Package 'antaresProcessing'

Description

This function computes 4 congestion variables of link (congestion frequency and congestion hours in direct and indirect direction) and adds them to an antaresData object. The input object must be at an hourly timestep.

Usage

addCongestionLink(x, timeStep = c("daily", "weekly", "monthly", "annual"))

Arguments

Value

addCongestionLink modifies its input by adding four columns:

congestionFrequencyDirect = round(sum((`CONG. PROB +` != 0)/.N), 2)

congestionFrequencyIndirect  = round(sum((`CONG. PROB -` != 0)/.N), 2)

congestionHoursDirect  = sum(`CONG. PROB +` != 0)

congestionHoursIndirect =  sum(`CONG. PROB -` != 0)

Examples

## Not run: 
# Data required by the function

mydata <- readAntares(links = "all")
mydata <- addCongestionLink(mydata, timeStep = "daily")
names(mydata)

mydata <- addCongestionLink(mydata, timeStep = c('daily'))

## End(Not run)

Description

This function computes isolated and interconnected downward margins of areas and add them to an antaresData object.

Usage

addDownwardMargin(x)

Arguments

Details

For a given area, downward margin is equal to the thermal minimum production (due must run production and minimum stable power of production units) plus the fatal productions minus the load and the pumping capacity. More formally it is equal to:

isolatedDownwardMargin = thermalPMin + `H. ROR` + WIND + SOLAR + `MISC. NDG` - LOAD - pumpingCapacity

The variable pumpingCapacity is automatically created when pumped storage areas are removed with function removeVirtualAreas. If there is not any such area, pumpingCapacity is assumed to be equal to 0.

Interconnected downward margin is the isolated downward margin plus the exports minus the imports:

interconnectedDownwardMargin = isolatedDownwardMargin + BALANCE - `ROW BAL.`

Value

The function modifies its input by adding to it two new columns isolatedDownwardMargin and interconnectedDownwardMargin. For convenience it invisibly returns x.

Examples

## Not run: 
# data required by the function
showAliases("downwardMargin")

mydata <- readAntares(select = "downwardMargin")
mydata <- removeVirtualAreas(mydata, getAreas(c("pump", "stor")))

addDownwardMargin(mydata)
names(mydata$areas)

## End(Not run)

Description

This function computes the export and import of areas or districts and add it to an antaresData object.

Usage

addExportAndImport(x, addCapacities = FALSE, opts = NULL)

Arguments

Value

addExportAndImport modifies its input by adding to it columns:

Examples

## Not run: 
# Data required by the function
showAliases("exportsImports")

mydata <- readAntares(select = "exportsImports")
addExportAndImport(mydata)
names(mydata$areas)


## End(Not run)

Description

This function computes the load factor of link and add it to an antaresData object.

Usage

addLoadFactorLink(x)

Arguments

Value

addLoadFactorLink modifies its input by adding to it two columns:

loadFactor = `FLOW LIN` / transCapacity

For convenience, the function invisibly returns the modified input.

Examples

## Not run: 
# Data required by the function
showAliases("loadFactorLink")

mydata <- readAntares(select = "loadFactorLink")
addLoadFactorLink(mydata)
names(mydata)

## End(Not run)

Description

This function computes the net load of areas or districts and add it to an antaresData object. Net load is the load of an area minus productions that are not controlled: wind, solar, hydraulic run of river, etc. the production of clusters in must run mode is also subtracted by default.

Usage

addNetLoad(x, ignoreMustRun = FALSE)

Arguments

Value

addNetLoad modifies its input by adding to it a column "netLoad". For convenience, it invisibly returns the modified input. formula = LOAD - 'ROW BAL.' - PSP - 'MISC. NDG' - 'H. ROR' - WIND - SOLAR - mustRunTotal

Examples

## Not run: 
# Data required by the function
showAliases("netLoad")

mydata <- readAntares(select = "netLoad")
addNetLoad(mydata)
names(mydata)


## End(Not run)

Description

This function computes isolated and interconnected upward margins of areas and add them to an antaresData object.

