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Calculate Energy Outputs of Individuals

Source: R/calculate_energy.R
calculate_energy.Rd

Calculate the energy output and efficiency rates of an individual in the current population under all given wind directions and speeds. If the terrain effect model is activated, the main calculations to model those effects will be done in this function.

Usage

calculate_energy(
  layout,
  reference_height,
  rotor_height,
  surface_roughness,
  wake_angle,
  wake_distance,
  area,
  rotor,
  wind,
  elevation = NULL,
  terrain = FALSE,
  ccl_raster = NULL,
  weibull = FALSE,
  park_center = NULL,
  plot = FALSE
)

Arguments

layout

One individual: matrix with X/Y (and typically cell IDs).

reference_height

Height at which wind$ws was measured.

rotor_height

Hub height in metres.

surface_roughness

Roughness length in metres. Per-cell when terrain is on.

wake_angle

Angle (degrees) beyond which wake influence is ignored.

wake_distance

Distance (metres) beyond which wake effects are ignored.

area

Site polygon (sf, SpatialPolygons, or coordinate matrix). Must be projected in metres.

rotor

Rotor radius in metres.

wind

Wind data.frame with ws, wd and optional probab. See windata_format().

elevation

Terrain list from terrain_model(). Unused when terrain is FALSE.

terrain

Terrain model (elevation + land cover). TRUE downloads a DEM via elevatr. Pass a DEM raster to skip the download. Per-cell values are computed once and stored in the result as terrainModel for plot_result() / random_search().

ccl_raster

Land-cover roughness raster from terrain_model().

weibull

If TRUE, hub-height speed comes from Weibull rasters; wind$ws is ignored.

park_center

Optional numeric of length 2 (x, y) used as rotation origin. Computed from the polygon bounding box when missing.

plot

If TRUE, the process will be plotted.

Value

Returns a list of an individual of the current generation with resulting wake effects, energy outputs, efficiency rates for every wind direction. The length of the list corresponds to the number of different wind directions.

See also

Other Wind Energy Calculation Functions: barometric_height(), circle_intersection(), get_dist_angles(), turbine_influences()

Examples

# \donttest{
## Create a random Polygon
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
area <- 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
)


## Assign the rotor radius and a factor of the radius for grid spacing.
rotor <- 50
fcr <- 3
resGrid <- grid_area(
  area = area, size = rotor * fcr, prop = 1,
  plot_grid = TRUE
)


## Assign the indexed data frame to new variable. Element 2 of the list
## is the grid, saved as Simple Feature Polygons.
resGrid1 <- resGrid[[1]]

## Create an initial population with the indexed Grid, 15 turbines and
## 100 individuals.
initpop <- init_population(grid = resGrid1, n = 15, n_start = 100)

## Calculate the expected energy output of the first individual of the
## population.
par(mfrow = c(1, 2))
plot(area)
points(initpop[[1]][, "X"], initpop[[1]][, "Y"], pch = 20, cex = 2)
plot(resGrid[[2]], add = TRUE)

resCalcEn <- calculate_energy(
  layout = initpop[[1]], reference_height = 50,
  rotor_height = 50, surface_roughness = 0.14, wake_angle = 20,
  wake_distance = 100000, wind = data.in,
  rotor = 50, area = area, terrain = FALSE,
  weibull = FALSE
)
resCalcEn <- as.data.frame(resCalcEn)
plot(area, main = resCalcEn[, "Energy_Output_Red"][[1]])
points(x = resCalcEn[, "Bx"], y = resCalcEn[, "By"], pch = 20)



## Create a variable and multidirectional wind data.frame and plot the
## resulting wind rose
data.in10 <- data.frame(ws = runif(10, 1, 25), wd = runif(10, 0, 360))
windrosePlot <- plot_windrose(
  data = data.in10, spd = data.in10$ws,
  dir = data.in10$wd, dirres = 10, spdmax = 20
)


## Calculate the energy outputs for the first individual with more than one
## wind direction.
resCalcEn <- calculate_energy(
  layout = initpop[[1]], reference_height = 50,
  rotor_height = 50, surface_roughness = 0.14, wake_angle = 20,
  wake_distance = 100000, wind = data.in10,
  rotor = 50, area = area, terrain = FALSE,
  weibull = FALSE
)
# }