workflows

Functions:

• openfield_pv_era5 –

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

• openfield_pv_era5_unvalidated –

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

• openfield_pv_era5pure –

  Simulation of an openfield PV openfield system based on ERA5 original

• openfield_pv_iconlam –

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

• openfield_pv_merra_ryberg2019 –

  openfield_pv_merra_ryberg2019(placements, merra_path, global_solar_atlas_ghi_path, module="WINAICO WSx-240P6", elev=300, tracking="fixed",

• openfield_pv_sarah_unvalidated –

  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)

openfield_pv_era5

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,
    time_slice=None,
)

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

Parameters:

• placements

  –

  Locations that you want to do the simulations for.
  Columns need to be lat (latitudes), lon (longitudes), tilt and capacity.
  

• era5_path

  –

  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

  –

  Path to the global solar atlas ghi data on your computer.
  

• global_solar_atlas_dni_path

  –

  Path to the global solar atlas dni data on your computer.
  

• module

  –

  Name of the module that you wanna use for the simulation.
  Default is Winaico Wsx-240P6
  

• elev

  –

  Elevation that you want to model your PV system at.
  

• tracking

  –

  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

  –

  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

  –

  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

  –

  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

  –

  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
  - 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
  

• GHI_nodata_fallback_scaling

  –

  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

  –

  Path to a file that you want to save your output NETCDF file at.
  Default is None
  

• output_variables

  –

  Output variables of the simulation that you want to save into your NETCDF Outputfile.
  

• gsa_nodata_fallback

  –

  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, default: 2050 ) –

      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.
  

• time_slice

  (slice, default: None ) –

  Limit the time span loaded from the ERA5 source. Only supported for
  Zarr-backed ERA5 sources, where it is strongly recommended to avoid
  loading whole multi-year cloud stores. Raises for netCDF4-backed ERA5
  sources; support for those is planned.
  

Returns:

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

Source code in reskit/solar/workflows/workflows.py

def 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,
    time_slice=None,
):
    """
    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
            - 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

    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.

    time_slice : slice, optional
            Limit the time span loaded from the ERA5 source. Only supported for
            Zarr-backed ERA5 sources, where it is strongly recommended to avoid
            loading whole multi-year cloud stores. Raises for netCDF4-backed ERA5
            sources; support for those is planned.

    Returns
    -------
    A xarray dataset including all the output variables you defined as your output_variables.
    """
    wf = SolarWorkflowManager(placements)
    wf.configure_cec_module(module, tech_year)

    # ensure the tracking parameter is correct
    assert tracking in [
        "fixed",
        "single_axis",
    ], f"tracking must be either 'fixed' or 'single_axis'"

    # estimates tilt, azimuth and elev
    wf.estimate_missing_params(elev)

    wf.read(
        variables=[
            "global_horizontal_irradiance",
            "direct_horizontal_irradiance",
            "surface_wind_speed",
            "surface_pressure",
            "surface_air_temperature",
            "surface_dew_temperature",
            "snow_albedo",
            "snow_density",
            "snow_depth_water_equivalent",
            "snowfall_water_equivalent",
        ],
        source_type="ERA5",
        source=era5_path,
        set_time_index=True,
        time_index_from="direct_horizontal_irradiance",
        time_slice=time_slice,
        verbose=False,
    )
    # If there is a need to resimulate old data, this line must be inserted.
    # wf.sim_data['global_horizontal_irradiance'] = wf.sim_data['global_horizontal_irradiance_archive']

    wf.determine_solar_position()
    wf.filter_positive_solar_elevation()

    wf.direct_normal_irradiance_from_trigonometry()

    # wf.spatial_disaggregation(
    #     variable='global_horizontal_irradiance',
    #     source_high_resolution=global_solar_atlas_ghi_path,
    #     source_low_resolution=rk_weather.GSAmeanSource.GHI_with_ERA5_pixel,
    # )

