etrago.cluster package

Submodules

etrago.cluster.networkclustering module

Networkclustering.py defines the methods to cluster power grid networks spatially for applications within the tool eTraGo.

etrago.cluster.networkclustering.cluster_on_extra_high_voltage(network, busmap, with_time=True)

Main function of the EHV-Clustering approach. Creates a new clustered pypsa.Network given a busmap mapping all bus_ids to other bus_ids of the same network.

Parameters:

• network (pypsa.Network) – Container for all network components.
• busmap (dict) – Maps old bus_ids to new bus_ids.
• with_time (bool) – If true time-varying data will also be aggregated.

Returns:

network – Container for all network components of the clustered network.

Return type:

pypsa.Network

etrago.cluster.networkclustering.graph_from_edges(edges)

Constructs an undirected multigraph from a list containing data on weighted edges.

Parameters:edges (list) – List of tuples each containing first node, second node, weight, key. Returns:M Return type::class:`networkx.classes.multigraph.MultiGraph

etrago.cluster.networkclustering.gen(nodes, n, graph)

Generator for applying multiprocessing.

Parameters:

• nodes (list) – List of nodes in the system.
• n (int) – Number of desired multiprocessing units.
• graph (:class:`networkx.classes.multigraph.MultiGraph) – Graph representation of an electrical grid.

Returns:

Return type:

None

etrago.cluster.networkclustering.shortest_path(paths, graph)

Finds the minimum path lengths between node pairs defined in paths.

Parameters:

• paths (list) – List of pairs containing a source and a target node
• graph (:class:`networkx.classes.multigraph.MultiGraph) – Graph representation of an electrical grid.

Returns:

df – DataFrame holding source and target node and the minimum path length.

Return type:

pd.DataFrame

etrago.cluster.networkclustering.busmap_by_shortest_path(network, session, scn_name, version, fromlvl, tolvl, cpu_cores=4)

Creates a busmap for the EHV-Clustering between voltage levels based on dijkstra shortest path. The result is automatically written to the model_draft on the <OpenEnergyPlatform>[www.openenergy-platform.org] database with the name ego_grid_pf_hv_busmap and the attributes scn_name (scenario name), bus0 (node before clustering), bus1 (node after clustering) and path_length (path length). An AssertionError occurs if buses with a voltage level are not covered by the input lists ‘fromlvl’ or ‘tolvl’.

Parameters:

• network (pypsa.Network object) – Container for all network components.
• session (sqlalchemy.orm.session.Session object) – Establishes interactions with the database.
• scn_name (str) – Name of the scenario.
• fromlvl (list) – List of voltage-levels to cluster.
• tolvl (list) – List of voltage-levels to remain.
• cpu_cores (int) – Number of CPU-cores.

Returns:

Return type:

None

etrago.cluster.networkclustering.busmap_from_psql(network, session, scn_name, version)

Retrieves busmap from model_draft.ego_grid_pf_hv_busmap on the <OpenEnergyPlatform>[www.openenergy-platform.org] by a given scenario name. If this busmap does not exist, it is created with default values.

Parameters:

• network (pypsa.Network object) – Container for all network components.
• session (sqlalchemy.orm.session.Session object) – Establishes interactions with the database.
• scn_name (str) – Name of the scenario.

Returns:

busmap – Maps old bus_ids to new bus_ids.

Return type:

dict

etrago.cluster.networkclustering.kmean_clustering(network, n_clusters=10, load_cluster=False, line_length_factor=1.25, remove_stubs=False, use_reduced_coordinates=False, bus_weight_tocsv=None, bus_weight_fromcsv=None, n_init=10, max_iter=300, tol=0.0001, n_jobs=1)

Main function of the k-mean clustering approach. Maps an original network to a new one with adjustable number of nodes and new coordinates.

Parameters:

• network (:class:`pypsa.Network) – Container for all network components.
• n_clusters (int) – Desired number of clusters.
• load_cluster (boolean) – Loads cluster coordinates from a former calculation.
• line_length_factor (float) – Factor to multiply the crow-flies distance between new buses in order to get new line lengths.
• remove_stubs (boolean) – Removes stubs and stubby trees (i.e. sequentially reducing dead-ends).
• use_reduced_coordinates (boolean) – If True, do not average cluster coordinates, but take from busmap.
• bus_weight_tocsv (str) – Creates a bus weighting based on conventional generation and load and save it to a csv file.
• bus_weight_fromcsv (str) – Loads a bus weighting from a csv file to apply it to the clustering algorithm.

Returns:

network – Container for all network components.

Return type:

pypsa.Network object

etrago.cluster.snapshot module

This module contains functions for calculating representative days/weeks based on a pyPSA network object. It is designed to be used for the lopf method. Essentially the tsam package ( https://github.com/FZJ-IEK3-VSA/tsam ), which is developed by Leander Kotzur is used.

Remaining questions/tasks:

• Does it makes sense to cluster normed values?
• Include scaling method for yearly sums

etrago.cluster.snapshot.snapshot_clustering(network, how='daily', clusters=10)

etrago.cluster.snapshot.tsam_cluster(timeseries_df, typical_periods=10, how='daily', extremePeriodMethod='None')

Parameters:

• df (pd.DataFrame) – DataFrame with timeseries to cluster
• extremePeriodMethod ({'None','append','new_cluster_center',) –

  ‘replace_cluster_center’}, default: ‘None’ Method how to integrate extreme Periods into to the typical period profiles. None: No integration at all. ‘append’: append typical Periods to cluster centers ‘new_cluster_center’: add the extreme period as additional cluster

  center. It is checked then for all Periods if they fit better to the this new center or their original cluster center.

  ’replace_cluster_center’: replaces the cluster center of the

  cluster where the extreme period belongs to with the periodly profile of the extreme period. (Worst case system design)

Returns:

timeseries – Clustered timeseries

Return type:

pd.DataFrame

etrago.cluster.snapshot.run(network, n_clusters=None, how='daily', normed=False)

etrago.cluster.snapshot.prepare_pypsa_timeseries(network, normed=False)

etrago.cluster.snapshot.update_data_frames(network, cluster_weights, dates, hours)

Updates the snapshots, snapshots weights and the dataframes based on the original data in the network and the medoids created by clustering these original data.

Parameters:

• network (pyPSA network object) –
• cluster_weights (dictionary) –
• dates (Datetimeindex) –

Returns:

Return type:

network

etrago.cluster.snapshot.manipulate_storage_invest(network, costs=None, wacc=0.05, lifetime=15)

etrago.cluster.snapshot.write_lpfile(network=None, path=None)

etrago.cluster.snapshot.fix_storage_capacity(network, resultspath, n_clusters)

Module contents