def test_teaser_four_element():
from modesto.LTIModels.RCmodels import TeaserFourElement
import pandas as pd
start_time = pd.Timestamp('20140101')
time_step = 3600
n_steps = 24
horizon = n_steps * time_step
RCmodel = TeaserFourElement('test', False)
time_index = pd.DatetimeIndex(start=start_time,
freq=str(time_step) + 'S',
periods=n_steps)
min_temp_room = pd.Series(16 + 273.15, index=time_index)
max_temp_room = pd.Series(24 + 273.15, index=time_index)
datapath = resource_filename('modesto', 'Data')
c_f = ut.read_time_data(
path=datapath,
name='ElectricityPrices/DAM_electricity_prices-2014_BE.csv'
)['price_BE']
params = {
'neighbName': 'OudWinterslag',
'streetName': 'Gierenshof',
'buildingName': 'Gierenshof_22_1589272',
'day_min_temperature': min_temp_room,
'day_max_temperature': max_temp_room,
'delta_T': 20,
'mult': 100,
'horizon': horizon,
'time_step': 100,
'horizon': 1000
}
for param in params:
RCmodel.change_param(param, params[param])
try:
RCmodel.compile(start_time='20140201', model=ConcreteModel())
return True
except ValueError:
return False
model, optimizers = representative(duration_repr=duration_repr,
selection=selection,
VWat=75000,
solArea=150000,
VSTC=100000,
time_step=time_step)
solve_repr(model, probe=False, solver='gurobi')
# ## Post-processing
# In[ ]:
import matplotlib.pyplot as plt
import numpy as np
t_amb = ut.read_time_data(DATAPATH, name='Weather/extT.csv', expand=True)
cmap = plt.get_cmap('gnuplot')
colors = [cmap(i) for i in np.linspace(0, 1, len(selection))]
coli = 0
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True)
for startday, reps in selection.items():
res = sum(
optimizers[startday].get_result(
'heat_flow', node=node, comp='tank', check_results=False)
for node in ['SolarArray', 'WaterscheiGarden', 'TermienWest'])
ax1.plot(res,
color=colors[coli],
label='S {} R {}'.format(startday, reps))
def setup_modesto(time_step=3600, n_steps=24 * 365, repr=False):
repr_days = get_json(
resource_filename(
'TimeSliceSelection',
'../Scripts/NoSeasons/ordered_solutions1_20bins_new.txt'),
'repr_days')
model = Modesto(pipe_model='ExtensivePipe',
graph=setup_graph(repr),
repr_days=repr_days[16] if repr else None)
heat_demand = ut.read_time_data(resource_filename('modesto',
'Data/HeatDemand'),
name='SH_GenkNet.csv')
weather_data = ut.read_time_data(resource_filename('modesto',
'Data/Weather'),
name='weatherData.csv')
model.opt_settings(allow_flow_reversal=False)
elec_cost = \
ut.read_time_data(
resource_filename('modesto', 'Data/ElectricityPrices'),
name='DAM_electricity_prices-2014_BE.csv')['price_BE']
general_params = {
'Te': weather_data['Te'],
'Tg': weather_data['Tg'],
'Q_sol_E': weather_data['QsolE'],
'Q_sol_W': weather_data['QsolW'],
'Q_sol_S': weather_data['QsolS'],
'Q_sol_N': weather_data['QsolN'],
'time_step': time_step,
'horizon': n_steps * time_step,
'cost_elec': pd.Series(0.1, index=weather_data.index)
}
model.change_params(general_params)
build_params = {
'delta_T': 20,
'mult': 1,
'heat_profile': heat_demand['WaterscheiGarden']
}
model.change_params(build_params, node='demand', comp='build')
stor_params = {
'temperature_supply': 80 + 273.15,
'temperature_return': 60 + 273.15,
'mflo_max': 110,
'mflo_min': -110,
'mult': 1,
'volume': 30000,
'ar': 1,
'dIns': 0.3,
'kIns': 0.024,
'heat_stor': 200000,
'mflo_use': pd.Series(0, index=weather_data.index)
}
model.change_params(dict=stor_params, node='demand', comp='stor')
model.change_init_type('heat_stor',
new_type='cyclic',
comp='stor',
node='demand')
sol_data = ut.read_time_data(resource_filename('modesto',
'Data/RenewableProduction'),
name='GlobalRadiation.csv')['0_40']
stc_params = {
'temperature_supply': 80 + 273.15,
'temperature_return': 60 + 273.15,
'solar_profile': sol_data,
'area': 2000
}
model.change_params(stc_params, node='STC', comp='solar')
pipe_data = {
'diameter': 500,
'temperature_supply': 80 + 273.15,
'temperature_return': 60 + 273.15
}
model.change_params(pipe_data, node=None, comp='pipe')
backup_params = {
'delta_T': 20,
'efficiency': 0.95,
'PEF': 1,
'CO2': 0.178,
'fuel_cost': elec_cost,
'Qmax': 500e8,
'ramp_cost': 0,
'ramp': 0
}
model.change_params(backup_params, node='STC', comp='backup')
return model
def construct_model():
G = nx.DiGraph()
G.add_node('ThorPark',
x=4000,
y=4000,
z=0,
comps={'plant': 'ProducerVariable'})
G.add_node('p1', x=2600, y=5000, z=0, comps={})
G.add_node('waterscheiGarden',
x=2500,
y=4600,
z=0,
comps={
'buildingD': 'RCmodel',
'storage': 'StorageVariable'
})
G.add_node('zwartbergNE',
x=2000,
y=5500,
z=0,
comps={'buildingD': 'RCmodel'})
G.add_edge('ThorPark', 'p1', name='bbThor')
G.add_edge('p1', 'waterscheiGarden', name='spWaterschei')
G.add_edge('p1', 'zwartbergNE', name='spZwartbergNE')
# nx.draw(G, with_labels=True, font_weight='bold')
# plt.show()
###################################
# Set up the optimization problem #
###################################
optmodel = Modesto(pipe_model='ExtensivePipe', graph=G, repr_days=None)
##################################
# Fill in the parameters #
##################################
df_weather = ut.read_time_data(resource_filename('modesto',
'Data/Weather'),
name='weatherData.csv')
df_userbehaviour = ut.read_time_data(resource_filename(
'modesto', 'Data/UserBehaviour'),
name='ISO13790.csv')
elec_data = ut.read_time_data(resource_filename('modesto', 'Data'),
name='ElectricityPrices/AvgPEF_CO2.csv')
t_amb = df_weather['Te']
t_g = df_weather['Tg']
QsolN = df_weather['QsolN']
QsolE = df_weather['QsolS']
QsolS = df_weather['QsolN']
QsolW = df_weather['QsolW']
day_max = df_userbehaviour['day_max']
day_min = df_userbehaviour['day_min']
night_max = df_userbehaviour['night_max']
night_min = df_userbehaviour['night_min']
bathroom_max = df_userbehaviour['bathroom_max']
bathroom_min = df_userbehaviour['bathroom_min']
floor_max = df_userbehaviour['floor_max']
floor_min = df_userbehaviour['floor_min']
Q_int_D = df_userbehaviour['Q_int_D']
Q_int_N = df_userbehaviour['Q_int_N']
optmodel.opt_settings(allow_flow_reversal=True)
