readAntares is a swiss-army-knife function used to read almost every
+possible time series of an antares Project at any desired time resolution
+(hourly, daily, weekly, monthly or annual).
+
+
It was first designed to read
+output time series, but it can also read input time series. The input time
+series are processed by the function to fit the query of the user (timeStep,
+synthetic results or Monte-Carlo simulation, etc.). The few data that are not
+read by readAntares can generally by read with other functions of the
+package starting with "read" (readClusterDesc,
+readLayout, readBindingConstraints)
+
Vector containing the names of the areas to import. If
+NULL no area is imported. The special value "all" tells the
+function to import all areas. By default, the value is "all" when no other argument is enter and "NULL" when other arguments are enter.
+
+
links
+
+
Vector containing the name of links to import. If NULL no
+area is imported. The special value "all" tells the function to
+import all areas. Use function getLinks to import all links
+connected to some areas.
+
+
clusters
+
+
Vector containing the name of the areas for which you want to
+import results at cluster level. If NULL no cluster is imported. The
+special value "all" tells the function to import clusters from all
+areas.
+
+
districts
+
+
Vector containing the names of the districts to import. If NULL,
+no district is imported. The special value "all" tells the function to import all
+districts.
+
+
misc
+
+
Vector containing the name of the areas for which you want to
+import misc.
+
+
thermalAvailabilities
+
+
Should thermal availabilities of clusters be imported ? If TRUE, the column
+"thermalAvailability" is added to the result and a new column "availableUnits"
+containing the number of available units in a cluster is created.If synthesis is set to TRUE then
+"availableUnits" contain the mean of avaible units on all MC Years.
+
+
hydroStorage
+
+
Should hydro storage be imported ?
+
+
hydroStorageMaxPower
+
+
Should hydro storage maximum power be imported ?
+
+
reserve
+
+
Should reserve be imported ?
+
+
linkCapacity
+
+
Should link capacities be imported ?
+
+
mustRun
+
+
Should must run productions be added to the result? If TRUE,
+then four columns are added: mustRun contains the production of
+clusters that are in complete must run mode; mustRunPartial
+contains the partial must run production of clusters; mustRunTotal
+is the sum of the two previous columns. Finally thermalPmin is
+similar to mustRunTotal except it also takes into account the production
+induced by the minimum stable power of the units of a cluster. More
+precisely, for a given cluster and a given time step, it is equal to
+min(NODU x min.stable.power, mustRunTotal).
+
+
thermalModulation
+
+
Should thermal modulation time series be imported ? If TRUE, the
+columns "marginalCostModulation", "marketBidModulation", "capacityModulation"
+and "minGenModulation" are added to the cluster data.
+
+
select
+
+
Character vector containing the name of the columns to import. If this
+argument is NULL, all variables are imported. Special names
+"allAreas" and "allLinks" indicate to the function to import
+all variables for areas or for links. Since version 1.0, values "misc",
+"thermalAvailabilities", "hydroStorage", "hydroStorageMaxPower", "reserve",
+"linkCapacity", "mustRun", "thermalModulation" are also accepted and can
+replace the corresponding arguments. The list of available variables can be
+seen with the command simOptions()$variables. Id variables like
+area, link or timeId are automatically imported.
+
+
mcYears
+
+
Index of the Monte-Carlo years to import. If NULL, synthetic results
+are read, else the specified Monte-Carlo simulations are imported. The
+special value all tells the function to import all Monte-Carlo
+simulations.
+
+
timeStep
+
+
Resolution of the data to import: hourly (default), daily,
+weekly, monthly or annual.
+
+
opts
+
+
list of simulation parameters returned by the function
+setSimulationPath
+
+
parallel
+
+
Should the importation be parallelized ? (See details)
+
+
simplify
+
+
If TRUE and only one type of output is imported then a
+data.table is returned. If FALSE, the result will always be a list of class
+"antaresData".
+
+
showProgress
+
+
If TRUE the function displays information about the progress of the
+importation.
+
+
+
+
+
Details
+
+
If parameters areas, links, clusters and districts
+are all NULL, readAntares will read output for all areas.
+By default the function reads synthetic results if they are available.
