Antares new features v8.6.0

This thumbnail will present the new features in line with Antares v8.6.0 (the link is here)

Create new study

dir_path <- tempdir()
createStudy(path = dir_path, 
            study_name = "test860", 
            antares_version = "8.6.0")
#> Warning: Parameter 'horizon' is missing or inconsistent with 'january.1st' and 'leapyear'. Assume correct year is 2018.
#> To avoid this warning message in future simulations, open the study with Antares and go to the simulation tab, put a valid year number in the cell 'horizon' and use consistent values for parameters 'Leap year' and '1st january'.

Create area

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

Create “st-storage”

We can create new st-storage cluster with new function createClusterST(). You can see function documentation with ?createClusterST.

By default you can call function only with two parameters (area, cluster_name).

inflows_data <- matrix(3, 8760)
ratio_values <- matrix(0.7, 8760)

createClusterST(area = "fr", 
                cluster_name = "test_storage", 
                storage_parameters = storage_values_default(), 
                PMAX_injection = ratio_values, 
                PMAX_withdrawal = ratio_values, 
                inflows = inflows_data, 
                lower_rule_curve = ratio_values, 
                upper_rule_curve = ratio_values, 
                overwrite = TRUE)
#> x being coerced from class: matrix to data.table
#> x being coerced from class: matrix to data.table
#> x being coerced from class: matrix to data.table
#> x being coerced from class: matrix to data.table
#> x being coerced from class: matrix to data.table

createClusterST(area = "it", 
                cluster_name = "test_storage", 
                storage_parameters = storage_values_default(), 
                PMAX_injection = ratio_values, 
                PMAX_withdrawal = ratio_values, 
                inflows = inflows_data, 
                lower_rule_curve = ratio_values, 
                upper_rule_curve = ratio_values, 
                overwrite = TRUE)
#> x being coerced from class: matrix to data.table
#> x being coerced from class: matrix to data.table
#> x being coerced from class: matrix to data.table
#> x being coerced from class: matrix to data.table
#> x being coerced from class: matrix to data.table

Now you can see informations in simulation options.

opts <- simOptions()
opts$areasWithSTClusters
#> [1] "fr" "it"

Read st-storages parameters

After creating “st-storage” clusters, you can read all information with specific function readClusterSTDesc().

tab <- readClusterSTDesc()
rmarkdown::paged_table(tab)

Read st-storages data

St-storages data are time series you can read for all areas or a specific area. 5 files contening one time series are generated (one per each function parameter):

PMAX-injection.txt
PMAX-withdrawal.txt
inflows.txt
lower-rule-curve.txt
upper-rule-curve.txt

data_st_storage <- readInputTS(st_storage = "all")
#> Importing st-storage
#>   |                                                                              |                                                                      |   0%  |                                                                              |===================================                                   |  50%  |                                                                              |======================================================================| 100%
rmarkdown::paged_table(head(data_st_storage))

As you can see, the last two columns (st-storage, name_file) give you value for each name file.
FYI : As default, reading option for hourly timestep is 8736 (see opts$timeIdMax).

Edit st-storage

It is possible to edit parameters values and data values like you want.

# edit parameters values 
list_params_st <- storage_values_default()
list_params_st$efficiency <- 0.5
list_params_st$reservoircapacity <- 50

# edit data values
inflows_data <- matrix(4, 8760)

editClusterST(area = "fr", 
              cluster_name = "test_storage", 
              storage_parameters = list_params_st,
              inflows = inflows_data,
              add_prefix = TRUE)
#> x being coerced from class: matrix to data.table

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


# read data
data_st_storage <- readInputTS(st_storage = "all")
#> Importing st-storage
#>   |                                                                              |                                                                      |   0%  |                                                                              |===================================                                   |  50%  |                                                                              |======================================================================| 100%
rmarkdown::paged_table(head(data_st_storage))

Remove st-storage

Creating or editing st-storage are done, you can also remove clusters from study.

# remove cluster
removeClusterST(area = "fr", 
                cluster_name = "test_storage", 
                add_prefix = TRUE)

# delete control 
opts <- simOptions()
opts$areasWithSTClusters
#> [1] "it"

The area fr is deleted cause we created only one cluster test_storage.

