R/get_margins.R
In rte-antares-rpackage/antaresXpansion: Antares Expansion Planning Tool
#' Compute generation margin in a given area on an Antares Study.
#'
#' - A positive generation margin means that the generation fleet of the area is over-sized
#' and the LOLD (Loss Of Load Duration) is below the adequacy criteria (typically 3h/y). In that
#' case, the margin corresponds to the extra constant load that the generation fleet can still supply while
#' respecting strictly the adequacy criteria.
#'
#' - A negative generation margin means that the generation fleet is under-sized and the LOLD
#' is above the adequacy criteria. In that case, the margin corresponds to the extra "perfect" production
#' capacity required to meet the adequacy criteria. A "perfect" capacity is a capacity which is available
#' all the time (i.e. without maintenance/outages).
#'
#' @param area
#' Area in which the margin is computed
#' @param cluster_name
#' Name of the complementary cluster if the margin is negative (lack of production).
#' The cluster can already exist, otherwise it will be created.
#' (example : If cluster_name = "gas_pcomp_peak" and area = "fr", the final name of the cluster will be "fr_gas_pcomp_peak")
#' @param LOLD
#' Adequacy criteria of the area in terms of Loss of Load Duration (given in h/year)
#' @param tolerance
#' Algorithmic tolerance about the adequacy criteria. Used as stop criterion.
#' (example : if tolerance = 0.5 and LOLD = 3.0, the aimed range of LOLD is between 2h30 and 3h30)
#' @param unit_size
#' Minimal step of the generation margin. Given in MW.
#' @param abaque
#' Function abaque : marge = f(LOLE)
#' @param initial_margin
#' First margin tested in the function. Given in MW.
#' @param path_solver
#' Character containing the Antares Solver path
#' @param display
#' Logical. If \code{TRUE} the advancement of the function
#' if displayed in the console
#' @param clean
#' Logical. If \code{TRUE} the output of the ANTARES simulations run by the
#' package will be deleted (except for the output of the simulation which brings
#' to the best solution).
#' @param parallel
#' Logical. If \code{TRUE} the ANTARES simulations will be run in parallel mode (Work
#' only with ANTARES v6.0.0 or more). In that case, the number of cores used by the simulation
#' is the one set in advanced_settings/simulation_cores (see ANTARES interface).
#' @param opts
#' list of simulation parameters returned by the function
#' \code{antaresRead::setSimulationPath}
#'
#' @return
#'
#' @importFrom assertthat assert_that
#' @importFrom antaresRead simOptions setSimulationPath getAreas readClusterDesc readInputTS
#' @importFrom rmarkdown render
#' @importFrom utils packageVersion tail
#' @importFrom antaresEditObject setPlaylist getPlaylist createCluster writeIni
#' @export
#'
get_margins <- function(area = "fr", cluster_name = "gas_pcomp_peak", LOLD = 3.00, tolerance = 0.1, unit_size = 100,
abaque = function(c){return(-3627*log(c)+3723.4)}, initial_margin = 0, path_solver, display = TRUE, clean = TRUE,
parallel = TRUE, opts = antaresRead::simOptions())
{
# ---- 0. initialize ----
LOLE <- LOLD
# reload study
opts <- setSimulationPath(opts$studyPath, simulation = 0)
# save current settings of the ANTARES study into a temporary file
save_general_settings(opts)
# reset options of the ANTARES study to their initial values when the function ends
on.exit(restore_general_settings(opts))
# set week and initial day
# we need to ensure the consistency between the weekly optimisation and the weekly
# aggregation of the output
month_name <- c("january", "december", "november", "october", "september", "august", "july", "june", "may", "april", "march", "february")
day_per_month <- c(0, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31, 28)
day_name <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
if (opts$parameters$general$leapyear)
{
day_per_month[12] <- 29
}
month_id <- which(month_name == opts$parameters$general$"first-month-in-year")
assertthat::assert_that(length(month_id) == 1)
n_day <- (-sum(day_per_month[1:month_id]) + opts$parameters$general$simulation.start - 1) %% 7
first_day_week <- day_name[((which(day_name == opts$parameters$general$january.1st) + n_day - 1) %% 7 ) +1]
antaresEditObject::updateGeneralSettings(first.weekday = first_day_week)
# adjust abacus
abaque_t <- abaque
a_0 <- uniroot(abaque_t, interval = c(0.01, 10))$root
abaque <- function(x){abaque_t(x * a_0 / LOLE)}
# initiate a few parameters
current_it <- 0
antaresEditObject::updateGeneralSettings(filtering = "true", year.by.year = "true", opts = opts)
filter_output_areas(areas = antaresRead::getAreas(opts = opts), filter = c("annual", "weekly"), type = c("year-by-year","synthesis"), opts = opts)
filter_output_links(links = antaresRead::getLinks(opts = opts), filter = c("annual"), type = c("synthesis"), opts = opts)
