init

.Rbuildignore

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+^\.travis\.yml$
+^appveyor\.yml$
+^antaresThermalTS\.Rproj$
+^\.Rproj\.user$

.gitignore

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+.Rhistory
+.RData
+.Rproj.user
+*.Rproj

DESCRIPTION

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+Package: antaresThermalTS
+Title: Extended Shutdown Simulation with 'Antares' for Power Groups
+Version: 0.0.0.9310
+Authors@R: c(
+    person("Victor", "Perrier", email = "[email protected]", role = c("aut", "cre")),
+    person("Fabiola", "Aravena-Rojas", email = "[email protected]", role = c("aut")),
+    person("RTE", role = c("cph", "fnd"))
+  )
+Description: Setup an 'Antares' study to simulate shutdown of power groups.
+License: GPL (>= 2) | file LICENSE
+Encoding: UTF-8
+LazyData: true
+Imports: 
+    antaresEditObject,
+    antaresRead,
+    readxl (>= 1.2.0),
+    janitor,
+    data.table,
+    lubridate,
+    stringr,
+    stringi,
+    zoo
+RoxygenNote: 6.1.1

LICENSE

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+COPYRIGHT HOLDER: RTE Réseau de transport d’électricité 

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(build_weekcal)
+export(compute_kd_coefs)
+export(compute_kipr)
+export(compute_kivt)
+export(create_clusters_edf)
+export(create_clusters_nuclear)
+export(create_clusters_other)
+export(read_calendar)
+export(read_cluster_desc)
+export(read_info)
+export(read_kd_cho)
+export(read_kd_cho_macro)
+export(read_planning)
+export(read_planning_rte)
+export(setup_study)
+importFrom(antaresEditObject,createArea)
+importFrom(antaresEditObject,createCluster)
+importFrom(antaresEditObject,createStudy)
+importFrom(antaresEditObject,updateGeneralSettings)
+importFrom(antaresRead,setSimulationPath)
+importFrom(antaresRead,simOptions)
+importFrom(data.table,"%chin%")
+importFrom(data.table,":=")
+importFrom(data.table,.SD)
+importFrom(data.table,copy)
+importFrom(data.table,data.table)
+importFrom(data.table,melt)
+importFrom(data.table,rbindlist)
+importFrom(data.table,setDT)
+importFrom(data.table,setnames)
+importFrom(data.table,setorder)
+importFrom(data.table,uniqueN)
+importFrom(janitor,clean_names)
+importFrom(lubridate,as_datetime)
+importFrom(lubridate,days)
+importFrom(lubridate,hours)
+importFrom(lubridate,years)
+importFrom(readxl,anchored)
+importFrom(readxl,cell_limits)
+importFrom(readxl,read_excel)
+importFrom(stats,setNames)
+importFrom(stringi,stri_replace_all_charclass)
+importFrom(stringr,str_replace_all)
+importFrom(utils,packageVersion)
+importFrom(zoo,na.locf)

R/build_weekcal.R

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+
+#' Build Week calendar
+#'
+#' @param start Starting year
+#' @param end Ending year
+#'
+#' @return a \code{data.table}
+#' @export
+#' 
+#' @importFrom data.table data.table :=
+#' @importFrom zoo na.locf
+#'
+#' @examples
+#' 
+#' build_weekcal()
+#' 
+build_weekcal <- function(start = 2018, end = 2020) {
+  weekcal <- data.table(
+    dates = seq(
+      from = as.Date(paste0(start, "-01-01")) - 7,
+      to = as.Date(paste0(end, "-12-31")) + 7,
+      by = "days"
+    )
+  )
+  weekcal[, saturdays := format(dates, format = "%u") %in% "6"]
+  weekcal[, year := format(dates + 7, format = "%Y")]
+  weekcal[saturdays == TRUE, week := sprintf("S%02d - %s", seq_along(saturdays), year), by = year]
+  weekcal[, week := zoo::na.locf(week, na.rm = FALSE)]
+  weekcal <- weekcal[!is.na(week)]
+
+  weekcal <- weekcal[, list(
+    week_start = min(dates),
+    week_end = max(dates),
+    n = .N
+  ), by = week]
+  weekcal <- weekcal[n == 7]
+  weekcal[, n := NULL]
+  weekcal[]
+}

