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322 lines
14 KiB
Python
322 lines
14 KiB
Python
import numpy as np
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import pandas as pd
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import backend.app.assumptions as assumptions
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QUARTERLY_ENERGY_PRICES = [
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# 2024 Q1
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{"start": "2024-01-01", "end": "2024-03-31", "electricity": 0.2, "gas": 0.042},
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# 2023 Q4
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{"start": "2023-10-01", "end": "2023-12-31", "electricity": 0.202, "gas": 0.51},
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# 2023 Q3
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{"start": "2023-07-01", "end": "2023-09-30", "electricity": 0.188, "gas": 0.46},
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# 2023 Q2
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{"start": "2023-04-01", "end": "2023-06-30", "electricity": 0.177, "gas": 0.456},
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]
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class AnnualBillSavings:
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"""
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This is a simple class which will estimate the annual bill savings, based on the kwh savings.
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This class uses data from Ofgem, including their price caps, to provide us with an estimate for
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1KWH of energy.
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"""
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# These gas an electricity consumption figures are based off of figures presented by Ofgem
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# https://www.ofgem.gov.uk/information-consumers/energy-advice-households/average-gas-and-electricity-use-explained
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AVERAGE_ELECTRICITY_CONSUMPTION = 2700
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AVERAGE_GAS_CONSUMPTION = 11500
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# Latest price cap figures from Ofgem are for April 2024
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# https://www.ofgem.gov.uk/energy-price-cap
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ELECTRICITY_PRICE_CAP = 0.2236
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GAS_PRICE_CAP = 0.0548
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# This is the most recent export payment figure, at 9.28p/kWh
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# Smart export guarantee rates can be found here:
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# https://www.sunsave.energy/solar-panels-advice/exporting-to-the-grid/best-seg-rates
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ELECTRICITY_EXPORT_PAYMENT = 0.0928
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# This is a weighted mean of the price caps, using the consumption figures above as weights
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PRICE_FACTOR = 0.09549999999999999
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# Daily standard charge, based on average across England, Scotland and Wales, and includes VAT
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DAILY_STANDARD_CHARGE_GAS = 0.3143
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DAILY_STANDARD_CHARGE_ELECTRICITY = 0.601
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# Based on https://www.nottenergy.com/advice-and-tools/project-energy-cost-comparison
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# For July 2024. These quotes are based on the east midlands region, so we
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FUEL_DATA = pd.DataFrame([
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{"Fuel": "Electricity Standard", "Price (p)": 28.58, "Unit": "kWh", "Boiler Efficiency (%)": 100,
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"Energy Content, Net Calorific value (kWh/unit)": 1.00, "Price per kWh (p) (inc boiler efficiency)": 28.58,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.275},
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{"Fuel": "Mains Gas Standard", "Price (p)": 6.31, "Unit": "kWh", "Boiler Efficiency (%)": 90,
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"Energy Content, Net Calorific value (kWh/unit)": 1.00, "Price per kWh (p) (inc boiler efficiency)": 7.01,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.213},
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{"Fuel": "Kerosene", "Price (p)": 62.49, "Unit": "Litre", "Boiler Efficiency (%)": 90,
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"Energy Content, Net Calorific value (kWh/unit)": 9.79, "Price per kWh (p) (inc boiler efficiency)": 7.09,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.298},
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{"Fuel": "Gas oil", "Price (p)": 94.50, "Unit": "Litre", "Boiler Efficiency (%)": 90,
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"Energy Content, Net Calorific value (kWh/unit)": 9.96, "Price per kWh (p) (inc boiler efficiency)": 10.54,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.316},
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{"Fuel": "LPG", "Price (p)": 55.00, "Unit": "Litre", "Boiler Efficiency (%)": 90,
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"Energy Content, Net Calorific value (kWh/unit)": 6.78, "Price per kWh (p) (inc boiler efficiency)": 9.01,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.240},
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{"Fuel": "Butane", "Price (p)": 216.58, "Unit": "Litre", "Boiler Efficiency (%)": 90,
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"Energy Content, Net Calorific value (kWh/unit)": 6.64, "Price per kWh (p) (inc boiler efficiency)": 36.24,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.248},
