Networks

Network

Constructor

class pypsa.Network(import_name: str | Path = '', name: str = 'Unnamed Network', ignore_standard_types: bool = False, override_components: DataFrame | None = None, override_component_attrs: Dict | None = None, **kwargs: Any)

Network container for all buses, one-ports and branches.

General methods

static(component_name)	Return the DataFrame of static components for component_name.
df(component_name)	Alias for [n.static][pypsa.Network.static].
dynamic(component_name)	Return the dictionary of DataFrames of varying components.
pnl(component_name)	Alias for [n.dynamic][pypsa.Network.dynamic].
to_crs(new)	Convert the network's geometries and bus coordinates to a new crs.
set_snapshots(snapshots[, ...])	Set the snapshots/time steps and reindex all time-dependent data.
set_investment_periods(periods)	Set the investment periods of the network.
add(class_name, name[, suffix, overwrite])	Add components to the network.
madd(class_name, names[, suffix])	Add multiple components to the network, along with their attributes.
remove(class_name, name[, suffix])	Remove a single component or a list of components from the network.
mremove(class_name, names)	Remove multiple components from the network.
copy([snapshots, investment_periods, ...])	Return a deep copy of Network object.
equals(other[, log_mode])	Check for equality of two networks.
branches()	Get branches.
passive_branches()	Get passive branches.
controllable_branches()	Get controllable branches.
determine_network_topology([...])	Build sub_networks from topology.
iterate_components([components, skip_empty])	Iterate over components.
consistency_check([check_dtypes, strict])	Check network for consistency.

Attributes

meta	Dictionary of the network meta data.
crs	Coordinate reference system of the network's geometries.
srid	Spatial reference system identifier of the network's geometries.
snapshots	Snapshots dimension of the network.
snapshot_weightings	Weightings applied to each snapshots during the optimization (LOPF).
investment_periods	Periods level of snapshots dimension.
investment_period_weightings	Weightings applied to each investment period during the optimization (LOPF).

Input and output methods

merge(other[, components_to_skip, inplace, ...])	Merge the components of two networks.
import_from_csv_folder(path[, encoding, ...])	Import network data from CSVs in a folder.
export_to_csv_folder(path[, encoding, ...])	Export network and components to a folder of CSVs.
import_from_excel(path[, skip_time, engine])	Import network data from an Excel file.
export_to_excel(path[, ...])	Export network and components to an Excel file.
import_from_hdf5(path[, skip_time])	Import network data from HDF5 store at path.
export_to_hdf5(path[, export_standard_types])	Export network and components to an HDF store.
import_from_netcdf(path[, skip_time])	Import network data from netCDF file or xarray Dataset at path.
export_to_netcdf([path, ...])	Export network and components to a netCDF file.
import_from_pypower_ppc(ppc[, ...])	Import network from PYPOWER PPC dictionary format version 2.
import_from_pandapower_net(net[, ...])	Import PyPSA network from pandapower net.
import_components_from_dataframe(dataframe, ...)	Import components from a pandas DataFrame.
import_series_from_dataframe(dataframe, ...)	Import time series from a pandas DataFrame.

Power flow methods

Also see Power Flow for all power flow functions.

calculate_dependent_values()	Calculate per unit impedances and append voltages to lines and shunt impedances.
lpf([snapshots, skip_pre])	Linear power flow for generic network.
pf([snapshots, skip_pre, x_tol, use_seed, ...])	Full non-linear power flow for generic network.

Contingency analysis

See Contingency Analysis.

Clustering methods

See Clustering.

Optimization methods

See Optimisation.

Statistics methods

See Statistics.

Plotting methods

See Plots.

Graph methods

graph([branch_components, weight, ...])	Build NetworkX graph.
adjacency_matrix([branch_components, ...])	Construct a sparse adjacency matrix (directed).
incidence_matrix([branch_components, busorder])	Construct a sparse incidence matrix (directed).

Descriptor methods

get_committable_i(c)	Getter function.
get_extendable_i(c)	Getter function.
get_switchable_as_dense(component, attr[, ...])	Return a Dataframe for a time-varying component attribute .
get_non_extendable_i(c)	Getter function.
get_active_assets(c[, investment_period])	Get active components mask of component type in investment period(s).

Sub-Network

Constructor

class pypsa.SubNetwork(n: Network, name: str)

SubNetwork for electric buses (AC or DC).

SubNetworks are generated by n.determine_network_topology() for electric buses with passive flows or isolated non-electric buses.

methods

adjacency_matrix([branch_components, ...])	Construct a sparse adjacency matrix (directed).
branches()	Get the branches in the sub-network.
branches_i([active_only])	Get the index of the branches in the sub-network.
buses()	Get the buses in the sub-network.
buses_i()	Get the index of the buses in the sub-network.
calculate_BODF([skip_pre])	Calculate the Branch Outage Distribution Factor (BODF) for sub_network.
calculate_B_H([skip_pre])	Calculate B and H matrices for AC or DC sub-networks.
calculate_PTDF([skip_pre])	Calculate the Power Transfer Distribution Factor (PTDF) for sub_network.
calculate_Y([skip_pre, active_branches_only])	Calculate bus admittance matrices for AC sub-networks.
find_bus_controls()	Find slack and all PV and PQ buses for a sub_network.
find_slack_bus()	Find the slack bus in a connected sub-network.
generators()	Get the generators in the sub-network.
generators_i()	Get the index of the generators in the sub-network.
graph([branch_components, weight, ...])	Build NetworkX graph.
incidence_matrix([branch_components, busorder])	Construct a sparse incidence matrix (directed).
iterate_components([components, skip_empty])	Iterate over components of the sub-network.
lines_i()	Get the index of the lines in the sub-network.
loads()	Get the loads in the sub-network.
loads_i()	Get the index of the loads in the sub-network.
lpf([snapshots, skip_pre])	Linear power flow for connected sub-network.
pf([snapshots, skip_pre, x_tol, use_seed, ...])	Non-linear power flow for connected sub-network.
shunt_impedances()	Get the shunt impedances in the sub-network.
shunt_impedances_i()	Get the index of the shunt impedances in the sub-network.
storage_units()	Get the storage units in the sub-network.
storage_units_i()	Get the index of the storage units in the sub-network.
stores_i()	Get the index of the stores in the sub-network.
transformers_i()	Get the index of the transformers in the sub-network.