physical_models.py

lib.physical_models.angles(hour, reg_ind, Crd_all, res_desired, orient)

This function creates multiple matrices for the whole scope, that represent the incidence, hour angles, declination, elevation, tilt, azimuth and orientation angles of every pixel with the desired resolution.

Parameters

  • hour (int) – Hour rank in a year (from 0 to 8759).

  • reg_ind (tuple of arrays) – indices of valid pixels within the spatial scope (pixels on land).

  • Crd_all (list) – Coordinates of the bounding box of the spatial scope.

  • res_desired (list) – Desired high resolution in degrees.

  • orient (int) – Azimuth orientation of the module in degrees.

Return (phi, omega, delta, alpha, beta, azi, orientation)

Rasters of latitude, hour, declination, elevation, tilt, azimuth and orientation angles.

Return type

tuple of arrays

lib.physical_models.calc_CF_solar(hour, reg_ind, param, merraData, rasterData, tech)

This function computes the hourly capacity factor for PV and CSP technologies for all valid pixels within the spatial scope for a given hour.

Parameters

  • hour (integer) – Hour within the year (from 0 to 8759).

  • reg_ind (tuple of arrays) – indices of valid pixels within the spatial scope (pixels on land).

  • param (dict) – Dictionary including the desired resolution, the coordinates of the bounding box of the spatial scope, and technology parameters.

  • merraData (dict) – Dictionary of numpy arrays containing the weather data for every point in reg_ind.

  • rasterData (dict) – Dictionary of numpy arrays containing land use types, Ross coefficients, albedo coefficients, and wind speed correction for every point in reg_ind.

  • tech (str) – Name of the technology ('PV' or 'CSP').

Return (CF_pv, CF_csp)

the capacity factors for all the points during that hour for PV and CSP.

Return type

tuple (numpy array, numpy array)

lib.physical_models.calc_CF_windoff(hour, reg_ind, turbine, m, n, merraData, rasterData)

This function computes the hourly capacity factor for onshore and offshore wind for all valid pixels within the spatial scope for a given hour.

Parameters

  • hour (integer) – Hour within the year (from 0 to 8759).

  • reg_ind (tuple of arrays) – indices of valid pixels within the spatial scope (pixels on land for onshore wind, on sea for offshore wind).

  • turbine (dict) – Dictionary including the turbine parameters (cut-in, cut-off and rated wind speed).

  • m (int) – number of rows.

  • n (int) – number of columns.

  • merraData (dict) – Dictionary of numpy arrays containing the weather data for every point in reg_ind.

  • rasterData (dict) – Dictionary of numpy arrays containing the wind speed correction for every point in reg_ind.

Return CF

Capacity factors for all the valid points during that hour.

Return type

numpy array

lib.physical_models.calc_CF_windon(hours, turbine, merraData, rasterData)

This function computes the hourly capacity factor for onshore and offshore wind for all valid pixels within the spatial scope for a given hour.

Parameters

  • hour (integer) – Hour within the year (from 0 to 8759).

  • reg_ind (tuple of arrays) – indices of valid pixels within the spatial scope (pixels on land for onshore wind, on sea for offshore wind).

  • turbine (dict) – Dictionary including the turbine parameters (cut-in, cut-off and rated wind speed).

  • m (int) – number of rows.

  • n (int) – number of columns.

  • merraData (dict) – Dictionary of numpy arrays containing the weather data for every point in reg_ind.

  • rasterData (dict) – Dictionary of numpy arrays containing the wind speed correction for every point in reg_ind.

Return CF

Capacity factors for all the valid points during that hour.

Return type

numpy array

lib.physical_models.coefficients(beta, ratio, R_b, A_i, f)

This function creates three weighting matrices for the spatial scope with the desired resolution, that correspond to the gains/losses caused by tilting to each component of the incident irradiance (direct, diffuse, and reflected).

Parameters

  • beta (numpy array) – Raster of tilt angles.

  • ratio (numpy array) – Diffuse fraction of global horizontal solar radiation using the Erbs model.

  • R_b (numpy array) – Ratio of incident beam to horizontal beam in the HDKR model.

  • A_i (numpy array) – Anisotropy index for forward scattering circumsolar diffuse irradiance in the HDKR model.

  • f (numpy array) – Modulating factor for horizontal brightening correction.

Return (F_direct, F_diffuse, F_reflected)

Rasters of direct, diffuse and reflected ratios of irradiance.

Return type

tuple of arrays

lib.physical_models.global2diff(k_t, dims)

This function estimates the global-to-diffuse irradiance ratio using the Erb model.

Parameters

  • k_t (numpy array) – Raster of clearness indices.

  • dims (tuple) – Dimensions of the output (similar to the dimension of the angles).

Return A_ratio

Raster of global-to-diffuse irradiance ratios.

Return type

numpy array

lib.physical_models.loss(G_tilt_h, TEMP, A_Ross, pv)

This function creates a temperature loss weighting matrix for the spatial scope.

Parameters

  • G_tilt_h (numpy array) – Raster of incident irradiance on the tilted panel.

  • TEMP (numpy array) – Raster of ambient temperatures in °C

  • A_Ross (numpy array) – Raster of Ross coefficients for temperature sensitivity.

  • pv (dict) – Dictionary containing PV-specific parameters for loss coefficient and rated temperature.

Return LOSS_TEMP

raster of weighting temperature loss.

Return type

numpy array

lib.physical_models.toa_hourly(alpha, hour)

This function returns the top of the atmosphere normal irradiance based on the solar constant, hour rank, and incidence angle.

Parameters

  • alpha (numpy array) – Raster of elevation angles.

  • hour (int) – Hour rank of the year (from 0 to 8759).

Return TOA_h

Raster of the normal top of the atmosphere irradiance.

Return type

numpy array

lib.physical_models.tracking(axis, A_phi, A_alpha, A_beta, A_azimuth)

This function computes the tilt angle and orientation based on the type of tracking, incidence, elevation tilt and azimuth angles.

Parameters

  • axis (int) – Number of tracking axes (0, 1, 2). The value 0 means no tracking (fixed rack), 1 means single-axis tracking in east-west dimension, and 2 means double-axis tracking.

  • A_phi (numpy array) – Raster of latitude angle.

  • A_alpha (numpy array) – Raster of elevation angle.

  • A_beta (numpy array) – Raster of tilt angle.

  • A_azimuth (numpy array) – Raster of azimuth angle.

Return (A_orient, A_beta)

Tuple of rasters for orientationa and tilt angles for specified tracking type.

Return type

tuple of arrays