unique_key <- paste(sample(c(0:9, letters), size = 3, replace = TRUE),collapse = "") # unique key used in output names
rowbalance_file_name <- paste0(opts$inputPath, "/misc-gen/miscgen-", area, ".txt", sep = "")
assertthat::assert_that(file.exists(rowbalance_file_name))
loop <- FALSE
if (file.info(rowbalance_file_name)$size != 0)
{
param_data <- read.table(rowbalance_file_name)
initial_row_balance <- param_data[, 8]
if (abs(sum(initial_row_balance)) > 0)
{
cat("Warning : initial row balance of the area ", area," is not null ! \n", sep="")
}
}
else {initial_row_balance <- rep(0,8760)}
list_margin <- data.frame(row.names = c("it", "margin", "LOLE"))
if(display){cat("------ Iteration 0 ------\n", sep="")}
# setplaylist and weeks
first_sim_week <- 1 + ceiling((opts$parameters$general$simulation.start - 1)/7)
n_w <- floor((opts$parameters$general$simulation.end - opts$parameters$general$simulation.start + 1)/7) # number of weeks
weeks <- initial_weeks <- first_sim_week:(first_sim_week + n_w - 1) # identifier of weeks to simulate for all expansion planning optimisation
playlist <- initial_playlist <- getPlaylist(opts) # identifier of mc years to simulate for all expansion planning optimisation
# set margin
set_margins_in_antares(margin = initial_margin, area, cluster_name, row_balance_init = initial_row_balance, unit_size = unit_size, opts = antaresRead::simOptions())
# ---- 1. Simulate with Antares once at the beginning : ----
simulation_name <- paste0("get-margins-", unique_key, "-it", current_it, "-margin_", initial_margin,"mw")
if(display){ cat(" ANTARES simulation running ", sep="")}
if(display){ cat("[",length(weeks)*length(playlist)," simulated weeks] ... ", sep = "")}
run_simulation(simulation_name, mode = "economy",
path_solver, wait = TRUE, show_output_on_console = FALSE, parallel = parallel, opts)
if(display){ cat("[done] \n", sep="")}
output_antares <- antaresRead::setSimulationPath(paste0(opts$studyPath, "/output/", get_whole_simulation_name(simulation_name, opts)))
output_LOLE <- antaresRead::readAntares(areas = area, links = NULL, mcYears = "all",
timeStep = "annual", opts = output_antares, showProgress = FALSE,
select = c("LOLD"))
# ---- 2. Define first LOLE : ----
new_LOLE <- mean(output_LOLE$LOLD)
if(display){ cat(" margin = ", initial_margin, " MW\n", sep="")}
if(display){ cat(" LOLD = ", new_LOLE ," h\n", sep="")}
list_margin <- rbind(list_margin,
data.frame(it = current_it,
margin = initial_margin,
LOLE = new_LOLE))
# if LOLE is above the target, report years and weeks which acutally have unsupplied energy
if(new_LOLE > LOLE)
{
ooo <- get_years_and_weeks_with_LOLD(area = area, opts = output_antares)
playlist <- intersect(playlist, ooo$mc_years)
weeks <- intersect(weeks, ooo$weeks)
}
# No zero with log !
new_LOLE_init <- new_LOLE
current_it <- 1
if(new_LOLE >= LOLE-tolerance & new_LOLE <= LOLE + tolerance )
{
if(display){ cat("The area ", area, " is already balanced with margin = ", initial_margin," MW \n")}
}
# ---- 3. Evaluate the fisrt margin with the abacus : ----
else
{
if (new_LOLE==0){margin <- 10000}
else if (new_LOLE > LOLE) {margin <- abaque(new_LOLE)}
else if (new_LOLE < LOLE) {margin <- abaque(new_LOLE) * 1.15}
margin <- initial_margin + margin
margin <- floor(margin/unit_size)*unit_size
if(display){ cat(" First margin evaluated with the abacus : ", margin," MW\n", sep="")}
# ---- 5. iterate until convergence : ----
has_converged <- FALSE
while(!has_converged)
{
if(display){cat("\n------ Iteration ", current_it, " ------\n", sep="")}
set_margins_in_antares(margin, area, cluster_name, row_balance_init = initial_row_balance, unit_size = unit_size, opts = antaresRead::simOptions())
# ----------- change playlist and simulated weeks -------------
set_simulation_period(weeks, opts)
antaresEditObject::setPlaylist(playlist, opts)
# ----------- Simulate -----------
antaresEditObject::updateGeneralSettings(filtering = "true", year.by.year = "true", opts = opts)
simulation_name <- paste0("get-margins-", unique_key, "-it", current_it, "-margin_", margin, "mw")
if(display){ cat(" ANTARES simulation running ", sep="")}
if(display){ cat("[",length(weeks)*length(playlist)," simulated weeks] ... ", sep = "")}
run_simulation(simulation_name, mode = "economy",
path_solver, wait = TRUE, show_output_on_console = FALSE, parallel = parallel, opts)
if(display){ cat("[done] \n", sep="")}
output_antares <- antaresRead::setSimulationPath(paste0(opts$studyPath, "/output/", get_whole_simulation_name(simulation_name, opts)))
output_LOLE <- antaresRead::readAntares(areas = area, links = NULL, mcYears = "all",
timeStep = "annual", opts = output_antares, showProgress = FALSE,
select = c("LOLD"))
new_LOLE <- sum(output_LOLE$LOLD)/length(initial_playlist)
if(display){ cat(" margin = " ,margin , " MW\n", sep="" )}
if(display){ cat(" LOLD = " ,new_LOLE , " h\n", sep="" )}
list_margin <- rbind(list_margin,
