API Reference

Network

class pypsa.Network(import_name=None, name='', ignore_standard_types=False, override_components=None, override_component_attrs=None, **kwargs)

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

Parameters
  • import_name (string) – Name of netCDF file, HDF5 .h5 store or folder from which to import CSVs of network data.

  • name (string, default "") – Network name.

  • ignore_standard_types (boolean, default False) – If True, do not read in PyPSA standard types into standard types DataFrames.

  • csv_folder_name (string) – Name of folder from which to import CSVs of network data. Overrides import_name.

  • override_components (pandas.DataFrame) – If you want to override the standard PyPSA components in pypsa.components.components, pass it a DataFrame with index of component name and columns of list_name and description, following the format of pypsa.components.components. See git repository examples/new_components/.

  • override_component_attrs (pypsa.descriptors.Dict of pandas.DataFrame) – If you want to override pypsa.component_attrs, follow its format. See git repository examples/new_components/.

  • kwargs – Any remaining attributes to set

Returns

Return type

None

Examples

>>> nw1 = pypsa.Network("my_store.h5")
>>> nw2 = pypsa.Network("/my/folder")
add(class_name, name, **kwargs)

Add a single component to the network.

Adds it to component DataFrame.

Any attributes which are not specified will be given the default value from Components.

This method is slow for many components; instead use madd or import_components_from_dataframe (see below).

Parameters
  • class_name (string) – Component class name in [“Bus”,”Generator”,”Load”,”StorageUnit”,”Store”,”ShuntImpedance”,”Line”,”Transformer”,”Link”]

  • name (string) – Component name

  • kwargs – Component attributes, e.g. x=0.1, length=123

Examples

>>> network.add("Bus","my_bus_0")
>>> network.add("Bus","my_bus_1",v_nom=380)
>>> network.add("Line","my_line_name",bus0="my_bus_0",bus1="my_bus_1",length=34,r=2,x=4)
adjacency_matrix(branch_components=None, busorder=None, weights=None)

Construct a sparse adjacency matrix (directed)

Parameters
  • branch_components (iterable sublist of branch_components) – Buses connected by any of the selected branches are adjacent (default: branch_components (network) or passive_branch_components (sub_network))

  • busorder (pd.Index subset of network.buses.index) – Basis to use for the matrix representation of the adjacency matrix (default: buses.index (network) or buses_i() (sub_network))

  • weights (pd.Series or None (default)) – If given must provide a weight for each branch, multi-indexed on branch_component name and branch name.

Returns

adjacency_matrix – Directed adjacency matrix

Return type

sp.sparse.coo_matrix

calculate_dependent_values()

Calculate per unit impedances and append voltages to lines and shunt impedances.

consistency_check()

Checks the network for consistency; e.g. that all components are connected to existing buses and that no impedances are singular.

Prints warnings if anything is potentially inconsistent.

Examples

>>> network.consistency_check()
copy(with_time=True, ignore_standard_types=False)

Returns a deep copy of the Network object with all components and time-dependent data.

Returns

network

Return type

pypsa.Network

Parameters
  • with_time (boolean, default True) – Copy snapshots and time-varying network.component_names_t data too.

  • ignore_standard_types (boolean, default False) – Ignore the PyPSA standard types.

Examples

>>> network_copy = network.copy()
determine_network_topology()

Build sub_networks from topology.

df(component_name)

Return the DataFrame of static components for component_name, i.e. network.component_names

Parameters

component_name (string) –

Returns

Return type

pandas.DataFrame

export_to_csv_folder(csv_folder_name, encoding=None, export_standard_types=False)

Export network and components to a folder of CSVs.

Both static and series attributes of all components are exported, but only if they have non-default values.

If csv_folder_name does not already exist, it is created.

Static attributes are exported in one CSV file per component, e.g. generators.csv.

Series attributes are exported in one CSV file per component per attribute, e.g. generators-p_set.csv.

Parameters
  • csv_folder_name (string) – Name of folder to which to export.

  • encoding (str, default None) – Encoding to use for UTF when reading (ex. ‘utf-8’). List of Python standard encodings

  • export_standard_types (boolean, default False) – If True, then standard types are exported too (upon reimporting you should then set “ignore_standard_types” when initialising the network).