# general parameters
datapath = resource_filename('modesto', 'Data')
c_f = ut.read_time_data(
path=datapath,
name='ElectricityPrices/DAM_electricity_prices-2014_BE.csv'
)['price_BE']
general_params = {
'Te': t_amb,
'Tg': t_g,
'Q_sol_E': QsolE,
'Q_sol_W': QsolW,
'Q_sol_S': QsolS,
'Q_sol_N': QsolN,
'time_step': time_step,
'horizon': n_steps * time_step,
'cost_elec': c_f,
'PEF_elec': elec_data['AvgPEF'],
'CO2_elec': elec_data['AvgCO2/kWh']
}
optmodel.change_params(general_params)
# building parameters
zw_building_params = {
'delta_T': 20,
'mult': 100,
'night_min_temperature': night_min,
'night_max_temperature': night_max,
'day_min_temperature': day_min,
'day_max_temperature': day_max,
'bathroom_min_temperature': bathroom_min,
'bathroom_max_temperature': bathroom_max,
'floor_min_temperature': floor_min,
'floor_max_temperature': floor_max,
'model_type': 'SFH_T_5_ins_TAB',
'Q_int_D': Q_int_D,
'Q_int_N': Q_int_N,
'TiD0': 20 + 273.15,
'TflD0': 20 + 273.15,
'TwiD0': 20 + 273.15,
'TwD0': 20 + 273.15,
'TfiD0': 20 + 273.15,
'TfiN0': 20 + 273.15,
'TiN0': 20 + 273.15,
'TwiN0': 20 + 273.15,
'TwN0': 20 + 273.15,
'max_heat': 6000
}
ws_building_params = zw_building_params.copy()
ws_building_params['mult'] = 200
ws_building_params['model_type'] = 'SFH_T_5_ins_TAB'
optmodel.change_params(zw_building_params,
node='zwartbergNE',
comp='buildingD')
optmodel.change_params(ws_building_params,
node='waterscheiGarden',
comp='buildingD')
optmodel.change_init_type(node='zwartbergNE',
comp='buildingD',
state='TiD0',
new_type='cyclic')
bbThor_params = {
'diameter': 500,
'temperature_supply': 80 + 273.15,
'temperature_return': 60 + 273.15
}
spWaterschei_params = bbThor_params.copy()
spWaterschei_params['diameter'] = 500
spZwartbergNE_params = bbThor_params.copy()
spZwartbergNE_params['diameter'] = 500
optmodel.change_params(bbThor_params, comp='bbThor')
optmodel.change_params(spWaterschei_params, comp='spWaterschei')
optmodel.change_params(bbThor_params, comp='spZwartbergNE')
# Storage parameters
stor_design = {
# Thi and Tlo need to be compatible with delta_T of previous
'temperature_supply': 80 + 273.15,
'temperature_return': 60 + 273.15,
'mflo_max': 110,
'mflo_min': -110,
'volume': 2e4,
'stor_type': 1,
'heat_stor': 0,
'mflo_use': pd.Series(0, index=t_amb.index),
'cost_inv': 1
}
optmodel.change_params(dict=stor_design,
node='waterscheiGarden',
comp='storage')
optmodel.change_state_bounds('heat_stor',
new_ub=10**12,
new_lb=0,
slack=False,
node='waterscheiGarden',
comp='storage')
# Production parameters
c_f = ut.read_time_data(
path=resource_filename('modesto', 'Data/ElectricityPrices'),
name='DAM_electricity_prices-2014_BE.csv')['price_BE']
# cf = pd.Series(0.5, index=t_amb.index)
prod_design = {
'delta_T': 20,
'efficiency': 0.95,
'CO2': 0.178, # based on HHV of CH4 (kg/KWh CH4)
'fuel_cost': c_f,
# http://ec.europa.eu/eurostat/statistics-explained/index.php/Energy_price_statistics (euro/kWh CH4)
'Qmax': 1.5e7,
'ramp_cost': 0.01,
'ramp': 1e6 / 3600,
'cost_inv': 1
}
optmodel.change_params(prod_design, 'ThorPark', 'plant')
##################################
# Print parameters #
##################################
# optmodel.print_all_params()
# optmodel.print_general_param('Te')
# optmodel.print_comp_param('ThorPark', 'plant')
# optmodel.print_comp_param('waterscheiGarden', 'storage')
# optmodel.print_comp_param('waterscheiGarden', 'storage', 'kIns', 'volume')
return optmodel
def setup_modesto_with_stor(graph, objtype='cost'):
numdays = 1
horizon = numdays * 24 * 3600
time_step = 3600
start_time = pd.Timestamp('20140101')
pipe_model = 'ExtensivePipe'
optmodel = Modesto(pipe_model=pipe_model,
graph=graph
)
from pkg_resources import resource_filename
datapath = resource_filename('modesto', 'Data')
wd = ut.read_time_data(datapath, name='Weather/weatherData.csv')
t_amb = wd['Te']
t_g = wd['Tg']
QsolN = wd['QsolN']
QsolE = wd['QsolS']
QsolS = wd['QsolN']
QsolW = wd['QsolW']
c_f = ut.read_time_data(path=datapath, name='ElectricityPrices/DAM_electricity_prices-2014_BE.csv')['price_BE']
elec_data = ut.read_time_data(datapath, name='ElectricityPrices/AvgPEF_CO2.csv')
general_params = {'Te': t_amb,
'Tg': t_g,
'Q_sol_E': QsolE,
'Q_sol_W': QsolW,
'Q_sol_S': QsolS,
'Q_sol_N': QsolN,
'time_step': time_step,
'horizon': horizon,
'cost_elec': c_f,
'CO2_elec': elec_data['AvgCO2/kWh'],
'PEF_elec': elec_data['AvgPEF']
}
optmodel.change_params(general_params)
Pnom = 4e4
# Building parameters
index = pd.DatetimeIndex(start=start_time, freq=str(time_step) + 'S', periods=horizon / time_step)
building_params = {
'temperature_supply': 50 + 273.15,
'temperature_return': 20 + 273.15,
'mult': 1,
'heat_profile': pd.Series(index=index, name='Heat demand', data=[0, 1, 0, 0, 1, 1] * 4 * numdays) * Pnom,
'DHW_demand': pd.Series(index=index, name='Heat demand', data=[0, 1, 0, 0, 1, 1] * 4 * numdays) * 60
}
optmodel.change_params(building_params, node='cons', comp='cons')
# Producer parameters
prod_design = {'delta_T': 30,
'efficiency': 0.95,
'CO2': 0.178, # based on HHV of CH4 (kg/KWh CH4)
'fuel_cost': c_f,
'Qmax': Pnom,
'ramp_cost': 0.00,
'ramp': Pnom,
'cost_inv': 1}
optmodel.change_params(prod_design, 'prod', 'prod')
# Storage parameters
stor_design = {'temperature_supply': 60 + 273.15,
'temperature_return': 20 + 273.15,
'mflo_max': 100,
'mflo_min': -100,
'volume': 5000,
'heat_stor': 10,
'stor_type': 0, # 0 pit, 1 tank
'mflo_use': pd.Series(index=c_f.index, data=0),
'cost_inv': 1}
for stor in ['stor', 'stor2']:
optmodel.change_params(stor_design, stor, 'stor')
optmodel.change_init_type('heat_stor', 'free', stor, 'stor')
# optmodel.change_param('stor', 'stor', 'dIns', 0.001)
# optmodel.change_param('stor', 'stor', 'kIns', 0.01)
# Pipe parameters
params = {
'diameter': 150
}
if pipe_model is 'ExtensivePipe':
params['temperature_supply'] = 30 + 273.15
params['temperature_return'] = 20 + 273.15
for p in ['pipe1', 'pipe2', 'pipe3']: optmodel.change_params(params, node=None, comp=p)
optmodel.compile(start_time=start_time)
