User guide"

In antaresEditObject: Edit an 'Antares' Simulation

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Prerequisites

First it's necessary to load the package:

library(antaresEditObject)

You need to set the path to an Antares study in "input" mode:

antaresRead::setSimulationPath(path = "path/to/study", simulation = "input")

Or you can simply create a new study:

createStudy("path/to/study")

Save study

Before modifying your study, you can save it in an archive:

backupStudy(what = "input")

This will create a .tar.gz file in your study folder.

Create a new area

You can create a new area with:

createArea(name = "myarea")

# The new area should appear here:
antaresRead::getAreas()

You can specify the localization of the area on the map, and also its color.

There are two helper functions for area parameters:

• filteringOptions() for filtering options, like filter-year-by-year
• nodalOptimizationOptions() for nodal optimizations options.

Create a new cluster

You can initialize a cluster with some parameters:

createCluster(
  area = "myarea", 
  cluster_name = "myareacluster",
  group = "other",
  unitcount = 1,
  nominalcapacity = 8400,
  `min-down-time` = 0,
  `marginal-cost` = 0.010000,
  `market-bid-cost` = 0.010000
)

You can also edit the settings of an existing cluster:

editCluster(
  area = "myarea", 
  cluster_name = "myareacluster", 
  nominalcapacity = 10600.000
)

Create a new link

createLink(
  from = "area1", 
  to = "area2", 
  propertiesLink = propertiesLinkOptions(
    hurdles_cost = FALSE,
    transmission_capacities = "enabled"
  ), 
  dataLink = NULL
)

You can edit the settings of an existing link:

editLink(
  from = "area1",
  to = "area2",
  transmission_capacities = "infinite"
)

Create a binding constraint

createBindingConstraint(
  name = "myconstraint", 
  values = matrix(data = c(rep(c(19200, 0, 0), each = 366)), ncol = 3), 
  enabled = FALSE, 
  timeStep = "daily",
  operator = "both",
  coefficients = c("fr%myarea" = 1)
)

Create several Pumped Storage Power plant (PSP)

pspData <- data.frame(
  area = c("a", "b"), 
  installedCapacity = c(800,900)
)

createPSP(
  areasAndCapacities = pspData, 
  efficiency = 0.75
)

Create several Demand Side Response (DSR)

dsrData <- data.frame(
  area = c("a", "b"),
  unit = c(10,20), 
  nominalCapacity = c(100, 120),
  marginalCost = c(52, 65),
  hour = c(3, 7)
)

createDSR(dsrData)

Update general settings

For example, set the output of simulation year by year, and limit the number of Monte-Carlo years to 10:

updateGeneralSettings(year.by.year = TRUE, nbyears = 10)

Remove methods

You can remove areas, links, clusters and binding constraints from input folder with remove* functions, e.g.:

removeArea("myarea")

Run Time-Series Generator

First, update general settings to activate time series to generate:

updateGeneralSettings(generate = "thermal")

Then run TS-generator:

runTsGenerator(
  path_solver = "C:/path/to/antares-solver.exe", 
  show_output_on_console = TRUE
)

Run an Antares simulation

Launch an Antares simulation from R:

runSimulation(
  name = "myAwesomeSimulation", 
  mode = "economy",
  path_solver = "C:/path/to/antares-solver.exe", 
  show_output_on_console = TRUE
)

antaresEditObject documentation built on Dec. 6, 2022, 5:10 p.m.