R/compute_kd_coefs.R

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+
+#' Compute Kd coefficients
+#'
+#' @param kipr Kipr coefficient from \code{\link{compute_kipr}}.
+#' @param kivt Kivt coefficient from \code{\link{compute_kivt}}.
+#' @param kd_cho Kd coefficients read with \code{\link{read_kd_cho}}.
+#'
+#' @return a \code{data.table}
+#' @export
+#'
+#' @importFrom data.table copy := setorder data.table
+compute_kd_coefs <- function(kipr, kivt, kd_cho) {
+  kipr <- copy(kipr)
+  kivt <- copy(kivt)
+  kd_cho <- copy(kd_cho)
+  code_palier_dic <- data.table(
+    type_groupe = c("Nucl\u00e9aire 1300", "Nucl\u00e9aire 900", "Nucl\u00e9aire N4"),
+    code_palier = c("p4", "cp0_cp_cp2", "n4")
+  )
+  kipr <- merge(
+    x = kipr[, .SD, .SDcols = c("week", "week_start", "week_end", "type_groupe", "kipr")],
+    y = code_palier_dic, by = "type_groupe"
+  )
+  kipr_kivt <- merge(
+    x = kipr,
+    y = kivt[, .SD, .SDcols = c("week", "type_groupe", "kivt")],
+    by = c("week", "type_groupe")
+  )
+  kipr_kivt[, type_groupe := NULL]
+
+  kd_coefs <- merge(
+    x = kipr_kivt,
+    y = kd_cho[, list(week = n_sem_annee, kif, kistretch, kienv, kihiver, kibouclage, code_palier, palier)],
+    by = c("week", "code_palier")
+  )
+  setorder(kd_coefs, week_start, code_palier)
+  kd_coefs[]
+}
+

R/compute_kipr.R

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+
+#' Compute Kipr coefficient
+#'
+#' @param calendar Calendar data read with \code{\link{read_calendar}}.
+#' @param clusters_desc Clusters / groups description read with \code{\link{read_cluster_desc}}.
+#' @param years Years to consider, if \code{NULL} (default), year range from \code{calendar} will be used.
+#'
+#' @return a \code{data.table}
+#' @export
+#' 
+#' @importFrom data.table copy setorder := uniqueN 
+#'
+#' @examples
+#' \dontrun{
+#' # Calendar data
+#' calendar <- read_calendar(path = "REF_Planning_5_ans_mars_2018.xlsx")
+#' 
+#' # Clusters description
+#' clusters <- read_cluster_desc("HypothesesRTE_CHO-4145.xlsx")
+#' 
+#' 
+#' # Kipr computation
+#' kipr <- compute_kipr(calendar, clusters)
+#' kipr
+#' }
+compute_kipr <- function(calendar, clusters_desc, years = NULL) {
+
+  calendar <- copy(calendar)
+  clusters_desc <- copy(clusters_desc)
+
+  if (is.null(years)) {
+    years <- range(calendar$date_debut, na.rm = TRUE)
+    years <- format(years, format = "%Y")
+  }
+
+  weekcal <- build_weekcal(start = years[1], end = years[2])
+
+  weekcal[, .id := 1]
+  calendar[, .id := 1]
+
+  week_groups <- weekcal[calendar[, list(
+    .id,
+    group = tranche,
+    shutdown_start = as.Date(date_de_fin_sans_prolongation),
+    shutdown_end = as.Date(date_de_fin_avec_prolongation)
+  )], on = ".id", allow.cartesian = TRUE]
+
+  week_groups[, n_overlaps := n_overlaps(week_start, week_end, shutdown_start, shutdown_end)]
+
+  clusters_desc <- clusters_desc[, list(group = corresp_groupes, type_groupe, pcn_mw)]
+  clusters_desc[, pcn_mw := as.numeric(pcn_mw)]
+  clusters_desc[, group_power := sum(pcn_mw), by = type_groupe]
+
+  weekclus <- merge(
+    x = week_groups, 
+    y = clusters_desc,
+    by = "group", all.x = TRUE, all.y = FALSE
+  )
+  weekclus <- weekclus[!is.na(type_groupe)]
+
+  setorder(weekclus, week, group, -n_overlaps)
+  weekclus <- unique(weekclus, by = c("week", "group"))
+
+  coef_kipr <- weekclus[, list(
+    kipr = sum(pcn_mw * n_overlaps / 7) / group_power * 100,
+    n_days = sum(n_overlaps), 
+    n = .N, n_group = uniqueN(group)
+  ), by = list(week, week_start, week_end, type_groupe, group_power)]
+  setorder(coef_kipr, week_start, type_groupe)
+  coef_kipr[]
+}