optmodel.set_objective(objtype)
optmodel.opt_settings(allow_flow_reversal=True)
return optmodel
def test_producer():
from modesto.main import Modesto
import pandas as pd
import networkx as nx
import modesto.utils as ut
from pkg_resources import resource_filename
def construct_model():
G = nx.DiGraph()
G.add_node('plant', x=0, y=0, z=0, comps={'gen': 'ProducerVariable'})
G.add_node('user', x=10, y=0, z=0, comps={'building': 'BuildingFixed'})
G.add_edge('plant', 'user', name='pipe')
return G
start_time = '20140101'
time_step = 3600
n_steps = 24
time_index = pd.DatetimeIndex(start=start_time,
freq=str(time_step) + 'S',
periods=n_steps)
t_amb = pd.Series(0, time_index)
t_g = pd.Series(0, time_index)
QsolN = pd.Series(0, time_index)
QsolE = pd.Series(0, time_index)
QsolS = pd.Series(0, time_index)
QsolW = pd.Series(0, time_index)
optmodel = Modesto(pipe_model='SimplePipe', graph=construct_model())
datapath = resource_filename('modesto', 'Data')
c_f = ut.read_time_data(
path=datapath,
name='ElectricityPrices/DAM_electricity_prices-2014_BE.csv'
)['price_BE']
elec_data = ut.read_time_data(datapath,
name='ElectricityPrices/AvgPEF_CO2.csv')
general_params = {
'Te': t_amb,
'Tg': t_g,
'Q_sol_E': QsolE,
'Q_sol_W': QsolW,
'Q_sol_S': QsolS,
'Q_sol_N': QsolN,
'horizon': n_steps * time_step,
'time_step': time_step,
'cost_elec': c_f,
'PEF_elec': elec_data['AvgPEF'],
'CO2_elec': elec_data['AvgCO2/kWh']
}
optmodel.change_params(general_params)
heat_profiles = [
pd.Series(([5e4, 1e5, 5e4] + [0, 1e4, 0]) * 4, index=time_index),
pd.Series(([6e4, 1e5, 6e4] + [0, 1e4, 0]) * 4, index=time_index),
pd.Series(([5e4, 1e5, 5e4] + [500] * 3) * 4, index=time_index)
]
building_params = {
'temperature_supply':
80 + 273.15,
'temperature_return':
60 + 273.15,
'mult':
1,
'DHW_demand':
pd.Series(([5e4, 1e5, 5e4] + [0, 1e4, 0]) * 4, index=time_index)
}
optmodel.change_params(building_params, node='user', comp='building')
c_f = pd.Series(1, time_index)
params = {
'delta_T': 20,
'efficiency': 0.95,
'CO2': 0.2052,
'fuel_cost': c_f,
'Qmax': 1e5,
'Qmin': 1e4,
'ramp_cost': 1,
'ramp': 1e5 / 2 / time_step,
'cost_inv': 1
}
optmodel.change_params(params, node='plant', comp='gen')
try:
flags = []
for heat_profile in heat_profiles:
optmodel.change_params({'heat_profile': heat_profile},
node='user',
comp='building')
optmodel.compile(start_time)
optmodel.set_objective('cost')
flags.append(optmodel.solve(tee=True))
if flags == [0, 1, 1]:
return True
except ValueError:
return False
def construct_model():
G = nx.DiGraph()
G.add_node('ThorPark',
x=4000,
y=4000,
z=0,
comps={'plant': 'ProducerVariable'})
G.add_node('p1', x=2600, y=5000, z=0, comps={})
G.add_node('waterscheiGarden',
x=2500,
y=4600,
z=0,
comps={
'buildingD': 'BuildingFixed',
'storage': 'StorageVariable'
})
G.add_node('zwartbergNE',
x=2000,
y=5500,
z=0,
comps={'buildingD': 'BuildingFixed'})
G.add_edge('ThorPark', 'p1', name='bbThor')
G.add_edge('p1', 'waterscheiGarden', name='spWaterschei')
G.add_edge('p1', 'zwartbergNE', name='spZwartbergNE')
# nx.draw(G, with_labels=True, font_weight='bold')
# plt.show()
###################################
# Set up the optimization problem #
###################################
optmodel = Modesto(pipe_model='SimplePipe', graph=G)
##################################
# Fill in the parameters #
##################################
heat_profile = ut.read_time_data(resource_filename('modesto',
'Data/HeatDemand/Old'),
name='HeatDemandFiltered.csv')
dhw_demand = ut.read_time_data(resource_filename('modesto',
'Data/HeatDemand'),
name='DHW_GenkNet.csv')
t_amb = ut.read_time_data(resource_filename('modesto', 'Data/Weather'),
name='extT.csv')['Te']
t_g = pd.Series(12 + 273.15, index=t_amb.index)
# Solar radiation
datapath = resource_filename('modesto', 'Data')
wd = ut.read_time_data(datapath, name='Weather/weatherData.csv')
QsolN = wd['QsolN']
QsolE = wd['QsolS']
QsolS = wd['QsolN']
QsolW = wd['QsolW']
optmodel.opt_settings(allow_flow_reversal=True)
# general parameters
c_f = ut.read_time_data(
path=datapath,
name='ElectricityPrices/DAM_electricity_prices-2014_BE.csv'
)['price_BE']
elec_data = ut.read_time_data(datapath,
name='ElectricityPrices/AvgPEF_CO2.csv')
general_params = {
'Te': t_amb,
'Tg': t_g,
'Q_sol_E': QsolE,
'Q_sol_W': QsolW,
'Q_sol_S': QsolS,
'Q_sol_N': QsolN,
'time_step': time_step,
'horizon': n_steps * time_step,
'cost_elec': c_f,
'PEF_elec': elec_data['AvgPEF'],
'CO2_elec': elec_data['AvgCO2/kWh']
}
optmodel.change_params(general_params)
# building parameters
zw_building_params = {
'temperature_supply': 80 + 273.15,
'temperature_return': 60 + 273.15,
'mult': 1,
'heat_profile': heat_profile['ZwartbergNEast'],
'DHW_demand': dhw_demand['ZwartbergNEast']
}
ws_building_params = zw_building_params.copy()
ws_building_params['mult'] = 1
ws_building_params['heat_profile'] = heat_profile['WaterscheiGarden']
optmodel.change_params(zw_building_params,
node='zwartbergNE',
comp='buildingD')
optmodel.change_params(ws_building_params,
node='waterscheiGarden',
comp='buildingD')
bbThor_params = {'diameter': 500}
spWaterschei_params = bbThor_params.copy()
spWaterschei_params['diameter'] = 500
spZwartbergNE_params = bbThor_params.copy()
spZwartbergNE_params['diameter'] = 500
optmodel.change_params(bbThor_params, comp='bbThor')
optmodel.change_params(spWaterschei_params, comp='spWaterschei')
optmodel.change_params(bbThor_params, comp='spZwartbergNE')
# Storage parameters
stor_design = { # Thi and Tlo need to be compatible with delta_T of previous
'temperature_supply': 80 + 273.15,
'temperature_return': 60 + 273.15,
'mflo_max': 110,
'mflo_min': -110,
'volume': 2e4,
'stor_type': 0,
'heat_stor': 0,
'mflo_use': pd.Series(0, index=t_amb.index),
'cost_inv': 1
}
optmodel.change_params(dict=stor_design,
node='waterscheiGarden',
comp='storage')
optmodel.change_state_bounds('heat_stor',
new_ub=10**12,
new_lb=0,
slack=False,
node='waterscheiGarden',
comp='storage')
# Production parameters
c_f = ut.read_time_data(
path=resource_filename('modesto', 'Data/ElectricityPrices'),
