Merge branch 'dev' into features/#665-interface-pre-market-market-opt…

…imization

etrago/appl.py

@@ -51,10 +51,11 @@
     "db": "egon-data",  # database session
     "gridversion": None,  # None for model_draft or Version number
     "method": {  # Choose method and settings for optimization
-        "type": "market_grid",  # type of optimization, currently only 'lopf'
+        "type": "market_grid",  # type of optimization, 'lopf' or 'market_grid'
         "n_iter": 1,  # abort criterion of iterative optimization, 'n_iter' or 'threshold'
         "pyomo": True,  # set if pyomo is used for model building
         "formulation": "pyomo",
+        "market_zones": "status_quo", # only used if type='market_grid'
         "rolling_horizon": { # Define parameter of market optimization
             "planning_horizon": 72, # number of snapshots in each optimization
             "overlap": 24, # number of overlapping hours

etrago/args.json

@@ -4,7 +4,8 @@
     "method": {
         "type": "lopf",
         "n_iter": 4,
-        "pyomo": true
+        "pyomo": true,
+        "market_zones": "status_quo"
     },
     "pf_post_lopf": {
         "active": false,

etrago/cluster/disaggregation.py

@@ -124,9 +124,9 @@ def from_busmap(x):
             if not left_external_connectors.empty:
                 ca_option = pd.get_option("mode.chained_assignment")
                 pd.set_option("mode.chained_assignment", None)
-                left_external_connectors.loc[
-                    :, "bus0"
-                ] = left_external_connectors.loc[:, "bus0"].apply(from_busmap)
+                left_external_connectors.loc[:, "bus0"] = (
+                    left_external_connectors.loc[:, "bus0"].apply(from_busmap)
+                )
                 pd.set_option("mode.chained_assignment", ca_option)
                 external_buses = pd.concat(
                     (external_buses, left_external_connectors.bus0)
@@ -139,9 +139,9 @@ def from_busmap(x):
             if not right_external_connectors.empty:
                 ca_option = pd.get_option("mode.chained_assignment")
                 pd.set_option("mode.chained_assignment", None)
-                right_external_connectors.loc[
-                    :, "bus1"
-                ] = right_external_connectors.loc[:, "bus1"].apply(from_busmap)
+                right_external_connectors.loc[:, "bus1"] = (
+                    right_external_connectors.loc[:, "bus1"].apply(from_busmap)
+                )
                 pd.set_option("mode.chained_assignment", ca_option)
                 external_buses = pd.concat(
                     (external_buses, right_external_connectors.bus1)
@@ -738,9 +738,11 @@ def solve_partial_network(
                     weight = reduce(
                         multiply,
                         (
-                            filtered.loc[:, key]
-                            if not timed(key)
-                            else pn_t[key].loc[:, filtered.index]
+                            (
+                                filtered.loc[:, key]
+                                if not timed(key)
+                                else pn_t[key].loc[:, filtered.index]
+                            )
                             for key in weights[s]
                         ),
                         1,

etrago/cluster/gas.py

@@ -514,12 +514,12 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None):
                 ]
                 if len(h2_idx) > 0:
                     h2_idx = h2_idx.index.tolist()[0]
-                    network_gasgrid_c.buses.loc[
-                        h2_idx, "x"
-                    ] = etrago.network.buses["x"].loc[medoid]
-                    network_gasgrid_c.buses.loc[
-                        h2_idx, "y"
-                    ] = etrago.network.buses["y"].loc[medoid]
+                    network_gasgrid_c.buses.loc[h2_idx, "x"] = (
+                        etrago.network.buses["x"].loc[medoid]
+                    )
+                    network_gasgrid_c.buses.loc[h2_idx, "y"] = (
+                        etrago.network.buses["y"].loc[medoid]
+                    )
 
                 network_gasgrid_c.buses.loc[i, "x"] = etrago.network.buses.loc[
                     medoid, "x"

etrago/cluster/snapshot.py

@@ -373,9 +373,9 @@ def segmentation_extreme_periods(
                 if date < maxi:
                     i = i + 1
                 else:
-                    timeseries[
-                        "SegmentDuration_Extreme"
-                    ] = timeseries.index.get_level_values("SegmentDuration")
+                    timeseries["SegmentDuration_Extreme"] = (
+                        timeseries.index.get_level_values("SegmentDuration")
+                    )
                     old_row = timeseries.iloc[i].copy()
                     old_row = pd.DataFrame(old_row).transpose()
 
