R/calcBP.R

In pik-piam/mrremind: MadRat REMIND Input Data Package

#' Calculate REMIND variables from historical BP values
#'
#' @md
#' @return A [`magpie`][magclass::magclass] object.
#'
#' @author Falk Benke
#' @importFrom dplyr select mutate left_join
#' @importFrom madrat toolGetMapping toolCountryFill
#' @importFrom magclass as.magpie mselect
#' @importFrom readxl read_excel
#' @importFrom rlang sym
#' @importFrom stats aggregate
#' @export

calcBP <- function() {
  .readFactors <- function() {
    factors <- toolGetMapping("BP_Renewable_Efficiency_Factors.csv", type = "sectoral", where = "mappingfolder")
    colnames(factors) <- c("year", "factor")
    factors <- rbind(
      factors,
      data.frame(year = seq(1965, 2000, 1), factor = 0.36)
    ) %>%
      filter(!is.na(suppressWarnings(as.numeric(!!sym("year")))))

    return(factors[order(factors$year), ])
  }

  mapping <- toolGetMapping("Mapping_BP.csv", type = "reportingVariables", where = "mappingfolder") %>%
    mutate(!!sym("conversion") := as.numeric(!!sym("Factor")) * !!sym("Weight")) %>%
    select("variable" = "BP", "REMIND", "conversion", "unit" = "Unit_BP", "Unit_REMIND")

  mapping$REMIND <- trimws(mapping$REMIND)

  .convert <- function(data) {
    data %>%
      mselect(data = unique(mapping$variable)) %>%
      as.data.frame() %>%
      as_tibble() %>%
      select(
        "region" = "Region", "variable" = "Data1",
        "year" = "Year", "value" = "Value"
      ) %>%
      return()
  }

  # Emission
  data <- .convert(readSource("BP", subtype = "Emission"))

  # Capacity
  data <- rbind(data, .convert(readSource("BP", subtype = "Capacity")))

  # Generation
  data <- rbind(data, .convert(readSource("BP", subtype = "Generation")))

  # Consumption

  # prepare consumption data
  consumption <- readSource("BP", subtype = "Consumption")

  # rescale renewables to direct equivalence method by multiplying with the numbers given on sheet "Methodology"
  renewables.factors <- .readFactors() %>%
    as.magpie() %>%
    dimReduce()
  renewables.vars <- c("Solar Consumption (EJ)", "Wind Consumption (EJ)", "Nuclear Consumption (EJ)", "Hydro Consumption (EJ)")

  # recalculate renewables to direct equivalent accounting
  consumption.renewables <- consumption[, , renewables.vars] * renewables.factors

  consumption.fossils <- consumption[, , c(renewables.vars, "Primary Energy Consumption (EJ)"), invert = T]

  # recalculate total pe consumption to direct equivalent accounting

  # 1. deduct original nuclear and electricity generation by renewables
  pe.elec.renewable <- readSource("BP", subtype = "Generation")[, , "Generation|Electricity|Renewable (EJ)"]
  pe.nuclear <- consumption[, , "Nuclear Consumption (EJ)"]
  consumption.pe <- consumption[, , "Primary Energy Consumption (EJ)"] - pe.nuclear - pe.elec.renewable

  # 2. add adjusted nuclear and renewables values
  pe.elec.renewable.dea <- pe.elec.renewable * renewables.factors
  pe.nuclear.dea <- pe.nuclear * renewables.factors
  consumption.pe <- consumption.pe + pe.elec.renewable.dea + pe.nuclear.dea

  data <- rbind(
    data,
    .convert(consumption.fossils),
    .convert(consumption.renewables),
    .convert(consumption.pe)
  )

  # Trade

  # calculate net oil trade
  trade.oil <- readSource("BP", subtype = "Trade Oil")
  trade.oil.net <- trade.oil[, , "Trade|Export|Oil (kb/d)"] - trade.oil[, , "Trade|Import|Oil (kb/d)"]
  getNames(trade.oil.net) <- c("Net Trade|Oil (kb/d)")
  getSets(trade.oil.net) <- c("region", "year", "data")

  # calculate net coal trade
  trade.coal <- readSource("BP", subtype = "Trade Coal")
  trade.coal.net <- trade.coal[, , "Trade|Export|Coal (EJ)"] - trade.coal[, , "Trade|Import|Coal (EJ)"]
  getNames(trade.coal.net) <- c("Net Trade|Coal (EJ)")
  getSets(trade.coal.net) <- c("region", "year", "data")

  # calculate net gas trade
  trade.gas <- readSource("BP", subtype = "Trade Gas")
  trade.gas.net <- trade.gas[, , "Trade|Export|Gas (bcm)"] - trade.gas[, , "Trade|Import|Gas (bcm)"]
  getNames(trade.gas.net) <- c("Net Trade|Gas (bcm)")
  getSets(trade.gas.net) <- c("region", "year", "data")

  data <- rbind(
    data,
    .convert(trade.oil),
    .convert(trade.oil.net),
    .convert(trade.gas),
    .convert(trade.gas.net),
    .convert(trade.coal),
    .convert(trade.coal.net)
  )

  # Trade

  p <- readSource("BP", subtype = "Price")
  data <- rbind(data, .convert(p))

  x <- left_join(
    data,
    mapping,
    by = "variable"
  ) %>%
    filter(!!sym("REMIND") != "") %>%
    mutate(
      !!sym("value") := !!sym("value") * !!sym("conversion"),
      !!sym("REMIND") := paste0(!!sym("REMIND"), " (", !!sym("Unit_REMIND"), ")")
    ) %>%
    select("variable" = "REMIND", "region", "year", "value")

  x <- aggregate(value ~ variable + region + year, x, sum) %>%
    as.magpie() %>%
    toolCountryFill(fill = 0, verbosity = 2)

  weights <- x
  weights[,,] <- NA
  weights[,,"US$2005/GJ", pmatch = T] <- 1

  return(list(
    x = x,
    weight = weights,
    mixed_aggregation = T,
    unit = c("Mt CO2/yr", "GW", "EJ/yr", "US$2005/GJ"),
    description = "Historical BP values as REMIND variables"
  ))
}