    # TODO remove the following mid 2024, also remove gsa_nodata_fallback in workflow args
    if gsa_nodata_fallback != "source":
        warnings.warn(
            "'gsa_nodata_fallback' is deprecated and will be removed soon. Use 'GHI_nodata_fallback' and 'GHI_nodata_fallback' instead.",
            DeprecationWarning,
        )
        # deprecated gsa nodata fallback has been changed!
        if GHI_nodata_fallback != 1.0 or DNI_nodata_fallback == 1.0:
            # also, changes have been made to GHI and DNI fallbacks
            raise ValueError(
                f"When GHI_nodata_fallback and DNI_nodata_fallback have been adapted, gsa_nodata_fallback must not be adapted (recommended to ignore, deprecated)"
            )
        else:
            # GHI and DNI fallbacks have not been changed, but 'source' has - adapt DNI and GHI fallbacks accordingly
            if gsa_nodata_fallback == "nan":
                GHI_nodata_fallback = np.nan
                DNI_nodata_fallback = np.nan
            else:
                raise ValueError(
                    f"'gsa_nodata_fallback' (deprecated) must be 'nan' or 'source'. Better use 'GHI_nodata_fallback' and 'GHI_nodata_fallback' instead, however."
                )

    wf.adjust_variable_to_long_run_average(
        variable="global_horizontal_irradiance",
        source_long_run_average=rk_weather.Era5Source.LONG_RUN_AVERAGE_GHI_2020_03,
        real_long_run_average=global_solar_atlas_ghi_path,
        real_lra_scaling=1000 / 24,  # cast to hourly average kWh
        nodata_fallback=GHI_nodata_fallback,
        nodata_fallback_scaling=GHI_nodata_fallback_scaling,
    )

    wf.adjust_variable_to_long_run_average(
        variable="direct_normal_irradiance",
        source_long_run_average=rk_weather.Era5Source.LONG_RUN_AVERAGE_DNI_2020_03,
        real_long_run_average=global_solar_atlas_dni_path,
        real_lra_scaling=1000 / 24,  # cast to hourly average kWh
        nodata_fallback=DNI_nodata_fallback,
        nodata_fallback_scaling=DNI_nodata_fallback_scaling,
    )

    wf.determine_extra_terrestrial_irradiance(model="spencer", solar_constant=1370)
    wf.determine_air_mass(model="kastenyoung1989")

    wf.diffuse_horizontal_irradiance_from_trigonometry()

    if tracking == "single_axis":
        wf.permit_single_axis_tracking(**tracking_args)

    wf.determine_angle_of_incidence()
    wf.estimate_plane_of_array_irradiances(transposition_model="perez")

    wf.apply_angle_of_incidence_losses_to_poa()

    wf.cell_temperature_from_sapm()

    wf.simulate_with_interpolated_single_diode_approximation(
        module=module,
        tech_year=tech_year,
    )

    if inverter is not None:
        wf.apply_inverter_losses(inverter=inverter, **inverter_kwargs)

    loss_factor = 0.115  # validation by d.franzmann, 2022/01/13
    variables = [_var for _var in ["capacity_factor", "total_system_generation"] if _var in wf.sim_data.keys()]
    wf.apply_loss_factor(loss_factor, variables=variables)

    return wf.to_xarray(output_netcdf_path=output_netcdf_path, output_variables=output_variables)

openfield_pv_era5_unvalidated

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.

Source code in reskit/solar/workflows/workflows.py

def 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.
    """
    # this is the github commit url with the latest workflow status
    commit_hash = "379645675cb1b2559ffa8d73c84be0dd0daef55e"
    raise rk_util.RESKitDeprecationError(commit_hash)

openfield_pv_era5pure

openfield_pv_era5pure(**kwargs)

Simulation of an openfield PV openfield system based on ERA5 original Data without further disaggregation.

Source code in reskit/solar/workflows/workflows.py

def openfield_pv_era5pure(**kwargs):
    """
    Simulation of an openfield  PV openfield system based on ERA5 original
    Data without further disaggregation.
    """
    # this is the github commit url with the latest workflow status
    commit_hash = "379645675cb1b2559ffa8d73c84be0dd0daef55e"
    raise rk_util.RESKitDeprecationError(commit_hash)

openfield_pv_iconlam

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

  –

  Locations that you want to do the simulations for.
  Columns need to be lat (latitudes), lon (longitudes), tilt and capacity.
  

• icon_lam_path

  –

  Path to the ICON-LAM Data on your computer.
  Can be a single ".nc" file, or a directory containing many ".nc" files.
  

• module

  –

  Name of the module that you wanna use for the simulation.
  Default is Winaico Wsx-240P6
  

• elev

  –

  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

  –

  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

  –

  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

  –

  Path to a file that you want to save your output NETCDF file at.
  Default is None
  

• output_variables

  –

  Output variables of the simulation that you want to save into your NETCDF Outputfile.
  

