Import and Export¶

PyPSA can handle several different data formats, such as CSV, netCDF and HDF5 files. It is also possible to build a pypsa.Network within a Python script. There is limited functionality to import from Pypower and Pandapower. Files can be imported from and exported to local files or cloud storage providers.

Adding, Removing & Merging¶

Networks can be built step-by-step for each component by calling n.add() to add a single or multiple components.

n.add("Bus", "my_bus")
n.add("Generator", "gen_1", bus="my_bus", p_nom=100, marginal_cost=100)

n.add("Load", ["load_1", "load_2"], bus="my_bus", p_set=[10, 20])

Components can be removed with n.remove().

n.remove("Load", ["load_1", "load_2"])
n.remove("Generator", "my_generator")

Two networks with a disjunct set of component indices can be merged with n1.merge(n2)

CSV Files¶

Create a folder with CSVs for each component type (e.g. generators.csv), then a CSV for each time-dependent variable (e.g. generators-p_max_pu.csv). Then run n.import_from_csv_folder() to import the network or simply provide the path in the pypsa.Network constructor.

Note

It is not necessary to add every single column, only those where values differ from the defaults listed in Components. All empty values/columns are filled with the defaults.

A network can be exported as a folder of csv files with n.export_to_csv_folder().

n.export_to_csv_folder("foo/bar")
n_import = pypsa.Network("foo/bar")

Excel¶

To import a network from an Excel file, run n.import_from_excel() or simply provide the path in the pypsa.Network constructor. To export a network to an Excel file, run n.export_to_excel(). Before using this import functionality, ensure that you install pypsa[excel] in your environment using pip install pypsa[excel].

n.export_to_excel("foo/bar.xlsx")
n_import = pypsa.Network("foo/bar.xlsx")

To create an Excel file compatible with PyPSA, structure the file with separate worksheets for each component type and their associated time-series data.

For static component data, name each worksheet using the exact component name as it appears in PyPSA (e.g., generators, buses, lines, storage_units). For time-series data, use the format <component>-<attribute> (e.g., generators-p_max_pu, loads-p_set, storage_units-inflow). Component and attribute names are case-sensitive and must match PyPSA conventions.

The I/O functionality will automatically ignore any worksheets that do not follow this naming pattern, allowing you to include documentation, notes, or auxiliary data in separate tabs without interfering with the import/export process.

The snapshots worksheet must contain the time-series index using an appropriate datetime format. Other time-series worksheets should have the same number of rows, with the index column either numbered sequentially (e.g., 1-8760 for hourly annual data) or using the same datetime format as the snapshots.

Note

To maintain data integrity and avoid conflicts, it is recommended to use the data validation feature in Excel: define your buses in the Bus worksheet and apply data validation to bus reference columns in component worksheets (e.g., bus0, bus1 fields in the Line worksheet) and similarly apply validation for carriers and time-series dataset references where applicable.
It is not necessary to add every single column, only those where values differ from the defaults. All empty values and columns are filled with the defaults.

Warning

Excel is resource-intensive and only appropriate for smaller networks. For larger datasets or production workflows, consider using netCDF files.

netCDF¶

netCDF files take up less space than CSV files and are faster to load.

To export network and components to a netCDF file run n.export_to_netcdf().

To import network data from netCDF file run n.import_from_netcdf() or simply provide the path in the pypsa.Network constructor.

Note

netCDF is preferred over HDF5 because netCDF is structured more cleanly, is easier to use from other programming languages, can limit float precision to save space and supports lazy loading.

n.export_to_netcdf("foo/bar.nc")
n_import = pypsa.Network("foo/bar.nc")

HDF5¶

To export the network to an HDF store, run n.export_to_hdf5().

To import network data from an HDF5 store, run n.import_from_hdf5() or simply provide the path in the pypsa.Network constructor.

n.export_to_hdf5("foo/bar.h5")
n_import = pypsa.Network("foo/bar.h5")

PYPOWER¶

To import a network from the PYPOWER ppc dictionary/numpy.array format version 2, run the function n.import_from_pypower_ppc().

Exporting to PYPOWER is not currently supported.

from pypower.api import case30
ppc = case30()
n.import_from_pypower_ppc(ppc)

Pandapower¶

To import a network from pandapower, run the function n.import_from_pandapower_net().

Exporting to pandapower is not currently supported.

Warning

Not all pandapower data fields are supported. For instance, three-winding transformers, switches, in_service status and tap positions of transformers.

import pandapower.networks as pn
net = pn.create_cigre_network_mv(with_der='all')
n = pypsa.Network()
n.import_from_pandapower_net(net, extra_line_data=True)

Cloud Object Storage¶

CSV, netCDF and HDF5 files in cloud object storage can be imported and exported by installing the cloudpathlib package. This is available through the [cloudpath] optional dependency, installable via pip install 'pypsa[cloudpath]'.

cloudpathlib supports AWS S3 (s3://), Google Cloud Storage (gs://) and Azure Blob Storage (az://) as cloud object storage providers. Users will need to additionally install the corresponding cloud storage provider client library to interface with the cloud storage provider via cloudpathlib (e.g. boto3, google-cloud-storage or azure-storage-blob).

from pypsa import Network
n = Network('examples/ac-dc-meshed/ac-dc-data')
n.export_to_csv_folder('s3://my-s3-bucket/ac-dc-data')
n = Network('s3://my-s3-bucket/ac-dc-data')
n.export_to_netcdf('gs://my-gs-bucket/ac-dc-data.nc')
n = Network('gs://my-gs-bucket/ac-dc-data.nc')
n.export_to_excel('az://my-az-bucket/ac-dc-data.xlsx')
n = Network('az://my-az-bucket/ac-dc-data.xlsx')