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, Path) – Path to netCDF file, HDF5 .h5 store or folder of CSV files from which to import network data. The string could be a URL.
name (string, default "") – Network name.
ignore_standard_types (boolean, default False) – If True, do not read in PyPSA standard types into standard types DataFrames.
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
- Return type
None
Examples
>>> nw1 = pypsa.Network("my_store.h5") >>> nw2 = pypsa.Network("/my/folder") >>> nw3 = pypsa.Network("https://github.com/PyPSA/PyPSA/raw/master/examples/scigrid-de/scigrid-with-load-gen-trafos.nc")
- 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
orimport_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, investment_period=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(check_dtypes=False)#
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, snapshots=None, investment_periods=None, ignore_standard_types=False)#
Returns a deep copy of the Network object with all components and time- dependent data.
- Returns
network
- Return type
- Parameters
with_time (boolean, default True) – Copy snapshots and time-varying network.component_names_t data too.
snapshots (list or index slice) – A list of snapshots to copy, must be a subset of network.snapshots, defaults to network.snapshots
ignore_standard_types (boolean, default False) – Ignore the PyPSA standard types.
Examples
>>> network_copy = network.copy()
- determine_network_topology(investment_period=None, skip_isolated_buses=False)#
Build sub_networks from topology.
For the default case investment_period=None, it is not taken into account whether the branch components are active (based on build_year and lifetime). If the investment_period is specified, the network topology is determined on the basis of the active branches.
- df(component_name)#
Return the DataFrame of static components for component_name, i.e. network.component_names.
- Parameters
component_name (string) –
- 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_folder(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, compression={'complevel': 4, 'zlib': True}, float32=False)#
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).
compression (dict|None) – Compression level to use for all features which are being prepared. The compression is handled via xarray.Dataset.to_netcdf(…). For details see: https://docs.xarray.dev/en/stable/generated/xarray.Dataset.to_netcdf.html To disable compression, set to None. As a trade-off between speed and compression, the default is {‘zlib’: True, ‘complevel’: 4}.
float32 (boolean, default False) – If True, typecasts values to float32.
- Returns
ds
- Return type
xarray.Dataset
Examples
>>> network.export_to_netcdf("my_file.nc")
- get_active_assets(c, investment_period)#
Getter function.
Get True values for elements of component c which are active at a given investment period. These are calculated from lifetime and the build year.
- get_committable_i(c)#
Getter function.
Get the index of commitable elements of a given component.
- get_extendable_i(c)#
Getter function.
Get the index of extendable elements of a given component.
- get_non_extendable_i(c)#
Getter function.
Get the index of non-extendable elements of a given component.
- get_switchable_as_dense(component, attr, snapshots=None, inds=None)#
Return a Dataframe for a time-varying component attribute with values for all non-time-varying components filled in with the default values for the attribute.
- Parameters
network (pypsa.Network) –
component (string) – Component object name, e.g. ‘Generator’ or ‘Link’
attr (string) – Attribute name
snapshots (pandas.Index) – Restrict to these snapshots rather than network.snapshots.
inds (pandas.Index) – Restrict to these components rather than network.components.index
- Return type
pandas.DataFrame
Examples
>>> get_switchable_as_dense(network, 'Generator', 'p_max_pu')
- 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
- 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")
See also
- 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_folder(csv_folder_name)
- import_from_hdf5(path, skip_time=False)#
Import network data from HDF5 store at path.
- Parameters
path (string, Path) – Name of HDF5 store. The string could be a URL.
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. The string could be a URL.
skip_time (bool, default False) – Skip reading in time dependent attributes
- import_from_pandapower_net(net, extra_line_data=False, use_pandapower_index=False)#
Import PyPSA network from pandapower net.
Importing from pandapower is still in beta; not all pandapower components are supported.
Unsupported features include: - three-winding transformers - switches - in_service status 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
use_pandapower_index (boolean, default: False) – if True, use integer numbers which is the pandapower index standard if False, use any net.name as index (e.g. ‘Bus 1’ (str) or 1 (int))
Examples
>>> network.import_from_pandapower_net(net) OR >>> import pypsa >>> import pandapower as pp >>> import pandapower.networks as pn >>> net = pn.create_cigre_network_mv(with_der='all') >>> network = pypsa.Network() >>> 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")
See also
- 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
- property investment_period_weightings#
Weightings applied to each investment period during the optimization (LOPF).
Objective weightings are multiplied with all cost coefficients in the objective function of the respective investment period (e.g. to include a social discount rate).
Years weightings denote the elapsed time until the subsequent investment period (e.g. used for global constraints CO2 emissions).
- property investment_periods#
Investment steps during the optimization.
- iplot(fig=None, bus_colors='cadetblue', bus_alpha=1, bus_sizes=10, bus_cmap=None, bus_colorbar=None, bus_text=None, line_colors='rosybrown', link_colors='darkseagreen', transformer_colors='orange', line_widths=3, link_widths=3, transformer_widths=3, line_text=None, link_text=None, transformer_text=None, layouter=None, title='', size=None, branch_components=None, iplot=True, jitter=None, mapbox=False, mapbox_style='open-street-map', mapbox_token='', mapbox_parameters={})#
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 “cadetblue”. If bus_sizes is a pandas.Series with a Multiindex, bus_colors defaults to the n.carriers[‘color’] column.
bus_alpha (float) – Adds alpha channel to buses, defaults to 1.
bus_sizes (float/pandas.Series) – Sizes of bus points, defaults to 10.
bus_cmap (plt.cm.ColorMap/str) – If bus_colors are floats, this color map will assign the colors
bus_colorbar (dict) – Plotly colorbar, e.g. {‘title’ : ‘my colorbar’}
bus_text (pandas.Series) – Text for each bus, defaults to bus names
line_colors (str/pandas.Series) – Colors for the lines, defaults to ‘rosybrown’.
link_colors (str/pandas.Series) – Colors for the links, defaults to ‘darkseagreen’.
transfomer_colors (str/pandas.Series) – Colors for the transfomer, defaults to ‘orange’.
line_widths (dict/pandas.Series) – Widths of lines, defaults to 1.5
link_widths (dict/pandas.Series) – Widths of links, defaults to 1.5
transformer_widths (dict/pandas.Series) – Widths of transformer, defaults to 1.5
line_text (pandas.Series) – Text for lines, defaults to line names.
link_text (pandas.Series) – Text for links, defaults to link names.
tranformer_text (pandas.Series) – Text for transformers, defaults to transformer names.
layouter (networkx.drawing.layout function, default None) – Layouting function from networkx which overrules coordinates given in
n.buses[['x', 'y']]
. See list of available options.title (string) – Graph title
size (None|tuple) – Tuple specifying width and height of figure; e.g. (width, heigh).
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
mapbox (bool, default False) – Switch to use Mapbox.
mapbox_style (str, default 'open-street-map') –
Define the mapbox layout style of the interactive plot. If this is set to a mapbox layout, the argument
mapbox_token
must be a valid Mapbox API access token.- Valid open layouts are:
open-street-map, white-bg, carto-positron, carto-darkmatter, stamen-terrain, stamen-toner, stamen-watercolor
- Valid mapbox layouts are:
basic, streets, outdoors, light, dark, satellite, satellite-streets
mapbox_token (string) – Mapbox API access token. Obtain from https://www.mapbox.com. Can also be included in mapbox_parameters as accesstoken=mapbox_token.
mapbox_parameters (dict) – Configuration parameters of the Mapbox layout. E.g. {“bearing”: 5, “pitch”: 10, “zoom”: 1, “style”: ‘dark’}.
- Returns
fig
- Return type
dictionary for plotly figure
- lopf(snapshots=None, pyomo=False, solver_name='glpk', solver_options={}, solver_logfile=None, formulation='kirchhoff', transmission_losses=0, keep_files=False, extra_functionality=None, multi_investment_periods=False, **kwargs)#
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
pyomo (bool, default False) – Whether to use pyomo for building and solving the model, setting this to False saves a lot of memory and time.
solver_name (string) – Must be a solver name that pyomo recognises and that is installed, e.g. “glpk”, “gurobi”
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)
solver_logfile (None|string) – If not None, sets the logfile option of the solver.
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”]
transmission_losses (int) – Whether an approximation of transmission losses should be included in the linearised power flow formulation. A passed number will denote the number of tangents used for the piecewise linear approximation. Defaults to 0, which ignores losses.
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.
multi_investment_periods (bool, default False) – Whether to optimise as a single investment period or to optimise in multiple investment periods. Then, snapshots should be a
pd.MultiIndex
.ptdf_tolerance (float) – Only taking effect when pyomo is True. Value below which PTDF entries are ignored
free_memory (set, default {'pyomo'}) – Only taking effect when pyomo is True. 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.
solver_io (string, default None) – Only taking effect when pyomo is True. Solver Input-Output option, e.g. “python” to use “gurobipy” for solver_name=”gurobi”
skip_pre (bool, default False) – Only taking effect when pyomo is True. Skip the preliminary steps of computing topology, calculating dependent values and finding bus controls.
extra_postprocessing (callable function) – Only taking effect when pyomo is True. 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.
skip_objective (bool, default False) – Only taking effect when pyomo is False. Skip writing the default objective function. If False, a custom objective has to be defined via extra_functionality.
warmstart (bool or string, default False) – Only taking effect when pyomo is False. Use this to warmstart the optimization. Pass a string which gives the path to the basis file. If set to True, a path to a basis file must be given in network.basis_fn.
store_basis (bool, default True) – Only taking effect when pyomo is False. Whether to store the basis of the optimization results. If True, the path to the basis file is saved in network.basis_fn. Note that a basis can only be stored if simplex, dual-simplex, or barrier with crossover is used for solving.
keep_references (bool, default False) – Only taking effect when pyomo is False. Keep the references of variable and constraint names withing the network. These can be looked up in n.vars and n.cons after solving.
keep_shadowprices (bool or list of component names) – Only taking effect when pyomo is False. Keep shadow prices for all constraints, if set to True. If a list is passed the shadow prices will only be parsed for those constraint names. Defaults to [‘Bus’, ‘Line’, ‘GlobalConstraint’]. After solving, the shadow prices can be retrieved using
pypsa.linopt.get_dual()
with corresponding namesolver_dir (str, default None) – Only taking effect when pyomo is False. Path to directory where necessary files are written, default None leads to the default temporary directory used by tempfile.mkstemp().
- Returns
status (str) – Status of optimization. Either “ok” if solution is optimal, or “warning” if not.
termination_condition (str) – More information on how the solver terminated. One of “optimal”, “suboptimal” (in which case a solution is still provided), “infeasible”, “infeasible or unbounded”, or “other”.
Deprecated since version 0.24: This will be removed in 1.0. Use linopy-based function
n.optimize()
instead. Migrate extra functionalities: https://pypsa.readthedocs.io/en/latest/examples/optimization-with-linopy-migrate-extra-functionalities.html.
- 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.
- 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)
- property meta#
Dictionary of the network meta data.
- 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, distribute_slack=False, slack_weights='p_set')#
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.
distribute_slack (bool, default False) – If
True
, distribute the slack power across generators proportional to generator dispatch by default or according to the distribution scheme provided inslack_weights
. IfFalse
only the slack generator takes up the slack.slack_weights (dict|str, default 'p_set') – Distribution scheme describing how to determine the fraction of the total slack power (of each sub network individually) a bus of the subnetwork takes up. Default is to distribute proportional to generator dispatch (‘p_set’). Another option is to distribute proportional to (optimised) nominal capacity (‘p_nom’ or ‘p_nom_opt’). Custom weights can be specified via a dictionary that has a key for each subnetwork index (
network.sub_networks.index
) and a pandas.Series/dict with buses or generators of the corresponding subnetwork as index/keys. When specifying custom weights with buses as index/keys the slack power of a bus is distributed among its generators in proportion to their nominal capacity (p_nom
) if given, otherwise evenly.
- 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
- plot(margin=0.05, ax=None, geomap=True, projection=None, bus_colors='cadetblue', bus_alpha=1, bus_sizes=0.02, bus_cmap=None, bus_norm=None, bus_split_circles=False, line_colors='rosybrown', link_colors='darkseagreen', transformer_colors='orange', line_alpha=1, link_alpha=1, transformer_alpha=1, line_widths=1.5, link_widths=1.5, transformer_widths=1.5, line_cmap=None, link_cmap=None, transformer_cmap=None, line_norm=None, link_norm=None, transformer_norm=None, flow=None, branch_components=None, layouter=None, title='', boundaries=None, geometry=False, jitter=None, color_geomap=None)#
Plot the network buses and lines using matplotlib and cartopy.