Examples

>>> network.export_to_csv(csv_folder_name)
export_to_hdf5(path, export_standard_types=False, **kwargs)

Export network and components to an HDF store.

Both static and series attributes of components are exported, but only if they have non-default values.

If path does not already exist, it is created.

Parameters
  • path (string) – Name of hdf5 file to which to export (if it exists, it is overwritten)

  • export_standard_types (boolean, default False) – If True, then standard types are exported too (upon reimporting you should then set “ignore_standard_types” when initialising the network).

  • **kwargs – Extra arguments for pd.HDFStore to specify f.i. compression (default: complevel=4)

Examples

>>> network.export_to_hdf5(filename)
export_to_netcdf(path=None, export_standard_types=False, least_significant_digit=None)

Export network and components to a netCDF file.

Both static and series attributes of components are exported, but only if they have non-default values.

If path does not already exist, it is created.

If no path is passed, no file is exported, but the xarray.Dataset is still returned.

Be aware that this cannot export boolean attributes on the Network class, e.g. network.my_bool = False is not supported by netCDF.

Parameters
  • path (string|None) – Name of netCDF file to which to export (if it exists, it is overwritten); if None is passed, no file is exported.

  • export_standard_types (boolean, default False) – If True, then standard types are exported too (upon reimporting you should then set “ignore_standard_types” when initialising the network).

  • least_significant_digit – This is passed to the netCDF exporter, but currently makes no difference to file size or float accuracy. We’re working on improving this…

Returns

ds

Return type

xarray.Dataset

Examples

>>> network.export_to_netcdf("my_file.nc")
graph(branch_components=None, weight=None, inf_weight=False)

Build NetworkX graph.

Parameters
  • network (Network|SubNetwork) –

  • branch_components ([str]) – Components to use as branches. The default are passive_branch_components in the case of a SubNetwork and branch_components in the case of a Network.

  • weight (str) – Branch attribute to use as weight

  • inf_weight (bool|float) – How to treat infinite weights (default: False). True keeps the infinite weight. False skips edges with infinite weight. If a float is given it is used instead.

Returns

graph – NetworkX graph

Return type

OrderedGraph

import_components_from_dataframe(dataframe, cls_name)

Import components from a pandas DataFrame.

If columns are missing then defaults are used.

If extra columns are added, these are left in the resulting component dataframe.

Parameters
  • dataframe (pandas.DataFrame) – A DataFrame whose index is the names of the components and whose columns are the non-default attributes.

  • cls_name (string) – Name of class of component, e.g. "Line","Bus","Generator", "StorageUnit"

Examples

>>> import pandas as pd
>>> buses = ['Berlin', 'Frankfurt', 'Munich', 'Hamburg']
>>> network.import_components_from_dataframe(
        pd.DataFrame({"v_nom" : 380, "control" : 'PV'},
                    index=buses),
                    "Bus")
>>> network.import_components_from_dataframe(
        pd.DataFrame({"carrier" : "solar", "bus" : buses, "p_nom_extendable" : True},
                    index=[b+" PV" for b in buses]),
                    "Generator")
import_from_csv_folder(csv_folder_name, encoding=None, skip_time=False)

Import network data from CSVs in a folder.

The CSVs must follow the standard form, see pypsa/examples.

Parameters
  • csv_folder_name (string) – Name of folder

  • encoding (str, default None) –

    Encoding to use for UTF when reading (ex. ‘utf-8’). List of Python standard encodings

  • skip_time (bool, default False) – Skip reading in time dependent attributes

Examples

>>> network.import_from_csv(csv_folder_name)
import_from_hdf5(path, skip_time=False)

Import network data from HDF5 store at path.

Parameters
  • path (string) – Name of HDF5 store

  • skip_time (bool, default False) – Skip reading in time dependent attributes

import_from_netcdf(path, skip_time=False)

Import network data from netCDF file or xarray Dataset at path.