data.frame(it = current_it,
margin = margin,
LOLE = new_LOLE))
# ---- update step ----
# if(min(abs(new_LOLE - LOLE + tolerance), abs(new_LOLE - LOLE - tolerance))>1.0){ step <- 8*unit_size}
# else{
# if(min(abs(new_LOLE - LOLE + tolerance), abs(new_LOLE - LOLE - tolerance))>0.75){step <- 4*unit_size}
# else{
# if(min(abs(new_LOLE - LOLE + tolerance), abs(new_LOLE - LOLE - tolerance))>0.5){step <- 2*unit_size}
# else{step <- unit_size}
# }
# }
if (new_LOLE==0){step <- 10000}
else {step <- abaque(new_LOLE)}
if(step > 0 && new_LOLE > LOLE){step <- -step}
if(step < 0 && new_LOLE < LOLE){step <- -step}
if(abs(step) < unit_size) {step <- sign(step)*unit_size}
else { step <- round(step/unit_size)*unit_size }
# ----------- Update : Lack of production -----------
if (new_LOLE > LOLE+tolerance)
{
margin <- margin + step
# If we had already had this margin previously
if(!is.null(list_margin$margin[list_margin$margin < margin - step]))
{
if (any(margin <= list_margin$margin[list_margin$margin < margin - step])) {
loop <- TRUE
# Get the nearest LOLE of the aimed range
ind_inf <- which(list_margin$LOLE< LOLE - tolerance)
ind_inf <- ind_inf[which.max(list_margin$margin[ind_inf])]
ind_sup <- which(list_margin$LOLE> LOLE + tolerance)
ind_sup <- ind_sup[which.min(list_margin$margin[ind_sup])]
if(abs(list_margin$margin[ind_inf]-list_margin$margin[ind_sup])==unit_size)
{
if(display){ cat("\n Units of ",unit_size," MW are too big to adapt to the tolerance \n")}
has_converged <- TRUE
if(display){ cat("Convergence with margin = ",list_margin$margin[ind_inf]," MW or margin = ",list_margin$margin[ind_sup], " MW \n", sep="")}
}
else{
margin <- floor((list_margin$margin[ind_inf]+list_margin$margin[ind_sup])/2/unit_size)*unit_size
}
}
}
}
# ----------- Update : Lack of consumption
else if (new_LOLE < LOLE - tolerance)
{
margin <- margin + step
# If we had already had this margin previously
if(!is.null(list_margin$margin[list_margin$margin > margin - step]))
{
if (any(margin >= list_margin$margin[list_margin$margin > margin - step])) {
loop <- TRUE
# Get the nearest LOLE of the aimed range
ind_inf <- which(list_margin$LOLE< LOLE - tolerance)
ind_inf <- ind_inf[which.max(list_margin$margin[ind_inf])]
ind_sup <- which(list_margin$LOLE> LOLE + tolerance)
ind_sup <- ind_sup[which.min(list_margin$margin[ind_sup])]
if(abs(list_margin$margin[ind_inf]-list_margin$margin[ind_sup])==unit_size)
{
if(display){ cat("\n Units of ",unit_size, " MW are too big to adapt to the tolerance \n")}
has_converged <- TRUE
if(display){ cat("Convergence with margin = ",list_margin$margin[ind_inf]," MW or margin = ",list_margin$margin[ind_sup], " MW \n", sep="")}
}
else{
margin <- floor((list_margin$margin[ind_inf]+list_margin$margin[ind_sup])/2/unit_size)*unit_size
}
}
}
}
else
{
if(display){ cat("\n --- Area ", area, " has been balanced with margin = ", margin, " MW \n", sep = "")}
has_converged <- TRUE
}
# if LOLE is above the target, report years and weeks which acutally have unsupplied energy
if(new_LOLE > LOLE)
{
ooo <- get_years_and_weeks_with_LOLD(area, output_antares)
playlist <- intersect(playlist, ooo$mc_years)
weeks <- intersect(weeks, ooo$weeks)
}
#---- 9. Clean ANTARES output ----
if(clean) { clean_output_benders(current_it, unique_key, output_name = "get-margins-", opts)}
current_it<-current_it+1
}
if(display){ cat("\n Summary : \n")}
if(display){ print(list_margin)}
}
}
#' Change the input of an Antares study with a given margin (positive or negative value
#' in MW) to be set in one area.
#' - if the margin is positive (implicitly, the generation fleet is over-sized and the LOLE
#' is below the adequacy criteria), ROW BALANCE is removed from the given area. In that case,
#' ROW BALANCE = row_balance_init - margin.
#' - if the margin is negative (implicitly, the generation fleet is under-sized and the LOLE
#' is above the adequacy criteria), a cluster is enabled and its installed capacity is set to
#' the amount of the margin. The cluster is "perfect", i.e. without any outages or maintenance
#' stops.
#'
#'
#' @param margin
#' given margin (positive or negative) to set in an Antares study.
#' @param area
#' area in which this margin should be set
#' @param cluster_name
#' Name of the complementary cluster if the margin is negative (lack of production).
#' The cluster can already exist, otherwise it will be created.
#' Example : if cluster_name = "gas_pcomp_peak" and area = "fr", the final name of the cluster will be "fr_gas_pcomp_peak".
#' @param row_balance_init
#' Initial row_balance in the given area. Vector of size 8760 or 1 (if constant).
#' @param unit_size
#' Minimal step of the generation margin. Given in MW.
#' @param opts
#' list of simulation parameters returned by the function
#' \code{antaresRead::setSimulationPath}
#'
#' @return
#' Nothing. Input file of the antares study are therefore modified.