name='DAM_electricity_prices-2014_BE.csv')['price_BE']
prod_design = {
'delta_T': 20,
'efficiency': 0.95,
'CO2': 0.178, # based on HHV of CH4 (kg/KWh CH4)
'fuel_cost': c_f,
# http://ec.europa.eu/eurostat/statistics-explained/index.php/Energy_price_statistics (euro/kWh CH4)
'Qmax': 1.5e8,
'ramp_cost': 0.01,
'ramp': 1e6 / 3600,
'cost_inv': 1
}
optmodel.change_params(prod_design, 'ThorPark', 'plant')
##################################
# Print parameters #
##################################
# optmodel.print_all_params()
# optmodel.print_general_param('Te')
# optmodel.print_comp_param('ThorPark', 'plant')
# optmodel.print_comp_param('waterscheiGarden', 'storage')
# optmodel.print_comp_param('waterscheiGarden', 'storage', 'kIns', 'volume')
##################################
# Solve #
##################################
return optmodel
if __name__ == '__main__':
optmodel = construct_model()
# compare_ramping_costs()
optmodel.opt_settings(allow_flow_reversal=False)
optmodel.compile(start_time)
optmodel.set_objective('cost')
optmodel.solve(tee=False, mipgap=0.01)
##################################
# Collect result s #
##################################
# Fuel costs
c_f = ut.read_time_data(path=resource_filename('modesto', 'Data/ElectricityPrices'),
name='DAM_electricity_prices-2014_BE.csv')
# Converting to euro per kWh
c_f = c_f['price_BE'] / 1000
fig4, ax4 = plt.subplots()
plotcf = ut.select_period_data(c_f, start_time=start_time, time_step=time_step, horizon=n_steps * time_step)
ax4.plot(plotcf,
label='Electricity day-ahead market')
# ax3.axhline(y=0, linewidth=2, color='k', lines tyle='--')
ax4.legend()
ax4.set_ylabel('Electricity price [euro/kWh]')
# fig3.tight_layout()
# c_f = [0.034] * int(n_steps/2) + [0.034] * int(n_steps/2) # http://ec.europa.eu/eurostat/statistics-explained/index.php/Energy_price_statistics (euro/kWh CH4)
# Heat flows
level=logging.DEBUG,
format='%(asctime)s %(name)-36s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M')
logger = logging.getLogger('Main.py')
###########################
# Set up Graph of network #
###########################
time_step = 900
n_steps = int(24 * 3 * 3600 / time_step)
start_time = pd.Timestamp('20140101')
df_weather = ut.read_time_data(resource_filename('modesto', 'Data/Weather'),
name='weatherData.csv',
expand=True)
df_userbehaviour = ut.read_time_data(resource_filename('modesto',
'Data/UserBehaviour'),
name='ISO13790.csv',
expand=True)
df_Qcon = ut.read_time_data(resource_filename('modesto', 'Data/UserBehaviour'),
name='QCon.csv',
expand=True)
df_Qrad = ut.read_time_data(resource_filename('modesto', 'Data/UserBehaviour'),
name='QRad.csv',
expand=True)
df_sh_day = ut.read_time_data(resource_filename('modesto',
'Data/UserBehaviour'),
name='sh_day.csv',
def construct_model():
G = nx.DiGraph()
G.add_node('waterscheiGarden',
x=2500,
y=4600,
z=0,
comps={
'plant': 'ProducerVariable',
'buildingD': 'TeaserFourElement'
})
###################################
# Set up the optimization problem #
###################################
optmodel = Modesto(pipe_model='SimplePipe', graph=G)
##################################
# Fill in the parameters #
##################################
t_amb = df_weather['Te']
t_g = df_weather['Tg']
QsolN = df_weather['QsolN']
QsolE = df_weather['QsolS']
QsolS = df_weather['QsolN']
QsolW = df_weather['QsolW']
day_max = df_userbehaviour['day_max']
day_min = ut.expand_df(df_sh_day['1'] + 273.15)
floor_max = df_userbehaviour['floor_max']
floor_min = df_userbehaviour['floor_min']
Q_int_con = ut.expand_df(df_Qcon['1'])
Q_int_rad = ut.expand_df(df_Qrad['1'])
c_f = ut.read_time_data(path=resource_filename('modesto',
'Data/ElectricityPrices'),
name='DAM_electricity_prices-2014_BE.csv',
expand=True)['price_BE']
# cf = pd.Series(0.5, index=t_amb.index)
optmodel.opt_settings(allow_flow_reversal=True)
# general parameters
general_params = {
'Te': t_amb,
'Tg': t_g,
'Q_sol_E': QsolE,
'Q_sol_W': QsolW,
'Q_sol_S': QsolS,
'Q_sol_N': QsolN,
'time_step': time_step,
'horizon': n_steps * time_step,
'cost_elec': c_f
}
optmodel.change_params(general_params)
# building parameters
ws_building_params = {
'TAir0': 20 + 273.15,
'TExt0': 12 + 273.15,
'TRoof0': 10 + 273.15,
'TFloor0': 10 + 273.15,
'delta_T': 20,
'mult': 10,
'day_min_temperature': day_min,
'day_max_temperature': day_max,
'floor_min_temperature': floor_min,
'floor_max_temperature': floor_max,
'neighbName': 'OudWinterslag',
'streetName': 'Gierenshof',
'buildingName': 'Gierenshof_17_1589280',
'Q_int_rad': Q_int_rad,
'Q_int_con': Q_int_con,
'max_heat': 2000000,
'fra_rad': 0.3,
'ACH': 0.4
}
optmodel.change_params(ws_building_params,
node='waterscheiGarden',
comp='buildingD')
# Production parameters
prod_design = {
'efficiency': 0.95,
'PEF': 1,
'CO2': 0.178, # based on HHV of CH4 (kg/KWh CH4)
'fuel_cost': c_f,
# http://ec.europa.eu/eurostat/statistics-explained/index.php/Energy_price_statistics (euro/kWh CH4)
'Qmax': 1.5e6,
'ramp_cost': 0.00,
'ramp': 0,
'delta_T': 20
}
optmodel.change_params(prod_design, 'waterscheiGarden', 'plant')
##################################
# Print parameters #
##################################
# optmodel.print_all_params()
# optmodel.print_general_param('Te')
# optmodel.print_comp_param('ThorPark', 'plant')
# optmodel.print_comp_param('waterscheiGarden', 'storage')
# optmodel.print_comp_param('waterscheiGarden', 'storage', 'kIns', 'volume')
return optmodel
def construct_model():
G = nx.DiGraph()
G.add_node('waterscheiGarden',
x=2500,
y=4600,
z=0,
comps={
'plant': 'ProducerVariable',
'buildingD': 'RCmodel'
})
###################################
# Set up the optimization problem #
###################################
optmodel = Modesto(pipe_model='SimplePipe', graph=G)
##################################
# Fill in the parameters #
##################################
df_weather = ut.read_time_data(resource_filename('modesto',
'Data/Weather'),
name='weatherData.csv',
expand=True)
df_userbehaviour = ut.read_time_data(resource_filename(
