Add scale_voltage_current_power to ModelChain.pvwatts_dc (#1138)

* add scale_voltage_current_power to ModelChain.pvwatts_dc, docstring edits, whatsnew

* extend pvsystem.scale_voltage_current_power to not require all columns, and accept Series

* handle tuples, use temporary DataFrame

* improve scale_voltage_current_power, handle tuples

* sort the new test

* improve scale function, fix multiple string test

* multiple string test runs

* add PR number

* Maintain column order in returned DataFrame

Co-authored-by: Will Holmgren <[email protected]>

* whatsnew entry formatting

Co-authored-by: Will Holmgren <[email protected]>

Co-authored-by: Will Holmgren <[email protected]>

Author: cwhanse

docs/sphinx/source/whatsnew/v0.9.0.rst

@@ -83,6 +83,10 @@ Enhancements
   automatically switch to using ``'effective_irradiance'`` (if available) for
   cell temperature models, when ``'poa_global'`` is not provided in input
   weather or calculated from input weather data.
+* :py:meth:`~pvlib.modelchain.ModelChain.pvwatts_dc` now scales the DC power
+  by ``pvsystem.PVSystem.modules_per_strings`` and
+  ``pvsystem.PVSystem.strings_per_inverter``. Note that both attributes still
+  default to 1. (:pull:`1138`)
 
 Bug fixes
 ~~~~~~~~~

pvlib/modelchain.py

@@ -730,8 +730,33 @@ def pvsyst(self):
         return self._singlediode(self.system.calcparams_pvsyst)
 
     def pvwatts_dc(self):
+        """Calculate DC power using the PVWatts model.
+
+        Results are stored in ModelChain.results.dc. DC power is computed
+        from PVSystem.module_parameters['pdc0'] and then scaled by
+        PVSystem.modules_per_string and PVSystem.strings_per_inverter.
+
+        Returns
+        -------
+        self
+
+        See also
+        --------
+        pvlib.pvsystem.PVSystem.pvwatts_dc
+        pvlib.pvsystem.PVSystem.scale_voltage_current_power
+        """
         self.results.dc = self.system.pvwatts_dc(
             self.results.effective_irradiance, self.results.cell_temperature)
+        if isinstance(self.results.dc, tuple):
+            temp = tuple(
+                pd.DataFrame(s, columns=['p_mp']) for s in self.results.dc)
+        else:
+            temp = pd.DataFrame(self.results.dc, columns=['p_mp'])
+        scaled = self.system.scale_voltage_current_power(temp)
+        if isinstance(scaled, tuple):
+            self.results.dc = tuple(s['p_mp'] for s in scaled)
+        else:
+            self.results.dc = scaled['p_mp']
         return self
 
     @property

pvlib/pvsystem.py

@@ -891,7 +891,7 @@ def scale_voltage_current_power(self, data):
         Parameters
         ----------
         data: DataFrame or tuple of DataFrame
-            Must contain columns `'v_mp', 'v_oc', 'i_mp' ,'i_x', 'i_xx',
+            May contain columns `'v_mp', 'v_oc', 'i_mp' ,'i_x', 'i_xx',
             'i_sc', 'p_mp'`.
 
         Returns
@@ -2626,13 +2626,13 @@ def i_from_v(resistance_shunt, resistance_series, nNsVth, voltage,
 
 def scale_voltage_current_power(data, voltage=1, current=1):
     """
-    Scales the voltage, current, and power of the DataFrames
-    returned by :py:func:`singlediode` and :py:func:`sapm`.
+    Scales the voltage, current, and power in data by the voltage
+    and current factors.
 
     Parameters
     ----------
     data: DataFrame
-        Must contain columns `'v_mp', 'v_oc', 'i_mp' ,'i_x', 'i_xx',
+        May contain columns `'v_mp', 'v_oc', 'i_mp' ,'i_x', 'i_xx',
         'i_sc', 'p_mp'`.
     voltage: numeric, default 1
         The amount by which to multiply the voltages.
@@ -2648,14 +2648,15 @@ def scale_voltage_current_power(data, voltage=1, current=1):
 
     # as written, only works with a DataFrame
     # could make it work with a dict, but it would be more verbose
-    data = data.copy()
-    voltages = ['v_mp', 'v_oc']
-    currents = ['i_mp', 'i_x', 'i_xx', 'i_sc']
-    data[voltages] *= voltage
-    data[currents] *= current
-    data['p_mp'] *= voltage * current
-
-    return data
+    voltage_keys = ['v_mp', 'v_oc']
+    current_keys = ['i_mp', 'i_x', 'i_xx', 'i_sc']
+    power_keys = ['p_mp']
+    voltage_df = data.filter(voltage_keys, axis=1) * voltage
+    current_df = data.filter(current_keys, axis=1) * current
+    power_df = data.filter(power_keys, axis=1) * voltage * current
+    df = pd.concat([voltage_df, current_df, power_df], axis=1)
+    df_sorted = df[data.columns]  # retain original column order
+    return df_sorted
 
 
 def pvwatts_dc(g_poa_effective, temp_cell, pdc0, gamma_pdc, temp_ref=25.):
@@ -2675,20 +2676,20 @@ def pvwatts_dc(g_poa_effective, temp_cell, pdc0, gamma_pdc, temp_ref=25.):
     Parameters
     ----------
     g_poa_effective: numeric
-        Irradiance transmitted to the PV cells in units of W/m**2. To be
+        Irradiance transmitted to the PV cells. To be
         fully consistent with PVWatts, the user must have already
         applied angle of incidence losses, but not soiling, spectral,
-        etc.
+        etc. [W/m^2]
     temp_cell: numeric
-        Cell temperature in degrees C.
+        Cell temperature [C].
     pdc0: numeric
-        Power of the modules at 1000 W/m2 and cell reference temperature.
+        Power of the modules at 1000 W/m^2 and cell reference temperature. [W]
     gamma_pdc: numeric
-        The temperature coefficient in units of 1/C. Typically -0.002 to
-        -0.005 per degree C.
+        The temperature coefficient of power. Typically -0.002 to
+        -0.005 per degree C. [1/C]
     temp_ref: numeric, default 25.0
         Cell reference temperature. PVWatts defines it to be 25 C and
-        is included here for flexibility.
+        is included here for flexibility. [C]
 
     Returns
     -------

pvlib/tests/test_modelchain.py

@@ -1180,6 +1180,25 @@ def test_dc_model_user_func(pvwatts_dc_pvwatts_ac_system, location, weather,
     assert not mc.results.ac.empty
 
 
+def test_pvwatts_dc_multiple_strings(pvwatts_dc_pvwatts_ac_system, location,
+                                     weather, mocker):
+    system = pvwatts_dc_pvwatts_ac_system
+    m = mocker.spy(system, 'scale_voltage_current_power')
+    mc1 = ModelChain(system, location,
+                     aoi_model='no_loss', spectral_model='no_loss')
+    mc1.run_model(weather)
+    assert m.call_count == 1
+    system.arrays[0].modules_per_string = 2
+    mc2 = ModelChain(system, location,
+                     aoi_model='no_loss', spectral_model='no_loss')
+    mc2.run_model(weather)
+    assert isinstance(mc2.results.ac, (pd.Series, pd.DataFrame))
+    assert not mc2.results.ac.empty
+    expected = pd.Series(data=[2., np.nan], index=mc2.results.dc.index,
+                         name='p_mp')
+    assert_series_equal(mc2.results.dc / mc1.results.dc, expected)
+
+
 def acdc(mc):
     mc.results.ac = mc.results.dc