def test_stratified_thermal_storage_invest_option_2_facade(self):
"""
Constraint test of a StratifiedThermalStorage with investment.
Ratio between capacity and storage_capacity is left open.
"""
bus_heat = solph.Bus(label='bus_heat')
facades.StratifiedThermalStorage(label='thermal_storage',
bus=bus_heat,
diameter=10,
temp_h=95,
temp_c=60,
temp_env=10,
u_value=0.5,
expandable=True,
capacity_cost=50,
storage_capacity_cost=400,
minimum_storage_capacity=1,
min_storage_level=0.975,
max_storage_level=0.025,
efficiency=1,
marginal_cost=0.0001)
self.compare_to_reference_lp(
'stratified_thermal_storage_invest_option_2.lp')
def test_stratified_thermal_storage_facade(self):
"""Constraint test of a StratifiedThermalStorage without investment.
"""
bus_heat = solph.Bus(label='bus_heat')
facades.StratifiedThermalStorage(label='thermal_storage',
bus=bus_heat,
diameter=10,
height=30,
temp_h=95,
temp_c=60,
temp_env=10,
u_value=0.5,
min_storage_level=0.975,
max_storage_level=0.025,
capacity=2,
efficiency=1,
marginal_cost=0.0001)
self.compare_to_reference_lp('stratified_thermal_storage.lp')
excess = Sink(label='excess', inputs={bus_heat: Flow()})
heat_demand = Sink(
label='heat_demand',
inputs={bus_heat: Flow(nominal_value=1, fix=demand_timeseries)})
thermal_storage = facades.StratifiedThermalStorage(
label='thermal_storage',
bus=bus_heat,
diameter=input_data[
'diameter'], # TODO: setting to zero should give an error
temp_h=input_data['temp_h'],
temp_c=input_data['temp_c'],
temp_env=input_data['temp_env'],
u_value=u_value,
expandable=True,
capacity_cost=50,
storage_capacity_cost=400,
minimum_storage_capacity=1, # TODO: setting to zero should give an error!
min_storage_level=input_data['min_storage_level'],
max_storage_level=input_data['max_storage_level'],
efficiency=1,
marginal_cost=0.0001)
energysystem.add(bus_heat, heat_source, shortage, excess, heat_demand,
thermal_storage)
# Create and solve the optimization model
optimization_model = Model(energysystem)
optimization_model.solve(solver=solver,
shortage = Source(label='shortage',
outputs={bus_heat: Flow(variable_costs=1e6)})
excess = Sink(label='excess', inputs={bus_heat: Flow()})
heat_demand = Sink(
label='heat_demand',
inputs={bus_heat: Flow(nominal_value=1, fix=demand_timeseries)})
thermal_storage = facades.StratifiedThermalStorage(
label='thermal_storage',
bus=bus_heat,
diameter=input_data['diameter'],
height=input_data['height'],
temp_h=input_data['temp_h'],
temp_c=input_data['temp_c'],
temp_env=input_data['temp_env'],
u_value=u_value,
min_storage_level=input_data['min_storage_level'],
max_storage_level=input_data['max_storage_level'],
capacity=input_data['maximum_heat_flow_charging'],
efficiency=1,
marginal_cost=0.0001)
energysystem.add(bus_heat, heat_source, shortage, excess, heat_demand,
thermal_storage)
# Create and solve the optimization model
optimization_model = Model(energysystem)
def run_storage_model(initial_storage_level, temp_h, temp_c):
data_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
'./data/validation_data.csv')
input_data = pd.read_csv(data_path, index_col=0, header=0)['var_value']
u_value = calculate_storage_u_value(
input_data['s_iso'],
input_data['lamb_iso'],
input_data['alpha_inside'],
input_data['alpha_outside'])
# Set up an energy system model
periods = 10
datetimeindex = pd.date_range('1/1/2019', periods=periods, freq='H')
demand_timeseries = np.zeros(periods)
heat_feedin_timeseries = np.zeros(periods)
energysystem = EnergySystem(timeindex=datetimeindex)
bus_heat = Bus(label='bus_heat')
heat_source = Source(
label='heat_source',
outputs={bus_heat: Flow(
nominal_value=1,
fix=heat_feedin_timeseries)})
shortage = Source(
label='shortage',
outputs={bus_heat: Flow(variable_costs=1e6)})
excess = Sink(
label='excess',
inputs={bus_heat: Flow()})
heat_demand = Sink(
label='heat_demand',
inputs={bus_heat: Flow(
nominal_value=1,
fix=demand_timeseries)})
thermal_storage = facades.StratifiedThermalStorage(
label='thermal_storage',
bus=bus_heat,
diameter=input_data['diameter'],
height=input_data['height'],
temp_h=temp_h,
temp_c=temp_c,
temp_env=input_data['temp_env'],
u_value=u_value, # W/(m2*K)
min_storage_level=input_data['min_storage_level'],
max_storage_level=input_data['max_storage_level'],
initial_storage_level=initial_storage_level,
capacity=input_data['maximum_heat_flow_charging'],
efficiency=1,
marginal_cost=0.0001
)
energysystem.add(
bus_heat,
heat_source,
shortage,
excess,
heat_demand,
thermal_storage)
# create and solve the optimization model
optimization_model = Model(energysystem)
optimization_model.write('storage_model_facades.lp',
io_options={'symbolic_solver_labels': True})
optimization_model.solve(solver='cbc', solve_kwargs={'tee': False})
energysystem.results['main'] = solph.processing.results(optimization_model)
string_results = solph.views.convert_keys_to_strings(energysystem.results['main'])
# Get time series of level (state of charge) of the thermal energy storage
TES_soc = (string_results['thermal_storage', 'None']['sequences'])
# Save results to csv file
TES_soc.to_csv("./data/storage_soc_calculated.csv")
return