#'
#' @noRd
#' @importFrom antaresRead readClusterDesc simOptions
#' @importFrom antaresEditObject createCluster readIniFile writeIni
#' @importFrom utils modifyList read.table write.table
#' @importFrom assertthat assert_that
#'
#'
#'
set_margins_in_antares <- function(margin, area, cluster_name, row_balance_init, unit_size, opts = antaresRead::simOptions())
{
# 1.check cluster existence
list_cluster <- antaresRead::readClusterDesc(opts = opts)
list_cluster <- list_cluster[area == area]
extended_cluster_name <- paste(area, cluster_name, sep = "_")
cluster_existence <- extended_cluster_name %in% list_cluster$cluster
if(!cluster_existence)
{
# create the cluster
antaresEditObject::createCluster(area = area,
cluster_name = cluster_name,
group = "gas",
unitcount = as.integer(1),
add_prefix = TRUE,
nominalcapacity = unit_size,
enabled = FALSE,
`marginal-cost` = 300,
`spread-cost` = 0,
`market-bid-cost` = 300,
time_series = rep(0,8760),
opts = opts)
warning("As it was not already present in the study, the cluster ", area, "_", cluster_name, " has been automatically created with default values (e.g. cost = 300€/MWh).")
}
# 2. If margins >=0, disable cluster + modify ROW Balance
if (margin >= 0)
{
# disable cluster
path_clusters_ini <- file.path(opts$inputPath, "thermal", "clusters", area, "list.ini")
previous_params <- antaresEditObject::readIniFile(file = path_clusters_ini)
params_cluster <- list(enabled = "false")
ind_cluster <- which(tolower(names(previous_params)) %in% tolower(extended_cluster_name))[1]
previous_params[[ind_cluster]] <- utils::modifyList(x = previous_params[[ind_cluster]], val = params_cluster)
antaresEditObject::writeIni(listData = previous_params,pathIni = path_clusters_ini,overwrite = TRUE)
# modify ROW Balance
update_row_balance(row_balance_init - margin, area = area, opts = opts)
}
# 3. if margins <0, enable cluster with good capacity + desactivate ROW Balance
else if (margin < 0)
{
# enable cluster and put its capacity equal to the margin
path_clusters_ini <- file.path(opts$inputPath, "thermal", "clusters", area, "list.ini")
previous_params <- antaresEditObject::readIniFile(file = path_clusters_ini)
params_cluster <- list(enabled = "true", unitcount = as.integer(1), nominalcapacity = - margin)
ind_cluster <- which(tolower(names(previous_params)) %in% tolower(extended_cluster_name))[1]
previous_params[[ind_cluster]] <- utils::modifyList(x = previous_params[[ind_cluster]], val = params_cluster)
antaresEditObject::writeIni(listData = previous_params,pathIni = path_clusters_ini,overwrite = TRUE)
# modify the time series of this cluster
ts_file_name <- paste0(opts$inputPath,"/thermal/series/" ,area,"/", extended_cluster_name,"/","series.txt",sep="")
assertthat::assert_that(file.exists(ts_file_name))
utils::write.table(rep(-margin, 8760),ts_file_name,sep = "\t",col.names = FALSE,row.names = FALSE)
# reset ROW Balance
update_row_balance(row_balance_init, area = area, opts = opts)
}
}
#' update ROW Balance with new given value
#'
#' @param row_balance
#' New row_balance. Vector of size 8760 or 1 (if constant).
#' @param area
#' area in which this margin should be set
#' @param opts
#' list of simulation parameters returned by the function
#' \code{antaresRead::setSimulationPath}
#'
#' @return
#' Nothing. Input file of the antares study are therefore modified.
#'
#' @noRd
#' @importFrom antaresRead simOptions
#' @importFrom antaresEditObject createCluster readIniFile writeIni
#' @importFrom utils read.table write.table
#' @importFrom assertthat assert_that
update_row_balance <- function(row_balance, area, opts = antaresRead::simOptions())
{
assertthat::assert_that(length(row_balance) %in% c(1, 8760))
rowbalance_file_name <- paste0(opts$inputPath,"/misc-gen/miscgen-",area,".txt",sep="")
assertthat::assert_that(file.exists(rowbalance_file_name))
if (file.info(rowbalance_file_name)$size != 0)
{
# read file
param_data <- utils::read.table(rowbalance_file_name)
# update column with new value and write it
param_data[,8] = row_balance
utils::write.table(param_data, rowbalance_file_name, sep="\t", col.names = FALSE, row.names = FALSE)
}
else if (file.info(rowbalance_file_name)$size ==0)
{
# The file exists but is empty : i.e. all column contains default value
# file is built from scratch
param_data <- as.table(matrix(0,8760,8))
# update column with new value and write it
param_data[,8] = row_balance
utils::write.table(param_data, link_file_name, sep="\t", col.names = FALSE, row.names = FALSE)
}
}
#' Returns list of mc_years and weeks with LOLD
#'
#' @param area
#' area in which LOLD is analyzed
#' @param opts
#' list of simulation parameters returned by the function
#' \code{antaresRead::setSimulationPath}
#'
#' @return
#' A list with two vectors, one with the MC years with LOLD and
#' on with the weeks with LOLD.
#'
#' @noRd
#' @importFrom antaresRead simOptions readAntares
get_years_and_weeks_with_LOLD <- function(area, opts = antaresRead::simOptions())
{
LOLD <- antaresRead::readAntares(areas = area, links = NULL, mcYears = "all",
timeStep = "weekly", opts = opts, showProgress = FALSE,
select = c("LOLD"))
# get mc-years and weeks with LOLD
mc_years <- unique(LOLD[LOLD > 0, mcYear])
weeks <- unique(LOLD[LOLD > 0, timeId])
# look for consecutive weeks
c_weeks <- min(weeks):max(weeks)
# build output
out <- list()
out$mc_years <- mc_years
out$weeks <- c_weeks
return(out)
}
rte-antares-rpackage/antaresXpansion documentation built on June 16, 2019, 2:35 p.m.
