Merge pull request #394 from PyPSA/introduce_modularity_clustering

new spatial clustering method: modularity

doc/configtables/clustering.csv

@@ -1,7 +1,7 @@
 ,Unit,Values,Description
 simplify_network,,,
 -- to_substations,bool,"{'true','false'}","Aggregates all nodes without power injection (positive or negative, i.e. demand or generation) to electrically closest ones"
--- algorithm,str,"One of {‘kmeans’, ‘hac’}",
+-- algorithm,str,"One of {‘kmeans’, ‘hac’, ‘modularity‘}",
 -- feature,str,"Str in the format ‘carrier1+carrier2+...+carrierN-X’, where CarrierI can be from {‘solar’, ‘onwind’, ‘offwind’, ‘ror’} and X is one of {‘cap’, ‘time’}.",
 cluster_network
 -- algorithm,str,"One of {‘kmeans’, ‘hac’}",

doc/release_notes.rst

@@ -62,6 +62,8 @@ PyPSA-Eur 0.5.0 (26th July 2022)
 * Hierarchical clustering was introduced. Distance metric is calculated from
   renewable potentials on hourly (feature entry ends with `-time`) or annual
   (feature entry in config end with `-cap`) values.
+  
+* Greedy modularity clustering was introduced. Distance metric is based on electrical distance taking into account the impedance of all transmission lines of the network.
 
 * Techno-economic parameters of technologies (e.g. costs and efficiencies) will
   now be retrieved from a separate repository `PyPSA/technology-data

envs/environment.yaml

@@ -10,7 +10,7 @@ dependencies:
   - python>=3.8
   - pip
 
-  - pypsa>=0.19.1
+  - pypsa>=0.20
   - atlite>=0.2.6
   - dask
 

scripts/cluster_network.py

@@ -135,7 +135,8 @@ import seaborn as sns
 
 from functools import reduce
 
-from pypsa.networkclustering import (busmap_by_kmeans, busmap_by_hac, get_clustering_from_busmap)
+from pypsa.networkclustering import (busmap_by_kmeans, busmap_by_hac,
+                                     busmap_by_greedy_modularity, get_clustering_from_busmap)
 
 import warnings
 warnings.filterwarnings(action='ignore', category=UserWarning)
@@ -313,6 +314,8 @@ def busmap_for_n_clusters(n, n_clusters, solver_name, focus_weights=None, algori
             return prefix + busmap_by_kmeans(n, weight, n_clusters[x.name], buses_i=x.index, **algorithm_kwds)
         elif algorithm == "hac":
             return prefix + busmap_by_hac(n, n_clusters[x.name], buses_i=x.index, feature=feature.loc[x.index])
+        elif algorithm == "modularity":
+            return prefix + busmap_by_greedy_modularity(n, n_clusters[x.name], buses_i=x.index)
         else:
             raise ValueError(f"`algorithm` must be one of 'kmeans' or 'hac'. Is {algorithm}.")