Usage

addUpwardMargin(x)

Arguments

Details

For a given area and time step, isolated upward margin is the difference between the available production capacity plus the fatal productions and the load. More formally it is equal to:

isolatedUpwardMargin = (`AVL DTG` + generatingMaxPower + storageCapacity) + (`H. ROR` + WIND + SOLAR + `MISC. NDG`) - LOAD

NB: in Antares v6 (and earlier versions) generatingMaxPower is replaced by hstorPMaxAvg.

The variable storageCapacity is automatically created when pumped storage areas are removed with function removeVirtualAreas. If there is not any such area, storageCapacity is assumed to be equal to 0.

Interconnected upward margin is the isolated upward margin plus the imports and minus the exports:

interconnectedUpwardMargin = isolatedUpwardMargin - BALANCE + `ROW BAL.`

Value

The function modifies its input by adding to it two new columns isolatedUpwardMargin and interconnectedUpwardMargin. For convenience it invisibly returns x.

Examples

## Not run: 
# Data required by the function
showAliases("upwardMargin")

mydata <- readAntares(select = "upwardMargin")
mydata <- removeVirtualAreas(mydata, getAreas(c("pump", "stor")))

addUpwardMargin(mydata)

## End(Not run)

Description

compare has been designed to compare two surpluses created with function "surplus" but it can be used to compare the values of two tables of class antaresData that contain the same type of data.

Usage

compare(x, y, method = c("diff", "ratio", "rate"))

Arguments

Value

a data.table of class antaresDataTable. It contains all shared rows and columns between 'x' and 'y'. The columns contain the statistic chosen: difference, ratio or rate of change.

Examples

## Not run: 
# First simulation
studyPath <- "path/to/study/"

setSimulationPath(studyPath, 1)
mydata1 <- readAntares("all", "all", synthesis = FALSE)
surplus1 <- surplus(mydata1, groupByDistrict = TRUE)

# Second simulation
setSimulationPath(studyPath, 2)
mydata2 <- readAntares("all", "all", synthesis = FALSE)
surplus2 <- surplus(mydata2, groupByDistrict = TRUE)

compare(surplus1, surplus2)

opts1 <- setSimulationPath(studyPath,-1)
mydata1<-readAntares(areas = "all",
links = "all",
select = c("allAreas", "allLinks"),
mcYears = c(1),
linkCapacity = TRUE)

opts2 <- setSimulationPath(studyPath,-2)
mydata2 <- readAntares(areas = "all",
links = "all",
select = c("allAreas", "allLinks"),
mcYears = c(1),
linkCapacity = TRUE)

opts3 <- setSimulationPath(studyPath,-3)
mydata3 <- readAntares(areas = "all",
links = "all",
select = c("allAreas", "allLinks"),
mcYears = c(1),
linkCapacity = TRUE)

opts4 <- setSimulationPath(studyPath, -4)
mydata4 <- readAntares(areas = "all",
links = "all",
select=c("allAreas", "allLinks"),
mcYears = c(1),
linkCapacity = TRUE)

opts5 <- setSimulationPath(studyPath, -5)
mydata5 <- readAntares(areas = "all",
links = "all",
select=c("allAreas", "allLinks"),
mcYears = c(1),
linkCapacity = TRUE)

resCompare1 <- compare(mydata2, mydata1, method = "diff")
resCompare2 <- compare(mydata3, mydata1, method = "diff")
resCompare3 <- compare(mydata4, mydata1, method = "diff")
resCompare4 <- compare(mydata5, mydata1, method = "diff")

listCompare <- list(resCompare1, resCompare2, resCompare3, resCompare4)

for (i in 1:length(listCompare)){
listCompare[[i]] <- removeVirtualAreas(listCompare[[i]],
                                       storageFlexibility =
                                       getAreas(select = c("z_dsr", "y_mul", "pum", "tur")))
}

ml <- readRDS("path/to/mapLayout.rds")
plotMap(listCompare, ml)

## End(Not run)

Description

This function computes the dependency in imports and export for each area or districts at a given time step. Dependency in imports represents moments where imports are required to have no loss of load. Dependency in exports represents moments where exports are required to have no spilled energy.

