potential.py

lib.potential.calc_FLH_solar(hours, args)

This function computes the full-load hours for all valid pixels specified in ind_nz in param. Due to parallel processing, most of the inputs are collected in the list args.

Parameters

  • hours (numpy array) – Filtered day hour ranks in a year (from 0 to 8759).

  • args (list) – List of arguments: * param (dict): Dictionary including multiple parameters such as the status bar limit, the name of the region, and others for calculating the hourly capacity factors. * tech (str): Name of the technology. * rasterData (dict): Dictionary of numpy arrays containing land use types, Ross coefficients, albedo coefficients, and wind speed correction for every point in reg_ind. * merraData (dict): Dictionary of numpy arrays containing the weather data for every point in reg_ind.

Return FLH

Full-load hours over the year for the technology.

Return type

numpy array

lib.potential.calc_FLH_windoff(hours, args)

This function computes the full-load hours for all valid pixels specified in ind_nz in param. Due to parallel processing, most of the inputs are collected in the list args.

Parameters

  • hours (numpy array) – Hour ranks in a year (from 0 to 8759).

  • args (list) – List of arguments: * param (dict): Dictionary including multiple parameters such as the status bar limit, the name of the region, and others for calculating the hourly capacity factors. * tech (str): Name of the technology. * rasterData (dict): Dictionary of numpy arrays containing land use types, Ross coefficients, albedo coefficients, and wind speed correction for every point in reg_ind. * merraData (dict): Dictionary of numpy arrays containing the weather data for every point in reg_ind.

Return FLH

Full-load hours over the year for the technology.

Return type

numpy array

lib.potential.calc_FLH_windon(param, tech, rasterData, merraData, GWA_array, b_xmin, b_xmax, b_ymin, b_ymax, x_gwa, y_gwa, pixles, list_results)

This function computes the full-load hours for all valid pixels specified in ind_nz in param. Due to parallel processing, most of the inputs are collected in the list args.

Parameters

  • hours (numpy array) – Hour ranks in a year (from 0 to 8759).

  • args (list) – List of arguments: * param (dict): Dictionary including multiple parameters such as the status bar limit, the name of the region, and others for calculating the hourly capacity factors. * tech (str): Name of the technology. * rasterData (dict): Dictionary of numpy arrays containing land use types, Ross coefficients, albedo coefficients, and wind speed correction for every point in reg_ind. * merraData (dict): Dictionary of numpy arrays containing the weather data for every point in reg_ind.

Return FLH

Full-load hours over the year for the technology.

Return type

numpy array

lib.potential.calc_gcr(Crd_all, m_high, n_high, res_desired, GCR)

This function creates a GCR weighting matrix for the desired geographic extent. The sizing of the PV system is conducted on a user-defined day for a shade-free exposure to the sun during a given number of hours.

Parameters

  • Crd_all (list) – Desired geographic extent of the whole region (north, east, south, west).

  • m_high (int) – Number of rows.

  • n_high (int) – Number of columns.

  • res_desired (list) – Resolution of the high resolution map.

  • GCR (dict) – Dictionary that includes the user-defined day and the duration of the shade-free period.

Return A_GCR

GCR weighting raster.

Return type

numpy array

lib.potential.calculate_full_load_hours(paths, param, tech, multiprocessing)

This function calculates the yearly FLH for a technology for all valid pixels in a spatial scope. Valid pixels are land pixels for WindOn, PV and CSP, and sea pixels for WindOff. The FLH values are calculated by summing up hourly capacity factors.

Parameters

  • paths (dict) – Dictionary of dictionaries containing the paths to the input weather data, land, sea and land use rasters, and correction rasters.

  • param (dict) – Dictionary of dictionaries containing the spatial scope, and technology and computation parameters.

  • tech (str) – Technology under study.

  • multiprocessing (bool) – Determines if the computation uses multiprocessing (True/False)

Returns

The raster of FLH potential is saved as mat and tif files, along with the json metadata file.