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#' Compute generation margin in a given area on an Antares Study.
#'
#' - A positive generation margin means that the generation fleet of the area is over-sized
#' and the LOLD (Loss Of Load Duration) is below the adequacy criteria (typically 3h/y). In that
#' case, the margin corresponds to the extra constant load that the generation fleet can still supply while
#' respecting strictly the adequacy criteria.
#'
#' - A negative generation margin means that the generation fleet is under-sized and the LOLD
#' is above the adequacy criteria. In that case, the margin corresponds to the extra "perfect" production
#' capacity required to meet the adequacy criteria. A "perfect" capacity is a capacity which is available
#' all the time (i.e. without maintenance/outages).
#'
#' @param area
#' Area in which the margin is computed
#' @param cluster_name
#' Name of the complementary cluster if the margin is negative (lack of production).
#' The cluster can already exist, otherwise it will be created.
#' (example : If cluster_name = "gas_pcomp_peak" and area = "fr", the final name of the cluster will be "fr_gas_pcomp_peak")
#' @param LOLD
#' Adequacy criteria of the area in terms of Loss of Load Duration (given in h/year)
#' @param tolerance
#' Algorithmic tolerance about the adequacy criteria. Used as stop criterion.
#' (example : if tolerance = 0.5 and LOLD = 3.0, the aimed range of LOLD is between 2h30 and 3h30)
#' @param unit_size
#' Minimal step of the generation margin. Given in MW.
#' @param abaque
#' Function abaque : marge = f(LOLE)
#' @param initial_margin
#' First margin tested in the function. Given in MW.
#' @param path_solver
#' Character containing the Antares Solver path
#' @param display
#' Logical. If \code{TRUE} the advancement of the function
#' if displayed in the console
#' @param clean
#' Logical. If \code{TRUE} the output of the ANTARES simulations run by the
#' package will be deleted (except for the output of the simulation which brings
#' to the best solution).
#' @param parallel
#' Logical. If \code{TRUE} the ANTARES simulations will be run in parallel mode (Work
#' only with ANTARES v6.0.0 or more). In that case, the number of cores used by the simulation
#' is the one set in advanced_settings/simulation_cores (see ANTARES interface).
#' @param opts
#' list of simulation parameters returned by the function
#' \code{antaresRead::setSimulationPath}
#'
#' @return
#'
#' @importFrom assertthat assert_that
#' @importFrom antaresRead simOptions setSimulationPath getAreas readClusterDesc readInputTS
#' @importFrom rmarkdown render
#' @importFrom utils packageVersion tail
#' @importFrom antaresEditObject setPlaylist getPlaylist createCluster writeIni
#' @export
#'
get_margins <- function(area = "fr", cluster_name = "gas_pcomp_peak", LOLD = 3.00, tolerance = 0.1, unit_size = 100,
abaque = function(c){return(-3627*log(c)+3723.4)}, initial_margin = 0, path_solver, display = TRUE, clean = TRUE,
parallel = TRUE, opts = antaresRead::simOptions())
{
# ---- 0. initialize ----
LOLE <- LOLD
# reload study
opts <- setSimulationPath(opts$studyPath, simulation = 0)
# save current settings of the ANTARES study into a temporary file
save_general_settings(opts)
# reset options of the ANTARES study to their initial values when the function ends
on.exit(restore_general_settings(opts))
# set week and initial day
# we need to ensure the consistency between the weekly optimisation and the weekly
# aggregation of the output
month_name <- c("january", "december", "november", "october", "september", "august", "july", "june", "may", "april", "march", "february")
day_per_month <- c(0, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31, 28)
day_name <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
if (opts$parameters$general$leapyear)
{
day_per_month[12] <- 29
}
month_id <- which(month_name == opts$parameters$general$"first-month-in-year")
assertthat::assert_that(length(month_id) == 1)
n_day <- (-sum(day_per_month[1:month_id]) + opts$parameters$general$simulation.start - 1) %% 7
first_day_week <- day_name[((which(day_name == opts$parameters$general$january.1st) + n_day - 1) %% 7 ) +1]
antaresEditObject::updateGeneralSettings(first.weekday = first_day_week)
# adjust abacus
abaque_t <- abaque
a_0 <- uniroot(abaque_t, interval = c(0.01, 10))$root
abaque <- function(x){abaque_t(x * a_0 / LOLE)}
# initiate a few parameters
current_it <- 0
antaresEditObject::updateGeneralSettings(filtering = "true", year.by.year = "true", opts = opts)
filter_output_areas(areas = antaresRead::getAreas(opts = opts), filter = c("annual", "weekly"), type = c("year-by-year","synthesis"), opts = opts)
filter_output_links(links = antaresRead::getLinks(opts = opts), filter = c("annual"), type = c("synthesis"), opts = opts)
