Expand export from the dispatch module to simplify analysis:

* New file with annual summary per-generation project that includes energy, capacity, costs & capacity factor
* New file with tech/annual summary for easily skimming results.
* Faster summation of tech/zone annual summary & tech/annual summary. Also include capacity, costs, and cap factor in those summary files.

Also, minor updates for python style.

Author: josiahjohnston

switch_model/generators/core/dispatch.py

@@ -12,9 +12,11 @@
 
 import logging
 import os, collections
+
+import pandas as pd
 from pyomo.environ import *
+
 from switch_model.reporting import write_table
-import pandas as pd
 
 dependencies = 'switch_model.timescales', 'switch_model.balancing.load_zones',\
     'switch_model.financials', 'switch_model.energy_sources.properties', \
@@ -389,6 +391,7 @@ def post_solve(instance, outdir):
 
     dispatch_normalized_dat = []
     for g, t in instance.GEN_TPS:
+        p = instance.tp_period[t]
         record = {
             "generation_project": g,
             "gen_dbid": instance.gen_dbid[g],
@@ -400,14 +403,19 @@ def post_solve(instance, outdir):
             "period": instance.tp_period[t],
             "DispatchGen_MW": value(instance.DispatchGen[g, t]),
             "Energy_GWh_typical_yr": value(
-                instance.DispatchGen[g, t] * instance.tp_weight_in_year[t] / 1000),
+                instance.DispatchGen[g, t] * instance.tp_weight_in_year[t] / 1000
+            ),
             "VariableCost_per_yr": value(
                 instance.DispatchGen[g, t] * instance.gen_variable_om[g] *
-                instance.tp_weight_in_year[t]),
+                instance.tp_weight_in_year[t]
+            ),
             "DispatchEmissions_tCO2_per_typical_yr": value(sum(
                 instance.DispatchEmissions[g, t, f] * instance.tp_weight_in_year[t]
                   for f in instance.FUELS_FOR_GEN[g]
-            )) if instance.gen_uses_fuel[g] else 0
+            )) if instance.gen_uses_fuel[g] else 0,
+            "GenCapacity_MW": value(instance.GenCapacity[g, p]),
+            "GenCapitalCosts": value(instance.GenCapitalCosts[g, p]),
+            "GenFixedOMCosts": value(instance.GenFixedOMCosts[g, p]),
         }
         try:
             try:
@@ -419,41 +427,102 @@ def post_solve(instance, outdir):
                 )
                 record['Discharge_GWh_typical_yr'] = record['Energy_GWh_typical_yr']
                 record['Energy_GWh_typical_yr'] -= record['Store_GWh_typical_yr']
+                record['is_storage'] = True
             except KeyError:
-                #record['ChargeStorage_MW'] = None
-                pass
+                record['ChargeStorage_MW'] = float("NaN")
+                record['Store_GWh_typical_yr'] = float("NaN")
+                record['Discharge_GWh_typical_yr'] = float("NaN")
+                record['is_storage'] = False
         except AttributeError:
             pass
         dispatch_normalized_dat.append(record)
     dispatch_full_df = pd.DataFrame(dispatch_normalized_dat)
     dispatch_full_df.set_index(["generation_project", "timestamp"], inplace=True)
-    dispatch_full_df.to_csv(os.path.join(outdir, "dispatch.csv"))
+    dispatch_full_df.to_csv(os.path.join(outdir, "dispatch.csv"),
+        columns=[])
 
     summary_columns=[
         "Energy_GWh_typical_yr",
         "VariableCost_per_yr",
-        "DispatchEmissions_tCO2_per_typical_yr"
+        "DispatchEmissions_tCO2_per_typical_yr",
+        "GenCapacity_MW",
+        "GenCapitalCosts",
+        "GenFixedOMCosts",
+        "capacity_factor",
     ]
     if 'ChargeStorage' in dir(instance):
         summary_columns.extend([
             'Store_GWh_typical_yr',
             'Discharge_GWh_typical_yr'
         ])
 
-    annual_summary = dispatch_full_df.groupby(['gen_tech', "gen_energy_source", "period"]).sum()
-    annual_summary.to_csv(
-        os.path.join(outdir, "dispatch_annual_summary.csv"),
+    # Annual summary of each generator
+    gen_sum = dispatch_full_df.groupby([
+        "generation_project",
+        "gen_dbid",
+        "gen_tech",
+        "gen_load_zone",
+        "gen_energy_source",
+        "period",
+        "GenCapacity_MW",
+        "GenCapitalCosts",
+        "GenFixedOMCosts"]).sum(min_count=1, skipna=False)
+    gen_sum.reset_index(inplace=True)
+    gen_sum.set_index(
+        inplace=True, 
+        keys=[
+            "generation_project",
+            "gen_dbid",
+            "gen_tech",
+            "gen_load_zone",
+            "gen_energy_source",
+            "period",
+        ]
+    )
+    gen_sum["Energy_out_avg_MW"] = (
+        gen_sum["Energy_GWh_typical_yr"] * 1000 / gen_sum["tp_weight_in_year_hrs"]
+    )
+    try:
+        idx = gen_sum["is_storage"].astype(bool)
+        gen_sum.loc[idx, "Energy_out_avg_MW"] = (
+            gen_sum.loc[idx, "Discharge_GWh_typical_yr"] * 1000 / 
+            gen_sum.loc[idx, "tp_weight_in_year_hrs"]
+        )
+    except KeyError:
+        pass
+    
+    def add_cap_factor(df):
+        df["capacity_factor"] = df["Energy_out_avg_MW"] / df["GenCapacity_MW"]
+        no_cap = (df["GenCapacity_MW"] == 0)
+        df.loc[no_cap, "capacity_factor"] = 0
+        return df
+
+    gen_sum = add_cap_factor(gen_sum)
+    gen_sum.to_csv(
+        os.path.join(outdir, "gen_project_annual_summary.csv"),
         columns=summary_columns)
 
-
-    zonal_annual_summary = dispatch_full_df.groupby(
-        ['gen_tech', "gen_load_zone", "gen_energy_source", "period"]
-    ).sum()
-    zonal_annual_summary.to_csv(
+    zone_sum = gen_sum.groupby([
+        'gen_tech', 
+        "gen_load_zone",
+        "gen_energy_source",
+        "period"
+    ]).sum()
+    zone_sum = add_cap_factor(zone_sum)
+    zone_sum.to_csv(
         os.path.join(outdir, "dispatch_zonal_annual_summary.csv"),
         columns=summary_columns
     )
 
+    annual_summary = zone_sum.groupby([
+        'gen_tech',
+        "gen_energy_source",
+        "period"]).sum()
+    annual_summary = add_cap_factor(annual_summary)
+    annual_summary.to_csv(
+        os.path.join(outdir, "dispatch_annual_summary.csv"),
+        columns=summary_columns)
+
     try:
         import plotnine as p9
         # Try to filter out spurious warnings from plotnine