reskit.solar

reskit.solar#

Submodules#

Classes#

SolarWorkflowManager

The WorkflowManager class assists with the construction of more specialized WorkflowManagers,

Functions#

`frank_correction_factors`(ghi, dni_extra, times, ...)	Applies the proposed transmissivity-based irradiance corrections to COSMO
`location_to_tilt`(locs[, convention])	Simple system tilt estimator based off latitude and longitude coordinates
`openfield_pv_era5`(placements, era5_path, ...[, ...])	Simulation of an openfield PV openfield system based on ERA5 Data.
`openfield_pv_era5_unvalidated`(**kwargs)	Simulation of an openfield PV openfield system based on ERA5 Data,
`openfield_pv_era5pure`(**kwargs)	Simulation of an openfield PV openfield system based on ERA5 original
`openfield_pv_iconlam`(placements, icon_lam_path[, ...])	Simulation of an openfield PV openfield system based on ICON-LAM Data.
`openfield_pv_merra_ryberg2019`(placements, merra_path, ...)	openfield_pv_merra_ryberg2019(placements, merra_path, global_solar_atlas_ghi_path, module="WINAICO WSx-240P6", elev=300, tracking="fixed",
`openfield_pv_sarah_unvalidated`(placements, sarah_path, ...)	openfield_pv_sarah_unvalidated(placements, sarah_path, era5_path, module="WINAICO WSx-240P6", elev=300, tracking="fixed", inverter=None, inverter_kwargs={}, tracking_args={}, output_netcdf_path=None, output_variables=None)

Package Contents#

reskit.solar.frank_correction_factors(ghi, dni_extra, times, solar_elevation)#

Applies the proposed transmissivity-based irradiance corrections to COSMO data based on Frank et al.

TODO: Add citation to Frank!

reskit.solar.location_to_tilt(locs, convention='Ryberg2020', **kwargs)#

Simple system tilt estimator based off latitude and longitude coordinates

Parameters:

locs (geokit.LocationSet or iterable of (lon,lat) pairs) – The locations at which to estimate system tilt angle
convention (str, optional) –
The calculation method used to suggest system tilts Options are:
- ”Ryberg2020”
- A string consumable by ‘eval’
- Can use the variable ‘latitude’
- Ex. “latitude*0.76”
- A path to a raster file
kwargs (Optional keyword arguments to use in geokit.raster.interpolateValues(...).) – Only applies when convention is a path to a raster file

Returns:

Suggested tilt angle at each of the provided locs. Has the same length as the number of locs.

Return type:

np.ndarray

Notes

“Ryberg2020”: When convention equals “Ryberg2020”, the following equation is followed:

\[42.327719357601396 * arctan( 1.5 * abs(latitude) )\]

class reskit.solar.SolarWorkflowManager(placements)#

Bases: reskit.workflow_manager.WorkflowManager

The WorkflowManager class assists with the construction of more specialized WorkflowManagers, such as the WindWorkflowManager or the SolarWorkflowManager. In addition to providing the general structure for simulation workflow management, the WorkflowManager also defines functionalities which should be common across all WorkflowManagers.

This includes:

Basic initialization
Time domain management
Reading weather data
Adjusting variables by a long-run-average value
Applying simple loss factors
Saving the state of WorkflowManagers to XArray datasets, either in memory or on disc

Initialization:#

WorkflowManager( placements )

__init_(self, placements)

Initialization of an instance of the generic SolarWorkflowManager class.

param placements:: The locations that the simulation should be run for. Columns must include “lon”, “lat”
type placements:: pandas Dataframe
rtype:: SolarWorkflorManager

_time_sel_ = None#

_time_index_ = None#

module = None#

estimate_tilt_from_latitude(convention)#

estimate_tilt_from_latitude(self, convention)

Estimates the tilt of the solar panels based on the latitude of the placements of the instance.

Parameters:: convention (str, optional) – The calculation method used to suggest system tilts. Option 1 of convention is “Ryberg2020”. Option 2 of convention is a string consumable by ‘eval’. This string can use the variable latitude. For example “latitude*0.76”. Option 3 of convention is a path to a rasterfile. To get more information check out reskit.solar.location_to_tilt for more information.
Return type:: Returns a reference to the invoking SolarWorkflowManager object

estimate_azimuth_from_latitude()#

estimate_azimuth_from_latitude(self)

Estimates the azimuth of the placements of the instance. For a positive latitude the azimuth is set to 180. For a negative latitude the azimuth is set to 0.