- Parameters
margin (float, defaults to 0.05) – Margin at the sides as proportion of distance between max/min x, y Will be ignored if boundaries are given.
ax (matplotlib ax, defaults to plt.gca()) – Axis to which to plot the network
geomap (bool/str, default True) – Switch to use Cartopy and draw geographical features. If string is passed, it will be used as a resolution argument, valid options are ‘10m’, ‘50m’ and ‘110m’.
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 “cadetblue”. If bus_sizes is a pandas.Series with a Multiindex, bus_colors defaults to the n.carriers[‘color’] column.
bus_alpha (float) – Adds alpha channel to buses, defaults to 1.
bus_sizes (dict/pandas.Series) – Sizes of bus points, defaults to 1e-2. If a multiindexed Series is passed, the function will draw pies for each bus (first index level) with segments of different color (second index level). Such a Series is ob- tained by e.g. n.generators.groupby([‘bus’, ‘carrier’]).p_nom.sum()
bus_cmap (plt.cm.ColorMap/str) – If bus_colors are floats, this color map will assign the colors
bus_norm (plt.Normalize|matplotlib.colors.*Norm) – The norm applied to the bus_cmap.
bus_split_circles (bool, default False) – Draw half circles if bus_sizes is a pandas.Series with a Multiindex. If set to true, the upper half circle per bus then includes all positive values of the series, the lower half circle all negative values. Defaults to False.
line_colors (str/pandas.Series) – Colors for the lines, defaults to ‘rosybrown’.
link_colors (str/pandas.Series) – Colors for the links, defaults to ‘darkseagreen’.
transfomer_colors (str/pandas.Series) – Colors for the transfomer, defaults to ‘orange’.
line_alpha (str/pandas.Series) – Alpha for the lines, defaults to 1.
link_alpha (str/pandas.Series) – Alpha for the links, defaults to 1.
transfomer_alpha (str/pandas.Series) – Alpha for the transfomer, defaults to 1.
line_widths (dict/pandas.Series) – Widths of lines, defaults to 1.5
link_widths (dict/pandas.Series) – Widths of links, defaults to 1.5
transformer_widths (dict/pandas.Series) – Widths of transformer, defaults to 1.5
line_cmap (plt.cm.ColorMap/str|dict) – If line_colors are floats, this color map will assign the colors.
link_cmap (plt.cm.ColorMap/str|dict) – If link_colors are floats, this color map will assign the colors.
transformer_cmap (plt.cm.ColorMap/str|dict) – If transformer_colors are floats, this color map will assign the colors.
line_norm (plt.Normalize|matplotlib.colors.*Norm) – The norm applied to the line_cmap.
link_norm (plt.Normalize|matplotlib.colors.*Norm) – The norm applied to the link_cmap.
transformer_norm (matplotlib.colors.Normalize|matplotlib.colors.*Norm) – The norm applied to the transformer_cmap.
flow (snapshot/pandas.Series/function/string) – Flow to be displayed in the plot, defaults to None. If an element of n.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.
layouter (networkx.drawing.layout function, default None) –
Layouting function from networkx which overrules coordinates given in
n.buses[['x', 'y']]
. See list of available options.title (string) – Graph title
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
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. If False, no geographical features are plotted.
- 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) –
- 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', pyomo=True, skip_pre=False, extra_functionality=None, solver_options={}, keep_files=False, formulation='kirchhoff', 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”
pyomo (bool, default True) – Whether to use pyomo for building and solving the model, setting this to False saves a lot of memory and time.
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, default "kirchhoff") – Formulation of the linear power flow equations to use; must be one of [“angles”, “cycles”, “kirchoff”, “ptdf”]
ptdf_tolerance (float) –
- Return type
None
Examples
>>> network.sclopf(network, branch_outages)
- set_investment_periods(periods)#
Set the investment periods of the network.
If the network snapshots are a pandas.MultiIndex, the investment periods have to be a subset of the first level. If snapshots are a single index, they and all time-series are repeated for all periods. This changes the network snapshots to be a MultiIndex (inplace operation) with the first level being the investment periods and the second level the snapshots.
- Parameters
n (pypsa.Network) –
periods (list) – List of periods to be selected/initialized.
- Return type
None.
- set_snapshots(snapshots: Union[List, Index, MultiIndex, DatetimeIndex], default_snapshot_weightings: float = 1.0, weightings_from_timedelta: bool = False) None #
Set the snapshots/time steps and reindex all time-dependent data.
Snapshot weightings, typically representing the hourly length of each snapshot, is filled with the default_snapshot_weighintgs value, or uses the timedelta of the snapshots if weightings_from_timedelta flag is True, and snapshots are of type pd.DatetimeIndex.
This will reindex all components time-dependent DataFrames (:role:`Network.pnl`); NaNs are filled with the default value for that quantity.
- Parameters
snapshots (list, pandas.Index, pd.MultiIndex or pd.DatetimeIndex) – All time steps.
default_snapshot_weightings (float) – The default weight for each snapshot. Defaults to 1.0.
weightings_from_timedelta (bool) – Wheter to use the timedelta of snapshots as snapshot_weightings if snapshots is of type pd.DatetimeIndex. Defaults to False.
- Return type
None
- property snapshot_weightings#
Weightings applied to each snapshots during the optimization (LOPF).
Objective weightings multiply the operational cost in the objective function.
Generator weightings multiply the impact of all generators in global constraints, e.g. multiplier of GHG emmissions.
Store weightings define the elapsed hours for the charge, discharge standing loss and spillage of storage units and stores in order to determine the state of charge.
- property snapshots#
Time steps of the network
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, investment_period=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
- 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
- iterate_components(components=None, skip_empty=True)#
Iterate over components of the subnetwork and extract corresponding data.
- Parameters
components (list-like, optional) – List of components (‘Generator’, ‘Line’, etc.) to iterate over, by default None
skip_empty (bool, optional) – Whether to skip a components with no assigned assets, by default True
- Yields
Component – Container for component data. See Component class for details.
- 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.
- Return type
None
- pf(snapshots=None, skip_pre=False, x_tol=1e-06, use_seed=False, distribute_slack=False, slack_weights='p_set')#
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.
distribute_slack (bool, default False) – If
True
, distribute the slack power across generators proportional to generator dispatch by default or according to the distribution scheme provided inslack_weights
. IfFalse
only the slack generator takes up the slack.slack_weights (pandas.Series|str, default 'p_set') – Distribution scheme describing how to determine the fraction of the total slack power a bus of the subnetwork takes up. Default is to distribute proportional to generator dispatch (‘p_set’). Another option is to distribute proportional to (optimised) nominal capacity (‘p_nom’ or ‘p_nom_opt’). Custom weights can be provided via a pandas.Series/dict that has the buses or the generators of the subnetwork as index/keys. When using custom weights with buses as index/keys the slack power of a bus is distributed among its generators in proportion to their nominal capacity (
p_nom
) if given, otherwise evenly.
- Returns
Tuple of three pandas.Series indicating number of iterations,
remaining error, and convergence status for each snapshot
Descriptors#
Descriptors for component attributes.
- class pypsa.descriptors.Dict#
Dict is a subclass of dict, which allows you to get AND SET items in the dict using the attribute syntax!
Stripped down from addict mewwts/addict .
- class pypsa.descriptors.OrderedGraph(incoming_graph_data=None, multigraph_input=None, **attr)#
- adjlist_dict_factory#
alias of
OrderedDict
- node_dict_factory#
alias of
OrderedDict
- pypsa.descriptors.allocate_series_dataframes(network, series)#
Populate time-varying outputs with default values.
- Parameters
network (pypsa.Network) –
series (dict) – Dictionary of components and their attributes to populate (see example)
- Return type
None
Examples
>>> allocate_series_dataframes(network, {'Generator': ['p'], 'Load': ['p']})
- pypsa.descriptors.expand_series(ser, columns)#
Helper function to quickly expand a series to a dataframe with according column axis and every single column being the equal to the given series.
- pypsa.descriptors.get_active_assets(n, c, investment_period)#
Getter function.
Get True values for elements of component c which are active at a given investment period. These are calculated from lifetime and the build year.
- pypsa.descriptors.get_activity_mask(n, c, sns=None, index=None)#
Getter function.
Get a boolean array with True values for elements of component c which are active at a specific snapshot. If the network is in multi_investment_period mode (given by n._multi_invest), these are calculated from lifetime and the build year. Otherwise all values are set to True.
- pypsa.descriptors.get_bounds_pu(n, c, sns, index=None, attr=None)#
Getter function to retrieve the per unit bounds of a given compoent for given snapshots and possible subset of elements (e.g. non-extendables). Depending on the attr you can further specify the bounds of the variable you are looking at, e.g. p_store for storage units.
- Parameters
n (pypsa.Network) –
c (string) – Component name, e.g. “Generator”, “Line”.
sns (pandas.Index/pandas.DateTimeIndex) – set of snapshots for the bounds
index (pd.Index, default None) – Subset of the component elements. If None (default) bounds of all elements are returned.
attr (string, default None) – attribute name for the bounds, e.g. “p”, “s”, “p_store”
- pypsa.descriptors.get_committable_i(n, c)#
Getter function.
Get the index of commitable elements of a given component.
- pypsa.descriptors.get_extendable_i(n, c)#
Getter function.
Get the index of extendable elements of a given component.
- pypsa.descriptors.get_non_extendable_i(n, c)#
Getter function.
Get the index of non-extendable elements of a given component.
- pypsa.descriptors.get_switchable_as_dense(network, component, attr, snapshots=None, inds=None)#
Return a Dataframe for a time-varying component attribute with values for all non-time-varying components filled in with the default values for the attribute.
- Parameters
network (pypsa.Network) –
component (string) – Component object name, e.g. ‘Generator’ or ‘Link’
attr (string) – Attribute name
snapshots (pandas.Index) – Restrict to these snapshots rather than network.snapshots.
inds (pandas.Index) – Restrict to these components rather than network.components.index
- Return type
pandas.DataFrame
Examples
>>> get_switchable_as_dense(network, 'Generator', 'p_max_pu')
- pypsa.descriptors.get_switchable_as_iter(network, component, attr, snapshots, inds=None)#
Return an iterator over snapshots for a time-varying component attribute with values for all non-time-varying components filled in with the default values for the attribute.
- Parameters
network (pypsa.Network) –
component (string) – Component object name, e.g. ‘Generator’ or ‘Link’
attr (string) – Attribute name
snapshots (pandas.Index) – Restrict to these snapshots rather than network.snapshots.
inds (pandas.Index) – Restrict to these items rather than all of network.{generators, ..}.index
- Return type
pandas.DataFrame
Examples
>>> get_switchable_as_iter(network, 'Generator', 'p_max_pu', snapshots)
- pypsa.descriptors.zsum(s, *args, **kwargs)#
Pandas 0.21.0 changes sum() behavior so that the result of applying sum over an empty DataFrame is NaN.
Meant to be set as pd.Series.zsum = zsum.
Input and Output#
Functions for importing and exporting data.
- pypsa.io.export_to_csv_folder(network, 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_folder(csv_folder_name)
- pypsa.io.export_to_hdf5(network, 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)
- pypsa.io.export_to_netcdf(network, path=None, export_standard_types=False, compression={'complevel': 4, 'zlib': True}, float32=False)#
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).
compression (dict|None) – Compression level to use for all features which are being prepared. The compression is handled via xarray.Dataset.to_netcdf(…). For details see: https://docs.xarray.dev/en/stable/generated/xarray.Dataset.to_netcdf.html To disable compression, set to None. As a trade-off between speed and compression, the default is {‘zlib’: True, ‘complevel’: 4}.
float32 (boolean, default False) – If True, typecasts values to float32.
- Returns
ds
- Return type
xarray.Dataset
Examples
>>> network.export_to_netcdf("my_file.nc")
- pypsa.io.import_components_from_dataframe(network, 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")
See also
- pypsa.io.import_from_csv_folder(network, 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_folder(csv_folder_name)
- pypsa.io.import_from_hdf5(network, path, skip_time=False)#
Import network data from HDF5 store at path.
- Parameters
path (string, Path) – Name of HDF5 store. The string could be a URL.
skip_time (bool, default False) – Skip reading in time dependent attributes
- pypsa.io.import_from_netcdf(network, 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. The string could be a URL.
skip_time (bool, default False) – Skip reading in time dependent attributes
- pypsa.io.import_from_pandapower_net(network, net, extra_line_data=False, use_pandapower_index=False)#
Import PyPSA network from pandapower net.
Importing from pandapower is still in beta; not all pandapower components are supported.
Unsupported features include: - three-winding transformers - switches - in_service status 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
use_pandapower_index (boolean, default: False) – if True, use integer numbers which is the pandapower index standard if False, use any net.name as index (e.g. ‘Bus 1’ (str) or 1 (int))
Examples
>>> network.import_from_pandapower_net(net) OR >>> import pypsa >>> import pandapower as pp >>> import pandapower.networks as pn >>> net = pn.create_cigre_network_mv(with_der='all') >>> network = pypsa.Network() >>> network.import_from_pandapower_net(net, extra_line_data=True)
- pypsa.io.import_from_pypower_ppc(network, 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)
- pypsa.io.import_series_from_dataframe(network, 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")
See also
Examples#
This module contains functions for retrieving/loading example networks provided by the PyPSA project.
