R/convertRogelj2017.R
In pik-piam/moinput: Model Operations Input Data Library
Defines functions
convertRogelj2017
#' Policy targets for NDCs from Rogelj2017
#' @description Converts conditional and unconditional capacity targets into total capacity (GW) in target year
#' the Generation targets are similar to the capacity targets but include the capacity factors,
#' the Emissions targets are the total (except land CO2) emissions in the target year
#' @param x MAgPIE object to be converted
#' @param subtype UNCapacity, COCapacity
#' @return Magpie object with Total Installed Capacity (GW) targets. The target years differ depending upon the database.
#' @author Aman Malik and Christoph Bertram
#' @importFrom R.utils isZero
convertRogelj2017 <- function(x,subtype){
if(subtype == "UNCapacity"){ # UNconditional policies
# x <- readSource("Rogelj2017","UNCapacity",convert=F)
x <- x[,,"conditional",invert=TRUE] # keep only unconditional policies
x[is.na(x)] <- 0 # Converting all NAs to zero
# reading historical data
hist_cap <- readSource(type="IRENA",subtype="Capacity")/1000 # converting from MW to GW
hist_gen <- readSource("IRENA", subtype = "Generation")# Units are GWh
# Real world capacity factor for hydro = Generation in last year/Capacity in last year
cf_realworld <- hist_gen[,2015,"Hydropower"]/(8760*hist_cap[,2015,"Hydropower"])
cf_realworld[is.na(cf_realworld)] <- 0
getNames(cf_realworld) <- "Hydro"
tech <- c("Wind","Solar","Hydro","Nuclear","Biomass")
target_years <- c(2020,2025,2030,2035)
x_tmp <- x
# 1. Cases when targets are given in non-model year. e.g., 2022, 2032
input_yr <- getYears(x,as.integer = TRUE)
for ( i in input_yr){
if (i>2020 & i<2025)
x_tmp[,2025,] <- as.vector(x[,i,])*(1+(2025-i)*0.05)
if (i>2025 & i<2030)
x_tmp[,2030,] <- as.vector(x[,i,])*(1+(2030-i)*0.05)
if (i>2030 & i<2035)
x_tmp[,2035,] <- as.vector(x[,i,])*(1+(2035-i)*0.05)
}
# Creating magpie object which at the end will only contain capacity targets
x_new <- new.magpie(getRegions(x_tmp),target_years,tech)
x_new[is.na(x_new)] <- 0
# Capacity factors in REMIND. From : calcOutput("Capacityfactor"...)
cf_biomass <- 0.75
cf_nuclear <- 0.85
cf_hydro <- cf_realworld
# Initialising all capacities for all model years to current capacities and converting generation to capacity
x_new[,,c("Wind","Solar")] <- setYears(hist_cap[getRegions(x_tmp),2015,c("Wind","Solar")])
x_new[,,"Biomass"] <- setYears(hist_cap[getRegions(x_tmp),2015,"Bioenergy"])
# special case for hydro.
x_new[,,"Hydro"] <- setYears(hist_gen[getRegions(x_new),2015,"Hydropower"])
# Special case for nuclear
hist_gen_nuclear <- readSource(type = "BP",subtype = "Generation")*1000 # TWh to GWh
for (i in target_years){
for (j in getNames(x_tmp[,,"Nuclear"])){
for (k in getRegions(x_tmp)){
if(x_tmp[k,i,j]!=0)
x_new[k,,"Nuclear"] <- setYears(hist_gen_nuclear[k,2015,"Nuclear"])/(8760*cf_nuclear)
}
}
}
x_tmp <- x_tmp[,target_years,]
x_current <- x_new # x_current contains current capacities except for hydro where current generation values are taken
x_new_abs <- x_new
x_new_abs[] <- 0
x_new_prod_nb <- x_new
x_new_prod_nb[] <- 0
x_new_tic <- x_new
x_new_tic[] <- 0
x_new_prod_swh <- x_new
x_new_prod_swh[] <- 0
# Converting additional capacity targets to absolute capacity targets
x_new_abs[,,c("Nuclear","Biomass","Wind","Solar")] <- x_current[,,c("Nuclear","Biomass","Wind","Solar")] +
x_tmp[,,c("AC-Absolute.Nuclear","AC-Absolute.Biomass","AC-Absolute.Wind","AC-Absolute.Solar"),drop=TRUE]
x_new_abs[,,"Hydro"] <- x_current[,,"Hydro"] + x_tmp[,,"AC-Absolute.Hydro",drop=TRUE]*setYears(cf_hydro[getRegions(x_tmp),,]*8760)
# Converting Production targets (GWh) to Capacity targets (TIC-Absolute) (GW) for nuclear and biomass
# pmax used to always take the higher value from existing capacity and new capacity (from production)
x_new_prod_nb[,,"Nuclear"] <- pmax( x_current[,,"Nuclear"], x_tmp[,,c("Production-Absolute.Nuclear")]/(8760*cf_nuclear))
x_new_prod_nb[,,"Biomass"] <- pmax(x_current[,,"Biomass"],x_tmp[,,c("Production-Absolute.Biomass")]/(8760*cf_biomass))
# Total installed capacity Targets
# target in target year should be the maximum from the target year and the current capacity
x_new_tic[,,c("Nuclear","Biomass","Wind","Solar")] <- pmax(x_current[,,c("Nuclear","Biomass","Wind","Solar")],x_tmp[,,c("Nuclear","Biomass","Wind","Solar")][,,"TIC-Absolute",drop = TRUE])
x_new_tic[,,"Hydro"] <- pmax(x_current[,,"Hydro"],x_tmp[,,"TIC-Absolute.Hydro",drop = TRUE]*setYears(cf_hydro[getRegions(x_tmp),,]))
# Converting Production targets to capacity targets for solar (pv and csp), hydro, and wind
# Obtaining the capacity factors (nur) values and associated maxproduction (maxprod) for Hydro, Wind, and Solar
data_wind <- calcOutput("PotentialWind", aggregate = FALSE)