@@ -395,12 +395,12 @@ def segmentation_extreme_periods(
                     if new_date.isin(
                         timeseries.index.get_level_values("dates")
                     ):
-                        timeseries[
-                            "dates"
-                        ] = timeseries.index.get_level_values("dates")
-                        timeseries[
-                            "SegmentNo"
-                        ] = timeseries.index.get_level_values("SegmentNo")
+                        timeseries["dates"] = (
+                            timeseries.index.get_level_values("dates")
+                        )
+                        timeseries["SegmentNo"] = (
+                            timeseries.index.get_level_values("SegmentNo")
+                        )
                         timeseries["SegmentDuration"] = timeseries[
                             "SegmentDuration_Extreme"
                         ]
@@ -428,12 +428,12 @@ def segmentation_extreme_periods(
                         for col in new_row.columns:
                             new_row[col][0] = old_row[col][0]
 
-                        timeseries[
-                            "dates"
-                        ] = timeseries.index.get_level_values("dates")
-                        timeseries[
-                            "SegmentNo"
-                        ] = timeseries.index.get_level_values("SegmentNo")
+                        timeseries["dates"] = (
+                            timeseries.index.get_level_values("dates")
+                        )
+                        timeseries["SegmentNo"] = (
+                            timeseries.index.get_level_values("SegmentNo")
+                        )
                         timeseries["SegmentDuration"] = timeseries[
                             "SegmentDuration_Extreme"
                         ]
@@ -457,9 +457,9 @@ def segmentation_extreme_periods(
                 else:
                     if i == -1:
                         i = 0
-                    max_val[
-                        "SegmentDuration"
-                    ] = timeseries.index.get_level_values("SegmentDuration")[i]
+                    max_val["SegmentDuration"] = (
+                        timeseries.index.get_level_values("SegmentDuration")[i]
+                    )
                     max_val.set_index(
                         ["dates", "SegmentNo", "SegmentDuration"], inplace=True
                     )
@@ -496,9 +496,9 @@ def segmentation_extreme_periods(
                 if date < mini:
                     i = i + 1
                 else:
-                    timeseries[
-                        "SegmentDuration_Extreme"
-                    ] = timeseries.index.get_level_values("SegmentDuration")
+                    timeseries["SegmentDuration_Extreme"] = (
+                        timeseries.index.get_level_values("SegmentDuration")
+                    )
                     old_row = timeseries.iloc[i].copy()
                     old_row = pd.DataFrame(old_row).transpose()
 
@@ -518,12 +518,12 @@ def segmentation_extreme_periods(
                     if new_date.isin(
                         timeseries.index.get_level_values("dates")
                     ):
-                        timeseries[
-                            "dates"
-                        ] = timeseries.index.get_level_values("dates")
-                        timeseries[
-                            "SegmentNo"
-                        ] = timeseries.index.get_level_values("SegmentNo")
+                        timeseries["dates"] = (
+                            timeseries.index.get_level_values("dates")
+                        )
+                        timeseries["SegmentNo"] = (
+                            timeseries.index.get_level_values("SegmentNo")
+                        )
                         timeseries["SegmentDuration"] = timeseries[
                             "SegmentDuration_Extreme"
                         ]
@@ -550,12 +550,12 @@ def segmentation_extreme_periods(
                         )
                         for col in new_row.columns:
                             new_row[col][0] = old_row[col][0]
-                        timeseries[
-                            "dates"
-                        ] = timeseries.index.get_level_values("dates")
-                        timeseries[
-                            "SegmentNo"
-                        ] = timeseries.index.get_level_values("SegmentNo")
+                        timeseries["dates"] = (
+                            timeseries.index.get_level_values("dates")
+                        )
+                        timeseries["SegmentNo"] = (
+                            timeseries.index.get_level_values("SegmentNo")
+                        )
                         timeseries["SegmentDuration"] = timeseries[
                             "SegmentDuration_Extreme"
                         ]
@@ -579,9 +579,9 @@ def segmentation_extreme_periods(
                 else:
                     if i == -1:
                         i = 0
-                    min_val[
-                        "SegmentDuration"
-                    ] = timeseries.index.get_level_values("SegmentDuration")[i]
+                    min_val["SegmentDuration"] = (
+                        timeseries.index.get_level_values("SegmentDuration")[i]
+                    )
                     min_val.set_index(
                         ["dates", "SegmentNo", "SegmentDuration"], inplace=True
                     )

etrago/cluster/spatial.py

@@ -591,9 +591,9 @@ def kmean_clustering(etrago, selected_network, weight, n_clusters):
             if kmean_settings["use_reduced_coordinates"]:
                 # TODO : FIX THIS HACK THAT HAS UNEXPECTED SIDE-EFFECTS,
                 # i.e. network is changed in place!!
-                network.buses.loc[
-                    busmap.index, ["x", "y"]
-                ] = network.buses.loc[busmap, ["x", "y"]].values
+                network.buses.loc[busmap.index, ["x", "y"]] = (
+                    network.buses.loc[busmap, ["x", "y"]].values
+                )
 
             clustering = get_clustering_from_busmap(
                 network,

etrago/execute/__init__.py

@@ -278,9 +278,9 @@ def iterate_lopf(
         ]
         etrago.network_tsa.transformers.s_nom_extendable = False
 
