For each area, the thermal cluster data are generated :
Writing .ini files
Writing time_series files
Writing prepro_data files
Writing prepro_modulation files
createClusterBulk(
cluster_object,
area_zone,
add_prefix = TRUE,
opts = antaresRead::simOptions()
)list mutiple list containing the parameters for writing each cluster
character name of area to create cluster
logical prefix cluster name with area name
List of simulation parameters returned by the function
antaresRead::setSimulationPath()
An updated list containing various information about the simulation.
list containing meta information about the simulation
see the example to write a cluster object,
see the original function createCluster()
Structure of cluster_object :
The list must be structured with named items
parameter : list of paramaters to write in .ini file
overwrite : logical to choose to overwrite an existing cluster (if not present, set to FALSE)
time_series : matrix or data.frame the "ready-made" 8760-hour time-series
prepro_data : matrix or data.frame Pre-process data
prepro_modulation : matrix or data.frame Pre-process modulation
Details for sublist cluster_object[["parameter"]] :
name : Name for the cluster,
it will prefixed by area name, unless you set add_prefix = FALSE
group : Group of the cluster, depends on cluster type
... : Parameters to write in the Ini file
if (FALSE) { # \dontrun{
# /!\/!\/!\ use or create a study /!\/!\/!\
# data preparation for sutructures
ts <- matrix(rep(c(0, 8000), each = 24*364),
ncol = 2)
df_pd <- matrix(rep(c(1, 1, 1, 0), each = 24*365),
ncol = 4)
df_pm <- matrix(data = c(rep(1, times = 365 * 24 * 3), rep(0, times = 365 * 24 * 1)),
ncol = 4)
# Example cluster object
zone_test_1 <- list(
`CCGT old 1`= list(
parameter= list(
name= "CCGT old 1",
group = "Other",
unitcount= 10L,
nominalcapacity= 100,
enabled= "true",
`min-stable-power`= 80L,
`min-up-time`= 20L,
`min-down_time`= 30L),
overwrite= TRUE,
time_series = ts_8760,
prepro_data = df_pd,
prepro_modulation = df_pm))
# overwrite existing cluster
zone_test_2 <- list(
`PEAK`= list(parameter= list(
name= "PEAK",
group = "Other"),
overwrite= TRUE,
time_series = ts,
prepro_data = df_pd,
prepro_modulation = df_pm))
# Create multiple areas with multiple clusters
list_areas <- antaresRead::getAreas()[1:5]
lapply(list_areas, createClusterBulk,
cluster_object = c(zone_test_1, zone_test_2),
add_prefix = TRUE)
} # }