R/compute_kivt.R

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+
+#' Compute Kivt coefficient
+#'
+#' @param calendar Calendar data read with \code{\link{read_calendar}}.
+#' @param clusters_desc Clusters / groups description read with \code{\link{read_cluster_desc}}.
+#' @param years Years to consider, if \code{NULL} (default), year range from \code{calendar} will be used.
+#'
+#' @return a \code{data.table}
+#' @export
+#' 
+#' @importFrom data.table copy setorder := uniqueN 
+#'
+#' @examples
+#' \dontrun{
+#' # Calendar data
+#' calendar <- read_calendar(path = "REF_Planning_5_ans_mars_2018.xlsx")
+#' 
+#' # Clusters description
+#' clusters <- read_cluster_desc("HypothesesRTE_CHO-4145.xlsx")
+#' 
+#' 
+#' # Kivt computation
+#' kivt <- compute_kivt(calendar, clusters)
+#' kivt
+#' }
+compute_kivt <- function(calendar, clusters_desc, years = NULL) {
+
+  calendar <- copy(calendar)
+  clusters_desc <- copy(clusters_desc)
+
+  if (is.null(years)) {
+    years <- range(calendar$date_debut, na.rm = TRUE)
+    years <- format(years, format = "%Y")
+  }
+
+  weekcal <- build_weekcal(start = years[1], end = years[2])
+
+  weekcal[, .id := 1]
+  calendar[, .id := 1]
+
+  week_groups <- weekcal[calendar[, list(
+    .id,
+    group = tranche,
+    shutdown_start = as.Date(date_debut),
+    shutdown_end = as.Date(date_de_fin_sans_prolongation) - 1
+  )], on = ".id", allow.cartesian = TRUE]
+
+  week_groups[, n_overlaps := n_overlaps(week_start, week_end, shutdown_start, shutdown_end)]
+
+  clusters_desc <- clusters_desc[, list(group = corresp_groupes, type_groupe, pcn_mw)]
+  clusters_desc[, pcn_mw := as.numeric(pcn_mw)]
+  clusters_desc[, group_power := sum(pcn_mw), by = type_groupe]
+
+  weekclus <- merge(
+    x = week_groups, 
+    y = clusters_desc,
+    by = "group", all.x = TRUE, all.y = FALSE
+  )
+  weekclus <- weekclus[!is.na(type_groupe)]
+
+  setorder(weekclus, week, group, -n_overlaps)
+  weekclus <- unique(weekclus, by = c("week", "group"))
+
+  coef_kivt <- weekclus[, list(
+    kivt = sum(pcn_mw * n_overlaps / 7) / group_power * 100,
+    n_days = sum(n_overlaps), 
+    n = .N, n_group = uniqueN(group)
+  ), by = list(week, week_start, week_end, type_groupe, group_power)]
+  setorder(coef_kivt, week_start, type_groupe)
+  coef_kivt[]
+}

