Feature/stratified therm storage a1

CHANGELOG.md

@@ -38,6 +38,8 @@ Here is a template for new release sections
 - Add benchmark test for json benchmark file from EPA (EPA-MVS compatability) (#781)
 - Add a try-except code block to catch fatal errors that cause simulation to terminate unsuccessfully (#754)
 
+- The parameters `fixed_losses_relative` and `fixed_losses_absolute` were added. It is now possible to model a stratified thermal energy storage. The usage of this new component has been tested and documented (#718)
+- It is now possible to model a stratified thermal energy storage. In this context, the two optional parameters `fixed_losses_relative` and `fixed_losses_absolute` were added and can be set in the `storage_*.csv` file. The usage of this new component was tested in `test_A1_csv_to_json.py`, `test_D1_model_components.py` and `test_benchmark_stratified_thermal_storage.py`. A documentation was added in the chapter `Modeling Assumptions of the MVS` (#718)
 
 
 ### Changed
@@ -59,6 +61,9 @@ Here is a template for new release sections
 - `C1.check_non_dispatchable_source_time_series()`, now verification not only applied to renewable assets, but all non-dispatchable assets (#726)
 - Add `MINIMAL_DEGREE_OF_AUTONOMY` to EPA-MVS parser (`utils.data_parser.convert_epa_params_to_mvs()`)(#726)
 - Provide the modeler with helpful messages enabling simpler identification and rectification of problems in their input files (#754)
+- In `test_A1_csv_to_json.py` tests were added that check whether default values of `0` are set for `fixed_losses_relative` and `fixed_losses_absolute` in case the user does not pass these two parameters (#718)
+- In `test_D1_model_components.py` tests were added that check whether the `GenericStorage` parameter `investment.minimum` is set to `0` in case `fixed_losses_relative` and `fixed_losses_absolute` are not passed and to `1` in case they are passed as times series or floats. At this time it is not possible to do an ivestment optimization of a stratified thermal energy storage without a non-zero `investment.minimum` (see this [issue](https://github.com/oemof/oemof-thermal/issues/174))  (#718)
+- The two optional parameters `fixed_losses_relative` and `fixed_losses_absolute` were added in `tests/inputs/mvs_config.json` (#718)
 
 ### Removed
 - Remove `MissingParameterWarning` and use `logging.warning` instead (#761)

docs/MVS_parameters.rst

@@ -174,6 +174,8 @@ The file `simulation_settings.csv` includes the following parameters:
 * :ref:`timestep-label`
 * :ref:`outputlpfile-label`
 
+.. _storage_csv:
+
 storage_*.csv
 ^^^^^^^^^^^^^^
 

docs/MVS_parameters_list.csv

@@ -14,6 +14,8 @@
  None, The number of days for which the simulation is to be run.,365, None, Numeric, Days,evaluated_period,evaluatedperiod-label
  None, Price received for feeding electricity into the grid.,0, None, Numeric, currency/kWh,feedin_tariff,feedintariff-label
  None, Name of the csv file containing the input PV generation time-series. E.g.: filename.csv, demand_harbor.csv, None, str, None,file_name,filename-label
+ 0, Thermal losses of storage independent of state of charge between two consecutive timesteps relative to nominal storage capacity., 0.0016, Between 0 and 1, Numeric, factor,fixed_thermal_losses_relative,fixed_thermal_losses_relative-label
+ 0, Thermal losses of storage independent of state of charge and independent of nominal storage capacity between two consecutive timesteps., 0.0003, Between 0 and 1, Numeric, factor,fixed_thermal_losses_absolute,fixed_thermal_losses_absolute-label
  None, The bus/component from which the energyVector is arriving into the asset., Electricity, None, str, None,inflow_direction,inflowdirection-label
  None," The already existing installed capacity in-place, which will also be replaced after its lifetime.",50, Each component in the energyProduction.csv should have a value., Numeric, kWp,installedCap,installedcap-label
  None, Name of the asset, Electricity grid DSO," Input the names in a computer readable format, preferably with underscores instead of spaces, and avoiding special characters (eg. pv_plant_01)", str, None,label,labl-label

docs/Model_Assumptions.rst

@@ -63,6 +63,65 @@ In case of excessive excess energy, a warning is given that it seems to be cheap
 High excess energy can for example result into an optimized inverter capacity that is smaller than the peak generation of installed PV.
 This becomes unrealistic when the excess is very high.
 
