costs_workflow/costs/cost.py

"""
Cost module
"""
import pandas as pd
from hub.city_model_structure.city import City

from configuration import Configuration
from capital_costs import LifeCycleCosts


class Cost:
  """
  Cost class
  """

  def __init__(self,
               city: City,
               number_of_years=31,
               percentage_credit=0,
               interest_rate=0.04,
               credit_years=15,
               consumer_price_index=0.04,
               electricity_peak_index=0.05,
               electricity_price_index=0.05,
               gas_price_index=0.05,
               discount_rate=0.03,
               retrofitting_year_construction=2020,
               factories_handler='montreal_custom'):
    self._city = city
    self._configuration = Configuration(number_of_years,
                                        percentage_credit,
                                        interest_rate, credit_years,
                                        consumer_price_index,
                                        electricity_peak_index,
                                        electricity_price_index,
                                        gas_price_index,
                                        discount_rate,
                                        retrofitting_year_construction,
                                        factories_handler)

  @property
  def life_cycle(self) -> pd.DataFrame:
    """
    Get complete life cycle costs
    :return: DataFrame
    """
    results = pd.DataFrame()
    for building in self._city.buildings:
      lcc = LifeCycleCosts(building, self._configuration)
      global_capital_costs, global_capital_incomes = lcc.calculate_capital_costs()
      global_end_of_life_costs = lcc.calculate_end_of_life_costs()
      global_operational_costs = lcc.calculate_total_operational_costs
      global_maintenance_costs = lcc.calculate_total_maintenance_costs()
      global_operational_incomes = lcc.calculate_total_operational_incomes()
      results[f'Scenario {retrofitting_scenario}'] = [life_cycle_costs_capital_skin,
                                                                 life_cycle_costs_capital_systems,
                                                                 life_cycle_costs_end_of_life_costs,
                                                                 life_cycle_operational_costs,
                                                                 life_cycle_maintenance_costs,
                                                                 life_cycle_operational_incomes,
                                                                 life_cycle_capital_incomes]

      life_cycle_results.index = ['total_capital_costs_skin',
                                  'total_capital_costs_systems',
                                  'end_of_life_costs',
                                  'total_operational_costs',
                                  'total_maintenance_costs',
                                  'operational_incomes',
                                  'capital_incomes']
    return results
    """

        if "gas" in building.energy_systems_archetype_name:
          FUEL_TYPE = 1
        else:
          FUEL_TYPE = 0

        
        full_path_output = Path(out_path / f'output {retrofitting_scenario} {building.name}.xlsx').resolve()
        with pd.ExcelWriter(full_path_output) as writer:
          global_capital_costs.to_excel(writer, sheet_name='global_capital_costs')
          global_end_of_life_costs.to_excel(writer, sheet_name='global_end_of_life_costs')
          global_operational_costs.to_excel(writer, sheet_name='global_operational_costs')
          global_maintenance_costs.to_excel(writer, sheet_name='global_maintenance_costs')
          global_operational_incomes.to_excel(writer, sheet_name='global_operational_incomes')
          global_capital_incomes.to_excel(writer, sheet_name='global_capital_incomes')

        df_capital_costs_skin = (
            global_capital_costs['B2010_opaque_walls'] + global_capital_costs['B2020_transparent'] +
            global_capital_costs['B3010_opaque_roof'] + global_capital_costs['B10_superstructure']
        )
        df_capital_costs_systems = (
            global_capital_costs['D3020_heat_generating_systems'] +
            global_capital_costs['D3030_cooling_generation_systems'] +
            global_capital_costs['D3080_other_hvac_ahu'] +
            global_capital_costs['D5020_lighting_and_branch_wiring'] +
            global_capital_costs['D301010_photovoltaic_system']
        )
        df_end_of_life_costs = global_end_of_life_costs['End_of_life_costs']
        df_operational_costs = (
            global_operational_costs['Fixed_costs_electricity_peak'] +
            global_operational_costs['Fixed_costs_electricity_monthly'] +
            global_operational_costs['Fixed_costs_electricity_peak'] +
            global_operational_costs['Fixed_costs_electricity_monthly'] +
            global_operational_costs['Variable_costs_electricity'] +
            global_operational_costs['Fixed_costs_gas'] +
            global_operational_costs['Variable_costs_gas']
        )
        df_maintenance_costs = (
            global_maintenance_costs['Heating_maintenance'] +
            global_maintenance_costs['Cooling_maintenance'] +
            global_maintenance_costs['PV_maintenance']
        )
        df_operational_incomes = global_operational_incomes['Incomes electricity']

        df_capital_incomes = (
            global_capital_incomes['Subsidies construction'] +
            global_capital_incomes['Subsidies HVAC'] +
            global_capital_incomes['Subsidies PV']
        )

        life_cycle_costs_capital_skin = npf.npv(self._discount_rate, list_cashflow)_npv_from_list(, df_capital_costs_skin.values.tolist())
        life_cycle_costs_capital_systems = _npv_from_list(DISCOUNT_RATE, df_capital_costs_systems.values.tolist())
        life_cycle_costs_end_of_life_costs = _npv_from_list(DISCOUNT_RATE, df_end_of_life_costs.values.tolist())
        life_cycle_operational_costs = _npv_from_list(DISCOUNT_RATE, df_operational_costs.values.tolist())
        life_cycle_maintenance_costs = _npv_from_list(DISCOUNT_RATE, df_maintenance_costs.values.tolist())
        life_cycle_operational_incomes = _npv_from_list(DISCOUNT_RATE, df_operational_incomes.values.tolist())
        life_cycle_capital_incomes = _npv_from_list(DISCOUNT_RATE, df_capital_incomes.values.tolist())

        life_cycle_costs = (
            life_cycle_costs_capital_skin +
            life_cycle_costs_capital_systems +
            life_cycle_costs_end_of_life_costs +
            life_cycle_operational_costs +
            life_cycle_maintenance_costs -
            life_cycle_operational_incomes -
            life_cycle_capital_incomes
        )

        life_cycle_results[f'Scenario {retrofitting_scenario}'] = [life_cycle_costs_capital_skin,
                                                                   life_cycle_costs_capital_systems,
                                                                   life_cycle_costs_end_of_life_costs,
                                                                   life_cycle_operational_costs,
                                                                   life_cycle_maintenance_costs,
                                                                   life_cycle_operational_incomes,
                                                                   life_cycle_capital_incomes]

        life_cycle_results.index = ['total_capital_costs_skin',
                                    'total_capital_costs_systems',
                                    'end_of_life_costs',
                                    'total_operational_costs',
                                    'total_maintenance_costs',
                                    'operational_incomes',
                                    'capital_incomes']

        print(life_cycle_results)
        print(f'Scenario {retrofitting_scenario} {life_cycle_costs}')
    return results


"""