- pypsa.examples.ac_dc_meshed(update=False, from_master=False, remove_link_p_set=True)#
Load the meshed AC-DC network example of pypsa stored in the PyPSA repository.
- Parameters
- Return type
- pypsa.examples.scigrid_de(update=False, from_master=False)#
Load the SciGrid network example of pypsa stored in the PyPSA repository.
- Parameters
update (bool, optional) – Whether to update the locally stored network data. The default is False.
- Return type
Network Graph#
Graph helper functions, which are attached to network and sub_network.
- pypsa.graph.adjacency_matrix(network, branch_components=None, investment_period=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
- pypsa.graph.graph(network, 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
- pypsa.graph.incidence_matrix(network, 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
Power Flow#
Power flow functionality.
- pypsa.pf.aggregate_multi_graph(sub_network)#
Aggregate branches between same buses and replace with a single branch with aggregated properties (e.g. s_nom is summed, length is averaged).
- pypsa.pf.apply_line_types(network)#
Calculate line electrical parameters x, r, b, g from standard types.
- pypsa.pf.apply_transformer_t_model(network)#
Convert given T-model parameters to PI-model parameters using wye-delta transformation.
- pypsa.pf.apply_transformer_types(network)#
Calculate transformer electrical parameters x, r, b, g from standard types.
- pypsa.pf.calculate_B_H(sub_network, skip_pre=False)#
Calculate B and H matrices for AC or DC sub-networks.
- pypsa.pf.calculate_PTDF(sub_network, 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.
- pypsa.pf.calculate_Y(sub_network, skip_pre=False)#
Calculate bus admittance matrices for AC sub-networks.
- pypsa.pf.calculate_dependent_values(network)#
Calculate per unit impedances and append voltages to lines and shunt impedances.
- pypsa.pf.find_bus_controls(sub_network)#
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.
- pypsa.pf.find_cycles(sub_network, weight='x_pu')#
Find all cycles in the sub_network and record them in sub_network.C.
networkx collects the cycles with more than 2 edges; then the 2-edge cycles from the MultiGraph must be collected separately (for cases where there are multiple lines between the same pairs of buses).
Cycles with infinite impedance are skipped.
- pypsa.pf.find_slack_bus(sub_network)#
Find the slack bus in a connected sub-network.
- pypsa.pf.find_tree(sub_network, weight='x_pu')#
Get the spanning tree of the graph, choose the node with the highest degree as a central “tree slack” and then see for each branch which paths from the slack to each node go through the branch.
- pypsa.pf.network_batch_lpf(network, snapshots=None)#
Batched linear power flow with numpy.dot for several snapshots.
- pypsa.pf.network_lpf(network, 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.
- Return type
None
- pypsa.pf.network_pf(network, snapshots=None, skip_pre=False, x_tol=1e-06, use_seed=False, distribute_slack=False, slack_weights='p_set')#
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.
distribute_slack (bool, default False) – If
True
, distribute the slack power across generators proportional to generator dispatch by default or according to the distribution scheme provided inslack_weights
. IfFalse
only the slack generator takes up the slack.slack_weights (dict|str, default 'p_set') – Distribution scheme describing how to determine the fraction of the total slack power (of each sub network individually) a bus of the subnetwork takes up. Default is to distribute proportional to generator dispatch (‘p_set’). Another option is to distribute proportional to (optimised) nominal capacity (‘p_nom’ or ‘p_nom_opt’). Custom weights can be specified via a dictionary that has a key for each subnetwork index (
network.sub_networks.index
) and a pandas.Series/dict with buses or generators of the corresponding subnetwork as index/keys. When specifying custom weights with buses as index/keys the slack power of a bus is distributed among its generators in proportion to their nominal capacity (p_nom
) if given, otherwise evenly.
- 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
- pypsa.pf.newton_raphson_sparse(f, guess, dfdx, x_tol=1e-10, lim_iter=100, distribute_slack=False, slack_weights=None)#
Solve f(x) = 0 with initial guess for x and dfdx(x).
dfdx(x) should return a sparse Jacobian. Terminate if error on norm of f(x) is < x_tol or there were more than lim_iter iterations.
- pypsa.pf.sub_network_lpf(sub_network, 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.
- Return type
None
- pypsa.pf.sub_network_pf(sub_network, snapshots=None, skip_pre=False, x_tol=1e-06, use_seed=False, distribute_slack=False, slack_weights='p_set')#
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.
distribute_slack (bool, default False) – If
True
, distribute the slack power across generators proportional to generator dispatch by default or according to the distribution scheme provided inslack_weights
. IfFalse
only the slack generator takes up the slack.slack_weights (pandas.Series|str, default 'p_set') – Distribution scheme describing how to determine the fraction of the total slack power a bus of the subnetwork takes up. Default is to distribute proportional to generator dispatch (‘p_set’). Another option is to distribute proportional to (optimised) nominal capacity (‘p_nom’ or ‘p_nom_opt’). Custom weights can be provided via a pandas.Series/dict that has the buses or the generators of the subnetwork as index/keys. When using custom weights with buses as index/keys the slack power of a bus is distributed among its generators in proportion to their nominal capacity (
p_nom
) if given, otherwise evenly.
- Returns
Tuple of three pandas.Series indicating number of iterations,
remaining error, and convergence status for each snapshot
- pypsa.pf.sub_network_pf_singlebus(sub_network, snapshots=None, skip_pre=False, distribute_slack=False, slack_weights='p_set', linear=False)#
Non-linear power flow for a sub-network consiting of a single bus.
- 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.
distribute_slack (bool, default False) – If
True
, distribute the slack power across generators proportional to generator dispatch by default or according to the distribution scheme provided inslack_weights
. IfFalse
only the slack generator takes up the slack.slack_weights (pandas.Series|str, default 'p_set') – Distribution scheme describing how to determine the fraction of the total slack power a bus of the subnetwork takes up. Default is to distribute proportional to generator dispatch (‘p_set’). Another option is to distribute proportional to (optimised) nominal capacity (‘p_nom’ or ‘p_nom_opt’). Custom weights can be provided via a pandas.Series/dict that has the generators of the single bus as index/keys.
- pypsa.pf.wye_to_delta(z1, z2, z3)#
Linopy Optimisation Module#
abstract.py
Build abstracted, extended optimisation problems from PyPSA networks with Linopy.
- pypsa.optimization.abstract.optimize_security_constrained(n, snapshots=None, branch_outages=None, multi_investment_periods=False, model_kwargs={}, **kwargs)#
Computes Security-Constrained Linear Optimal Power Flow (SCLOPF).
This ensures that no branch is overloaded even given the branch outages.
- Parameters
n (pypsa.Network) –
snapshots (list-like, optional) – Set of snapshots to consider in the optimization. The default is None.
branch_outages (list-like/pandas.Index/pandas.MultiIndex, optional) – Subset of passive branches to consider as possible outages. If a list or a pandas.Index is passed, it is assumed to identify lines. If a multiindex is passed, its first level has to contain the component names, the second the assets. The default None results in all passive branches to be considered.
multi_investment_periods (bool, default False) – Whether to optimise as a single investment period or to optimise in multiple investment periods. Then, snapshots should be a
pd.MultiIndex
.model_kwargs (dict) – Keyword arguments used by linopy.Model, such as solver_dir or chunk.
**kwargs – Keyword argument used by linopy.Model.solve, such as solver_name, problem_fn or solver options directly passed to the solver.
- Return type
None
- pypsa.optimization.abstract.optimize_transmission_expansion_iteratively(n, snapshots=None, msq_threshold=0.05, min_iterations=1, max_iterations=100, track_iterations=False, **kwargs)#
Iterative linear optimization updating the line parameters for passive AC and DC lines. This is helpful when line expansion is enabled. After each successful solving, line impedances and line resistance are recalculated based on the optimization result. If warmstart is possible, it uses the result from the previous iteration to fasten the optimization.
- Parameters
snapshots (list or index slice) – A list of snapshots to optimise, must be a subset of network.snapshots, defaults to network.snapshots
msq_threshold (float, default 0.05) – Maximal mean square difference between optimized line capacity of the current and the previous iteration. As soon as this threshold is undercut, and the number of iterations is bigger than ‘min_iterations’ the iterative optimization stops
min_iterations (integer, default 1) – Minimal number of iteration to run regardless whether the msq_threshold is already undercut
max_iterations (integer, default 100) – Maximal number of iterations to run regardless whether msq_threshold is already undercut
track_iterations (bool, default False) – If True, the intermediate branch capacities and values of the objective function are recorded for each iteration. The values of iteration 0 represent the initial state.
**kwargs – Keyword arguments of the n.optimize function which runs at each iteration
common.py
Use common methods for optimization problem definition with Linopy.
- pypsa.optimization.common.get_strongly_meshed_buses(n, threshold=45)#
Get the buses which are strongly meshed in the network.
- pypsa.optimization.common.reindex(ds, dim, index)#
Index a xarray.DataArray by a pandas.Index while renaming according to the new index name.
- Parameters
ds (xr.DataArray) –
dim (str) –
index (pd.Index) –
- Returns
Reindexed dataarray with renamed dimension.
- Return type
ds
- pypsa.optimization.common.set_from_frame(n, c, attr, df)#
Update values in time-dependent attribute from new dataframe.
compat.py
Use compatibility methods for optimization problem definition with Linopy.
This module intends to make the transition from the native pypsa optimization code to the linopy implementation easier.
- pypsa.optimization.compat.define_constraints(n, lhs, sense, rhs, name, attr='', axes=None, spec='', mask=None, **kwargs)#
Define a constraint for a given network.
This function mimics the behavior of pypsa.linopt.define_constraints, and was created for compatibility reasons.
- pypsa.optimization.compat.define_variables(n, lower, upper, name, attr='', axes=None, spec='', mask=None, **kwargs)#
Define a variable for a given network.
This function mimics the behavior of pypsa.linopt.define_variables, and was created for compatibility reasons.
- pypsa.optimization.compat.get_var(n, c, key)#
Get variables directly from network.
- pypsa.optimization.compat.join_exprs(arr, **kwargs)#
Sum over linear expression.
This function mimics the behavior of pypsa.linopt.join_exprs, and was created for compatibility reasons.
- pypsa.optimization.compat.linexpr(*tuples, as_pandas=True, return_axes=False)#
Define a linear expression.
This function mimics the behavior of pypsa.linopt.linexpr, and was created for compatibility reasons.
constraints.py
Define optimisation constraints from PyPSA networks with Linopy.
- pypsa.optimization.constraints.define_fixed_nominal_constraints(n, c, attr)#
Sets constraints for fixing static variables of a given component and attribute to the corresponding values in n.df(c)[attr + ‘_set’].
- Parameters
n (pypsa.Network) –
c (str) – name of the network component
attr (str) – name of the attribute, e.g. ‘p’
- pypsa.optimization.constraints.define_fixed_operation_constraints(n, sns, c, attr)#
Sets constraints for fixing time-dependent variables of a given component and attribute to the corresponding values in n.pnl(c)[attr + ‘_set’].
- Parameters
n (pypsa.Network) –
c (str) – name of the network component
attr (str) – name of the attribute, e.g. ‘p’
- pypsa.optimization.constraints.define_kirchhoff_voltage_constraints(n, sns)#
Defines Kirchhoff voltage constraints.
- pypsa.optimization.constraints.define_nodal_balance_constraints(n, sns, transmission_losses=0, buses=None, suffix='')#
Defines nodal balance constraints.
- pypsa.optimization.constraints.define_nominal_constraints_for_extendables(n, c, attr)#
Sets capacity expansion constraints for extendable assets for a given component and a given attribute.
Note: As GLPK does not like inf values on the right-hand-side we as masking these out.
- Parameters
n (pypsa.Network) –
c (str) – name of the network component
attr (str) – name of the attribute, e.g. ‘p’
- pypsa.optimization.constraints.define_operational_constraints_for_committables(n, sns, c)#
Sets power dispatch constraints for committable devices for a given component and a given attribute. The linearized approximation of the unit commitment problem is inspired by Hua et al. (2017) DOI: 10.1109/TPWRS.2017.2735026.
- Parameters
n (pypsa.Network) –
sns (pd.Index) – Snapshots of the constraint.
c (str) – name of the network component
- pypsa.optimization.constraints.define_operational_constraints_for_extendables(n, sns, c, attr, transmission_losses)#
Sets power dispatch constraints for extendable devices for a given component and a given attribute.
