def test_calculate_capacities():
params = {
'volume': 1000, # m3
'temp_h': 100, # deg C
'temp_c': 50, # deg C
}
nominal_storage_capacity = calculate_capacities(**params)
assert nominal_storage_capacity == 56.62804059111111
def build_solph_components(self):
"""
"""
self.inflow_conversion_factor = sequence(self.efficiency)
self.outflow_conversion_factor = sequence(self.efficiency)
self.loss_rate = sequence(self.loss_rate)
self.fixed_losses_relative = sequence(self.fixed_losses_relative)
self.fixed_losses_absolute = sequence(self.fixed_losses_absolute)
# make it investment but don't set costs (set below for flow (power))
self.investment = self._investment()
if self.investment:
self.invest_relation_input_output = 1
for attr in ["invest_relation_input_output"]:
if getattr(self, attr) is None:
raise AttributeError(
("You need to set attr "
"`{}` "
"for component {}").format(attr, self.label))
# set capacity costs at one of the flows
fi = Flow(investment=Investment(
ep_costs=self.capacity_cost,
maximum=self.capacity_potential,
existing=self.capacity,
),
**self.input_parameters)
# set investment, but no costs (as relation input / output = 1)
fo = Flow(investment=Investment(),
variable_costs=self.marginal_cost,
**self.output_parameters)
# required for correct grouping in oemof.solph.components
self._invest_group = True
else:
self.volume = calculate_storage_dimensions(self.height,
self.diameter)[0]
self.nominal_storage_capacity = calculate_capacities(
self.volume, self.temp_h, self.temp_c, **{
key: value
for key, value in self.water_properties.items()
if value is not None
})
fi = Flow(nominal_value=self._nominal_value(),
**self.input_parameters)
fo = Flow(nominal_value=self._nominal_value(),
variable_costs=self.marginal_cost,
**self.output_parameters)
self.inputs.update({self.bus: fi})
self.outputs.update({self.bus: fo})
self._set_flows()
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,
)
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'])
volume, surface = calculate_storage_dimensions(input_data['height'],
input_data['diameter'])
nominal_storage_capacity = calculate_capacities(volume, input_data['temp_h'],
input_data['temp_c'])
loss_rate, fixed_losses_relative, fixed_losses_absolute = calculate_losses(
u_value, input_data['diameter'], input_data['temp_h'],
input_data['temp_c'], input_data['temp_env'])
maximum_heat_flow_charging = 2
maximum_heat_flow_discharging = 2
def print_parameters():
parameter = {
'U-value [W/(m2*K)]': u_value,
'Volume [m3]': volume,
'Surface [m2]': surface,
'Nominal storage capacity [MWh]': nominal_storage_capacity,
data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'stratified_thermal_storage.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'])
volume, surface = calculate_storage_dimensions(input_data['height'],
input_data['diameter'])
nominal_storage_capacity, max_storage_level, min_storage_level = calculate_capacities(
volume, input_data['temp_h'], input_data['temp_c'],
input_data['nonusable_storage_volume'], input_data['heat_capacity'],
input_data['density'])
loss_rate, fixed_losses_relative, fixed_losses_absolute = calculate_losses(
u_value, input_data['diameter'], input_data['temp_h'],
input_data['temp_c'], input_data['temp_env'])
maximum_heat_flow_charging = 2
maximum_heat_flow_discharging = 2
def print_results():
parameter = {
'U-value [W/(m2*K)]': u_value,
'Volume [m3]': volume,
'Surface [m2]': surface,
