def test_calculate_storage_u_value():
params = {
's_iso': 50, # mm
'lamb_iso': 0.05, # W/(m*K)
'alpha_inside': 1, # W/(m2*K)
'alpha_outside': 1 # W/(m2*K)
}
u_value = calculate_storage_u_value(**params)
assert u_value == 1 / 3
from oemof.thermal.stratified_thermal_storage import calculate_storage_u_value
from oemof.thermal import facades
from oemof.solph import (processing, Source, Sink, Bus, Flow, Model,
EnergySystem)
# Set paths
data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'data/stratified_thermal_storage.csv')
# Read input data
input_data = pd.read_csv(data_path, index_col=0, header=0)['var_value']
# Precalculation
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
solver = 'cbc'
periods = 100
datetimeindex = pd.date_range('1/1/2019', periods=periods, freq='H')
demand_timeseries = np.zeros(periods)
demand_timeseries[-5:] = 1
heat_feedin_timeseries = np.zeros(periods)
heat_feedin_timeseries[:10] = 1
energysystem = EnergySystem(timeindex=datetimeindex)
bus_heat = Bus(label='bus_heat')
def calc_strat_tes_param(
weather,
temperature_col="temp_air",
user_inputs_pvcompare_directory=None,
user_inputs_mvs_directory=None,
):
"""
This function does the precalculations of the stratified thermal storage.
It calculates the following parameters:
1. nominal_storage_capacity
2. loss_rate
3. fixed_losses_relative
4. fixed_losses_absolute
from the storage's input data provided in `stratified_thermal_storage.csv`
and using functions implemented in oemof.thermal (github.com/oemof/oemof-thermal).
Parameters
----------
weather : :pandas:`pandas.DataFrame<frame>`
Contains weather data time series. Required: ambient temperature in
column `temperature_col`.
temperature_col : str
Name of column in `weather` containing ambient temperature.
Default: "temp_air".
user_inputs_pvcompare_directory: str or None
Directory of the user inputs. If None,
`constants.DEFAULT_USER_INPUTS_PVCOMPARE_DIRECTORY` is used as user_inputs_pvcompare_directory.
Default: None.
user_inputs_mvs_directory: str or None
Path to input directory containing files that describe the energy
system and that are an input to MVS. Default:
DEFAULT_MVS_OUTPUT_DIRECTORY (see :func:`~pvcompare.constants`.
Returns
-------
nominal_storage_capacity : numeric
Maximum amount of stored thermal energy [MWh]
loss_rate : numeric (sequence or scalar)
The relative loss of the storage capacity between two consecutive
timesteps [-]
fixed_losses_relative : numeric (sequence or scalar)
Losses independent of state of charge between two consecutive
timesteps relative to nominal storage capacity [-]
fixed_losses_absolute : numeric (sequence or scalar)
Losses independent of state of charge and independent of
nominal storage capacity between two consecutive timesteps [MWh]
"""
# *********************************************************************************************
# Set paths - Read and prepare data
# *********************************************************************************************
if user_inputs_pvcompare_directory is None:
input_directory = constants.DEFAULT_USER_INPUTS_PVCOMPARE_DIRECTORY
if user_inputs_mvs_directory == None:
user_inputs_mvs_directory = constants.DEFAULT_USER_INPUTS_MVS_DIRECTORY
input_data_filename = os.path.join(user_inputs_pvcompare_directory,
"stratified_thermal_storage.csv")
input_data = pd.read_csv(input_data_filename, header=0,
index_col=0)["var_value"]
# Prepare ambient temperature for precalculations
ambient_temperature = weather[temperature_col]
# *********************************************************************************************
# Precalculations
# *********************************************************************************************
u_value = strat_tes.calculate_storage_u_value(
input_data["s_iso"],
input_data["lamb_iso"],
input_data["alpha_inside"],
input_data["alpha_outside"],
)
volume, surface = strat_tes.calculate_storage_dimensions(
input_data["height"], input_data["diameter"])
nominal_storage_capacity = (strat_tes.calculate_capacities(
volume, input_data["temp_h"], input_data["temp_c"]) * 1000)
try:
int(float(nominal_storage_capacity))
except ValueError:
if math.isnan(nominal_storage_capacity) is True:
nominal_storage_capacity = 0
(
loss_rate,
fixed_losses_relative,
fixed_losses_absolute,
) = strat_tes.calculate_losses(
u_value,
input_data["diameter"],
input_data["temp_h"], # TODO: In future heat pump temp here
input_data["temp_c"], # TODO: In future relation to temp_h here
ambient_temperature,
)
return (
nominal_storage_capacity,
loss_rate,
fixed_losses_relative,
fixed_losses_absolute,
)
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