- Parameters
n (pypsa.Network) –
sns (pd.Index) – Snapshots of the constraint.
c (str) – name of the network component
attr (str) – name of the attribute, e.g. ‘p’
- pypsa.optimization.constraints.define_operational_constraints_for_non_extendables(n, sns, c, attr, transmission_losses)#
Sets power dispatch constraints for non-extendable and non-commitable assets for a given component and a given attribute.
- Parameters
n (pypsa.Network) –
sns (pd.Index) – Snapshots of the constraint.
c (str) – name of the network component
attr (str) – name of the attribute, e.g. ‘p’
- pypsa.optimization.constraints.define_ramp_limit_constraints(n, sns, c, attr)#
Defines ramp limits for assets with valid ramplimit.
- Parameters
n (pypsa.Network) –
c (str) – name of the network component
- pypsa.optimization.constraints.define_storage_unit_constraints(n, sns)#
Defines energy balance constraints for storage units. In principal the constraints states:
previous_soc + p_store - p_dispatch + inflow - spill == soc
- pypsa.optimization.constraints.define_store_constraints(n, sns)#
Defines energy balance constraints for stores. In principal the constraints states:
previous_e - p == e
global_constraints.py
Define global constraints for optimisation problems with Linopy.
- pypsa.optimization.global_constraints.define_growth_limit(n, sns)#
Constraint new installed capacity per investment period.
- Parameters
n (pypsa.Network) –
sns (list-like) – Set of snapshots to which the constraint should be applied.
- pypsa.optimization.global_constraints.define_nominal_constraints_per_bus_carrier(n, sns)#
Set an capacity expansion limit for assets of the same carrier at the same bus (e.g. ‘onwind’ at bus ‘1’). The function searches for columns in the buses dataframe matching the pattern “nom_{min/max}_{carrier}”. In case the constraint should only be defined for one investment period, the column name can be constructed according to “nom_{min/max}_{carrier}_{period}” where period must be in n.investment_periods.
- Parameters
n (pypsa.Network) –
sns (list-like) – Set of snapshots to which the constraint should be applied.
- Return type
None.
- pypsa.optimization.global_constraints.define_operational_limit(n, sns)#
Defines operational limit constraints. It limits the net production of a carrier taking into account generator, storage units and stores.
- Parameters
n (pypsa.Network) –
sns (list-like) – Set of snapshots to which the constraint should be applied.
- Return type
None.
- pypsa.optimization.global_constraints.define_primary_energy_limit(n, sns)#
Defines primary energy constraints. It limits the byproducts of primary energy sources (defined by carriers) such as CO2.
- Parameters
n (pypsa.Network) –
sns (list-like) – Set of snapshots to which the constraint should be applied.
- Return type
None.
- pypsa.optimization.global_constraints.define_tech_capacity_expansion_limit(n, sns)#
Defines per-carrier and potentially per-bus capacity expansion limits.
- Parameters
n (pypsa.Network) –
sns (list-like) – Set of snapshots to which the constraint should be applied.
- Return type
None.
- pypsa.optimization.global_constraints.define_transmission_expansion_cost_limit(n, sns)#
Set a limit for line expansion costs. For the capacity expansion only the carriers ‘AC’ and ‘DC’ are considered.
- Parameters
n (pypsa.Network) –
sns (list-like) – Set of snapshots to which the constraint should be applied.
- Return type
None.
- pypsa.optimization.global_constraints.define_transmission_volume_expansion_limit(n, sns)#
Set a limit for line volume expansion. For the capacity expansion only the carriers ‘AC’ and ‘DC’ are considered.
- Parameters
n (pypsa.Network) –
sns (list-like) – Set of snapshots to which the constraint should be applied.
- Return type
None.
optimize.py
Build optimisation problems from PyPSA networks with Linopy.
- class pypsa.optimization.optimize.OptimizationAccessor(network)#
Optimization accessor for building and solving models using linopy.
- add_load_shedding(suffix=' load shedding', buses=None, sign=0.001, marginal_cost=100.0, p_nom=1000000000.0)#
Add load shedding in form of generators to all or a subset of buses.
For more information on load shedding see http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full
- Parameters
buses (pandas.Index, optional) – Subset of buses where load shedding should be available. Defaults to all buses.
sign (float/Series, optional) – Scaling of the load shedding. This is used to scale the price of the load shedding. The default is 1e-3 which translates to a measure in kW instead of MW.
marginal_cost (float/Series, optional) – Price of the load shedding. The default is 1e2.
p_nom (float/Series, optional) – Maximal load shedding. The default is 1e9 (kW).
- assign_duals()#
Map dual values i.e. shadow prices to network components.
- assign_solution()#
Map solution to network components.
- create_model(snapshots=None, multi_investment_periods=False, transmission_losses=0, linearized_unit_commitment=False, **kwargs)#
Create a linopy.Model instance from a pypsa network.
The model is stored at n.model.
- Parameters
n (pypsa.Network) –
snapshots (list or index slice) – A list of snapshots to optimise, must be a subset of network.snapshots, defaults to network.snapshots
multi_investment_periods (bool, default False) – Whether to optimise as a single investment period or to optimize in multiple investment periods. Then, snapshots should be a
pd.MultiIndex
.transmission_losses (int, default 0) –
linearized_unit_commitment (bool, default False) – Whether to optimise using the linearised unit commitment formulation or not.
**kwargs – Keyword arguments used by linopy.Model(), such as solver_dir or chunk.
- Return type
linopy.model
- fix_optimal_capacities()#
Fix capacities of extendable assets to optimized capacities.
Use this function when a capacity expansion optimization was already performed and a operational optimization should be done afterwards.
- fix_optimal_dispatch()#
Fix dispatch of all assets to optimized values.
Use this function when the optimal dispatch should be used as an starting point for power flow calculation (Network.pf).
- optimize_security_constrained(snapshots=None, branch_outages=None, multi_investment_periods=False, model_kwargs={}, **kwargs)#
Computes Security-Constrained Linear Optimal Power Flow (SCLOPF).
This ensures that no branch is overloaded even given the branch outages.
- Parameters
n (pypsa.Network) –
snapshots (list-like, optional) – Set of snapshots to consider in the optimization. The default is None.
branch_outages (list-like/pandas.Index/pandas.MultiIndex, optional) – Subset of passive branches to consider as possible outages. If a list or a pandas.Index is passed, it is assumed to identify lines. If a multiindex is passed, its first level has to contain the component names, the second the assets. The default None results in all passive branches to be considered.
multi_investment_periods (bool, default False) – Whether to optimise as a single investment period or to optimise in multiple investment periods. Then, snapshots should be a
pd.MultiIndex
.model_kwargs (dict) – Keyword arguments used by linopy.Model, such as solver_dir or chunk.
**kwargs – Keyword argument used by linopy.Model.solve, such as solver_name, problem_fn or solver options directly passed to the solver.
- Return type
None
- optimize_transmission_expansion_iteratively(snapshots=None, msq_threshold=0.05, min_iterations=1, max_iterations=100, track_iterations=False, **kwargs)#
Iterative linear optimization updating the line parameters for passive AC and DC lines. This is helpful when line expansion is enabled. After each successful solving, line impedances and line resistance are recalculated based on the optimization result. If warmstart is possible, it uses the result from the previous iteration to fasten the optimization.
- Parameters
snapshots (list or index slice) – A list of snapshots to optimise, must be a subset of network.snapshots, defaults to network.snapshots
msq_threshold (float, default 0.05) – Maximal mean square difference between optimized line capacity of the current and the previous iteration. As soon as this threshold is undercut, and the number of iterations is bigger than ‘min_iterations’ the iterative optimization stops
min_iterations (integer, default 1) – Minimal number of iteration to run regardless whether the msq_threshold is already undercut
max_iterations (integer, default 100) – Maximal number of iterations to run regardless whether msq_threshold is already undercut
track_iterations (bool, default False) – If True, the intermediate branch capacities and values of the objective function are recorded for each iteration. The values of iteration 0 represent the initial state.
**kwargs – Keyword arguments of the n.optimize function which runs at each iteration
- post_processing()#
Post-process the optimzed network.
This calculates quantities derived from the optimized values such as power injection per bus and snapshot, voltage angle.
- solve_model(solver_name='glpk', solver_options={}, **kwargs)#
Solve an already created model and assign its solution to the network.
- pypsa.optimization.optimize.assign_duals(n)#
Map dual values i.e. shadow prices to network components.
- pypsa.optimization.optimize.assign_solution(n)#
Map solution to network components.
- pypsa.optimization.optimize.create_model(n, snapshots=None, multi_investment_periods=False, transmission_losses=0, linearized_unit_commitment=False, **kwargs)#
Create a linopy.Model instance from a pypsa network.
The model is stored at n.model.
- Parameters
n (pypsa.Network) –
snapshots (list or index slice) – A list of snapshots to optimise, must be a subset of network.snapshots, defaults to network.snapshots
multi_investment_periods (bool, default False) – Whether to optimise as a single investment period or to optimize in multiple investment periods. Then, snapshots should be a
pd.MultiIndex
.transmission_losses (int, default 0) –
linearized_unit_commitment (bool, default False) – Whether to optimise using the linearised unit commitment formulation or not.
**kwargs – Keyword arguments used by linopy.Model(), such as solver_dir or chunk.
- Return type
linopy.model
- pypsa.optimization.optimize.define_objective(n, sns)#
Defines and writes out the objective function.
- pypsa.optimization.optimize.optimize(n, snapshots=None, multi_investment_periods=False, transmission_losses=0, linearized_unit_commitment=False, model_kwargs={}, extra_functionality=None, solver_name='glpk', solver_options={}, **kwargs)#
Optimize the pypsa network using linopy.
- Parameters
n (pypsa.Network) –
snapshots (list or index slice) – A list of snapshots to optimise, must be a subset of n.snapshots, defaults to n.snapshots
multi_investment_periods (bool, default False) – Whether to optimise as a single investment period or to optimise in multiple investment periods. Then, snapshots should be a
pd.MultiIndex
.transmission_losses (int, default 0) – Whether an approximation of transmission losses should be included in the linearised power flow formulation. A passed number will denote the number of tangents used for the piecewise linear approximation. Defaults to 0, which ignores losses.
linearized_unit_commitment (bool, default False) – Whether to optimise using the linearised unit commitment formulation or not.
model_kwargs (dict) – Keyword arguments used by linopy.Model, such as solver_dir or chunk.
extra_functionality (callable) – 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_name (str) – Name of the solver to use.
solver_options (dict) – Keyword arguments used by the solver. Can also be passed via **kwargs.
**kwargs – Keyword argument used by linopy.Model.solve, such as solver_name, problem_fn or solver options directly passed to the solver.
- Return type
None.
- pypsa.optimization.optimize.post_processing(n)#
Post-process the optimzed network.
This calculates quantities derived from the optimized values such as power injection per bus and snapshot, voltage angle.
variables.py
Define optimisation variables from PyPSA networks with Linopy.
- pypsa.optimization.variables.define_loss_variables(n, sns, c)#
Initializes variables for transmission losses.
- pypsa.optimization.variables.define_nominal_variables(n, c, attr)#
Initializes variables for nominal capacities for a given component and a given attribute.
- Parameters
n (pypsa.Network) –
c (str) – network component of which the nominal capacity should be defined
attr (str) – name of the variable, e.g. ‘p_nom’
- pypsa.optimization.variables.define_operational_variables(n, sns, c, attr)#
Initializes variables for power dispatch for a given component and a given attribute.
- Parameters
n (pypsa.Network) –
c (str) – name of the network component
attr (str) – name of the attribute, e.g. ‘p’
- pypsa.optimization.variables.define_spillage_variables(n, sns)#
Defines the spillage variables for storage units.
Optimisation Module#
Tools for fast Linear Problem file writing. This module contains.
io functions for writing out variables, constraints and objective into a lp file.
functions to create lp format based linear expression
solver functions which read the lp file, run the problem and return the solution
This module supports the linear optimal power flow calculation without using pyomo (see module linopt.py)
- pypsa.linopt.align_with_static_component(n, c, attr)#
Alignment of time-dependent variables with static components.
If c is a pypsa.component name, it will sort the columns of the variable according to the static component.
- pypsa.linopt.broadcasted_axes(*dfs)#
Helper function which, from a collection of arrays, series, frames and other values, retrieves the axes of series and frames which result from broadcasting operations.
It checks whether index and columns of given series and frames, repespectively, are aligned. Using this function allows to subsequently use pure numpy operations and keep the axes in the background.
- pypsa.linopt.define_binaries(n, axes, name, attr='', spec='', mask=None)#
Defines binary-variable(s) for pypsa-network. The variables are stored in the network object under n.vars with key of the variable name. For each entry for the pd.Series of pd.DataFrame spanned by the axes argument the function defines a binary.
