genetic_algorithm: 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
)

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.

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.

Examples

Run this code

if (FALSE) {
## 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)
}