merge dev into features/#21-import-connect-genos

doc/units_table.csv

@@ -8,4 +8,4 @@ Resistance;R;Ohm or Ohm/km;Ohm/km applies to lines
 Reactance;X;Ohm or Ohm/km;Ohm/km applies to lines
 Voltage;V;kV;
 Inductance;L;mH/km;
-Capacitance;C;nF/km;
+Capacitance;C;µF/km;

edisgo/config/config_costs_default.cfg

@@ -0,0 +1,57 @@
+# This file is part of eDisGo, a python package for distribution grid
+# analysis and optimization.
+#
+# It is developed in the project open_eGo: https://openegoproject.wordpress.com
+#
+# eDisGo lives at github: https://github.com/openego/edisgo/
+# The documentation is available on RTD: http://edisgo.readthedocs.io
+
+# the key refers to column 'name' in equipment files
+
+[costs_lv_cables]
+
+NAYY 4x1x300 = 0
+NAYY 4x1x240 = 0
+NAYY 4x1x185 = 0
+NAYY 4x1x150 = 0
+NAYY 4x1x120 = 0
+NAYY 4x1x95 = 0
+NAYY 4x1x50 = 0
+NAYY 4x1x35 = 0
+
+[costs_mv_cables]
+
+NA2XS2Y 3x1x185 RM/25 = 0
+NA2XS2Y 3x1x240 RM/25 = 0
+NA2XS2Y 3x1x300 RM/25 = 0
+NA2XS2Y 3x1x400 RM/35 = 0
+NA2XS2Y 3x1x500 RM/35 = 0
+NA2XS2Y 3x1x150 RE/25 = 0
+NA2XS2Y 3x1x240 = 0
+NA2XS(FL)2Y 3x1x300 RM/25 = 0
+NA2XS(FL)2Y 3x1x400 RM/35 = 0
+NA2XS(FL)2Y 3x1x500 RM/35 = 0
+
+[costs_mv_overhead_lines]
+
+48-AL1/8-ST1A = 0
+94-AL1/15-ST1A = 0
+122-AL1/20-ST1A = 0
+
+[costs_lv_transformers]
+
+50 kVA = 0
+100 kVA = 0
+160 kVA = 0
+250 kVA = 0
+400 kVA = 0
+630 kVA = 0
+800 kVA = 0
+1000 kVA = 0
+
+[costs_mv_transformers]
+
+20 MVA = 0
+32 MVA = 0
+40 MVA = 0
+63 MVA = 0

edisgo/config/config_flexopt_default.cfg

@@ -8,17 +8,57 @@
 
 [grid_expansion]
 
-# standard equipment (column 'name' in equipment files)
+# standard equipment
+# ==================
+# (column 'name' in equipment files)
 std_hv_mv_transformer = 40 MVA
 std_mv_lv_transformer = 630 kVA
 std_mv_line = NA2XS2Y 3x1x185 RM/25
 std_lv_line = NAYY 4x1x150
 
 # allowed voltage deviations
-mv_max_v_deviation = 0.04
-lv_max_v_deviation = 0.04
+# ==========================
+# COMBINED MV+LV
+# --------------
+# hv_mv_trafo_offset:
+#     offset which is set at HV-MV station
+#     (pos. if op. voltage is increased, neg. if decreased)
+hv_mv_trafo_offset = 0.025
+
+# hv_mv_trafo_control_deviation:
+#     control deviation of HV-MV station
+#     (always pos. in config; pos. or neg. usage depending on case in edisgo)
+hv_mv_trafo_control_deviation = 0.015
+
+# mv_lv_max_v_deviation:
+#     max. allowed voltage deviation according to DIN EN 50160
+#     caution: offset and control deviation at HV-MV station must be considered in calculations!
+mv_lv_max_v_deviation = 0.10
+
+# MV ONLY
+# -------
+# mv_load_case_max_v_deviation:
+#     max. allowed voltage deviation in MV grids (load case)
+mv_load_case_max_v_deviation = 0.05
+
+# mv_feedin_case_max_v_deviation:
+#     max. allowed voltage deviation in MV grids (feedin case)
+#     according to BDEW
+mv_feedin_case_max_v_deviation = 0.02
+
+# LV ONLY
+# -------
+# lv_feedin_case_max_v_deviation:
+#     max. allowed voltage deviation in LV grids (load case)
+lv_load_case_max_v_deviation = 0.05
+
+# lv_feedin_case_max_v_deviation:
+#     max. allowed voltage deviation in LV grids (feedin case)
+#     according to VDE-AR-N 4105
+lv_feedin_case_max_v_deviation = 0.03
 