- Parameters
n (pypsa.Network) –
axes (pd.Index or tuple of pd.Index objects) – Specifies the axes and therefore the shape of the variables.
name (str) –
general name of the variable (or component which the variable is referring to). The variable will then be stored under:
n.vars[name].pnl if the variable is two-dimensional
n.vars[name].df if the variable is one-dimensional
attr (str default '') – Specifying name of the variable, defines under which name the variable(s) are stored in n.vars[name].pnl if two-dimensional or in n.vars[name].df if one-dimensional
mask (pd.DataFrame/np.array) – Boolean mask with False values for variables which are skipped. The shape of the mask has to match the shape given by axes.
See also
- pypsa.linopt.define_constraints(n, lhs, sense, rhs, name, attr='', axes=None, spec='', mask=None)#
Defines constraint(s) for pypsa-network with given left hand side (lhs), sense and right hand side (rhs). The constraints are stored in the network object under n.cons with key of the constraint name. If multiple constraints are defined at ones, only using np.arrays, then the axes argument can be used for defining the axes for the constraints (this is especially recommended for time-dependent constraints). If one of lhs, sense and rhs is a pd.Series/pd.DataFrame the axes argument is not necessary.
- Parameters
n (pypsa.Network) –
lhs (pd.Series/pd.DataFrame/np.array/str/float) – left hand side of the constraint(s), created with
pypsa.linot.linexpr()
.sense (pd.Series/pd.DataFrame/np.array/str/float) – sense(s) of the constraint(s)
rhs (pd.Series/pd.DataFrame/np.array/str/float) – right hand side of the constraint(s), must only contain pure constants, no variables
name (str) –
general name of the constraint (or component which the constraint is referring to). The constraint will then be stored under:
n.cons[name].pnl if the constraint is two-dimensional
n.cons[name].df if the constraint is one-dimensional
attr (str default '') – Specifying name of the constraint, defines under which name the constraint(s) are stored in n.cons[name].pnl if two-dimensional or in n.cons[name].df if one-dimensional
axes (pd.Index or tuple of pd.Index objects, default None) – Specifies the axes if all of lhs, sense and rhs are np.arrays or single strings or floats.
mask (pd.DataFrame/np.array) – Boolean mask with False values for constraints which are skipped. The shape of the mask has to match the shape of the array that come out when combining lhs, sense and rhs.
Example
Let’s say we want to constraint all gas generators to a maximum of 100 MWh during the first 10 snapshots. We then firstly get all operational variables for this subset and constraint there sum to less equal 100.
>>> from pypsa.linopt import get_var, linexpr, define_constraints >>> gas_i = n.generators.query('carrier == "Natural Gas"').index >>> gas_vars = get_var(n, 'Generator', 'p').loc[n.snapshots[:10], gas_i] >>> lhs = linexpr((1, gas_vars)).sum().sum() >>> define_constraints(n, lhs, '<=', 100, 'Generator', 'gas_power_limit')
Now the constraint references can be accessed by
pypsa.linopt.get_con()
using>>> cons = get_var(n, 'Generator', 'gas_power_limit')
Under the hood they are stored in n.cons.Generator.pnl.gas_power_limit. For retrieving their shadow prices add the general name of the constraint to the keep_shadowprices argument.
Note that this is useful for the extra_functionality argument.
- pypsa.linopt.define_variables(n, lower, upper, name, attr='', axes=None, spec='', mask=None)#
Defines variable(s) for pypsa-network with given lower bound(s) and upper bound(s). The variables are stored in the network object under n.vars with key of the variable name. If multiple variables are defined at ones, at least one of lower and upper has to be an array (including pandas) of shape > (1, ) or axes have to define the dimensions of the variables.
- Parameters
n (pypsa.Network) –
lower (pd.Series/pd.DataFrame/np.array/str/float) – lower bound(s) for the variable(s)
upper (pd.Series/pd.DataFrame/np.array/str/float) – upper bound(s) for the variable(s)
name (str) –
general name of the variable (or component which the variable is referring to). The variable will then be stored under:
n.vars[name].pnl if the variable is two-dimensional
n.vars[name].df if the variable is one-dimensional
but can easily be accessed with
get_var(n, name, attr)()
attr (str default '') – Specifying name of the variable, defines under which name the variable(s) are stored in n.vars[name].pnl if two-dimensional or in n.vars[name].df if one-dimensional
axes (pd.Index or tuple of pd.Index objects, default None) – Specifies the axes and therefore the shape of the variables if bounds are single strings or floats. This is helpful when multiple variables have the same upper and lower bound.
mask (pd.DataFrame/np.array) – Boolean mask with False values for variables which are skipped. The shape of the mask has to match the shape the added variables.
Example
Let’s say we want to define a demand-side-managed load at each bus of network n, which has a minimum of 0 and a maximum of 10. We then define lower bound (lb) and upper bound (ub) and pass it to define_variables
>>> from pypsa.linopt import define_variables, get_var >>> lb = pd.DataFrame(0, index=n.snapshots, columns=n.buses.index) >>> ub = pd.DataFrame(10, index=n.snapshots, columns=n.buses.index) >>> define_variables(n, lb, ub, 'DSM', 'variableload')
Now the variables can be accessed by
pypsa.linopt.get_var()
using>>> variables = get_var(n, 'DSM', 'variableload')
Note that this is usefull for the extra_functionality argument.
- pypsa.linopt.get_con(n, c, attr, pop=False)#
Retrieves constraint references for a given static or time-depending attribute of a give component.
- Parameters
n (pypsa.Network) –
c (str) – component name to which the constraint belongs
attr (str) – attribute name of the constraints
Example
get_con(n, ‘Generator’, ‘mu_upper’)
- pypsa.linopt.get_dual(n, name, attr='')#
Retrieves shadow price for a given constraint. Note that for retrieving shadow prices of a custom constraint, its name has to be passed to keep_references in the lopf, or keep_references has to be set to True. Note that a lookup of all stored shadow prices is given in n.dualvalues.
- Parameters
n (pypsa.Network) –
c (str) – constraint name to which the constraint belongs
attr (str) – attribute name of the constraints
Example
get_dual(n, ‘Generator’, ‘mu_upper’)
- pypsa.linopt.get_sol(n, name, attr='')#
Retrieves solution for a given variable. Note that a lookup of all stored solutions is given in n.solutions.
- Parameters
n (pypsa.Network) –
c (str) – general variable name (or component name if variable is attached to a component)
attr (str) – attribute name of the variable
Example
get_dual(n, ‘Generator’, ‘mu_upper’)
- pypsa.linopt.get_var(n, c, attr, pop=False)#
Retrieves variable references for a given static or time-depending attribute of a given component. The function looks into n.variables to detect whether the variable is a time-dependent or static.
- Parameters
n (pypsa.Network) –
c (str) – component name to which the constraint belongs
attr (str) – attribute name of the constraints
Example
>>> get_var(n, 'Generator', 'p')
- pypsa.linopt.join_exprs(df)#
Helper function to join arrays, series or frames of strings together.
- pypsa.linopt.linexpr(*tuples, as_pandas=True, return_axes=False)#
Elementwise concatenation of tuples in the form (coefficient, variables). Coefficient and variables can be arrays, series or frames. Per default returns a pandas.Series or pandas.DataFrame of strings. If return_axes is set to True the return value is split into values and axes, where values are the numpy.array and axes a tuple containing index and column if present.
- Parameters
tuples (tuple of tuples) –
Each tuple must of the form (coeff, var), where
coeff is a numerical value, or a numerical array, series, frame
var is a str or a array, series, frame of variable strings
as_pandas (bool, default True) – Whether to return to resulting array as a series, if 1-dimensional, or a frame, if 2-dimensional. Supersedes return_axes argument.
return_axes (Boolean, default False) – Whether to return index and column (if existent)
Example
Initialize coefficients and variables
>>> coeff1 = 1 >>> var1 = pd.Series(['a1', 'a2', 'a3']) >>> coeff2 = pd.Series([-0.5, -0.3, -1]) >>> var2 = pd.Series(['b1', 'b2', 'b3'])
Create the linear expression strings
>>> linexpr((coeff1, var1), (coeff2, var2)) 0 +1.0 a1 -0.5 b1 1 +1.0 a2 -0.3 b2 2 +1.0 a3 -1.0 b3 dtype: object
For a further step the resulting frame can be used as the lhs of
pypsa.linopt.define_constraints()
For retrieving only the values:
>>> linexpr((coeff1, var1), (coeff2, var2), as_pandas=False) array(['+1.0 a1 -0.5 b1', '+1.0 a2 -0.3 b2', '+1.0 a3 -1.0 b3'], dtype=object)
- pypsa.linopt.run_and_read_cbc(n, problem_fn, solution_fn, solver_logfile, solver_options, warmstart=None, store_basis=True)#
Solving function. Reads the linear problem file and passes it to the cbc solver. If the solution is successful it returns variable solutions and constraint dual values.
For more information on the solver options, run ‘cbc’ in your shell
- pypsa.linopt.run_and_read_cplex(n, problem_fn, solution_fn, solver_logfile, solver_options, warmstart=None, store_basis=True)#
Solving function.
Reads the linear problem file and passes it to the cplex solver. If the solution is successful it returns variable solutions and constraint dual values. Cplex must be installed for using this function
- pypsa.linopt.run_and_read_glpk(n, problem_fn, solution_fn, solver_logfile, solver_options, warmstart=None, store_basis=True)#
Solving function. Reads the linear problem file and passes it to the glpk solver. If the solution is successful it returns variable solutions and constraint dual values.
For more information on the glpk solver options: https://kam.mff.cuni.cz/~elias/glpk.pdf
- pypsa.linopt.run_and_read_gurobi(n, problem_fn, solution_fn, solver_logfile, solver_options, warmstart=None, store_basis=True)#
Solving function. Reads the linear problem file and passes it to the gurobi solver. If the solution is successful it returns variable solutions and constraint dual values. Gurobipy must be installed for using this function.
For more information on solver options, see <https://www.gurobi.com/documentation/{gurobi_verion}/refman/parameter_descriptions.html>_
- pypsa.linopt.run_and_read_highs(n, problem_fn, solution_fn, solver_logfile, solver_options={}, warmstart=None, store_basis=True)#
Highs solver function. Reads a linear problem file and passes it to the highs solver. If the solution is feasible the function returns the objective, solution and dual constraint variables. Highs must be installed for usage. Documentation: https://www.maths.ed.ac.uk/hall/HiGHS/
Notes
- The script might only work for version HiGHS 1.1.1. Installation steps::
sudo apt-get install cmake # if not installed git clone git@github.com:ERGO-Code/HiGHS.git cd HiGHS git checkout 95342daa73543cc21e5b27db3e0fbf7330007541 # moves to HiGHS 1.1.1 mkdir build cd build cmake .. make ctest
- Then in .bashrc add paths of executables and library ::
export PATH=”${PATH}:/foo/HiGHS/build/bin” export LD_LIBRARY_PATH=”${LD_LIBRARY_PATH}:/foo/HiGHS/build/lib” source .bashrc
- Now when typing
highs
in the terminal you should see something like :: Running HiGHS 1.1.1 [date: 2021-11-14, git hash: 95342daa]
The function reads and execute (i.e. subprocess.Popen, …) terminal commands of the solver. Meaning the command can be also executed at your command window/terminal if HiGHs is installed. Executing the commands on your local terminal helps to identify the raw outputs that are useful for developing the interface further.
All functions below the “process = …” do only read and save the outputs generated from the HiGHS solver. These parts are solver specific and depends on the solver output.
Solver options are read by the 1) command window and the 2) option_file.txt
1) An example list of solver options executable by the command window is given here: Examples: –model_file arg File of model to solve. –presolve arg Presolve: “choose” by default - “on”/”off” are alternatives. –solver arg Solver: “choose” by default - “simplex”/”ipm” are alternatives. –parallel arg Parallel solve: “choose” by default - “on”/”off” are alternatives. –time_limit arg Run time limit (double). –options_file arg File containing HiGHS options. -h, –help Print help.
2) The options_file.txt gives some more options, see a full list here: https://www.maths.ed.ac.uk/hall/HiGHS/HighsOptions.set By default, we insert a couple of options for the ipm solver. The dictionary can be overwritten by simply giving the new values. For instance, you could write a dictionary replacing some of the default values or adding new options:
``` solver_options = {
name: highs, method: ipm, parallel: “on”, <option_name>: <value>,
}#
Note, the <option_name> and <value> must be equivalent to the name convention of HiGHS. Some function exist that are not documented, check their GitHub file: ERGO-Code/HiGHS
- returns
status (string,) – “ok” or “warning”
termination_condition (string,) – Contains “optimal”, “infeasible”,
variables_sol (series)
constraints_dual (series)
objective (float)
- pypsa.linopt.run_and_read_xpress(n, problem_fn, solution_fn, solver_logfile, solver_options, keep_files, warmstart=None, store_basis=True)#
Solving function. Reads the linear problem file and passes it to the Xpress solver. If the solution is successful it returns variable solutions and constraint dual values. The xpress module must be installed for using this function.