# control removed parameters
tab <- readClusterSTDesc()
rmarkdown::paged_table(head(tab))


# control removed data
data_st_storage <- readInputTS(st_storage = "all")
#> Importing st-storage
#>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%
rmarkdown::paged_table(head(data_st_storage))


unique(data_st_storage$area)
#> [1] "it"

Parameters and data concerning this cluster in this area are removed.

Thermal pollutants parameters

Antares version 8.6.0 now provide pollutants parameters for thermal clusters. You can see the documentation on thermal clusters here.

You have global list of pollutants given by function list_pollutants_values(). By default, parameters are set to NULL, you can initialize all parameters with value or customize parameters.

# create cluster with pollutants

# pollutants
all_param_pollutants <- list_pollutants_values(multi_values = 0.25)

createCluster(area = "fr", 
              cluster_name = "test_pollutant", 
              unitcount = 1L, 
              marginal_cost = 50,
              list_pollutants = all_param_pollutants, 
              time_series = matrix(rep(c(0, 8000), each = 24*364), ncol = 2),
              prepro_modulation = matrix(rep(c(1, 1, 1, 0), each = 24*365), ncol = 4) 
              )

# read parameters
param_th_cluster <- readClusterDesc()
rmarkdown::paged_table(param_th_cluster)

Let’s see how to edit 3 parameters nh3, nox, pm2_5.

# editing
edit_param_pollutants <- list_pollutants_values(multi_values = 0.3)[1:3]

editCluster(area = "fr", 
            cluster_name = "test_pollutant",
            unitcount = 2L, 
            list_pollutants = edit_param_pollutants)

# read parameters
param_th_cluster <- readClusterDesc()
rmarkdown::paged_table(param_th_cluster)

Hydro - MINGEN file

Antares version 8.6.0 provides new file mingen.txt, this file must respect some conditions.
The first condition to respect is the dimension with file mod.txt.
The second one is the consistency of the data between 3 files (mingen.txt, mod.txt, maxpower_{area}.txt).

Full documentation is available in the function writeInputTS(). We will see further information for values checks.

Values checks :

Checks depends of values of parameters in hydro.ini file.

After creating study, .txt files containing time series are empty. We will describe steps to edit mingen.txt.

Initial values :

# see hydro parameters 
path_file_hydro <- file.path("input", "hydro", "hydro.ini")
hydro_ini_values <- readIni(pathIni = path_file_hydro)
hydro_params <- c('follow load', 'use heuristic', "reservoir")
hydro_ini_values[hydro_params]
#> $`follow load`
#> $`follow load`$fr
#> [1] TRUE
#> 
#> $`follow load`$it
#> [1] TRUE
#> 
#> 
#> $`use heuristic`
#> $`use heuristic`$fr
#> [1] TRUE
#> 
#> $`use heuristic`$it
#> [1] TRUE
#> 
#> 
#> $reservoir
#> $reservoir$fr
#> [1] FALSE
#> 
#> $reservoir$it
#> [1] FALSE

Steps to create mingen file :

# Initialize mingen data (time series)
mingen_data = matrix(0.06,8760,5)

# 1 - edit mod file (time series)
mod_data = matrix(6,365,5)
suppressWarnings(
  writeInputTS(area = "fr", type = "hydroSTOR", 
             data = mod_data, 
             overwrite = TRUE)
)
#> Importing mingen
#>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%
#> Importing hydroStorage
#>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%

# 2 - edit maxpower 
maxpower_data <- matrix(6,365,4)
suppressWarnings(
  writeHydroValues(area = "fr", 
                 type = "maxpower", 
                 data = maxpower_data)
)
#> x being coerced from class: matrix to data.table
#> Importing mingen
#>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%
#> Importing hydroStorageMaxPower
#>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%

# 3 - edit mingen
suppressWarnings(
  writeInputTS(area = "fr", type = "mingen", 
             data = mingen_data, 
             overwrite = TRUE)
)
#> Importing mingen
#>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%
#> Importing hydroStorage
#>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%
#> Importing mingen
#>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%
#> Importing hydroStorageMaxPower
#>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%

Now we can read time series.

# read input time series
read_ts_file <- readInputTS(mingen = "all")
#> Importing mingen
#>   |                                                                              |                                                                      |   0%  |                                                                              |===================================                                   |  50%  |                                                                              |======================================================================| 100%
rmarkdown::paged_table(head(read_ts_file))