• tech_year

  (int, default: 2050 ) –

      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.
  

Returns:

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

Source code in reskit/solar/workflows/workflows.py

def 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.

    Returns
    -------
    A xarray dataset including all the output variables you defined as your output_variables.
    """
    wf = SolarWorkflowManager(placements)
    wf.configure_cec_module(module, tech_year)

    # ensure the tracking parameter is correct
    assert tracking in [
        "fixed",
        "single_axis",
    ], f"tracking must be either 'fixed' or 'single_axis'"

    # estimates tilt, azimuth and elev
    wf.estimate_missing_params(elev)

    wf.read(
        variables=[
            "global_horizontal_irradiance",
            "direct_horizontal_irradiance",
            "surface_wind_speed",
            "surface_pressure",
            "surface_air_temperature",
            "surface_dew_temperature",
        ],
        source_type="ICON-LAM",
        source=icon_lam_path,
        set_time_index=True,
        time_index_from="direct_horizontal_irradiance",
        spatial_interpolation_mode="near",
        verbose=False,
    )

    wf.determine_solar_position()
    wf.filter_positive_solar_elevation()

    wf.direct_normal_irradiance_from_trigonometry()

    wf.determine_extra_terrestrial_irradiance(model="spencer", solar_constant=1370)
    wf.determine_air_mass(model="kastenyoung1989")

    wf.diffuse_horizontal_irradiance_from_trigonometry()

    if tracking == "single_axis":
        wf.permit_single_axis_tracking(**tracking_args)

    wf.determine_angle_of_incidence()
    wf.estimate_plane_of_array_irradiances(transposition_model="perez")

    wf.apply_angle_of_incidence_losses_to_poa()

    wf.cell_temperature_from_sapm()

    wf.simulate_with_interpolated_single_diode_approximation(
        module=module,
        tech_year=tech_year,
    )

    if inverter is not None:
        wf.apply_inverter_losses(inverter=inverter, **inverter_kwargs)

    # this loss_factor was particularly tuned for ERA5GSA RESKit solar workflow
    # loss_factor = 0.115  # validation by d.franzmann, 2022/01/13
    # wf.apply_loss_factor(
    #     loss_factor, variables=["capacity_factor", "total_system_generation"]
    # )

    loss_factor = 0.107  # general loss_factor by s.chen, 2024/05/08
    wf.apply_loss_factor(loss_factor, variables=["capacity_factor", "total_system_generation"])

    return wf.to_xarray(output_netcdf_path=output_netcdf_path, output_variables=output_variables)

openfield_pv_merra_ryberg2019

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

  –

  Locations where to perform simulations at. Columns need to be lat (latitudes), lon (longitudes), tilt and capacity.

• merra_path

  –

  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

  –

  Path to the global solar atlas ghi data on your computer.

• module

  –

  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

  –

  Elevation that you want to model your PV system at.

• tracking

  –

      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

  –

  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

  –

  Path to a file that you want to save your output NETCDF file at. Default is None

• output_variables

  –

  Output variables of the simulation that you want to save into your NETCDF Outputfile.

• tech_year

  (int, default: 2050 ) –

      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.
  

Returns:

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

Source code in reskit/solar/workflows/workflows.py

def 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_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.

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

    """
    wf = SolarWorkflowManager(placements)
    wf.configure_cec_module(module, tech_year)
    # ensure the tracking parameter is correct
    assert tracking in [
        "fixed",
        "single_axis",
    ], f"tracking must be either 'fixed' or 'single_axis'"

    # estimates tilt, azimuth and elev
    wf.estimate_missing_params(elev)

    wf.read(
        variables=[
            "surface_wind_speed",
            "surface_pressure",
            "surface_air_temperature",
            "surface_dew_temperature",
            "global_horizontal_irradiance",
        ],
        source_type="MERRA",
        source=merra_path,
        set_time_index=True,
        verbose=False,
    )

    wf.adjust_variable_to_long_run_average(
        variable="global_horizontal_irradiance",
        source_long_run_average=rk_weather.MerraSource.LONG_RUN_AVERAGE_GHI,
        real_long_run_average=global_solar_atlas_ghi_path,
        real_lra_scaling=1000 / 24,  # cast to hourly average kWh
    )

