105 lines
4.5 KiB
Python
105 lines
4.5 KiB
Python
"""
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Total operational costs module
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2024 Project Coder Saeed Ranjbar saeed.ranjbar@mail.concordia.ca
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Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
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"""
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import math
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import pandas as pd
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from hub.city_model_structure.building import Building
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import hub.helpers.constants as cte
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from scripts.costs.configuration import Configuration
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from scripts.costs.cost_base import CostBase
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from scripts.costs.peak_load import PeakLoad
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class TotalOperationalCosts(CostBase):
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"""
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Total Operational costs class
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"""
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def __init__(self, building: Building, configuration: Configuration):
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super().__init__(building, configuration)
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columns_list = self.columns()
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self._yearly_operational_costs = pd.DataFrame(
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index=self._rng,
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columns=columns_list,
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dtype='float'
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)
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def calculate(self) -> pd.DataFrame:
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"""
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Calculate total operational costs
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:return: pd.DataFrame
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"""
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building = self._building
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fuel_consumption_breakdown = building.energy_consumption_breakdown
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archetype = self._archetype
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total_floor_area = self._total_floor_area
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if archetype.function == 'residential':
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factor = total_floor_area / 80
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else:
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factor = 1
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total_electricity_consumption = sum(self._building.energy_consumption_breakdown[cte.ELECTRICITY].values())
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peak_electricity_load = PeakLoad(self._building).electricity_peak_load
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peak_load_value = peak_electricity_load.max(axis=1)
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peak_electricity_demand = peak_load_value[1] / 1000 # self._peak_electricity_demand adapted to kW
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fuels = archetype.operational_cost.fuels
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for fuel in fuels:
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if fuel.type in fuel_consumption_breakdown.keys():
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if fuel.type == cte.ELECTRICITY:
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variable_electricity_cost_year_0 = (
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total_electricity_consumption * fuel.variable[0] / 1000
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)
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peak_electricity_cost_year_0 = peak_electricity_demand * fuel.fixed_power * 12
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monthly_electricity_cost_year_0 = fuel.fixed_monthly * 12 * factor
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for year in range(1, self._configuration.number_of_years + 1):
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price_increase_electricity = math.pow(1 + self._configuration.electricity_price_index, year)
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price_increase_peak_electricity = math.pow(1 + self._configuration.electricity_peak_index, year)
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self._yearly_operational_costs.at[year, 'Fixed Costs Electricity Peak'] = (
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peak_electricity_cost_year_0 * price_increase_peak_electricity
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)
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self._yearly_operational_costs.at[year, 'Fixed Costs Electricity Monthly'] = (
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monthly_electricity_cost_year_0 * price_increase_peak_electricity
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)
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if not isinstance(variable_electricity_cost_year_0, pd.DataFrame):
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variable_costs_electricity = variable_electricity_cost_year_0 * price_increase_electricity
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else:
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variable_costs_electricity = float(variable_electricity_cost_year_0.iloc[0] * price_increase_electricity)
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self._yearly_operational_costs.at[year, 'Variable Costs Electricity'] = (
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variable_costs_electricity
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)
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else:
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fuel_fixed_cost = fuel.fixed_monthly * 12 * factor
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if fuel.type == cte.BIOMASS:
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conversion_factor = 1
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else:
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conversion_factor = fuel.density[0]
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variable_cost_fuel = (
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((sum(fuel_consumption_breakdown[fuel.type].values()) * 3600)/(1e6*fuel.lower_heating_value[0] * conversion_factor)) * fuel.variable[0])
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for year in range(1, self._configuration.number_of_years + 1):
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price_increase_gas = math.pow(1 + self._configuration.gas_price_index, year)
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self._yearly_operational_costs.at[year, f'Fixed Costs {fuel.type}'] = fuel_fixed_cost * price_increase_gas
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self._yearly_operational_costs.at[year, f'Variable Costs {fuel.type}'] = (
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variable_cost_fuel * price_increase_gas)
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self._yearly_operational_costs.fillna(0, inplace=True)
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return self._yearly_operational_costs
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def columns(self):
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columns_list = []
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fuels = [key for key in self._building.energy_consumption_breakdown.keys()]
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for fuel in fuels:
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if fuel == cte.ELECTRICITY:
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columns_list.append('Fixed Costs Electricity Peak')
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columns_list.append('Fixed Costs Electricity Monthly')
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columns_list.append('Variable Costs Electricity')
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else:
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columns_list.append(f'Fixed Costs {fuel}')
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columns_list.append(f'Variable Costs {fuel}')
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return columns_list
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