191 lines
9.3 KiB
Python
191 lines
9.3 KiB
Python
"""
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Costs Workflow
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2022 Project Author Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
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Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
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"""
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from pathlib import Path
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import numpy_financial as npf
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import pandas as pd
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from energy_systems_sizing import EnergySystemsSizing
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from hub.catalog_factories.costs_catalog_factory import CostCatalogFactory
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from hub.helpers.dictionaries import Dictionaries
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from hub.imports.construction_factory import ConstructionFactory
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from hub.imports.geometry_factory import GeometryFactory
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from hub.imports.usage_factory import UsageFactory
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from hub.imports.weather_factory import WeatherFactory
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from hub.helpers import constants as cte
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from monthly_energy_balance_engine import MonthlyEnergyBalanceEngine
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from sra_engine import SraEngine
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from life_cycle_costs import LifeCycleCosts
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# import constants
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from costs import CLIMATE_REFERENCE_CITY, WEATHER_FILE, WEATHER_FORMAT, CONSTRUCTION_FORMAT, USAGE_FORMAT
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from costs import ENERGY_SYSTEM_FORMAT, ATTIC_HEATED_CASE, BASEMENT_HEATED_CASE, RETROFITTING_SCENARIOS, NUMBER_OF_YEARS
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from costs import CONSUMER_PRICE_INDEX, ELECTRICITY_PEAK_INDEX, ELECTRICITY_PRICE_INDEX, GAS_PRICE_INDEX, DISCOUNT_RATE
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from costs import SKIN_RETROFIT, SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV
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# import paths
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from costs import file_path, tmp_folder, out_path
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def _npv_from_list(npv_discount_rate, list_cashflow):
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lcc_value = npf.npv(npv_discount_rate, list_cashflow)
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return lcc_value
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def _search_archetype(costs_catalog, building_function):
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costs_archetypes = costs_catalog.entries('archetypes').archetypes
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for building_archetype in costs_archetypes:
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if str(building_function) == str(building_archetype.function):
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return building_archetype
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raise KeyError('archetype not found')
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life_cycle_results = pd.DataFrame()
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print('[city creation start]')
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city = GeometryFactory('geojson',
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path=file_path,
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height_field='heightmax',
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year_of_construction_field='ANNEE_CONS',
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function_field='CODE_UTILI',
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function_to_hub=Dictionaries().montreal_function_to_hub_function).city
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city.climate_reference_city = CLIMATE_REFERENCE_CITY
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city.climate_file = (tmp_folder / f'{CLIMATE_REFERENCE_CITY}.cli').resolve()
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print(f'city created from {file_path}')
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WeatherFactory(WEATHER_FORMAT, city, file_name=WEATHER_FILE).enrich()
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print('enrich weather... done')
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ConstructionFactory(CONSTRUCTION_FORMAT, city).enrich()
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print('enrich constructions... done')
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UsageFactory(USAGE_FORMAT, city).enrich()
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print('enrich usage... done')
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for building in city.buildings:
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building.energy_systems_archetype_name = 'system 1 gas'
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EnergySystemsFactory(ENERGY_SYSTEM_FORMAT, city).enrich()
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print('enrich systems... done')
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print('exporting:')
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catalog = CostCatalogFactory('montreal_custom').catalog
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print('costs catalog access... done')
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sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
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SraEngine(city, sra_file, tmp_folder, WEATHER_FILE)
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print(' sra processed...')
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for building in city.buildings:
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building.attic_heated = ATTIC_HEATED_CASE
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building.basement_heated = BASEMENT_HEATED_CASE
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for retrofitting_scenario in RETROFITTING_SCENARIOS:
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if retrofitting_scenario in (SKIN_RETROFIT, SYSTEM_RETROFIT_AND_PV):
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for building in city.buildings:
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building.year_of_construction = retrofitting_year_of_construction
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ConstructionFactory(CONSTRUCTION_FORMAT, city).enrich()
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print('enrich retrofitted constructions... done')
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if retrofitting_scenario in (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
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for building in city.buildings:
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building.energy_systems_archetype_name = 'system 6 electricity pv'
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EnergySystemsFactory(ENERGY_SYSTEM_FORMAT, city).enrich()
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print('enrich systems... done')
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MonthlyEnergyBalanceEngine(city, tmp_folder)
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EnergySystemsSizing(city).enrich()
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print(f'beginning costing scenario {retrofitting_scenario} systems... done')
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for building in city.buildings:
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function = Dictionaries().hub_function_to_montreal_custom_costs_function[building.function]
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archetype = _search_archetype(catalog, function)
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print('lcc for first building started')
