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.
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.
python
import pandas as pd import matplotlib.pyplot as plt
# seaborn makes the plots look nicer import seaborn as sns sns.set_color_codes()
times = pd.DatetimeIndex(start='2015', end='2016', freq='1h')
# very approximate # latitude, longitude, name, altitude coordinates = [(30, -110, 'Tucson', 700), (35, -105, 'Albuquerque', 1500), (40, -120, 'San Francisco', 10), (50, 10, 'Berlin', 34)]
import pvlib
# get the module and inverter specifications from SAM sandia_modules = pvlib.pvsystem.retrieve_sam('SandiaMod') sapm_inverters = pvlib.pvsystem.retrieve_sam('sandiainverter') module = sandia_modules['Canadian_Solar_CS5P_220M___2009'] inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014']
# specify constant ambient air temp and wind for simplicity temp_air = 20 wind_speed = 0
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:
python
- system = {'module': module, 'inverter': inverter,
'surface_azimuth': 180}
energies = {} 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
energies = pd.Series(energies)
# based on the parameters specified above, these are in W*hrs print(energies.round(0))
energies.plot(kind='bar', rot=0) @savefig proc-energies.png width=6in plt.ylabel('Yearly energy yield (W hr)')
pvlib-python provides a :py~pvlib.modelchain.basic_chain function that implements much of the code above. Use this function with a full understanding of what it is doing internally!
python
from pvlib.modelchain import basic_chain
energies = {} for latitude, longitude, name, altitude in coordinates: dc, ac = basic_chain(times, latitude, longitude, module, inverter, altitude=altitude, orientation_strategy='south_at_latitude_tilt') 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))
energies.plot(kind='bar', rot=0) @savefig basic-chain-energies.png width=6in plt.ylabel('Yearly energy yield (W hr)')
The first object oriented paradigm uses a model where a :py~pvlib.pvsystem.PVSystem object represents an assembled collection of modules, inverters, etc., a :py~pvlib.location.Location object represents a particular place on the planet, and a :py~pvlib.modelchain.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 :py~pvlib.location.Location, :py~pvlib.pvsystem.PVSystem, and :py~pvlib.modelchain.ModelChain objects to accomplish our system modeling goal:
python
from pvlib.pvsystem import PVSystem from pvlib.location import Location from pvlib.modelchain import ModelChain
- system = PVSystem(module_parameters=module,
inverter_parameters=inverter)
energies = {} for latitude, longitude, name, altitude in coordinates: location = Location(latitude, longitude, name=name, altitude=altitude) # very experimental 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(times) annual_energy = mc.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))
energies.plot(kind='bar', rot=0) @savefig modelchain-energies.png width=6in plt.ylabel('Yearly energy yield (W hr)')
The second object oriented paradigm uses a model where a :py~pvlib.pvsystem.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 :py~pvlib.pvsystem.LocalizedPVSystem object to accomplish our modeling goal:
python
from pvlib.pvsystem import LocalizedPVSystem
energies = {} 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
energies = pd.Series(energies)
# based on the parameters specified above, these are in W*hrs print(energies.round(0))
energies.plot(kind='bar', rot=0) @savefig localized-pvsystem-energies.png width=6in plt.ylabel('Yearly energy yield (W hr)')
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.
The best way to get support is to make an issue on our GitHub issues page .
We're so glad you asked! Please see our wiki for information and instructions on how to contribute. We really appreciate it!
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.