---
title: "Antares new features v8.6.0"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Antares new features v8.6.0}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup}
# CRAN limite CPU usage
data.table::setDTthreads(2)
library(antaresEditObject)
```
This thumbnail will present the new features in line with **Antares v8.6.0** (the link is [here](https://antares-simulator.readthedocs.io/en/stable/developer-guide/CHANGELOG/#branch-86x-end-of-support-062025))
There are 3 new features :
> - Add new storage type "short-term storage".
> - Update parameters of thermal clusters with "pollutant emission factors"
> - "Hydro Pmin" : new file "mingen.txt"
## Create new study
```{r init}
dir_path <- tempdir()
createStudy(path = dir_path,
study_name = "test860",
antares_version = "8.6.0")
```
## Create area
```{r areas}
createArea(name = "fr")
createArea(name = "it")
```
## Create "st-storage"
We can create new st-storage cluster with new function `createClusterST()`. You can see function documentation with `?createClusterST`.
By default you can call function only with two parameters (`area`, `cluster_name`).
```{r st-storage}
inflows_data <- matrix(3, 8760)
ratio_values <- matrix(0.7, 8760)
createClusterST(area = "fr",
cluster_name = "test_storage",
storage_parameters = storage_values_default(),
PMAX_injection = ratio_values,
PMAX_withdrawal = ratio_values,
inflows = inflows_data,
lower_rule_curve = ratio_values,
upper_rule_curve = ratio_values,
overwrite = TRUE)
createClusterST(area = "it",
cluster_name = "test_storage",
storage_parameters = storage_values_default(),
PMAX_injection = ratio_values,
PMAX_withdrawal = ratio_values,
inflows = inflows_data,
lower_rule_curve = ratio_values,
upper_rule_curve = ratio_values,
overwrite = TRUE)
```
Now you can see informations in simulation options.
```{r study options}
opts <- simOptions()
opts$areasWithSTClusters
```
## Read st-storages parameters
After creating "st-storage" clusters, you can read all information with specific function `readClusterSTDesc()`.
```{r read st-storage param}
tab <- readClusterSTDesc()
rmarkdown::paged_table(tab)
```
## Read st-storages data
St-storages data are time series you can read for all areas or a specific area. 5 files contening one time series are generated (one per each function parameter):
- PMAX-injection.txt
- PMAX-withdrawal.txt
- inflows.txt
- lower-rule-curve.txt
- upper-rule-curve.txt
```{r read st-storage data}
data_st_storage <- readInputTS(st_storage = "all")
rmarkdown::paged_table(head(data_st_storage))
```
As you can see, the last two columns (`st-storage`, `name_file`) give you value for each name file.
**FYI** : As default, reading option for hourly timestep is `r opts$timeIdMax` (see `opts$timeIdMax`).
## Edit st-storage
It is possible to edit parameters values and data values like you want.
```{r edit st-storage}
# edit parameters values
list_params_st <- storage_values_default()
list_params_st$efficiency <- 0.5
list_params_st$reservoircapacity <- 50
# edit data values
inflows_data <- matrix(4, 8760)
editClusterST(area = "fr",
cluster_name = "test_storage",
storage_parameters = list_params_st,
inflows = inflows_data,
add_prefix = TRUE)
# read parameters
tab <- readClusterSTDesc()
rmarkdown::paged_table(tab)
# read data
data_st_storage <- readInputTS(st_storage = "all")
rmarkdown::paged_table(head(data_st_storage))
```
## Remove st-storage
Creating or editing st-storage are done, you can also remove clusters from study.
```{r remove st-storage opts}
# remove cluster
removeClusterST(area = "fr",
cluster_name = "test_storage",
add_prefix = TRUE)
# delete control
opts <- simOptions()
opts$areasWithSTClusters
```
The area `fr` is deleted cause we created only one cluster `test_storage`.
```{r remove st-storage}
# control removed parameters
tab <- readClusterSTDesc()
rmarkdown::paged_table(head(tab))
# control removed data
data_st_storage <- readInputTS(st_storage = "all")
rmarkdown::paged_table(head(data_st_storage))
unique(data_st_storage$area)
```
Parameters and data concerning this cluster in this area are removed.
