calcCoolingSharesFuture.R

#' Calculate Cooling Type Shares for Future Timesteps
#'
#' This function calculates REMIND input data for the shares of cooling types
#' per electricity technology and REMIND region in post-2020, using as initial
#' information the Davies (2013) data per electricity technology and GCAM
#' region. The source data provide most required information but some
#' assumptions on missing data are also made.
#'
#'
#' @return MAgPIE object on cooling type shares per elecricity technology and
#' REMIND region
#' @author Ioanna Mouratiadou
#' @seealso \code{\link{calcOutput}}, \code{\link{readDaviesCooling}},
#' \code{\link{convertDaviesCooling}},
#' \code{\link{calcCoolingSharesAll}},\code{\link{calcCoolingSharesBase}}
#' @examples
#' \dontrun{
#' a <- calcOutput("CoolingSharesFuture")
#' }
#'
calcCoolingSharesFuture <- function() {
  # read in data
  data <- readSource("DaviesCooling", subtype = "dataFuture")
  getNames(data)[grepl("^Sea", getNames(data))] <- "Sea.NA"

  # seperate data for Sea water
  Sea <- data[, , "Sea"]
  data <- data[, , -which(getNames(data) == "Sea.NA")]

  id <- getNames(data, dim = 1)

  # calculate pond
  pond <- new.magpie(getRegions(data), getYears(data), id)
  for (i in id) {
    pond[, , i] <- 100 - dimSums(data[, , i], dim = 3.2)
  }
  getNames(pond) <- paste(getNames(pond), "Pond", sep = ".")

  # add pond to data
  data <- mbind(data, pond)

  # calculate sea water
  sea_new <- new.magpie(getRegions(data), getYears(data), id)
  for (i in id) {
    sea_new[, , i] <- data[, , paste(i, "1-thru", sep = ".")] * Sea / 100
  }
  getNames(sea_new) <- paste(getNames(sea_new), "Sea", sep = ".")

  # correct 1-thru-data
  data[, , "1-thru"] <- data[, , "1-thru"] * (1 - Sea / 100)
  # add sea data to data
  data <- mbind(data, sea_new)

  # check if all categories sum up to 100%
  check <- new.magpie(getRegions(data), getYears(data), id)
  for (i in id) {
    check[, , i] <- dimSums(data[, , i], dim = 3.2)
  }
  if (!all(check == 100)) {
    stop("sum of categorie XXX is not 100%")
  }

  # read in mapping to REMIND technologies
  map_table <- toolGetMapping(
    type = "sectoral",
    name = "techmappingDaviesToREMIND.csv",
    where = "mrremind"
  )
  map <- list()
  map$davies <- paste(map_table$Davies.Source.Technology, map_table$Davies.Cooling, sep = ".")
  map$remind <- paste(map_table$REMIND.Technology, map_table$REMIND.Cooling, sep = ".")


  # calculate REMIND input in REMIND categories
  output <- new.magpie(getRegions(data), getYears(data), map$remind)
  output[, , ] <- 0
  for (d in 1:length(map$davies)) {
    if (!map$davies[d] == "-.-") {
      output[, , map$remind[d]] <- data[, , map$davies[d]]
    }
  }

  # remove no longer used technologies pcc and pco
  output <- output[, , c("pcc", "pco"), invert = TRUE]

  # add assumed data
  output[, , "geohdr.tower"] <- 70
  output[, , "geohdr.dry"] <- 20
  output[, , "geohdr.hybrid"] <- 10
  output[, , "hydro.default"] <- 100
  output[, , "wind.default"] <- 100
  output[, , "spv.default"] <- 100
  output[, , "csp.tower"] <- 70
  output[, , "csp.dry"] <- 20
  output[, , "csp.hybrid"] <- 10

  outputFuture <- new.magpie(getRegions(output), c(2020), getNames(output))
  outputFuture[, , ] <- output[, , ]

  # assign aggregation weight
  weight <- dimSums(calcOutput("IO", subtype = "output", aggregate = FALSE)[, 2010, c("feelb", "feeli")], dim = 3)

  # set weights to zero for countries that were not contained in the GCAM2ISO mapping
  weight["ALA", , ] <- 0
  weight["ATA", , ] <- 0
  weight["BES", , ] <- 0
  weight["BLM", , ] <- 0
  weight["CUW", , ] <- 0
  weight["GGY", , ] <- 0
  weight["IMN", , ] <- 0
  weight["JEY", , ] <- 0
  weight["MAF", , ] <- 0
  weight["PSE", , ] <- 0
  weight["SSD", , ] <- 0
  weight["SXM", , ] <- 0

  return(list(
    x = outputFuture,
    weight = weight,
    unit = "% of cooling type technologies",
    description = c(
      "Cooling shares for different cooling technologies based on ",
      "Davies et al. (2013) publication and using electricity use weights ",
      "(aggregated based on IEA World Energy Balances, 2014) for regional mapping"
    )
  ))
}