# Reordering dim=3 for data_wind so that 1st position corresponds to maxprod.nur.1 and not maxprod.nur.9
data_wind_sorted <- mbind(data_wind[,,"1"],data_wind[,,"2"],data_wind[,,"3"],data_wind[,,"4"],
data_wind[,,"5"],data_wind[,,"6"],data_wind[,,"7"],data_wind[,,"8"],data_wind[,,"9"])
data_hydro <- calcOutput("PotentialHydro", aggregate = FALSE)
#data_solar <- calcOutput("Solar", aggregate = FALSE)
setConfig(regionmapping = "regionmappingTCD.csv")
data_solar <- calcOutput("Solar")
setConfig(regionmapping = "regionmappingH12.csv")
names_solar <- paste0("Solar.",getNames(collapseNames((mselect(data_solar,type=c("nur","maxprod"),technology="spv")),collapsedim = 2)))
names_hydro <- paste0("Hydro.",getNames(data_hydro))
names_wind <- paste0("Wind.",getNames(data_wind_sorted))
data_combined <- new.magpie(getRegions(data_hydro), NULL, c(names_solar,names_hydro,names_wind))
data_combined[,,"Hydro"] <- data_hydro
data_combined[,,"Wind"] <- data_wind_sorted
data_combined[c("TCD","JPN"),,"Solar"][,,"maxprod"] <- as.vector(data_solar[c("TCD","JPN"),,"maxprod"][,,"spv"])
data_combined[c("TCD","JPN"),,"Solar"][,,"nur"] <- as.vector(data_solar[c("TCD","JPN"),,"nur"][,,"spv"])
data_combined <- data_combined[getRegions(x_tmp),,]
for (n in getNames(data_combined,dim=1)){
name=paste0(n,".maxprod")
# Conversion from EJ/a to GWh
data_combined[,,name,pmatch=TRUE] <- data_combined[,,name,pmatch=TRUE]*277777.778
}
data_combined[is.na(data_combined)] <- 0
# Production/Generation targets are converted into capacity targets by alloting production to certain capacity factors based on maxprod.
final <- numeric(length(getRegions(x_tmp)))
names(final) <- getRegions(x_tmp)
tmp_target <- numeric(10)
#x_tmp[,,"Production-Absolute.Hydro"] <- pmax(x_tmp[,,"Production-Absolute.Hydro"],x_new[,,"Hydro"])
x_tmp[,,"Production-Absolute.Hydro"] <- pmax(x_tmp[,,"Production-Absolute.Hydro"],x_new_tic[,,"Hydro"],x_new_abs[,,"Hydro"])
x_tmp[is.na(x_tmp)] <- 0
# For all countries which have non-zero generation values but zero or negative maxprod(),
# replace x_tmp[,,"Production-Absolute.Hydro]==0
# Even if there is one +ve production absolute value for Hydro but all maxprod are zero
for (r in names(final)){
if(any(x_tmp[r,,"Production-Absolute.Hydro"]!=0) &
all(data_combined[r,,"Hydro.maxprod"]==0)|any(data_combined[r,,"Hydro.maxprod"]<0) )
x_tmp[r,,"Production-Absolute.Hydro"] <- 0
}
for (t in c("Solar","Wind","Hydro")){
data_sel <- data_combined[,,t]
data_in_use <- data_sel[,,"maxprod"]/data_sel[,,"nur"]
for (y in target_years){
final[] <-0
for (r in names(final)){
tmp_target <- numeric(10)
name <- paste0(t,".maxprod")
name2 <- paste0("Production-Absolute.",t)
if (!isZero(x_tmp[,,"Production-Absolute"][,,t])[r,y,] &
dimSums(data_combined[r,,name],na.rm=T) > max(x_tmp[r,,name2])){
# extracting the first non-zero location of maxprod
name <- paste0(t,".maxprod")
loc <- min(which(!R.utils::isZero(data_combined[r,,name,pmatch=TRUE])))
tmp_target[1] <- x_tmp[r,y,"Production-Absolute"][,,t]
if (data_sel[r,,"maxprod"][,,loc] > tmp_target[1]){
final[r] <- tmp_target[1]/(8760*data_sel[r,,"nur"][,,loc])
} else {tmp_target[2] <- tmp_target[1] - data_sel[r,,"maxprod"][,,loc]
if(data_sel[r,,"maxprod"][,,loc+1] > tmp_target[2]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + tmp_target[1]/data_sel[r,,"nur"][,,loc+1])
} else {tmp_target[3] <- tmp_target[2] - data_sel[r,,"maxprod"][,,loc+1]
if(data_sel[r,,"maxprod"][,,loc+2] > tmp_target[3]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1]
+ tmp_target[2]/data_sel[r,,"nur"][,,loc+2])
} else {tmp_target[4] <- tmp_target[3] - data_sel[r,,"maxprod"][,,loc+2]
if(data_sel[r,,"maxprod"][,,loc+3] > tmp_target[4]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
tmp_target[3]/data_sel[r,,"nur"][,,loc+3])
final[r] <- tmp_target[1]
} else {tmp_target[5] <- tmp_target[4] - data_sel[r,,"maxprod"][,,loc+3]
if(data_sel[r,,"maxprod"][loc+4] > tmp_target[5]){
final[r] <-(1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
data_in_use[r,,][,,loc+3] + tmp_target[4]/data_sel[r,,"nur"][,,loc+4])
} else {tmp_target[6] <- tmp_target[5] - data_sel[r,,"maxprod"][,,loc+4]
if(data_sel[r,,"maxprod"][loc+5] > tmp_target[6]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
data_in_use[r,,][,,loc+3] + data_in_use[r,,][,,loc+4] +
tmp_target[5]/data_sel[r,,"nur"][,,loc+5])
}
}
}
}
}
}
}
}
x_new_prod_swh[,y,t] <-final
}
}
x_new_gen <- mbind(x_new_prod_swh[,,c("Solar","Wind","Hydro")],x_new_prod_nb[,,c("Biomass","Nuclear")])
x_new[,,c("Solar","Wind","Hydro","Biomass","Nuclear")] <- pmax(x_new_abs[,,c("Solar","Wind","Hydro","Biomass","Nuclear")],