+
+
readAntares is able to read input time series, but when they are not
+stored in output, these time series may have changed since a simulation has
+been run. In such a case the function will remind you this danger with a
+warning.
+
+
When individual Monte-Carlo simulations are read, the function may crash
+because of insufficient memory. In such a case, it is necessary to reduce
+size of the output. Different strategies are available depending on your
+objective:
+
+
+
+
Use a larger time step (parameter timeStep)
+
+
+
Filter the elements to import (parameters areas,links,
+clusters and districts)
+
+
+
Select only a few columns (parameter select)
+
+
+
read only a subset of Monte-Carlo simulations (parameter
+mcYears). For instance one can import a random sample of
+100 simulations with mcYears = sample(simOptions()$mcYears, 100)
+
+
+
+
+
+
Value
+
+
If simplify = TRUE and only one type of output is imported
+then the result is a data.table.
+
+
Else an object of class "antaresDataList" is returned. It is a list of
+data.tables, each element representing one type of element (areas, links,
+clusters)
+
+
+
+
Parallelization
+
+
If you import several elements of the same type (areas, links, clusters), you
+can use parallelized importation to improve performance. Setting the
+parameter parallel = TRUE is not enough to parallelize the
+importation, you also have to install the package
+foreach
+and a package that provides a parallel backend (for instance the package
+doParallel).
+
+
Before running the function with argument parallel=TRUE, you need to
+register your parallel backend. For instance, if you use package "doParallel"
+you need to use the function registerDoParallel once per
+session.
+
+## Not run:
+# Import areas and links separately
+
+areas <- readAntares() # equivalent to readAntares(areas="all")
+links <- readAntares(links="all")
+
+# Import areas and links at same time
+
+output <- readAntares(areas = "all", links = "all")
+
+# Add input time series to the object returned by the function
+areas <- readAntares(areas = "all", misc = TRUE, reserve = TRUE)
+
+# Get all output for one area
+
+myArea <- sample(simOptions()$areaList, 1)
+myArea
+
+myAreaOutput <- readAntares(area = myArea,
+ links = getLinks(myArea, regexpSelect=FALSE),
+ clusters = myArea)
+
+# Or equivalently:
+myAreaOutput <- readAntaresAreas(myArea)
+
+# Use parameter "select" to read only some columns.
+
+areas <- readAntares(select = c("LOAD", "OV. COST"))
+
+# Aliases can be used to select frequent groups of columns. use showAliases()
+# to view a list of available aliases
+
+areas <- readAntares(select="economy")
+
+
+## End(Not run)
+
+
+
+
diff --git a/inst/application/www/removeVirtualAreas.html b/inst/application/www/removeVirtualAreas.html
new file mode 100644
index 0000000..842c7fb
--- /dev/null
+++ b/inst/application/www/removeVirtualAreas.html
@@ -0,0 +1,178 @@
+R: Remove virtual areas
+
+
+
+
+
removeVirtualAreas
R Documentation
+
+
Remove virtual areas
+
+
Description
+
+
This function removes virtual areas from an antaresDataList object and
+corrects the data for the real areas. The antaresDataList object
+should contain area and link data to function correctly.
+
An object of class antaresDataList with at least components
+areas and links.
+
+
storageFlexibility
+
+
A vector containing the names of the virtual
+storage/flexibility areas.
+
+
production
+
+
A vector containing the names of the virtual production
+areas.
+
+
reassignCosts
+
+
If TRUE, the production costs of the virtual areas are
+reallocated to the real areas they are connected to. If the virtual areas
+are connected to a virtual hub, their costs are first reallocated to the
+hub and then the costs of the hub are reallocated to the real areas.
+
+
newCols
+
+
If TRUE, new columns containing the production of the virtual
+areas are added. If FALSE their production is added to the production of
+the real areas they are connected to.
+
+
+
+
+
Details
+
+
Two types of virtual areas have been defined corresponding to different types
+of modeling in Antares and different types of post-treatment to do:
+
+
+
+
Flexibility/storage areas are areas created to model
+pumping unit or any other flexibility that behave as a storage. For those
+virtual areas, the important results are flows on the links.
