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cost_effective_hp.py
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cost_effective_hp.py
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import numpy as np
import matplotlib.pyplot as plt
import os
output_directory = 'S:\ZacharySubins_Documents\Projects\Building Electrification Market Assessment\Efficiency Analysis'
fmt = 'pdf'
gas_prices = [12, 24] # $/mmBTU
scenario_names = ['$12 per mmBTU Gas', '$24 per mmBTU Gas']
for i in range(len(gas_prices)):
eff = np.arange(2, 5, 0.1) # COP
rate = np.arange(0.05, 0.3, 0.01) # electricity price in $/kWh
gas_price = gas_prices[i] # $/mmBTU
GJ_per_MMBTU = 1.055
tonnes_per_GJ = 0.05 # NG emissions intensity
gas_price += 20*tonnes_per_GJ*GJ_per_MMBTU #add CO2 price
print('gas_price = ' + str(gas_price))
service_demand = 20 # mmBTU/yr
gas_eff = 0.9 # COP
gas_costs = service_demand / gas_eff * gas_price # $/yr
y, x = np.meshgrid(eff, rate) # x-axis is rate, y-axis is efficiency
mmbtu_per_kWh_thermal = 3.6e-3/GJ_per_MMBTU
COP_to_HSPF = 3.6/GJ_per_MMBTU
CRF = -np.pmt(0.05, 15, 1)
print('CRF = ' + str(CRF))
net_fuel_cost = service_demand / y * x / mmbtu_per_kWh_thermal - gas_costs # mmBTU / COP * $/kWh / (mmBTU/kWh)
breakeven_cap_cost = -net_fuel_cost / CRF
plt.close()
plt.figure()
vscale = 2500
y *= COP_to_HSPF # convert to HSPF
plot = plt.contourf(x, y, breakeven_cap_cost, cmap='coolwarm_r', vmin=-vscale, vmax=vscale)
#plt.clabel(plot)
plt.colorbar()
#plt.clim([-5000, 2500])
plt.title('Breakeven Capital Cost Relative to Gas Furnace ($)\n' + scenario_names[i])
plt.xlabel('Electricity Rate ($/kWh)')
plt.ylabel('Heat Pump Efficiency (HSPF)')
plt.savefig(os.path.join(output_directory, 'Breakeven Capital Cost, ' + scenario_names[i] + '.' + fmt), format=fmt)