It is easy to run PyPSA-Eur for multiple scenarios using the wildcards feature of snakemake.
Wildcards allow to generalise a rule to produce all files that follow a regular expression pattern
which e.g. defines one particular scenario. One can think of a wildcard as a parameter that shows
up in the input/output file names of the Snakefile and thereby determines which rules to run,
what data to retrieve and what files to produce.
!!! note
Detailed explanations of how wildcards work in snakemake can be found in the
relevant section of the documentation.
The {cutout} wildcard facilitates running the rule [build_cutout][]
for all cutout configurations specified under atlite: cutouts:.
These cutouts will be stored in a folder specified by {cutout}.
The {technology} wildcard specifies for which renewable energy technology to produce availability time
series and potentials using the rule [build_renewable_profiles][].
It can take the values onwind, offwind-ac, offwind-dc, offwind-float, and solar but not hydro
(since hydroelectric plant profiles are created by a different rule)
The {clusters} wildcard specifies the number of buses a detailed
network model should be reduced to in the rule [cluster_network][].
The number of clusters must be lower than the total number of nodes
and higher than the number of countries. However, a country counts twice if
it has two asynchronous subnetworks (e.g. Denmark or Italy).
The {opts} wildcard is used for electricity-only studies. It triggers
optional constraints, which are activated in either [prepare_network][] or
the [solve_network][] step. It may hold multiple triggers separated by -,
i.e. Co2L-3h contains the Co2L trigger and the 3h switch. There are
currently:
| Trigger | Description |
|---|---|
Co2L |
Add an pointwise or global CO2 limit constraint |
Co2L{p} |
Set CO2 limit to p times the 1990 CO2 emissions |
Ep |
Add an pointwise or global emission pricing constraint |
Ep{p} |
Set the emission price to p EUR/tCO2 |
{n}h |
Temporal averaging/down-sampling by n hours |
{n}seg |
Apply n time segments using the tsam package |
!!! note
The wildcard options are processed in scripts/prepare_network.py and scripts/solve_network.py.
!!! warning More comprehensive documentation for this wildcard will be added soon. To really understand the options here, look in scripts/prepare_sector_network.py
The {sector_opts} wildcard is only used for sector-coupling studies.
| Trigger | Description |
|---|---|
cb{x}ex{y} |
Set a carbon budget of x Gt CO2 with exponential decay starting at linear growth rate y |
Co2L{p} |
Set CO2 limit to p times the 1990 CO2 emissions |
{n}h |
Temporal averaging/down-sampling by n hours |
{n}seg |
Apply n time segments using the tsam package |
!!! note
The wildcard options for sector-coupled studies are processed in scripts/prepare_sector_network.py.
For more details, refer to the source code.
!!! warning More comprehensive documentation for this wildcard will be added soon.
The {planning_horizons} wildcard is only used for sector-coupling studies.
It takes years as values, e.g. 2020, 2030, 2040, 2050.