+Energy storage
+###########
+
+Generic storages are defined with file `energyStorage.csv` and `storage_*.csv` and have subassets, which are listed in :ref:`storage_csv`.
+
+Stratified thermal energy storage uses the two optional parameters `fixed_losses_relative` and `fixed_losses_absolute`. If these two are not included in `storage_*.csv` or are equal to zero, then a normal generic storage is simulated.
+They are used to take into account temperature dependent losses of a thermal storage. To model a thermal energy storage without stratification, the two parameters are not set. The default values of `fixed_losses_relative` and `fixed_losses_absolute` are zero.
+Except for these two additional parameters the stratified thermal storage is implemented in the same way as other storage components.
+
+Precalculations of the `installedCap`, `efficiency`, `fixed_losses_relative` and `fixed_losses_absolute` can be done orientating on the stratified thermal storage component of `oemof.thermal  <https://github.com/oemof/oemof-thermal>`__.
+The parameters `U-value`, `volume` and `surface` of the storage, which are required to calculate `installedCap`, can be precalculated as well.
+
+The efficiency :math:`\eta` of the storage is calculated as follows:
+
+.. math::
+   \eta = 1 - loss{\_}rate
+
+This example shows how to do precalculations using stratified thermal storage specific input data:
+
+
+.. code-block:: python
+
+        from oemof.thermal.stratified_thermal_storage import (
+        calculate_storage_u_value,
+        calculate_storage_dimensions,
+        calculate_capacities,
+        calculate_losses,
+        )
+
+        # 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'])
+
+Please see the `oemof.thermal` `examples <https://github.com/oemof/oemof-thermal/tree/dev/examples/stratified_thermal_storage>`__ and the `documentation  <https://oemof-thermal.readthedocs.io/en/latest/stratified_thermal_storage.html>`__ for further information.
+
+For an investment optimization the height of the storage should be left open in the precalculations and `installedCap` should be set to 0 or NaN.
+
+An implementation of the stratified thermal storage component has been done in `pvcompare <https://github.com/greco-project/pvcompare>`__. You can find the precalculations of the stratified thermal energy storage made in `pvcompare` `here <https://github.com/greco-project/pvcompare/tree/dev/pvcompare/stratified_thermal_storage.py>`__.
+
 Energy providers (DSOs)
 -----------------------
 
@@ -742,6 +801,8 @@ A benchmark is a point of reference against which results are compared to assess
 
 * Parser converting an energy system model from EPA to MVS (`data <https://github.com/rl-institut/multi-vector-simulator/tree/dev/tests/benchmark_test_inputs/epa_benchmark.json>`__/`pytest <https://github.com/rl-institut/multi-vector-simulator/blob/dev/tests/test_benchmark_scenarios.py>`__)
 
+* Stratified thermal energy storage (`data <https://github.com/rl-institut/multi-vector-simulator/tree/dev/tests/benchmark_test_inputs/Feature_stratified_thermal_storage>`__/`pytest <https://github.com/rl-institut/multi-vector-simulator/tree/dev/tests/test_benchmark_stratified_thermal_storage.py>`__): With fixed thermal losses absolute and relative reduced storage capacity only if these losses apply
+
 More tests can still be implemented with regard to:
 
 * The investment model within the MVS

src/multi_vector_simulator/A1_csv_to_json.py

@@ -77,6 +77,8 @@
     SOC_INITIAL,
     SOC_MAX,
     SOC_MIN,
+    THERM_LOSSES_REL,
+    THERM_LOSSES_ABS,
     STORAGE_CAPACITY,
     FILENAME,
     UNIT,
@@ -216,6 +218,11 @@ def create_json_from_csv(
         add_storage_components() the
         parameter is set to True
 