'modesto', 'Data/UserBehaviour'),
name='ISO13790.csv',
expand=True)
t_amb = df_weather['Te']
t_g = df_weather['Tg']
QsolN = df_weather['QsolN']
QsolE = df_weather['QsolS']
QsolS = df_weather['QsolN']
QsolW = df_weather['QsolW']
day_max = df_userbehaviour['day_max']
day_min = df_userbehaviour['day_min']
bathroom_min = df_userbehaviour['bathroom_min']
bathroom_max = df_userbehaviour['bathroom_max']
night_max = df_userbehaviour['night_max']
night_min = df_userbehaviour['night_min']
floor_max = df_userbehaviour['floor_max']
floor_min = df_userbehaviour['floor_min']
Q_int_D = df_userbehaviour['Q_int_D']
Q_int_N = df_userbehaviour['Q_int_N']
optmodel.opt_settings(allow_flow_reversal=True)
# general parameters
general_params = {
'Te': t_amb,
'Tg': t_g,
'Q_sol_E': QsolE,
'Q_sol_W': QsolW,
'Q_sol_S': QsolS,
'Q_sol_N': QsolN,
'time_step': time_step,
'horizon': n_steps * time_step
}
optmodel.change_params(general_params)
# building parameters
ws_building_params = {
'delta_T': 20,
'mult': 100,
'night_min_temperature': night_min,
'night_max_temperature': night_max,
'day_min_temperature': day_min,
'day_max_temperature': day_max,
'bathroom_min_temperature': bathroom_min,
'bathroom_max_temperature': bathroom_max,
'floor_min_temperature': floor_min,
'floor_max_temperature': floor_max,
'model_type': 'SFH_T_5_ins_TAB',
'Q_int_D': Q_int_D,
'Q_int_N': Q_int_N,
'TiD0': 20 + 273.15,
'TflD0': 20 + 273.15,
'TwiD0': 20 + 273.15,
'TwD0': 20 + 273.15,
'TfiD0': 20 + 273.15,
'TfiN0': 20 + 273.15,
'TiN0': 20 + 273.15,
'TwiN0': 20 + 273.15,
'TwN0': 20 + 273.15,
'max_heat': 60000
}
optmodel.change_params(ws_building_params,
node='waterscheiGarden',
comp='buildingD')
# Production parameters
c_f = ut.read_time_data(
path=resource_filename('modesto', 'Data/ElectricityPrices'),
name='DAM_electricity_prices-2014_BE.csv')['price_BE']
# cf = pd.Series(0.5, index=t_amb.index)
prod_design = {
'efficiency': 0.95,
'PEF': 1,
'CO2': 0.178, # based on HHV of CH4 (kg/KWh CH4)
'fuel_cost': c_f,
# http://ec.europa.eu/eurostat/statistics-explained/index.php/Energy_price_statistics (euro/kWh CH4)
'Qmax': 1.5e6,
'ramp_cost': 0.00,
'ramp': 1e6
}
optmodel.change_params(prod_design, 'waterscheiGarden', 'plant')
##################################
# Print parameters #
##################################
# optmodel.print_all_params()
# optmodel.print_general_param('Te')
# optmodel.print_comp_param('ThorPark', 'plant')
# optmodel.print_comp_param('waterscheiGarden', 'storage')
# optmodel.print_comp_param('waterscheiGarden', 'storage', 'kIns', 'volume')
return optmodel
def setup_opt(horizon=365 * 24 * 3600, time_step=6 * 3600):
G = nx.DiGraph()
G.add_node('WaterscheiGarden', x=0, y=0, z=0,
comps={'neighb': 'BuildingFixed'})
G.add_node('p1', x=1000, y=2400, z=0, comps={})
G.add_node('p2', x=4000, y=2800, z=0, comps={})
G.add_node('TermienWest', x=4200, z=0, y=4600,
comps={'neighb': 'BuildingFixed'})
G.add_node('Production', x=6000, y=4000, z=0, comps={'backup': 'ProducerVariable',
'tank': 'StorageVariable'})
G.add_node('TermienEast', x=5400, y=200, z=0, comps={'neighb': 'BuildingFixed'})
G.add_edge('p1', 'WaterscheiGarden', name='servWat')
G.add_edge('p1', 'p2', name='backBone')
G.add_edge('p2', 'TermienWest', name='servTer')
G.add_edge('p2', 'TermienEast', name='servBox')
G.add_edge('Production', 'p2', name='servPro')
# pos = {}
# for node in G:
# # print node
# pos[node] = (G.nodes[node]['x'], G.nodes[node]['y'])
#
# fig, ax = plt.subplots()
# nx.draw_networkx(G, with_labels=True, pos=pos, ax=ax)
# ax.set_xlim(-1500, 7000)
# fig.savefig('img/NetworkLayout.svg') # , dpi=150)
# # Setting up modesto
# Decide the following characteristics of the optimization problem:
# * **Horizon** of the optimization problem (in seconds)
# * **Time step** of the (discrete) problem (in seconds)
# * **Start time** (should be a pandas TimeStamp). Currently, weather and prixe data for 2014 are available in modesto.
# * **Pipe model**: The type of model used to model the pipes. Only one type can be selected for the whole optimization problem (unlike the component model types). Possibilities: SimplePipe (= perfect pipe, no losses, no time delays), ExtensivePipe (limited mass flows and heat losses, no time delays) and NodeMethod (heat losses and time delays, but requires mass flow rates to be known in advance)
horizon = horizon
time_step = time_step
pipe_model = 'ExtensivePipe'
# And create the modesto object
model = Modesto(pipe_model=pipe_model,
graph=G)
# # Adding data
# modesto is now aware of the position and interconnections between components, nodes and edges, but still needs information rergarding, weather, prices, customer demands, component sizing, etc.
#
# ## Collect data
# modesto provides some useful data handling methods (found in modesto.utils). Most notable is read_time_data, that can load time-variable data from a csv file. In this example, the data that is available in the folder modesto/Data is used.
# #### Weather data:
from pkg_resources import resource_filename
datapath = resource_filename('modesto', 'Data')
wd = ut.read_time_data(datapath, name='Weather/weatherData.csv')
t_amb = wd['Te']
t_g = wd['Tg']
QsolN = wd['QsolN']
QsolE = wd['QsolS']
QsolS = wd['QsolN']
QsolW = wd['QsolW']
# #### Electricity price
# In[11]:
c_f = ut.read_time_data(path=datapath, name='ElectricityPrices/DAM_electricity_prices-2014_BE.csv')['price_BE']
elec_data = ut.read_time_data(datapath, name='ElectricityPrices/AvgPEF_CO2.csv')
# ## Changing parameters
# In order to solve the problem, all parameters of the optimization probkem need to get a value. A list of the parameters that modesto needs and their description can be found with the following command:
general_params = {'Te': t_amb,
'Tg': t_g,
'Q_sol_E': QsolE,
'Q_sol_W': QsolW,
'Q_sol_S': QsolS,
'Q_sol_N': QsolN,
'time_step': time_step,
'horizon': horizon,
'cost_elec': c_f,
'PEF_elec': elec_data['AvgPEF'],
'CO2_elec': elec_data['AvgCO2/kWh']}
model.change_params(general_params)
