modify investment variables

NAMESPACE

@@ -136,7 +136,6 @@ importFrom(ggplot2,ggtitle)
 importFrom(ggplot2,labs)
 importFrom(ggplot2,scale_color_manual)
 importFrom(ggplot2,scale_fill_brewer)
-importFrom(ggplot2,scale_fill_discrete)
 importFrom(ggplot2,scale_fill_manual)
 importFrom(ggplot2,scale_linetype_identity)
 importFrom(ggplot2,scale_x_continuous)
@@ -148,7 +147,6 @@ importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_bw)
 importFrom(ggplot2,theme_minimal)
 importFrom(ggplot2,unit)
-importFrom(ggplot2,xlab)
 importFrom(gms,getLine)
 importFrom(gms,readDefaultConfig)
 importFrom(lucode2,getScenNames)
@@ -222,7 +220,6 @@ importFrom(quitte,overwrite)
 importFrom(quitte,read.gdx)
 importFrom(quitte,read.quitte)
 importFrom(quitte,revalue.levels)
-importFrom(quitte,sum_total)
 importFrom(readr,read_csv)
 importFrom(readr,write_rds)
 importFrom(remulator,calc_supplycurve)

R/modifyInvestmentVariables.R

@@ -0,0 +1,19 @@
+#' Modify Investment Variables
+#'
+#' a helper to adapt investment variables in reporting after changes in https://github.com/remindmodel/remind/pull/1238
+#'
+#' calculates the value of each timestep for a given maglcass object
+#' as the average of this timestep and the next timestep (last timestep remains unchanged)
+#'
+#'
+#' @author Falk Benke
+modifyInvestmentVariables <- function(x) {
+  # obtain 't+1'
+  idx <- c(tail(getYears(x, as.integer = TRUE), -1), tail(getYears(x, as.integer = TRUE), 1))
+  # retrieve values for 't+1'  and assign to 't'
+  tmp <- x[, idx, ]
+  getItems(tmp, dim = 2) <- getItems(x, dim = 2)
+  # calculate average of 't' and 't'+1
+  out <- (x + tmp) / 2
+  return(out)
+}

R/plotLCOE.R

@@ -34,7 +34,6 @@ plotLCOE <- function(LCOEfile, gdx, y = c(2015, 2020, 2030, 2040, 2050, 2060),
   variable <- NULL
   value <- NULL
   type <- NULL
-  vm_deltaCap <- NULL
   LCOE <- NULL
   all_te <- NULL
   output <- NULL
@@ -117,8 +116,8 @@ plotLCOE <- function(LCOEfile, gdx, y = c(2015, 2020, 2030, 2040, 2050, 2060),
   ### end plot settings
 
   # read in capacity additions from .mif, for second y axes of plot
-  vm_deltaCap <- readGDX(gdx, "vm_deltaCap", field = "l", restore_zeros = FALSE)
-
+  vm_deltaCap <- readGDX(gdx, "vm_deltaCap", field = "l", restore_zeros = FALSE) %>%
+    modifyInvestmentVariables()
 
   # calculate 15-year moving average capacity additions
   df.dC <- as.quitte(dimSums(vm_deltaCap, dim = 3.2) * 1e3) %>%

R/reportCapacity.R

@@ -50,6 +50,7 @@ reportCapacity <- function(gdx, regionSubsetList = NULL, t = c(seq(2005, 2060, 5
   vm_cap      <- vm_cap[, ttot, ]
   vm_deltaCap <- vm_deltaCap[teall2rlf]
   vm_deltaCap <- vm_deltaCap[, ttot, ]
+  vm_deltaCap <- modifyInvestmentVariables(vm_deltaCap)
   v_earlyreti <-   v_earlyreti[, ttot, ]
   t2005 <- ttot[ttot > 2004]
 

R/reportCapitalStock.R

@@ -31,21 +31,22 @@ reportCapitalStock <- function(gdx, regionSubsetList = NULL, t = c(seq(2005, 206
   pm_conv_cap_2_MioLDV <- 650  # The world has ~715million cars in 2005 (IEA TECO2)
 
   # read sets
-  teall2rlf    <- readGDX(gdx, name = c("te2rlf", "teall2rlf"), format = "first_found")
-  teue2rlf     <- readGDX(gdx, name = c("teue2rlf", "tees2rlf"), format = "first_found")
+  teall2rlf <- readGDX(gdx, name = c("te2rlf", "teall2rlf"), format = "first_found")
+  teue2rlf <- readGDX(gdx, name = c("teue2rlf", "tees2rlf"), format = "first_found")
   # read variables
-  vm_cap       <- readGDX(gdx, name = c("vm_cap"), field = "l", format = "first_found")
-  vm_deltaCap  <- readGDX(gdx, name = c("vm_deltaCap"), field = "l", format = "first_found")
+  vm_cap <- readGDX(gdx, name = c("vm_cap"), field = "l", format = "first_found")
+  vm_deltaCap <- readGDX(gdx, name = c("vm_deltaCap"), field = "l", format = "first_found")
+  vm_deltaCap <- modifyInvestmentVariables(vm_deltaCap)
   v_investcost <- readGDX(gdx, name = c("vm_costTeCapital", "v_costTeCapital", "v_investcost"), field = "l", format = "first_found")
-  vm_cesIO     <- readGDX(gdx, name = "vm_cesIO", field = "l")
+  vm_cesIO <- readGDX(gdx, name = "vm_cesIO", field = "l")
   # read parameters
   ppfKap_Ind <- readGDX(gdx, name = "ppfkap_industry_dyn37", react = "silent")
   steel_process_based <- "steel" %in% readGDX(gdx, "secInd37Prc", react = "silent")
 
