# packages library( antaresViz ) library( antaresWeeklyMargin ) library( data.table ) library( dygraphs ) library( magrittr ) # datas marge_seul_fr <- readRDS(file = "datas/marge_fr_seule.rds") marge_seul_fr_e <- marge_seul_fr[, c(1, sample.int(n = 2040, size = 200) + 1)] data_fr <- readRDS("datas/data_fr.rds") marge_inter_fr <- readRDS(file = "datas/marge_fr_inter.rds") data_all <- readRDS(file = "datas/data_all_mc50.rds") # Monotone data mono_data <- readRDS(file = "datas/mono.rds") load("datas/ml.rda") week <- 9 year_mc <- 50 date_study <- "28-02-2018 10:00:00" date_debut <- "2018-02-24" n_scenario <- 2040
draw_series(data_fr, "LOAD", mcYears = 1)
Prévision utilisateur CNES
draw_series(data_fr, "WIND")
51 Forecast scenarios Meteologica
draw_series(data_fr, "SOLAR")
51 Forecast scenarios Meteologica
#Analyse sur le graphique marge_pays_seul
draw_upward_margin( upward_margin = marge_seul_fr_e, area = "fr", type = "seul", nb_MC = ncol(marge_seul_fr_e) - 1, num_week = week )
r n_scenario
scenarii
Table of quantiles :
marg <- margins_quantiles(marge_seul_fr_e) ft_margins_quantiles(marg, layout = "horizontal", language = "en")
#Analyse sur le graphique marge_pays_interconnecté
draw_upward_margin( upward_margin = marge_inter_fr, area = "fr", type = "inter", nb_MC = ncol(marge_inter_fr) - 1, num_week = week )
r n_scenario
scenarii
Table of quantiles :
marg_i <- margins_quantiles(marge_inter_fr) ft_margins_quantiles(marg_i, layout = "horizontal", language = "en")
#Analyse sur le graphique probabilité de défaillance
draw_stack_hist(marge_seul_fr, marge_inter_fr, "fr")
available power in the country > needs - imports are required - power still available in the country, but final remaining capacity = 0 - imports are required & final remaining capacity = 0 - inadequacy
draw_mono(mono_data$mono_france, main = paste0("Monotone des flux imports/exports pour France ", date_study))
draw_mono(mono_data$mono_cwe)
r year_mc
r year_mc
– FlowsantaresViz::exchangesStack(data_all$links, area = "fr", interactive = FALSE)#$widgets[[1]]$widget[[1]]
#Analyse sur le graphique exports/imports #<ul> #<li>France exports during the weekend.</li> #<li>During the weekdays France is a net importer.</li> #<li>During the day France exports to Belgium, except during hours of inadequacy as it was the case of 10 Nov 2016 17:00 UTC (no more flows between the 2 countries)</li> #</ul>
r year_mc
– Productiondraw_prod_MC(data_fr, date_i = date_debut, mc_year = year_mc) #$widgets[[1]]$widget[[1]]
#Analyse sur le graphique prod_mc #<ul> #<li>In France, thermal production is low because of unavailability of nuclear plants.</li> #<li>Because of that, France is a net importer during the weekdays.</li> #<li>We can also observe time steps where there are unsupplied energy.</li> #<li>Water is pumped during off-peak hours but it is not enough to avoid inadequacy.</li> #</ul>
r year_mc
– MapplotMap( x = data_all, mapLayout = ml, interactive = FALSE, colLinkVar = "abs_loadFactor", sizeLinkVar = "FLOW LIN.", colAreaVar = "marges_inter", labelAreaVar = "marges_inter", options = plotMapOptions( areaDefaultSize = 50, areaColorScaleOpts = colorScaleOptions( breaks = c(-3000, 0, 0.1, 70000), colors = c("#ff0000", "#cd853f", "#008000", "#008000") ), linkColorScaleOpts = colorScaleOptions( breaks = c(0, 0.25, 0.5, 0.75, 0.999, 1), colors = c("#88cc8a","#a4ce3b","#ffff30", "#f49518", "#ff0000") ) ) )
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