# Tutorial¶

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

Running the tutorial requires limited computational resources compared to the full model, which allows the user to explore most of its functionalities on a local machine. It takes approximately five minutes to complete and requires 3 GB of memory along with 1 GB free disk space.

If not yet completed, follow the Installation steps first.

The tutorial will cover examples on how to

• configure and customise the PyPSA-Eur model and
• run the snakemake workflow step by step from network creation to the solved network.

The configuration of the tutorial is included in the config.tutorial.yaml. To run the tutorial, use this as your configuration file config.yaml.

.../pypsa-eur % cp config.tutorial.yaml config.yaml


This configuration is set to download a reduced data set via the rules retrieve_databundle, retrieve_natura_raster, retrieve_cutout totalling at less than 250 MB. The full set of data dependencies would consume 5.3 GB. For more information on the data dependencies of PyPSA-Eur, continue reading Rules retrieve*.

## How to customise PyPSA-Eur?¶

The model can be adapted to only include selected countries (e.g. Belgium) instead of all European countries to limit the spatial scope.




Likewise, the example’s temporal scope can be restricted (e.g. to a single month).

snapshots:
start: "2013-03-01"
end: "2013-04-01"
closed: 'left' # end is not inclusive


It is also possible to allow less or more carbon-dioxide emissions. Here, we limit the emissions of Germany 100 Megatonnes per year.

snapshots:
end: "2013-04-01"


PyPSA-Eur also includes a database of existing conventional powerplants. We can select which types of powerplants we like to be included with fixed capacities:

snapshots:
co2limit: 100.e+6


To accurately model the temporal and spatial availability of renewables such as wind and solar energy, we rely on historical weather data. It is advisable to adapt the required range of coordinates to the selection of countries.

atlite:
nprocesses: 4
cutouts:
be-03-2013-era5:
module: era5
x: [4., 15.]
y: [46., 56.]
time: ["2013-03", "2013-03"]


We can also decide which weather data source should be used to calculate potentials and capacity factor time-series for each carrier. For example, we may want to use the ERA-5 dataset for solar and not the default SARAH-2 dataset.

    clip_p_max_pu: 1.e-2
offwind-dc:


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).

    battery: 0.
emission_prices: # in currency per tonne emission, only used with the option Ep


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!

## How to use the snakemake rules?¶

Open a terminal, go into the PyPSA-Eur directory, and activate the pypsa-eur environment with

.../pypsa-eur % conda activate pypsa-eur


Let’s say based on the modifications above we would like to solve a very simplified model clustered down to 6 buses and every 24 hours aggregated to one snapshot. The command

.../pypsa-eur % snakemake -j 1 results/networks/elec_s_6_ec_lcopt_Co2L-24H.nc


orders snakemake to run the script solve_network that produces the solved network and stores it in .../pypsa-eur/results/networks with the name elec_s_6_ec_lcopt_Co2L-24H.nc:

rule solve_network:
input: "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
output: "results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
[...]
script: "scripts/solve_network.py"


This triggers a workflow of multiple preceding jobs that depend on each rule’s inputs and outputs:

In the terminal, this will show up as a list of jobs to be run:

Building DAG of jobs...
Using shell: /bin/bash
Provided cores: 1
Rules claiming more threads will be scaled down.
Unlimited resources: mem
Job counts:
count   jobs
1       add_electricity
1       base_network
1       build_bus_regions
4       build_renewable_profiles
1       build_shapes
1       cluster_network
1       prepare_network
1       simplify_network
1       solve_network
12


snakemake then runs these jobs in the correct order.

A job (here simplify_network) will display its attributes and normally some logs in the terminal:

[<DATETIME>]
rule simplify_network:
input: networks/elec.nc, data/costs.csv, resources/regions_onshore.geojson, resources/regions_offshore.geojson
output: networks/elec_s.nc, resources/regions_onshore_elec_s.geojson, resources/regions_offshore_elec_s.geojson, resources/clustermaps_elec_s.h5
jobid: 3
benchmark: benchmarks/simplify_network/elec_s
wildcards: network=elec, simpl=
resources: mem=4000

INFO:pypsa.io:Imported network elec.nc has buses, carriers, generators, lines, links, loads, storage_units, transformers
INFO:__main__:Mapping all network lines onto a single 380kV layer
INFO:__main__:Simplifying connected link components
INFO:__main__:Removing stubs
INFO:__main__:Displacing offwind-ac generator(s) and adding connection costs to capital_costs: 20128 Eur/MW/a for 5718 offwind-ac
INFO:__main__:Displacing offwind-dc generator(s) and adding connection costs to capital_costs: 14994 Eur/MW/a for 5718 offwind-dc, 26939 Eur/MW/a for 5724 offwind-dc, 29621 Eur/MW/a for 5725 offwind-dc
INFO:pypsa.io:Exported network elec_s.nc has lines, carriers, links, storage_units, loads, buses, generators
[<DATETIME>]
Finished job 3.
9 of 12 steps (75%) done


Once the whole worktree is finished, it should show state so in the terminal:

Finished job 0.
12 of 12 steps (100%) done
Complete log: /home/XXXX/pypsa-eur/.snakemake/log/20XX-XX-XXTXX.snakemake.log
snakemake results/networks/elec_s_6_ec_lcopt_Co2L-24H.nc  519,84s user 34,26s system 242% cpu 3:48,83 total


You will notice that many intermediate stages are saved, namely the outputs of each individual snakemake rule.

You can produce any output file occuring in the Snakefile by running

.../pypsa-eur % snakemake -j 1 <output file>


For example, you can explore the evolution of the PyPSA networks by running

1. .../pypsa-eur % snakemake -j 1 networks/base.nc
2. .../pypsa-eur % snakemake -j 1 networks/elec.nc
3. .../pypsa-eur % snakemake -j 1 networks/elec_s.nc
4. .../pypsa-eur % snakemake -j 1 networks/elec_s_6.nc
5. .../pypsa-eur % snakemake -j 1 networks/elec_s_6_ec_lcopt_Co2L-24H.nc

There’s a special rule: If you simply run

.../pypsa-eur % snakemake


the wildcards given in scenario in the configuration file config.yaml are used:

scenario:
simpl: ['']
ll: ['copt']
clusters: [5]
opts: [Co2L-24H]


In this example we would not only solve a 6-node model of Germany but also a 2-node model.

## How to analyse solved networks?¶

The solved networks can be analysed just like any other PyPSA network (e.g. in Jupyter Notebooks).

import pypsa

network = pypsa.Network("results/networks/elec_s_6_ec_lcopt_Co2L-24H.nc")


For inspiration, read the examples section in the PyPSA documentation.