1 Introduction

antaresViz-0.15 is on CRAN. It includes new features. This document introduces some of them.

Install antaresViz with :

install.packages("antaresViz")

2 Reference study

runAppAntaresViz() run a shiny app. You can now use a reference study in your analysis process. Someone can now compare several strategies from a reference study. This process use the function ‘antaresProcessing::compare’. You can find the description of compare here.

3 Reference study with interactive set to FALSE

You can also use a reference study outside the shiny app. prodStack(), exchangesStack, plot and plotMap have a new parameter refStudy to set the reference study.

Import your data before comparing.

#here import your data 

refStudy <- "pathToRefStudy"
mlLayout <- load("myLayout")
#reference study
optsRef <- setSimulationPath(path = refStudy, simulation = -3)
refData <- readAntares(areas = "all", 
                       links = "all", 
                       opts = optsRef)
#strategie 1
optsAlter1 <- setSimulationPath(path = refStudy, simulation = -2)
myData01 <- readAntares(areas = "all", 
                        links = "all", 
                        opts = optsRef)
#strategie 2
optsAlter2 <- setSimulationPath(path = refStudy, simulation = -1)
myData02 <- readAntares(areas = "all", 
                        links = "all", 
                        opts = optsRef)

3.1 prodStack

We can compare easily the production between the strategies and the reference study. For example, for strategy one, we produce more gas than the reference study and less nuclear.

myDataRange <- c("2029-01-09 00:00:00", 
                 "2029-01-16 00:00:00")

prodStack(x = list(myData01, myData02),
          refStudy = refData,
          interactive = FALSE,
          areas = "fr",
          dateRange = myDataRange)

3.2 exchangesStack

Flows can be very different between the two strategies. What is the best strategy for exporting more in January?

exchangesStack(x = list(myData01, myData02),
               refStudy = refData,
               interactive = FALSE,
               area = "fr",
               dateRange = myDataRange)

3.3 plotMap

You can also compare the production and flows evolutions on a map.

plotMap(x = list(myData01, myData02),
        refStudy = refData,
        mapLayout = mlLayout,
        type = "avg",
        interactive = FALSE,
        colAreaVar = "LOAD",
        typeSizeAreaVars = TRUE,
        aliasSizeAreaVars = c("generation", "renewable"),
        colLinkVar = "CONG. PROB +",
        sizeLinkVar = "FLOW LIN.",
        sizeMiniPlot = TRUE)

3.4 plot

We can also focus our attention on the evolution of a variable.

plot(x = list(myData01, myData02),
     refStudy = refData,
     interactive = FALSE,
     elements = "fr",
     table = "areas",
     variable = "NUCLEAR",
     type = "density")