def generic():
inputs_basics = myInputs.heatpump_basics()
inputs_demands = myInputs.demands()
weather = myInputs.weather()
HeatPump = heatpump.HeatPump(
inputs_basics['heat_pump_type'], inputs_basics['modelling_approach'],
inputs_basics['capacity'], inputs_basics['ambient_delta_t'],
inputs_basics['operation'], inputs_basics['minimum_output'],
inputs_basics['data_input'], inputs_demands['source_temp'],
inputs_demands['return_temp_DH'], weather)
print HeatPump.performance()
def lorentz():
inputs_basics = myInputs.heatpump_basics()
inputs_lorentz = myInputs.heatpump_lorentz()
inputs_demands = myInputs.demands()
weather = myInputs.weather()
HeatPump = heatpump.HeatPump(inputs_basics['heat_pump_type'],
inputs_basics['modelling_approach'],
inputs_basics['capacity'],
inputs_basics['ambient_delta_t'],
inputs_basics['operation'],
inputs_basics['minimum_output'],
inputs_basics['data_input'],
inputs_demands['source_temp'],
inputs_demands['return_temp_DH'],
weather,
lorentz_inputs=inputs_lorentz)
# print HeatPump.performance()
HeatPump.flow_temp_source = [70] * 8760
hp1 = HeatPump.performance()
HeatPump.flow_temp_source = [60] * 8760
hp2 = HeatPump.performance()
cop1 = []
duty1 = []
cop2 = []
duty2 = []
for time in range(8760):
cop1.append(hp1[time]['cop'])
duty1.append(hp1[time]['duty'])
cop2.append(hp2[time]['cop'])
duty2.append(hp2[time]['duty'])
cop1 = pd.DataFrame(cop1)
duty1 = pd.DataFrame(duty1)
cop2 = pd.DataFrame(cop2)
duty2 = pd.DataFrame(duty2)
plt.plot(cop1)
plt.plot(cop2)
plt.show()
def standard_regression():
myInputs = inputs.Inputs('WWHC.xlsx', 'hp_1000_ts_50000')
inputs_basics = myInputs.heatpump_basics()
inputs_standard_regression = myInputs.heatpump_standard_regression()
inputs_demands = myInputs.demands()
weather = myInputs.weather()
HeatPump = heatpump.HeatPump(
inputs_basics['heat_pump_type'],
inputs_basics['modelling_approach'],
inputs_basics['capacity'],
inputs_basics['ambient_delta_t'],
inputs_basics['operation'],
inputs_basics['minimum_output'],
inputs_basics['data_input'],
inputs_demands['source_temp'],
inputs_demands['return_temp_DH'],
weather,
standard_test_regression_inputs=inputs_standard_regression)
HeatPump.flow_temp_source = [70] * 8760
hp1 = HeatPump.performance()
HeatPump.flow_temp_source = [60] * 8760
hp2 = HeatPump.performance()
cop1 = []
duty1 = []
cop2 = []
duty2 = []
for time in range(8760):
cop1.append(hp1[time]['cop'])
duty1.append(hp1[time]['duty'])
cop2.append(hp2[time]['cop'])
duty2.append(hp2[time]['duty'])
cop1 = pd.DataFrame(cop1)
duty1 = pd.DataFrame(duty1)
cop2 = pd.DataFrame(cop2)
duty2 = pd.DataFrame(duty2)
plt.plot(cop1)
plt.plot(cop2)
plt.show()
def init(name, subname):
myInputs = inputs.Inputs(name, subname)
# initialise instance of classes
input_windturbine = myInputs.windturbine_user()
input_weather = myInputs.weather()
myUserWindturbine = renewables.Windturbine(
turbine_name=input_windturbine['turbine_name'],
hub_height=input_windturbine['hub_height'],
rotor_diameter=input_windturbine['rotor_diameter'],
multiplier=input_windturbine['multiplier'],
nominal_power=input_windturbine['nominal_power'],
power_curve=input_windturbine['power_curve'],
weather_input=input_weather)
input_windturbine = myInputs.windturbine_database()
myDatabaseWindturbine = renewables.Windturbine(
turbine_name=input_windturbine['turbine_name'],
hub_height=input_windturbine['hub_height'],
rotor_diameter=input_windturbine['rotor_diameter'],
multiplier=input_windturbine['multiplier'],
weather_input=input_weather)
input_PV_model = myInputs.PV_model()
myPV = renewables.PV(module_name=input_PV_model['module_name'],
inverter_name=input_PV_model['inverter_name'],
multiplier=input_PV_model['multiplier'],
surface_tilt=input_PV_model['surface_tilt'],
surface_azimuth=input_PV_model['surface_azimuth'],
surface_type=input_PV_model['surface_type'],
loc_name=input_PV_model['loc_name'],
latitude=input_PV_model['latitude'],
longitude=input_PV_model['longitude'],
altitude=input_PV_model['altitude'],
weather_input=input_weather)
ts_inputs = myInputs.hot_water_tank()
myHotWaterTank = hot_water_tank.HotWaterTank(
ts_inputs['capacity'],
ts_inputs['insulation'],
ts_inputs['location'],
ts_inputs['number_nodes'],
ts_inputs['dimensions'],
ts_inputs['tank_openings'],
ts_inputs['correction_factors'],
air_temperature=input_weather)
inputs_basics = myInputs.heatpump_basics()
modelling_approach = inputs_basics['modelling_approach']
if modelling_approach == 'Simple':
inputs_simple = myInputs.heatpump_simple()
inputs_demands = myInputs.demands()
myHeatPump = heatpump.HeatPump(inputs_basics['heat_pump_type'],
inputs_basics['modelling_approach'],
inputs_basics['capacity'],
inputs_basics['ambient_delta_t'],