Parameters:: None
Return type:: Returns a reference to the invoking SolarWorkflowManager object

apply_elevation(elev, fallback_elev=0)#

apply_elevation(self)

Adds an elevation (name: ‘elev’) column to the placements data frame.

Parameters:

elev (str, int, iterable) – If a string is given it must be a path to a rasterfile including the elevations. If an iterable is given it has to include the elevations at each location and be of equal length to self.placements dataframe. If an integer is given, it will be applied to all locations equally.
fallback_elev (int, optional) – The fallback value that will be used in case that elev is a raster path and the extraction of the elevation from raster fails (applied only to no-data locations). By default 0.

Return type:

Returns a reference to the invoking SolarWorkflowManager object

determine_solar_position(lon_rounding=1, lat_rounding=1, elev_rounding=-2)#

determine_solar_position(self, lon_rounding=1, lat_rounding=1, elev_rounding=-2)

Calculates azimuth and apparent zenith for each location using the pvlib function pvlib.solarposition.spa_python() [1]. Adds azimuth and apparent zenit to the sim_data dictionary.

Parameters:

lon_rounding (int, optional) – Decimal places that the longitude should be rounded to. Default is 1.
lat_rounding (int, optional) – Decimal places that the latitude should be rounded to. Default is 1.
elev_rounding (int, optional) – Decimal places that the elevation should be rounded to. Default is -2.

Return type:

Returns a reference to the invoking SolarWorkflowManager object

Notes

Required columns in the placements dataframe to use this functions are ‘lon’, ‘lat’ and ‘elev’. Required data in the sim_data dictionary are ‘surface_pressure’ and ‘surface_air_temperature’.

References

[1] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.solarposition.spa_python.html

[2] I. Reda and A. Andreas, Solar position algorithm for solar: radiation applications. Solar Energy, vol. 76, no. 5, pp. 577-589, 2004.
[3] I. Reda and A. Andreas, Corrigendum to Solar position algorithm for: solar radiation applications. Solar Energy, vol. 81, no. 6, p. 838, 2007.
[4] USNO delta T:: http://www.usno.navy.mil/USNO/earth-orientation/eo-products/long-term

filter_positive_solar_elevation()#

filter_positive_solar_elevation(self)

Filters positive solar elevations so that future operations are only executed for time steps when the sun is above (or at least near-to) the horizon

Parameters:: None
Return type:: Returns a reference to the invoking SolarWorkflowManager object

Notes

Required data in the sim_data dictionary are ‘apparent_solar_zenith’.

determine_extra_terrestrial_irradiance(**kwargs)#

determine_extra_terrestrial_irradiance(self, **kwargs)

Determines extra terrestrial irradiance using the pvlib.irradiance.get_extra_radiation() function [1].

Parameters:: None
Return type:: Returns a reference to the invoking SolarWorkflowManager object.

References

[1] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.irradiance.get_extra_radiation.html

[2] M. Reno, C. Hansen, and J. Stein, “Global Horizontal Irradiance Clear Sky Models: Implementation and Analysis”, Sandia National Laboratories, SAND2012-2389, 2012.

[3] <http://solardat.uoregon.edu/SolarRadiationBasics.html>, Eqs. SR1 and SR2

[4] Partridge, G. W. and Platt, C. M. R. 1976. Radiative Processes in Meteorology and Climatology.

[5] Duffie, J. A. and Beckman, W. A. 1991. Solar Engineering of Thermal Processes, 2nd edn. J. Wiley and Sons, New York.

[6] ASCE, 2005. The ASCE Standardized Reference Evapotranspiration Equation, Environmental and Water Resources Institute of the American Civil Engineers, Ed. R. G. Allen et al.

determine_air_mass(model='kastenyoung1989')#

determine_air_mass(self, model=’kastenyoung1989’)

Determines air mass using the pvlib function pvlib.atmosphere.get_relative_airmass() [1].

