knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
# CRAN limite CPU usage data.table::setDTthreads(2) library(antaresEditObject)
This thumbnail will present the new features in line with Antares v8.6.0 (the link is here)
There are 3 new features :
Add new storage type "short-term storage".
Update parameters of thermal clusters with "pollutant emission factors"
"Hydro Pmin" : new file "mingen.txt"
dir_path <- tempdir() createStudy(path = dir_path, study_name = "test860", antares_version = "8.6.0")
createArea(name = "fr") createArea(name = "it")
We can create new st-storage cluster with new function createClusterST()
. You can see function documentation with ?createClusterST
.
By default you can call function only with two parameters (area
, cluster_name
).
inflows_data <- matrix(3, 8760) ratio_values <- matrix(0.7, 8760) createClusterST(area = "fr", cluster_name = "test_storage", storage_parameters = storage_values_default(), PMAX_injection = ratio_values, PMAX_withdrawal = ratio_values, inflows = inflows_data, lower_rule_curve = ratio_values, upper_rule_curve = ratio_values, overwrite = TRUE) createClusterST(area = "it", cluster_name = "test_storage", storage_parameters = storage_values_default(), PMAX_injection = ratio_values, PMAX_withdrawal = ratio_values, inflows = inflows_data, lower_rule_curve = ratio_values, upper_rule_curve = ratio_values, overwrite = TRUE)
Now you can see informations in simulation options.
opts <- simOptions() opts$areasWithSTClusters
After creating "st-storage" clusters, you can read all information with specific function readClusterSTDesc()
.
tab <- readClusterSTDesc() rmarkdown::paged_table(tab)
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):
data_st_storage <- readInputTS(st_storage = "all") 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 r opts$timeIdMax
(see opts$timeIdMax
).
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) # read parameters tab <- readClusterSTDesc() rmarkdown::paged_table(tab) # read data data_st_storage <- readInputTS(st_storage = "all") rmarkdown::paged_table(head(data_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$areasWithSTClusters
The area fr
is deleted cause we created only one cluster test_storage
.
# control removed parameters tab <- readClusterSTDesc() rmarkdown::paged_table(head(tab)) # control removed data data_st_storage <- readInputTS(st_storage = "all") rmarkdown::paged_table(head(data_st_storage)) unique(data_st_storage$area)
Parameters and data concerning this cluster in this area are removed.
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 r names(list_pollutants_values())[1:3]
.
# 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)
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.
knitr::include_graphics("schemas/mingen_hydro_rules.png")
# path_image <- sourcedir860 <- system.file("doc/schemas", package = "antaresEditObject") # knitr::include_graphics(file.path(path_image,"mingen.png")) knitr::include_graphics("schemas/mingen_draw.png")
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]
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) )
# 2 - edit maxpower maxpower_data <- matrix(6,365,4) suppressWarnings( writeHydroValues(area = "fr", type = "maxpower", data = maxpower_data) )
# 3 - edit mingen suppressWarnings( writeInputTS(area = "fr", type = "mingen", data = mingen_data, overwrite = TRUE) )
Now we can read time series.
# read input time series read_ts_file <- readInputTS(mingen = "all") rmarkdown::paged_table(head(read_ts_file))
# Delete study unlink(opts$studyPath, recursive = TRUE) # clean global options options(antares = NULL)
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