Parameters
  • path (string|xr.Dataset) – Path to netCDF dataset or instance of xarray Dataset

  • skip_time (bool, default False) – Skip reading in time dependent attributes

import_from_pandapower_net(net, extra_line_data=False)

Import network from pandapower net.

Importing from pandapower is still in beta; not all pandapower data is supported.

Unsupported features include: - three-winding transformers - switches - in_service status, - shunt impedances, and - tap positions of transformers.”

Parameters
  • net (pandapower network) –

  • extra_line_data (boolean, default: False) – if True, the line data for all parameters is imported instead of only the type

Examples

>>> network.import_from_pandapower_net(net)
OR
>>> import pandapower as pp
>>> import pandapower.networks as pn
>>> net = pn.create_cigre_network_mv(with_der='all')
>>> pp.runpp(net)
>>> network.import_from_pandapower_net(net, extra_line_data=True)
import_from_pypower_ppc(ppc, overwrite_zero_s_nom=None)

Import network from PYPOWER PPC dictionary format version 2.

Converts all baseMVA to base power of 1 MVA.

For the meaning of the pypower indices, see also pypower/idx_*.

Parameters
  • ppc (PYPOWER PPC dict) –

  • overwrite_zero_s_nom (Float or None, default None) –

Examples

>>> from pypower.api import case30
>>> ppc = case30()
>>> network.import_from_pypower_ppc(ppc)
import_series_from_dataframe(dataframe, cls_name, attr)

Import time series from a pandas DataFrame.

Parameters
  • dataframe (pandas.DataFrame) – A DataFrame whose index is network.snapshots and whose columns are a subset of the relevant components.

  • cls_name (string) – Name of class of component

  • attr (string) – Name of time-varying series attribute

Examples

>>> import numpy as np
>>> network.set_snapshots(range(10))
>>> network.import_series_from_dataframe(
        pd.DataFrame(np.random.rand(10,4),
            columns=network.generators.index,
                        index=range(10)),
                    "Generator",
                    "p_max_pu")
incidence_matrix(branch_components=None, busorder=None)

Construct a sparse incidence matrix (directed)

Parameters
  • branch_components (iterable sublist of branch_components) – Buses connected by any of the selected branches are adjacent (default: branch_components (network) or passive_branch_components (sub_network))

  • busorder (pd.Index subset of network.buses.index) – Basis to use for the matrix representation of the adjacency matrix (default: buses.index (network) or buses_i() (sub_network))

Returns

incidence_matrix – Directed incidence matrix

Return type

sp.sparse.csr_matrix

iplot(fig=None, bus_colors='blue', bus_colorscale=None, bus_colorbar=None, bus_sizes=10, bus_text=None, line_colors='green', line_widths=2, line_text=None, title='', branch_components=['Line', 'Link'], iplot=True, jitter=None)

Plot the network buses and lines interactively using plotly.

Parameters
  • fig (dict, default None) – If not None, figure is built upon this fig.

  • bus_colors (dict/pandas.Series) – Colors for the buses, defaults to “b”

  • bus_colorscale (string) – Name of colorscale if bus_colors are floats, e.g. ‘Jet’, ‘Viridis’

  • bus_colorbar (dict) – Plotly colorbar, e.g. {‘title’ : ‘my colorbar’}

  • bus_sizes (dict/pandas.Series) – Sizes of bus points, defaults to 10

  • bus_text (dict/pandas.Series) – Text for each bus, defaults to bus names

  • line_colors (dict/pandas.Series) – Colors for the lines, defaults to “g” for Lines and “cyan” for Links. Colors for branches other than Lines can be specified using a pandas Series with a MultiIndex.

  • line_widths (dict/pandas.Series) – Widths of lines, defaults to 2. Widths for branches other than Lines can be specified using a pandas Series with a MultiIndex.

  • line_text (dict/pandas.Series) – Text for lines, defaults to line names. Text for branches other than Lines can be specified using a pandas Series with a MultiIndex.

  • title (string) – Graph title

  • branch_components (list of str) – Branch components to be plotted, defaults to Line and Link.

  • iplot (bool, default True) – Automatically do an interactive plot of the figure.