Parameters:

model (str, optional) –

default ‘kastenyoung1989’ [1]

’simple’ - secant(apparent zenith angle) - Note that this gives -inf at zenith=90 [2] ‘kasten1966’ - See reference [2] - requires apparent sun zenith [2] ‘youngirvine1967’ - See reference [3] - requires true sun zenith [2] ‘kastenyoung1989’ - See reference [4] - requires apparent sun zenith [2] ‘gueymard1993’ - See reference [5] - requires apparent sun zenith [2] ‘young1994’ - See reference [6] - requires true sun zenith [2] ‘pickering2002’ - See reference [7] - requires apparent sun zenith [2]

Return type:

Nothing is returned.

Notes

Required data in the sim_data dictionary are ‘apparent_solar_zenith’.

References

[1] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.atmosphere.get_relative_airmass.html

[2] Fritz Kasten. “A New Table and Approximation Formula for the Relative Optical Air Mass”. Technical Report 136, Hanover, N.H.: U.S. Army Material Command, CRREL.

[3] A. T. Young and W. M. Irvine, “Multicolor Photoelectric Photometry of the Brighter Planets,” The Astronomical Journal, vol. 72, pp. 945-950, 1967.

[4] Fritz Kasten and Andrew Young. “Revised optical air mass tables and approximation formula”. Applied Optics 28:4735-4738

[5] C. Gueymard, “Critical analysis and performance assessment of clear sky solar irradiance models using theoretical and measured data,” Solar Energy, vol. 51, pp. 121-138, 1993.

[6] A. T. Young, “AIR-MASS AND REFRACTION,” Applied Optics, vol. 33, pp. 1108-1110, Feb 1994.

[7] Keith A. Pickering. “The Ancient Star Catalog”. DIO 12:1, 20,

[8] Matthew J. Reno, Clifford W. Hansen and Joshua S. Stein, “Global Horizontal Irradiance Clear Sky Models: Implementation and Analysis” Sandia Report, (2012).

apply_DIRINT_model(use_pressure=False, use_dew_temperature=False)#

apply_DIRINT_model(self, use_pressure=False, use_dew_temperature=False)

Determines direct normal irradiance (DNI) using the pvlib.irradiance.dirint() function [1].

Parameters:

use_pressure (boolian, optional) – Default: False
use_dew_temperature (boolian, optional) – Default: False

Return type:

Returns a reference to the invoking SolarWorkflowManager object.

Notes

Required data in the sim_data dictionary are ‘global_horizontal_irradiance’, ‘surface_pressure’, ‘surface_dew_temperature’, ‘apparent_solar_zenith’, ‘air_mass’ and ‘extra_terrestrial_irradiance’.

References

[1] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.irradiance.dirint.html

[2] Perez, R., P. Ineichen, E. Maxwell, R. Seals and A. Zelenka, (1992). “Dynamic Global-to-Direct Irradiance Conversion Models”. ASHRAE Transactions-Research Series, pp. 354-369

[3] Maxwell, E. L., “A Quasi-Physical Model for Converting Hourly Global Horizontal to Direct Normal Insolation”, Technical Report No. SERI/TR-215-3087, Golden, CO: Solar Energy Research Institute, 1987.

diffuse_horizontal_irradiance_from_trigonometry()#

diffuse_horizontal_irradiance_from_trigonometry(self)

Calculates the diffuse horizontal irradiance from global horizontal irradiance, direct normal irradiance and apparent zenith.

[TODO: Add a simple equation such as the one given in ‘direct_normal_irradiance_from_trigonometry’]

Parameters:: None
Return type:: Returns a reference to the invoking SolarWorkflowManager object.

Notes

Required data in the sim_data dictionary are ‘global_horizontal_irradiance’, ‘direct_normal_irradiance’ and ‘apparent_solar_zenith’.

direct_normal_irradiance_from_trigonometry()#

direct_normal_irradiance_from_trigonometry(self):

Parameters:: None
Return type:: Returns a reference to the invoking SolarWorkflowManager object.

Notes

Required columns in the placements dataframe to use this functions are ‘lon’, ‘lat’ and ‘elev’. Required data in the sim_data dictionary are ‘direct_horizontal_irradiance’ and ‘apparent_solar_zenith’.