x_new_gen[,,c("Solar","Wind","Hydro","Biomass","Nuclear")],
x_new_tic[,,c("Solar","Wind","Hydro","Biomass","Nuclear")])
# x_new[,,c("Solar","Wind")] <- pmax(x_new_copy[,,c("Solar","Wind")],x_new[,,c("Solar","Wind")])
x_new[,,"Hydro"] <- x_new_gen[,,"Hydro"]
# # Making sure that targets in subsequent years are always same or greater than the proceeding year
for (r in getRegions(x_tmp)){
for (t in tech){
for(i in c(2020,2025,2030)){
if(x_new[r,i+5,t] < setYears(x_new[r,i,t])){
x_new[r,i+5,t] <- setYears(x_new[r,i,t])
}else{
x_new[r,i,t] <- x_new[r,i,t]
}
}
}
}
# countries not in the database
rest_regions <- getRegions(hist_cap)[!(getRegions(hist_cap) %in% getRegions(x_new))]
x_other <- new.magpie(rest_regions,target_years,tech)
x_other[,,c("Wind","Solar")] <- setYears(hist_cap[rest_regions,2015,c("Solar","Wind")])
x_other[,,"Nuclear"] <- 0
x_other[,,"Biomass"] <- setYears(hist_cap[rest_regions,2015,"Bioenergy"])
x_other[,,"Hydro"] <- setYears(hist_cap[rest_regions,2015,"Hydropower"])*setYears(cf_hydro[rest_regions,,])
x_final <- magpiesort(mbind(x_new,x_other))
x_final[is.na(x_final)] <- 0
x <- x_final
getNames(x) <- c("wind","spv","hydro","tnrs","bioigcc")
}
if(subtype == "COCapacity"){
# x <- readSource("Rogelj2017",subtype = "COCapacity",convert = F)
# loop to make conditional targets at least as good as unconditional targets
for (r in getRegions(x)){
for (t in getYears(x)){
for (tech in getNames(x[,,"conditional",drop=TRUE]) )
if(is.na(x[r,t,paste0("conditional",".",tech)]))
x[r,t,paste0("conditional",".",tech)] <- x[r,t,paste0("unconditional",".",tech)]
}
}
x <- x[,,"unconditional",invert=TRUE]# drop unconditional targets
x[is.na(x)] <- 0 # Converting all NAs to zero
# reading historical data
hist_cap <- readSource(type="IRENA",subtype="Capacity")/1000 # converting from MW to GW
hist_gen <- readSource("IRENA", subtype = "Generation")# Units are GWh
# Real world capacity factor for hydro = Generation in last year/Capacity in last year
cf_realworld <- hist_gen[,2015,"Hydropower"]/(8760*hist_cap[,2015,"Hydropower"])
cf_realworld <- cf_realworld<1
getNames(cf_realworld) <- "Hydro"
tech <- c("Wind","Solar","Hydro","Nuclear","Biomass")
target_years <- c(2020,2025,2030,2035)
x_tmp <- x
# 1. Cases when targets are given in non-model year. e.g., 2022, 2032
input_yr <- getYears(x,as.integer = TRUE)
for ( i in input_yr){
if (i>2020 & i<2025)
x_tmp[,2025,] <- as.vector(x[,i,])*(1+(2025-i)*0.05)
if (i>2025 & i<2030)
x_tmp[,2030,] <- as.vector(x[,i,])*(1+(2030-i)*0.05)
if (i>2030 & i<2035)
x_tmp[,2035,] <- as.vector(x[,i,])*(1+(2035-i)*0.05)
}
# Creating magpie object which at the end will only contain capacity targets
x_new <- new.magpie(getRegions(x_tmp),target_years,tech)
x_new[is.na(x_new)] <- 0
# Capacity factors in REMIND. From : calcOutput("Capacityfactor"...)
cf_biomass <- 0.75
cf_nuclear <- 0.85
cf_hydro <- cf_realworld
# Initialising all capacities for all model years to current capacities and converting generation to capacity
x_new[,,c("Wind","Solar")] <- setYears(hist_cap[getRegions(x_tmp),2015,c("Wind","Solar")])
x_new[,,"Biomass"] <- setYears(hist_cap[getRegions(x_tmp),2015,"Bioenergy"])
# special case for hydro.
x_new[,,"Hydro"] <- setYears(hist_gen[getRegions(x_new),2015,"Hydropower"])
# Special case for nuclear
hist_gen_nuclear <- readSource(type = "BP",subtype = "Generation")*1000 # TWh to GWh
for (i in target_years){
for (j in getNames(x_tmp[,,"Nuclear"])){
for (k in getRegions(x_tmp)){
if(x_tmp[k,i,j]!=0)
x_new[k,,"Nuclear"] <- setYears(hist_gen_nuclear[k,2015,"Nuclear"])/(8760*cf_nuclear)
}
}
}
x_current <- x_new # x_current contains current capacity values. x_new contains capacity targets
x_tmp <- x_tmp[,target_years,]
x_current <- x_new # x_current contains current capacities except for hydro where current generation values are taken
x_new_abs <- x_new
x_new_abs[] <- 0
x_new_prod_nb <- x_new
x_new_prod_nb[] <- 0
x_new_tic <- x_new
x_new_tic[] <- 0
x_new_prod_swh <- x_new
x_new_prod_swh[] <- 0
# Converting additional capacity targets to absolute capacity targets
x_new_abs[,,c("Nuclear","Biomass","Wind","Solar")] <- x_current[,,c("Nuclear","Biomass","Wind","Solar")] +
x_tmp[,,c("AC-Absolute.Nuclear","AC-Absolute.Biomass","AC-Absolute.Wind","AC-Absolute.Solar"),drop=TRUE]
x_new_abs[,,"Hydro"] <- x_current[,,"Hydro"] + x_tmp[,,"AC-Absolute.Hydro",drop=TRUE]*setYears(cf_hydro[getRegions(x_tmp),,]*8760)
# Converting Production targets (GWh) to Capacity targets (TIC-Absolute) (GW) for nuclear and biomass
# pmax used to always take the higher value from existing capacity and new capacity (from production)
x_new_prod_nb[,,"Nuclear"] <- pmax( x_current[,,"Nuclear"], x_tmp[,,c("Production-Absolute.Nuclear")]/(8760*cf_nuclear))
