Record total capacities and total costs

Snakefile

@@ -219,6 +219,7 @@ rule make_summary:
         #heat_demand_name='data/heating/daily_heat_demand.h5'
     output:
         costs=config['summary_dir'] + '/' + config['run'] + '/csvs/costs.csv',
+        capacities=config['summary_dir'] + '/' + config['run'] + '/csvs/capacities.csv',
         curtailment=config['summary_dir'] + '/' + config['run'] + '/csvs/curtailment.csv',
         energy=config['summary_dir'] + '/' + config['run'] + '/csvs/energy.csv',
         supply=config['summary_dir'] + '/' + config['run'] + '/csvs/supply.csv',

config.yaml

@@ -2,13 +2,13 @@ logging_level: INFO
 
 results_dir: 'results/'
 summary_dir: results
-run: '190501-256'
+run: '190502-181'
 
 scenario:
   sectors: [E] # ,E+EV,E+BEV,E+BEV+V2G] # [ E+EV, E+BEV, E+BEV+V2G ]
   simpl: ['']
   lv: [1.0,1.25]#[1.0, 1.125, 1.25, 1.5, 2.0, opt]# or opt
-  clusters: [256] #[90, 128, 181] #[45, 64, 90, 128, 181, 256] #, 362] # (2**np.r_[5.5:9:.5]).astype(int) minimum is 37
+  clusters: [181] #[90, 128, 181] #[45, 64, 90, 128, 181, 256] #, 362] # (2**np.r_[5.5:9:.5]).astype(int) minimum is 37
   opts: ['']  #for pypsa-eur
   sector_opts: [Co2L0-3H-T-H-B-I-solar3,Co2L0-3H-T-H-B-I-onwind0p25-solar3]#,Co2L0p05-3H-T-H-B-I,Co2L0p10-3H-T-H-B-I,Co2L0p20-3H-T-H-B-I,Co2L0p30-3H-T-H-B-I,Co2L0p50-3H-T-H-B-I]#[Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0-3H-T-H,Co2L0p20-3H-T-H]   #Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p20-3H-T-HCo2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p30-3H-T-H,Co2L0p50-3H-T-H] #Co2L-3H,Co2L-3H-T,, LC-FL, LC-T, Ep-T, Co2L-T]
   # Co2L will give default (5%); Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions

scripts/make_summary.py

@@ -75,6 +75,11 @@ def calculate_costs(n,label,costs):
         else:
             p = c.pnl.p.multiply(n.snapshot_weightings,axis=0).sum()
 
+        #correct sequestration cost
+        if c.name == "Store":
+            items = c.df.index[(c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.)]
+            c.df.loc[items,"marginal_cost"] = -20.
+
         marginal_costs = p*c.df.marginal_cost
 
         marginal_costs_grouped = marginal_costs.groupby(c.df.carrier).sum()
@@ -100,6 +105,18 @@ def calculate_costs(n,label,costs):
 
 
 
+def calculate_capacities(n,label,capacities):
+
+    for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
+        capacities_grouped = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby(c.df.carrier).sum()
+
+        capacities = capacities.reindex(capacities.index|pd.MultiIndex.from_product([[c.list_name],capacities_grouped.index]))
+
+        capacities.loc[idx[c.list_name,list(capacities_grouped.index)],label] = capacities_grouped.values
+
+    return capacities
+
+
 def calculate_curtailment(n,label,curtailment):
 
     avail = n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt).sum().groupby(n.generators.carrier).sum()
@@ -373,6 +390,7 @@ def calculate_price_statistics(n, label, price_statistics):
 
 
 outputs = ["costs",
+           "capacities",
            "curtailment",
            "energy",
            "supply",
@@ -449,4 +467,6 @@ if __name__ == "__main__":
 
     df = make_summaries(networks_dict)
 
+    df["metrics"].loc["total costs"] =  df["costs"].sum()
+
     to_csv(df)