Package Overview¶
Introduction¶
The core mission of pvlib-python is to provide open, reliable, interoperable, and benchmark implementations of PV system models.
There are at least as many opinions about how to model PV systems as there are modelers of PV systems, so pvlib-python provides several modeling paradigms.
Modeling paradigms¶
The backbone of pvlib-python is well-tested procedural code that implements PV system models. pvlib-python also provides a collection of classes for users that prefer object-oriented programming. These classes can help users keep track of data in a more organized way, provide some “smart” functions with more flexible inputs, and simplify the modeling process for common situations. The classes do not add any algorithms beyond what’s available in the procedural code, and most of the object methods are simple wrappers around the corresponding procedural code.
Let’s use each of these pvlib modeling paradigms to calculate the yearly energy yield for a given hardware configuration at a handful of sites listed below.
In [1]: import pandas as pd
In [2]: import matplotlib.pyplot as plt
# seaborn makes the plots look nicer
In [3]: import seaborn as sns
In [4]: sns.set_color_codes()
In [5]: times = pd.DatetimeIndex(start='2015', end='2016', freq='1h')
# very approximate
# latitude, longitude, name, altitude
In [6]: coordinates = [(30, -110, 'Tucson', 700),
...: (35, -105, 'Albuquerque', 1500),
...: (40, -120, 'San Francisco', 10),
...: (50, 10, 'Berlin', 34)]
...:
In [7]: import pvlib
# get the module and inverter specifications from SAM
In [8]: sandia_modules = pvlib.pvsystem.retrieve_sam('SandiaMod')
In [9]: sapm_inverters = pvlib.pvsystem.retrieve_sam('sandiainverter')
In [10]: module = sandia_modules['Canadian_Solar_CS5P_220M___2009_']
In [11]: inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']
# specify constant ambient air temp and wind for simplicity
In [12]: temp_air = 20
In [13]: wind_speed = 0
Procedural¶
The straightforward procedural code can be used for all modeling steps in pvlib-python.
The following code demonstrates how to use the procedural code to accomplish our system modeling goal:
In [14]: system = {'module': module, 'inverter': inverter,
....: 'surface_azimuth': 180}
....:
In [15]: energies = {}
In [16]: for latitude, longitude, name, altitude in coordinates:
....: system['surface_tilt'] = latitude
....: cs = pvlib.clearsky.ineichen(times, latitude, longitude, altitude=altitude)
....: solpos = pvlib.solarposition.get_solarposition(times, latitude, longitude)
....: dni_extra = pvlib.irradiance.extraradiation(times)
....: dni_extra = pd.Series(dni_extra, index=times)
....: airmass = pvlib.atmosphere.relativeairmass(solpos['apparent_zenith'])
....: pressure = pvlib.atmosphere.alt2pres(altitude)
....: am_abs = pvlib.atmosphere.absoluteairmass(airmass, pressure)
....: aoi = pvlib.irradiance.aoi(system['surface_tilt'], system['surface_azimuth'],
....: solpos['apparent_zenith'], solpos['azimuth'])
....: total_irrad = pvlib.irradiance.total_irrad(system['surface_tilt'],
....: system['surface_azimuth'],
....: solpos['apparent_zenith'],
....: solpos['azimuth'],
....: cs['dni'], cs['ghi'], cs['dhi'],
....: dni_extra=dni_extra,
....: model='haydavies')
....: temps = pvlib.pvsystem.sapm_celltemp(total_irrad['poa_global'],
....: wind_speed, temp_air)
....: dc = pvlib.pvsystem.sapm(module, total_irrad['poa_direct'],
....: total_irrad['poa_diffuse'], temps['temp_cell'],
....: am_abs, aoi)
....: ac = pvlib.pvsystem.snlinverter(inverter, dc['v_mp'], dc['p_mp'])
....: annual_energy = ac.sum()
....: energies[name] = annual_energy
....:
In [17]: energies = pd.Series(energies)
# based on the parameters specified above, these are in W*hrs
In [18]: print(energies.round(0))
Albuquerque 512634
Berlin 399650
San Francisco 458228
Tucson 476996
dtype: float64
In [19]: energies.plot(kind='bar', rot=0)
Out[19]: <matplotlib.axes._subplots.AxesSubplot at 0x7f18b704eb50>
In [20]: plt.ylabel('Yearly energy yield (W hr)')
Out[20]: <matplotlib.text.Text at 0x7f18b7a1e410>
Object oriented (Location, PVSystem, ModelChain)¶
The first object oriented paradigm uses a model where
a PVSystem
object represents an
assembled collection of modules, inverters, etc.,
a Location
object represents a
particular place on the planet,
and a ModelChain
object describes
the modeling chain used to calculate PV output at that Location.