Usage

externalDependency(x, timeStep = "annual", synthesis = FALSE, opts = NULL)

Arguments

Value

A data.table of class antaresDataTable with the following columns:

Examples

## Not run: 
# Data required by the function
showAliases("externalDependency")

mydata <- readAntares(select = "externalDependency")
addNetLoad(mydata)
externalDependency(mydata)

# if there are some virtual pumping/storage areas, remove them with
# removeVirtualAreas
mydata <- removeVirtualAreas(mydata, c("pumping", "storage"))
externalDependency(mydata, ignoreMustRun = TRUE)

## End(Not run)

Description

Get all the values of a variable for some years Monte Carlo

Usage

getValues(data = NULL, variable = NULL, mcyear = "all")

Arguments

Examples

## Not run: 

mydata <- readAntares(areas="all",clusters="all", select="LOAD")
getValues(mydata$areas, variable="LOAD")
getValues(myData$clusters, variable = "production")


## End(Not run)

Description

This function computes the load factor and other related statistics for cluster of a study.

Usage

loadFactor(
  x,
  timeStep = "annual",
  synthesis = FALSE,
  clusterDesc = NULL,
  loadFactorAvailable = FALSE,
  opts = NULL
)

Arguments

Value

a data.table of class antaresDataTablecontaining the following columns:

#'

Examples

## Not run: 
# data required by the function
showAliases("loadfactor")

mydata <- readAntares(select = "loadfactor")
loadFactor(mydata, synthesis = TRUE)

## End(Not run)

Description

Merge all antaresDataSets

Usage

mergeAllAntaresData(dta)

Arguments

Examples

## Not run: 
setSimulationPath("Mystud", 1)
dta <- readAntares(areas = "all", links = "all", clusters = "all", districts = "all")
dta <- mergeAllAntaresData(dta)

## End(Not run)

Description

This function computes the modulation of cluster units or of sectors.

Usage

modulation(
  x,
  timeStep = "annual",
  synthesis = FALSE,
  by = c("cluster", "sector"),
  clusterDesc = NULL,
  opts = NULL
)

Arguments

Value

A data.table of class antaresDataTable or a list of such tables with the following columns:

Notice that if by="cluster", the function computes the modulation per unit, i.e. the modulation of a cluster divided by the number of units of the cluster. On the opposite, if by="sector", the function returns the modulation of the global production of the sector. Moreover, if parameter x contains area and district data, the function returns a list with components areas and districts.

Examples

## Not run: 
# data required by the function
showAliases("modulation")

mydata <- readAntares(select="modulation")

# Modulation of cluster units
modulation(mydata)

# Aggregate Monte-Carlo scenarios
modulation(mydata, synthesis = TRUE)

# Modulation of sectors
modulation(mydata, by = "sector")

# Modulation of sectors per district
modulation(mydata, by = "sector")

## End(Not run)

Description

This function computes the ramp of the consumption and the balance of areas and/or districts.

Usage

netLoadRamp(
  x,
  timeStep = "hourly",
  synthesis = FALSE,
  ignoreMustRun = FALSE,
  opts = NULL
)

Arguments

Value

netLoadRamp returns a data.table or a list of data.tables with the following columns:

For convenience the function invisibly returns the modified input.

Examples

## Not run: 
# data required by the function
showAliases("netLoadRamp")

mydata <- readAntares(select="netLoadRamp")
netLoadRamp(mydata, timeStep = "annual")

## End(Not run)

Description

This function computes the economic surplus for the consumers, the producers and the global surplus of an area.

Usage

surplus(
  x,
  timeStep = "annual",
  synthesis = FALSE,
  groupByDistrict = FALSE,
  hurdleCost = TRUE,
  opts = NULL
)

Arguments

Value

A data.table with the following columns:

Examples

## Not run: 
showAliases("surplus")

mydata <- readAntares(select="surplus")
surplus(mydata)

surplus(mydata, synthesis = TRUE)
surplus(mydata, synthesis = TRUE, groupByDistrict = TRUE)

## End(Not run)

Description

This function computes the surplus of clusters of interest. The surplus of a cluster is equal to its production times the marginal cost of the area it belongs to minus variable, fixed and startup costs.