 # load factors
+# ============
 load_factor_hv_mv_transformer = 1.0
 load_factor_mv_lv_transformer = 1.0
 load_factor_mv_line = 0.6

edisgo/config/config_scenario_default.cfg

@@ -10,3 +10,16 @@
 
 name = nep2035
 #name = ego100
+
+pfac_mv_gen = 0.9
+pfac_mv_load = 0.9
+pfac_lv_gen = 0.95
+pfac_lv_load = 0.95
+
+# scale factors describe actual power to nominal power ratio of generators and loads
+# follows values provided by "dena-Verteilnetzstudie. Ausbau- und
+# Innovationsbedarf der Stromverteilnetze in Deutschland bis 2030", .p. 98
+scale_factor_mv_load = 0.15
+scale_factor_lv_load = 0.1
+scale_factor_feedin_pv = 0.85
+scale_factor_feedin_other = 1

edisgo/data/import_data.py

@@ -125,7 +125,7 @@ def _build_lv_grid(ding0_grid, network):
                     grid_district={
                         'geom': ding0_lv_grid.grid_district.geo_data,
                         'population': ding0_lv_grid.grid_district.population},
-                    voltage_nom=ding0_lv_grid.v_level,
+                    voltage_nom=ding0_lv_grid.v_level / 1e3,
                     network=network)
 
                 station = {repr(_):_
@@ -160,7 +160,10 @@ def _build_lv_grid(ding0_grid, network):
 
                 # Create list of branch tee instances and add these to grid's graph
                 branch_tees = {
-                    _: BranchTee(id=_.id_db, geom=_.geo_data, grid=lv_grid)
+                    _: BranchTee(id=_.id_db,
+                                 geom=_.geo_data,
+                                 grid=lv_grid,
+                                 in_building=_.in_building)
                     for _ in ding0_lv_grid._cable_distributors}
                 lv_grid.graph.add_nodes_from(branch_tees.values(),
                                               type='branch_tee')
@@ -182,7 +185,8 @@ def _build_lv_grid(ding0_grid, network):
                           {'line': Line(
                               id=_['branch'].id_db,
                               type=_['branch'].type,
-                              length=_['branch'].length,
+                              length=_['branch'].length / 1e3,
+                              kind=_['branch'].kind,
                               grid=lv_grid)
                           })
                          for _ in edges]
@@ -238,7 +242,8 @@ def _build_mv_grid(ding0_grid, network):
         aggregated, aggr_stations, dingo_import_data = _determine_aggregated_nodes(la_centers)
         network.dingo_import_data = dingo_import_data
     else:
-        aggregated = aggr_stations = []
+        aggregated = {}
+        aggr_stations = []
 
     # Create list of load instances and add these to grid's graph
     loads = {_: Load(
@@ -269,7 +274,10 @@ def _build_mv_grid(ding0_grid, network):
                               type='disconnection_point')
 
     # Create list of branch tee instances and add these to grid's graph
-    branch_tees = {_: BranchTee(id=_.id_db, geom=_.geo_data, grid=grid)
+    branch_tees = {_: BranchTee(id=_.id_db,
+                                geom=_.geo_data,
+                                grid=grid,
+                                in_building=False)
                    for _ in ding0_grid._cable_distributors}
     grid.graph.add_nodes_from(branch_tees.values(), type='branch_tee')
 
