Merge pull request #524 from johannah-pik/EDGETrefactoring

edgeTransport 2.0 release

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '362724990'
+ValidationKey: '36475351'
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'
 - 'Warning: namespace ''.*'' is not available and has been replaced'
@@ -10,6 +10,7 @@ AcceptedNotes:
 - unable to verify current time
 - Imports includes .* non-default packages.
 - installed size is
+- 'Undefined global functions or variables:'
 AutocreateReadme: yes
 allowLinterWarnings: yes
 enforceVersionUpdate: yes

CITATION.cff

@@ -2,8 +2,8 @@ cff-version: 1.2.0
 message: If you use this software, please cite it using the metadata from this file.
 type: software
 title: 'mrremind: MadRat REMIND Input Data Package'
-version: 0.182.10
-date-released: '2024-07-15'
+version: 0.183.1
+date-released: '2024-07-17'
 abstract: The mrremind packages contains data preprocessing for the REMIND model.
 authors:
 - family-names: Baumstark

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: mrremind
 Title: MadRat REMIND Input Data Package
-Version: 0.182.10
-Date: 2024-07-15
+Version: 0.183.1
+Date: 2024-07-17
 Authors@R: c(
     person("Lavinia", "Baumstark", , "[email protected]", role = c("aut", "cre")),
     person("Renato", "Rodrigues", role = "aut"),
@@ -38,7 +38,7 @@ Description: The mrremind packages contains data preprocessing for the
 License: LGPL-3 | file LICENSE
 URL: https://github.com/pik-piam/mrremind
 Depends:
-    edgeTransport (>= 0.17.0),
+    edgeTransport (>= 1.5.5),
     madrat (>= 3.7.1),
     magclass (>= 6.16.1),
     mrcommons (>= 1.38.0),
@@ -64,7 +64,6 @@ Imports:
     readxl,
     reshape2,
     rlang,
-    rmndt,
     tibble,
     tidyr,
     tidyselect,

NAMESPACE

@@ -84,12 +84,9 @@ importFrom(assertr,not_na)
 importFrom(assertr,verify)
 importFrom(assertr,within_bounds)
 importFrom(data.table,":=")
-importFrom(data.table,as.data.table)
 importFrom(data.table,data.table)
-importFrom(data.table,fread)
 importFrom(data.table,rbindlist)
 importFrom(data.table,setDT)
-importFrom(data.table,setcolorder)
 importFrom(data.table,setkeyv)
 importFrom(data.table,setnames)
 importFrom(dplyr,across)
@@ -151,6 +148,8 @@ importFrom(ggplot2,scale_linetype_manual)
 importFrom(ggplot2,scale_shape_manual)
 importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_minimal)
+importFrom(madrat,madratAttach)
+importFrom(madrat,toolAggregate)
 importFrom(magclass,matchDim)
 importFrom(magrittr,"%>%")
 importFrom(quitte,add_countrycode_)

R/calcEDGETransport.R

@@ -1,132 +1,106 @@
 #' @title Prepare EDGETransport inputs
 #'
-#' @return magpie object of EDGEtransport iterative inputs
-#' @author Alois Dirnaichner, Marianna Rottoli
+#' @author Johanna Hoppe
 #' @seealso \code{\link{readSource}}
-#' @param subtype refer to the code for a list of allowed subtypes.
-#'
+#' @param subtype  REMIND/iterative EDGE-T input data subtypes
+#' @return REMIND/iterative EDGE-T input data for all scenario combinations
 #' @examples
-#' \dontrun{ a <- calcOutput(type="EDGETransport", subtype="logit_exponent", aggregate=F)
+#' \dontrun{ a <- calcOutput(type = "EDGETransport", subtype = "CAPEXandNonFuelOPEX", aggregate = F)
 #' }
-#'
-#'@importFrom data.table as.data.table
-calcEDGETransport <- function(subtype = "logit_exponent") {
-  value <- i.value <- NULL
 
-  if (subtype %in% c("logit_exponent", "ptab4W")) {
-      conv = FALSE
-   } else {
-      conv = TRUE
-   }
+calcEDGETransport <- function(subtype) {
 