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{"Fuel": "Propane", "Price (p)": 157.67, "Unit": "Litre", "Boiler Efficiency (%)": 90,
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"Energy Content, Net Calorific value (kWh/unit)": 7.22, "Price per kWh (p) (inc boiler efficiency)": 24.25,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.239},
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{"Fuel": "Kiln Dried (logs)", "Price (p)": 36.52, "Unit": "kg", "Boiler Efficiency (%)": 85,
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"Energy Content, Net Calorific value (kWh/unit)": 4.09, "Price per kWh (p) (inc boiler efficiency)": 10.51,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.024},
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{"Fuel": "Pellets (Bagged)", "Price (p)": 39.62, "Unit": "kg", "Boiler Efficiency (%)": 90,
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"Energy Content, Net Calorific value (kWh/unit)": 4.80, "Price per kWh (p) (inc boiler efficiency)": 9.17,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.049},
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{"Fuel": "Pellets (Blown bulk)", "Price (p)": 33.92, "Unit": "kg", "Boiler Efficiency (%)": 90,
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"Energy Content, Net Calorific value (kWh/unit)": 4.80, "Price per kWh (p) (inc boiler efficiency)": 7.85,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.049},
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{"Fuel": "Smokeless fuel", "Price (p)": 67.26, "Unit": "kg", "Boiler Efficiency (%)": 75,
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"Energy Content, Net Calorific value (kWh/unit)": 6.70, "Price per kWh (p) (inc boiler efficiency)": 13.38,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.404},
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{"Fuel": "Coal", "Price (p)": 48.50, "Unit": "kg", "Boiler Efficiency (%)": 75,
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"Energy Content, Net Calorific value (kWh/unit)": 7.95, "Price per kWh (p) (inc boiler efficiency)": 8.13,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.404},
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{"Fuel": "GSHP", "Price (p)": 28.58, "Unit": "kWh", "Boiler Efficiency (%)": 350,
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"Energy Content, Net Calorific value (kWh/unit)": 1.00, "Price per kWh (p) (inc boiler efficiency)": 8.17,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.079},
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{"Fuel": "ASHP", "Price (p)": 28.58, "Unit": "kWh", "Boiler Efficiency (%)": 294,
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"Energy Content, Net Calorific value (kWh/unit)": 1.00, "Price per kWh (p) (inc boiler efficiency)": 9.72,
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"CO2eq emission factor kgCO2eq/kWh (Gross CV)": 0.094}
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])
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EPC_BANDS = ["G", "F", "E", "D", "C", "B", "A"]
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@classmethod
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def estimate(cls, kwh: float):
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"""
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Estimate the annual bill savings based on the kwh savings
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:param kwh: The kwh savings
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:return: An estimate for annual bill savings
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"""
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return cls.PRICE_FACTOR * kwh
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@classmethod
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def estimate_electric(cls, kwh: float):
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"""
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Estimate the annual bill savings based on the kwh savings
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:param kwh: The kwh savings
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:return: An estimate for annual bill savings
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"""
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return cls.ELECTRICITY_PRICE_CAP * kwh
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@classmethod
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def calculate_annual_bill(cls, kwh, mains_gas=True):
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"""
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This method will estimate the total annual bill for a property
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It assumed gas & electricity are used
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:param kwh: The total kwh consumption
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:param mains_gas: Whether the property uses mains gas
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:return: An estimate for annual bill
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"""
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if mains_gas:
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return cls.PRICE_FACTOR * kwh + (
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cls.DAILY_STANDARD_CHARGE_GAS + cls.DAILY_STANDARD_CHARGE_ELECTRICITY * 365)
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return cls.ELECTRICITY_PRICE_CAP * kwh + (cls.DAILY_STANDARD_CHARGE_ELECTRICITY * 365)
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@staticmethod
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def calculate_occupants(total_floor_area):
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"""
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From Table 1b of the SAP 2012 documentation https://bregroup.com/documents/d/bre-group/sap-2012_9-92
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Provides a methodology to estimate occupancy, based on floor area. This is used to calculate the amount of
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electricity used be appliances and during cooking.