For more information on solver options, see <https://www.fico.com/fico-xpress-optimization/docs/latest/solver/GUID-ACD7E60C-7852-36B7-A78A-CED0EA291CDD.html>_
- pypsa.linopt.set_conref(n, constraints, c, attr, spec='')#
Sets constraint references to the network. One-dimensional constraint references will be collected at n.cons[c].df, two-dimensional in n.cons[c].pnl For example:
constraints for nominal capacity variables for generators are stored in n.cons.Generator.df.mu_upper
operational capacity limits for generators are stored in n.cons.Generator.pnl.mu_upper
- pypsa.linopt.set_varref(n, variables, c, attr, spec='')#
Sets variable references to the network. One-dimensional variable references will be collected at n.vars[c].df, two-dimensional varaibles in n.vars[c].pnl. For example:
nominal capacity variables for generators are stored in n.vars.Generator.df.p_nom
operational variables for generators are stored in n.vars.Generator.pnl.p
- pypsa.linopt.to_pandas(array, *axes)#
Convert a numpy array to pandas.Series if 1-dimensional or to a pandas.DataFrame if 2-dimensional.
Provide index and columns if needed
- pypsa.linopt.write_binary(n, axes, mask=None)#
Writer function for writing out multiple binary-variables at a time.
According to the axes it writes out binaries for each entry the pd.Series or pd.DataFrame spanned by axes. Returns a series or frame with variable references.
- pypsa.linopt.write_bound(n, lower, upper, axes=None, mask=None)#
Writer function for writing out multiple variables at a time.
If lower and upper are floats it demands to give pass axes, a tuple of (index, columns) or (index), for creating the variable of same upper and lower bounds. Return a series or frame with variable references.
- pypsa.linopt.write_constraint(n, lhs, sense, rhs, axes=None, mask=None)#
Writer function for writing out multiple constraints to the corresponding constraints file.
If lower and upper are numpy.ndarrays it axes must not be None but a tuple of (index, columns) or (index). Return a series or frame with constraint references.
- pypsa.linopt.write_objective(n, terms)#
Writer function for writing out one or multiple objective terms.
- Parameters
n (pypsa.Network) –
terms (str/numpy.array/pandas.Series/pandas.DataFrame) – String or array of strings which represent new objective terms, built with
linexpr()
Power System Optimisation#
Build optimisation problems from PyPSA networks without Pyomo.
Originally retrieved from nomopyomo ( -> ‘no more Pyomo’).
- pypsa.linopf.assign_solution(n, sns, variables_sol, constraints_dual, keep_references=False, keep_shadowprices=None)#
Map solution to network components.
Helper function. Assigns the solution of a succesful optimization to the network.
- pypsa.linopf.define_committable_generator_constraints(n, sns)#
Deprecated since version 0.23: This will be removed in 0.24. Use define_unit_commitment_constraints instead.
- pypsa.linopf.define_dispatch_for_extendable_and_committable_variables(n, sns, c, attr)#
Initializes variables for power dispatch for a given component and a given attribute.
- Parameters
n (pypsa.Network) –
c (str) – name of the network component
attr (str) – name of the attribute, e.g. ‘p’
- pypsa.linopf.define_dispatch_for_extendable_constraints(n, sns, c, attr, transmission_losses)#
Sets power dispatch constraints for extendable devices for a given component and a given attribute.
- Parameters
n (pypsa.Network) –
c (str) – name of the network component
attr (str) – name of the attribute, e.g. ‘p’
- pypsa.linopf.define_dispatch_for_non_extendable_variables(n, sns, c, attr, transmission_losses)#
Initializes variables for power dispatch for a given component and a given attribute.
- Parameters
n (pypsa.Network) –
c (str) – name of the network component
attr (str) – name of the attribute, e.g. ‘p’
- pypsa.linopf.define_fixed_variable_constraints(n, sns, c, attr, pnl=True)#
Sets constraints for fixing variables of a given component and attribute to the corresponding values in n.df(c)[attr + ‘_set’] if pnl is True, or n.pnl(c)[attr + ‘_set’]
- Parameters
n (pypsa.Network) –
c (str) – name of the network component
attr (str) – name of the attribute, e.g. ‘p’
pnl (bool, default True) – Whether variable which should be fixed is time-dependent
- pypsa.linopf.define_generator_status_variables(n, sns)#
Deprecated since version 0.23: This will be removed in 0.24. Use define_unit_commitment_status_variables instead.
- pypsa.linopf.define_global_constraints(n, sns)#
Defines global constraints for the optimization. Possible types are.
- primary_energy
Use this to constraint the byproducts of primary energy sources as CO2
- transmission_volume_expansion_limit
Use this to set a limit for line volume expansion. Possible carriers are ‘AC’ and ‘DC’
- transmission_expansion_cost_limit
Use this to set a limit for line expansion costs. Possible carriers are ‘AC’ and ‘DC’
- tech_capacity_expansion_limit
Use this to se a limit for the summed capacitiy of a carrier (e.g. ‘onwind’) for each investment period at choosen nodes. This limit could e.g. represent land resource/ building restrictions for a technology in a certain region. Currently, only the capacities of extendable generators have to be below the set limit.
- pypsa.linopf.define_growth_limit(n, sns, c, attr)#
Constraint new installed capacity per investment period.
- Parameters
n (pypsa.Network) –
c (str) – network component of which the nominal capacity should be defined
attr (str) – name of the variable, e.g. ‘p_nom’
- pypsa.linopf.define_kirchhoff_constraints(n, sns)#
Defines Kirchhoff voltage constraints.
- pypsa.linopf.define_nodal_balance_constraints(n, sns, transmission_losses)#
Defines nodal balance constraint.
- pypsa.linopf.define_nominal_for_extendable_variables(n, c, attr)#
Initializes variables for nominal capacities for a given component and a given attribute.
- Parameters
n (pypsa.Network) –
c (str) – network component of which the nominal capacity should be defined
attr (str) – name of the variable, e.g. ‘p_nom’
- pypsa.linopf.define_objective(n, sns)#
Defines and writes out the objective function.
- pypsa.linopf.define_ramp_limit_constraints(n, sns, c)#
Defines ramp limits for a given component with valid ramplimit.
- pypsa.linopf.define_storage_unit_constraints(n, sns)#
Defines state of charge (soc) constraints for storage units. In principal the constraints states:
previous_soc + p_store - p_dispatch + inflow - spill == soc
- pypsa.linopf.define_store_constraints(n, sns)#
Defines energy balance constraints for stores. In principal this states:
previous_e - p == e
- pypsa.linopf.ilopf(n, snapshots=None, msq_threshold=0.05, min_iterations=1, max_iterations=100, track_iterations=False, **kwargs)#
Iterative linear optimization updating the line parameters for passive AC and DC lines. This is helpful when line expansion is enabled. After each sucessful solving, line impedances and line resistance are recalculated based on the optimization result. If warmstart is possible, it uses the result from the previous iteration to fasten the optimization.
- Parameters
snapshots (list or index slice) – A list of snapshots to optimise, must be a subset of network.snapshots, defaults to network.snapshots
msq_threshold (float, default 0.05) – Maximal mean square difference between optimized line capacity of the current and the previous iteration. As soon as this threshold is undercut, and the number of iterations is bigger than ‘min_iterations’ the iterative optimization stops
min_iterations (integer, default 1) – Minimal number of iteration to run regardless whether the msq_threshold is already undercut
max_iterations (integer, default 100) – Maximal number of iterations to run regardless whether msq_threshold is already undercut
track_iterations (bool, default False) – If True, the intermediate branch capacities and values of the objective function are recorded for each iteration. The values of iteration 0 represent the initial state.
**kwargs – Keyword arguments of the lopf function which runs at each iteration
- pypsa.linopf.network_lopf(n, snapshots=None, solver_name='cbc', solver_logfile=None, extra_functionality=None, multi_investment_periods=False, skip_objective=False, skip_pre=False, extra_postprocessing=None, formulation='kirchhoff', transmission_losses=0, keep_references=False, keep_files=False, keep_shadowprices=['Bus', 'Line', 'Transformer', 'Link', 'GlobalConstraint'], solver_options=None, warmstart=False, store_basis=False, solver_dir=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_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)
solver_dir (str, default None) – Path to directory where necessary files are written, default None leads to the default temporary directory used by tempfile.mkstemp().
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”]
transmission_losses (int, default 0) – Whether an approximation of transmission losses should be included in the linearised power flow formulation. A passed number will denote the number of tangents used for the piecewise linear approximation. Defaults to 0 which ignores losses.
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.
skip_pre (bool, default False) – Skip the preliminary steps of computing topology.
skip_objective (bool, default False) – Skip writing the default objective function. If False, a custom objective has to be defined via extra_functionality.
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.
warmstart (bool or string, default False) – Use this to warmstart the optimization. Pass a string which gives the path to the basis file. If set to True, a path to a basis file must be given in network.basis_fn.
store_basis (bool, default False) – Whether to store the basis of the optimization results. If True, the path to the basis file is saved in network.basis_fn. Note that a basis can only be stored if simplex, dual-simplex, or barrier with crossover is used for solving.
keep_references (bool, default False) – Keep the references of variable and constraint names withing the network. These can be looked up in n.vars and n.cons after solving.
keep_shadowprices (bool or list of component names) – Keep shadow prices for all constraints, if set to True. If a list is passed the shadow prices will only be parsed for those constraint names. Defaults to [‘Bus’, ‘Line’, ‘GlobalConstraint’]. After solving, the shadow prices can be retrieved using
pypsa.linopt.get_dual()
with corresponding name
- pypsa.linopf.prepare_lopf(n, snapshots=None, keep_files=False, skip_objective=False, extra_functionality=None, solver_dir=None, transmission_losses=0)#
Sets up the linear problem and writes it out to a lp file.
- Returns
Tuple (fdp, problem_fn) indicating the file descriptor and the file name of
the lp file
Pyomo Optimisation Module#
Tools for fast Pyomo linear problem building.
Essentially this library replaces Pyomo expressions with more strict objects with a pre-defined affine structure.
- class pypsa.opt.LConstraint(lhs=None, sense='==', rhs=None)#
Constraint of optimisation variables.
Linear constraint of the form:
lhs sense rhs
- Parameters
lhs (LExpression) –
sense (string) –
rhs (LExpression) –
- class pypsa.opt.LExpression(variables=None, constant=0.0)#
Affine expression of optimisation variables.
Affine expression of the form:
constant + coeff1*var1 + coeff2*var2 + ….
- pypsa.opt.l_constraint(model, name, constraints, *args)#
A replacement for pyomo’s Constraint that quickly builds linear constraints.
Instead of
model.name = Constraint(index1, index2, …, rule=f)
call instead
l_constraint(model, name, constraints, index1, index2, …)
where constraints is a dictionary of constraints of the form:
constraints[i] = LConstraint object
OR using the soon-to-be-deprecated list format:
constraints[i] = [[(coeff1, var1), (coeff2, var2), …], sense, constant_term]
i.e. the first argument is a list of tuples with the variables and their coefficients, the second argument is the sense string (must be one of “==”, “<=”, “>=”, “><”) and the third argument is the constant term (a float). The sense “><” allows lower and upper bounds and requires constant_term to be a 2-tuple.
Variables may be repeated with different coefficients, which pyomo will sum up.
- Parameters
model (pyomo.environ.ConcreteModel) –
name (string) – Name of constraints to be constructed
constraints (dict) – A dictionary of constraints (see format above)
*args – Indices of the constraints
- pypsa.opt.l_objective(model, objective=None, sense=1)#
A replacement for pyomo’s Objective that quickly builds linear objectives.
Instead of
model.objective = Objective(expr=sum(vars[i]*coeffs[i] for i in index)+constant)
call instead
l_objective(model, objective, sense)
where objective is an LExpression.
Variables may be repeated with different coefficients, which pyomo will sum up.
- Parameters
model (pyomo.environ.ConcreteModel) –
objective (LExpression) –
sense (minimize / maximize) –
Pyomo Power System Optimisation#
Optimal Power Flow functions.
- pypsa.opf.define_nodal_balances(network, snapshots)#
Construct the nodal balance for all elements except the passive branches.
Store the nodal balance expression in network._p_balance.
- pypsa.opf.define_passive_branch_flows_with_kirchhoff(network, snapshots, skip_vars=False)#
Define passive branch flows with the kirchoff method.
- pypsa.opf.define_sub_network_cycle_constraints(subnetwork, snapshots, passive_branch_p, attribute)#
Constructs cycle_constraints for a particular subnetwork.