x_new_prod_nb[,,"Biomass"] <- pmax(x_current[,,"Biomass"],x_tmp[,,c("Production-Absolute.Biomass")]/(8760*cf_biomass))
# Total installed capacity Targets
# target in target year should be the maximum from the target year and the current capacity
x_new_tic[,,c("Nuclear","Biomass","Wind","Solar")] <- pmax(x_current[,,c("Nuclear","Biomass","Wind","Solar")],x_tmp[,,c("Nuclear","Biomass","Wind","Solar")][,,"TIC-Absolute",drop = TRUE])
x_new_tic[,,"Hydro"] <- pmax(x_current[,,"Hydro"],x_tmp[,,"TIC-Absolute.Hydro",drop = TRUE]*setYears(cf_hydro[getRegions(x_tmp),,]))
# Converting Production targets to capacity targets for solar (pv and csp), hydro, and wind
# Obtaining the capacity factors (nur) values and associated maxproduction (maxprod) for Hydro, Wind, and Solar
data_wind <- calcOutput("PotentialWind", aggregate = FALSE)
# Reordering dim=3 for data_wind so that 1st position corresponds to maxprod.nur.1 and not maxprod.nur.9
data_wind_sorted <- mbind(data_wind[,,"1"],data_wind[,,"2"],data_wind[,,"3"],data_wind[,,"4"],
data_wind[,,"5"],data_wind[,,"6"],data_wind[,,"7"],data_wind[,,"8"],data_wind[,,"9"])
data_hydro <- calcOutput("PotentialHydro", aggregate = FALSE)
# data_solar <- calcOutput("Solar", aggregate = FALSE)
setConfig(regionmapping = "regionmappingTCD.csv")
data_solar <- calcOutput("Solar")
names_solar <- paste0("Solar.",getNames(collapseNames((mselect(data_solar,type=c("nur","maxprod"),technology="spv")),collapsedim = 2)))
setConfig(regionmapping = "regionmappingH12.csv")
names_hydro <- paste0("Hydro.",getNames(data_hydro))
names_wind <- paste0("Wind.",getNames(data_wind_sorted))
data_combined <- new.magpie(getRegions(data_hydro), NULL, c(names_solar,names_hydro,names_wind))
data_combined[,,"Hydro"] <- data_hydro
data_combined[,,"Wind"] <- data_wind_sorted
data_combined[c("TCD","JPN"),,"Solar"][,,"maxprod"] <- as.vector(data_solar[c("TCD","JPN"),,"maxprod"][,,"spv"])
data_combined[c("TCD","JPN"),,"Solar"][,,"nur"] <- as.vector(data_solar[c("TCD","JPN"),,"nur"][,,"spv"])
data_combined <- data_combined[getRegions(x_tmp),,]
for (n in getNames(data_combined,dim=1)){
name=paste0(n,".maxprod")
# Conversion from EJ/a to GWh
data_combined[,,name,pmatch=TRUE] <- data_combined[,,name,pmatch=TRUE]*277777.778
}
data_combined[is.na(data_combined)] <- 0
# Production/Generation targets are converted into capacity targets by alloting production to certain capacity factors based on maxprod.
final <- numeric(length(getRegions(x_tmp)))
names(final) <- getRegions(x_tmp)
tmp_target <- numeric(10)
x_new_copy <- x_new
# x_tmp[,,"Production-Absolute.Hydro"] <- pmax(x_tmp[,,"Production-Absolute.Hydro"],x_new[,,"Hydro"])
x_tmp[,,"Production-Absolute.Hydro"] <- pmax(x_tmp[,,"Production-Absolute.Hydro"],x_new_tic[,,"Hydro"],x_new_abs[,,"Hydro"])
x_tmp[is.na(x_tmp)] <- 0
# For all countries which have non-zero generation values but zero or negative maxprod(),
# replace x_tmp[,,"Production-Absolute.Hydro]==0
#
for (r in names(final)){
if(any(x_tmp[r,,"Production-Absolute.Hydro"]!=0) &
all(data_combined[r,,"Hydro.maxprod"]==0)|any(data_combined[r,,"Hydro.maxprod"]<0) )
x_tmp[r,,"Production-Absolute.Hydro"] <- 0
}
for (t in c("Solar","Wind","Hydro")){
data_sel <- data_combined[,,t]
data_in_use <- data_sel[,,"maxprod"]/data_sel[,,"nur"]
for (y in target_years){
final[] <-0
for (r in names(final)){
tmp_target <- numeric(10)
name <- paste0(t,".maxprod")
name2 <- paste0("Production-Absolute.",t)
if (!isZero(x_tmp[,,"Production-Absolute"][,,t])[r,y,] &
dimSums(data_combined[r,,name],na.rm=T) > max(x_tmp[r,,name2])){
# extracting the first non-zero location of maxprod
name <- paste0(t,".maxprod")
loc <- min(which(!R.utils::isZero(data_combined[r,,name,pmatch=TRUE])))
tmp_target[1] <- x_tmp[r,y,"Production-Absolute"][,,t]
if (data_sel[r,,"maxprod"][,,loc] > tmp_target[1]){
final[r] <- tmp_target[1]/(8760*data_sel[r,,"nur"][,,loc])
} else {tmp_target[2] <- tmp_target[1] - data_sel[r,,"maxprod"][,,loc]
if(data_sel[r,,"maxprod"][,,loc+1] > tmp_target[2]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + tmp_target[1]/data_sel[r,,"nur"][,,loc+1])
} else {tmp_target[3] <- tmp_target[2] - data_sel[r,,"maxprod"][,,loc+1]
if(data_sel[r,,"maxprod"][,,loc+2] > tmp_target[3]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1]
+ tmp_target[2]/data_sel[r,,"nur"][,,loc+2])
} else {tmp_target[4] <- tmp_target[3] - data_sel[r,,"maxprod"][,,loc+2]
if(data_sel[r,,"maxprod"][,,loc+3] > tmp_target[4]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
tmp_target[3]/data_sel[r,,"nur"][,,loc+3])
final[r] <- tmp_target[1]
