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Workshop: What the tool can do

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kais-siala edited this page Oct 25, 2019 · 2 revisions

What the tool can do

The features and applications are taken from README.md.

Features

  • Generation of potential maps and time series for user-defined regions within the globe

    • potential maps: Full-load hours (FLH), which is the sum of hourly capacity factors over the year
    • time series: of the capacity factors for particular locations
    • user-defined regions: using custom shapefiles
    • global: suggested sources for inputs are global, but you can use inputs for specific regions (small changes necessary)

  • Modeled technologies: onshore wind, offshore wind, PV, CSP (user-defined technology characteristics)

    • CSP not tested extensively (as of Oct 2019)
    • the user has to define the characteristics of the technology

  • Use of MERRA-2 reanalysis data, with the option to detect and correct outliers

    • If you want to use ERA5 or something else, you need to write one module similar to input_maps.generate_weather_files
    • possible corrections: if very high discrepancy between cell and neighbors --> outlier --> replaced by average of neighbors; correction with one constant factor; correction of low wind values in high altitude areas; redistribution of wind based on terrain roughness

  • High resolution potential taking into account the land use suitability/availability, topography, bathymetry, slope, distance to urban areas, etc.

    • suitability is binary
    • availability is a percentage of the area of the pixel
    • topography = elevation
    • bathymetry = depth of the sea (relevant for offshore)
    • slope: you can set a limit for the suitable areas (slope < threshold)
    • distance to urban areas: in pixels, not in meter! (relevant for onshore wind)

  • Statistical reports with summaries (available area, maximum capacity, maximum energy output, etc.) for each user-defined region

    • show example

  • Generation of several time series for each technology and region, based on user's preferences

    • the user can set the quantiles
    • work in progress: the user can set coordinates of particular points

  • Possibility to combine the time series into one using linear regression to match given full-load hours and temporal fluctuations

Applications

This code is useful if:

  • You want to estimate the theoretical and/or technical potential of an area, which you can define through a shapefile
  • You want to obtain high resolution maps
  • You want to define your own technology characteristics
  • You want to generate time series for an area after excluding parts of it that are not suitable for renewable power plants
  • You want to generate multiple time series for the same area (best site, upper 10%, median, lower 25%, etc.)
  • You want to match historical capacity factors of countries from the IRENA database

You do not need to use the code (but you can) if:

Overall concept

The user defines the region of interest, the geographic scope of their work. This is done through a shapefile, for which only the bounding box actually matters.

In the first part of the code, everything will be done for this scope (generating input maps and potential maps). This is rather slow, but is done only once for a given set of technology characteristics.

In the second part, the user defines subregions of interest. In case the scope is Europe, the subregions could be a shapefile of all European countries, or a shapefile of mega-regions (Iberian peninsula, Scandinavia), or a shapefile of one small region (Black forest, Garching, TUM campuses). Anything within the scope... well, not exactly. It can exceed the scope, but the parts outside of the scope will be cropped out (maybe you are interested in Europe but do not need the Azores or the Canary islands, which technically belong to European countries...). This step is fast and is meant to be done many times, provided that the first step is done. Statistical reports and time series for each subregion are generated at this step.

The third step is advanced and includes a linear regression to combine the time series and match a given shape and FLH value, or to group them in categories (high wind / low wind, for example).

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