genetic_algorithm: Run a Genetic Algorithm to optimize a wind farm layout
In windfarmGA: Genetic Algorithm for Wind Farm Layout Optimization

genetic_algorithm

R Documentation

Run a Genetic Algorithm to optimize a wind farm layout

Description

Run a Genetic Algorithm to optimize the layout of wind turbines on a given area. The algorithm works with a fixed amount of turbines, a fixed rotor radius and a mean wind speed value for every incoming wind direction.

Usage

genetic_algorithm(
  Polygon1,
  GridMethod,
  Rotor,
  n,
  fcrR,
  referenceHeight,
  RotorHeight,
  SurfaceRoughness,
  Proportionality,
  iteration,
  mutr,
  vdirspe,
  topograp,
  elitism,
  nelit,
  selstate,
  crossPart1,
  trimForce,
  Projection,
  sourceCCL,
  sourceCCLRoughness,
  weibull,
  weibullsrc,
  Parallel,
  numCluster,
  verbose = FALSE,
  plotit = FALSE
)

Arguments

`Polygon1`	The considered area as SpatialPolygon, SimpleFeature Polygon or coordinates as matrix/data.frame
`GridMethod`	Should the polygon be divided into rectangular or hexagonal grid cells? The default is `Rectangular` grid. Hexagonal grids are computed when assigning `h` or `hexagon` to this input variable.
`Rotor`	The rotor radius in meter
`n`	The amount of turbines
`fcrR`	A numeric value used for grid spacing. Default is `5`
`referenceHeight`	The height at which the incoming wind speeds were measured. Default is `RotorHeight`
`RotorHeight`	The height of the turbine hub
`SurfaceRoughness`	A surface roughness length in meters. With the terrain effect model, a surface roughness is calculated for every grid cell using the elevation and land cover data. Default is `0.3`
`Proportionality`	A numeric value used for the grid calculation, as it determines the percentage a grid cell must overlay the area. Default is `1`
`iteration`	The number of iterations. Default is `20`
`mutr`	A numeric mutation rate. Default is `0.008`
`vdirspe`	A data.frame containing the wind speeds, directions and probabilities. See `windata_format`.
`topograp`	Boolean value, which indicates if the terrain effect model should be enabled or not. Default is `FALSE`
`elitism`	Boolean value, which indicates whether elitism should be activated or not. Default is `TRUE`
`nelit`	If `elitism` is TRUE, this input determines the amount of individuals in the elite group. Default is 7
`selstate`	Determines which selection method is used, "FIX" selects a constant percentage and "VAR" selects a variable percentage, depending on the development of the fitness values. Default is "FIX"
`crossPart1`	Determines which crossover method is used, "EQU" divides the genetic code at equal intervals and "RAN" divides the genetic code at random locations. Default is `"EQU"`
`trimForce`	If `TRUE` the algorithm will use a probabilistic approach to correct the windfarms to the desired amount of turbines. If `FALSE` the adjustment will be random. Default is `FALSE`
`Projection`	A spatial reference system. Depending on your PROJ-version, it should either be a numeric `EPSG-code` or a `Proj4-string`. Default is `EPSG:3035`
`sourceCCL`	The path to the Corine Land Cover raster (.tif). Only required when the terrain effect model is activated.
`sourceCCLRoughness`	The source to the adapted Corine Land Cover legend as .csv file. Only required when terrain effect model is activated. As default a .csv file within this package (‘~/extdata’) is taken that was already adapted manually.
`weibull`	A boolean value that specifies whether to take Weibull parameters into account. If `TRUE`, the wind speed values of `vdirspe` are ignored. The algorithm will calculate the mean wind speed for every wind turbine according to the Weibull parameters. Default is `FALSE`
`weibullsrc`	A list of Weibull parameter rasters, where the first list item must be the shape parameter raster `k` and the second item must be the scale parameter raster `a` of the Weibull distribution. If no list is given, then rasters included in the package are used instead, which currently only cover Austria. This variable is only used if `weibull = TRUE`.
`Parallel`	A boolean value, indicating whether parallel processing should be used. The parallel and doParallel packages are used for parallel processing. Default is `FALSE`
`numCluster`	If `Parallel` is TRUE, this variable defines the number of clusters to be used. Default is `2`
`verbose`	If TRUE it will print information for every generation. Default is `FALSE`
`plotit`	If TRUE it will plot the best windfarm of every generation. Default is `FALSE`

Details

A terrain effect model can be included in the optimization process. Therefore, a digital elevation model will be downloaded automatically via the elevatr::get_elev_raster function. A land cover raster can also downloaded automatically from the EEA-website, or the path to a raster file can be passed to sourceCCL. The algorithm uses an adapted version of the Raster legend ("clc_legend.csv"), which is stored in the package directory ‘~/inst/extdata’. To use other values for the land cover roughness lengths, insert a column named "Rauhigkeit_z" to the .csv file, assign a surface roughness length to all land cover types. Be sure that all rows are filled with numeric values and save the file with ";" separation. Assign the path of the file to the input variable sourceCCLRoughness of this function.

Value

The result is a matrix with aggregated values per generation; the best individual regarding energy and efficiency per generation, some fuzzy control variables per generation, a list of all fitness values per generation, the amount of individuals after each process, a matrix of all energy, efficiency and fitness values per generation, the selection and crossover parameters, a matrix with the generational difference in maximum and mean energy output, a matrix with the given inputs, a dataframe with the wind information, the mutation rate per generation and a matrix with all tested wind farm layouts.

Examples

## Not run: 
## Create a random rectangular shapefile
library(sf)

Polygon1 <- sf::st_as_sf(sf::st_sfc(
  sf::st_polygon(list(cbind(
    c(4498482, 4498482, 4499991, 4499991, 4498482),
    c(2668272, 2669343, 2669343, 2668272, 2668272)
  ))),
  crs = 3035
))

## Create a uniform and unidirectional wind data.frame and plot the
## resulting wind rose
data.in <- data.frame(ws = 12, wd = 0)
windrosePlot <- plot_windrose(
  data = data.in, spd = data.in$ws,
  dir = data.in$wd, dirres = 10, spdmax = 20
)

## Runs an optimization run for 20 iterations with the
## given shapefile (Polygon1), the wind data.frame (data.in),
## 12 turbines (n) with rotor radii of 30m (Rotor) and rotor height of 100m.
result <- genetic_algorithm(
  Polygon1 = Polygon1,
  n = 12,
  vdirspe = data.in,
  Rotor = 30,
  RotorHeight = 100
)
plot_windfarmGA(result = result, Polygon1 = Polygon1)

## End(Not run)

windfarmGA documentation built on April 4, 2025, 3:39 a.m.