knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
First it's necessary to load the package:
# CRAN limite CPU usage data.table::setDTthreads(2) library(antaresEditObject)
You need to set the path to an Antares study in "input" mode:
antaresRead::setSimulationPath(path = "path/to/study", simulation = "input")
Or you can simply create a new study:
createStudy("path/to/study")
Before modifying your study, you can save it in an archive:
backupStudy(what = "input")
This will create a .tar.gz
file in your study folder.
You can create a new area with:
createArea(name = "myarea") # The new area should appear here: antaresRead::getAreas()
You can specify the localization of the area on the map, and also its color.
There are two helper functions for area parameters:
filteringOptions()
for filtering options, like filter-year-by-year
nodalOptimizationOptions()
for nodal optimizations options.You can initialize a cluster with some parameters:
createCluster( area = "myarea", cluster_name = "myareacluster", group = "other", unitcount = 1, nominalcapacity = 8400, `min-down-time` = 0, `marginal-cost` = 0.010000, `market-bid-cost` = 0.010000 )
You can also edit the settings of an existing cluster:
editCluster( area = "myarea", cluster_name = "myareacluster", nominalcapacity = 10600.000 )
createLink( from = "area1", to = "area2", propertiesLink = propertiesLinkOptions( hurdles_cost = FALSE, transmission_capacities = "enabled" ), dataLink = NULL )
You can edit the settings of an existing link:
editLink( from = "area1", to = "area2", transmission_capacities = "infinite" )
createBindingConstraint( name = "myconstraint", values = matrix(data = c(rep(c(19200, 0, 0), each = 366)), ncol = 3), enabled = FALSE, timeStep = "daily", operator = "both", coefficients = c("fr%myarea" = 1) )
pspData <- data.frame( area = c("a", "b"), installedCapacity = c(800,900) ) createPSP( areasAndCapacities = pspData, efficiency = 0.75 )
dsrData <- data.frame( area = c("a", "b"), unit = c(10,20), nominalCapacity = c(100, 120), marginalCost = c(52, 65), hour = c(3, 7) ) createDSR(dsrData)
For example, set the output of simulation year by year, and limit the number of Monte-Carlo years to 10:
updateGeneralSettings(year.by.year = TRUE, nbyears = 10)
You can remove areas, links, clusters and binding constraints from input folder
with remove*
functions, e.g.:
removeArea("myarea")
First, update general settings to activate time series to generate:
updateGeneralSettings(generate = "thermal")
Then run TS-generator:
runTsGenerator( path_solver = "C:/path/to/antares-solver.exe", show_output_on_console = TRUE )
Launch an Antares simulation from R:
runSimulation( name = "myAwesomeSimulation", mode = "economy", path_solver = "C:/path/to/antares-solver.exe", show_output_on_console = TRUE )
To update an existing time series and write it, you can use the following commands :
# Filepath of the study, version >= 820 my_study <- file.path("", "", "") opts <- setSimulationPath(my_study, simulation ="input") opts$timeIdMax <- 8760 # Links, use only one link my_link <- as.character(getLinks()[1]) ts_input <- readInputTS(linkCapacity = my_link, opts = opts) # Sort the data to ensure its reliability data.table::setorder(ts_input, cols = "tsId", "timeId") # Reshape to wide format : writeInputTS expects a 8760 * N matrix metrics <- c("transCapacityDirect", "transCapacityIndirect") ts_input_reformatted <- data.table::dcast(ts_input, timeId ~ tsId, value.var = metrics ) # Add a value my_param to your matrix my_param <- 123 writeInputTS(data = ts_input_reformatted[,2:ncol(ts_input_reformatted)] + my_param, type = "tsLink", link = my_link, overwrite = TRUE, opts = opts ) # Thermal, use only one area and one cluster my_area <- "zone" my_cluster <- "mon_cluster" ts_input <- readInputTS(thermalAvailabilities = my_area, opts = opts) ts_input <- ts_input[cluster == paste0(my_area,"_",my_cluster)] # Sort the data to ensure its reliability data.table::setorder(ts_input, cols = "tsId", "timeId") # Reshape to wide format : writeInputTS expects a 8760 * N matrix metrics <- c("ThermalAvailabilities") ts_input_reformatted <- data.table::dcast(ts_input, timeId ~ tsId, value.var = metrics ) # Add a value my_param to your matrix my_param <- 1000 editCluster(area = my_area, cluster_name = my_cluster, time_series = ts_input_reformatted[,2:ncol(ts_input_reformatted)] + my_param, opts = opts ) # Run of River, use only one area my_area <- "zone" ts_input <- readInputTS(ror = my_area, opts = opts) # Sort the data to ensure its reliability data.table::setorder(ts_input, cols = "tsId", "timeId") # Reshape to wide format : writeInputTS expects a 8760 * N matrix metrics <- c("ror") ts_input_reformatted <- data.table::dcast(ts_input, timeId ~ tsId, value.var = metrics ) # Add a value my_param to your matrix my_param <- 1000 writeInputTS(area = my_area, type = "hydroROR", data = ts_input_reformatted[,2:ncol(ts_input_reformatted)] + my_param, overwrite = TRUE, opts = opts )
# set the path to an Antares study my_study <- file.path("", "", "") opts <- setSimulationPath(my_study, simulation ="input") # choose geographic trimming when creating new Antares areas # default filtering : c("hourly","daily","weekly","monthly","annual") initial_filtering_synthesis <- c("weekly","monthly") initial_filtering_year_by_year <- c("monthly","annual") opts <- createArea(name = "area1", filtering = filteringOptions( filter_synthesis = initial_filtering_synthesis, filter_year_by_year = initial_filtering_year_by_year), opts = opts) opts <- createArea(name = "area2",opts = opts) opts <- createLink(from = "area1", to = "area2", propertiesLink = propertiesLinkOptions( filter_synthesis = initial_filtering_synthesis, filter_year_by_year = initial_filtering_year_by_year), opts = opts) # check the initial filters initial_GT <- getGeographicTrimming(areas="area1",links=TRUE,opts=opts) print(initial_GT$areas[["area1"]]) print(initial_GT$links[["area1 - area2"]]) # edit geographic trimming of an existing area or link new_filtering_synthesis <- c("monthly") new_filtering_year_by_year <- c("annual") opts <- editArea(name = "area1", filtering = list( "filter_synthesis" = paste(new_filtering_synthesis,collapse = ", ") "filter_year_by_year" = paste(new_filtering_year_by_year,collapse = ", ")), opts = opts) opts <- editLink(from = "area1", to = "area2", filter_year_by_year = new_filtering_year_by_year, filter_synthesis = new_filtering_synthesis, opts = opts) # check the new filters new_GT <- antaresRead::getGeographicTrimming(areas="area1",links=TRUE,opts=opts) print(new_GT$areas[["area1"]]) print(new_GT$links[["area1 - area2"]]) # important : make sure that `geographic-trimming` parameter is activated in general settings opts <- updateGeneralSettings(geographic.trimming = TRUE,opts = opts)
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