   # calculate maximal temporal resolution
   y <- Reduce(intersect, list(getYears(vm_cap), getYears(v_investcost)))
-  vm_cap       <- vm_cap[, y, ]
-  vm_deltaCap  <- vm_deltaCap[, y, ]
+  vm_cap <- vm_cap[, y, ]
+  vm_deltaCap <- vm_deltaCap[, y, ]
   v_investcost <- v_investcost[, y, ]
 
   tmp <- NULL

R/reportEnergyInvestment.R

@@ -52,22 +52,26 @@ reportEnergyInvestment <- function(gdx, regionSubsetList = NULL,
   grid  <- readGDX(gdx, name = c("teGrid", "grid"), format = "first_found")
   pebio  <- readGDX(gdx, c("peBio", "pebio"), format = "first_found")
   ttot        <- readGDX(gdx, name = "ttot", format = "first_found")
+
   # read variables
+
+  # investment cost per technology
   v_directteinv <- readGDX(gdx, name = c("v_costInvTeDir", "vm_costInvTeDir", "v_directteinv"),
                            field = "l", format = "first_found")
+  # adjustment cost per technology
   v_adjustteinv <- readGDX(gdx, name = c("v_costInvTeAdj", "vm_costInvTeAdj", "v_adjustteinv"),
                            field = "l", format = "first_found")
 
   # read parameters for additional calculations:
   pm_data   <- readGDX(gdx, c("pm_data"), format = "first_found")
 
   # data preparation
+
   ttot <- as.numeric(as.vector(readGDX(gdx, "ttot", format = "first_found")))
-  v_directteinv <- v_directteinv[, ttot, ]
-  v_adjustteinv <- v_adjustteinv[, ttot, ]
+  v_directteinv <- modifyInvestmentVariables(v_directteinv[, ttot, ])
+  v_adjustteinv <- modifyInvestmentVariables(v_adjustteinv[, ttot, ])
   costRatioTdelt2Tdels <- pm_data[, , "inco0.tdelt"] / pm_data[, , "inco0.tdels"]
 
-
   ####### internal function for reporting ###########
   ## "ie" stands for input energy, "oe" for output energy
   inv_se <- function(ie, oe, settofilter = pe2se, adjte, v_directteinv, v_adjustteinv, te = pe2se$all_te) {

R/reportLCOE.R

@@ -138,9 +138,19 @@ reportLCOE <- function(gdx, output.type = "both") {
     pm_SEPrice <- readGDX(gdx, "pm_SEPrice", restore_zeros = FALSE)
 
     ## variables
-    vm_costInvTeDir <- readGDX(gdx, name = c("vm_costInvTeDir", "v_costInvTeDir", "v_directteinv"), field = "l", format = "first_found")[, ttot, ] ## Total direct Investment Cost in Timestep
-    vm_costInvTeAdj <- readGDX(gdx, name = c("vm_costInvTeAdj", "v_costInvTeAdj"), field = "l", format = "first_found")[, ttot, ] ## total adjustment cost in period
-    vm_deltaCap   <- readGDX(gdx, name = c("vm_deltaCap"), field = "l", format = "first_found")[, ttot, ]
+
+    ## Total direct Investment Cost in Timestep
+    vm_costInvTeDir <- readGDX(gdx, name = c("vm_costInvTeDir", "v_costInvTeDir", "v_directteinv"), field = "l", format = "first_found")[, ttot, ]
+    vm_costInvTeDir <- modifyInvestmentVariables(vm_costInvTeDir)
+
+    ## total adjustment cost in period
+    vm_costInvTeAdj <- readGDX(gdx, name = c("vm_costInvTeAdj", "v_costInvTeAdj"), field = "l", format = "first_found")[, ttot, ]
+    vm_costInvTeAdj <- modifyInvestmentVariables(vm_costInvTeAdj)
+
+    # capacity additions per year
+    vm_deltaCap <- readGDX(gdx, name = c("vm_deltaCap"), field = "l", format = "first_found")[, ttot, ]
+    vm_deltaCap <- modifyInvestmentVariables(vm_deltaCap)
+
     vm_demPe      <- readGDX(gdx, name = c("vm_demPe", "v_pedem"), field = "l", restore_zeros = FALSE, format = "first_found")
     v_investcost  <- readGDX(gdx, name = c("vm_costTeCapital", "v_costTeCapital", "v_investcost"), field = "l", format = "first_found")[, ttot, ]
     vm_cap        <- readGDX(gdx, name = c("vm_cap"), field = "l", format = "first_found")