Calculates the direct normal irradiance from the following equation:: \[dir_nor_irr = dir_hor_irr / cos( solar_zenith )\]

Where: dir_nor_irr -> The direct irradiance on the normal plane dir_hor_irr -> The direct irradiance on the horizontal plane solar_zenith -> The solar zenith angle in radians

permit_single_axis_tracking(max_angle=90, backtrack=True, gcr=2.0 / 7.0)#

permit_single_axis_tracking(self, max_angle=90, backtrack=True, gcr=2.0 / 7.0)

Permits single axis tracking in the simulation using the pvlib.tracking.singleaxis() function [1].

Parameters:

max_angle (float, optional) – default 90 A value denoting the maximum rotation angle, in decimal degrees, of the one-axis tracker from its horizontal position (horizontal if axis_tilt = 0). A max_angle of 90 degrees allows the tracker to rotate to a vertical position to point the panel towards a horizon. max_angle of 180 degrees allows for full rotation [1].
backtrack (bool, optional) – default True Controls whether the tracker has the capability to “backtrack” to avoid row-to-row shading. False denotes no backtrack capability. True denotes backtrack capability [1].
gcr (float, optional) – default 2.0/7.0 A value denoting the ground coverage ratio of a tracker system which utilizes backtracking; i.e. the ratio between the PV array surface area to total ground area. A tracker system with modules 2 meters wide, centered on the tracking axis, with 6 meters between the tracking axes has a gcr of 2/6=0.333. If gcr is not provided, a gcr of 2/7 is default. gcr must be <=1 [1].

Return type:

Returns a reference to the invoking SolarWorkflowManager object.

Notes

Required columns in the placements dataframe to use this functions are ‘lon’, ‘lat’, ‘elev’, ‘tilt’ and ‘azimuth’. Required data in the sim_data dictionary are ‘apparent_solar_zenith’ and ‘solar_azimuth’.

References

[1] https://wholmgren-pvlib-python-new.readthedocs.io/en/doc-reorg2/generated/tracking/pvlib.tracking.singleaxis.html

[2] Lorenzo, E et al., 2011, “Tracking and back-tracking”, Prog. in Photovoltaics: Research and Applications, v. 19, pp. 747-753.

determine_angle_of_incidence()#

determine_angle_of_incidence(self)

Determines the angle of incidence [TODO: credit the PVLib function as you’ve done in previous examples].

Parameters:: None
Return type:: Returns a reference to the invoking SolarWorkflowManager object.

Notes

Required data in the sim_data dictionary are ‘apparent_solar_zenith’ and ‘solar_azimuth’.

estimate_plane_of_array_irradiances(transposition_model='perez', albedo=0.25, **kwargs)#

estimate_plane_of_array_irradiances(self, transposition_model=”perez”, albedo=0.25, **kwargs)

Estimates the plane of array irradiance using the pvlib.irradiance.get_total_irradiance() function [1].

Parameters:

transportion_model (str, optional) – default “perez”
albedo (numeric, optional) – default 0.25 Surface albedo [1].

Return type:

Returns a reference to the invoking SolarWorkflowManager object.

Notes

Required data in the sim_data dictionary are ‘apparent_solar_zenith’, ‘solar_azimuth’, ‘direct_normal_irradiance’, ‘global_horizontal_irradiance’, ‘diffuse_horizontal_irradiance’, ‘extra_terrestrial_irradiance’ and ‘air_mass’.

References

[1] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.irradiance.get_total_irradiance.html

_fix_bad_plane_of_array_values()#

cell_temperature_from_sapm(mounting='glass_open_rack')#

cell_temperature_from_sapm(self, mounting=”glass_open_rack”)

Calculates the cell temperature based on the pvlib.temperature.sapm_cell() function [1].

Parameters:: mounting (str) – Options: “glass_open_rack” [1] “glass_close_roof” [1] “polymer_open_rack” [1] “polymer_insulated_back” [1]
Return type:: Returns a reference to the invoking SolarWorkflowManager object.

Notes

Required data in the sim_data dictionary are ‘surface_wind_speed’, ‘surface_air_temperature’ and ‘poa_global’.

References

[1] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.temperature.sapm_cell.html

apply_angle_of_incidence_losses_to_poa()#

apply_angle_of_incidence_losses_to_poa(self)

Applies the angle of incidence losses to the plane-of-array irradiance using the pvlib.pvsystem.iam.physical() function [1].