+
+
+
Production areas are areas created to isolate some generation from
+the "real" areas. They can be isolate for several reasons: to distinguish
+time-series (for example wind onshore/offshore), to select some specific
+unit to participate to day-ahead reserve, etc.
+
+
+
+
removeVirtualAreas performs different corrections:
+
+
+
+
Correct the balance of the real areas by removing the flows
+to or from virtual areas.
+
+
+
If parameter reassignCosts is TRUE, then the costs of the
+virtual areas are reassigned to the real areas they are connected to. The
+affected columns are OV. COST, OP. COST, CO2 EMIS.
+and NP COST. If a virtual area is connected to a single real area,
+all its costs are attributed to the real area. If it is connected to
+several real areas, then costs at a given time step are divided between
+them proportionally to the flows between them and the virtual area.
+
+
+
For each storage/flexibility area, a column named like the area is
+created. It contains the values of the flow between the virtual area and
+the real areas. This column is interpreted as a production of
+electricity: it is positive if the flow from the virtual area to the real
+area is positive and negative otherwise. If parameter newCols is
+FALSE, the values are added to the variable PSP and the
+columns is removed.
+
+
+
If the parameter production is specified, then the non null
+productions of the virtual areas are either added to the ones of the real
+areas they are connected to if newCols = FALSE or put in new
+columns if newCols = TRUE. In the second case the columns are
+named *_virtual where "*" is a type of
+production (wind, solar, nuclear, ...). Productions that are zero for
+all virtual areas are omited.
+If virtual production areas contains clusters then they will be move to the
+real area.
+
+
+
Finally, virtual areas and the links connected to them are removed
+from the data.
+
+
+
+
The functions makes a few assumptions about the network. if they are
+violated it will not act correctly:
+
+
+
+
storage/flexibility
+areas can be connected to other storage/flexibility areas (hubs), but at
+least one of them is connected to a real area. That means that there is
+no group of virtual areas disconnected from the real network. If such a
+group exists, you can either remove them manually or simply not import
+them.
+
+
+
production areas are connected to one and only one real area. They
+cannot be connected to virtual areas. But a real area may by connected to
+several production areas.
+
+
+
+
+
+
Value
+
+
An antaresDataList object in which virtual areas have been removed and
+data of the real has been corrected. See details for an explanation of the
+corrections.
+
+
+
+
Examples
+
+
+## Not run:
+
+# Assume we have a network with two virtual areas acting as pump storage and
+# an area representing offshore production
+#
+# offshore
+# |
+# real area - psp in
+# \
+# psp out
+#
+
+data <- readAntares(areas="all", links="all")
+
+# Remove pump storage virtual areas
+
+correctedData <- removeVirtualAreas(data,
+ storageFlexibility = c("psp in", "psp out"),
+ production = "offshore")
+
+## End(Not run)
+
+
+
+
+
diff --git a/inst/application/www/spinner.gif b/inst/application/www/spinner.gif
new file mode 100644
index 0000000..d79561d
Binary files /dev/null and b/inst/application/www/spinner.gif differ
diff --git a/inst/application/www/writeAntaresH5.html b/inst/application/www/writeAntaresH5.html
new file mode 100644
index 0000000..addf33a
--- /dev/null
+++ b/inst/application/www/writeAntaresH5.html
@@ -0,0 +1,161 @@
+R: Convert antares output to h5 file
+
+
+
+
+
character folder where h5 file will be write (default getwd())
+
+
timeSteps
+
+
character timeSteps
+
+
opts
+
+
list of simulation parameters returned by the function setSimulationPath. Defaut to antaresRead::simOptions()
+
+
writeMcAll
+
+
boolean write mc-all
+
+
compress
+
+
numeric compress level
+
+
misc
+
+
boolean see readAntares
+
+
thermalAvailabilities
+
+
boolean see readAntares
+
+
hydroStorage
+
+
boolean see readAntares
+
+
hydroStorageMaxPower
+
+
boolean see readAntares
+
+
reserve
+
+
boolean see readAntares
+
+
linkCapacity
+
+
boolean see readAntares
+
+
mustRun
+
+
boolean see readAntares
+
+
thermalModulation
+
+
boolean see readAntares
+
+
allData
+
+
boolean add all data with a single call (writeMcAll, misc, thermalAvailabilities, hydroStorage, hydroStorageMaxPower
+reserve, linkCapacity, mustRun, thermalModulation).
