This article will present the new features in line with Antares v9.2 (cf Antares Simulator)
dir_path <- tempdir()
suppressWarnings(
createStudy(path = dir_path,
study_name = "test920",
antares_version = "9.2")
)For Antares Simulator, format version is now “9.2”. For packages, we make the adjustment and we keep “920”.
Check version of my current study :
Some minor changes to the creation of the study.
Initializes the study by updating the generaldata.ini
file :
shedding-policy parameter is changed
to “accurate shave peaks”hydro-pmax = “daily”We just need create areas to create st-storages.
A new parameter overflow spilled cost difference in
hydro.ini file (input/hydro/hydro.ini) is
initiated (default value = 1).
We can create new clusters, st-storage (from v8.6), with function
createClusterST(). You can see function documentation with
?createClusterST.
By default you can call function only with two parameters
(area, cluster_name).
Clusters are created with default properties and time series.
Default group is still “Other1” and now you can
create/edit your own group name (only for version
study >= 9.2).
# creation
createClusterST(area = "fr",
cluster_name = "test_storage",
group = "my_own_group")
createClusterST(area = "it",
cluster_name = "test_storage",
group = "my_own_group_again")
# edit group of existing st-storage cluster
editClusterST(area = "fr",
cluster_name = "test_storage",
group = "my_own_group_Pondage")
# read cluster properties
tab <- readClusterSTDesc()
rmarkdown::paged_table(tab)you can create or edit new clusters with new properties (see doc
?createClusterST).
# new properties (default values)
rmarkdown::paged_table(as.data.frame(storage_values_default(), check.names = FALSE))# creation
my_parameters <- storage_values_default()
my_parameters$efficiencywithdrawal <- 0.5
my_parameters$`penalize-variation-injection` <- TRUE
my_parameters$`penalize-variation-withdrawal` <- TRUE
createClusterST(area = "fr",
cluster_name = "test_storage",
group = "new_properties",
storage_parameters = my_parameters,
overwrite = TRUE)
createClusterST(area = "it",
cluster_name = "test_storage",
group = "new_properties",
storage_parameters = my_parameters,
overwrite = TRUE)
# read cluster properties
tab <- readClusterSTDesc()
rmarkdown::paged_table(tab)# edit properties of existing st-storage cluster
my_parameters$efficiencywithdrawal <- 0.9
my_parameters$`penalize-variation-injection` <- FALSE
my_parameters$`penalize-variation-withdrawal` <- FALSE
editClusterST(area = "fr",
cluster_name = "test_storage",
storage_parameters = my_parameters)
# read cluster properties
tab <- readClusterSTDesc()
rmarkdown::paged_table(tab)We have five new .txt files containing one series of dimension \({N=8760, P=1}\) :
# creation
ratio_value <- matrix(0.7, 8760)
# default properties with new optional TS
createClusterST(area = "fr",
cluster_name = "good_ts_value",
cost_injection = ratio_value,
cost_withdrawal = ratio_value,
cost_level = ratio_value,
cost_variation_injection = ratio_value,
cost_variation_withdrawal = ratio_value)
# read cluster TS values
tab <- readInputTS(st_storage = "all",
showProgress = FALSE)
rmarkdown::paged_table(head(tab))# edit TS values of existing st-storage cluster
new_ratio_value <- matrix(0.85, 8760)
# edit everything or anyone you want
editClusterST(area = "fr",
cluster_name = "good_ts_value",
cost_injection = new_ratio_value,
cost_withdrawal = new_ratio_value)
# read cluster TS values
tab <- readInputTS(st_storage = "all",
showProgress = FALSE)
rmarkdown::paged_table(head(tab))We can add additional constraints via the dedicated endpoints:
The function read_storages_constraints() reads all short-term storage constraints from API or disk and returns them as a nested R list. Each top-level element corresponds to an area. Inside each area you will find, for each storage cluster, two main sub-lists:
properties – general information and constraint definitions, such as:
values - time-series data linked to those constraints, for example a rhs_test element containing a numeric vector of hourly values (length 8760).
