212 lines
9.7 KiB
Python
212 lines
9.7 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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import glob
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import os
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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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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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file_path = (Path(__file__).parent.parent / 'costs_workflow' / 'input_files' / 'selected_building_2864.geojson')
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climate_reference_city = 'Montreal'
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weather_file = 'CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw'
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weather_format = 'epw'
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construction_format = 'nrcan'
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usage_format = 'comnet'
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energy_systems_format = 'montreal_custom'
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attic_heated_case = 0
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basement_heated_case = 1
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tmp_folder = (Path(__file__).parent.parent / 'monthly_energy_balance_workflow' / 'tmp')
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out_path = (Path(__file__).parent.parent / 'costs_workflow' / 'out_files')
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files = glob.glob(f'{out_path}/*')
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for file in files:
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if file != '.gitignore':
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os.remove(file)
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number_of_years = 31
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consumer_price_index = 0.04
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electricity_peak_index = 0.05
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electricity_price_index = 0.05
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gas_price_index = 0.05
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discount_rate = 0.03
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retrofitting_year_of_construction = 2020
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retrofitting_scenarios = [0, 1, 2, 3]
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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_systems_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 == 1 or retrofitting_scenario == 3:
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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 == 2 or retrofitting_scenario == 3:
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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_systems_format, city).enrich()
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print('enrich systems... done')
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MonthlyEnergyBalanceEngine(city, tmp_folder)
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lightingdemand = city.buildings[0].lighting_electrical_demand[cte.YEAR]['insel meb'] / 1000
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print(f'lighting first {lightingdemand}')
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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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print(f'fuel type {fuel_type}')
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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 = [f'total_capital_costs_skin',
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f'total_capital_costs_systems', f'end_of_life_costs',
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f'total_operational_costs', f'total_maintenance_costs',
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f'operational_incomes', f'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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