unique_key <- paste(sample(c(0:9, letters), size = 3, replace = TRUE),collapse = "") # unique key used in output names
rowbalance_file_name <- paste0(opts$inputPath, "/misc-gen/miscgen-", area, ".txt", sep = "")
assertthat::assert_that(file.exists(rowbalance_file_name))
loop <- FALSE
if (file.info(rowbalance_file_name)$size != 0)
{
param_data <- read.table(rowbalance_file_name)
initial_row_balance <- param_data[, 8]
if (abs(sum(initial_row_balance)) > 0)
{
cat("Warning : initial row balance of the area ", area," is not null ! \n", sep="")
}
}
else {initial_row_balance <- rep(0,8760)}
list_margin <- data.frame(row.names = c("it", "margin", "LOLE"))
if(display){cat("------ Iteration 0 ------\n", sep="")}
# setplaylist and weeks
first_sim_week <- 1 + ceiling((opts$parameters$general$simulation.start - 1)/7)
n_w <- floor((opts$parameters$general$simulation.end - opts$parameters$general$simulation.start + 1)/7) # number of weeks
weeks <- initial_weeks <- first_sim_week:(first_sim_week + n_w - 1) # identifier of weeks to simulate for all expansion planning optimisation
playlist <- initial_playlist <- getPlaylist(opts) # identifier of mc years to simulate for all expansion planning optimisation
# set margin
set_margins_in_antares(margin = initial_margin, area, cluster_name, row_balance_init = initial_row_balance, unit_size = unit_size, opts = antaresRead::simOptions())
# ---- 1. Simulate with Antares once at the beginning : ----
simulation_name <- paste0("get-margins-", unique_key, "-it", current_it, "-margin_", initial_margin,"mw")
if(display){ cat(" ANTARES simulation running ", sep="")}
if(display){ cat("[",length(weeks)*length(playlist)," simulated weeks] ... ", sep = "")}
run_simulation(simulation_name, mode = "economy",
path_solver, wait = TRUE, show_output_on_console = FALSE, parallel = parallel, opts)
if(display){ cat("[done] \n", sep="")}
output_antares <- antaresRead::setSimulationPath(paste0(opts$studyPath, "/output/", get_whole_simulation_name(simulation_name, opts)))
output_LOLE <- antaresRead::readAntares(areas = area, links = NULL, mcYears = "all",
timeStep = "annual", opts = output_antares, showProgress = FALSE,
select = c("LOLD"))
# ---- 2. Define first LOLE : ----
new_LOLE <- mean(output_LOLE$LOLD)
if(display){ cat(" margin = ", initial_margin, " MW\n", sep="")}
if(display){ cat(" LOLD = ", new_LOLE ," h\n", sep="")}
list_margin <- rbind(list_margin,
data.frame(it = current_it,
margin = initial_margin,
LOLE = new_LOLE))
# if LOLE is above the target, report years and weeks which acutally have unsupplied energy
if(new_LOLE > LOLE)
{
ooo <- get_years_and_weeks_with_LOLD(area = area, opts = output_antares)
playlist <- intersect(playlist, ooo$mc_years)
weeks <- intersect(weeks, ooo$weeks)
}
# No zero with log !
new_LOLE_init <- new_LOLE
current_it <- 1
if(new_LOLE >= LOLE-tolerance & new_LOLE <= LOLE + tolerance )
{
if(display){ cat("The area ", area, " is already balanced with margin = ", initial_margin," MW \n")}
}
# ---- 3. Evaluate the fisrt margin with the abacus : ----
else
{
if (new_LOLE==0){margin <- 10000}
else if (new_LOLE > LOLE) {margin <- abaque(new_LOLE)}
else if (new_LOLE < LOLE) {margin <- abaque(new_LOLE) * 1.15}
margin <- initial_margin + margin
margin <- floor(margin/unit_size)*unit_size
if(display){ cat(" First margin evaluated with the abacus : ", margin," MW\n", sep="")}
# ---- 5. iterate until convergence : ----
has_converged <- FALSE
while(!has_converged)
{
if(display){cat("\n------ Iteration ", current_it, " ------\n", sep="")}
set_margins_in_antares(margin, area, cluster_name, row_balance_init = initial_row_balance, unit_size = unit_size, opts = antaresRead::simOptions())
# ----------- change playlist and simulated weeks -------------
set_simulation_period(weeks, opts)
antaresEditObject::setPlaylist(playlist, opts)
# ----------- Simulate -----------
antaresEditObject::updateGeneralSettings(filtering = "true", year.by.year = "true", opts = opts)
simulation_name <- paste0("get-margins-", unique_key, "-it", current_it, "-margin_", margin, "mw")
if(display){ cat(" ANTARES simulation running ", sep="")}
if(display){ cat("[",length(weeks)*length(playlist)," simulated weeks] ... ", sep = "")}
run_simulation(simulation_name, mode = "economy",
path_solver, wait = TRUE, show_output_on_console = FALSE, parallel = parallel, opts)
if(display){ cat("[done] \n", sep="")}
output_antares <- antaresRead::setSimulationPath(paste0(opts$studyPath, "/output/", get_whole_simulation_name(simulation_name, opts)))
output_LOLE <- antaresRead::readAntares(areas = area, links = NULL, mcYears = "all",
timeStep = "annual", opts = output_antares, showProgress = FALSE,
select = c("LOLD"))
new_LOLE <- sum(output_LOLE$LOLD)/length(initial_playlist)
if(display){ cat(" margin = " ,margin , " MW\n", sep="" )}
if(display){ cat(" LOLD = " ,new_LOLE , " h\n", sep="" )}
list_margin <- rbind(list_margin,