+
+
writeAllSimulations
+
+
boolean, write all simulations of your antares study.
+
+
nbCores
+
+
numeric, number of cores to use, only used if writeAllSimulations is TRUE
+
+
removeVirtualAreas
+
+
boolean, remove virtual areas, see removeVirtualAreas
+
+
storageFlexibility
+
+
character, see removeVirtualAreas
+
+
production
+
+
character, see removeVirtualAreas
+
+
reassignCosts
+
+
boolean, see removeVirtualAreas
+
+
newCols
+
+
boolean, see removeVirtualAreas
+
+
overwrite
+
+
boolean, overwrite old file
+
+
supressMessages
+
+
boolean, supress messages from readAntares and removeVirtualAreas
+
+
+
+
+
Examples
+
+
+
+## Not run:
+# Write simulation one by one
+setSimulationPath("C:/Users/MyUser/Mystudy", 1)
+writeAntaresH5()
+
+# Write all simulations
+setSimulationPath("C:/Users/MyUser/Mystudy")
+writeAntaresH5(writeAllSimulations = TRUE)
+
+# Choose timestep to write
+setSimulationPath("C:/Users/MyUser/Mystudy", 1)
+writeAntaresH5(timeSteps = "hourly")
+
+# Write with additionnal information
+writeAntaresH5(timeSteps = "hourly",
+ misc = TRUE, thermalAvailabilities = TRUE,
+ hydroStorage = TRUE, hydroStorageMaxPower = TRUE, reserve = TRUE,
+ linkCapacity = TRUE, mustRun = TRUE, thermalModulation = TRUE)
+
+# Write all data with a shorcut
+writeAntaresH5(allData = TRUE)
+
+
+## End(Not run)
+
+
+
+
diff --git a/inst/color.csv b/inst/color.csv
new file mode 100644
index 0000000..b2dcc13
--- /dev/null
+++ b/inst/color.csv
@@ -0,0 +1,21 @@
+red;green;blue;Column
+17;71;185;PSP
+120;136;194;mustRunTotal
+120;236;194;mustRunPartial
+220;236;94;mustRun
+22;106;87;MISC. NDG
+116;205;185;WIND
+242;116;6;SOLAR
+245;179;0;NUCLEAR
+243;10;10;GAS
+172;140;53;COAL
+135;86;39;LOAD
+39;114;178;H. STOR
+180;130;43;LIGNITE
+131;86;162;OIL
+127;84;156;MIX. FUEL
+173;255;47;MISC. DTG
+61;96;125;H. ROR
+84;151;208;H. STOR
+101;180;197;netLoad
+1;1;1;AVL DTG
diff --git a/inst/htmlwidgets/leafletDragPoints.js b/inst/htmlwidgets/leafletDragPoints.js
index 8e6041a..b3de06f 100644
--- a/inst/htmlwidgets/leafletDragPoints.js
+++ b/inst/htmlwidgets/leafletDragPoints.js
@@ -14,14 +14,22 @@ HTMLWidgets.widget({
).addTo(map);
var points = [];
+ var mapLayer;
+ var markersLayer = [];
+
+ function clear_polyline() {
+ map.removeLayer( linesFeatureLayer );
+ }
// Function that updates shiny input
function updateShinyInput() {
var coords = points.map(function(p) {
return p.marker.getLatLng();
});
- Shiny.onInputChange(el.id + "_coords", coords);
- Shiny.onInputChange(el.id + "_mapcenter", map.getCenter());
+ if(HTMLWidgets.shinyMode){
+ Shiny.onInputChange(el.id + "_coords", coords);
+ Shiny.onInputChange(el.id + "_mapcenter", map.getCenter());
+ }
}
map.on("moveend", updateShinyInput);
@@ -32,37 +40,66 @@ HTMLWidgets.widget({
return {
renderValue: function(x) {
+ if(x.init){
+ for(var i=0;i
+ %\VignetteIndexEntry{Vignette Title}
+ %\VignetteEngine{knitr::rmarkdown}
+ %\VignetteEncoding{UTF-8}
+---
+
+
+
+
+
+This package works along with RTE's adequacy software ANTARES : https://antares.rte-france.com/
+
+`antaresViz` is a package which proposes relevant graphs and maps to vizualize the results of an ANTARES study, with numerous settings and possible customizations.
