The genetic algorithm is designed to optimize small wind farms with irregular shapes. The algorithm works with a fixed amount of turbines, a fixed rotor radius and a mean wind speed value for every incoming wind direction. If required, it can include a terrain effect model which downloads an 'SRTM' elevation model automatically and loads a Corine Land Cover raster, which has to be downloaded previously. Further information can be found in the description of the function 'windfarmGA'. To start an optimization run, either the function 'windfarmGA' or 'genAlgo' can be used. The function 'windfarmGA' checks the user inputs interactively and then runs the function 'genAlgo'. If the input parameters are already known, an optimization can be run directly via the function 'genAlgo'. Their output is identical.
Switch to matrices instead of data.frames and a lot of restructuring and performance optimization of the whole algorithm.
windfarmGA and the plotting functions now accept SimpleFeature Polygons or coordinates in table format
with long, lat or x, y column names. The terrain effect model can now be activated only by setting
topograp to TRUE and it will attempt to download the land cover raster from the European Environment Agency website.
viewshedA new set of functions, to analyze the visual impact of a wind farm.
plot_farm_3dExperimental rayshader function
The output of
windfarmGA can be further randomized/optimized with the following
RandomSearch is used to randomize all turbines of the layout whereas
RandomSearchTurb is used to randomize a single turbine
RandomSearchPlot is used to plot the outputs of those functions, compared with the
loadloadRes = RandomSearchTurbRandomSearchPlot
## Runs the same optimization, but with parallel processing and 3 cores.result_par <- genAlgoPlotWindfarmGA
result_hex <- genAlgoPlotWindfarmGA