select correct sector name in endogeneous retrofitting

config/config.default.yaml

@@ -446,6 +446,7 @@ sector:
     decentral: 3
     central: 180
   boilers: true
+  resistive_heaters: true
   oil_boilers: false
   biomass_boiler: true
   chp: true

doc/configtables/sector.csv

@@ -62,7 +62,8 @@ tes,--,"{true, false}",Add option for storing thermal energy in large water pits
 tes_tau,,,The time constant used to calculate the decay of thermal energy in thermal energy storage (TES): 1- :math:`e^{-1/24τ}`.
 -- decentral,days,float,The time constant in decentralized thermal energy storage (TES)
 -- central,days,float,The time constant in centralized thermal energy storage (TES)
-boilers,--,"{true, false}",Add option for transforming electricity into heat using resistive heater
+boilers,--,"{true, false}",Add option for transforming gas into heat using gas boilers
+resistive_heaters,--,"{true, false}",Add option for transforming electricity into heat using resistive heaters (independently from gas boilers)
 oil_boilers,--,"{true, false}",Add option for transforming oil into heat using boilers
 biomass_boiler,--,"{true, false}",Add option for transforming biomass into heat using boilers
 chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP)

rules/common.smk

@@ -4,7 +4,7 @@
 
 import os, sys, glob
 
-helper_source_path = [match for match in glob.glob('**/_helpers.py', recursive=True)]
+helper_source_path = [match for match in glob.glob("**/_helpers.py", recursive=True)]
 
 for path in helper_source_path:
     path = os.path.dirname(os.path.abspath(path))

scripts/build_retro_cost.py

@@ -533,16 +533,16 @@ def prepare_temperature_data():
     """
     temperature = xr.open_dataarray(snakemake.input.air_temperature).to_pandas()
     d_heat = (
-        temperature.groupby(temperature.columns.str[:2], axis=1)
+        temperature.T.groupby(temperature.columns.str[:2])
         .mean()
-        .resample("1D")
+        .T.resample("1D")
         .mean()
         < t_threshold
     ).sum()
     temperature_average_d_heat = (
-        temperature.groupby(temperature.columns.str[:2], axis=1)
+        temperature.T.groupby(temperature.columns.str[:2])
         .mean()
-        .apply(
+        .T.apply(
             lambda x: get_average_temperature_during_heating_season(x, t_threshold=15)
         )
     )
@@ -610,7 +610,7 @@ def calculate_costs(u_values, l, cost_retro, window_assumptions):
             cost_retro.loc[x.name[3], "cost_var"]
             * 100
             * float(l)
-            * l_weight.loc[x.name[3]][0]
+            * l_weight.loc[x.name[3]].iloc[0]
             + cost_retro.loc[x.name[3], "cost_fix"]
         )
         * x.A_element
@@ -720,6 +720,7 @@ def map_to_lstrength(l_strength, df):
         .swaplevel(axis=1)
         .dropna(axis=1)
     )
+
     return pd.concat([df.drop([2, 3], axis=1, level=1), l_strength_df], axis=1)
 
 
@@ -800,6 +801,7 @@ def calculate_heat_losses(u_values, data_tabula, l_strength, temperature_factor)
         * data_tabula.A_envelope
         / data_tabula.A_C_Ref
     )
+
     heat_transfer_perm2 = pd.concat(
         [
             heat_transfer_perm2,

scripts/prepare_sector_network.py

@@ -455,7 +455,7 @@ def update_wind_solar_costs(n, costs):
 
             logger.info(
                 "Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}".format(
-                    connection_cost[0].min(), connection_cost[0].max(), tech
+                    connection_cost.min(), connection_cost.max(), tech
                 )
             )
 
@@ -497,6 +497,9 @@ def add_carrier_buses(n, carrier, nodes=None):
         capital_cost=capital_cost,
     )
 
+    if carrier == "gas":
+        return
+
     n.madd(
         "Generator",
         nodes,
@@ -1870,7 +1873,7 @@ def add_heat(n, costs):
                 lifetime=costs.at[name_type + " water tank storage", "lifetime"],
             )
 
