View source: R/createClusterBulk.R
createClusterBulk | R Documentation |
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()
)
cluster_object |
|
area_zone |
|
add_prefix |
|
opts |
List of simulation parameters returned by the function
|
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
An updated list containing various information about the simulation.
list
containing meta information about the simulation
## Not run:
# /!\/!\/!\ 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)
## End(Not run)
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