inputs_basics['minimum_runtime'],
inputs_basics['minimum_output'],
inputs_basics['data_input'],
inputs_demands['source_temp'],
inputs_demands['return_temp_DH'],
input_weather,
simple_cop=inputs_simple)
if modelling_approach == 'Lorentz':
inputs_lorentz = myInputs.heatpump_lorentz()
inputs_demands = myInputs.demands()
myHeatPump = heatpump.HeatPump(inputs_basics['heat_pump_type'],
inputs_basics['modelling_approach'],
inputs_basics['capacity'],
inputs_basics['ambient_delta_t'],
inputs_basics['minimum_runtime'],
inputs_basics['minimum_output'],
inputs_basics['data_input'],
inputs_demands['source_temp'],
inputs_demands['return_temp_DH'],
input_weather,
lorentz_inputs=inputs_lorentz)
elif modelling_approach == 'Generic regression':
inputs_demands = myInputs.demands()
myHeatPump = heatpump.HeatPump(
inputs_basics['heat_pump_type'],
inputs_basics['modelling_approach'], inputs_basics['capacity'],
inputs_basics['ambient_delta_t'], inputs_basics['minimum_runtime'],
inputs_basics['minimum_output'], inputs_basics['data_input'],
inputs_demands['source_temp'], inputs_demands['return_temp_DH'],
input_weather)
elif modelling_approach == 'Standard test regression':
inputs_standard_regression = myInputs.heatpump_standard_regression()
inputs_demands = myInputs.demands()
myHeatPump = heatpump.HeatPump(
inputs_basics['heat_pump_type'],
inputs_basics['modelling_approach'],
inputs_basics['capacity'],
inputs_basics['ambient_delta_t'],
inputs_basics['minimum_runtime'],
inputs_basics['minimum_output'],
inputs_basics['data_input'],
inputs_demands['source_temp'],
inputs_demands['return_temp_DH'],
input_weather,
standard_test_regression_inputs=inputs_standard_regression)
i = myInputs.electrical_storage()
myElectricalStorage = electrical_storage.ElectricalStorage(
i['capacity'], i['initial_state'], i['charge_max'], i['discharge_max'],
i['charge_eff'], i['discharge_eff'], i['self_discharge'])
aux_inputs = myInputs.aux()
myAux = auxiliary.Aux(aux_inputs['fuel'], aux_inputs['efficiency'],
aux_inputs['fuel_info'])
grid_inputs = myInputs.grid()
export = grid_inputs['export']
tariff_choice = grid_inputs['tariff_choice']
balancing_mechanism = grid_inputs['balancing_mechanism']
grid_services = grid_inputs['grid_services']
tariff_choice = grid_inputs['tariff_choice']
balancing_mechanism = grid_inputs['balancing_mechanism']
grid_services = grid_inputs['grid_services']
variable_periods_year = grid_inputs['variable_periods_year']
premium = grid_inputs['wm_info']['premium']
maximum = grid_inputs['wm_info']['maximum']
lower_percent = grid_inputs['ppa_info']['lower_percent']
higher_percent = grid_inputs['ppa_info']['higher_percent']
lower_penalty = grid_inputs['ppa_info']['lower_penalty']
higher_discount = grid_inputs['ppa_info']['higher_discount']
if tariff_choice == 'Flat rates':
fr = grid_inputs['flat_rates']
myGrid = grid.Grid(name,
subname,
export,
tariff_choice,
balancing_mechanism,
grid_services,
flat_rate=fr)
elif tariff_choice == 'Variable periods':
vp = grid_inputs['variable_periods']
myGrid = grid.Grid(name,
subname,
export,
tariff_choice,
balancing_mechanism,
grid_services,
variable_periods=vp,
variable_periods_year=variable_periods_year)
elif tariff_choice == 'Time of use - WM':
twm = grid_inputs['wholesale_market']
myGrid = grid.Grid(name,
subname,
export,
tariff_choice,
balancing_mechanism,
grid_services,
wholesale_market=twm,
premium=premium,
maximum=maximum)
elif tariff_choice == 'Time of use - PPA + FR':
fr = grid_inputs['flat_rates']
myGrid = grid.Grid(name,
subname,
export,
tariff_choice,
balancing_mechanism,
grid_services,
flat_rate=fr,
lower_percent=lower_percent,
higher_percent=higher_percent,
higher_discount=higher_discount,
lower_penalty=lower_penalty)
elif tariff_choice == 'Time of use - PPA + WM':
twm = grid_inputs['wholesale_market']
myGrid = grid.Grid(name,
subname,
export,
tariff_choice,
balancing_mechanism,
grid_services,
wholesale_market=twm,
premium=premium,
maximum=maximum,
lower_percent=lower_percent,
higher_percent=higher_percent,
higher_discount=higher_discount,
lower_penalty=lower_penalty)
elif tariff_choice == 'Time of use - PPA + VP':
vp = grid_inputs['variable_periods']
myGrid = grid.Grid(name,
subname,
export,
tariff_choice,
balancing_mechanism,
grid_services,
variable_periods=vp,
variable_periods_year=variable_periods_year,
lower_percent=lower_percent,
higher_percent=higher_percent,
higher_discount=higher_discount,
lower_penalty=lower_penalty)
dict = {
'myUserWindturbine': myUserWindturbine,
'myDatabaseWindturbine': myDatabaseWindturbine,
'myPV': myPV,
'myHotWaterTank': myHotWaterTank,
'myHeatPump': myHeatPump,
'myElectricalStorage': myElectricalStorage,
'myAux': myAux,
'myGrid': myGrid
}
return dict