  • jitter (None|float) – Amount of random noise to add to bus positions to distinguish overlapping buses

Returns

fig

Return type

dictionary for plotly figure

lopf(snapshots=None, solver_name='glpk', solver_io=None, skip_pre=False, extra_functionality=None, solver_logfile=None, solver_options={}, keep_files=False, formulation='angles', ptdf_tolerance=0.0, free_memory={}, extra_postprocessing=None)

Linear optimal power flow for a group of snapshots.

Parameters
  • snapshots (list or index slice) – A list of snapshots to optimise, must be a subset of network.snapshots, defaults to network.snapshots

  • solver_name (string) – Must be a solver name that pyomo recognises and that is installed, e.g. “glpk”, “gurobi”

  • solver_io (string, default None) – Solver Input-Output option, e.g. “python” to use “gurobipy” for solver_name=”gurobi”

  • skip_pre (bool, default False) – Skip the preliminary steps of computing topology, calculating dependent values and finding bus controls.

  • extra_functionality (callable function) – This function must take two arguments extra_functionality(network,snapshots) and is called after the model building is complete, but before it is sent to the solver. It allows the user to add/change constraints and add/change the objective function.

  • solver_logfile (None|string) – If not None, sets the logfile option of the solver.

  • solver_options (dictionary) – A dictionary with additional options that get passed to the solver. (e.g. {‘threads’:2} tells gurobi to use only 2 cpus)

  • keep_files (bool, default False) – Keep the files that pyomo constructs from OPF problem construction, e.g. .lp file - useful for debugging

  • formulation (string) – Formulation of the linear power flow equations to use; must be one of [“angles”,”cycles”,”kirchhoff”,”ptdf”]

  • ptdf_tolerance (float) – Value below which PTDF entries are ignored

  • free_memory (set, default {'pyomo'}) – Any subset of {‘pypsa’, ‘pyomo’}. Allows to stash pypsa time-series data away while the solver runs (as a pickle to disk) and/or free pyomo data after the solution has been extracted.

  • extra_postprocessing (callable function) – This function must take three arguments extra_postprocessing(network,snapshots,duals) and is called after the model has solved and the results are extracted. It allows the user to extract further information about the solution, such as additional shadow prices.

Returns

Return type

None

lpf(snapshots=None, skip_pre=False)

Linear power flow for generic network.

Parameters
  • snapshots (list-like|single snapshot) – A subset or an elements of network.snapshots on which to run the power flow, defaults to network.snapshots

  • skip_pre (bool, default False) – Skip the preliminary steps of computing topology, calculating dependent values and finding bus controls.

Returns

Return type

None

lpf_contingency(snapshots=None, branch_outages=None)

Computes linear power flow for a selection of branch outages.

Parameters
  • snapshots (list-like|single snapshot) – A subset or an elements of network.snapshots on which to run the power flow, defaults to network.snapshots NB: currently this only works for a single snapshot

  • branch_outages (list-like) – A list of passive branches which are to be tested for outages. If None, it’s take as all network.passive_branches_i()

Returns

p0 – num_passive_branch x num_branch_outages DataFrame of new power flows

Return type

pandas.DataFrame

Examples

>>> network.lpf_contingency(snapshot, branch_outages)
madd(class_name, names, suffix='', **kwargs)

Add multiple components to the network, along with their attributes.

Make sure when adding static attributes as pandas Series that they are indexed by names. Make sure when adding time-varying attributes as pandas DataFrames that their index is a superset of network.snapshots and their columns are a subset of names.

Any attributes which are not specified will be given the default value from Components.

Parameters
  • class_name (string) – Component class name in [“Bus”,”Generator”,”Load”,”StorageUnit”,”Store”,”ShuntImpedance”,”Line”,”Transformer”,”Link”]

  • names (list-like or pandas.Index) – Component names

  • suffix (string, default '') – All components are named after names with this added suffix. It is assumed that all Series and DataFrames are indexed by the original names.

