Merge 768e044 into 8032080

docs/index.rst

@@ -15,6 +15,14 @@ Welcome to oemof.thermal's documentation!
    getting_started
    examples
 
+.. toctree::
+   :maxdepth: 1
+   :caption: Model validation
+
+   validation_compression_heat_pumps_and_chillers
+   validation_concentrating_solar_power
+   validation_solar_thermal_collector
+   validation_stratified_thermal_storage
 
 .. toctree::
    :maxdepth: 1

docs/validation_stratified_thermal_storage.rst

@@ -0,0 +1,119 @@
+.. _validation_stratified_thermal_storage_label:
+
+
+Stratified thermal storage
+===================================
+
+Scope
+_____
+
+The validation of the stratified thermal storage has been conducted within the oemof_heat project.
+Measurement data of a reference storage has been provided by the energy supplier Naturstrom AG.
+The set of data contains the storage geometry (height, diameter, insulation thickness),
+temperatures at top and bottom of the storage and a time series of the storage level.
+
+Method
+_______
+
+In order to calculate the storage level using the StratifiedThermalStorage
+component from oemof-thermal
+the storage geometry,
+the temperatures of the hot and cold layers (top and bottom),
+the temperature of the environment,
+the heat conductivity of the insulation and
+the heat transfer coefficients inside and outside of the storage surface
+are required.
+Tab.1 shows the required input parameter and the respective values of the reference storage.
+For some parameters assumptions had to be made.
+
+================================ =============================
+    Name                          Value
+================================ =============================
+    height                          2.96 m
+    diameter                        1.15 m
+    insulation thickness            100 mm
+    temperature of hot layer        82°C
+    temperature of cold layer       55°C
+    **Own Assumptions**
+--------------------------------------------------------------
+    temperature of environment      25°C
+    conductivity of insulation      0.039 W/(m*K)
+    heat transfer coef. inside      7 W/(m2*K)
+    heat transfer coef. outside     4 W/(m2*K)
+================================ =============================
+Tab.1: Input parameters used for the model validation
+
+Please see the
+`USER'S GUIDE <https://oemof-thermal.readthedocs.io/en/latest/stratified_thermal_storage.html>`_ on the stratified thermal storage for further information.
+
+The level of the reference storage is not measured directly but is
+determined from the temperatures at different heights :math:`T_{i}` in the storage.
+
+.. math::
+        level = \frac{T_\mathrm{mean} - T_\mathrm{cold}}{T_\mathrm{hot} - T_\mathrm{cold}}
+
+where :math:`T_\mathrm{mean}` is the arithmetic mean temperature of the storage.
+
+.. math::
+        T_\mathrm{mean}  = \frac{\sum_{i=1}^{n} T_{i}}{n}
+
+where :math:`n` is the amount of temperature sensors.
+
+Measurement data
+_______
+
+The measurement data come from an energy system that contains several identical storages.
+Here, only a single storage is calculated to keep the model simple.
+
+The validation aims on checking how accurately the losses of the storage are predicted.
+This does not include losses during the charging and discharging
+(inflow_conversion_factor and outflow_conversion_factor).
+Therefore a short time series of measurement data (see Tab.2) is used for the
+validation where no charging or discharging occurs.
+
+======= ==============
+time      level in %
+======= ==============
+0.0     	78.50
+0.25     	78.21
+0.5      	78.38
+0.75    	78.00
+1.0	        78.25
+1.25    	77.79
+1.5     	77.75
+1.75    	77.04
+2.0       	77.17
+2.25        77.63
+2.5         78.00
+2.75        77.71
+3.0         77.79
+3.15        77.29
+3.5         77.00
+3.75        76.38
+4.0         77.33
+4.25        77.21
+4.5         77.00
+4.75        77.29
+5.0	        77.08
+5.25        76.54
+5.5         76.33
+======= ==============
+Tab.2: Measured storage level.
+
+Results
+________
+
+
+
+.. figure:: _pics/storage_level.png
+    :width: 80 %
+    :alt: storage_level.png
+    :align: center
+    :figclass: align-center
+
+    Fig.1: Measured storage level (red) and calculated storage level (blue).
+
+
+
+References
+__________

