Antares new features v9.2

In antaresEditObject: Edit an 'Antares' Simulation

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(antaresEditObject)

This article will present the new features in line with Antares v9.2 (cf Antares Simulator)

Create new study v9.2

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 :

current_study_opts <- simOptions()
current_study_opts$antaresVersion

Some minor changes to the creation of the study.

Initializes the study by updating the generaldata.ini file :

• the value of the shedding-policy parameter is changed to "accurate shave peaks"
• a new "compatibility" section is created with parameter hydro-pmax = "daily"

Create area

We just need create areas to create st-storages.

createArea(name = "fr")
createArea(name = "it")

A new parameter overflow spilled cost difference in hydro.ini file (input/hydro/hydro.ini) is initiated (default value = 1).

Short term storage

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.

Dynamic group names

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)

New properties

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)

New optional time series

We have five new .txt files containing one series of dimension ${N=8760, P=1}$ :

• cost-injection.txt
• cost-withdrawal.txt
• cost-level.txt
• cost-variation-injection.txt
• cost-variation-withdrawal.txt

# 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))

Additional

We can add additional constraints via the dedicated endpoints:

• constraints_properties
• TS constraints rhs_*

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:

• variable (e.g. "withdrawal")
• operator (e.g. "equal")
• hours (e.g. "[1, 3, 5]")
• enabled (logical flag)

values - time-series data linked to those constraints, for example a rhs_test element containing a numeric vector of hourly values (length 8760).

Properties

# 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]")
                 )))

Values

# 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()

library(listviewer)
jsonedit(res, mode = "view", options = list(collapsed = 1))

Remove clusters

Nothing has changed to remove clusters.

# read cluster names
levels(readClusterSTDesc()$cluster)

# remove a cluster
removeClusterST(area = "fr", 
                cluster_name = "good_ts_value")

# read cluster 
tab <- readClusterSTDesc()
rmarkdown::paged_table(tab)

Generaldata

The “generaldata.ini” settings file (settings/generaldata.ini) contains several sections.

Antares Simulator v9.2 deletes some parameters:

• adequacy patch/enable-first-step
• adequacy patch/set-to-null-ntc-between-physical-out-for-first-step
• other preferences/initial-reservoir-levels

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)
updateAdequacySettings(enable_first_step = FALSE)

Scenario builder

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
  )

  # update scenearionbuilder.dat
  updateScenarioBuilder(ldata = ldata,
                        series = "hfl")

  readScenarioBuilder(as_matrix = TRUE)

# Delete study
unlink(current_study_opts$studyPath, 
       recursive = TRUE)
# clean global options
options(antares = NULL)

antaresEditObject documentation built on Nov. 5, 2025, 7:30 p.m.