} else {tmp_target[5] <- tmp_target[4] - data_sel[r,,"maxprod"][,,loc+3]
if(data_sel[r,,"maxprod"][loc+4] > tmp_target[5]){
final[r] <-(1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
data_in_use[r,,][,,loc+3] + tmp_target[4]/data_sel[r,,"nur"][,,loc+4])
} else {tmp_target[6] <- tmp_target[5] - data_sel[r,,"maxprod"][,,loc+4]
if(data_sel[r,,"maxprod"][loc+5] > tmp_target[6]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
data_in_use[r,,][,,loc+3] + data_in_use[r,,][,,loc+4] +
tmp_target[5]/data_sel[r,,"nur"][,,loc+5])
}
}
}
}
}
}
}
}
x_new_prod_swh[,y,t] <-final
}
}
x_new_gen <- mbind(x_new_prod_swh[,,c("Solar","Wind","Hydro")],x_new_prod_nb[,,c("Biomass","Nuclear")])
x_new[,,c("Solar","Wind","Hydro","Biomass","Nuclear")] <- pmax(x_new_abs[,,c("Solar","Wind","Hydro","Biomass","Nuclear")],
x_new_gen[,,c("Solar","Wind","Hydro","Biomass","Nuclear")],
x_new_tic[,,c("Solar","Wind","Hydro","Biomass","Nuclear")])
# x_new[,,c("Solar","Wind")] <- pmax(x_new_copy[,,c("Solar","Wind")],x_new[,,c("Solar","Wind")])
x_new[,,"Hydro"] <- x_new_gen[,,"Hydro"]
#x_new[,,c("Solar","Wind")] <- pmax(x_new_copy[,,c("Solar","Wind")],x_new[,,c("Solar","Wind")])
#x_new[,,"Hydro"] <- x_new_copy[,,"Hydro"]
# # Making sure that targets in subsequent years are always same or greater than the proceeding year
for (r in getRegions(x_tmp)){
for (t in tech){
for(i in c(2020,2025,2030)){
if(x_new[r,i+5,t] < setYears(x_new[r,i,t])){
x_new[r,i+5,t] <- setYears(x_new[r,i,t])
}else{
x_new[r,i,t] <- x_new[r,i,t]
}
}
}
}
# countries not in the database
rest_regions <- getRegions(hist_cap)[!(getRegions(hist_cap) %in% getRegions(x_new))]
x_other <- new.magpie(rest_regions,target_years,tech)
x_other[,,c("Wind","Solar")] <- setYears(hist_cap[rest_regions,2015,c("Solar","Wind")])
x_other[,,"Nuclear"] <- 0
x_other[,,"Biomass"] <- setYears(hist_cap[rest_regions,2015,"Bioenergy"])
x_other[,,"Hydro"] <- setYears(hist_cap[rest_regions,2015,"Hydropower"])*setYears(cf_hydro[rest_regions,,])
x_final <- magpiesort(mbind(x_new,x_other))
x_final[is.na(x_final)] <- 0
x <- x_final
getNames(x) <- c("wind","spv","hydro","tnrs","bioigcc")
}
x <- toolCountryFill(x,fill = 0)
return(x)
}
pik-piam/moinput documentation built on June 9, 2020, 12:23 p.m.
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Defines functions convertRogelj2017
#' Policy targets for NDCs from Rogelj2017
#' @description Converts conditional and unconditional capacity targets into total capacity (GW) in target year
#' the Generation targets are similar to the capacity targets but include the capacity factors,
#' the Emissions targets are the total (except land CO2) emissions in the target year
#' @param x MAgPIE object to be converted
#' @param subtype UNCapacity, COCapacity
#' @return Magpie object with Total Installed Capacity (GW) targets. The target years differ depending upon the database.
#' @author Aman Malik and Christoph Bertram
#' @importFrom R.utils isZero
convertRogelj2017 <- function(x,subtype){
if(subtype == "UNCapacity"){ # UNconditional policies
# x <- readSource("Rogelj2017","UNCapacity",convert=F)
x <- x[,,"conditional",invert=TRUE] # keep only unconditional policies
x[is.na(x)] <- 0 # Converting all NAs to zero
# reading historical data
hist_cap <- readSource(type="IRENA",subtype="Capacity")/1000 # converting from MW to GW
hist_gen <- readSource("IRENA", subtype = "Generation")# Units are GWh
# Real world capacity factor for hydro = Generation in last year/Capacity in last year
cf_realworld <- hist_gen[,2015,"Hydropower"]/(8760*hist_cap[,2015,"Hydropower"])
cf_realworld[is.na(cf_realworld)] <- 0
getNames(cf_realworld) <- "Hydro"
tech <- c("Wind","Solar","Hydro","Nuclear","Biomass")
target_years <- c(2020,2025,2030,2035)
x_tmp <- x
# 1. Cases when targets are given in non-model year. e.g., 2022, 2032
input_yr <- getYears(x,as.integer = TRUE)
for ( i in input_yr){
if (i>2020 & i<2025)
x_tmp[,2025,] <- as.vector(x[,i,])*(1+(2025-i)*0.05)
if (i>2025 & i<2030)
x_tmp[,2030,] <- as.vector(x[,i,])*(1+(2030-i)*0.05)
if (i>2030 & i<2035)
x_tmp[,2035,] <- as.vector(x[,i,])*(1+(2035-i)*0.05)
}
# Creating magpie object which at the end will only contain capacity targets
x_new <- new.magpie(getRegions(x_tmp),target_years,tech)
x_new[is.na(x_new)] <- 0
# Capacity factors in REMIND. From : calcOutput("Capacityfactor"...)
cf_biomass <- 0.75
cf_nuclear <- 0.85
cf_hydro <- cf_realworld
# Initialising all capacities for all model years to current capacities and converting generation to capacity
x_new[,,c("Wind","Solar")] <- setYears(hist_cap[getRegions(x_tmp),2015,c("Wind","Solar")])
x_new[,,"Biomass"] <- setYears(hist_cap[getRegions(x_tmp),2015,"Bioenergy"])