+    :notes:
+    Tested with:
+    - test_default_values_storage_without_thermal_losses()
+    - test_default_values_storage_with_thermal_losses()
+
     :return: dict
         the converted dictionary
     """
@@ -312,7 +319,13 @@ def create_json_from_csv(
                     )
                 # add column specific parameters
                 if column == STORAGE_CAPACITY:
-                    extra = [SOC_INITIAL, SOC_MAX, SOC_MIN]
+                    extra = [
+                        SOC_INITIAL,
+                        SOC_MAX,
+                        SOC_MIN,
+                        THERM_LOSSES_REL,
+                        THERM_LOSSES_ABS,
+                    ]
                 elif column == INPUT_POWER or column == OUTPUT_POWER:
                     extra = [C_RATE, DISPATCH_PRICE]
                 else:
@@ -325,10 +338,42 @@ def create_json_from_csv(
                 column_parameters = parameters + extra
                 # check if required parameters are missing
                 for i in set(column_parameters) - set(df_copy.index):
-                    raise MissingParameterError(
-                        f"In file {filename}.csv the parameter {i}"
-                        f" in column {column} is missing."
-                    )
+                    if i == THERM_LOSSES_REL:
+                        logging.debug(
+                            f"You are not using the parameter {THERM_LOSSES_REL}, which allows considering relative thermal energy losses (Values: Float). This is an advanced setting that most users can ignore."
+                        )
+                        # Set 0 as default parameter for losses in storage capacity
+                        losses = pd.DataFrame(
+                            data={
+                                "": THERM_LOSSES_REL,
+                                df_copy.columns[0]: ["factor"],
+                                df_copy.columns[1]: [0],
+                            }
+                        )
+                        losses = losses.set_index("")
+                        # Append missing parameter to dataframe
+                        df_copy = df_copy.append(losses, ignore_index=False, sort=False)
+                    elif i == THERM_LOSSES_ABS:
+                        logging.debug(
+                            f"You are not using the parameter {THERM_LOSSES_ABS}, which allows considering relative thermal energy losses (Values: Float). This is an advanced setting that most users can ignore."
+                        )
+                        # Set 0 as default parameter for losses in storage capacity
+                        losses = pd.DataFrame(
+                            data={
+                                "": THERM_LOSSES_ABS,
+                                df_copy.columns[0]: ["kWh"],
+                                df_copy.columns[1]: [0],
+                            }
+                        )
+                        losses = losses.set_index("")
+                        # Append missing parameter to dataframe
+                        df_copy = df_copy.append(losses, ignore_index=False, sort=False)
+                    else:
+                        raise MissingParameterError(
+                            f"In file {filename}.csv the parameter {i}"
+                            f" in column {column} is missing."
+                        )
+
                 for i in df_copy.index:
                     if i not in column_parameters:
                         # check if not required parameters are set to Nan and
@@ -339,6 +384,8 @@ def create_json_from_csv(
                             SOC_INITIAL,
                             SOC_MAX,
                             SOC_MIN,
+                            THERM_LOSSES_REL,
+                            THERM_LOSSES_ABS,
                         ]:
                             warnings.warn(
                                 WrongParameterWarning(

src/multi_vector_simulator/C0_data_processing.py

@@ -350,7 +350,13 @@ def energyStorage(dict_values, group):
             )
 
             # check if parameters are provided as timeseries
-            for parameter in [EFFICIENCY, SOC_MIN, SOC_MAX]:
+            for parameter in [
+                EFFICIENCY,
+                SOC_MIN,
+                SOC_MAX,
+                THERM_LOSSES_REL,
+                THERM_LOSSES_ABS,
+            ]:
                 if parameter in dict_values[group][asset][subasset] and (
                     FILENAME in dict_values[group][asset][subasset][parameter]
                     and HEADER in dict_values[group][asset][subasset][parameter]