+
+
+
+## Where everything starts
+
+
+```{r loadPackage, message = FALSE, warning=FALSE}
+library(antaresViz)
+```
+
+
+The data from an ANTARES study can be imported and easily manipulated using the [antaresRead](https://github.com/rte-antares-rpackage/antaresRead) package.
+
+The examples presented below have been build on a fictionnal study whose output have been loaded with antaresRead. They contains different types of elements :
+
+ * outputs with an annual and an hourly time step,
+ * synthetic outputs (averaged on several Monte-Carlo years) and detailled outputs (given year by year),
+ * outputs for areas, links, clusters and districts.
+
+
+```{r loadData, message = FALSE}
+load("data_for_antaresViz_vignette_extralight.Rdata")
+```
+
+The aim of this vignette is to give a quick overview of the possibilities offered by the package antaresViz. The data with which the following graphs have been plotted are fictionnal.
+
+## The simple power of the `plot` function
+
+The `plot()` function, used with an antaresDataList object (i.e. an object returned by the `antaresRead::readAntares()` function) offers different possible vizualisations.
+
+Moreover, this function is __really easy to use__. It opens a shiny interface which let the user decide :
+
+* Which type of __element__ he wants to study : areas, links or disctricts.
+* Which __variable__ he wants to analyze.
+* With which type of __graph__ : barplot, time series, probability density function, heatmap, etc.
+* For which area(s), link(s), or district(s).
+* And during which __time period__.
+
+
+```r
+plot(data_hourly_synthesis)
+```
+
+
+
+
+Note that all the interface manipulation can also be set directly in the arguments of the `plot` function. Some examples of the multiple graphs that can be returned by this function are given below.
+
+###### Barplot of Loss of Load Duration (LOLD) for several districts of the study
+
+```{r barplot, eval = FALSE}
+plot(data_annual_synthesis, table = "districts" ,variable = "LOLD", type = "barplot", elements = c("00_a", "00_b", "00_c", "00_d", "00_e", "00_f", "00_g", "00_h", "00_i"), interactive = FALSE, width = "100%", height = 400)
+```
+
+
+###### Time series of the load in an area with average value and 95% confidence interval.
+
+```{r ts, eval=FALSE}
+plot(data_hourly_allmc, table = "areas" ,variable = "LOAD", type = "ts", elements = "23_b", confInt = 0.95, dateRange = c("2018-01-08", "2018-01-14"), width = "100%", height = 400, interactive = FALSE)
+```
+
+
+###### Probability density function of the wind power generation in two areas
+```{r density, eval=FALSE}
+plot(data_hourly_synthesis, table = "areas" ,variable = "WIND", type = "density", elements = c("01_a", "02_a"), interactive = FALSE, width = "100%", height = 400)
+```
+
+
+###### Heatmap of the congestion probability of one interconnection
+
+```{r heatmap, eval=FALSE}
+plot(data_hourly_synthesis_1year, table = "links", variable = "CONG. PROB +", type = "heatmap", elements = "25_c - 26_d", interactive = FALSE, width = "100%", height = 400, main = "Congestion probability")
+```
+
+
+Note that the `plot()` function also contains a `compare` argument which allows comparisons between :
+
+* different variables
+* different areas/links/district
+* different studies
+
+
+## When the production meets the demand
+
+The `prodStack` function builds a graph which contains the time series of demand in one area (or the sum of the demand of a set of areas) along with the generation of this area (or set of areas), divided between the different fuel types (e.g. nuclear, gas, wind, etc).
+
+
+```r
+prodStack(data_hourly_synthesis)
+```
+
+Once again, this function is easy to use and opens a shiny interface which let the user manipulate the selected areas and time range. Some settings can also be passed to the function through its (optionnal) arguments.