# special case for hydro.
x_new[,,"Hydro"] <- setYears(hist_gen[getRegions(x_new),2015,"Hydropower"])
# Special case for nuclear
hist_gen_nuclear <- readSource(type = "BP",subtype = "Generation")*1000 # TWh to GWh
for (i in target_years){
for (j in getNames(x_tmp[,,"Nuclear"])){
for (k in getRegions(x_tmp)){
if(x_tmp[k,i,j]!=0)
x_new[k,,"Nuclear"] <- setYears(hist_gen_nuclear[k,2015,"Nuclear"])/(8760*cf_nuclear)
}
}
}
x_tmp <- x_tmp[,target_years,]
x_current <- x_new # x_current contains current capacities except for hydro where current generation values are taken
x_new_abs <- x_new
x_new_abs[] <- 0
x_new_prod_nb <- x_new
x_new_prod_nb[] <- 0
x_new_tic <- x_new
x_new_tic[] <- 0
x_new_prod_swh <- x_new
x_new_prod_swh[] <- 0
# Converting additional capacity targets to absolute capacity targets
x_new_abs[,,c("Nuclear","Biomass","Wind","Solar")] <- x_current[,,c("Nuclear","Biomass","Wind","Solar")] +
x_tmp[,,c("AC-Absolute.Nuclear","AC-Absolute.Biomass","AC-Absolute.Wind","AC-Absolute.Solar"),drop=TRUE]
x_new_abs[,,"Hydro"] <- x_current[,,"Hydro"] + x_tmp[,,"AC-Absolute.Hydro",drop=TRUE]*setYears(cf_hydro[getRegions(x_tmp),,]*8760)
# Converting Production targets (GWh) to Capacity targets (TIC-Absolute) (GW) for nuclear and biomass
# pmax used to always take the higher value from existing capacity and new capacity (from production)
x_new_prod_nb[,,"Nuclear"] <- pmax( x_current[,,"Nuclear"], x_tmp[,,c("Production-Absolute.Nuclear")]/(8760*cf_nuclear))
x_new_prod_nb[,,"Biomass"] <- pmax(x_current[,,"Biomass"],x_tmp[,,c("Production-Absolute.Biomass")]/(8760*cf_biomass))
# Total installed capacity Targets
# target in target year should be the maximum from the target year and the current capacity
x_new_tic[,,c("Nuclear","Biomass","Wind","Solar")] <- pmax(x_current[,,c("Nuclear","Biomass","Wind","Solar")],x_tmp[,,c("Nuclear","Biomass","Wind","Solar")][,,"TIC-Absolute",drop = TRUE])
x_new_tic[,,"Hydro"] <- pmax(x_current[,,"Hydro"],x_tmp[,,"TIC-Absolute.Hydro",drop = TRUE]*setYears(cf_hydro[getRegions(x_tmp),,]))
# Converting Production targets to capacity targets for solar (pv and csp), hydro, and wind
# Obtaining the capacity factors (nur) values and associated maxproduction (maxprod) for Hydro, Wind, and Solar
data_wind <- calcOutput("PotentialWind", aggregate = FALSE)
# Reordering dim=3 for data_wind so that 1st position corresponds to maxprod.nur.1 and not maxprod.nur.9
data_wind_sorted <- mbind(data_wind[,,"1"],data_wind[,,"2"],data_wind[,,"3"],data_wind[,,"4"],
data_wind[,,"5"],data_wind[,,"6"],data_wind[,,"7"],data_wind[,,"8"],data_wind[,,"9"])
data_hydro <- calcOutput("PotentialHydro", aggregate = FALSE)
#data_solar <- calcOutput("Solar", aggregate = FALSE)
setConfig(regionmapping = "regionmappingTCD.csv")
data_solar <- calcOutput("Solar")
setConfig(regionmapping = "regionmappingH12.csv")
names_solar <- paste0("Solar.",getNames(collapseNames((mselect(data_solar,type=c("nur","maxprod"),technology="spv")),collapsedim = 2)))
names_hydro <- paste0("Hydro.",getNames(data_hydro))
names_wind <- paste0("Wind.",getNames(data_wind_sorted))
data_combined <- new.magpie(getRegions(data_hydro), NULL, c(names_solar,names_hydro,names_wind))
data_combined[,,"Hydro"] <- data_hydro
data_combined[,,"Wind"] <- data_wind_sorted
data_combined[c("TCD","JPN"),,"Solar"][,,"maxprod"] <- as.vector(data_solar[c("TCD","JPN"),,"maxprod"][,,"spv"])
data_combined[c("TCD","JPN"),,"Solar"][,,"nur"] <- as.vector(data_solar[c("TCD","JPN"),,"nur"][,,"spv"])
data_combined <- data_combined[getRegions(x_tmp),,]
for (n in getNames(data_combined,dim=1)){
name=paste0(n,".maxprod")
# Conversion from EJ/a to GWh
data_combined[,,name,pmatch=TRUE] <- data_combined[,,name,pmatch=TRUE]*277777.778
}
data_combined[is.na(data_combined)] <- 0
# Production/Generation targets are converted into capacity targets by alloting production to certain capacity factors based on maxprod.