-        etrago.network_tsa.storage_units[
-            "p_nom"
-        ] = etrago.network.storage_units["p_nom_opt"]
+        etrago.network_tsa.storage_units["p_nom"] = (
+            etrago.network.storage_units["p_nom_opt"]
+        )
         etrago.network_tsa.storage_units["p_nom_extendable"] = False
 
         etrago.network_tsa.stores["e_nom"] = etrago.network.stores["e_nom_opt"]
@@ -471,13 +471,13 @@ def dispatch_disaggregation(self):
                 index=transits,
             )
             for storage in self.network.storage_units.index:
-                self.conduct_dispatch_disaggregation[
-                    storage
-                ] = self.network.storage_units_t.state_of_charge[storage]
+                self.conduct_dispatch_disaggregation[storage] = (
+                    self.network.storage_units_t.state_of_charge[storage]
+                )
             for store in sto.index:
-                self.conduct_dispatch_disaggregation[
-                    store
-                ] = self.network.stores_t.e[store]
+                self.conduct_dispatch_disaggregation[store] = (
+                    self.network.stores_t.e[store]
+                )
 
             extra_func = self.args["extra_functionality"]
             self.args["extra_functionality"] = {}
@@ -517,9 +517,9 @@ def dispatch_disaggregation(self):
         self.network.transformers.s_nom_extendable = (
             self.network_tsa.transformers.s_nom_extendable
         )
-        self.network.storage_units[
-            "p_nom_extendable"
-        ] = self.network_tsa.storage_units["p_nom_extendable"]
+        self.network.storage_units["p_nom_extendable"] = (
+            self.network_tsa.storage_units["p_nom_extendable"]
+        )
         self.network.stores["e_nom_extendable"] = self.network_tsa.stores[
             "e_nom_extendable"
         ]
@@ -807,9 +807,9 @@ def drop_foreign_components(network):
 
     # Assign generators control strategy
     ac_bus = network.buses[network.buses.carrier == "AC"]
-    network.generators.control[
-        network.generators.bus.isin(ac_bus.index)
-    ] = "PV"
+    network.generators.control[network.generators.bus.isin(ac_bus.index)] = (
+        "PV"
+    )
     network.generators.control[
         network.generators.carrier == "load shedding"
     ] = "PQ"
@@ -1049,9 +1049,7 @@ def calc_line_losses(network, converged):
     """
     # Line losses
     # calculate apparent power S = sqrt(p² + q²) [in MW]
-    s0_lines = (network.lines_t.p0**2 + network.lines_t.q0**2).apply(
-        np.sqrt
-    )
+    s0_lines = (network.lines_t.p0**2 + network.lines_t.q0**2).apply(np.sqrt)
     # in case some snapshots did not converge, discard them from the
     # calculation
     s0_lines.loc[converged[converged is False].index, :] = np.nan
@@ -1061,9 +1059,7 @@ def calc_line_losses(network, converged):
     )
     # calculate losses per line and timestep network.\
     # lines_t.line_losses = I² * R [in MW]
-    network.lines_t.losses = np.divide(
-        i0_lines**2 * network.lines.r, 1000000
-    )
+    network.lines_t.losses = np.divide(i0_lines**2 * network.lines.r, 1000000)
     # calculate total losses per line [in MW]
     network.lines = network.lines.assign(
         losses=np.sum(network.lines_t.losses).values

etrago/execute/grid_optimization.py

@@ -216,9 +216,13 @@ def add_redispatch_generators(self):
     # Set maximum feed-in limit for ramp up generators based on feed-in of
     # (disaggregated) generators from the market optimization and potential
     # feedin time series
+    p_max_pu_all = self.network.get_switchable_as_dense(
+        "Generator", "p_max_pu"
+    )
+
     self.network.generators_t.p_max_pu.loc[:, gens_redispatch + " ramp_up"] = (
         (
-            self.network.generators_t.p_max_pu.loc[:, gens_redispatch].mul(
+            p_max_pu_all.loc[:, gens_redispatch].mul(
                 self.network.generators.loc[gens_redispatch, "p_nom"]
             )
             - (self.market_model.generators_t.p.loc[self.network.snapshots,
@@ -319,14 +323,6 @@ def add_redispatch_generators(self):
     #         self.network.buses.index.isin(['47085', '47086', '37865', '37870'
     #                                        ])].index, inplace=True)
 
-    # TEMPORAL
-    self.network.generators.loc[
-        self.network.generators.index.str.contains("run_of_river"), "p_max_pu"
-    ] = 0.65
-    self.network.generators.loc[
-        self.network.generators.index.str.contains("reservoir"), "p_max_pu"
-    ] = 0.65
-
 
 def extra_functionality():
     return None