Fix errors in the calculation of costs (#563)

* Fix calculation of material costs

* Fix calculation of variable costs

* Revert change to output

* Fix error in variable cost calculation

* Fix consumption function to allow non-1 outputs

* Revert "Fix consumption function to allow non-1 outputs"

This reverts commit c9d4c3e.

* One more material costs error

* Better name for tech_activity

* Fix error in fixed and variable costs

* Select products only when calculating variable costs

Author: tsmbland

src/muse/costs.py

@@ -91,6 +91,9 @@ def net_present_value(
     products = is_enduse(technologies.comm_usage)
     fuels = is_fuel(technologies.comm_usage)
 
+    # Calculate consumption
+    cons = consumption(technologies=techs, production=production, prices=prices)
+
     # Revenue
     prices_non_env = filter_input(prices, commodity=products, year=years.values)
     raw_revenues = (production * prices_non_env * rates).sum(("commodity", "year"))
@@ -112,34 +115,41 @@ def net_present_value(
 
     # Fuel/energy costs
     prices_fuel = filter_input(prices, commodity=fuels, year=years.values)
-    fuel = consumption(technologies=techs, production=production, prices=prices)
-    fuel_costs = (fuel * prices_fuel * rates).sum(("commodity", "year"))
+    fuel_costs = (cons * prices_fuel * rates).sum(("commodity", "year"))
 
     # Cost related to material other than fuel/energy and environmentals
     prices_material = filter_input(prices, commodity=material, year=years.values)
-    material_costs = (production * prices_material * rates).sum(("commodity", "year"))
+    material_costs = (cons * prices_material * rates).sum(("commodity", "year"))
 
-    # Fixed and Variable costs
-    fixed_costs = convert_timeslice(
-        techs.fix_par * (capacity**techs.fix_exp),
-        prices.timeslice,
-        QuantityType.EXTENSIVE,
+    # Fixed costs
+    fixed_costs = (
+        convert_timeslice(
+            techs.fix_par * (capacity**techs.fix_exp),
+            prices.timeslice,
+            QuantityType.EXTENSIVE,
+        )
+        * rates
+    ).sum("year")
+
+    # Variable costs
+    tech_activity = (production.sel(commodity=products) / techs.fixed_outputs).max(
+        "commodity"
     )
-    variable_costs = techs.var_par * (
-        (production.sel(commodity=products).sum("commodity")) ** techs.var_exp
+    variable_costs = ((techs.var_par * tech_activity**techs.var_exp) * rates).sum(
+        "year"
     )
-    assert set(fixed_costs.dims) == set(variable_costs.dims)
-    fixed_and_variable_costs = ((fixed_costs + variable_costs) * rates).sum("year")
 
-    results = raw_revenues - (
+    # Net present value
+    result = raw_revenues - (
         installed_capacity_costs
         + fuel_costs
         + environmental_costs
         + material_costs
-        + fixed_and_variable_costs
+        + fixed_costs
+        + variable_costs
     )
 
-    return results
+    return result
 
 
 def net_present_cost(
@@ -259,6 +269,9 @@ def lifetime_levelized_cost_of_energy(
     products = is_enduse(technologies.comm_usage)
     fuels = is_fuel(technologies.comm_usage)
 
+    # Calculate consumption
+    cons = consumption(technologies=techs, production=production, prices=prices)
+
     # Cost of installed capacity
     installed_capacity_costs = convert_timeslice(
         techs.cap_par * (capacity**techs.cap_exp),
@@ -276,31 +289,47 @@ def lifetime_levelized_cost_of_energy(
 
     # Fuel/energy costs
     prices_fuel = filter_input(prices, commodity=fuels, year=years.values)
-    fuel = consumption(technologies=techs, production=production, prices=prices)
-    fuel_costs = (fuel * prices_fuel * rates).sum(("commodity", "year"))
+    fuel_costs = (cons * prices_fuel * rates).sum(("commodity", "year"))
 
     # Cost related to material other than fuel/energy and environmentals
     prices_material = filter_input(prices, commodity=material, year=years.values)
-    material_costs = (production * prices_material * rates).sum(("commodity", "year"))
+    material_costs = (cons * prices_material * rates).sum(("commodity", "year"))
 
-    # Fixed and Variable costs
-    fixed_costs = convert_timeslice(
-        techs.fix_par * (capacity**techs.fix_exp),
-        prices.timeslice,
-        QuantityType.EXTENSIVE,
+    # Fixed costs
+    fixed_costs = (
+        convert_timeslice(
+            techs.fix_par * (capacity**techs.fix_exp),
+            prices.timeslice,
+            QuantityType.EXTENSIVE,
+        )
+        * rates
+    ).sum("year")
+
+    # Variable costs
+    tech_activity = (production.sel(commodity=products) / techs.fixed_outputs).max(
+        "commodity"
     )
-    variable_costs = (
-        techs.var_par * production.sel(commodity=products) ** techs.var_exp
-    ).sum("commodity")
-    fixed_and_variable_costs = ((fixed_costs + variable_costs) * rates).sum("year")
-    denominator = production.where(production > 0.0, 1e-6)
+    variable_costs = ((techs.var_par * tech_activity**techs.var_exp) * rates).sum(
+        "year"
+    )
+
+    # Production
+    prod = (
+        production.where(production > 0.0, 1e-6)
+        .sel(commodity=products)
+        .sum("commodity")
+    )
+    total_prod = (prod * rates).sum("year")
+
+    # LCOE
     result = (
         installed_capacity_costs
         + fuel_costs
         + environmental_costs
         + material_costs
-        + fixed_and_variable_costs
-    ) / (denominator.sel(commodity=products).sum("commodity") * rates).sum("year")
+        + fixed_costs
+        + variable_costs
+    ) / total_prod
 
     return result
 

src/muse/outputs/mca.py

@@ -694,7 +694,7 @@ def sectory_consumption(
 def metric_fuel_costs(
     market: xr.Dataset, sectors: list[AbstractSector], **kwargs
 ) -> pd.DataFrame:
-    """Current lifetime levelised cost across all sectors."""
+    """Current fuel costs across all sectors."""
     return _aggregate_sectors(sectors, market, op=sector_fuel_costs)
 
 
@@ -878,7 +878,7 @@ def sector_emission_costs(
 def metric_lcoe(
     market: xr.Dataset, sectors: list[AbstractSector], **kwargs
 ) -> pd.DataFrame:
-    """Current emission costs across all sectors."""
+    """Current lifetime levelised cost across all sectors."""
     return _aggregate_sectors(sectors, market, op=sector_lcoe)