    wf.determine_solar_position()
    wf.filter_positive_solar_elevation()
    wf.determine_extra_terrestrial_irradiance(model="spencer", solar_constant=1370)
    wf.determine_air_mass(model="kastenyoung1989")
    wf.apply_DIRINT_model()
    wf.diffuse_horizontal_irradiance_from_trigonometry()

    if tracking == "single_axis":
        wf.permit_single_axis_tracking(**tracking_args)

    wf.determine_angle_of_incidence()
    wf.estimate_plane_of_array_irradiances(transposition_model="perez")

    wf.apply_angle_of_incidence_losses_to_poa()

    wf.cell_temperature_from_sapm()

    wf.simulate_with_interpolated_single_diode_approximation(
        module=module,
        tech_year=tech_year,
    )

    if inverter is not None:
        wf.apply_inverter_losses(inverter=inverter, **inverter_kwargs)

    variables = [_var for _var in ["capacity_factor", "total_system_generation"] if _var in wf.sim_data.keys()]
    wf.apply_loss_factor(0.20, variables=variables)

    return wf.to_xarray(output_netcdf_path=output_netcdf_path, output_variables=output_variables)

openfield_pv_sarah_unvalidated

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

  –

          Locations that you want to do the simulations for.
          Columns need to be lat (latitudes), lon (longitudes), tilt and capacity.
  

• sarah_path

  –

      Path to the SARAH Data on your computer.
      Can be a single ".nc" file, or a directory containing many ".nc" files.
  

• era5_path

  –

      Path to the ERA5 Data on your computer.
      Can be a single ".nc" file, or a directory containing many ".nc" files.
  

• module

  –

  Name of the module that you wanna use for the simulation.
  Default is Winaico Wsx-240P6
  

• elev

  –

  Elevation that you want to model your PV system at.
  

• tracking

  –

      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

  –

      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

  –

              Path to a file that you want to save your output NETCDF file at.
              Default is None
  

• output_variables

  –

              Output variables of the simulation that you want to save into your NETCDF Outputfile.
  

• tech_year

  (int, default: 2050 ) –

      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.
  

Returns:

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

Source code in reskit/solar/workflows/workflows.py

def 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_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.

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

    """
    wf = SolarWorkflowManager(placements)
    wf.configure_cec_module(module, tech_year)
    # ensure the tracking parameter is correct
    assert tracking in [
        "fixed",
        "single_axis",
    ], f"tracking must be either 'fixed' or 'single_axis'"

    if not "tilt" in wf.placements.columns:
        wf.estimate_tilt_from_latitude(convention="Ryberg2020")
    if not "azimuth" in wf.placements.columns:
        wf.estimate_azimuth_from_latitude()
    if not "elev" in wf.placements.columns:
        wf.apply_elevation(elev)

    wf.read(
        variables=["direct_normal_irradiance", "global_horizontal_irradiance"],
        source_type="SARAH",
        source=sarah_path,
        set_time_index=True,
        verbose=False,
    )

    wf.read(
        variables=[
            "surface_wind_speed",
            "surface_pressure",
            "surface_air_temperature",
            "surface_dew_temperature",
        ],
        source_type="ERA5",
        source=era5_path,
        set_time_index=False,
        time_index_from="direct_horizontal_irradiance",
        verbose=False,
    )

    wf.determine_solar_position()
    wf.filter_positive_solar_elevation()
    wf.determine_extra_terrestrial_irradiance(model="spencer", solar_constant=1370)
    wf.determine_air_mass(model="kastenyoung1989")

    wf.diffuse_horizontal_irradiance_from_trigonometry()

    if tracking == "single_axis":
        wf.permit_single_axis_tracking(**tracking_args)

    wf.determine_angle_of_incidence()
    wf.estimate_plane_of_array_irradiances(transposition_model="perez")

    wf.apply_angle_of_incidence_losses_to_poa()

    wf.cell_temperature_from_sapm()

    wf.simulate_with_interpolated_single_diode_approximation(
        module=module,
        tech_year=tech_year,
    )

    if inverter is not None:
        wf.apply_inverter_losses(inverter=inverter, **inverter_kwargs)

    variables = [_var for _var in ["capacity_factor", "total_system_generation"] if _var in wf.sim_data.keys()]
    wf.apply_loss_factor(0.20, variables=variables)

    return wf.to_xarray(output_netcdf_path=output_netcdf_path, output_variables=output_variables)