Parameters:: None
Return type:: Returns a reference to the invoking SolarWorkflowManager object.

Notes

Required data in the sim_data dictionary are ‘poa_direct’, ‘poa_ground_diffuse’ and ‘poa_sky_diffuse’.

References

[1] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.iam.physical.html

configure_cec_module(module='WINAICO WSx-240P6', tech_year=2050)#

configure_cec_module(self, module=”WINAICO WSx-240P6”)

Configures CEC of a module based on the outputs of the pvlib.pvsystem.retrieve_sam() function [1].

Parameters:

module (str or dict) –
Must be one of:
- A module found in the pvlib.pvsystem.retrieve_sam(“CECMod”) database
- ”WINAICO WSx-240P6” -> Good for open-field applications
- ”LG Electronics LG370Q1C-A5” -> Good for rooftop applications
- A dict containing a set of module parameters, including:
  T_NOCT, A_c, N_s, I_sc_ref, V_oc_ref, I_mp_ref, V_mp_ref, alpha_sc, beta_oc, a_ref, I_L_ref, I_o_ref, R_s, R_sh_ref, Adjust, gamma_r, PTC
tech_year (int, optional) – If given in combination with the projected module str names “WINAICO WSx-240P6” or “LG Electronics LG370Q1C-A5”, the effifiency will be scaled linearly to the given year. Must then be between year of market comparison in analysis (2019) and 2050. Will be ignored when non-projected existing module names or specific parameters are given, can then be None. By default 2050.

Return type:

Returns a reference to the invoking SolarWorkflowManager object

References

[1] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.pvsystem.retrieve_sam.html

simulate_with_interpolated_single_diode_approximation(module='WINAICO WSx-240P6', tech_year=2050)#

simulate_with_interpolated_single_diode_approximation(self, module=”WINAICO WSx-240P6”)

Does the simulation with an interpolated single diode approximation using the pvlib.pvsystem.calcparams_desoto() [1] function and the pvlib.pvsystem.singlediode() [2] function.

Parameters:

module (str) –
Must be one of:
- A module found in the pvlib.pvsystem.retrieve_sam(“CECMod”) database
- ”WINAICO WSx-240P6” -> Good for open-field applications
- ”LG Electronics LG370Q1C-A5” -> Good for rooftop applications
tech_year (int, optional) – If given in combination with the projected module str names “WINAICO WSx-240P6” or “LG Electronics LG370Q1C-A5”, the effifiency will be scaled linearly to the given year. Must then be between year of market comparison in analysis (2019) and 2050. Will be ignored when non-projected existing module names or specific parameters are given, can then be None. By default 2050.

Return type:

Returns a reference to the invoking SolarWorkflowManager object.

Notes

Required columns in the placements dataframe to use this functions are ‘lon’, ‘lat’, ‘elev’, ‘tilt’ and ‘azimuth’. Required data in the sim_data dictionary are ‘poa_global’ and ‘cell_temperature’.

References

[1] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.pvsystem.calcparams_desoto.html

[2] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.pvsystem.singlediode.html

[3] (1, 2) W. De Soto et al., “Improvement and validation of a model for photovoltaic array performance”, Solar Energy, vol 80, pp. 78-88, 2006.

[4] System Advisor Model web page. https://sam.nrel.gov.

[5] A. Dobos, “An Improved Coefficient Calculator for the California Energy Commission 6 Parameter Photovoltaic Module Model”, Journal of Solar Energy Engineering, vol 134, 2012.

[6] O. Madelung, “Semiconductors: Data Handbook, 3rd ed.” ISBN 3-540-40488-0

[7] S.R. Wenham, M.A. Green, M.E. Watt, “Applied Photovoltaics” ISBN 0 86758 909 4

[8] A. Jain, A. Kapoor, “Exact analytical solutions of the parameters of real solar cells using Lambert W-function”, Solar Energy Materials and Solar Cells, 81 (2004) 269-277.

[9] D. King et al, “Sandia Photovoltaic Array Performance Model”, SAND2004-3535, Sandia National Laboratories, Albuquerque, NM

[10] “Computer simulation of the effects of electrical mismatches in photovoltaic cell interconnection circuits” JW Bishop, Solar Cell (1988) https://doi.org/10.1016/0379-6787(88)90059-2

apply_inverter_losses(inverter, method='sandia')#

apply_inverter_losses(self, inverter, method=”sandia”, )

Applies inverter losses using the pvlib.pvsystem.snlinverter() function [1], the pvlib.pvsystem.retrieve_sam() function [2] and the pvlib.pvsystem.adrinverter() function [3].