+
+```{r prodStack, eval = FALSE}
+prodStack(data_hourly_synthesis, stack = "eco2mix", areas = "37_h", dateRange = c("2018-01-08", "2018-01-21"), main = "Production stack", unit = "GWh", interactive = FALSE, width = "100%", height = 500)
+```
+
+The graphical template used by default is the one of the RTE's application [eco2mix](http://www.rte-france.com/fr/eco2mix/eco2mix-mix-energetique). This template can though be redefined completely by the user with the function `setProdStackAlias()`.
+
+The `exchangesStack()` function proposes similar graphs with a superposition of all the imports and exports of an area.
+
+```{r exchangeStack, eval = FALSE}
+exchangesStack(data_hourly_synthesis, area = "37_h", dateRange = c("2018-01-08", "2018-01-21"), main = "Import/Export of area 37_h", unit = "GWh", interactive = FALSE, width = "100%", height = 500)
+```
+
+## Everything looks better on a map
+
+Last but not least, `antaresViz` proposes several function to vizualise the results of a study on a map.
+
+To do so, the first function to use is `mapLayout()`. This function launches an interactive application that let the user place areas of the ANTARES study on a map.
+
+
+```r
+antares_layout <- antaresRead::readLayout(opts = antaresRead::setSimulationPath(study_path))
+map_layout <- mapLayout(layout = antares_layout)
+```
+
+
+
+(Once again : the study presented here is fictionnal !)
+
+
+The function `plotMap()` then generates an interactive map that let the user visually explore the results of an Antares simulation. By default the function starts a Shiny gadget that let the user choose :
+
+ * Which __variable__ to represent
+ * With which __type of vizualisation__
+ - areas : color, size, popup, label
+ - links : color, width of the link, popup
+ * For which __time step__ or __date range__
+
+
+```r
+plotMap(data_hourly_synthesis, map_layout)
+```
+
+Some examples of results returned by the `plotMap()` function are depicted below.
+
+
+
+###### C02 emissions
+
+This first map depicts the annual CO2 emissions of all the areas of the ANTARES study.
+
+
+```{r plotmap_co2, eval = FALSE}
+plotMap(data_annual_filtered, map_layout, showLabels = TRUE, sizeAreaVar = "CO2 EMIS.", interactive = FALSE, labelAreaVar = "CO2 EMIS.", colAreaVar = "CO2 EMIS.", type = "avg", options = plotMapOptions(areaDefaultSize = 30, labelMaxSize = 8, labelMinSize = 14, areaColorScaleOpts = colorScaleOptions(zeroCol = "white", posCol = "red2")), width = "100%", height = 600)
+```
+
+
+###### Average balance and flows in the system
+
+This map illustrates the annual balance (MWh exported if positive - or imported if negative - during an hour) of each area and the annual flows of each link.
+
+```{r plotmap_flows, eval = FALSE}
+plotMap(data_annual_synthesis, map_layout, showLabels = TRUE, colAreaVar = "BALANCE", interactive = FALSE, labelAreaVar = "BALANCE", sizeLinkVar = "FLOW LIN.", type = "avg", options = plotMapOptions(areaDefaultSize = 30, labelMaxSize = 10, labelMinSize = 8, areaColorScaleOpts = colorScaleOptions(negCol = "tomato3", zeroCol = "white", posCol = "blue3")), width = "100%", height = 600)
+```
+
+
+###### Energy mixes
+
+The proportion of each energy type in the annual production of each area is illustrated on this next map. The actual generated energies (in MWh) can be known by clicking on the pie charts.
+
+```{r plotmap_mix, eval = FALSE}
+data_annual_synthesis$areas <- data_annual_synthesis$areas[, `:=`(THERMAL = NUCLEAR + LIGNITE + COAL + GAS + OIL + `MIX. FUEL` + `MISC. DTG`, HYDRO =`H. ROR` + `H. STOR`)]
+
+plotMap(data_annual_synthesis, map_layout, interactive = FALSE, sizeAreaVars = c("HYDRO", "SOLAR", "WIND", "THERMAL"), popupAreaVars = c("HYDRO", "SOLAR", "WIND", "THERMAL"),areaChartType = "pie", type = "avg", options = plotMapOptions(areaDefaultSize = 25), width = "100%", height = 600)
+```
+
+
+
+## Let's get started
+
+The `antaresViz` package is available on the CRAN and can be installed with :
+```r
+install.packages("antaresViz")
+```
+
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