src/multi_vector_simulator/D1_model_components.py

@@ -31,6 +31,8 @@
     SOC_INITIAL,
     SOC_MAX,
     SOC_MIN,
+    THERM_LOSSES_REL,
+    THERM_LOSSES_ABS,
     STORAGE_CAPACITY,
     TIMESERIES,
     TIMESERIES_NORMALIZED,
@@ -578,6 +580,8 @@ def storage_fix(model, dict_asset, **kwargs):
         },  # maximum discharge possible in one timestep
         loss_rate=1
         - dict_asset[STORAGE_CAPACITY][EFFICIENCY][VALUE],  # from timestep to timestep
+        fixed_losses_absolute=dict_asset[STORAGE_CAPACITY][THERM_LOSSES_ABS][VALUE],
+        fixed_losses_relative=dict_asset[STORAGE_CAPACITY][THERM_LOSSES_REL][VALUE],
         min_storage_level=dict_asset[STORAGE_CAPACITY][SOC_MIN][VALUE],
         max_storage_level=dict_asset[STORAGE_CAPACITY][SOC_MAX][VALUE],
         initial_storage_level=dict_asset[STORAGE_CAPACITY][SOC_INITIAL][
@@ -602,16 +606,39 @@ def storage_optimize(model, dict_asset, **kwargs):
     -----
     Tested with:
     - test_storage_optimize()
+    - test_storage_optimize_investment_minimum_0_float()
+    - test_storage_optimize_investment_minimum_0_time_series()
+    - test_storage_optimize_investment_minimum_1_rel_float()
+    - test_storage_optimize_investment_minimum_1_abs_float()
+    - test_storage_optimize_investment_minimum_1_rel_times_series()
+    - test_storage_optimize_investment_minimum_1_abs_times_series()
 
     Returns
     -------
     Indirectly updated `model` and dict of asset in `kwargs` with the storage object.
 
     """
+    # investment.minimum for an InvestmentStorage is 0 as default
+    minimum = 0
+
+    # Set investment.minimum to 1 if
+    # non-zero fixed_thermal_losses_relative or fixed_thermal_losses_absolute exist as
+    for losses in [THERM_LOSSES_REL, THERM_LOSSES_ABS]:
+        # 1. float or
+        try:
+            float(dict_asset[STORAGE_CAPACITY][losses][VALUE])
+            if dict_asset[STORAGE_CAPACITY][losses][VALUE] != 0:
+                minimum = 1
+        # 2. time series
+        except TypeError:
+            if sum(dict_asset[STORAGE_CAPACITY][losses][VALUE]) != 0:
+                minimum = 1
+
     storage = solph.components.GenericStorage(
         label=dict_asset[LABEL],
         investment=solph.Investment(
             ep_costs=dict_asset[STORAGE_CAPACITY][SIMULATION_ANNUITY][VALUE],
+            minimum=minimum,
             maximum=dict_asset[STORAGE_CAPACITY][MAXIMUM_CAP][VALUE],
             existing=dict_asset[STORAGE_CAPACITY][INSTALLED_CAP][VALUE],
         ),
@@ -641,6 +668,8 @@ def storage_optimize(model, dict_asset, **kwargs):
         },  # maximum discharge power
         loss_rate=1
         - dict_asset[STORAGE_CAPACITY][EFFICIENCY][VALUE],  # from timestep to timestep
+        fixed_losses_absolute=dict_asset[STORAGE_CAPACITY][THERM_LOSSES_ABS][VALUE],
+        fixed_losses_relative=dict_asset[STORAGE_CAPACITY][THERM_LOSSES_REL][VALUE],
         min_storage_level=dict_asset[STORAGE_CAPACITY][SOC_MIN][VALUE],
         max_storage_level=dict_asset[STORAGE_CAPACITY][SOC_MAX][VALUE],
         initial_storage_level=dict_asset[STORAGE_CAPACITY][SOC_INITIAL][

src/multi_vector_simulator/utils/constants_json_strings.py

@@ -110,6 +110,8 @@
 SOC_INITIAL = "soc_initial"
 SOC_MAX = "soc_max"
 SOC_MIN = "soc_min"
+THERM_LOSSES_REL = "fixed_thermal_losses_relative"
+THERM_LOSSES_ABS = "fixed_thermal_losses_absolute"
 