# Notice how all parameters are first grouped together in a dictionary and then given all at once to modesto.
#
# If we print the parameters again, we can see the values have now been added:
building_params_common = {
'temperature_supply': 70 + 273.15,
'temperature_return': 30 + 273.15,
'mult': 1
}
heat_profile = ut.read_time_data(datapath, name='HeatDemand/Old/HeatDemandFiltered.csv')
dhw_demand = ut.read_time_data(datapath, name='HeatDemand/DHW_GenkNet.csv')
print('#######################')
print('# Sum of heat demands #')
print('#######################')
print('')
for name in ['WaterscheiGarden', 'TermienWest',
'TermienEast']: # ['Boxbergheide', 'TermienWest', 'WaterscheiGarden']:
build_param = building_params_common
build_param['heat_profile'] = heat_profile[name]
build_param['DHW_demand'] = dhw_demand[name]
print(name, ':', str(sum(heat_profile[name]['2014']) / 1e9)) # Quarterly data
model.change_params(build_param, node=name, comp='neighb')
# ### Heat generation unit
prod_design = {'delta_T': 40,
'efficiency': 0.95,
'CO2': 0.178, # based on HHV of CH4 (kg/KWh CH4)
'fuel_cost': c_f,
'Qmax': 65e6,
'ramp_cost': 0.01,
'ramp': 120e6 / 3600,
'cost_inv': 1}
model.change_params(prod_design, 'Production', 'backup')
prod_stor_design = {
'temperature_supply': 70 + 273.15,
'temperature_return': 30 + 273.15,
'mflo_max': 1100,
'mflo_min': -1100,
'mflo_use': pd.Series(0, index=t_amb.index),
'volume': 3e3,
'stor_type': 1, # 1 tank
'heat_stor': 0,
'cost_inv': 1
}
model.change_params(prod_stor_design, node='Production', comp='tank')
model.change_init_type('heat_stor', 'cyclic', node='Production', comp='tank')
# ### Pipes
pipeDiam = {
'backBone': 500,
'servWat': 400,
'servTer': 250,
'servPro': 500,
'servBox': 250
}
for pipe, DN in pipeDiam.items():
model.change_param(node=None, comp=pipe, param='diameter', val=DN)
model.change_param(node=None, comp=pipe, param='temperature_supply', val=70 + 273.15)
model.change_param(node=None, comp=pipe, param='temperature_return', val=30 + 273.15)
return model
def construct_model():
G = nx.DiGraph()
G.add_node('ThorPark', x=4000, y=4000, z=0,
comps={'plant': 'ProducerVariable'})
G.add_node('p1', x=2600, y=5000, z=0,
comps={})
G.add_node('waterscheiGarden', x=2500, y=4600, z=0,
comps={'buildingD': 'FixedProfile',
}
)
G.add_node('zwartbergNE', x=2000, y=5500, z=0,
comps={'buildingD': 'FixedProfile'})
G.add_edge('ThorPark', 'p1', name='bbThor')
G.add_edge('p1', 'waterscheiGarden', name='spWaterschei')
G.add_edge('p1', 'zwartbergNE', name='spZwartbergNE')
nx.draw(G, with_labels=True)
optmodel = Modesto(pipe_model='NodeMethod', graph=G, repr_days=None)
##################################
# Set up data #
##################################
# Initial temperatures in network
supply_temp = 333.15
return_temp = 303.15
# Heat profiles
linear = np.linspace(0, 1000, n_steps).tolist()
step = [0] * int(n_steps / 2) + [1000] * int(n_steps / 2)
sine = 600 + 400 * np.sin(
[i / int(86400 / time_step) * 2 * np.pi - np.pi / 2 for i in range(int(5 * 86400 / time_step))])
# TODO fix sine profiles
time_index = pd.DatetimeIndex(start=start_time, freq=str(time_step) + 'S', periods=n_steps)
heat_profile_step = pd.Series(step, index=time_index)
heat_profile_linear = pd.Series(linear, index=time_index)
heat_profile_sine = pd.Series(sine[0:n_steps], index=time_index)
heat_profile = heat_profile_sine
# Ambient temperature
t_amb = ut.read_time_data(path=resource_filename('modesto', 'Data/Weather'),
name='extT.csv')
# Ground temperature
t_g = pd.Series(12 + 273.15, index=t_amb.index)
# Solar radiation
datapath = resource_filename('modesto', 'Data')
wd = ut.read_time_data(datapath, name='Weather/weatherData.csv')
QsolN = wd['QsolN']
QsolE = wd['QsolS']
QsolS = wd['QsolN']
QsolW = wd['QsolW']
# Historical temperatures and mass flows
temp_history_return = pd.Series([return_temp] * 20, index=list(range(20)))
temp_history_supply = pd.Series([supply_temp] * 20, index=list(range(20)))
mass_flow_history = pd.Series([10] * 20, index=list(range(20)))
# Fuel costs
c_f = ut.read_time_data(path=resource_filename('modesto', 'Data/ElectricityPrices'),
name='DAM_electricity_prices-2014_BE.csv')
elec_data = ut.read_time_data(datapath, name='ElectricityPrices/AvgPEF_CO2.csv')
# c_f = [0.034] * int(n_steps/2) + [0.034] * int(n_steps/2) # http://ec.europa.eu/eurostat/statistics-explained/index.php/Energy_price_statistics (euro/kWh CH4)
###########################
# Set parameters #
###########################
# general_parameters
general_params = {'Te': t_amb['Te'],
'Tg': t_g,
'Q_sol_E': QsolE,
'Q_sol_W': QsolW,
'Q_sol_S': QsolS,
'Q_sol_N': QsolN,
'time_step': time_step,
'horizon': n_steps*time_step,
'cost_elec': c_f['price_BE'],
'PEF_elec': elec_data['AvgPEF'],
'CO2_elec': elec_data['AvgCO2/kWh']
}
optmodel.change_params(general_params)
optmodel.test = 'Test'
# building parameters
ZW_building_params = {'mult': 500,
'heat_profile': heat_profile,
'temperature_return': return_temp,
'temperature_supply': supply_temp,
'temperature_max': 363.15,
'temperature_min': 283.15}
WS_building_params = ZW_building_params.copy()
WS_building_params['mult'] = 1000
# Calculating mass flows through network
mfcalc = MfCalculation(G, time_step, time_step*n_steps)
mfcalc.add_mf(node='waterscheiGarden', name='buildingD',
mf_df=WS_building_params['mult'] * heat_profile / 4186 / (WS_building_params['temperature_supply'] - WS_building_params['temperature_return']))
mfcalc.add_mf(node='zwartbergNE', name='buildingD',
mf_df=ZW_building_params['mult'] * heat_profile / 4186 / (ZW_building_params['temperature_supply'] - ZW_building_params['temperature_return']))
mfcalc.set_producer_node('ThorPark')
mfcalc.set_producer_component('plant')
mfcalc.calculate_mf()
ZW_building_params['mass_flow'] = mfcalc.get_comp_mf(node='zwartbergNE', comp='buildingD')
WS_building_params['mass_flow'] = mfcalc.get_comp_mf(node='waterscheiGarden', comp='buildingD')
optmodel.change_params(ZW_building_params, node='zwartbergNE', comp='buildingD')
optmodel.change_params(WS_building_params, node='waterscheiGarden', comp='buildingD')
# pipe parameters
bbThor_params = {'diameter': 200,
'mass_flow_history': mass_flow_history,
'temperature_history_return': temp_history_return,
'temperature_history_supply': temp_history_supply,
'wall_temperature_supply': supply_temp,
'wall_temperature_return': return_temp,
'temperature_out_supply': supply_temp,
'temperature_out_return': return_temp,
'mass_flow': mfcalc.get_edge_mf('bbThor')}
spWaterschei_params = bbThor_params.copy()
spWaterschei_params['diameter'] = 150
spWaterschei_params['mass_flow'] = mfcalc.get_edge_mf('spWaterschei')