Parameters:

inverter (str) – Describes the inverter. [TODO: Add a more detailed description following the example of ‘configure_cec_module’]
method (str) – Options: “scandia” “driesse” Describes the used method to apply the inverter losses.

Return type:

Returns a reference to the invoking SolarWorkflowManager object.

Notes

Required data in the sim_data dictionary are ‘module_dc_power_at_mpp’ and ‘module_dc_voltage_at_mpp’. Required data in the placements dataframe are ‘modules_per_string’ and ‘strings_per_inverter’. Cannot simultaneously provide ‘capacity’ and inverter-string parameters.

References

[1] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.pvsystem.snlinverter.html

[2] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.pvsystem.retrieve_sam.html

[3] https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.pvsystem.adrinverter.html

[4] SAND2007-5036, “Performance Model for Grid-Connected Photovoltaic Inverters by D. King, S. Gonzalez, G. Galbraith, W. Boyson

[5] System Advisor Model web page. https://sam.nrel.gov.

[6] Beyond the Curves: Modeling the Electrical Efficiency of Photovoltaic Inverters, PVSC 2008, Anton Driesse et. al.

estimate_missing_params(elev, convention='Ryberg2020')#

reskit.solar.openfield_pv_era5(placements, era5_path, global_solar_atlas_ghi_path, global_solar_atlas_dni_path, module='WINAICO WSx-240P6', elev=300, tracking='fixed', inverter=None, inverter_kwargs={}, tracking_args={}, DNI_nodata_fallback=1.0, DNI_nodata_fallback_scaling=1.0, GHI_nodata_fallback=1.0, GHI_nodata_fallback_scaling=1.0, output_netcdf_path=None, output_variables=None, gsa_nodata_fallback='source', tech_year=2050)#

Simulation of an openfield PV openfield system based on ERA5 Data.

Parameters:

placements (Pandas Dataframe) – Locations that you want to do the simulations for. Columns need to be lat (latitudes), lon (longitudes), tilt and capacity.
era5_path (str) – Path to the ERA5 Data on your computer. Can be a single “.nc” file, or a directory containing many “.nc” files.
global_solar_atlas_ghi_path (str) – Path to the global solar atlas ghi data on your computer.
global_solar_atlas_dni_path (str) – Path to the global solar atlas dni data on your computer.
module (str) – Name of the module that you wanna use for the simulation. Default is Winaico Wsx-240P6
elev (float) – Elevation that you want to model your PV system at.
tracking (str) – Determines whether your PV system is fixed or not. Default is fixed. Option 1 is ‘fixed’ meaning that the module does not have any tracking capabilities. Option 2 is ‘single_axis’ meaning that the module has single_axis tracking capabilities.
inverter (str) – Determines whether you want to model your PV system with an inverter or not. Default is None. See reskit.solar.SolarWorkflowManager.apply_inverter_losses for more usage information.
DNI_nodata_fallback (str, optional) – When global_solar_atlas_dni_path has no data, one can decide between different fallback options, by default 1.0: - np.nan or None : return np.nan for missing values in global_solar_atlas_dni_path - float : Apply this float value as a scaling factor for all no-data locations only: source_long_run_average * DNI_nodata_fallback. NOTE: A value of 1.0 will return the source lra value in case of missing global_solar_atlas_dni_path values. - str : Will be interpreted as a filepath to a raster with alternative absolute global_solar_atlas_dni_path values - callable : any callable method taking the arguments (all iterables): ‘locs’ and ‘source_long_run_average_value’ (the locations as gk.geom.point objects and original value from source data). The output values will be considered as the new real_long_run_average for missing locations only. NOTE: np.nan will also be returned in case that the nodata fallback does not yield values either.
DNI_nodata_fallback_scaling (float, optional) – The scaling factor that will be applied to the DNI nodata fallback e.g. in case of different units compared to source data. By default 1.0, i.e. no effect.
GHI_nodata_fallback (str, optional) –
When global_solar_atlas_ghi_path has no data, one can decide between different fallback options, by default 1.0: - np.nan or None : return np.nan for missing values in global_solar_atlas_ghi_path - float : Apply this float value as a scaling factor for all no-data locations only: source_long_run_average * GHI_nodata_fallback.