R/corresp_gps.R

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+
+
+
+corresp_gps <- function() {
+  data.table(
+    groupe = c("BELLEVILLE 1", "BELLEVILLE 2", "BLAYAIS 1", "BLAYAIS 2",
+               "BLAYAIS 3", "BLAYAIS 4", "BUGEY 2", "BUGEY 3", "BUGEY 4",
+               "BUGEY 5", "CATTENOM 1", "CATTENOM 2", "CATTENOM 3", "CATTENOM 4",
+               "CHINON 1", "CHINON 2", "CHINON 3", "CHINON 4", "CHOOZ 1",
+               "CHOOZ 2", "CIVAUX 1", "CIVAUX 2", "CRUAS 1", "CRUAS 2", "CRUAS 3",
+               "CRUAS 4", "DAMPIERRE 1", "DAMPIERRE 2", "DAMPIERRE 3",
+               "DAMPIERRE 4", "FESSENHEIM 1", "FESSENHEIM 2", "FLAMANVILLE 1",
+               "FLAMANVILLE 2", "GOLFECH 1", "GOLFECH 2", "GRAVELINES 1",
+               "GRAVELINES 2", "GRAVELINES 3", "GRAVELINES 4", "GRAVELINES 5",
+               "GRAVELINES 6", "NOGENT 1", "NOGENT 2", "PALUEL 1", "PALUEL 2", "PALUEL 3",
+               "PALUEL 4", "PENLY 1", "PENLY 2", "ST ALBAN 1", "ST ALBAN 2",
+               "ST LAURENT 1", "ST LAURENT 2", "TRICASTIN 1", "TRICASTIN 2",
+               "TRICASTIN 3", "TRICASTIN 4", "CORDEMAIS 4", "CORDEMAIS 5", "HAVRE 4",
+               "PROVENCE 5", "EMILE HUCHET 6", "DK6 - Braek 1",
+               "DK6 - Braek 2", "BOUCHAIN 7", "MARTIGUES PONTEAU 5", "MARTIGUES PONTEAU 6",
+               "SPEM", "SPEM Pointe", "Grand-Riviere (SPEM)", "CYCOFOS",
+               "BLENOD 5", "COMBIGOLFE", "EMILE HUCHET 7", "EMILE HUCHET 8",
+               "Croix-de-Metz", "Pont-sur-Sambre", "FR-GA-MORANT1", "GENNEVILLIERS 1",
+               "AMFARD14", "AMFARD15", "CORDEMAIS 2", "CORDEMAIS 3",
+               "PORCHEVILLE 1", "PORCHEVILLE 2", "PORCHEVILLE 3", "PORCHEVILLE 4",
+               "ARAMON 1", "ARAMON 2", "MONTEREAU FIOUL 6", "MONTEREAU FIOUL 5",
+               "MONTEREAU GAZ 6", "MONTEREAU GAZ 5", "VAIRES 1", "VAIRES 2",
+               "VAIRES 3", "ARRIGHI 1", "ARRIGHI 2", "DIRINON 1", "DIRINON 2",
+               "BRENNILIS 1", "BRENNILIS 2", "BRENNILIS 3", "BILHOT01", "FOSCHT 2",
+               "Calais Tioxide", "DD HOA 1", "DD HOA 2", "DD HOA DALKIA",
+               "DD OA - ELD RESTE France", "DD OA - ERDF RESTE France", "DDBZHT 1",
+               "DK6 TV1", "DK6 TV2"),
+    code_gp = c("BVIL7T 1", "BVIL7T 2", "BLAYAT 1", "BLAYAT 2", "BLAYAT 3",
+                "BLAYAT 4", "BUGEYT 2", "BUGEYT 3", "BUGEYT 4", "BUGEYT 5",
+                "CATTET 1", "CATTET 2", "CATTET 3", "CATTET 4", "CHIN2T 1",
+                "CHIN2T 2", "CHIN2T 3", "CHIN2T 4", "CHOO2T 1", "CHOO2T 2",
+                "CIVAUT 1", "CIVAUT 2", "CRUA5T 1", "CRUA5T 2", "CRUA5T 3", "CRUA5T 4",
+                "D.BURT 1", "D.BURT 2", "D.BURT 3", "D.BURT 4", "FESS5T 1",
+                "FESS5T 2", "FLAMAT 1", "FLAMAT 2", "GOLF5T 1", "GOLF5T 2",
+                "GRAV5T 1", "GRAV5T 2", "GRAV5T 3", "GRAV5T 4", "GRAV5T 5", "GRAV5T 6",
+                "N.SE5T 1", "N.SE5T 2", "PALUET 1", "PALUET 2", "PALUET 3",
+                "PALUET 4", "PENLYT 1", "PENLYT 2", "SSAL7T 1", "SSAL7T 2",
+                "SSEA2T 1", "SSEA2T 2", "TRICAT 1", "TRICAT 2", "TRICAT 3", "TRICAT 4",
+                "CORD5T 4", "CORD5T 5", "HAVRET 4", "PROVET 5", "E.HUCT 6", NA,
+                NA, "BOUCHT 7", "M.PONT 5", "M.PONT 6", NA, NA, NA, NA,