@@ -288,7 +296,7 @@ def _build_mv_grid(ding0_grid, network):
                             geom=_.geo_data,
                             voltage_op=t.v_level,
                             type=pd.Series(dict(
-                                S=t.s_max_a, X=t.x, R=t.r))
+                                S_nom=t.s_max_a, X=t.x, R=t.r))
                         ) for (count, t) in enumerate(_.transformers(), 1)])
                 for _ in ding0_grid._graph.nodes()
                 if isinstance(_, LVStationDing0) and _ not in aggr_stations}
@@ -326,7 +334,7 @@ def _build_mv_grid(ding0_grid, network):
               {'line': Line(
                   id=_['branch'].id_db,
                   type=_['branch'].type,
-                  length=_['branch'].length,
+                  length=_['branch'].length / 1e3,
                   grid=grid)
               })
              for _ in ding0_grid.graph_edges()
@@ -403,20 +411,20 @@ def aggregate_generators(gen, aggr):
         -------
 
         """
+
         if gen.v_level not in aggr['generation']:
             aggr['generation'][gen.v_level] = {}
-        if gen.subtype not in aggr['generation'][gen.v_level]:
-            aggr['generation'][gen.v_level].update(
-                {gen.subtype:
-                     {'ids': [gen.id_db],
-                      'capacity': gen.capacity,
-                      'type': gen.type
-                      }
-                 }
-            )
+        if gen.type not in aggr['generation'][gen.v_level]:
+            aggr['generation'][gen.v_level][gen.type] = {}
+        if gen.subtype not in aggr['generation'][gen.v_level][gen.type]:
+            aggr['generation'][gen.v_level][gen.type].update(
+                     {gen.subtype: {'ids': [gen.id_db],
+                                'capacity': gen.capacity}})
         else:
-            aggr['generation'][gen.v_level][gen.subtype]['ids'].append(gen.id_db)
-            aggr['generation'][gen.v_level][gen.subtype]['capacity'] += gen.capacity
+            aggr['generation'][gen.v_level][gen.type][gen.subtype][
+                'ids'].append(gen.id_db)
+            aggr['generation'][gen.v_level][gen.type][gen.subtype][
+                'capacity'] += gen.capacity
 
         return aggr
 
@@ -451,9 +459,10 @@ def aggregate_loads(la_center, aggr):
 
         return aggr
 
-    aggregated = []
+    aggregated = {}
     aggr_stations = []
 
+    # TODO: The variable generation_aggr is further used -> delete this code
     generation_aggr = {}
     for la in la_centers[0].grid.grid_district._lv_load_areas:
         for lvgd in la._lv_grid_districts:
@@ -498,7 +507,7 @@ def aggregate_loads(la_center, aggr):
             aggr_stations.append(_.lv_grid.station())
 
         # add elements to lists
-        aggregated.append(aggr)
+        aggregated.update({repr(la_center): aggr})
 
 
     return aggregated, aggr_stations, dingo_import_data
@@ -526,19 +535,20 @@ def _attach_aggregated(network, grid, aggregated, ding0_grid):
     aggr_line_type = ding0_grid.network._static_data['MV_cables'].iloc[
         ding0_grid.network._static_data['MV_cables']['I_max_th'].idxmax()]
 
-    for la in aggregated:
+    for la_id, la in aggregated.items():
         # add aggregated generators
         for v_level, val in la['generation'].items():
-            for subtype, val2 in val.items():
-                gen = Generator(
-                    id='agg_' + '_'.join(str(_) for _ in val2['ids']),
-                    nominal_capacity=val2['capacity'],
-                    type=val2['type'],
-                    subtype=subtype,
-                    geom=grid.station.geom,
-                    grid=grid,
-                    v_level=4)
-                grid.graph.add_node(gen, type='generator')
+            for type, val2 in val.items():
+                for subtype, val3 in val2.items():
+                    gen = Generator(
+                        id='agg_' + '_'.join([la_id] + [str(_) for _ in val3['ids']]),
+                        nominal_capacity=val3['capacity'],
+                        type=type,
+                        subtype=subtype,
+                        geom=grid.station.geom,
+                        grid=grid,
+                        v_level=4)
+                    grid.graph.add_node(gen, type='generator')
 