NOTE: A value of 1.0 will return the source lra value in case of missing global_solar_atlas_ghi_path values.
- str : Will be interpreted as a filepath to a raster with alternative absolute global_solar_atlas_ghi_path values
- callableany callable method taking the arguments (all iterables): ‘locs’ and ‘source_long_run_average_value’
  (the locations as gk.geom.point objects and original value from source data). The output values will be considered as the new real_long_run_average for missing locations only.
NOTE: np.nan will also be returned in case that the nodata fallback does not yield values either
GHI_nodata_fallback_scaling (float, optional) – The scaling factor that will be applied to the GHI nodata fallback e.g. in case of different units compared to source data. By default 1.0, i.e. no effect.
output_netcdf_path (str) – Path to a file that you want to save your output NETCDF file at. Default is None
output_variables (str) – Output variables of the simulation that you want to save into your NETCDF Outputfile.
gsa_nodata_fallback (str, optional) – NOTE: DEPRECATED! Will be removed soon! When real_long_run_average has no data, it can be decided between fallback options: -‘source’: use source data (ERA5 raw simulation) -‘nan’: return np.nan for missing values get flags for missing values: - f’missing_values_{os.path.basename(path_to_LRA_source)}
tech_year (int, optional) – If given in combination with the projected module str names “WINAICO WSx-240P6” or “LG Electronics LG370Q1C-A5”, the effifiency will be scaled linearly to the given year. Must then be between year of market introduction for that module and 2050. Will be ignored when non-projected existing module names or specific parameters are given, can then be None. By default 2050.

Return type:

A xarray dataset including all the output variables you defined as your output_variables.

reskit.solar.openfield_pv_era5_unvalidated(**kwargs)#: Simulation of an openfield PV openfield system based on ERA5 Data, with unvalidated default loss factor of 0.107 based on literature.

reskit.solar.openfield_pv_era5pure(**kwargs)#: Simulation of an openfield PV openfield system based on ERA5 original Data without further disaggregation.

reskit.solar.openfield_pv_iconlam(placements, icon_lam_path, module='WINAICO WSx-240P6', elev=300, tracking='fixed', inverter=None, inverter_kwargs={}, tracking_args={}, output_netcdf_path=None, output_variables=None, tech_year=2050)#

Simulation of an openfield PV openfield system based on ICON-LAM Data.

Parameters:

placements (Pandas Dataframe) – Locations that you want to do the simulations for. Columns need to be lat (latitudes), lon (longitudes), tilt and capacity.
icon_lam_path (str) – Path to the ICON-LAM Data on your computer. Can be a single “.nc” file, or a directory containing many “.nc” files.
module (str) – Name of the module that you wanna use for the simulation. Default is Winaico Wsx-240P6
elev (float) – Elevation that you want to model your PV system at. SChen: Or you can provide a string directory when a terrain raster can be found
tracking (str) – Determines whether your PV system is fixed or not. Default is fixed. Option 1 is ‘fixed’ meaning that the module does not have any tracking capabilities. Option 2 is ‘single_axis’ meaning that the module has single_axis tracking capabilities.
inverter (str) – Determines whether you want to model your PV system with an inverter or not. Default is None. See reskit.solar.SolarWorkflowManager.apply_inverter_losses for more usage information.
output_netcdf_path (str) – Path to a file that you want to save your output NETCDF file at. Default is None
output_variables (str) – Output variables of the simulation that you want to save into your NETCDF Outputfile.
tech_year (int, optional) – If given in combination with the projected module str names “WINAICO WSx-240P6” or “LG Electronics LG370Q1C-A5”, the effifiency will be scaled linearly to the given year. Must then be between year of market introduction for that module and 2050. Will be ignored when non-projected existing module names or specific parameters are given, can then be None. By default 2050.

