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