Add transposition gain gallery example (pvlib#979)

* add transposition gain example

* whatsnew

* review suggestions

* flat -> horizontal

Author: kandersolar

docs/examples/plot_transposition_gain.py

@@ -0,0 +1,122 @@
+"""
+Modeling Transposition Gain
+===========================
+
+Calculating the gain in insolation of a tilted module over a flat module.
+"""
+
+# %%
+# This example shows how to evaluate the transposition gain of a racking
+# strategy.  The transposition gain is the additional insolation collected
+# by orienting at a tilt instead of horizontal; using PV modeling lingo, it's
+# the increase in POA (plane of array) insolation over GHI (global horizontal
+# irradiance) insolation.
+#
+# This example uses a TMY dataset and the
+# :py:meth:`pvlib.irradiance.get_total_irradiance` function to transpose
+# irradiance components to POA irradiance for various fixed tilts.  It also
+# models a single-axis tracking system for comparison. The monthly POA
+# insolation is calculated for each strategy to show how orientation affects
+# seasonal irradiance collection.
+
+import pvlib
+from pvlib import location
+from pvlib import irradiance
+from pvlib import tracking
+from pvlib.iotools import read_tmy3
+import pandas as pd
+from matplotlib import pyplot as plt
+import pathlib
+
+# get full path to the data directory
+DATA_DIR = pathlib.Path(pvlib.__file__).parent / 'data'
+
+# get TMY3 dataset
+tmy, metadata = read_tmy3(DATA_DIR / '723170TYA.CSV', coerce_year=1990)
+# TMY3 datasets are right-labeled (AKA "end of interval") which means the last
+# interval of Dec 31, 23:00 to Jan 1 00:00 is labeled Jan 1 00:00. When rolling
+# up hourly irradiance to monthly insolation, a spurious January value is
+# calculated from that last row, so we'll just go ahead and drop it here:
+tmy = tmy.iloc[:-1, :]
+
+# create location object to store lat, lon, timezone
+location = location.Location.from_tmy(metadata)
+
+# calculate the necessary variables to do transposition.  Note that solar
+# position doesn't depend on array orientation, so we just calculate it once.
+# Note also that TMY datasets are right-labeled hourly intervals, e.g. the
+# 10AM to 11AM interval is labeled 11.  We should calculate solar position in
+# the middle of the interval (10:30), so we subtract 30 minutes:
+times = tmy.index - pd.Timedelta('30min')
+solar_position = location.get_solarposition(times)
+# but remember to shift the index back to line up with the TMY data:
+solar_position.index += pd.Timedelta('30min')
+
+
+# create a helper function to do the transposition for us
+def calculate_poa(tmy, solar_position, surface_tilt, surface_azimuth):
+    # Use the get_total_irradiance function to transpose the irradiance
+    # components to POA irradiance
+    poa = irradiance.get_total_irradiance(
+        surface_tilt=surface_tilt,
+        surface_azimuth=surface_azimuth,
+        dni=tmy['DNI'],
+        ghi=tmy['GHI'],
+        dhi=tmy['DHI'],
+        solar_zenith=solar_position['apparent_zenith'],
+        solar_azimuth=solar_position['azimuth'],
+        model='isotropic')
+    return poa['poa_global']  # just return the total in-plane irradiance
+
+
+# create a dataframe to keep track of our monthly insolations
+df_monthly = pd.DataFrame()
+
+# fixed-tilt:
+for tilt in range(0, 50, 10):
+    # we will hardcode azimuth=180 (south) for all fixed-tilt cases
+    poa_irradiance = calculate_poa(tmy, solar_position, tilt, 180)
+    column_name = "FT-{}".format(tilt)
+    # TMYs are hourly, so we can just sum up irradiance [W/m^2] to get
+    # insolation [Wh/m^2]:
+    df_monthly[column_name] = poa_irradiance.resample('m').sum()
+
+# single-axis tracking:
+orientation = tracking.singleaxis(solar_position['apparent_zenith'],
+                                  solar_position['azimuth'],
+                                  axis_tilt=0,  # flat array
+                                  axis_azimuth=180,  # south-facing azimuth
+                                  max_angle=60,  # a common maximum rotation
+                                  backtrack=True,  # backtrack for a c-Si array
+                                  gcr=0.4)  # a common ground coverage ratio
+
+poa_irradiance = calculate_poa(tmy,
+                               solar_position,
+                               orientation['surface_tilt'],
+                               orientation['surface_azimuth'])
+df_monthly['SAT-0.4'] = poa_irradiance.resample('m').sum()
+
+# calculate the percent difference from GHI
+ghi_monthly = tmy['GHI'].resample('m').sum()
+df_monthly = 100 * (df_monthly.divide(ghi_monthly, axis=0) - 1)
+
+df_monthly.plot()
+plt.xlabel('Month of Year')
+plt.ylabel('Monthly Transposition Gain [%]')
+plt.show()
+
+
+# %%
+# Note that in summer, steeper tilts actually collect less insolation than
+# flatter tilts because the sun is so high in the sky at solar noon.  However,
+# the steeper tilts significantly increase insolation capture in winter when
+# the sun is lower in the sky.  In contrast to the highly seasonal gain shown
+# by fixed tilts, the tracker system shows a much more consistent gain
+# year-round.
+#
+# Because the seasonality of the fixed-tilt transposition gain is driven by
+# solar position angles, the relative behavior of different orientations will
+# be different for different locations.  For example, a common rule of thumb
+# (considered somewhat outdated today) used to be to set tilt equal to the
+# latitude of the system location.  At higher latitudes, the sun doesn't get
+# as high in the sky, so steeper tilts make more sense.

docs/sphinx/source/whatsnew/v0.8.0.rst

@@ -32,6 +32,7 @@ Documentation
 * Clarify units for heat loss factors in
   :py:func:`pvlib.temperature.pvsyst_cell` and
   :py:func:`pvlib.temperature.faiman`. (:pull:`960`)
+* Add a transposition gain example to the gallery.  (:pull:`979`)
 * Add a gallery example of calculating diffuse IAM using
   :py:func:`pvlib.iam.marion_diffuse`. (:pull:`984`)