data.frame(it = current_it,
margin = margin,
LOLE = new_LOLE))
# ---- update step ----
# if(min(abs(new_LOLE - LOLE + tolerance), abs(new_LOLE - LOLE - tolerance))>1.0){ step <- 8*unit_size}
# else{
# if(min(abs(new_LOLE - LOLE + tolerance), abs(new_LOLE - LOLE - tolerance))>0.75){step <- 4*unit_size}
# else{
# if(min(abs(new_LOLE - LOLE + tolerance), abs(new_LOLE - LOLE - tolerance))>0.5){step <- 2*unit_size}
# else{step <- unit_size}
# }
# }
if (new_LOLE==0){step <- 10000}
else {step <- abaque(new_LOLE)}
if(step > 0 && new_LOLE > LOLE){step <- -step}
if(step < 0 && new_LOLE < LOLE){step <- -step}
if(abs(step) < unit_size) {step <- sign(step)*unit_size}
else { step <- round(step/unit_size)*unit_size }
# ----------- Update : Lack of production -----------
if (new_LOLE > LOLE+tolerance)
{
margin <- margin + step
# If we had already had this margin previously
if(!is.null(list_margin$margin[list_margin$margin < margin - step]))
{
if (any(margin <= list_margin$margin[list_margin$margin < margin - step])) {
loop <- TRUE
# Get the nearest LOLE of the aimed range
ind_inf <- which(list_margin$LOLE< LOLE - tolerance)
ind_inf <- ind_inf[which.max(list_margin$margin[ind_inf])]
ind_sup <- which(list_margin$LOLE> LOLE + tolerance)
ind_sup <- ind_sup[which.min(list_margin$margin[ind_sup])]
if(abs(list_margin$margin[ind_inf]-list_margin$margin[ind_sup])==unit_size)
{
if(display){ cat("\n Units of ",unit_size," MW are too big to adapt to the tolerance \n")}
has_converged <- TRUE
if(display){ cat("Convergence with margin = ",list_margin$margin[ind_inf]," MW or margin = ",list_margin$margin[ind_sup], " MW \n", sep="")}
}
else{
margin <- floor((list_margin$margin[ind_inf]+list_margin$margin[ind_sup])/2/unit_size)*unit_size
}
}
}
}
# ----------- Update : Lack of consumption
else if (new_LOLE < LOLE - tolerance)
{
margin <- margin + step
# If we had already had this margin previously
if(!is.null(list_margin$margin[list_margin$margin > margin - step]))
{
if (any(margin >= list_margin$margin[list_margin$margin > margin - step])) {
loop <- TRUE
# Get the nearest LOLE of the aimed range
ind_inf <- which(list_margin$LOLE< LOLE - tolerance)
ind_inf <- ind_inf[which.max(list_margin$margin[ind_inf])]
ind_sup <- which(list_margin$LOLE> LOLE + tolerance)
ind_sup <- ind_sup[which.min(list_margin$margin[ind_sup])]
if(abs(list_margin$margin[ind_inf]-list_margin$margin[ind_sup])==unit_size)
{
if(display){ cat("\n Units of ",unit_size, " MW are too big to adapt to the tolerance \n")}
has_converged <- TRUE
if(display){ cat("Convergence with margin = ",list_margin$margin[ind_inf]," MW or margin = ",list_margin$margin[ind_sup], " MW \n", sep="")}
}
else{
margin <- floor((list_margin$margin[ind_inf]+list_margin$margin[ind_sup])/2/unit_size)*unit_size
}
}
}
}
else
{
if(display){ cat("\n --- Area ", area, " has been balanced with margin = ", margin, " MW \n", sep = "")}
has_converged <- TRUE
}
# if LOLE is above the target, report years and weeks which acutally have unsupplied energy
if(new_LOLE > LOLE)
{
ooo <- get_years_and_weeks_with_LOLD(area, output_antares)
playlist <- intersect(playlist, ooo$mc_years)
weeks <- intersect(weeks, ooo$weeks)
}
#---- 9. Clean ANTARES output ----
if(clean) { clean_output_benders(current_it, unique_key, output_name = "get-margins-", opts)}
current_it<-current_it+1
}
if(display){ cat("\n Summary : \n")}
if(display){ print(list_margin)}
}
}
#' Change the input of an Antares study with a given margin (positive or negative value
#' in MW) to be set in one area.
#' - if the margin is positive (implicitly, the generation fleet is over-sized and the LOLE
#' is below the adequacy criteria), ROW BALANCE is removed from the given area. In that case,
#' ROW BALANCE = row_balance_init - margin.
#' - if the margin is negative (implicitly, the generation fleet is under-sized and the LOLE
#' is above the adequacy criteria), a cluster is enabled and its installed capacity is set to
#' the amount of the margin. The cluster is "perfect", i.e. without any outages or maintenance
#' stops.
#'
#'
#' @param margin
#' given margin (positive or negative) to set in an Antares study.
#' @param area
#' area in which this margin should be set
#' @param cluster_name
#' Name of the complementary cluster if the margin is negative (lack of production).
#' The cluster can already exist, otherwise it will be created.
#' Example : if cluster_name = "gas_pcomp_peak" and area = "fr", the final name of the cluster will be "fr_gas_pcomp_peak".
#' @param row_balance_init
#' Initial row_balance in the given area. Vector of size 8760 or 1 (if constant).
#' @param unit_size
#' Minimal step of the generation margin. Given in MW.
#' @param opts
#' list of simulation parameters returned by the function
#' \code{antaresRead::setSimulationPath}
#'
#' @return
#' Nothing. Input file of the antares study are therefore modified.