final <- numeric(length(getRegions(x_tmp)))
names(final) <- getRegions(x_tmp)
tmp_target <- numeric(10)
#x_tmp[,,"Production-Absolute.Hydro"] <- pmax(x_tmp[,,"Production-Absolute.Hydro"],x_new[,,"Hydro"])
x_tmp[,,"Production-Absolute.Hydro"] <- pmax(x_tmp[,,"Production-Absolute.Hydro"],x_new_tic[,,"Hydro"],x_new_abs[,,"Hydro"])
x_tmp[is.na(x_tmp)] <- 0
# For all countries which have non-zero generation values but zero or negative maxprod(),
# replace x_tmp[,,"Production-Absolute.Hydro]==0
# Even if there is one +ve production absolute value for Hydro but all maxprod are zero
for (r in names(final)){
if(any(x_tmp[r,,"Production-Absolute.Hydro"]!=0) &
all(data_combined[r,,"Hydro.maxprod"]==0)|any(data_combined[r,,"Hydro.maxprod"]<0) )
x_tmp[r,,"Production-Absolute.Hydro"] <- 0
}
for (t in c("Solar","Wind","Hydro")){
data_sel <- data_combined[,,t]
data_in_use <- data_sel[,,"maxprod"]/data_sel[,,"nur"]
for (y in target_years){
final[] <-0
for (r in names(final)){
tmp_target <- numeric(10)
name <- paste0(t,".maxprod")
name2 <- paste0("Production-Absolute.",t)
if (!isZero(x_tmp[,,"Production-Absolute"][,,t])[r,y,] &
dimSums(data_combined[r,,name],na.rm=T) > max(x_tmp[r,,name2])){
# extracting the first non-zero location of maxprod
name <- paste0(t,".maxprod")
loc <- min(which(!R.utils::isZero(data_combined[r,,name,pmatch=TRUE])))
tmp_target[1] <- x_tmp[r,y,"Production-Absolute"][,,t]
if (data_sel[r,,"maxprod"][,,loc] > tmp_target[1]){
final[r] <- tmp_target[1]/(8760*data_sel[r,,"nur"][,,loc])
} else {tmp_target[2] <- tmp_target[1] - data_sel[r,,"maxprod"][,,loc]
if(data_sel[r,,"maxprod"][,,loc+1] > tmp_target[2]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + tmp_target[1]/data_sel[r,,"nur"][,,loc+1])
} else {tmp_target[3] <- tmp_target[2] - data_sel[r,,"maxprod"][,,loc+1]
if(data_sel[r,,"maxprod"][,,loc+2] > tmp_target[3]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1]
+ tmp_target[2]/data_sel[r,,"nur"][,,loc+2])
} else {tmp_target[4] <- tmp_target[3] - data_sel[r,,"maxprod"][,,loc+2]
if(data_sel[r,,"maxprod"][,,loc+3] > tmp_target[4]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
tmp_target[3]/data_sel[r,,"nur"][,,loc+3])
final[r] <- tmp_target[1]
} else {tmp_target[5] <- tmp_target[4] - data_sel[r,,"maxprod"][,,loc+3]
if(data_sel[r,,"maxprod"][loc+4] > tmp_target[5]){
final[r] <-(1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
data_in_use[r,,][,,loc+3] + tmp_target[4]/data_sel[r,,"nur"][,,loc+4])
} else {tmp_target[6] <- tmp_target[5] - data_sel[r,,"maxprod"][,,loc+4]
if(data_sel[r,,"maxprod"][loc+5] > tmp_target[6]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
data_in_use[r,,][,,loc+3] + data_in_use[r,,][,,loc+4] +
tmp_target[5]/data_sel[r,,"nur"][,,loc+5])
}
}
}
}
}
}
}
}
x_new_prod_swh[,y,t] <-final
}
}
x_new_gen <- mbind(x_new_prod_swh[,,c("Solar","Wind","Hydro")],x_new_prod_nb[,,c("Biomass","Nuclear")])
x_new[,,c("Solar","Wind","Hydro","Biomass","Nuclear")] <- pmax(x_new_abs[,,c("Solar","Wind","Hydro","Biomass","Nuclear")],
x_new_gen[,,c("Solar","Wind","Hydro","Biomass","Nuclear")],
x_new_tic[,,c("Solar","Wind","Hydro","Biomass","Nuclear")])
# x_new[,,c("Solar","Wind")] <- pmax(x_new_copy[,,c("Solar","Wind")],x_new[,,c("Solar","Wind")])
x_new[,,"Hydro"] <- x_new_gen[,,"Hydro"]
# # Making sure that targets in subsequent years are always same or greater than the proceeding year
for (r in getRegions(x_tmp)){
for (t in tech){
for(i in c(2020,2025,2030)){
if(x_new[r,i+5,t] < setYears(x_new[r,i,t])){
x_new[r,i+5,t] <- setYears(x_new[r,i,t])
}else{
x_new[r,i,t] <- x_new[r,i,t]
}
}
}
}
# countries not in the database
rest_regions <- getRegions(hist_cap)[!(getRegions(hist_cap) %in% getRegions(x_new))]
x_other <- new.magpie(rest_regions,target_years,tech)
x_other[,,c("Wind","Solar")] <- setYears(hist_cap[rest_regions,2015,c("Solar","Wind")])
x_other[,,"Nuclear"] <- 0
x_other[,,"Biomass"] <- setYears(hist_cap[rest_regions,2015,"Bioenergy"])
x_other[,,"Hydro"] <- setYears(hist_cap[rest_regions,2015,"Hydropower"])*setYears(cf_hydro[rest_regions,,])
x_final <- magpiesort(mbind(x_new,x_other))
x_final[is.na(x_final)] <- 0
x <- x_final
getNames(x) <- c("wind","spv","hydro","tnrs","bioigcc")
}
if(subtype == "COCapacity"){
# x <- readSource("Rogelj2017",subtype = "COCapacity",convert = F)
# loop to make conditional targets at least as good as unconditional targets
for (r in getRegions(x)){
for (t in getYears(x)){
for (tech in getNames(x[,,"conditional",drop=TRUE]) )
if(is.na(x[r,t,paste0("conditional",".",tech)]))
x[r,t,paste0("conditional",".",tech)] <- x[r,t,paste0("unconditional",".",tech)]
}
}
x <- x[,,"unconditional",invert=TRUE]# drop unconditional targets
x[is.na(x)] <- 0 # Converting all NAs to zero
# reading historical data
hist_cap <- readSource(type="IRENA",subtype="Capacity")/1000 # converting from MW to GW
hist_gen <- readSource("IRENA", subtype = "Generation")# Units are GWh
# Real world capacity factor for hydro = Generation in last year/Capacity in last year
cf_realworld <- hist_gen[,2015,"Hydropower"]/(8760*hist_cap[,2015,"Hydropower"])
cf_realworld <- cf_realworld<1
getNames(cf_realworld) <- "Hydro"
tech <- c("Wind","Solar","Hydro","Nuclear","Biomass")
target_years <- c(2020,2025,2030,2035)
x_tmp <- x
# 1. Cases when targets are given in non-model year. e.g., 2022, 2032
input_yr <- getYears(x,as.integer = TRUE)
for ( i in input_yr){
if (i>2020 & i<2025)
x_tmp[,2025,] <- as.vector(x[,i,])*(1+(2025-i)*0.05)
if (i>2025 & i<2030)
x_tmp[,2030,] <- as.vector(x[,i,])*(1+(2030-i)*0.05)
if (i>2030 & i<2035)
x_tmp[,2035,] <- as.vector(x[,i,])*(1+(2035-i)*0.05)
}
# Creating magpie object which at the end will only contain capacity targets
x_new <- new.magpie(getRegions(x_tmp),target_years,tech)