+                "BLENOT 5", NA, "E.HUCT 7", "E.HUCT 8", "C.ME5T01", "SAMBRT1", "MORANT 1",
+                "GENN3T 1", "AMFART14", "AMFART15", "CORD5T 2", "CORD5T 3",
+                "PORC2T 1", "PORC2T 2", "PORC2T 3", "PORC2T 4", NA, NA, "MTERFT 6",
+                "MTERFT 5", "MTERGT 6", "MTERGT 5", "VAIR6T 1", "VAIR6T 2",
+                "VAIR6T 3", "ARRI5T 1", "ARRI5T 2", "DIRINT 1", "DIRINT 2",
+                "BRENNT 1", "BRENNT 2", "BRENNT 3", NA, NA, "CALAIT1", "DFDC1T 1",
+                "DFDC2T 1", "DFDCOT 1", "DFELDT 1", "DFEGST 1", "DDBZHT 1", NA, NA),
+    name_desc = c("nuclear_p4", "nuclear_p4", "nuclear_cp0_cp_cp2",
+                  "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2",
+                  "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2",
+                  "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2", "nuclear_p4", "nuclear_p4",
+                  "nuclear_p4", "nuclear_p4", "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2",
+                  "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2", "nuclear_n4",
+                  "nuclear_n4", "nuclear_n4", "nuclear_n4", "nuclear_cp0_cp_cp2",
+                  "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2",
+                  "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2",
+                  "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2",
+                  "nuclear_p4", "nuclear_p4", "nuclear_p4", "nuclear_p4",
+                  "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2",
+                  "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2",
+                  "nuclear_p4", "nuclear_p4", "nuclear_p4", "nuclear_p4",
+                  "nuclear_p4", "nuclear_p4", "nuclear_p4", "nuclear_p4", "nuclear_p4",
+                  "nuclear_p4", "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2",
+                  "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2", "nuclear_cp0_cp_cp2",
+                  "nuclear_cp0_cp_cp2", "hard_coal_old_1", "hard_coal_old_1",
+                  "hard_coal_old_1", "hard_coal_old_1", "hard_coal_old_1", "gas_ccgt_new",
+                  "gas_ccgt_new", "gas_ccgt_new", "gas_ccgt_new", "gas_ccgt_new",
+                  "gas_ccgt_new", "gas_ccgt_new", "gas_ccgt_new", "gas_ccgt_new",
+                  "gas_ccgt_new", "gas_ccgt_new", "gas_ccgt_new", "gas_ccgt_new",
+                  "gas_ccgt_new", "gas_ccgt_new", "gas_ccgt_new", "gas_ocgt_old",
+                  "gas_cogen_market", "gas_cogen_market", "heavy_oil_old_1",
+                  "heavy_oil_old_1", "heavy_oil_old_1", "heavy_oil_old_1",
+                  "heavy_oil_old_1", "heavy_oil_old_1", "heavy_oil_old_1", "heavy_oil_old_1",
+                  "light_oil", "light_oil", "gas_ocgt_new", "gas_ocgt_new",
+                  "light_oil", "light_oil", "light_oil", "light_oil", "light_oil",
+                  "light_oil", "light_oil", "light_oil", "light_oil", "light_oil",
+                  "gas_cogen_market", "gas_cogen_market", "gas_cogen_market", "light_oil",
+                  "light_oil", "light_oil", "light_oil", "light_oil", "light_oil",
+                  "gas_ccgt_new", "gas_ccgt_new")
+  )
+}