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wholmgren merged 2 commits into from
Jan 26, 2021
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Update intro tutorial (#1116) #1144

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0699bea
Update intro tutorial (#1116)
Peque Jan 24, 2021
c86a71c
fixup! Update intro tutorial (#1116)
Peque Jan 25, 2021
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146 changes: 97 additions & 49 deletions docs/sphinx/source/introtutorial.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -27,19 +27,18 @@ configuration at a handful of sites listed below.

.. ipython:: python

import pvlib
import pandas as pd
import matplotlib.pyplot as plt

naive_times = pd.date_range(start='2015', end='2016', freq='1h')

# very approximate
# latitude, longitude, name, altitude, timezone
coordinates = [(30, -110, 'Tucson', 700, 'Etc/GMT+7'),
(35, -105, 'Albuquerque', 1500, 'Etc/GMT+7'),
(40, -120, 'San Francisco', 10, 'Etc/GMT+8'),
(50, 10, 'Berlin', 34, 'Etc/GMT-1')]

import pvlib
coordinates = [
(30, -110, 'Tucson', 700, 'Etc/GMT+7'),
(35, -105, 'Albuquerque', 1500, 'Etc/GMT+7'),
(40, -120, 'San Francisco', 10, 'Etc/GMT+8'),
(50, 10, 'Berlin', 34, 'Etc/GMT-1'),
]

# get the module and inverter specifications from SAM
sandia_modules = pvlib.pvsystem.retrieve_sam('SandiaMod')
Expand All @@ -48,9 +47,32 @@ configuration at a handful of sites listed below.
inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208__208V_']
temperature_model_parameters = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']

# specify constant ambient air temp and wind for simplicity
temp_air = 20
wind_speed = 0

In order to retrieve meteorological data for the simulation, we can make use of
the :ref:`iotools` module. In this example we will be using PVGIS, one of the
data sources available, to retrieve a Typical Meteorological Year (TMY) which
includes irradiation, temperature and wind speed.

.. note:: PVGIS uses different naming conventions, so it is required to rename
the weather data variables before using them. Data is already UTC-localized,
so conversion to local timezone is optional.

.. ipython:: python

variables_translation = {
"Gb(n)": "dni",
"G(h)": "ghi",
"Gd(h)": "dhi",
"T2m": "temp_air",
"WS10m": "wind_speed",
}
tmys = []
for location in coordinates:
latitude, longitude, name, altitude, timezone = location
weather = pvlib.iotools.get_pvgis_tmy(latitude, longitude)[0]
weather = weather.rename(columns=variables_translation)
weather.index.name = "utc_time"
tmys.append(weather)


Procedural
Expand All @@ -69,41 +91,60 @@ to accomplish our system modeling goal:

energies = {}

for latitude, longitude, name, altitude, timezone in coordinates:
times = naive_times.tz_localize(timezone)
for location, weather in zip(coordinates, tmys):
latitude, longitude, name, altitude, timezone = location
system['surface_tilt'] = latitude
solpos = pvlib.solarposition.get_solarposition(times, latitude, longitude)
dni_extra = pvlib.irradiance.get_extra_radiation(times)
solpos = pvlib.solarposition.get_solarposition(
time=weather.index,
latitude=latitude,
longitude=longitude,
altitude=altitude,
temperature=weather["temp_air"],
pressure=pvlib.atmosphere.alt2pres(altitude),
)
dni_extra = pvlib.irradiance.get_extra_radiation(weather.index)
airmass = pvlib.atmosphere.get_relative_airmass(solpos['apparent_zenith'])
pressure = pvlib.atmosphere.alt2pres(altitude)
am_abs = pvlib.atmosphere.get_absolute_airmass(airmass, pressure)
tl = pvlib.clearsky.lookup_linke_turbidity(times, latitude, longitude)
cs = pvlib.clearsky.ineichen(solpos['apparent_zenith'], am_abs, tl,
dni_extra=dni_extra, altitude=altitude)
aoi = pvlib.irradiance.aoi(system['surface_tilt'], system['surface_azimuth'],
solpos['apparent_zenith'], solpos['azimuth'])
total_irrad = pvlib.irradiance.get_total_irradiance(system['surface_tilt'],
system['surface_azimuth'],
solpos['apparent_zenith'],
solpos['azimuth'],
cs['dni'], cs['ghi'], cs['dhi'],
dni_extra=dni_extra,
model='haydavies')
tcell = pvlib.temperature.sapm_cell(total_irrad['poa_global'],
temp_air, wind_speed,
**temperature_model_parameters)
aoi = pvlib.irradiance.aoi(
system['surface_tilt'],
system['surface_azimuth'],
solpos["apparent_zenith"],
solpos["azimuth"],
)
total_irradiance = pvlib.irradiance.get_total_irradiance(
system['surface_tilt'],
system['surface_azimuth'],
solpos['apparent_zenith'],
solpos['azimuth'],
weather['dni'],
weather['ghi'],
weather['dhi'],
dni_extra=dni_extra,
model='haydavies',
)
cell_temperature = pvlib.temperature.sapm_cell(
total_irradiance['poa_global'],
weather["temp_air"],
weather["wind_speed"],
**temperature_model_parameters,
)
effective_irradiance = pvlib.pvsystem.sapm_effective_irradiance(
total_irrad['poa_direct'], total_irrad['poa_diffuse'],
am_abs, aoi, module)
dc = pvlib.pvsystem.sapm(effective_irradiance, tcell, module)
total_irradiance['poa_direct'],
total_irradiance['poa_diffuse'],
am_abs,
aoi,
module,
)
dc = pvlib.pvsystem.sapm(effective_irradiance, cell_temperature, module)
ac = pvlib.inverter.sandia(dc['v_mp'], dc['p_mp'], inverter)
annual_energy = ac.sum()
energies[name] = annual_energy

energies = pd.Series(energies)

# based on the parameters specified above, these are in W*hrs
print(energies.round(0))
print(energies)

energies.plot(kind='bar', rot=0)
@savefig proc-energies.png width=6in
Expand Down Expand Up @@ -150,28 +191,35 @@ by examining the parameters defined for the module.
from pvlib.location import Location
from pvlib.modelchain import ModelChain

system = PVSystem(module_parameters=module,
inverter_parameters=inverter,
temperature_model_parameters=temperature_model_parameters)
system = PVSystem(
module_parameters=module,
inverter_parameters=inverter,
temperature_model_parameters=temperature_model_parameters,
)

energies = {}
for latitude, longitude, name, altitude, timezone in coordinates:
times = naive_times.tz_localize(timezone)
location = Location(latitude, longitude, name=name, altitude=altitude,
tz=timezone)
weather = location.get_clearsky(times)
mc = ModelChain(system, location,
orientation_strategy='south_at_latitude_tilt')
# model results (ac, dc) and intermediates (aoi, temps, etc.)
# assigned as mc object attributes
mc.run_model(weather)
annual_energy = mc.results.ac.sum()
for location, weather in zip(coordinates, tmys):
latitude, longitude, name, altitude, timezone = location
location = Location(
latitude,
longitude,
name=name,
altitude=altitude,
tz=timezone,
)
mc = ModelChain(
system,
location,
orientation_strategy='south_at_latitude_tilt',
)
results = mc.run_model(weather)
annual_energy = results.ac.sum()
energies[name] = annual_energy

energies = pd.Series(energies)

# based on the parameters specified above, these are in W*hrs
print(energies.round(0))
print(energies)

energies.plot(kind='bar', rot=0)
@savefig modelchain-energies.png width=6in
Expand Down
3 changes: 3 additions & 0 deletions docs/sphinx/source/whatsnew/v0.9.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -100,6 +100,9 @@ Testing
Documentation
~~~~~~~~~~~~~

* Update intro tutorial to highlight the use of historical meteorological data
and to make the procedural and OO results match exactly.

Requirements
~~~~~~~~~~~~
* ``dataclasses`` is required for python 3.6
Expand Down