  • kwargs – Component attributes, e.g. x=[0.1,0.2], can be list, pandas.Series of pandas.DataFrame for time-varying

Returns

new_names – Names of new components (including suffix)

Return type

pandas.index

Examples

Short Example:

>>> network.madd("Load", ["load 1", "load 2"],
...        bus=["1","2"],
...        p_set=np.random.rand(len(network.snapshots),2))

Long Example:

>>> import pandas as pd, numpy as np
>>> buses = range(13)
>>> snapshots = range(7)
>>> n = pypsa.Network()
>>> n.set_snapshots(snapshots)
>>> n.madd("Bus", buses)
>>> # add load as numpy array
>>> n.madd("Load",
...        n.buses.index + " load",
...        bus=buses,
...        p_set=np.random.rand(len(snapshots),len(buses)))
>>> # add wind availability as pandas DataFrame
>>> wind = pd.DataFrame(np.random.rand(len(snapshots),len(buses)),
...        index=n.snapshots,
...        columns=buses)
>>> #use a suffix to avoid boilerplate to rename everything
>>> n.madd("Generator",
...        buses,
...        suffix=' wind',
...        bus=buses,
...        p_nom_extendable=True,
...        capital_cost=1e5,
...        p_max_pu=wind)
mremove(class_name, names)

Removes multiple components from the network.

Removes them from component DataFrames.

Parameters
  • class_name (string) – Component class name

  • name (list-like) – Component names

Examples

>>> network.mremove("Line", ["line x", "line y"])
opf(snapshots=None)

Optimal power flow for snapshots.

pf(snapshots=None, skip_pre=False, x_tol=1e-06, use_seed=False)

Full non-linear power flow for generic network.

Parameters
  • snapshots (list-like|single snapshot) – A subset or an elements of network.snapshots on which to run the power flow, defaults to network.snapshots

  • skip_pre (bool, default False) – Skip the preliminary steps of computing topology, calculating dependent values and finding bus controls.

  • x_tol (float) – Tolerance for Newton-Raphson power flow.

  • use_seed (bool, default False) – Use a seed for the initial guess for the Newton-Raphson algorithm.

Returns

Dictionary with keys ‘n_iter’, ‘converged’, ‘error’ and dataframe values indicating number of iterations, convergence status, and iteration error for each snapshot (rows) and sub_network (columns)

Return type

dict

plot(margin=0.05, ax=None, geomap=True, projection=None, bus_colors='b', line_colors={'Line': 'g', 'Link': 'cyan'}, bus_sizes=10, line_widths={'Line': 2, 'Link': 2}, flow=None, title='', line_cmap=None, bus_cmap=None, boundaries=None, geometry=False, branch_components=['Line', 'Link'], jitter=None, basemap=None, basemap_parameters=None, color_geomap=None)

Plot the network buses and lines using matplotlib and Basemap.

Parameters
  • margin (float) – Margin at the sides as proportion of distance between max/min x,y

  • ax (matplotlib ax, defaults to plt.gca()) – Axis to which to plot the network

  • geomap (bool/str, default True) – Switch to use Basemap or Cartopy (depends on what is installed). If string is passed, it will be used as a resolution argument. For Basemap users ‘c’ (crude), ‘l’ (low), ‘i’ (intermediate), ‘h’ (high), ‘f’ (full) are valid resolutions options. For Cartopy users ‘10m’, ‘50m’, ‘110m’ are valid resolutions options.

  • projection (cartopy.crs.Projection, defaults to None) – Define the projection of your geomap, only valid if cartopy is installed. If None (default) is passed the projection for cartopy is set to cartopy.crs.PlateCarree

  • bus_colors (dict/pandas.Series) – Colors for the buses, defaults to “b”

  • bus_sizes (dict/pandas.Series) – Sizes of bus points, defaults to 10

  • line_colors (dict/pandas.Series) – Colors for the lines, defaults to “g” for Lines and “cyan” for Links. Colors for branches other than Lines can be specified using a pandas Series with a MultiIndex.

  • line_widths (dict/pandas.Series) – Widths of lines, defaults to 2. Widths for branches other than Lines can be specified using a pandas Series with a MultiIndex.