-  weightInt <- calcOutput("GDP", aggregate = F)
-  get_weight <- function(data, weightInt) {
-    year_inter = getYears(data)
-    ## define weight for intensive entries (for weighted average)
-    weightInt <- time_interpolate(
-      weightInt,
-      year_inter,
-      extrapolation_type="constant")[,, getNames(data, dim=1)]
-    ## create an empty object that has the same dimensions as data
-    weight <- new.magpie(cells_and_regions = getRegions(data), years = getYears(data), names = getNames(data), fill = 0)
-    ## use the GPD for each SSP in data to fill up the empty weight (it needs as many repetitions as SSP* is called in data)
-    regions <- getRegions(weightInt)
-    years <- getYears(weightInt)
-    for (gdpscen in getNames(weightInt)) {
-      mselect(weight, region=regions, year=years, data=gdpscen) <- weightInt[regions, years, gdpscen]
-    }
-    getSets(weight, fulldim=F)[3] <- getSets(data, fulldim=F)[3]
-    return(weight)
-  }
-
-  if(subtype == "pm_trp_demand"){
-    fe_dem <- as.data.table(as.quitte(readSource("EDGETransport", "fe_demand_tech")))[
-    , c("model", "scenario", "variable", "unit") := NULL
-    ]
-    fe2es  <- as.data.table(as.quitte(readSource("EDGETransport", "fe2es")))[
-    , c("model", "scenario", "variable", "unit") := NULL
-    ]
-    es_dem <- fe_dem[fe2es, on=c("period", "region", "GDP_scenario", "DEM_scenario", "EDGE_scenario", "all_teEs")][
-    , sum(value * i.value), by=c("period", "region", "GDP_scenario", "DEM_scenario", "EDGE_scenario", "all_in")
-    ]
-    data <- as.magpie(es_dem, spatial=2, temporal=1, datacol=7)
-  }else{
-    data <- readSource("EDGETransport", subtype, convert = conv)
-  }
+  x <- readSource("EDGETransport", subtype)
 
   switch(subtype,
-         "logit_exponent" = {
-           weight = NULL
-           unit = "[-]"
-           description = "Logit exponent values for transport alternatives"
+         "f35_esCapCost" = {
+           weight <- readSource("EDGETransport", subtype = "weightESdemand")
+           #Rule out numerical errors after disaggregating very small numbers
+           weight[weight < 1e-5] <- 0
+           #check whether weightsum is zero for some cases
+           #if so, the values should just be aggregated equally in order to prevent zeros in the results
+           weight <- magpie2dt(weight)
+           regMap <- toolGetMapping(getConfig("regionmapping"), type = "regional", where = "mappingfolder")
+           setnames(regMap, "CountryCode", "all_regi")
+           weight <- merge(weight, regMap[, c("all_regi", "RegionCode")], by = "all_regi")
+           weight[, weightSum := sum(value), by = c("RegionCode", "GDP_scenario", "DEM_scenario", "EDGE_scenario", "all_teEs", "tall")]
+           weight[weightSum == 0, value := 1]
+           weight[, c("RegionCode", "weightSum") := NULL]
+           weight <- as.magpie(weight)
+           unit = "2005US$/(p|t)km"
+           description = "Capital cost (purchase) per energy service demand on CES level."
          },
-         "harmonized_intensities" = {
-           weight = get_weight(data, weightInt)
-           unit = "Passenger transport: [EJ/Mpkm]; freight transport: [EJ/Mtkm]"
-           description = "Energy intensity for transport modes and fuel, harmonized on IEA balances"
+         "f35_fe2es" = {
+           weight = readSource("EDGETransport", "f35_demByTech")
+           weight[weight < 1e-5] <- 0
+           #check whether weightsum is zero for some cases
+           #if so, the values should just be aggregated equally in order to prevent zeros in the results
+           weight <- magpie2dt(weight)
+           regMap <- toolGetMapping(getConfig("regionmapping"), type = "regional", where = "mappingfolder")
+           setnames(regMap, "CountryCode", "all_regi")
+           weight <- merge(weight, regMap[, c("all_regi", "RegionCode")], by = "all_regi")
+           weight[, weightSum := sum(value), by = c("RegionCode", "GDP_scenario", "DEM_scenario", "EDGE_scenario", "all_enty", "all_in", "all_teEs", "tall")]
+           weight[weightSum == 0, value := 1]
+           weight[, c("RegionCode", "weightSum", "all_enty", "all_in") := NULL]
+           weight <- as.magpie(weight)
+           unit = "trn (p|t)km/Twa"
+           description = "Energy efficiency on CES level."
          },
-         "value_time" = {
-           weight = get_weight(data, weightInt)
-           unit = "Passenger transport: [2005US$/pkm]; freight transport: [2005US$/tkm]"
-           description = "Value of time for passenger transport modes"
-         },
-         "pref" = {
-           weight = get_weight(data, weightInt)
-           unit = "LDVs 4wheelers: inconvenience cost [2005US$/pkm]; all other modes: [-] share weight, a dimensionless parameter reflecting consumer preferences"
-           description = "Inconvenience cost reflecting availability of infrastructure"
-         },
-         "ptab4W" = {
+         "f35_demByTech" = {
            weight = NULL
-           unit = "[-]"
-           description = "LDVs 4wheelers: factors for the inconvenience costs."
-         },
-         "price_nonmot" = {
-           weight = get_weight(data, weightInt)
-           unit = "Passenger transport: [2005US$/pkm]; freight transport: [2005US$/tkm]"
-           description = "Price for non motorized transport modes (Walking; Cycling)"
+           unit = "TWa"
+           description = "Final energy demand on CES level."
          },
-         "UCD_NEC_iso" = {
-           weight = get_weight(data, weightInt)
-           unit = "Passenger transport: [2005US$/pkm]; freight transport: [2005US$/tkm]"
-           description = "Non energy costs for all motorized transport modes, both total and purchase for LDVs"
+         "f29_trpdemand" = {
+           weight = NULL
+           unit = "trillion pkm/trillion tkm"
+           description = "Energy service demand on CES level."
          },
-         "loadFactor" = {
-           weight = get_weight(data, weightInt)
-           unit = "Passenger transport: [pass/veh]; freight transport: [ton/veh]"
-           description = "Load factor for all motorized transport modes"
+         "CAPEXandNonFuelOPEX" = {
+           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
+           weight = gdp |> time_interpolate(getYears(x))
+           unit = "2005US$/(p|t)km"
+           description = "Capital cost (purchase) and non-fuel operational costs on technology level."
          },
-         "annual_mileage" = {
-           weight = get_weight(data, weightInt)
-           unit = "km/veh/year"
-           description = "Annual mileage for selected transport modes"
+         "scenSpecPrefTrends" = {
+           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
+           weight = gdp |> time_interpolate(getYears(x))
+           unit = "-"
+           description = "Scenario specific preference trends on technology level."
          },
-         "esCapCost" = {
-           weight = get_weight(data, weightInt)
-           unit = "Passenger transport [2005US$/pkm]; freight transport: [2005US$/tkm]"
-           description = "Transport ES demand trajectories for the CES tree."
+         "scenSpecLoadFactor" = {
+           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
+           weight = gdp |> time_interpolate(getYears(x))
+           unit = "-"
+           description = "Scenario specific load factor on technology level."
          },
-         "fe2es" = {
-           weight = get_weight(data, weightInt)
-           unit = "Passenger transport [trn pkm/Twa], freight transport [trn tkm/Twa]"
-           description = "Energy efficiency of CES level nodes for Transport."
+         "scenSpecEnIntensity" = {
+           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
+           weight = gdp |> time_interpolate(getYears(x))
+           unit = "MJ/vehkm"
+           description = "Scenario specific energy intensity on technology level."
          },
-         "pm_trp_demand" = {
-           weight = NULL
-           unit = "Passenger transport [trn pkm], freight transport [trn tkm]"
-           description = "CES level transport demand."
+         "initialIncoCosts" = {
+           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
+           weight = gdp |> time_interpolate(getYears(x))
+           unit = "2005US$/(p|t)km"
+           description = "Initial inconvenience cost values."
          },
-         "fe_demand_tech" = {
-           weight = NULL
-           unit = "TWa"
-           description = "FE demand divided by technologies for different ES on the CES level."
+         "annualMileage" = {
+           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
+           weight = gdp |> time_interpolate(getYears(x))
+           unit = "vehkm/yr"
+           description = "Annual vehicle km traveled."
          },
-         "pm_fe_demand_EDGETbased" = {
-           weight = NULL
-           unit = "EJ"
-           description = "UE demand divided by technologies for different ES on the CES level."
-         })
+         "timeValueCosts" = {
+           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
+           weight = gdp |> time_interpolate(getYears(x))
+           unit = "2005US$/(p|t)km"
+           description = "Value of time cost equivalent."
+         }
+       )
 