Return type

None

lib.potential.get_merra_raster_data(paths, param, tech)

This function returns a tuple of two dictionaries containing weather and correction rasters for specified technology.

Parameters

  • paths (dict) – Dictionary of dictionaries containing the paths to the input weather and raster data.

  • param (dict) – Dictionary of dictionaries containing land use, Ross coefficients, albedo, and Hellmann coefficients.

  • tech (str) – Technology under study.

Return (merraData, rasterData)

Dictionaries for the weather data and for the correction data.

Return type

tuple (dict, dict)

lib.potential.mask_potential_maps(paths, param, tech)

This function first reads the rasters for land use, slope, bathymetry, and protected areas for the scope. Based on user-defined assumptions on their suitabilities, it generates a masking raster to exclude the unsuitable pixels. Both the mask itself and the masked potential rasters can be saved.

Parameters

  • paths (dict) – Dictionary of dictionaries containing user-defined parameters for masking, protected areas, and landuse.

  • param (dict) – Dictionary of dictionaries containing the paths to the land use, protected areas, slope and bathymetry, in addition to output paths.

  • tech (str) – Technology under study.

Returns

The files for the mask and the masked FLH are saved as tif and mat files, along with their metadata json files.

Return type

None

lib.potential.redistribution_array(param, merraData, i, j, xmin, xmax, ymin, ymax, GWA_array, x_gwa, y_gwa)

What does this function do?

Parameters

  • param –

  • merraData –

  • i –

  • j –

  • xmin –

  • xmax –

  • ymin –

  • ymax –

  • GWA_array –

  • x_gwa –

  • y_gwa –

Return reMerra

Return type

numpy array

Aim:

Increase the resolution of the MERRA wind data by using Global Wind Atlas data. For this reason, the low resolution MERRA data is redistributed by the energy distribution of the higher resolution Global Wind Atlas data

Algorithm:

  1. Import wind data from Global Wind Atlas

  2. Select pixels that are within one MERRA pixle

  3. Convert from wind speed to energy

  4. Redistribute MERRA data

ToDo: Where do the borders/limits come from? -> not shape file ?!?

lib.potential.report_potentials(paths, param, tech)

This function reads the FLH files and the subregion shapefile, and creates a CSV file containing various statistics:

  • Available number of pixels, before and after masking

  • Available area in in km²

  • FLH mean, median, max, min values, before and after masking

  • FLH standard deviation after masking

  • Power Potential in GW, before and after weighting

  • Energy Potential in TWh in total, after weighting, and after masking and weighting

  • Sorted sample of FLH values for each region

Parameters

  • paths (dict) – Dictionary of dictionaries containing the paths to FLH, Masking, Weighting, and Area rasters.

  • param (dict) – Dictionary of dictionaries containing technology parameters and sampling parameters.

  • tech (str) – Technology under study.

Returns

The CSV files with the report and the sorted FLH are saved directly in the desired paths, along with the corresponding metadata in JSON files.

Return type

None

lib.potential.sampled_sorting(Raster, sampling)

This function returns a list with a defined length of sorted values sampled from a numpy array.

Parameters

  • Raster (numpy array) – Input raster to be sorted.

  • sampling (int) – Number of values to be sampled from the raster, defines length of output list.

Return s

List of sorted values sampled from Raster.

Return type

list

lib.potential.weight_potential_maps(paths, param, tech)

This function weights the power potential by including assumptions on the power density and the available area. Therefore, it reads the rasters for land use and protected areas for the scope. Based on user-defined assumptions on their availabilities, it generates a weighting raster to exclude the unsuitable pixels. Both the weight itself and the weighted potential rasters can be saved.

Parameters

  • paths (dict) – Dictionary of dictionaries containing user-defined parameters for weighting, protected areas, and landuse.

  • param (dict) – Dictionary of dictionaries containing the paths to the land use, protected areas, area, in addition to output paths.

  • tech (str) – Technology under study.

Returns

The files for the weight and the weighted FLH are saved as tif and mat files, along with their metadata json files.

Return type

None