examples/stratified_thermal_storage/data/storage_soc_measured.csv

@@ -0,0 +1,24 @@
+,level
+0,78.5
+1,78.21
+2,78.38
+3,78.0
+4,78.25
+5,77.79
+6,77.75
+7,77.04
+8,77.17
+9,77.63
+10,78.0
+11,77.71
+12,77.79
+13,77.29
+14,77.0
+15,76.38
+16,77.33
+17,77.21
+18,77.0
+19,77.29
+20,77.08
+21,76.54
+22,76.33

examples/stratified_thermal_storage/data/validation_data.csv

@@ -0,0 +1,17 @@
+stratified_thermal_storage.csvvar_name,var_value,var_unit
+height,2.96,m
+diameter,1.15,m
+temp_h,82,degC
+temp_c,55,degC
+temp_env,25,degC
+inflow_conversion_factor,1,
+outflow_conversion_factor,1,
+min_storage_level,0.25,
+max_storage_level,0.95,
+initial_storage_level,0.785,
+maximum_heat_flow_charging,2,MW
+maximum_heat_flow_discharging,2,MW
+s_iso,100,mm
+lamb_iso,0.039,W/(m*K)
+alpha_inside,7,W/(m2*K)
+alpha_outside,4,W/(m2*K)

examples/stratified_thermal_storage/model_validation.py

@@ -0,0 +1,172 @@
+import os
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import oemof.solph as solph
+from oemof.thermal.stratified_thermal_storage import (  # noqa
+    calculate_storage_u_value,
+    calculate_storage_dimensions,
+    calculate_capacities
+)
+from oemof.thermal import facades
+
+Source = solph.Source
+Sink = solph.Sink
+Bus = solph.Bus
+Flow = solph.Flow
+Model = solph.Model
+EnergySystem = solph.EnergySystem
+
+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']
+
+
+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
+
+
+initial_storage_level = input_data['initial_storage_level']
+
+run_storage_model(initial_storage_level=input_data['initial_storage_level'],
+                  temp_h=input_data['temp_h'],
+                  temp_c=input_data['temp_c'])
+
+volume, surface = calculate_storage_dimensions(
+    input_data['height'],
+    input_data['diameter'])
+
+# Max capacity in MWh
+nominal_storage_capacity = calculate_capacities(
+    volume,
+    input_data['temp_h'],
+    input_data['temp_c'])
+
+# Get measurement data
+filename = './data/storage_soc_measured.csv'
+level_meas = pd.read_csv(filename, header=0)
+
+# Get simulation results (hourly values)
+filename = './data/storage_soc_calculated.csv'
+TES_soc_df = pd.read_csv(filename, header=0)
+
+# Convert to list
+TES_soc_hourly = TES_soc_df['storage_content'].values
+
+# Convert simulation data to relative values in %
+TES_soc_relative = [soc / nominal_storage_capacity * 100 for soc in
+                    TES_soc_hourly]
+
+end_step = 7
+
+# Make list with time steps for x-axes in plot
+t_meas = np.arange(0, (len(level_meas) / 4), 0.25)
+
+plt.style.use('ggplot')
+fig, ax = plt.subplots()
+
+# Plot horizontal line (initial level)
+init_level = level_meas['level'][0]
+plt.plot([init_level] * end_step, '--', color='gray')
+
+# Plot simulation data
+TES_soc_relative_list = [initial_storage_level * 100]
+[TES_soc_relative_list.append(TES_soc_relative[i]) for i in range(10)]
+plt.plot(TES_soc_relative_list[:end_step],
+         label="storage level (simulation)")
+
+# Plot measurement data
+plt.plot(t_meas, level_meas['level'], label="storage level (measurement)")
+
+plt.legend()
+ax.set_xlabel("Time in h")
+ax.set_ylabel("Storage level in %")
+ax.set_xlim([0, 5.5])
+
+ax.set_ylim([75, 80])
+
+plt.savefig("validation.png")