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:param total_floor_area:
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:return:
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"""
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if total_floor_area <= 13.9:
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return 1
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return 1 + (1.76 * (1 - np.exp(-0.000349 * (total_floor_area - 13.9) * (total_floor_area - 13.9))) + 0.0013 * (
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total_floor_area - 13.9))
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@staticmethod
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def estimate_electrical_appliances(occupants, total_floor_area):
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"""
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From secion L2 of SAP2012 Electrical appliances
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https://bregroup.com/documents/d/bre-group/sap-2012_9-92
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Used to estimate the amount of energy used by electrical appliances
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:param occupants:
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:param total_floor_area:
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:return:
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"""
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e_a = 207.8 * np.power(total_floor_area * occupants, 0.4717)
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days_in_month = {
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1: 31,
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2: 28,
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3: 31,
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4: 30,
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5: 31,
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6: 30,
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7: 31,
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8: 31,
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9: 30,
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10: 31,
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11: 30,
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12: 31
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}
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eam = 0
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for m in range(1, 13):
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nm = days_in_month[m]
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eam += e_a * (1 + 0.157 * np.cos(2 * np.pi * (m - 1.78) / 12)) * nm / 365
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return eam
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@classmethod
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def estimate_appliances_energy_use(cls, total_floor_area):
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# The EPC energy consumption does not factor in cooking and applicance use, so this is estimated using the
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# methodology outlined in SAP, and is discussed in the UCL paper in section 3.1.1
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estimated_occupants = cls.calculate_occupants(total_floor_area=total_floor_area)
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appliances_energy_use = cls.estimate_electrical_appliances(estimated_occupants, total_floor_area)
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return appliances_energy_use
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@classmethod
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def adjust_energy_to_metered(cls, epc_energy, current_epc_rating):
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"""
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The over-prediction of energy use by EPCs in Great Britain: A comparison
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of EPC-modelled and metered primary energy use intensity
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Which can be found here: https://www.sciencedirect.com/science/article/pii/S0378778823002542
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We implement the results on page 10
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This is used to just re-map the cost from the EPC to the metered cost
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epc_energy could be cost or kwh
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:return:
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"""
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gradients = {
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"A": -0.1,
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"B": -0.1,
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"C": -0.43,
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"D": -0.52,
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"E": -0.7,
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"F": -0.76,
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"G": -0.76
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}
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intercepts = {
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"A": 28,
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"B": 28,
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"C": 97,
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"D": 119,
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"E": 160,
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"F": 157,
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"G": 157
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}
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gradient = gradients[current_epc_rating]
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intercept = intercepts[current_epc_rating]
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# This should be negative
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consumption_difference = gradient * epc_energy + intercept
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consumption_difference = 0 if consumption_difference > 0 else consumption_difference
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adjusted_consumption = (epc_energy + consumption_difference)
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if adjusted_consumption < 0:
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raise ValueError("consumption_difference should be negative")
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return adjusted_consumption
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@classmethod
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def adjust_expected_band(cls, expected_epc_rating, current_epc_rating):
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"""
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Because of the differing intercepts and intercepts when adjusting, it's possible for
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expected_adjusted_energy to be bigger than current_adjusted_energy. In this case, we'll
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adjust, against at most 1 EPC band above the curent. This function performs the EPC adjustment
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:param expected_epc_rating: The expected EPC rating
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:param current_epc_rating: The current EPC rating
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"""
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# Find index of expected EPC rating
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expected_index = cls.EPC_BANDS.index(expected_epc_rating)
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current_index = cls.EPC_BANDS.index(current_epc_rating)
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if expected_index - 1 < current_index:
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return current_epc_rating
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return cls.EPC_BANDS[expected_index - 1]
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@staticmethod
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def cost_per_kwh(price_per_unit, energy_content_per_unit):
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"""
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Calculate the cost of fuel per kWh given the price per unit in GBP and the energy content per unit in kWh.