-        if options["boilers"]:
+        if options["resistive_heaters"]:
             key = f"{name_type} resistive heater"
 
             n.madd(
@@ -1885,6 +1888,7 @@ def add_heat(n, costs):
                 lifetime=costs.at[key, "lifetime"],
             )
 
+        if options["boilers"]:
             key = f"{name_type} gas boiler"
 
             n.madd(
@@ -2022,7 +2026,7 @@ def add_heat(n, costs):
             )
         w_space["tot"] = (
             heat_demand_r["services space"] + heat_demand_r["residential space"]
-        ) / heat_demand_r.groupby(level=[1], axis=1).sum()
+        ) / heat_demand_r.T.groupby(level=[1]).sum().T
 
         for name in n.loads[
             n.loads.carrier.isin([x + " heat" for x in heat_systems])
@@ -2031,11 +2035,21 @@ def add_heat(n, costs):
             ct = pop_layout.loc[node, "ct"]
 
             # weighting 'f' depending on the size of the population at the node
-            f = urban_fraction[node] if "urban" in name else (1 - urban_fraction[node])
+            if "urban central" in name:
+                f = dist_fraction[node]
+            elif "urban decentral" in name:
+                f = urban_fraction[node] - dist_fraction[node]
+            else:
+                f = 1 - urban_fraction[node]
             if f == 0:
                 continue
             # get sector name ("residential"/"services"/or both "tot" for urban central)
-            sec = [x if x in name else "tot" for x in sectors][0]
+            if "urban central" in name:
+                sec = "tot"
+            if "residential" in name:
+                sec = "residential"
+            if "services" in name:
+                sec = "services"
 
             # get floor aread at node and region (urban/rural) in m^2
             floor_area_node = (
@@ -2079,14 +2093,15 @@ def add_heat(n, costs):
                 strengths = strengths.drop(s)
 
             # reindex normed time profile of space heat demand back to hourly resolution
-            space_pu = space_pu.reindex(index=heat_demand.index).fillna(method="ffill")
+            space_pu = space_pu.reindex(index=heat_demand.index).ffill()
 
             # add for each retrofitting strength a generator with heat generation profile following the profile of the heat demand
             for strength in strengths:
+                node_name = " ".join(name.split(" ")[2::])
                 n.madd(
                     "Generator",
                     [node],
-                    suffix=" retrofitting " + strength + " " + name[6::],
+                    suffix=" retrofitting " + strength + " " + node_name,
                     bus=name,
                     carrier="retrofitting",
                     p_nom_extendable=True,
@@ -2998,8 +3013,9 @@ def add_industry(n, costs):
 
     if options["co2_spatial"] or options["co2network"]:
         p_set = (
-            -industrial_demand.loc[nodes, "process emission"]
-            .rename(index=lambda x: x + " process emissions")
+            -industrial_demand.loc[nodes, "process emission"].rename(
+                index=lambda x: x + " process emissions"
+            )
             / nhours
         )
     else:
@@ -3417,7 +3433,7 @@ def define_clustering(attributes, aggregate_dict):
                 ),
                 inplace=True,
             )
-            pnl[k] = pnl[k].groupby(level=0, axis=1).agg(agg[k], **agg_group_kwargs)
+            pnl[k] = pnl[k].T.groupby(level=0).agg(agg[k], **agg_group_kwargs).T
 
         # remove unclustered assets of service/residential
         to_drop = c.df.index.difference(df.index)
@@ -3674,7 +3690,7 @@ def lossy_bidirectional_links(n, carrier, efficiencies={}):
     if "I" in opts:
         add_industry(n, costs)
 
-    if "I" in opts and "H" in opts:
+    if "H" in opts:
         add_waste_heat(n)
 
     if "A" in opts:  # requires H and I

scripts/solve_network.py

@@ -856,7 +856,7 @@ def solve_network(n, config, solving, opts="", **kwargs):
     kwargs["assign_all_duals"] = cf_solving.get("assign_all_duals", False)
 
     if kwargs["solver_name"] == "gurobi":
-        logging.getLogger('gurobipy').setLevel(logging.CRITICAL)
+        logging.getLogger("gurobipy").setLevel(logging.CRITICAL)
 
     rolling_horizon = cf_solving.pop("rolling_horizon", False)
     skip_iterations = cf_solving.pop("skip_iterations", False)