Incorporated co2 in costs workflow. Constants initialised in init, and calculations done in __main__emissions.py

costs/__init__.py

@@ -36,6 +36,15 @@ RETROFITTING_SCENARIOS = [
   SYSTEM_RETROFIT_AND_PV,
   SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV
 ]
+
+EMISSION_FACTOR_GAS_QUEBEC = 0.25
+EMISSION_FACTOR_ELECTRICITY_QUEBEC = 0.0015 #https://www.cer-rec.gc.ca/en/data-analysis/energy-markets/provincial-territorial-energy-profiles/provincial-territorial-energy-profiles-quebec.html#:~:text=GHG%20Emissions,-Quebec's%20GHG%20emissions&text=The%20largest%20emitting%20sectors%20in,2.3%20MT%20CO2e.
+EMISSION_FACTOR_GAS_QUEBEC = 0.183 #https://www.canada.ca/en/environment-climate-change/services/climate-change/pricing-pollution-how-it-will-work/output-based-pricing-system/federal-greenhouse-gas-offset-system/emission-factors-reference-values.html
+EMISSION_FACTOR_BIOMASS_QUEBEC = 0.035 #Data from Spain. https://www.miteco.gob.es/es/cambio-climatico/temas/mitigacion-politicas-y-medidas/factoresemision_tcm30-479095.pdf
+EMISSION_FACTOR_FUEL_OIL_QUEBEC = 0.274
+EMISSION_FACTOR_DIESEL_QUEBEC = 0.240
+
+
 tmp_folder = Path('./tmp').resolve()
 out_path = Path('./outputs').resolve()
 files = glob.glob(f'{out_path}/*')

costs/__main__.py

@@ -29,6 +29,9 @@ from costs import CONSUMER_PRICE_INDEX, ELECTRICITY_PEAK_INDEX, ELECTRICITY_PRIC
 from costs import SKIN_RETROFIT, SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV
 from costs import RETROFITTING_YEAR_CONSTRUCTION
 
+from costs import EMISSION_FACTOR_GAS_QUEBEC, EMISSION_FACTOR_ELECTRICITY_QUEBEC, EMISSION_FACTOR_GAS_QUEBEC,\
+  EMISSION_FACTOR_BIOMASS_QUEBEC, EMISSION_FACTOR_FUEL_OIL_QUEBEC, EMISSION_FACTOR_DIESEL_QUEBEC
+
 # import paths
 from costs import file_path, tmp_folder, out_path
 

costs/__main__emissions.py

@@ -0,0 +1,68 @@
+"""
+Costs Workflow
+SPDX - License - Identifier: LGPL - 3.0 - or -later
+Copyright © 2022 Project Author Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
+Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
+"""
+
+from pathlib import Path
+
+import pandas as pd
+from hub.helpers.dictionaries import Dictionaries
+from hub.catalog_factories.costs_catalog_factory import CostCatalogFactory
+
+from costs import EMISSION_FACTOR_ELECTRICITY_QUEBEC, EMISSION_FACTOR_GAS_QUEBEC, EMISSION_FACTOR_BIOMASS_QUEBEC, \
+  EMISSION_FACTOR_FUEL_OIL_QUEBEC, EMISSION_FACTOR_DIESEL_QUEBEC, NUMBER_OF_YEARS
+
+def _search_archetype(costs_catalog, building_function):
+  costs_archetypes = costs_catalog.entries('archetypes').archetypes
+  for building_archetype in costs_archetypes:
+    if str(building_function) == str(building_archetype.function):
+      return building_archetype
+  raise KeyError('archetype not found')
+
+catalog = CostCatalogFactory('montreal_custom').catalog
+
+for building in city.buildings:
+  building_heating_consumption = 1000
+  building_domestic_water_consumption = 1000
+  building_cooling_consumption = 1000
+  distribution_systems_electrical_consumption = 1000
+  lighting_electrical_demand = 1000
+  appliances_electrical_demand = 1000
+  rng = range(NUMBER_OF_YEARS)
+
+  function = Dictionaries().hub_function_to_montreal_custom_costs_function[building.function]
+  archetype = _search_archetype(catalog, function)
+
+  print('co2 for first building started')
+  if "gas" in building.energy_systems_archetype_name:
+    gas_consumption = building_heating_consumption + building_domestic_water_consumption
+    electricity_consumption = building_cooling_consumption + distribution_systems_electrical_consumption + \
+                              lighting_electrical_demand + appliances_electrical_demand
+    biomass_consumption = 0
+    fuel_oil_consumption = 0
+    diesel_consumption = 0
+  else:
+    gas_consumption = 0
+    electricity_consumption = building_heating_consumption + building_domestic_water_consumption + \
+                              building_cooling_consumption + distribution_systems_electrical_consumption + \
+                              lighting_electrical_demand + appliances_electrical_demand
+    biomass_consumption = 0
+    fuel_oil_consumption = 0
+    diesel_consumption = 0
+
+  CO2_emissions = pd.DataFrame(index=rng, columns=['CO2 emissions gas', 'CO2 emissions electricity',
+                                                   'CO2 Emissions biomass', 'CO2 emissions fueloil',
+                                                   'CO2 emissions diesel'], dtype='float')
+
+  for year in range(1, NUMBER_OF_YEARS+1):
+
+    CO2_emissions.at[year,'CO2 emissions gas'] = gas_consumption * EMISSION_FACTOR_GAS_QUEBEC
+    CO2_emissions.at[year, 'CO2 emissions electricity'] = electricity_consumption * EMISSION_FACTOR_ELECTRICITY_QUEBEC
+    CO2_emissions.at[year, 'CO2 emissions biomass'] = biomass_consumption * EMISSION_FACTOR_BIOMASS_QUEBEC
+    CO2_emissions.at[year, 'CO2 emissions fueloil'] = fuel_oil_consumption * EMISSION_FACTOR_FUEL_OIL_QUEBEC
+    CO2_emissions.at[year, 'CO2 emissions diesel'] = diesel_consumption * EMISSION_FACTOR_DIESEL_QUEBEC
+
+  CO2_emissions_total = CO2_emissions.sum()
+

costs/life_cycle_costs.py

@@ -280,6 +280,7 @@ class LifeCycleCosts:
         electricity_heating + electricity_cooling + electricity_lighting + domestic_hot_water_electricity +
         electricity_plug_loads + electricity_distribution
     )
+    print(f'electricity consumption {total_electricity_consumption}')
 
     # todo: change when peak electricity demand is coded. Careful with factor residential
     peak_electricity_demand = 100  # self._peak_electricity_demand