def METHOD_NAME(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