#'
#' @noRd
#' @importFrom antaresRead readClusterDesc simOptions
#' @importFrom antaresEditObject createCluster readIniFile writeIni
#' @importFrom utils modifyList read.table write.table
#' @importFrom assertthat assert_that
#'
#'
#'
set_margins_in_antares <- function(margin, area, cluster_name, row_balance_init, unit_size, opts = antaresRead::simOptions())
{
# 1.check cluster existence
list_cluster <- antaresRead::readClusterDesc(opts = opts)
list_cluster <- list_cluster[area == area]
extended_cluster_name <- paste(area, cluster_name, sep = "_")
cluster_existence <- extended_cluster_name %in% list_cluster$cluster
if(!cluster_existence)
{
# create the cluster
antaresEditObject::createCluster(area = area,
cluster_name = cluster_name,
group = "gas",
unitcount = as.integer(1),
add_prefix = TRUE,
nominalcapacity = unit_size,
enabled = FALSE,
`marginal-cost` = 300,
`spread-cost` = 0,
`market-bid-cost` = 300,
time_series = rep(0,8760),
opts = opts)
warning("As it was not already present in the study, the cluster ", area, "_", cluster_name, " has been automatically created with default values (e.g. cost = 300€/MWh).")
}
# 2. If margins >=0, disable cluster + modify ROW Balance
if (margin >= 0)
{
# disable cluster
path_clusters_ini <- file.path(opts$inputPath, "thermal", "clusters", area, "list.ini")
previous_params <- antaresEditObject::readIniFile(file = path_clusters_ini)
params_cluster <- list(enabled = "false")
ind_cluster <- which(tolower(names(previous_params)) %in% tolower(extended_cluster_name))[1]
previous_params[[ind_cluster]] <- utils::modifyList(x = previous_params[[ind_cluster]], val = params_cluster)
antaresEditObject::writeIni(listData = previous_params,pathIni = path_clusters_ini,overwrite = TRUE)
# modify ROW Balance
update_row_balance(row_balance_init - margin, area = area, opts = opts)
}
# 3. if margins <0, enable cluster with good capacity + desactivate ROW Balance
else if (margin < 0)
{
# enable cluster and put its capacity equal to the margin
path_clusters_ini <- file.path(opts$inputPath, "thermal", "clusters", area, "list.ini")
previous_params <- antaresEditObject::readIniFile(file = path_clusters_ini)
params_cluster <- list(enabled = "true", unitcount = as.integer(1), nominalcapacity = - margin)
ind_cluster <- which(tolower(names(previous_params)) %in% tolower(extended_cluster_name))[1]
previous_params[[ind_cluster]] <- utils::modifyList(x = previous_params[[ind_cluster]], val = params_cluster)
antaresEditObject::writeIni(listData = previous_params,pathIni = path_clusters_ini,overwrite = TRUE)
# modify the time series of this cluster
ts_file_name <- paste0(opts$inputPath,"/thermal/series/" ,area,"/", extended_cluster_name,"/","series.txt",sep="")
assertthat::assert_that(file.exists(ts_file_name))
utils::write.table(rep(-margin, 8760),ts_file_name,sep = "\t",col.names = FALSE,row.names = FALSE)
# reset ROW Balance
update_row_balance(row_balance_init, area = area, opts = opts)
}
}
#' update ROW Balance with new given value
#'
#' @param row_balance
#' New row_balance. Vector of size 8760 or 1 (if constant).
#' @param area
#' area in which this margin should be set
#' @param opts
#' list of simulation parameters returned by the function
#' \code{antaresRead::setSimulationPath}
#'
#' @return
#' Nothing. Input file of the antares study are therefore modified.
#'
#' @noRd
#' @importFrom antaresRead simOptions
#' @importFrom antaresEditObject createCluster readIniFile writeIni
#' @importFrom utils read.table write.table
#' @importFrom assertthat assert_that
update_row_balance <- function(row_balance, area, opts = antaresRead::simOptions())
{
assertthat::assert_that(length(row_balance) %in% c(1, 8760))
rowbalance_file_name <- paste0(opts$inputPath,"/misc-gen/miscgen-",area,".txt",sep="")
assertthat::assert_that(file.exists(rowbalance_file_name))
if (file.info(rowbalance_file_name)$size != 0)
{
# read file
param_data <- utils::read.table(rowbalance_file_name)
# update column with new value and write it
param_data[,8] = row_balance
utils::write.table(param_data, rowbalance_file_name, sep="\t", col.names = FALSE, row.names = FALSE)
}
else if (file.info(rowbalance_file_name)$size ==0)
{
# The file exists but is empty : i.e. all column contains default value
# file is built from scratch
param_data <- as.table(matrix(0,8760,8))
# update column with new value and write it
param_data[,8] = row_balance
utils::write.table(param_data, link_file_name, sep="\t", col.names = FALSE, row.names = FALSE)
}
}
#' Returns list of mc_years and weeks with LOLD
#'
#' @param area
#' area in which LOLD is analyzed
#' @param opts
#' list of simulation parameters returned by the function
#' \code{antaresRead::setSimulationPath}
#'
#' @return
#' A list with two vectors, one with the MC years with LOLD and
#' on with the weeks with LOLD.
#'
#' @noRd
#' @importFrom antaresRead simOptions readAntares
get_years_and_weeks_with_LOLD <- function(area, opts = antaresRead::simOptions())
{
LOLD <- antaresRead::readAntares(areas = area, links = NULL, mcYears = "all",
timeStep = "weekly", opts = opts, showProgress = FALSE,
select = c("LOLD"))
# get mc-years and weeks with LOLD
mc_years <- unique(LOLD[LOLD > 0, mcYear])
weeks <- unique(LOLD[LOLD > 0, timeId])
# look for consecutive weeks
c_weeks <- min(weeks):max(weeks)
# build output
out <- list()
out$mc_years <- mc_years
out$weeks <- c_weeks
return(out)
}
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