- pypsa.opf.network_lopf(network, snapshots=None, solver_name='glpk', solver_io=None, skip_pre=False, extra_functionality=None, multi_investment_periods=False, solver_logfile=None, solver_options={}, keep_files=False, formulation='angles', transmission_losses=0, 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”]
transmission_losses (int) – Whether an approximation of transmission losses should be included in the linearised power flow formulation. A passed number will denote the number of tangents used for the piecewise linear approximation. Defaults to 0, which ignores losses.
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.
- Return type
None
- pypsa.opf.network_lopf_build_model(network, snapshots=None, skip_pre=False, formulation='angles', transmission_losses=0, ptdf_tolerance=0.0)#
Build pyomo model for 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
skip_pre (bool, default False) – Skip the preliminary steps of computing topology, calculating dependent values and finding bus controls.
formulation (string) – Formulation of the linear power flow equations to use; must be one of [“angles”, “cycles”, “kirchhoff”, “ptdf”]
transmission_losses (int, 0) –
ptdf_tolerance (float) – Value below which PTDF entries are ignored
- Return type
network.model
- pypsa.opf.network_lopf_prepare_solver(network, solver_name='glpk', solver_io=None)#
Prepare solver for linear optimal power flow.
- Parameters
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”
- Return type
None
- pypsa.opf.network_lopf_solve(network, snapshots=None, formulation='angles', transmission_losses=0, solver_options={}, solver_logfile=None, keep_files=False, free_memory={'pyomo'}, extra_postprocessing=None)#
Solve linear optimal power flow for a group of snapshots and extract results.
- Parameters
snapshots (list or index slice) – A list of snapshots to optimise, must be a subset of network.snapshots, defaults to network.snapshots
formulation (string) – Formulation of the linear power flow equations to use; must be one of [“angles”, “cycles”, “kirchhoff”, “ptdf”]; must match formulation used for building the model.
transmission_losses (int, default 0) –
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)
solver_logfile (None|string) – If not None, sets the logfile option of the solver.
keep_files (bool, default False) – Keep the files that pyomo constructs from OPF problem construction, e.g. .lp file - useful for debugging
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.
- Return type
None
- pypsa.opf.network_opf(network, snapshots=None)#
Optimal power flow for snapshots.
Contingency Analysis#
Functionality for contingency analysis, such as branch outages.
- pypsa.contingency.calculate_BODF(sub_network, 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()
- pypsa.contingency.network_lpf_contingency(network, 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)
- pypsa.contingency.network_sclopf(network, snapshots=None, branch_outages=None, solver_name='glpk', pyomo=True, skip_pre=False, extra_functionality=None, solver_options={}, keep_files=False, formulation='kirchhoff', 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”
pyomo (bool, default True) – Whether to use pyomo for building and solving the model, setting this to False saves a lot of memory and time.
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, default "kirchhoff") – Formulation of the linear power flow equations to use; must be one of [“angles”, “cycles”, “kirchoff”, “ptdf”]
ptdf_tolerance (float) –
- Return type
None
Examples
>>> network.sclopf(network, branch_outages)
Statistics#
Statistics Accessor.
- class pypsa.statistics.StatisticsAccessor(network)#
Accessor to calculate different statistical values.
- capacity_factor(comps=None, aggregate_time='mean', aggregate_groups='sum', groupby=None)#
Calculate the capacity factor of components in the network.
For information on the list of arguments, see the docs in Network.statistics or pypsa.statitics.StatisticsAccessor.
- capex(comps=None, aggregate_groups='sum', groupby=None)#
Calculate the capital expenditure of the network.
For information on the list of arguments, see the docs in Network.statistics or pypsa.statitics.StatisticsAccessor.
- curtailment(comps=None, aggregate_time='sum', aggregate_groups='sum', groupby=None)#
Calculate the curtailment of components in the network.
The calculation only considers assets with a p_max_pu time series, which is used to quantify the available power potential.
For information on the list of arguments, see the docs in Network.statistics or pypsa.statitics.StatisticsAccessor.
- expanded_capacity(comps=None, aggregate_groups='sum', groupby=None)#
Calculate the expanded capacity of the network components.
For information on the list of arguments, see the docs in Network.statistics or pypsa.statistics.StatisticsAccessor.
- get_bus_and_carrier(c)#
Get the buses and nice carrier names for a component.
- get_carrier(c)#
Get the buses and nice carrier names for a component.
- installed_capacity(comps=None, aggregate_groups='sum', groupby=None)#
Calculate the installed capacity of the network components.
For information on the list of arguments, see the docs in Network.statistics or pypsa.statitics.StatisticsAccessor.
- market_value(comps=None, aggregate_time='mean', aggregate_groups='sum', groupby=None)#
Calculate the market value of components in the network.
For information on the list of arguments, see the docs in Network.statistics or pypsa.statitics.StatisticsAccessor.
- opex(comps=None, aggregate_time='sum', aggregate_groups='sum', groupby=None)#
Calculate the operational expenditure in the network.
For information on the list of arguments, see the docs in Network.statistics or pypsa.statitics.StatisticsAccessor.
- optimal_capacity(comps=None, aggregate_groups='sum', groupby=None)#
Calculate the optimal capacity of the network components.
For information on the list of arguments, see the docs in Network.statistics or pypsa.statitics.StatisticsAccessor.
- revenue(comps=None, aggregate_time='sum', aggregate_groups='sum', groupby=None)#
Calculate the revenue of components in the network.
For information on the list of arguments, see the docs in Network.statistics or pypsa.statitics.StatisticsAccessor.
- supply(comps=None, aggregate_time='sum', aggregate_groups='sum', groupby=None)#
Calculate the supply of components in the network.
For information on the list of arguments, see the docs in Network.statistics or pypsa.statitics.StatisticsAccessor.
- withdrawal(comps=None, aggregate_time='sum', aggregate_groups='sum', groupby=None)#
Calculate the withdrawal of components in the network.
For information on the list of arguments, see the docs in Network.statistics or pypsa.statitics.StatisticsAccessor.
- pypsa.statistics.aggregate_components(n, func, agg='sum', comps=None, groupby=None)#
Apply a function and group the result for a collection of components.
- pypsa.statistics.aggregate_timeseries(df, weights, agg='sum')#
Calculate the weighed sum or average of a DataFrame or Series.
- pypsa.statistics.get_bus_and_carrier(n, c)#
Get the buses and nice carrier names for a component.
- pypsa.statistics.get_carrier(n, c)#
Get the nice carrier names for a component.
- pypsa.statistics.get_operation(n, c)#
Get the operation time series of a component.
- pypsa.statistics.get_weightings(n, c)#
Get the relevant snapshot weighting for a component.
Plotting Networks#
Functions for plotting networks.
- class pypsa.plot.HandlerCircle(patch_func=None, **kwargs)#
Legend Handler used to create circles for legend entries.
This handler resizes the circles in order to match the same dimensional scaling as in the applied axis.
- create_artists(legend, orig_handle, xdescent, ydescent, width, height, fontsize, trans)#
Return the legend artists generated.
- Parameters
legend (~matplotlib.legend.Legend) – The legend for which these legend artists are being created.
orig_handle (~matplotlib.artist.Artist or similar) – The object for which these legend artists are being created.
xdescent (int) – The rectangle (xdescent, ydescent, width, height) that the legend artists being created should fit within.
ydescent (int) – The rectangle (xdescent, ydescent, width, height) that the legend artists being created should fit within.
width (int) – The rectangle (xdescent, ydescent, width, height) that the legend artists being created should fit within.
height (int) – The rectangle (xdescent, ydescent, width, height) that the legend artists being created should fit within.
fontsize (int) – The fontsize in pixels. The legend artists being created should be scaled according to the given fontsize.
trans (~matplotlib.transforms.Transform) – The transform that is applied to the legend artists being created. Typically from unit coordinates in the handler box to screen coordinates.
- pypsa.plot.add_legend_circles(ax, sizes, labels, srid=4326, patch_kw={}, legend_kw={})#
Add a legend for reference circles.
- Parameters
ax (matplotlib ax) –
sizes (list-like, float) – Size of the reference circle; for example [3, 2, 1]
labels (list-like, str) – Label of the reference circle; for example [“30 GW”, “20 GW”, “10 GW”]
patch_kw (defaults to {}) – Keyword arguments passed to matplotlib.patches.Circle
legend_kw (defaults to {}) – Keyword arguments passed to ax.legend
- pypsa.plot.add_legend_lines(ax, sizes, labels, patch_kw={}, legend_kw={})#
Add a legend for lines and links.
- Parameters
ax (matplotlib ax) –
sizes (list-like, float) – Size of the line reference; for example [3, 2, 1]
labels (list-like, str) – Label of the line reference; for example [“30 GW”, “20 GW”, “10 GW”]
patch_kw (defaults to {}) – Keyword arguments passed to plt.Line2D
legend_kw (defaults to {}) – Keyword arguments passed to ax.legend
- pypsa.plot.add_legend_patches(ax, colors, labels, patch_kw={}, legend_kw={})#
Add patches for color references.
- Parameters
ax (matplotlib ax) –
colors (list-like, float) – Color of the patch; for example [“r”, “g”, “b”]
labels (list-like, str) – Label of the patch; for example [“wind”, “solar”, “gas”]
patch_kw (defaults to {}) – Keyword arguments passed to matplotlib.patches.Patch
legend_kw (defaults to {}) – Keyword arguments passed to ax.legend
- pypsa.plot.autogenerate_coordinates(n, assign=False, layouter=None)#
Automatically generate bus coordinates for the network graph according to a layouting function from networkx.
- Parameters
n (pypsa.Network) –
assign (bool, default False) – Assign generated coordinates to the network bus coordinates at
n.buses[['x', 'y']]
.layouter (networkx.drawing.layout function, default None) –
Layouting function from networkx. See list of available options. By default coordinates are determined for a planar layout if the network graph is planar, otherwise for a Kamada-Kawai layout.
- Returns
coordinates – DataFrame containing the generated coordinates with buses as index and [‘x’, ‘y’] as columns.
- Return type
pd.DataFrame
Examples
>>> autogenerate_coordinates(network) >>> autogenerate_coordinates(network, assign=True, layouter=nx.circle_layout)
- pypsa.plot.compute_bbox_with_margins(margin, x, y)#
Helper function to compute bounding box for the plot.
- pypsa.plot.directed_flow(coords, flow, color, area_factor=1, cmap=None, alpha=1)#
Helper function to generate arrows from flow data.
- pypsa.plot.iplot(n, fig=None, bus_colors='cadetblue', bus_alpha=1, bus_sizes=10, bus_cmap=None, bus_colorbar=None, bus_text=None, line_colors='rosybrown', link_colors='darkseagreen', transformer_colors='orange', line_widths=3, link_widths=3, transformer_widths=3, line_text=None, link_text=None, transformer_text=None, layouter=None, title='', size=None, branch_components=None, iplot=True, jitter=None, mapbox=False, mapbox_style='open-street-map', mapbox_token='', mapbox_parameters={})#
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 “cadetblue”. If bus_sizes is a pandas.Series with a Multiindex, bus_colors defaults to the n.carriers[‘color’] column.
bus_alpha (float) – Adds alpha channel to buses, defaults to 1.
bus_sizes (float/pandas.Series) – Sizes of bus points, defaults to 10.
bus_cmap (plt.cm.ColorMap/str) – If bus_colors are floats, this color map will assign the colors
bus_colorbar (dict) – Plotly colorbar, e.g. {‘title’ : ‘my colorbar’}
bus_text (pandas.Series) – Text for each bus, defaults to bus names
line_colors (str/pandas.Series) – Colors for the lines, defaults to ‘rosybrown’.
link_colors (str/pandas.Series) – Colors for the links, defaults to ‘darkseagreen’.
transfomer_colors (str/pandas.Series) – Colors for the transfomer, defaults to ‘orange’.
line_widths (dict/pandas.Series) – Widths of lines, defaults to 1.5
link_widths (dict/pandas.Series) – Widths of links, defaults to 1.5
transformer_widths (dict/pandas.Series) – Widths of transformer, defaults to 1.5
line_text (pandas.Series) – Text for lines, defaults to line names.
link_text (pandas.Series) – Text for links, defaults to link names.
tranformer_text (pandas.Series) – Text for transformers, defaults to transformer names.
layouter (networkx.drawing.layout function, default None) –
Layouting function from networkx which overrules coordinates given in
n.buses[['x', 'y']]
. See list of available options.title (string) – Graph title
size (None|tuple) – Tuple specifying width and height of figure; e.g. (width, heigh).