Usage

surplusClusters(
  x,
  timeStep = "annual",
  synthesis = FALSE,
  surplusLastUnit = FALSE,
  clusterDesc = NULL,
  opts = NULL
)

Arguments

Value

A data.table of class antaresDataTable with the following columns:

Examples

## Not run: 
# Data required by the function:
showAliases("surplusClusters")

mydata <- readAntares(select = "surplusClusters")
surplusClusters(mydata)

# Computing the surplus of the last unit of a cluster requires the additional
# column "availableUnits". To add this column, one has to use parameter
# "thermalAvailabilities = TRUE" in readAntares.

mydata <- readAntares(select = c("surplusClusters", "thermalAvailabilities"))
surplusClusters(mydata, surplusLastUnit = TRUE)


## End(Not run)

Description

This function computes the surplus of sectors for each area and time step. For sectors wind, solar, hydraulic storage and run of river, production costs are assumed to be equal to 0.

Usage

surplusSectors(
  x,
  sectors = c("thermal", "renewable"),
  timeStep = "annual",
  synthesis = FALSE,
  groupByDistrict = FALSE,
  clusterDesc = NULL,
  opts = NULL
)

Arguments

Value

A data.table of class "antaresData". It contains one column per sector containing the surplus of that sector for a given area and timeId.

Examples

## Not run: 

# Data required by the function:
showAliases("surplusSectors")

mydata <- readAntares(select = "surplusSectors")
surplusSectors(mydata)

# Note that if the parameter "sectors" is modified, the function can require
# more or less data. For instance, if one only wants surplus for thermal
# sectors:
mydata <- readAntares(areas = "all", clusters = "all", synthesis = FALSE,
                      select = "MRG. PRICE")
surplusSectors(mydata, sectors = "thermal")


## End(Not run)

Description

This function takes as input an object of class antaresData containing detailed results of a simulation and creates a synthesis of the results. The synthesis contains the average value of each variable over Monte-Carlo scenarios and eventually other aggregated statistics

Usage

synthesize(x, ..., prefixForMeans = "", useTime = TRUE)

Arguments

Details

Additional statistics can be asked in three different ways:

1. A character string in "min", "max", "std", "median" or "qXXX" where "XXX" is a real number between 0 and 100. It will add for each column respectively the minimum or maximum value, the standard deviation, the median or a quantile.

2. A named argument whose value is a function or one of the previous aliases. For instance med = median will calculate the median of each variable. The name of the resulting column will be prefixed by "med_". Similarly, l = "q5" will compute the 5 each variable and put the result in a column with name prefixed by "l_"

3. A named argument whose value is a list. It has to contain an element fun equal to a function or an alias and optionally an element only containing the names of the columns to which to apply the function. For instance med = list(fun = median, only = c("LOAD", "MRG. PRICE")) will compute the median of variables "LOAD" and "MRG. PRICE". The result will be stored in columns "med_LOAD" and "med_MRG. PRICE".

The computation of custom statistics can take some time, especially with hourly data. To improve performance, prefer the third form and compute custom statistics only on a few variables.

Value

Synthetic version of the input data. It has the same structure as x except that column mcYear has been removed. All variables are averaged across Monte-Carlo scenarios and eventually some additional columns have been added corresponding to the requested custom statistics.

Examples

## Not run: 
mydata <- readAntares("all", timeStep = "annual")

synthesize(mydata)

# Add minimum and maximum for all variables
synthesize(mydata, "min", "max")

# Compute a custom statistic for all columns
synthesize(mydata, log = function(x) mean(log(1 + x)))

# Same but only for column "LOAD"
synthesize(mydata,
           log = list(fun = function(x) mean(log(1 + x)),
                      only = "LOAD"))

# Compute the proportion of time balance is positive

synthesize(mydata, propPos = list(fun = function(x) mean(x > 0),
                                  only = "BALANCE"))

# Compute 95% confidence interval for the marginal price
synthesize(mydata,
           l = list(fun = "q2.5", only = "MRG. PRICE"),
           u = list(fun = "q97.5", only = "MRG. PRICE"))

## End(Not run)

Description

compute thermal capacities from study

Usage

thermalGroupCapacities(opts = simOptions())

Arguments