-  return(list(x           = data,
+  return(list(x           = x,
               weight      = weight,
               unit        = unit,
               description = description))

R/calcEU_ReferenceScenario.R

@@ -12,7 +12,7 @@ calcEU_ReferenceScenario <- function() {
 
   weights <- x
   weights[, , ] <- NA
-  weights[, , "Price|Secondary Energy|Electricity (US$2005/GJ)"] <- 1
+  weights[, , "Price|Secondary Energy|Electricity (EUR2013/GJ)"] <- 1
 
   return(list(
     x = x, weight = weights, mixed_aggregation = TRUE,

R/calcIO.R

@@ -102,6 +102,15 @@ calcIO <- function(subtype = c("input", "output", "output_biomass", "trade",
   # read in data and convert from ktoe to EJ
   data <- readSource("IEA", subtype = ieaSubtype) * 4.1868e-5
 
+  # Correct transport reporting issue in IEA data for NONBIODIES.MARBUNK in RUS
+  # FE is reported in 1990 and 2010 but not in the years in between. This cause problems in the harmonization of EDGE-Transport
+  # and the IEA data in 2005 as there is no MARBUNK demand at all for REF regions.
+  data["RUS", seq(1990, 2010, 1), "NONBIODIES.MARBUNK"] <-
+    data["RUS", c(1990, 2010),"NONBIODIES.MARBUNK"]|> time_interpolate(seq(1990, 2010, 1))
+  #Adjust totals
+  data["RUS", seq(1991, 2009, 1),"TOTAL.MARBUNK"] <-
+    data["RUS", seq(1991, 2009, 1),"TOTAL.MARBUNK"] + data["RUS", seq(1991, 2009, 1), "NONBIODIES.MARBUNK"]
+
   ieamatch <- read.csv2(mapping, stringsAsFactors = FALSE, na.strings = "")
 
   # add total buildings electricity demand (feelb = feelcb + feelhpb + feelrhb)