Tutorials#
This is a brief tutorial on how to recreate the scenarios of the study The Role of Energy Storage in Germany. Unlike the original PyPSA-Eur repository, this version comes pre-configured with scenarios that you can test run.
Build the tutorial model#
First, open the base configuration file for this study, called config.form.yaml`, using a code editor of your choice, and navigate to the ``run section:
run:
prefix: "scenarios"
name: "baseline-mds"
scenarios:
enable: true
file: config/scenarios.form.yaml
disable_progressbar: false
shared_resources:
policy: false
exclude: []
shared_cutouts: true
as you can see, baseline-mds is the name of the scenario that is chosen.
The content of baseline-mds as well as a comprehensive list of all studied scenarios is defined in scenarios.form.yaml as described in Scenarios Configuration.
baseline-mds:
sector:
stores: ["H2"]
storage_units: ["li-ion battery", "vanadium", "lair", "pair", "iron-air battery"]
To create your own scenarios, it’s recommended to define the configurations in scenarios.form.yaml by either modifying an existing scenario or duplicating and renaming a scenario.
Note
If you want to create a model with lower computational requirements, you can adjust the clustering: temporal: resolution_sector setting here:
clustering:
focus_weights:
DE: 0.6
temporal:
resolution_sector: 4380SEG
The general rules of thumb are as follows:
1H: 1-hour time resolution4380SEG: 3 times faster computational time compared to1H2920SEG: 4 times faster computational time compared to1H100H: significantly quicker to solve compared to1H
If you want to test run this model on your computer, replace 4380SEG with 100H.
Using a lower time resolution, such as that from SEG, can still capture the desired variability in the model.
See Segmentation Temporal Clustering for more details
Run the model#
Before running the model, it’s recommended to test run the snakemake command:
snakemake solve_sector_networks --configfile config/config.form.yaml -n
The -n at the end indicates a dry run. If no errors occur, it will list the jobs that would be run:
Job stats:
job count
------------------------------------------------ -------
add_electricity 1
add_existing_baseyear 1
add_transmission_projects_and_dlr 1
base_network 1
build_ammonia_production 1
build_biomass_potentials 1
build_central_heating_temperature_profiles 1
build_clustered_population_layouts 1
build_cop_profiles 1
build_daily_heat_demand 1
build_direct_heat_source_utilisation_profiles 1
build_district_heat_share 1
build_electricity_demand 1
build_electricity_demand_base 1
build_energy_totals 1
build_existing_heating_distribution 1
build_gas_input_locations 1
build_gas_network 1
build_heat_totals 1
build_hourly_heat_demand 1
build_hydro_profile 1
build_industrial_distribution_key 1
build_industrial_energy_demand_per_country_today 1
build_industrial_energy_demand_per_node 1
build_industrial_energy_demand_per_node_today 1
build_industrial_production_per_country 1
build_industrial_production_per_country_tomorrow 1
build_industrial_production_per_node 1
build_industry_sector_ratios 1
build_industry_sector_ratios_intermediate 1
build_population_layouts 1
build_population_weighted_energy_totals 2
build_powerplants 1
build_renewable_profiles 6
build_salt_cavern_potentials 1
build_shapes 1
build_ship_raster 1
build_shipping_demand 1
build_solar_thermal_profiles 1
build_temperature_profiles 1
build_transmission_projects 1
build_transport_demand 1
cluster_gas_network 1
cluster_network 1
determine_availability_matrix 6
final_adjustment_myopic 1
prepare_network 1
prepare_sector_network 1
retrieve_cost_data 1
retrieve_electricity_demand 1
retrieve_gas_infrastructure_data 1
retrieve_osm_prebuilt 1
retrieve_ship_raster 1
simplify_network 1
solve_sector_network_myopic 1
solve_sector_networks 1
time_aggregation 1
total 68
Note
If an error occurs due to missing data, the first solution is to check the enable: setting in config.form.yaml.
enable:
retrieve: auto
retrieve_databundle: false
retrieve_cost_data: true
build_cutout: false
retrieve_cutout: false
custom_busmap: false
drop_leap_day: true
final_adjustment: true
retrieve_databundle: set this to true if a file indata/is missingretrieve_cost_data: set this to true ifresources/{run}/cost_2035.csvis missingretrieve_cutout: set this to true ifcutouts/europe-{weather year}-sarah3-era5.ncis missing
The weather data cutouts for PyPSA-Eur are available for all the weather years included in the scenarios.
If the weather year you’ve selected is not on that list, you’ll need to set build_cutout to true.
To create and solve the model, run the snakemake command:
snakemake solve_sector_networks --configfile config/config.form.yaml
This triggers a workflow of multiple preceding jobs that depend on each rule’s inputs and outputs. To create and solve the model and automatically generate all KPI plots of the study, run the snakemake command:
snakemake all --configfile config/config.form.yaml
Analyze the model#
There are several ways to analyze the results of the Snakemake run:
By analyzing the PyPSA network directly, see Package_unpackage_networks.ipynb for more details.
By using the CSV files and graphs located in
results/{run}/csvs/andresults/{run}/graphs/By using a pre-configured Jupyter Notebook in
notebooks/or any Jupyter Notebook of your choice.By utilizing the customizable KPIs in
results/{run}/maps/
The automatic generation of KPI plots is a feature from this project and may be upstreamed in the future. To create your own custom KPI plots, see KPI Configuration
To generate the KPI plots, run the snakemake command:
snakemake plot_KPIs_all --configfile config/config.form.yaml
The Jupyter Notebook notebooks/Scenarios_comparison.ipynb uses the script functions in plot_KPIs to plot and compare the results of multiple scenarios.