provided default configuration that is comprehensibly documented in Configuration. There are many more configuration options beyond what is adapted for the tutorial!

A good starting point to customize your model are settings of the default configuration file config.default. You may want to do a reserve copy of your current configuration file and then overwrite it by a default configuration:

.../pypsa-za (pypsa-za) % cp config.default.yaml config.yaml

How to execute different parts of the workflow?

Snakemake is a workflow management tool inherited by PyPSA-RSA from PyPSA-Eur. Snakemake decomposes a large software process into a set of subtasks, or ’rules’, that are automatically chained to obtain the desired output.

Note

Snakemake, which is one of the major dependencies, will be automatically installed in the environment pypsa-za, thereby there is no need to install it manually.

The snakemake included in the conda environment pypsa-za can be used to execute any custom rule with the following command:

.../pypsa-za (pypsa-za) % snakemake < your custom rule >

Starting with essential usability features, the implemented PyPSA-RSA Snakemake procedure that allows to flexibly execute the entire workflow with various options without writing a single line of python code. For instance, you can model South Africa’s energy system using the required data. Wildcards, which are special generic keys that can assume multiple values depending on the configuration options, help to execute large workflows with parameter sweeps and various options.

You can execute some parts of the workflow in case you are interested in some specific it’s parts. E.g. renewable resource potentials for onshore wind in redz areas for a single node model may be generated with the following command which refers to the script name:

.../pypsa-earth (pypsa-earth) % snakemake -j 1 resources/profile_onwind_1-supply_redz.nc

How to use PyPSA-RSA for your energy problem?

PyPSA-RSA mostly relies on input datasets specific to South Africa but can be tailored to represent any part of the world in a few steps. The following procedure is recommended.

1. Adjust the model configuration

The main parameters needed to customize the inputs for your national-specific data are defined in the configuration file config.yaml. The configuration settings should be adjusted according to a particular problem you are intending to model. The main country-dependent parameters are:

regions parameter which defines the network topology;
resareas parameter which defines zones suitable for renewable expansion based on country specific policies;
cutouts and cutout parameters which refer to a name of the climate data archive (so called cutout)

to be used for calculation of the renewable potential.

Apart of that, it’s worth to check that there is a proper match between the temporal and spatial parameters across the configuration file as it is essential to build the model properly. Generally, if there are any mysterious error message appearing during the first model run, there are chances that it can be resolved by a simple config check.

It could be helpful to keep in mind the following points:

the cutout name should be the same across the whole configuration file (there are several entries, one under atlite and some under each of the renewable parameters);
the country of interest given as a shape file in data/supply_regions/ should be covered by the cutout area;
the cutout time dimension, the weather year used for demand modelling and the actual snapshot should match.

2. Build the custom cutout

The cutout is the main concept of climate data management in PyPSA ecosystem introduced in atlite package. The cutout is an archive containing a spatio-temporal subset of one or more topology and weather datasets. Since such datasets are typically global and span multiple decades, the Cutout class allows atlite to reduce the scope to a more manageable size. More details about the climate data processing concepts are contained in JOSS paper.

The pre-built cutout for South Africa is available for 2012 year and can be loaded directly from zenodo through the rule retrieve_cutout.

In case you are interested in other parts of the world you have to generate a cutout yourself using the build_cutouts rule. To run it you will need to:

be registered on the Copernicus Climate Data Store;
install cdsapi package (can be installed with pip);
setup your CDS API key as described on their website.

These steps are required to use CDS API which allows an automatic file download while executing build_cutouts rule.

Normally cutout extent is calculated from the shape of the requested region defined by the countries parameter in the configuration file config.yaml. It could make sense to set the countries list as big as it’s feasible when generating a cutout. A considered area can be narrowed anytime when building a specific model by adjusting content of the countries list.

There is also option to set the cutout extent specifying x and y values directly. However, these values will overwrite values extracted from the countries shape. Which means that nothing prevents build_cutout to extract data which has no relation to the requested countries. Please use direct definition of x and y only if you really understand what and why you are doing.

The build_cutout flag should be set true to generate the cutout. After the cutout is ready, it’s recommended to set build_cutout to false to avoid overwriting the existing cutout by accident.

3. Build a natura.tiff raster

A raster file natura.tiff is used to store shapes of the protected and reserved nature areas. Such landuse restrictions can be taking into account when calculating the renewable potential with build_renewable_profiles.

Note

Skip this recommendation if the region of your interest is within Africa

A pre-built natura.tiff is loaded along with other data needed to run a model with retrieve_databundle_light rule. Currently this raster is valid for Africa, global natura.tiff raster is under development. You may generate the natura.tiff for a region of interest using build_natura_raster rule which aggregates data on protected areas along the cutout extent.