  • flow (snapshot/pandas.Series/function/string) – Flow to be displayed in the plot, defaults to None. If an element of network.snapshots is given, the flow at this timestamp will be displayed. If an aggregation function is given, is will be applied to the total network flow via pandas.DataFrame.agg (accepts also function names). Otherwise flows can be specified by passing a pandas Series with MultiIndex including all necessary branch components. Use the line_widths argument to additionally adjust the size of the flow arrows.

  • title (string) – Graph title

  • line_cmap (plt.cm.ColorMap/str|dict) – If line_colors are floats, this color map will assign the colors. Use a dict to specify colormaps for more than one branch type.

  • bus_cmap (plt.cm.ColorMap/str) – If bus_colors are floats, this color map will assign the colors

  • boundaries (list of four floats) – Boundaries of the plot in format [x1,x2,y1,y2]

  • branch_components (list of str) – Branch components to be plotted, defaults to Line and Link.

  • jitter (None|float) – Amount of random noise to add to bus positions to distinguish overlapping buses

  • basemap_parameters (dict) – Specify a dict with additional constructor parameters for the Basemap. Will disable Cartopy. Use this feature to set a custom projection. (e.g. {‘projection’: ‘tmerc’, ‘lon_0’:10.0, ‘lat_0’:50.0})

  • color_geomap (dict or bool) – Specify colors to paint land and sea areas in. If True, it defaults to {‘ocean’: ‘lightblue’, ‘land’: ‘whitesmoke’}. If no dictionary is provided, colors are white.

Returns

bus_collection, branch_collection1, … – Collections for buses and branches.

Return type

tuple of Collections

pnl(component_name)

Return the dictionary of DataFrames of varying components for component_name, i.e. network.component_names_t

Parameters

component_name (string) –

Returns

Return type

dict of pandas.DataFrame

remove(class_name, name)

Removes a single component from the network.

Removes it from component DataFrames.

Parameters
  • class_name (string) – Component class name

  • name (string) – Component name

Examples

>>> network.remove("Line","my_line 12345")
sclopf(snapshots=None, branch_outages=None, solver_name='glpk', skip_pre=False, extra_functionality=None, solver_options={}, keep_files=False, formulation='angles', ptdf_tolerance=0.0)

Computes Security-Constrained Linear Optimal Power Flow (SCLOPF).

This ensures that no branch is overloaded even given the branch outages.

Parameters
  • snapshots (list or index slice) – A list of snapshots to optimise, must be a subset of network.snapshots, defaults to network.snapshots

  • branch_outages (list-like) – A list of passive branches which are to be tested for outages. If None, it’s take as all network.passive_branches_i()

  • solver_name (string) – Must be a solver name that pyomo recognises and that is installed, e.g. “glpk”, “gurobi”

  • skip_pre (bool, default False) – Skip the preliminary steps of computing topology, calculating dependent values and finding bus controls.

  • extra_functionality (callable function) – This function must take two arguments extra_functionality(network,snapshots) and is called after the model building is complete, but before it is sent to the solver. It allows the user to add/change constraints and add/change the objective function.

  • solver_options (dictionary) – A dictionary with additional options that get passed to the solver. (e.g. {‘threads’:2} tells gurobi to use only 2 cpus)

  • keep_files (bool, default False) – Keep the files that pyomo constructs from OPF problem construction, e.g. .lp file - useful for debugging

  • formulation (string) – Formulation of the linear power flow equations to use; must be one of [“angles”,”cycles”,”kirchoff”,”ptdf”]

  • ptdf_tolerance (float) –

Returns

Return type

None

Examples

>>> network.sclopf(network, branch_outages)
set_snapshots(snapshots)

Set the snapshots and reindex all time-dependent data.

This will reindex all pandas.Panels of time-dependent data; NaNs are filled with the default value for that quantity.

Parameters

snapshots (list or pandas.Index) – All time steps.

Returns

Return type

None

Sub-Network

class pypsa.SubNetwork(network, name='')

Connected network of electric buses (AC or DC) with passive flows or isolated non-electric buses.