 # Constraints
 CONSTRAINTS = "constraints"

src/multi_vector_simulator/utils/data_parser.py

@@ -93,6 +93,8 @@
     TIMESERIES_SOC,
     TYPE_ASSET,
     DSM,
+    THERM_LOSSES_REL,
+    THERM_LOSSES_ABS,
 )
 
 from multi_vector_simulator.utils.exceptions import MissingParameterError
@@ -388,6 +390,23 @@ def convert_epa_params_to_mvs(epa_dict):
                 # TODO remove this when change has been made on EPA side
                 if asset_group == ENERGY_STORAGE:
 
+                    if (
+                        THERM_LOSSES_REL
+                        not in dict_asset[asset_label][STORAGE_CAPACITY]
+                    ):
+                        dict_asset[asset_label][STORAGE_CAPACITY][THERM_LOSSES_REL] = {
+                            UNIT: "factor",
+                            VALUE: 0,
+                        }
+                    if (
+                        THERM_LOSSES_ABS
+                        not in dict_asset[asset_label][STORAGE_CAPACITY]
+                    ):
+                        dict_asset[asset_label][STORAGE_CAPACITY][THERM_LOSSES_ABS] = {
+                            UNIT: "kWh",
+                            VALUE: 0,
+                        }
+
                     if OPTIMIZE_CAP not in dict_asset[asset_label]:
                         dict_asset[asset_label][OPTIMIZE_CAP] = {
                             UNIT: TYPE_BOOL,

tests/benchmark_test_inputs/Feature_stratified_thermal_storage/csv_elements/constraints.csv

@@ -0,0 +1,4 @@
+,unit,constraints
+minimal_renewable_factor,factor,0
+maximum_emissions,kgCO2eq/a,None
+minimal_degree_of_autonomy,factor,0

tests/benchmark_test_inputs/Feature_stratified_thermal_storage/csv_elements/economic_data.csv

@@ -0,0 +1,5 @@
+,unit,economic_data
+project_duration,year,30
+currency,str,EUR
+discount_factor,factor,0.08
+tax,factor,0.0 

tests/benchmark_test_inputs/Feature_stratified_thermal_storage/csv_elements/energyBusses.csv

@@ -0,0 +1,3 @@
+,unit,Electricity,Heat
+energyVector,str,Electricity,Heat
+

tests/benchmark_test_inputs/Feature_stratified_thermal_storage/csv_elements/energyConsumption.csv

@@ -0,0 +1,8 @@
+,unit,demand_elec,demand_heat
+dsm,str,False,False
+file_name,str,demand_elec.csv,demand_heat.csv
+type_asset,str,demand,demand
+type_oemof,str,sink,sink
+energyVector,str,Electricity,Heat
+inflow_direction,str,Electricity,Heat
+unit,str,kW,kWh

tests/benchmark_test_inputs/Feature_stratified_thermal_storage/csv_elements/energyConversion.csv

@@ -0,0 +1,16 @@
+,unit,Heat pump
+age_installed,year,0
+development_costs,currency,0
+specific_costs,currency/kW,1000
+efficiency,factor,3.8
+inflow_direction,str,Electricity
+installedCap,kW,0
+maximumCap,kW,None
+lifetime,year,20
+specific_costs_om,currency/kW/year,20
+dispatch_price,currency/kWh,0
+optimizeCap,bool,True
+outflow_direction,str,Heat
+energyVector,str,Heat
+type_oemof,str,transformer
+unit,str,kW

tests/benchmark_test_inputs/Feature_stratified_thermal_storage/csv_elements/energyProduction.csv

@@ -0,0 +1,17 @@
+,unit
+age_installed,year
+development_costs,currency
+specific_costs,currency/unit
+file_name
+installedCap,kWp
+maximumCap, kWp
+lifetime,year
+specific_costs_om,currency/unit/year
+dispatch_price,currency/kWh
+optimizeCap,bool
+outflow_direction,str
+type_oemof,str
+unit,str
+energyVector,str
+renewableAsset,bool
+emission_factor,kgCO2eq/unit