Merge branch 'paper/kmedoids-dijkstra-2' of https://github.com/openeg…

…o/eTraGo into paper/kmedoids-dijkstra-2
openego · May 15, 2024 · 80da97d · 80da97d
2 parents c5a94d5 + 4f7f76c
commit 80da97d
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diff --git a/etrago/appl_ehv.py b/etrago/appl_ehv.py
@@ -67,7 +67,7 @@
         "FeasibilityTol": 1e-05,
         "crossover": 0,
         "logFile": "solver_etrago.log",
-        "threads": 10,
+        "threads": 16,
         "method": 2,
         "BarHomogeneous": 1
     },
@@ -139,7 +139,7 @@
         "random_state": 42,  # random state for replicability of kmeans results
         "active": True,  # choose if clustering is activated
         "method": "kmedoids-dijkstra",  # choose clustering method: kmeans or kmedoids-dijkstra
-        "n_clusters_AC": 30,  # total number of resulting AC nodes (DE+foreign)
+        "n_clusters_AC": 300,  # total number of resulting AC nodes (DE+foreign)
         "cluster_foreign_AC": False,  # take foreign AC buses into account, True or False
         "method_gas": "kmedoids-dijkstra",  # choose clustering method: kmeans or kmedoids-dijkstra
         "n_clusters_gas": 17,  # total number of resulting CH4 nodes (DE+foreign)
@@ -156,7 +156,7 @@
         "n_init": 10,  # affects clustering algorithm, only change when neccesary
         "max_iter": 100,  # affects clustering algorithm, only change when neccesary
         "tol": 1e-6, # affects clustering algorithm, only change when neccesary
-        "CPU_cores": 10, # number of cores used during clustering. "max" for all cores available.
+        "CPU_cores": 16, # number of cores used during clustering. "max" for all cores available.
     },
     "sector_coupled_clustering": {
         "active": True,  # choose if clustering is activated
@@ -483,19 +483,23 @@ def run_etrago(args, json_path):
 
     # import network from database
     etrago.build_network_from_db()
-
-    print(' ')
-    print(etrago.network.buses.carrier.unique())
-    print(' ')
 
-    etrago.network.transformers.loc["99999", :] = etrago.network.transformers.loc["27248",:]
-    etrago.network.transformers.loc["99999","bus0"] = "32756"
-    etrago.network.transformers.loc["99999","bus1"] = "36163"
+    new_trafo = etrago.network.transformers.loc["27248",:]
+    new_trafo["bus0"] = "32756"
+    new_trafo["bus1"] = "36163"
+    new_trafo.name = "99999"
+    new_trafo = new_trafo.to_frame()
+    new_trafo = new_trafo.T
+    etrago.network.import_components_from_dataframe(new_trafo, "Transformer" )
+
+    new_line = etrago.network.lines.loc["26463", :]
+    new_line["bus0"] = "32788"
+    new_line["bus1"] = "35597"
+    new_line.name = "99999"
+    new_line = new_line.to_frame()
+    new_line = new_line.T
+    etrago.network.import_components_from_dataframe(new_line, "Line" )
 
-    etrago.network.lines.loc["99999", :] = etrago.network.lines.loc["26463", :]
-    etrago.network.lines.loc["99999", "bus0"] = "32788"
-    etrago.network.lines.loc["99999", "bus1"] = "32786"
-
     etrago.network.storage_units.lifetime = np.inf
     etrago.network.transformers.lifetime = 40  # only temporal fix
     etrago.network.lines.lifetime = 40  # only temporal fix until either the
@@ -656,9 +660,9 @@ def run_etrago(args, json_path):
     etrago.ehv_clustering()
 
     print(' ')
-    print(etrago.network.buses)
+    print('spatial resolution after ehv clustering:')
     print(len(etrago.network.buses))
-    print(' ')
+    print(' ')   
 
     print(' ')
     print('start spatial clustering')
@@ -672,7 +676,7 @@ def run_etrago(args, json_path):
     print('stop spatial clustering')
     print(datetime.datetime.now())
     print(' ')
-
+      
     from etrago.tools.utilities import modular_weight
     print(' ')
     print('Modularity')
@@ -698,16 +702,17 @@ def run_etrago(args, json_path):
     #etrago.network.storage_units.p_nom_extendable=False
     #etrago.export_to_csv("original")
     #etrago = Etrago(csv_folder_name="original")
-    etrago.network.lines.loc[etrago.network.lines.r == 0.0, "r"] = 0.1
+
     # start linear optimal powerflow calculations
     # needs to be adjusted for new sectors
+    etrago.network.lines.loc[etrago.network.lines.r == 0.0, "r"] = 0.1
     etrago.lopf()
 
     # conduct lopf with full complex timeseries for dispatch disaggregation
-    etrago.dispatch_disaggregation()
-
+    #etrago.dispatch_disaggregation()
+    #etrago.network.lines.loc[etrago.network.lines.r == 0.0, "r"] = 10
     # start power flow based on lopf results
-    etrago.pf_post_lopf()
+    #etrago.pf_post_lopf()
 
     # spatial disaggregation
     # needs to be adjusted for new sectors