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"""
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cost_per_kwh = price_per_unit / energy_content_per_unit
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# Tgis data is returned in pennies so we convert to pounds
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return cost_per_kwh / 100
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@classmethod
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def calculate_recommendation_fuel_cost(cls, kwh, fuel, cop):
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if fuel == "Electricity":
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return (kwh / cop) * cls.ELECTRICITY_PRICE_CAP
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if fuel in ["Natural Gas", "Natural Gas (Community Scheme)"]:
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return (kwh / cop) * cls.GAS_PRICE_CAP
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if fuel == "LPG":
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# Get the cost per kwh
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price_data = cls.FUEL_DATA[cls.FUEL_DATA["Fuel"] == "LPG"].squeeze()
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cost_per_kwh = cls.cost_per_kwh(
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price_data["Price (p)"], price_data["Energy Content, Net Calorific value (kWh/unit)"]
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)
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return (kwh / cop) * cost_per_kwh
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if fuel in ["Wood Logs", "Wood Pellets"]:
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price_data = cls.FUEL_DATA[cls.FUEL_DATA["Fuel"] == "Pellets (Bagged)"].squeeze()
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cost_per_kwh = cls.cost_per_kwh(
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price_data["Price (p)"], price_data["Energy Content, Net Calorific value (kWh/unit)"]
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)
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return (kwh / cop) * cost_per_kwh
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if fuel == "Natural Gas + Solar Thermal":
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# The solar thermal covers a % of the heating kwh, so we need to adjust the cost
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return (kwh / cop) * assumptions.SOLAR_CONSUMPTION_PROPORTION * cls.GAS_PRICE_CAP
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if fuel == "Electricity + Solar Thermal":
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# The solar thermal covers a % of the heating kwh, so we need to adjust the cost
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return (kwh / cop) * assumptions.SOLAR_CONSUMPTION_PROPORTION * cls.ELECTRICITY_PRICE_CAP
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if fuel == "LPG + Solar Thermal":
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# The solar thermal covers a % of the heating kwh, so we need to adjust the cost
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price_data = cls.FUEL_DATA[cls.FUEL_DATA["Fuel"] == "LPG"].squeeze()
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cost_per_kwh = cls.cost_per_kwh(
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price_data["Price (p)"], price_data["Energy Content, Net Calorific value (kWh/unit)"]
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)
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return (kwh / cop) * cost_per_kwh * assumptions.SOLAR_CONSUMPTION_PROPORTION
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if fuel == "Oil":
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price_data = cls.FUEL_DATA[cls.FUEL_DATA["Fuel"] == "Kerosene"].squeeze()
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cost_per_kwh = cls.cost_per_kwh(
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price_data["Price (p)"], price_data["Energy Content, Net Calorific value (kWh/unit)"]
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)
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return (kwh / cop) * cost_per_kwh
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if fuel in ["Smokeless Fuel", "Anthracite"]:
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price_data = cls.FUEL_DATA[cls.FUEL_DATA["Fuel"] == "Smokeless fuel"].squeeze()
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cost_per_kwh = cls.cost_per_kwh(
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price_data["Price (p)"], price_data["Energy Content, Net Calorific value (kWh/unit)"]
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)
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return (kwh / cop) * cost_per_kwh
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# We use coal's values for
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if fuel == "Coal":
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price_data = cls.FUEL_DATA[cls.FUEL_DATA["Fuel"] == "Coal"].squeeze()
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cost_per_kwh = cls.cost_per_kwh(
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price_data["Price (p)"], price_data["Energy Content, Net Calorific value (kWh/unit)"]
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)
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return (kwh / cop) * cost_per_kwh
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raise Exception("Fuel not recognised")
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