User guide

Prerequisites

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")

Save 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.

Create a new area

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.

Create a new cluster

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
)

Create a new link

 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"
)

Create a binding constraint

 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)
)

Create several Pumped Storage Power plant (PSP)

pspData <- data.frame(
 area = c("a", "b"), 
 installedCapacity = c(800,900)
)
 
 createPSP(
 areasAndCapacities = pspData, 
 efficiency = 0.75
)

Create several Demand Side Response (DSR)

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)

Update general settings

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)

Remove methods

You can remove areas, links, clusters and binding constraints from input folder with remove* functions, e.g.:

 removeArea("myarea")

Run Time-Series Generator

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
)

Run an Antares simulation

Launch an Antares simulation from R:

 runSimulation(
 name = "myAwesomeSimulation", 
 mode = "economy",
 path_solver = "C:/path/to/antares-solver.exe", 
 show_output_on_console = TRUE
)

Read a time series, update it and write it

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
)

Edit geographic trimming

 # 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)