## Thermal pollutants parameters
Antares version 8.6.0 now provide pollutants parameters for thermal clusters. You can see the documentation on thermal clusters [here](https://rte-antares-rpackage.github.io/antaresEditObject/articles/antaresEditObject.html#create-a-new-cluster).
You have global `list` of pollutants given by function `list_pollutants_values()`. By default, parameters are set to NULL, you can initialize all parameters with value or customize parameters.
```{r}
# create cluster with pollutants
# pollutants
all_param_pollutants <- list_pollutants_values(multi_values = 0.25)
createCluster(area = "fr",
cluster_name = "test_pollutant",
unitcount = 1L,
marginal_cost = 50,
list_pollutants = all_param_pollutants,
time_series = matrix(rep(c(0, 8000), each = 24*364), ncol = 2),
prepro_modulation = matrix(rep(c(1, 1, 1, 0), each = 24*365), ncol = 4)
)
```
```{r pollutants param}
# read parameters
param_th_cluster <- readClusterDesc()
rmarkdown::paged_table(param_th_cluster)
```
Let's see how to edit 3 parameters **`r names(list_pollutants_values())[1:3]`**.
```{r edit pollutants}
# editing
edit_param_pollutants <- list_pollutants_values(multi_values = 0.3)[1:3]
editCluster(area = "fr",
cluster_name = "test_pollutant",
unitcount = 2L,
list_pollutants = edit_param_pollutants)
# read parameters
param_th_cluster <- readClusterDesc()
rmarkdown::paged_table(param_th_cluster)
```
## Hydro - MINGEN file
Antares version 8.6.0 provides new file `mingen.txt`, this file must respect some conditions.
The first condition to respect is the dimension with file `mod.txt`.
The second one is the consistency of the data between 3 files (`mingen.txt`, `mod.txt`, `maxpower_{area}.txt`).
Full documentation is available in the function `writeInputTS()`. We will see further information for values checks.
**Values checks : **
Checks depends of values of parameters in `hydro.ini` file.
```{r schema combinatoire , echo=FALSE, fig.cap="", out.width = '50%', fig.align='center'}
knitr::include_graphics("schemas/mingen_hydro_rules.png")
```
```{r schema, echo=FALSE, fig.cap="", out.width = '75%', fig.align='center'}
# path_image <- sourcedir860 <- system.file("doc/schemas", package = "antaresEditObject")
# knitr::include_graphics(file.path(path_image,"mingen.png"))
knitr::include_graphics("schemas/mingen_draw.png")
```
After creating study, `.txt` files containing time series are empty. We will describe steps to edit `mingen.txt`.
**Initial values :**
```{r hydro ini}
# see hydro parameters
path_file_hydro <- file.path("input", "hydro", "hydro.ini")
hydro_ini_values <- readIni(pathIni = path_file_hydro)
hydro_params <- c('follow load', 'use heuristic', "reservoir")
hydro_ini_values[hydro_params]
```
**Steps to create mingen file :**
```{r edit mod}
# Initialize mingen data (time series)
mingen_data = matrix(0.06,8760,5)
# 1 - edit mod file (time series)
mod_data = matrix(6,365,5)
suppressWarnings(
writeInputTS(area = "fr", type = "hydroSTOR",
data = mod_data,
overwrite = TRUE)
)
```
```{r edit maxpower}
# 2 - edit maxpower
maxpower_data <- matrix(6,365,4)
suppressWarnings(
writeHydroValues(area = "fr",
type = "maxpower",
data = maxpower_data)
)
```
```{r edit mingen}
# 3 - edit mingen
suppressWarnings(
writeInputTS(area = "fr", type = "mingen",
data = mingen_data,
overwrite = TRUE)
)
```
Now we can read time series.
```{r read mingen}
# read input time series
read_ts_file <- readInputTS(mingen = "all")
rmarkdown::paged_table(head(read_ts_file))
```
```{r delete study, include=FALSE}
# Delete study
unlink(opts$studyPath,
recursive = TRUE)
# clean global options
options(antares = NULL)
```