spZwartbergNE_params = bbThor_params.copy()
spZwartbergNE_params['diameter'] = 150
spZwartbergNE_params['mass_flow'] = mfcalc.get_edge_mf('spZwartbergNE')
optmodel.change_params(spWaterschei_params, comp='spWaterschei')
optmodel.change_params(spZwartbergNE_params, comp='spZwartbergNE')
optmodel.change_params(bbThor_params, comp='bbThor')
# production parameters
prod_design = {'efficiency': 3.5,
'CO2': 0.178, # based on HHV of CH4 (kg/KWh CH4)
'fuel_cost': c_f['price_BE'],
'Qmax': 2e6,
'temperature_supply': supply_temp,
'temperature_return': return_temp,
'temperature_max': 363.15,
'temperature_min': 323.15,
'ramp': 1e6 / 3600,
'ramp_cost': 0.01,
'mass_flow': mfcalc.get_comp_mf(node='ThorPark', comp='plant'),
'cost_inv': 1}
optmodel.change_params(prod_design, node='ThorPark', comp='plant')
##################################
# Print parameters #
##################################
# optmodel.print_all_params()
# optmodel.print_general_param('Te')
# optmodel.print_comp_param(node='ThorPark', comp='plant')
# optmodel.print_comp_param('waterscheiGarden.storage')
# optmodel.print_comp_param('waterscheiGarden.storage', 'kIns', 'volume')
return optmodel
def setup_modesto(time_step=3600, n_steps=24 * 30):
model = Modesto(pipe_model='ExtensivePipe', graph=setup_graph())
heat_demand = ut.read_time_data(resource_filename('modesto', 'Data/HeatDemand/Old'), name='HeatDemandFiltered.csv')
dhw_demand = ut.read_time_data(resource_filename('modesto', 'Data/HeatDemand'), name='DHW_GenkNet.csv')
weather_data = ut.read_time_data(resource_filename('modesto', 'Data/Weather'), name='weatherData.csv')
model.opt_settings(allow_flow_reversal=False)
elec_cost = ut.read_time_data(resource_filename('modesto', 'Data/ElectricityPrices'),
name='DAM_electricity_prices-2014_BE.csv')['price_BE']
elec_data = ut.read_time_data(resource_filename('modesto', 'Data/ElectricityPrices'), name='AvgPEF_CO2.csv')
general_params = {'Te': weather_data['Te'],
'Tg': weather_data['Tg'],
'Q_sol_E': weather_data['QsolE'],
'Q_sol_W': weather_data['QsolW'],
'Q_sol_S': weather_data['QsolS'],
'Q_sol_N': weather_data['QsolN'],
'time_step': time_step,
'horizon': n_steps * time_step,
'cost_elec': pd.Series(0.1, index=weather_data.index),
'PEF_elec': elec_data['AvgPEF'],
'CO2_elec': elec_data['AvgCO2/kWh']
}
model.change_params(general_params)
build_params = {
'temperature_supply': 80 + 273.15,
'temperature_return': 60 + 273.15,
'mult': 1,
'heat_profile': heat_demand['ZwartbergNEast'],
'DHW_demand': dhw_demand['ZwartbergNEast']
}
model.change_params(build_params, node='demand', comp='build')
stor_params = {
'temperature_supply': 80 + 273.15,
'temperature_return': 60 + 273.15,
'mflo_max': 110,
'mflo_min': -110,
'volume': 30000,
'stor_type': 0,
'heat_stor': 0,
'mflo_use': pd.Series(0, index=weather_data.index),
'cost_inv': 1
}
model.change_params(dict=stor_params, node='demand', comp='stor')
model.change_init_type('heat_stor', new_type='fixedVal', comp='stor', node='demand')
stc_params = {
'temperature_supply': 80 + 273.15,
'temperature_return': 60 + 273.15,
'area': 2000
}
model.change_params(stc_params, node='STC', comp='solar')
pipe_data = {
'diameter': 250,
'temperature_supply': 80 + 273.15,
'temperature_return': 60 + 273.15
}
model.change_params(pipe_data, node=None, comp='pipe')
backup_params = {
'delta_T': 20,
'efficiency': 0.95,
'CO2': 0.178,
'fuel_cost': elec_cost,
'Qmax': 7e6,
'ramp_cost': 0,
'ramp': 0,
'cost_inv': 1
}
model.change_params(backup_params, node='STC', comp='backup')
return model
def setup_modesto(graph, objtype='cost'):
"""
Instantiate and compile Modesto object using network graph supplied.
:param graph: nx.DiGraph object specifying network lay-out
:return:
"""
numdays = 1
horizon = numdays * 24 * 3600
time_step = 3600
start_time = pd.Timestamp('20140101')
pipe_model = 'ExtensivePipe'
optmodel = Modesto(pipe_model=pipe_model,
graph=graph
)
from pkg_resources import resource_filename
datapath = resource_filename('modesto', 'Data')
wd = ut.read_time_data(datapath, name='Weather/weatherData.csv')
t_amb = wd['Te']
t_g = wd['Tg']
QsolN = wd['QsolN']
QsolE = wd['QsolS']
QsolS = wd['QsolN']
QsolW = wd['QsolW']
c_f = ut.read_time_data(path=datapath, name='ElectricityPrices/DAM_electricity_prices-2014_BE.csv')['price_BE']
elec_data = ut.read_time_data(datapath, name='ElectricityPrices/AvgPEF_CO2.csv')
general_params = {'Te': t_amb,
'Tg': t_g,
'Q_sol_E': QsolE,
'Q_sol_W': QsolW,
'Q_sol_S': QsolS,
'Q_sol_N': QsolN,
'time_step': time_step,
'horizon': horizon,
'cost_elec': c_f,
'CO2_elec': elec_data['AvgCO2/kWh'],
'PEF_elec': elec_data['AvgPEF']}
optmodel.change_params(general_params)
Pnom = 5e6
# Building parameters
index = pd.DatetimeIndex(start=start_time, freq=str(time_step) + 'S', periods=horizon / time_step)
building_params = {
'temperature_supply': 50 + 273.15,
'temperature_return': 20 + 273.15,
'mult': 1,
'heat_profile': pd.Series(index=index, name='Heat demand', data=[0, 1, 0, 0, 1, 1] * 4 * numdays) * Pnom,
'DHW_demand': pd.Series(index=index, name='Heat demand', data=[0, 1, 0, 0, 1, 1] * 4 * numdays) * 60
}
optmodel.change_params(building_params, node='cons', comp='cons')
# Producer parameters
prod_design = {'delta_T': 30,
'efficiency': 0.95,
'CO2': 0.178, # based on HHV of CH4 (kg/KWh CH4)
'fuel_cost': c_f,
'Qmax': Pnom * 1.5,
'ramp_cost': 0.01,
'ramp': Pnom / 3500,
'cost_inv': 1}
optmodel.change_params(prod_design, 'prod', 'prod')
# Pipe parameters
params = {
'diameter': 150,
'temperature_supply': 50 + 273.15,
'temperature_return': 20 + 273.15
}
optmodel.change_params(params, node=None, comp='pipe')
optmodel.compile(start_time=start_time)
optmodel.set_objective(objtype)
optmodel.opt_settings(allow_flow_reversal=True)
return optmodel
#!/usr/bin/env python
"""
Description
"""
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
import pandas as pd
from pkg_resources import resource_filename
import modesto.utils as ut
moddata = resource_filename('modesto', 'Data')
weadat = ut.read_time_data(moddata, name='Weather/weatherData.csv')[:]['2014']
headat = ut.read_time_data(moddata,
name='HeatDemand/HeatDemandFiltered.csv')[:]['2014']
fig, axs = plt.subplots(2, 1, sharex=True)
axs[0].plot(weadat['Te'] - 273.15,
label='Ambient',
color='red',
linewidth=0.75)
axs[0].plot(weadat['Tg'] - 273.15, label='Ground', color='orange', ls='-.')
axs[0].set_ylabel('Temperature [$^\circ$C]')
axs[0].legend(loc='best')
# TODO markers
axs[1].plot(headat['WaterscheiGarden'] / 1e6, label='A', linewidth=0.75)
def set_params(model,
pipe_model,
verbose=True,
repr=False,
horizon=3600 * 24,
time_step=3600):
"""
Set all necessary parameters (can still be changed before compilation).