This can be a useful paradigm if you prefer to think about
the PV system and its location as separate concepts or if
you develop your own ModelChain subclasses.
It can also be helpful if you make extensive use of Location-specific
methods for other calculations.
The following code demonstrates how to use
Location
,
PVSystem
, and
ModelChain
objects to accomplish our system modeling goal:
In [21]: from pvlib.pvsystem import PVSystem
In [22]: from pvlib.location import Location
In [23]: from pvlib.modelchain import ModelChain
In [24]: system = PVSystem(module_parameters=module,
....: inverter_parameters=inverter)
....:
In [25]: energies = {}
In [26]: for latitude, longitude, name, altitude in coordinates:
....: location = Location(latitude, longitude, name=name, altitude=altitude)
....: mc = ModelChain(system, location,
....: orientation_strategy='south_at_latitude_tilt')
....: dc, ac = mc.run_model(times)
....: annual_energy = ac.sum()
....: energies[name] = annual_energy
....:
In [27]: energies = pd.Series(energies)
# based on the parameters specified above, these are in W*hrs
In [28]: print(energies.round(0))
Albuquerque 512385
Berlin 399219
San Francisco 458016
Tucson 476795
dtype: float64
In [29]: energies.plot(kind='bar', rot=0)
Out[29]: <matplotlib.axes._subplots.AxesSubplot at 0x7f18b71fdd50>
In [30]: plt.ylabel('Yearly energy yield (W hr)')
Out[30]: <matplotlib.text.Text at 0x7f18b704e850>
Object oriented (LocalizedPVSystem)¶
The second object oriented paradigm uses a model where a
LocalizedPVSystem
represents a
PV system at a particular place on the planet.
This can be a useful paradigm if you’re thinking about
a power plant that already exists.
The following code demonstrates how to use a
LocalizedPVSystem
object to accomplish our modeling goal:
In [31]: from pvlib.pvsystem import LocalizedPVSystem
In [32]: energies = {}
In [33]: for latitude, longitude, name, altitude in coordinates:
....: localized_system = LocalizedPVSystem(module_parameters=module,
....: inverter_parameters=inverter,
....: surface_tilt=latitude,
....: surface_azimuth=180,
....: latitude=latitude,
....: longitude=longitude,
....: name=name,
....: altitude=altitude)
....: clearsky = localized_system.get_clearsky(times)
....: solar_position = localized_system.get_solarposition(times)
....: total_irrad = localized_system.get_irradiance(solar_position['apparent_zenith'],
....: solar_position['azimuth'],
....: clearsky['dni'],
....: clearsky['ghi'],
....: clearsky['dhi'])
....: temps = localized_system.sapm_celltemp(total_irrad['poa_global'],
....: wind_speed, temp_air)
....: aoi = localized_system.get_aoi(solar_position['apparent_zenith'],
....: solar_position['azimuth'])
....: airmass = localized_system.get_airmass(solar_position=solar_position)
....: dc = localized_system.sapm(total_irrad['poa_direct'],
....: total_irrad['poa_diffuse'],
....: temps['temp_cell'],
....: airmass['airmass_absolute'],
....: aoi)
....: ac = localized_system.snlinverter(dc['v_mp'], dc['p_mp'])
....: annual_energy = ac.sum()
....: energies[name] = annual_energy
....:
In [34]: energies = pd.Series(energies)
# based on the parameters specified above, these are in W*hrs
In [35]: print(energies.round(0))
Albuquerque 512601
Berlin 399650
San Francisco 458228
Tucson 476982
dtype: float64
In [36]: energies.plot(kind='bar', rot=0)
Out[36]: <matplotlib.axes._subplots.AxesSubplot at 0x7f18b7110f50>
In [37]: plt.ylabel('Yearly energy yield (W hr)')
Out[37]: <matplotlib.text.Text at 0x7f18b7121490>
User extensions¶
There are many other ways to organize PV modeling code. We encourage you to build on these paradigms and to share your experiences with the pvlib community via issues and pull requests.
Getting support¶
The best way to get support is to make an issue on our GitHub issues page .
How do I contribute?¶
We’re so glad you asked! Please see our wiki for information and instructions on how to contribute. We really appreciate it!
Credits¶
The pvlib-python community thanks Sandia National Lab for developing PVLIB Matlab and for supporting Rob Andrews of Calama Consulting to port the library to Python. Will Holmgren thanks the DOE EERE Postdoctoral Fellowship program for support. The pvlib-python maintainers thank all of pvlib’s contributors of issues and especially pull requests. The pvlib-python community thanks all of the maintainers and contributors to the PyData stack.