Fix bug where 0 flow meant no constraint

Author: staadecker

switch_model/generators/core/dispatch.py

@@ -729,16 +729,24 @@ def graph_total_dispatch(tools):
 
     tools.bar_label()
 
+
 @graph(
     "energy_balance",
     title="Energy Balance For Every Month",
     supports_multi_scenario=True,
-    is_long=True
+    is_long=True,
 )
 def energy_balance(tools):
     # Get dispatch dataframe
-    cols = ["timestamp", "gen_tech", "gen_energy_source", "DispatchGen_MW", "scenario_name", "scenario_index",
-            "Curtailment_MW"]
+    cols = [
+        "timestamp",
+        "gen_tech",
+        "gen_energy_source",
+        "DispatchGen_MW",
+        "scenario_name",
+        "scenario_index",
+        "Curtailment_MW",
+    ]
     df = tools.get_dataframe("dispatch.csv", drop_scenario_info=False)[cols]
     df = tools.transform.gen_type(df)
 
@@ -749,11 +757,16 @@ def energy_balance(tools):
     # Sum dispatch across all the projects of the same type and timepoint
     key_columns = ["timestamp", "gen_type", "scenario_name", "scenario_index"]
     df = df.groupby(key_columns, as_index=False).sum()
-    df = df.melt(id_vars=key_columns, value_vars=["Dispatch", "Dispatch Limit"], var_name="Type")
+    df = df.melt(
+        id_vars=key_columns, value_vars=["Dispatch", "Dispatch Limit"], var_name="Type"
+    )
     df = df.rename({"gen_type": "Source"}, axis=1)
 
-    discharge = df[(df["Source"] == "Storage") & (df["Type"] == "Dispatch")].drop(["Source", "Type"], axis=1).rename(
-        {"value": "discharge"}, axis=1)
+    discharge = (
+        df[(df["Source"] == "Storage") & (df["Type"] == "Dispatch")]
+        .drop(["Source", "Type"], axis=1)
+        .rename({"value": "discharge"}, axis=1)
+    )
 
     # Get load dataframe
     load = tools.get_dataframe("load_balance.csv", drop_scenario_info=False)
@@ -764,23 +777,21 @@ def energy_balance(tools):
     load = load.groupby(key_columns, as_index=False).sum()
 
     # Subtract storage dispatch from generation and add it to the storage charge to get net flow
-    load = load.merge(
-        discharge,
-        how="left",
-        on=key_columns,
-        validate="one_to_one"
-    )
+    load = load.merge(discharge, how="left", on=key_columns, validate="one_to_one")
     load["ZoneTotalCentralDispatch"] -= load["discharge"]
     load["StorageNetCharge"] += load["discharge"]
     load = load.drop("discharge", axis=1)
 
     # Rename and convert from wide to long format
-    load = load.rename({
-        "ZoneTotalCentralDispatch": "Total Generation (excl. storage discharge)",
-        "TXPowerNet": "Transmission Losses",
-        "StorageNetCharge": "Storage Net Flow",
-        "zone_demand_mw": "Demand",
-    }, axis=1).sort_index(axis=1)
+    load = load.rename(
+        {
+            "ZoneTotalCentralDispatch": "Total Generation (excl. storage discharge)",
+            "TXPowerNet": "Transmission Losses",
+            "StorageNetCharge": "Storage Net Flow",
+            "zone_demand_mw": "Demand",
+        },
+        axis=1,
+    ).sort_index(axis=1)
     load = load.melt(id_vars=key_columns, var_name="Source")
     load["Type"] = "Dispatch"
 
@@ -796,26 +807,34 @@ def energy_balance(tools):
     FREQUENCY = "1W"
 
     def groupby_time(df):
-        return df.groupby([
-            "scenario_name",
-            "period",
-            "Source",
-            "Type",
-            tools.pd.Grouper(key="datetime", freq=FREQUENCY, origin="start")
-        ])["value"]
+        return df.groupby(
+            [
+                "scenario_name",
+                "period",
+                "Source",
+                "Type",
+                tools.pd.Grouper(key="datetime", freq=FREQUENCY, origin="start"),
+            ]
+        )["value"]
 
     df = groupby_time(df).sum().reset_index()
 
     # Get the state of charge data
-    soc = tools.get_dataframe("StateOfCharge.csv", dtype={"STORAGE_GEN_TPS_1": str}, drop_scenario_info=False)
-    soc = soc.rename({"STORAGE_GEN_TPS_2": "timepoint", "StateOfCharge": "value"}, axis=1)
+    soc = tools.get_dataframe(
+        "StateOfCharge.csv", dtype={"STORAGE_GEN_TPS_1": str}, drop_scenario_info=False
+    )
+    soc = soc.rename(
+        {"STORAGE_GEN_TPS_2": "timepoint", "StateOfCharge": "value"}, axis=1
+    )
     # Sum over all the projects that are in the same scenario with the same timepoint
     soc = soc.groupby(["timepoint", "scenario_name"], as_index=False).sum()
     soc["Source"] = "State Of Charge"
     soc["value"] /= 1e6  # Convert to TWh
 