Return type:

A xarray dataset including all the output variables you defined as your output_variables.

reskit.solar.openfield_pv_merra_ryberg2019(placements, merra_path, global_solar_atlas_ghi_path, module='WINAICO WSx-240P6', elev=300, tracking='fixed', inverter=None, inverter_kwargs={}, tracking_args={}, output_netcdf_path=None, output_variables=None, tech_year=2050)#

openfield_pv_merra_ryberg2019(placements, merra_path, global_solar_atlas_ghi_path, module=”WINAICO WSx-240P6”, elev=300, tracking=”fixed”,: inverter=None, inverter_kwargs={}, tracking_args={}, output_netcdf_path=None, output_variables=None)

Simulation of an openfield PV openfield system based on MERRA Data.

Parameters:

placements (Pandas Dataframe) – Locations where to perform simulations at. Columns need to be lat (latitudes), lon (longitudes), tilt and capacity.
merra_path (str) – Path to the MERRA Data on your computer. Can be a single “.nc” file, or a directory containing many “.nc” files.
global_solar_atlas_ghi_path (str) – Path to the global solar atlas ghi data on your computer.
module (str) – Name of the module that you want to use for the simulation. Default is Winaico Wsx-240P6. See reskit.solar.SolarWorkflowManager.configure_cec_module for more usage information.
elev (float) – Elevation that you want to model your PV system at.
tracking (str) – Option 1 is ‘fixed’ meaning that the module does not have any tracking capabilities. Option 2 is ‘single_axis’ meaning that the module has single_axis tracking capabilities.

inverter: str: Determines whether or not you want to model your PV system with an inverter. Default is None, meaning no inverter is assumed See reskit.solar.SolarWorkflowManager.apply_inverter_losses for more usage information
output_netcdf_path: str: Path to a file that you want to save your output NETCDF file at. Default is None
output_variables: str: Output variables of the simulation that you want to save into your NETCDF Outputfile.
tech_yearint, optional: If given in combination with the projected module str names “WINAICO WSx-240P6” or “LG Electronics LG370Q1C-A5”, the effifiency will be scaled linearly to the given year. Must then be between year of market introduction for that module and 2050. Will be ignored when non-projected existing module names or specific parameters are given, can then be None. By default 2050.

Return type:: A xarray dataset including all the output variables you defined as your output_variables.

reskit.solar.openfield_pv_sarah_unvalidated(placements, sarah_path, era5_path, module='WINAICO WSx-240P6', elev=300, tracking='fixed', inverter=None, inverter_kwargs={}, tracking_args={}, output_netcdf_path=None, output_variables=None, tech_year=2050)#

openfield_pv_sarah_unvalidated(placements, sarah_path, era5_path, module=”WINAICO WSx-240P6”, elev=300, tracking=”fixed”, inverter=None, inverter_kwargs={}, tracking_args={}, output_netcdf_path=None, output_variables=None)

Simulation of an openfield PV openfield system based on Sarah and ERA5 Data.

Parameters:

placements (Pandas Dataframe) – Locations that you want to do the simulations for. Columns need to be lat (latitudes), lon (longitudes), tilt and capacity.
sarah_path (str) – Path to the SARAH Data on your computer. Can be a single “.nc” file, or a directory containing many “.nc” files.
era5_path (str) – Path to the ERA5 Data on your computer. Can be a single “.nc” file, or a directory containing many “.nc” files.

module: str: Name of the module that you wanna use for the simulation. Default is Winaico Wsx-240P6
elev: float: Elevation that you want to model your PV system at.
tracking: str: Determines whether your PV system is fixed or not. Default is fixed. Option 1 is ‘fixed’ meaning that the module does not have any tracking capabilities. Option 2 is ‘single_axis’ meaning that the module has single_axis tracking capabilities.
inverter: str: Determines whether you want to model your PV system with an inverter or not. Default is None. See reskit.solar.SolarWorkflowManager.apply_inverter_losses for more usage information.
output_netcdf_path: str: Path to a file that you want to save your output NETCDF file at. Default is None
output_variables: str: Output variables of the simulation that you want to save into your NETCDF Outputfile.
tech_yearint, optional: If given in combination with the projected module str names “WINAICO WSx-240P6” or “LG Electronics LG370Q1C-A5”, the effifiency will be scaled linearly to the given year. Must then be between year of market introduction for that module and 2050. Will be ignored when non-projected existing module names or specific parameters are given, can then be None. By default 2050.

Return type:: A xarray dataset including all the output variables you defined as your output_variables.