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
mapbox (bool, default False) – Switch to use Mapbox.
mapbox_style (str, default 'open-street-map') –
Define the mapbox layout style of the interactive plot. If this is set to a mapbox layout, the argument
mapbox_token
must be a valid Mapbox API access token.- Valid open layouts are:
open-street-map, white-bg, carto-positron, carto-darkmatter, stamen-terrain, stamen-toner, stamen-watercolor
- Valid mapbox layouts are:
basic, streets, outdoors, light, dark, satellite, satellite-streets
mapbox_token (string) – Mapbox API access token. Obtain from https://www.mapbox.com. Can also be included in mapbox_parameters as accesstoken=mapbox_token.
mapbox_parameters (dict) – Configuration parameters of the Mapbox layout. E.g. {“bearing”: 5, “pitch”: 10, “zoom”: 1, “style”: ‘dark’}.
- Returns
fig
- Return type
dictionary for plotly figure
- pypsa.plot.plot(n, margin=0.05, ax=None, geomap=True, projection=None, bus_colors='cadetblue', bus_alpha=1, bus_sizes=0.02, bus_cmap=None, bus_norm=None, bus_split_circles=False, line_colors='rosybrown', link_colors='darkseagreen', transformer_colors='orange', line_alpha=1, link_alpha=1, transformer_alpha=1, line_widths=1.5, link_widths=1.5, transformer_widths=1.5, line_cmap=None, link_cmap=None, transformer_cmap=None, line_norm=None, link_norm=None, transformer_norm=None, flow=None, branch_components=None, layouter=None, title='', boundaries=None, geometry=False, jitter=None, color_geomap=None)#
Plot the network buses and lines using matplotlib and cartopy.
- Parameters
margin (float, defaults to 0.05) – Margin at the sides as proportion of distance between max/min x, y Will be ignored if boundaries are given.
ax (matplotlib ax, defaults to plt.gca()) – Axis to which to plot the network
geomap (bool/str, default True) – Switch to use Cartopy and draw geographical features. If string is passed, it will be used as a resolution argument, valid options are ‘10m’, ‘50m’ and ‘110m’.
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 “cadetblue”. If bus_sizes is a pandas.Series with a Multiindex, bus_colors defaults to the n.carriers[‘color’] column.
bus_alpha (float) – Adds alpha channel to buses, defaults to 1.
bus_sizes (dict/pandas.Series) – Sizes of bus points, defaults to 1e-2. If a multiindexed Series is passed, the function will draw pies for each bus (first index level) with segments of different color (second index level). Such a Series is ob- tained by e.g. n.generators.groupby([‘bus’, ‘carrier’]).p_nom.sum()
bus_cmap (plt.cm.ColorMap/str) – If bus_colors are floats, this color map will assign the colors
bus_norm (plt.Normalize|matplotlib.colors.*Norm) – The norm applied to the bus_cmap.
bus_split_circles (bool, default False) – Draw half circles if bus_sizes is a pandas.Series with a Multiindex. If set to true, the upper half circle per bus then includes all positive values of the series, the lower half circle all negative values. Defaults to False.
line_colors (str/pandas.Series) – Colors for the lines, defaults to ‘rosybrown’.
link_colors (str/pandas.Series) – Colors for the links, defaults to ‘darkseagreen’.
transfomer_colors (str/pandas.Series) – Colors for the transfomer, defaults to ‘orange’.
line_alpha (str/pandas.Series) – Alpha for the lines, defaults to 1.
link_alpha (str/pandas.Series) – Alpha for the links, defaults to 1.
transfomer_alpha (str/pandas.Series) – Alpha for the transfomer, defaults to 1.
line_widths (dict/pandas.Series) – Widths of lines, defaults to 1.5
link_widths (dict/pandas.Series) – Widths of links, defaults to 1.5
transformer_widths (dict/pandas.Series) – Widths of transformer, defaults to 1.5
line_cmap (plt.cm.ColorMap/str|dict) – If line_colors are floats, this color map will assign the colors.
link_cmap (plt.cm.ColorMap/str|dict) – If link_colors are floats, this color map will assign the colors.
transformer_cmap (plt.cm.ColorMap/str|dict) – If transformer_colors are floats, this color map will assign the colors.
line_norm (plt.Normalize|matplotlib.colors.*Norm) – The norm applied to the line_cmap.
link_norm (plt.Normalize|matplotlib.colors.*Norm) – The norm applied to the link_cmap.
transformer_norm (matplotlib.colors.Normalize|matplotlib.colors.*Norm) – The norm applied to the transformer_cmap.
flow (snapshot/pandas.Series/function/string) – Flow to be displayed in the plot, defaults to None. If an element of n.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.
layouter (networkx.drawing.layout function, default None) –
Layouting function from networkx which overrules coordinates given in
n.buses[['x', 'y']]
. See list of available options.title (string) – Graph title
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
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. If False, no geographical features are plotted.
- Returns
bus_collection, branch_collection1, … – Collections for buses and branches.
- Return type
tuple of Collections
- pypsa.plot.projected_area_factor(ax, original_crs=4326)#
Helper function to get the area scale of the current projection in reference to the default projection.
The default ‘original crs’ is assumed to be 4326, which translates to the cartopy default cartopy.crs.PlateCarree()
Network Clustering#
Functions for computing network clusters.
- class pypsa.clustering.spatial.Clustering(network, busmap, linemap, linemap_positive, linemap_negative)#
- busmap#
Alias for field number 1
- linemap#
Alias for field number 2
- linemap_negative#
Alias for field number 4
- linemap_positive#
Alias for field number 3
- network#
Alias for field number 0
- pypsa.clustering.spatial.busmap_by_greedy_modularity(network, n_clusters, buses_i=None)#
Create a busmap according to Clauset-Newman-Moore greedy modularity maximization [1].
- Parameters
network (pypsa.Network) –
n_clusters (int) – Final number of clusters desired.
buses_i (None | pandas.Index, default=None) – Subset of buses to cluster. If None, all buses are considered.
- Returns
busmap – Mapping of network.buses to clusters (indexed by non-negative integers).
- Return type
pandas.Series
References
- 1
Clauset, A., Newman, M. E., & Moore, C. “Finding community structure in very large networks.” Physical Review E 70(6), 2004.
- pypsa.clustering.spatial.busmap_by_hac(network, n_clusters, buses_i=None, branch_components=None, feature=None, affinity='euclidean', linkage='ward', **kwargs)#
Create a busmap according to Hierarchical Agglomerative Clustering.
- Parameters
network (pypsa.Network) –
n_clusters (int) – Final number of clusters desired.
buses_i (None | pandas.Index, default=None) – Subset of buses to cluster. If None, all buses are considered.
branch_components (List, default=None) – Subset of all branch_components in the network. If None, all branch_components are considered.
feature (None | pandas.DataFrame, default=None) – Feature to be considered for the clustering. The DataFrame must be indexed with buses_i. If None, all buses have the same similarity.
affinity (str or callable, default=’euclidean’) – Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”, “manhattan”, “cosine”, or “precomputed”. If linkage is “ward”, only “euclidean” is accepted. If “precomputed”, a distance matrix (instead of a similarity matrix) is needed as input for the fit method.
linkage (‘ward’, ‘complete’, ‘average’ or ‘single’, default=’ward’) – Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion. - ‘ward’ minimizes the variance of the clusters being merged. - ‘average’ uses the average of the distances of each observation of the two sets. - ‘complete’ or ‘maximum’ linkage uses the maximum distances between all observations of the two sets. - ‘single’ uses the minimum of the distances between all observations of the two sets.
kwargs – Any remaining arguments to be passed to Hierarchical Clustering (e.g. memory, connectivity).
- Returns
busmap – Mapping of network.buses to clusters (indexed by non-negative integers).
- Return type
pandas.Series
- pypsa.clustering.spatial.busmap_by_kmeans(network, bus_weightings, n_clusters, buses_i=None, **kwargs)#
Create a bus map from the clustering of buses in space with a weighting.
- Parameters
network (pypsa.Network) – The buses must have coordinates x, y.
bus_weightings (pandas.Series) – Series of integer weights for buses, indexed by bus names.
n_clusters (int) – Final number of clusters desired.
buses_i (None|pandas.Index) – If not None (default), subset of buses to cluster.
kwargs – Any remaining arguments to be passed to KMeans (e.g. n_init, n_jobs).
- Returns
busmap – Mapping of network.buses to k-means clusters (indexed by non-negative integers).
- Return type
pandas.Series
- pypsa.clustering.spatial.busmap_by_stubs(network, matching_attrs=None)#
Create a busmap by reducing stubs and stubby trees (i.e. sequentially reducing dead-ends).
- Parameters
network (pypsa.Network) –
matching_attrs (None|[str]) – bus attributes clusters have to agree on
- Returns
busmap – Mapping of network.buses to k-means clusters (indexed by non-negative integers).
- Return type
pandas.Series
- pypsa.clustering.spatial.greedy_modularity_clustering(network, n_clusters, buses_i=None, line_length_factor=1.0)#
Create a busmap according to Clauset-Newman-Moore greedy modularity maximization [1].
- Parameters
network (pypsa.Network) –
n_clusters (int) – Final number of clusters desired.
buses_i (None | pandas.Index, default=None) – Subset of buses to cluster. If None, all buses are considered.
line_length_factor (float, default=1.0) – Factor to multiply the spherical distance between two new buses to get new line lengths.
- Returns
Clustering – A named tuple containing network, busmap and linemap.
- Return type
named tuple
References
- 1
Clauset, A., Newman, M. E., & Moore, C. “Finding community structure in very large networks.” Physical Review E 70(6), 2004.
- pypsa.clustering.spatial.hac_clustering(network, n_clusters, buses_i=None, branch_components=None, feature=None, affinity='euclidean', linkage='ward', line_length_factor=1.0, **kwargs)#
Cluster the network using Hierarchical Agglomerative Clustering.
- Parameters
network (pypsa.Network) – The buses must have coordinates x, y.
buses_i (None | pandas.Index, default=None) – Subset of buses to cluster. If None, all buses are considered.
branch_components (List, default=["Line", "Link"]) – Subset of all branch_components in the network.
feature (None | pandas.DataFrame, default=None) – Feature to be considered for the clustering. The DataFrame must be indexed with buses_i. If None, all buses have the same similarity.
affinity (str or callable, default=’euclidean’) – Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”, “manhattan”, “cosine”, or “precomputed”. If linkage is “ward”, only “euclidean” is accepted. If “precomputed”, a distance matrix (instead of a similarity matrix) is needed as input for the fit method.
linkage (‘ward’, ‘complete’, ‘average’ or ‘single’, default=’ward’) – Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion. - ‘ward’ minimizes the variance of the clusters being merged. - ‘average’ uses the average of the distances of each observation of the two sets. - ‘complete’ or ‘maximum’ linkage uses the maximum distances between all observations of the two sets. - ‘single’ uses the minimum of the distances between all observations of the two sets.
line_length_factor (float, default=1.0) – Factor to multiply the spherical distance between two new buses in order to get new line lengths.
kwargs – Any remaining arguments to be passed to Hierarchical Clustering (e.g. memory, connectivity).
- Returns
Clustering – A named tuple containing network, busmap and linemap
- Return type
named tuple
- pypsa.clustering.spatial.kmeans_clustering(network, bus_weightings, n_clusters, line_length_factor=1.0, **kwargs)#
Cluster the network according to k-means clustering of the buses.
Buses can be weighted by an integer in the series bus_weightings.
Note that this clustering method completely ignores the branches of the network.
- Parameters
network (pypsa.Network) – The buses must have coordinates x, y.
bus_weightings (pandas.Series) – Series of integer weights for buses, indexed by bus names.
n_clusters (int) – Final number of clusters desired.
line_length_factor (float) – Factor to multiply the spherical distance between new buses in order to get new line lengths.
kwargs – Any remaining arguments to be passed to KMeans (e.g. n_init, n_jobs)
- Returns
Clustering – A named tuple containing network, busmap and linemap
- Return type
named tuple
Georeferencing Utilities#
Functionality to help with georeferencing and calculate distances/areas.
- pypsa.geo.haversine(a, b)#
Compute the distance in km between two sets of points in long/lat.
One dimension of a* should be 2; longitude then latitude. Uses haversine formula.
- Parameters
a (array/list of at most 2 dimensions) – One dimension must be 2
b (array/list of at most 2 dimensions) – One dimension must be 2
- Returns
distance_km – 2-dimensional array of distances in km between points in a, b
- Return type
array
Examples
>>> haversine([10.1, 52.6], [[10.8, 52.1], [-34, 56.]]) array([[ 73.15416698, 2836.6707696 ]])
See also
haversine_pts
Determine pointwise crow-fly distance
- pypsa.geo.haversine_pts(a, b)#
Determines crow-flies distance between points in a and b.
ie. distance[i] = crow-fly-distance between a[i] and b[i]
- Parameters
a (N x 2 - array of dtype float) – Geographical coordinates in longitude, latitude ordering
b (N x 2 - array of dtype float) – Geographical coordinates in longitude, latitude ordering
- Returns
c – Distance in km
- Return type
N - array of dtype float
See also
haversine
Matrix of distances between all pairs in a and b