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if "gas" in building.energy_systems_archetype_name:
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FUEL_TYPE = 1
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else:
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FUEL_TYPE = 0
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lcc = LifeCycleCosts(building, archetype, NUMBER_OF_YEARS, CONSUMER_PRICE_INDEX, ELECTRICITY_PEAK_INDEX,
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ELECTRICITY_PRICE_INDEX, GAS_PRICE_INDEX, DISCOUNT_RATE, retrofitting_scenario, FUEL_TYPE)
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global_capital_costs, global_capital_incomes = lcc.calculate_capital_costs()
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global_end_of_life_costs = lcc.calculate_end_of_life_costs()
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global_operational_costs = lcc.calculate_total_operational_costs()
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global_maintenance_costs = lcc.calculate_total_maintenance_costs()
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global_operational_incomes = lcc.calculate_total_operational_incomes()
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full_path_output = Path(out_path / f'output {retrofitting_scenario} {building.name}.xlsx').resolve()
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with pd.ExcelWriter(full_path_output) as writer:
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global_capital_costs.to_excel(writer, sheet_name='global_capital_costs')
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global_end_of_life_costs.to_excel(writer, sheet_name='global_end_of_life_costs')
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global_operational_costs.to_excel(writer, sheet_name='global_operational_costs')
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global_maintenance_costs.to_excel(writer, sheet_name='global_maintenance_costs')
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global_operational_incomes.to_excel(writer, sheet_name='global_operational_incomes')
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global_capital_incomes.to_excel(writer, sheet_name='global_capital_incomes')
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df_capital_costs_skin = (
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global_capital_costs['B2010_opaque_walls'] + global_capital_costs['B2020_transparent'] +
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global_capital_costs['B3010_opaque_roof'] + global_capital_costs['B10_superstructure']
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)
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df_capital_costs_systems = (
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global_capital_costs['D3020_heat_generating_systems'] +
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global_capital_costs['D3030_cooling_generation_systems'] +
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global_capital_costs['D3080_other_hvac_ahu'] +
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global_capital_costs['D5020_lighting_and_branch_wiring'] +
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global_capital_costs['D301010_photovoltaic_system']
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)
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df_end_of_life_costs = global_end_of_life_costs['End_of_life_costs']
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df_operational_costs = (
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global_operational_costs['Fixed_costs_electricity_peak'] +
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global_operational_costs['Fixed_costs_electricity_monthly'] +
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global_operational_costs['Fixed_costs_electricity_peak'] +
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global_operational_costs['Fixed_costs_electricity_monthly'] +
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global_operational_costs['Variable_costs_electricity'] +
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global_operational_costs['Fixed_costs_gas'] +
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global_operational_costs['Variable_costs_gas']
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)
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df_maintenance_costs = (
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global_maintenance_costs['Heating_maintenance'] +
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global_maintenance_costs['Cooling_maintenance'] +
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global_maintenance_costs['PV_maintenance']
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)
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df_operational_incomes = global_operational_incomes['Incomes electricity']
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df_capital_incomes = global_capital_incomes['Subsidies construction'] + global_capital_incomes['Subsidies HVAC'] + \
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global_capital_incomes['Subsidies PV']
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life_cycle_costs_capital_skin = _npv_from_list(DISCOUNT_RATE, df_capital_costs_skin.values.tolist())
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life_cycle_costs_capital_systems = _npv_from_list(DISCOUNT_RATE, df_capital_costs_systems.values.tolist())
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life_cycle_costs_end_of_life_costs = _npv_from_list(DISCOUNT_RATE, df_end_of_life_costs.values.tolist())
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life_cycle_operational_costs = _npv_from_list(DISCOUNT_RATE, df_operational_costs.values.tolist())
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life_cycle_maintenance_costs = _npv_from_list(DISCOUNT_RATE, df_maintenance_costs.values.tolist())
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life_cycle_operational_incomes = _npv_from_list(DISCOUNT_RATE, df_operational_incomes.values.tolist())
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life_cycle_capital_incomes = _npv_from_list(DISCOUNT_RATE, df_capital_incomes.values.tolist())
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life_cycle_costs = (
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life_cycle_costs_capital_skin +
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life_cycle_costs_capital_systems +
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life_cycle_costs_end_of_life_costs +
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life_cycle_operational_costs +
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life_cycle_maintenance_costs -
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life_cycle_operational_incomes -
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life_cycle_capital_incomes
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)
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life_cycle_results[f'Scenario {retrofitting_scenario}'] = [life_cycle_costs_capital_skin,
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life_cycle_costs_capital_systems,
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life_cycle_costs_end_of_life_costs,
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life_cycle_operational_costs,
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life_cycle_maintenance_costs,
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life_cycle_operational_incomes,
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life_cycle_capital_incomes]
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life_cycle_results.index = ['total_capital_costs_skin',
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f'total_capital_costs_systems','end_of_life_costs',
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'total_operational_costs', 'total_maintenance_costs',
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'operational_incomes', 'capital_incomes']
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print(life_cycle_results)
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print(f'Scenario {retrofitting_scenario} {life_cycle_costs}')
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