Use 'wind_farm_efficiency' instead of 'constant_efficiency' and 'power_efficiency_curve'

Author: SabineHaas

example/turbine_cluster_modelchain_example.ipynb

@@ -129,7 +129,7 @@ class that provides all necessary steps to calculate the power output of a
         WindTurbineCluster object.
 
     """
-
+    example_farm.efficiency = 0.9
     # power output calculation for example_farm
     # initialize TurbineClusterModelChain with default parameters and use
     # run_model method to calculate power output
@@ -141,10 +141,10 @@ class that provides all necessary steps to calculate the power output of a
     # own specifications for TurbineClusterModelChain setup
     modelchain_data = {
         'wake_losses_model':
-            None,  # 'dena_mean' (default) or name of another wind efficiency
-                   # curve (see
-                   # :py:func:`~.wake_losses.get_wind_efficiency_curve`) or
-                   # 'wind_farm_efficiency' or None
+            'wind_farm_efficiency',  # 'dena_mean' (default), None,
+                                     # 'wind_farm_efficiency' or name
+                                     #  of another wind efficiency curve
+                #  see :py:func:`~.wake_losses.get_wind_efficiency_curve`
         'smoothing': True,  # False (default) or True
         'block_width': 0.5,  # default: 0.5
         'standard_deviation_method': 'Staffell_Pfenninger',  #

example/turbine_cluster_modelchain_example.py

@@ -75,7 +75,7 @@ def test_run_model(self):
         assert_series_equal(test_tc_mc.power_output, power_output_exp)
 
         # Test constant efficiency
-        parameters['wake_losses_model'] = 'constant_efficiency'
+        parameters['wake_losses_model'] = 'wind_farm_efficiency'
         test_wind_farm = wf.WindFarm(**self.test_farm)
         test_wind_farm.efficiency = 0.9
         power_output_exp = pd.Series(data=[4420994.806920091,
@@ -138,7 +138,7 @@ def test_run_model_turbine_cluster(self):
         assert_series_equal(test_tc_mc.power_output, power_output_exp)
 
         # Test constant efficiency
-        parameters['wake_losses_model'] = 'constant_efficiency'
+        parameters['wake_losses_model'] = 'wind_farm_efficiency'
         test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
         for farm in test_cluster.wind_farms:
             farm.efficiency = 0.9

tests/test_turbine_cluster_modelchain.py

@@ -183,8 +183,7 @@ def assign_power_curve(self, weather_df):
             'turbulence_intensity' in
             weather_df.columns.get_level_values(0) else None)
         # Assign power curve
-        if (self.wake_losses_model == 'power_efficiency_curve' or
-                self.wake_losses_model == 'constant_efficiency' or
+        if (self.wake_losses_model == 'wind_farm_efficiency' or
                 self.wake_losses_model is None):
             wake_losses_model_to_power_curve = self.wake_losses_model
             if self.wake_losses_model is None:
@@ -262,8 +261,7 @@ def run_model(self, weather_df):
         density_hub = (None if (self.power_output_model == 'power_curve' and
                                 self.density_correction is False)
                        else self.density_hub(weather_df))
-        if (self.wake_losses_model != 'power_efficiency_curve' and
-                self.wake_losses_model != 'constant_efficiency' and
+        if (self.wake_losses_model != 'wind_farm_efficiency' and
                 self.wake_losses_model is not None):
             # Reduce wind speed with wind efficiency curve
             wind_speed_hub = wake_losses.reduce_wind_speed(

windpowerlib/turbine_cluster_modelchain.py

@@ -186,7 +186,7 @@ def get_installed_power(self):
             wind_dict['number_of_turbines']
             for wind_dict in self.wind_turbine_fleet)
 
-    def assign_power_curve(self, wake_losses_model='power_efficiency_curve',
+    def assign_power_curve(self, wake_losses_model='wind_farm_efficiency',
                            smoothing=False, block_width=0.5,
                            standard_deviation_method='turbulence_intensity',
                            smoothing_order='wind_farm_power_curves',
@@ -207,7 +207,7 @@ def assign_power_curve(self, wake_losses_model='power_efficiency_curve',
         wake_losses_model : str
             Defines the method for taking wake losses within the farm into
             consideration. Options: 'wind_farm_efficiency' or None.
-            Default: 'wind_farm_efficiency'. # todo adapt
+            Default: 'wind_farm_efficiency'.
         smoothing : bool
             If True the power curves will be smoothed before or after the
             aggregation of power curves depending on `smoothing_order`.
@@ -308,8 +308,7 @@ def assign_power_curve(self, wake_losses_model='power_efficiency_curve',
                 wind_farm_power_curve['value'],
                 standard_deviation_method=standard_deviation_method,
                 block_width=block_width, **kwargs)
-        if (wake_losses_model == 'constant_efficiency' or
-                wake_losses_model == 'power_efficiency_curve'):
+        if wake_losses_model == 'wind_farm_efficiency':
             if self.efficiency is not None:
                 wind_farm_power_curve = (
                     power_curves.wake_losses_to_power_curve(