:param model: Model in which parameters are to be set
:param pipe_model: Type of pipe model.
:return:
"""
# # Adding data
# modesto is now aware of the position and interconnections between components, nodes and edges, but still needs information rergarding, weather, prices, customer demands, component sizing, etc.
#
# ## Collect data
# modesto provides some useful data handling methods (found in modesto.utils). Most notable is read_time_data, that can load time-variable data from a csv file. In this example, the data that is available in the folder modesto/Data is used.
# #### Weather data:
from pkg_resources import resource_filename
datapath = resource_filename('modesto', 'Data')
wd = utils.read_time_data(datapath,
name='Weather/weatherData.csv',
expand=repr)
t_amb = wd['Te']
t_g = wd['Tg']
QsolN = wd['QsolN']
QsolE = wd['QsolS']
QsolS = wd['QsolN']
QsolW = wd['QsolW']
# #### Electricity price
# In[11]:
c_f = utils.read_time_data(
path=datapath,
name='ElectricityPrices/DAM_electricity_prices-2014_BE.csv',
expand=repr)['price_BE']
elec_data = utils.read_time_data(datapath,
name='ElectricityPrices/AvgPEF_CO2.csv')
# ## Changing parameters
# In order to solve the problem, all parameters of the optimization probkem need to get a value. A list of the parameters that modesto needs and their description can be found with the following command:
general_params = {
'Te': t_amb,
'Tg': t_g,
'Q_sol_E': QsolE,
'Q_sol_W': QsolW,
'Q_sol_S': QsolS,
'Q_sol_N': QsolN,
'time_step': time_step,
'horizon': horizon,
'cost_elec': c_f,
'PEF_elec': elec_data['AvgPEF'],
'CO2_elec': elec_data['AvgCO2/kWh']
}
model.change_params(general_params)
# Notice how all parameters are first grouped together in a dictionary and then given all at once to modesto.
#
# If we print the parameters again, we can see the values have now been added:
building_params_common = {
'temperature_supply': 70 + 273.15,
'temperature_return': 30 + 273.15,
'mult': 1
}
heat_profile = utils.read_time_data(
datapath, name='HeatDemand/Old/HeatDemandFiltered.csv', expand=repr)
dhw_demand = utils.read_time_data(datapath,
name='HeatDemand/DHW_GenkNet.csv',
expand=repr)
if verbose:
print('#######################')
print('# Sum of heat demands #')
print('#######################')
print('')
print(
sum(heat_profile[i] for i in
['WaterscheiGarden', 'TermienWest', 'TermienEast']).max())
for name in ['WaterscheiGarden', 'TermienWest', 'TermienEast'
]: # ['Boxbergheide', 'TermienWest', 'WaterscheiGarden']:
build_param = building_params_common
build_param['heat_profile'] = heat_profile[name]
build_param['DHW_demand'] = dhw_demand[name]
if verbose:
print(name, ':',
str(sum(heat_profile[name]['2014']) / 1e9)) # Quarterly data
model.change_params(build_param, node=name, comp='neighb')
# ### Heat generation unit
prod_design = {
'delta_T': 40,
'efficiency': 0.97,
'CO2': 0.178, # based on HHV of CH4 (kg/KWh CH4)
'fuel_cost': c_f,
'Qmax': 7.5e7,
'ramp_cost': 0.00,
'ramp': 0,
'cost_inv': 1
}
model.change_params(prod_design, 'Production', 'backup')
STOR_COST = resource_filename('modesto', 'Data/Investment/Storage.xlsx')
pit_cost = utils.read_xlsx_data(STOR_COST, use_sheet='Pit')['Cost']
tank_cost = utils.read_xlsx_data(STOR_COST, use_sheet='Tank')['Cost']
prod_stor_design = {
'temperature_supply': 70 + 273.15,
'temperature_return': 30 + 273.15,
'mflo_max': 1100,
'mflo_min': -1100,
'mflo_use': pd.Series(0, index=t_amb.index),
'volume': 3e3,
'stor_type': 1,
'heat_stor': 0,
'cost_inv': tank_cost
}
model.change_init_type('heat_stor',
'cyclic',
node='Production',
comp='tank')
model.change_params(prod_stor_design, node='Production', comp='tank')
for node in ['SolarArray', 'TermienWest', 'WaterscheiGarden']:
model.change_init_type('heat_stor', 'cyclic', node=node, comp='tank')
# ### Storage Unit
stor_design = {
'temperature_supply': 70 + 273.15,
'temperature_return': 30 + 273.15,
'mflo_max': 11000,
'mflo_min': -11000,
'mflo_use': pd.Series(0, index=t_amb.index),
'volume': 50e3,
'stor_type': 0,
'heat_stor': 0,
'cost_inv': pit_cost
}
model.change_params(stor_design, node='SolarArray', comp='tank')
stor_design = {
'TermienWest': {
'temperature_supply': 70 + 273.15,
'temperature_return': 30 + 273.15,
'mflo_max': 11000,
'mflo_min': -11000,
'mflo_use': pd.Series(0, index=t_amb.index),
'volume': 20e3,
'stor_type': 0, # Pit
'heat_stor': 0,
'cost_inv': pit_cost
},
'WaterscheiGarden': {
'temperature_supply': 70 + 273.15,
'temperature_return': 30 + 273.15,
'mflo_max': 11000,
'mflo_min': -11000,
'mflo_use': pd.Series(0, index=t_amb.index),
'volume': 60e3,
'stor_type': 0, # pit
'heat_stor': 0,
'cost_inv': pit_cost
}
}
for node in ['TermienWest', 'WaterscheiGarden']:
model.change_params(stor_design[node], node=node, comp='tank')
for node in [
'Production', 'SolarArray', 'TermienWest', 'WaterscheiGarden'
]:
if repr:
model.change_init_type('heat_stor', 'free', node=node, comp='tank')
else:
model.change_init_type('heat_stor',
'cyclic',
node=node,
comp='tank')
# ### Pipes
pipeDiam = {
'backBone': 500,
'servWat': 400,
'servTer': 250,
'servPro': 500,
'servSol': 500,
'servBox': 250
}
for pipe, DN in pipeDiam.items():
model.change_param(node=None, comp=pipe, param='diameter', val=DN)
if pipe_model == 'ExtensivePipe':
model.change_param(node=None,
comp=pipe,
param='temperature_supply',
val=70 + 273.15)
model.change_param(node=None,
comp=pipe,
param='temperature_return',
val=30 + 273.15)
# ### Solar collector
solData = utils.read_time_data(
datapath, name='RenewableProduction/GlobalRadiation.csv', expand=False)
solParam = {
'temperature_supply': 70 + 273.15,
'temperature_return': 30 + 273.15,
'area': 300000
}
model.change_params(solParam, node='SolarArray', comp='solar')
return model
