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adding fuel pricing table
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Khalim Conn-Kowlessar
parent
18b3487438
commit
4b2c4cb0a3
3 changed files with 83 additions and 1 deletions
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@ -343,6 +343,8 @@ def extract_propert_on_site_recommendations(config, already_installed, non_invas
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for rec in property_non_invasive_recommendations["recommendations"]:
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if isinstance(rec, str):
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transformed.append({"type": rec, })
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else:
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transformed.append(rec)
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property_non_invasive_recommendations["recommendations"] = str(transformed)
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@ -720,6 +722,8 @@ async def trigger_plan(body: PlanTriggerRequest):
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)
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# We now insert kwh estimates and costs into the recommendations
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# TODO: We should join the methodology which maps the heating and hot water descriptions to the fuel types in
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# Recommendations, but also the Property class
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for property_id in recommendations.keys():
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property_recommendations = recommendations[property_id]
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property_instance = [p for p in input_properties if p.id == property_id][0]
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@ -1,4 +1,5 @@
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import numpy as np
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import pandas as pd
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QUARTERLY_ENERGY_PRICES = [
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# 2024 Q1
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@ -40,6 +41,53 @@ class AnnualBillSavings:
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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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@ -200,6 +248,15 @@ class AnnualBillSavings:
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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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@ -207,3 +264,22 @@ class AnnualBillSavings:
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if fuel == "Natural Gas":
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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 == "Wood":
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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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raise Exception("Fuel not recognised")
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@ -27,6 +27,8 @@ DESCRIPTIONS_TO_FUEL_TYPES = {
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"Room heaters, electric": {"fuel": 'Electricity', "cop": 1},
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"Electric immersion, standard tariff": {"fuel": 'Electricity', "cop": 1},
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"Portable electric heaters assumed for most rooms": {"fuel": 'Electricity', "cop": 1},
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"Boiler and radiators, LPG": {"fuel": 'LPG', "cop": 0.9},
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"Room heaters, dual fuel (mineral and wood)": {"fuel": 'Wood', "cop": 1},
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}
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STARTING_DUMMY_ID_VALUE = -9999
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@ -516,7 +518,7 @@ class Recommendations:
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mapped = DESCRIPTIONS_TO_FUEL_TYPES[heating_description]
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heating_fuel = mapped["fuel"]
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if hotwater_description == "From main system":
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if hotwater_description in ["From main system", "From main system, no cylinder thermostat"]:
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return {
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"heating_fuel_type": heating_fuel, "hotwater_fuel_type": heating_fuel,
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"heating_cop": mapped["cop"], "hotwater_cop": mapped["cop"]
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