Generated by network.determine_network_topology().

adjacency_matrix(branch_components=None, busorder=None, weights=None)

Construct a sparse adjacency matrix (directed)

Parameters
  • branch_components (iterable sublist of branch_components) – Buses connected by any of the selected branches are adjacent (default: branch_components (network) or passive_branch_components (sub_network))

  • busorder (pd.Index subset of network.buses.index) – Basis to use for the matrix representation of the adjacency matrix (default: buses.index (network) or buses_i() (sub_network))

  • weights (pd.Series or None (default)) – If given must provide a weight for each branch, multi-indexed on branch_component name and branch name.

Returns

adjacency_matrix – Directed adjacency matrix

Return type

sp.sparse.coo_matrix

calculate_BODF(skip_pre=False)

Calculate the Branch Outage Distribution Factor (BODF) for sub_network.

Sets sub_network.BODF as a (dense) numpy array.

The BODF is a num_branch x num_branch 2d array.

For the outage of branch l, the new flow on branch k is given in terms of the flow before the outage

f_k^after = f_k^before + BODF_{kl} f_l^before

Note that BODF_{ll} = -1.

Parameters
  • sub_network (pypsa.SubNetwork) –

  • skip_pre (bool, default False) – Skip the preliminary step of computing the PTDF.

Examples

>>> sub_network.caculate_BODF()
calculate_B_H(skip_pre=False)

Calculate B and H matrices for AC or DC sub-networks.

calculate_PTDF(skip_pre=False)

Calculate the Power Transfer Distribution Factor (PTDF) for sub_network.

Sets sub_network.PTDF as a (dense) numpy array.

Parameters
  • sub_network (pypsa.SubNetwork) –

  • skip_pre (bool, default False) – Skip the preliminary steps of computing topology, calculating dependent values, finding bus controls and computing B and H.

calculate_Y(skip_pre=False)

Calculate bus admittance matrices for AC sub-networks.

find_bus_controls()

Find slack and all PV and PQ buses for a sub_network. This function also fixes sub_network.buses_o, a DataFrame ordered by control type.

find_slack_bus()

Find the slack bus in a connected sub-network.

graph(branch_components=None, weight=None, inf_weight=False)

Build NetworkX graph.

Parameters
  • network (Network|SubNetwork) –

  • branch_components ([str]) – Components to use as branches. The default are passive_branch_components in the case of a SubNetwork and branch_components in the case of a Network.

  • weight (str) – Branch attribute to use as weight

  • inf_weight (bool|float) – How to treat infinite weights (default: False). True keeps the infinite weight. False skips edges with infinite weight. If a float is given it is used instead.

Returns

graph – NetworkX graph

Return type

OrderedGraph

incidence_matrix(branch_components=None, busorder=None)

Construct a sparse incidence matrix (directed)

Parameters
  • branch_components (iterable sublist of branch_components) – Buses connected by any of the selected branches are adjacent (default: branch_components (network) or passive_branch_components (sub_network))

  • busorder (pd.Index subset of network.buses.index) – Basis to use for the matrix representation of the adjacency matrix (default: buses.index (network) or buses_i() (sub_network))

Returns

incidence_matrix – Directed incidence matrix

Return type

sp.sparse.csr_matrix

lpf(snapshots=None, skip_pre=False)

Linear power flow for connected sub-network.

Parameters
  • snapshots (list-like|single snapshot) – A subset or an elements of network.snapshots on which to run the power flow, defaults to network.snapshots

  • skip_pre (bool, default False) – Skip the preliminary steps of computing topology, calculating dependent values and finding bus controls.

Returns

Return type

None

pf(snapshots=None, skip_pre=False, x_tol=1e-06, use_seed=False)

Non-linear power flow for connected sub-network.

Parameters
  • snapshots (list-like|single snapshot) – A subset or an elements of network.snapshots on which to run the power flow, defaults to network.snapshots

  • skip_pre (bool, default False) – Skip the preliminary steps of computing topology, calculating dependent values and finding bus controls.

  • x_tol (float) – Tolerance for Newton-Raphson power flow.

  • use_seed (bool, default False) – Use a seed for the initial guess for the Newton-Raphson algorithm.

Returns

  • Tuple of three pandas.Series indicating number of iterations,

  • remaining error, and convergence status for each snapshot