     # Group by time
-    soc = tools.transform.timestamp(soc, use_timepoint=True, key_col="timepoint").astype({"period": str})
+    soc = tools.transform.timestamp(
+        soc, use_timepoint=True, key_col="timepoint"
+    ).astype({"period": str})
     soc["Type"] = "Dispatch"
     soc = groupby_time(soc).mean().reset_index()
 
@@ -827,35 +846,47 @@ def groupby_time(df):
     # Plot
     # Get the colors for the lines
     colors = tools.get_colors()
-    colors.update({
-        "Transmission Losses": "brown",
-        "Storage Net Flow": "cadetblue",
-        "Demand": "black",
-        "Total Generation (excl. storage discharge)": "black",
-        "State Of Charge": "green"
-    })
+    colors.update(
+        {
+            "Transmission Losses": "brown",
+            "Storage Net Flow": "cadetblue",
+            "Demand": "black",
+            "Total Generation (excl. storage discharge)": "black",
+            "State Of Charge": "green",
+        }
+    )
 
     # plot
     num_periods = df["period"].nunique()
     pn = tools.pn
-    plot = pn.ggplot(df) + \
-           pn.geom_line(pn.aes(x="day", y="value", color="Source", linetype="Type")) + \
-           pn.facet_grid("period ~ scenario_name") + \
-           pn.labs(y="Contribution to Energy Balance (TWh)") + \
-           pn.scales.scale_color_manual(values=colors, aesthetics="color", na_value=colors["Other"]) + \
-           pn.scales.scale_x_continuous(
-               name="Month",
-               labels=["J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D"],
-               breaks=(15, 46, 76, 106, 137, 167, 198, 228, 259, 289, 319, 350),
-               limits=(0, 366)) + \
-           pn.scales.scale_linetype_manual(
-               values={"Dispatch Limit": "dotted", "Dispatch": "solid"}
-           ) + \
-           pn.theme(
-               figure_size=(pn.options.figure_size[0] * tools.num_scenarios, pn.options.figure_size[1] * num_periods))
+    plot = (
+        pn.ggplot(df)
+        + pn.geom_line(pn.aes(x="day", y="value", color="Source", linetype="Type"))
+        + pn.facet_grid("period ~ scenario_name")
+        + pn.labs(y="Contribution to Energy Balance (TWh)")
+        + pn.scales.scale_color_manual(
+            values=colors, aesthetics="color", na_value=colors["Other"]
+        )
+        + pn.scales.scale_x_continuous(
+            name="Month",
+            labels=["J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D"],
+            breaks=(15, 46, 76, 106, 137, 167, 198, 228, 259, 289, 319, 350),
+            limits=(0, 366),
+        )
+        + pn.scales.scale_linetype_manual(
+            values={"Dispatch Limit": "dotted", "Dispatch": "solid"}
+        )
+        + pn.theme(
+            figure_size=(
+                pn.options.figure_size[0] * tools.num_scenarios,
+                pn.options.figure_size[1] * num_periods,
+            )
+        )
+    )
 
     tools.save_figure(plot.draw())
 
+
 @graph(
     "curtailment_per_period",
     title="Percent of total dispatchable capacity curtailed",

switch_model/generators/extensions/hydro_simple.py

@@ -171,10 +171,7 @@ def define_components(mod):
     )
 
     mod.hydro_avg_flow_mw = Param(
-        mod.HYDRO_GEN_TS_RAW,
-        within=NonNegativeReals,
-        input_file="hydro_timeseries.csv",
-        default=0.0,
+        mod.HYDRO_GEN_TS_RAW, within=NonNegativeReals, input_file="hydro_timeseries.csv"
     )
 
     # We use a scaling factor to improve the numerical properties
@@ -184,9 +181,7 @@ def define_components(mod):
     enforce_hydro_avg_flow_scaling_factor = 1e1
     mod.Enforce_Hydro_Avg_Flow = Constraint(
         mod.HYDRO_GEN_TS,
-        rule=lambda m, g, hts: Constraint.Skip
-        if m.hydro_avg_flow_mw[g, hts] == 0
-        else enforce_hydro_avg_flow_scaling_factor *
+        rule=lambda m, g, hts: enforce_hydro_avg_flow_scaling_factor *
         # Compute the weighted average of the dispatch
         sum(m.DispatchGen[g, t] * m.tp_weight[t] for t in m.TPS_IN_HTS[hts])
         / sum(m.tp_weight[tp] for tp in m.TPS_IN_HTS[hts])