# Create
createClusterST(area = "fr",
cluster_name = "Additional_Properties",
storage_parameters = my_parameters,
PMAX_injection = NULL,
PMAX_withdrawal = NULL,
inflows = NULL,
lower_rule_curve = NULL,
upper_rule_curve = NULL,
cost_injection = NULL,
cost_withdrawal = NULL,
cost_level = NULL,
cost_variation_injection = NULL,
cost_variation_withdrawal =NULL,
constraints_properties = list(
"test"=list(
variable = "withdrawal",
operator = "equal",
hours = c("[1,3,5]"),
"test2"=list(
variable = "netting",
operator = "less",
hours = c("[1, 168]")
)
)))
# Edit
editClusterST (area = "fr",
cluster_name = "Additional_Properties",
storage_parameters = my_parameters,
PMAX_injection = NULL,
PMAX_withdrawal = NULL,
inflows = NULL,
lower_rule_curve = NULL,
upper_rule_curve = NULL,
cost_injection = NULL,
cost_withdrawal = NULL,
cost_level = NULL,
cost_variation_injection = NULL,
cost_variation_withdrawal =NULL,
constraints_properties <- list(
"test"=list(
variable = "withdrawal",
operator = "equal",
hours = c("[1,3,5]",
"[120,121,122,123,124,125,126,127,128]"),
enabled = FALSE
),
"test2"=list(
variable = "netting",
operator = "less",
hours = c("[1, 168]")
)))# Create
good_ts <- matrix(0.7, nrow = 8760, ncol = 1)
createClusterST(area = "fr",
cluster_name = "Additional_Values",
storage_parameters = my_parameters,
PMAX_injection = NULL,
PMAX_withdrawal = NULL,
inflows = NULL,
lower_rule_curve = NULL,
upper_rule_curve = NULL,
cost_injection = NULL,
cost_withdrawal = NULL,
cost_level = NULL,
cost_variation_injection = NULL,
cost_variation_withdrawal =NULL,
constraints_properties = list(
"test"=list(
variable = "withdrawal",
operator = "equal",
hours = c("[1,3,5]",
"[120,121,122,123,124,125,126,127,128]")
#enabled = FALSE
),
"test2"=list(
variable = "netting",
operator = "less",
hours = c("[1, 168]")
)),
# constraints_ts
constraints_ts = list(
"test" = good_ts,
"test2" = good_ts
))
# Edit
editClusterST (area = "fr",
cluster_name = "Additional_Values",
constraints_ts = list(
"test" = good_ts,
"test2" = good_ts+1
) ,
add_prefix = TRUE)
#Read
res=read_storages_constraints()#> levelName
#> 1 Root
#> 2 °--fr
#> 3 ¦--fr_additional_properties
#> 4 ¦ ¦--properties
#> 5 ¦ ¦ °--test
#> 6 ¦ °--values
#> 7 °--fr_additional_values
#> 8 ¦--properties
#> 9 ¦ ¦--test
#> 10 ¦ °--test2
#> 11 °--values
#> 12 ¦--rhs_test
#> 13 °--rhs_test2Nothing has changed to remove clusters.
# read cluster names
levels(readClusterSTDesc()$cluster)
#> [1] "fr_additional_properties" "fr_additional_values"
#> [3] "fr_good_ts_value" "fr_test_storage"
#> [5] "it_test_storage"
# remove a cluster
removeClusterST(area = "fr",
cluster_name = "good_ts_value")
# read cluster
tab <- readClusterSTDesc()
rmarkdown::paged_table(tab)The “generaldata.ini” settings file (settings/generaldata.ini) contains several sections.
Antares Simulator v9.2 deletes some parameters:
A message is displayed and parameters are set to NULL
for the [adequacy patch] section.
For the [other preferences] section, the
initial-reservoir-levels parameter is not explicitly used
by a dedicated function.
# user messages
updateAdequacySettings(
set_to_null_ntc_between_physical_out_for_first_step = FALSE)
#> Warning: The `set_to_null_ntc_between_physical_out_for_first_step` argument of
#> `updateAdequacySettings()` is deprecated as of antaresEditObject 2.9.2.
#> ℹ This parameter are no longer supported for an Antares version >= '9.2', the
#> values will be ignored.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
updateAdequacySettings(enable_first_step = FALSE)
#> Warning: The `enable_first_step` argument of `updateAdequacySettings()` is deprecated as
#> of antaresEditObject 2.9.2.
#> ℹ This parameter are no longer supported for an Antares version >= '9.2', the
#> values will be ignored.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.The scenario builder allows you to use a new code hfl
for ‘hydro final level’. This new feature is similar to hl
(‘hydro levels’) and is used in the same way with the
‘coef_hydro_levels’ parameter.
# the number of coeff is equivalent to the number of areas
my_coef <- runif(length(getAreas()))
opts <- simOptions()
# build data
ldata <- scenarioBuilder(
n_scenario = 10,
n_mc = 10,
areas = getAreas(),
coef_hydro_levels = my_coef
)
#> Warning: Specified number of Monte-Carlo years differ from the one in Antares
#> general parameter
# update scenearionbuilder.dat
updateScenarioBuilder(ldata = ldata,
series = "hfl")
readScenarioBuilder(as_matrix = TRUE)
#> $hfl
#> [,1]
#> fr 0.3920503
#> it 0.3070450
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