                 # backup reference of geno to LV geno list (save geno
                 # where the former LV genos are aggregated in)
@@ -562,13 +572,13 @@ def _attach_aggregated(network, grid, aggregated, ding0_grid):
                 geom=grid.station.geom,
                 consumption={sector: sectoral_load},
                 grid=grid,
-                id='_'.join(['Load_aggregated', sector, repr(grid)]))
+                id='_'.join(['Load_aggregated', sector, repr(grid), la_id]))
 
             grid.graph.add_node(load, type='load')
 
             # connect aggregated load to MV station
             line = {'line': Line(
-                id='_'.join(['line_aggr_load', sector]),
+                id='_'.join(['line_aggr_load', sector, la_id]),
                 type=aggr_line_type,
                 length=.5,
                 grid=grid)

edisgo/equipment/equipment-parameters_LV_transformers.csv

@@ -1,4 +1,4 @@
-name,s_nom,R,X
+name,S_nom,R,X
 #-,kVA,Ohm,Ohm
 # Source 100..1000 kVar transformers: Torsten Werth; Netzberechnung mit Erzeugungsprofilen --
 # Grundlagen, Berechnung, Anwendung; Springer; 2016

edisgo/equipment/equipment-parameters_MV_transformers.csv

@@ -1,4 +1,4 @@
-name,s_nom,R,X
+name,S_nom,R,X
 #-,kVA,Ohm,Ohm
 20 MVA,20000,0,0
 32 MVA,32000,0,0

edisgo/examples/example.py

@@ -3,23 +3,42 @@
 import os
 import pickle
 
+import logging
+logging.basicConfig(filename='example.log',
+                    format='%(asctime)s %(message)s',
+                    datefmt='%m/%d/%Y %I:%M:%S %p',
+                    level=logging.INFO)
+logger = logging.getLogger('edisgo')
+logger.setLevel(logging.DEBUG)
+
 timeseries = TimeSeries()
-scenario = Scenario(timeseries=timeseries)
+scenario = Scenario(timeseries=timeseries,
+                    power_flow='worst-case')
 
 import_network = False
 
 if import_network:
     network = Network.import_from_ding0(
-        os.path.join('data', 'ding0_grids_example.pkl'),
+        os.path.join('data', 'ding0_grids__76.pkl'),
         id='Test grid',
         scenario=scenario
     )
     # Do non-linear power flow analysis with PyPSA
     network.analyze()
-    network.pypsa = None
-    pickle.dump(network, open('test_network.pkl', 'wb'))
+    network.pypsa.export_to_csv_folder('data/pypsa_export')
+    #network.pypsa = None
+    #pickle.dump(network, open('test_network.pkl', 'wb'))
 else:
-    network = pickle.load(open('test_network.pkl', 'rb'))
+    network = None #pickle.load(open('test_network.pkl', 'rb'))
+
+# from pypsa import Network as PyPSANetwork
+# pypsa_network = PyPSANetwork(csv_folder_name='data/pypsa_export_80_stations')
+# # q unterscheidet sich
+# b1 = pypsa_network.transformers_t['q0']
+# b2 = network.pypsa.transformers_t['q0']
+# b3 = b1 - b2
+# b1 = pypsa_network.loads_t['q_set']
+# b2 = network.pypsa.loads_t['q_set']
 
 # # Print LV station secondary side voltage levels returned by PFA
 # print(network.results.v_res(
@@ -77,12 +96,12 @@
 #     print("O MWh")
 
 reinforce_grid.reinforce_grid(network)
+#print(network.results.grid_expansion_costs)
 
 # liste aller lv grids
 # [_ for _ in network.mv_grid.lv_grids]
 
 # nx.draw_spectral(list(network.mv_grid.lv_grids)[0].graph)
 
-# ToDo: feedin und load case auswahl
 # ToDo: Möglichkeit MV und LV getrennt zu rechnen
-# ToDo: Abbruchkriterium einführen - Anzahl paralleler lines
+