x_new[is.na(x_new)] <- 0
# Capacity factors in REMIND. From : calcOutput("Capacityfactor"...)
cf_biomass <- 0.75
cf_nuclear <- 0.85
cf_hydro <- cf_realworld
# Initialising all capacities for all model years to current capacities and converting generation to capacity
x_new[,,c("Wind","Solar")] <- setYears(hist_cap[getRegions(x_tmp),2015,c("Wind","Solar")])
x_new[,,"Biomass"] <- setYears(hist_cap[getRegions(x_tmp),2015,"Bioenergy"])
# special case for hydro.
x_new[,,"Hydro"] <- setYears(hist_gen[getRegions(x_new),2015,"Hydropower"])
# Special case for nuclear
hist_gen_nuclear <- readSource(type = "BP",subtype = "Generation")*1000 # TWh to GWh
for (i in target_years){
for (j in getNames(x_tmp[,,"Nuclear"])){
for (k in getRegions(x_tmp)){
if(x_tmp[k,i,j]!=0)
x_new[k,,"Nuclear"] <- setYears(hist_gen_nuclear[k,2015,"Nuclear"])/(8760*cf_nuclear)
}
}
}
x_current <- x_new # x_current contains current capacity values. x_new contains capacity targets
x_tmp <- x_tmp[,target_years,]
x_current <- x_new # x_current contains current capacities except for hydro where current generation values are taken
x_new_abs <- x_new
x_new_abs[] <- 0
x_new_prod_nb <- x_new
x_new_prod_nb[] <- 0
x_new_tic <- x_new
x_new_tic[] <- 0
x_new_prod_swh <- x_new
x_new_prod_swh[] <- 0
# Converting additional capacity targets to absolute capacity targets
x_new_abs[,,c("Nuclear","Biomass","Wind","Solar")] <- x_current[,,c("Nuclear","Biomass","Wind","Solar")] +
x_tmp[,,c("AC-Absolute.Nuclear","AC-Absolute.Biomass","AC-Absolute.Wind","AC-Absolute.Solar"),drop=TRUE]
x_new_abs[,,"Hydro"] <- x_current[,,"Hydro"] + x_tmp[,,"AC-Absolute.Hydro",drop=TRUE]*setYears(cf_hydro[getRegions(x_tmp),,]*8760)
# Converting Production targets (GWh) to Capacity targets (TIC-Absolute) (GW) for nuclear and biomass
# pmax used to always take the higher value from existing capacity and new capacity (from production)
x_new_prod_nb[,,"Nuclear"] <- pmax( x_current[,,"Nuclear"], x_tmp[,,c("Production-Absolute.Nuclear")]/(8760*cf_nuclear))
x_new_prod_nb[,,"Biomass"] <- pmax(x_current[,,"Biomass"],x_tmp[,,c("Production-Absolute.Biomass")]/(8760*cf_biomass))
# Total installed capacity Targets
# target in target year should be the maximum from the target year and the current capacity
x_new_tic[,,c("Nuclear","Biomass","Wind","Solar")] <- pmax(x_current[,,c("Nuclear","Biomass","Wind","Solar")],x_tmp[,,c("Nuclear","Biomass","Wind","Solar")][,,"TIC-Absolute",drop = TRUE])
x_new_tic[,,"Hydro"] <- pmax(x_current[,,"Hydro"],x_tmp[,,"TIC-Absolute.Hydro",drop = TRUE]*setYears(cf_hydro[getRegions(x_tmp),,]))
# Converting Production targets to capacity targets for solar (pv and csp), hydro, and wind
# Obtaining the capacity factors (nur) values and associated maxproduction (maxprod) for Hydro, Wind, and Solar
data_wind <- calcOutput("PotentialWind", aggregate = FALSE)
# Reordering dim=3 for data_wind so that 1st position corresponds to maxprod.nur.1 and not maxprod.nur.9
data_wind_sorted <- mbind(data_wind[,,"1"],data_wind[,,"2"],data_wind[,,"3"],data_wind[,,"4"],
data_wind[,,"5"],data_wind[,,"6"],data_wind[,,"7"],data_wind[,,"8"],data_wind[,,"9"])
data_hydro <- calcOutput("PotentialHydro", aggregate = FALSE)
# data_solar <- calcOutput("Solar", aggregate = FALSE)
setConfig(regionmapping = "regionmappingTCD.csv")
data_solar <- calcOutput("Solar")
names_solar <- paste0("Solar.",getNames(collapseNames((mselect(data_solar,type=c("nur","maxprod"),technology="spv")),collapsedim = 2)))
setConfig(regionmapping = "regionmappingH12.csv")
names_hydro <- paste0("Hydro.",getNames(data_hydro))
names_wind <- paste0("Wind.",getNames(data_wind_sorted))
data_combined <- new.magpie(getRegions(data_hydro), NULL, c(names_solar,names_hydro,names_wind))
data_combined[,,"Hydro"] <- data_hydro
data_combined[,,"Wind"] <- data_wind_sorted
data_combined[c("TCD","JPN"),,"Solar"][,,"maxprod"] <- as.vector(data_solar[c("TCD","JPN"),,"maxprod"][,,"spv"])
data_combined[c("TCD","JPN"),,"Solar"][,,"nur"] <- as.vector(data_solar[c("TCD","JPN"),,"nur"][,,"spv"])
data_combined <- data_combined[getRegions(x_tmp),,]
for (n in getNames(data_combined,dim=1)){
name=paste0(n,".maxprod")
# Conversion from EJ/a to GWh
data_combined[,,name,pmatch=TRUE] <- data_combined[,,name,pmatch=TRUE]*277777.778
}
data_combined[is.na(data_combined)] <- 0
# Production/Generation targets are converted into capacity targets by alloting production to certain capacity factors based on maxprod.
final <- numeric(length(getRegions(x_tmp)))
names(final) <- getRegions(x_tmp)
tmp_target <- numeric(10)
x_new_copy <- x_new
# x_tmp[,,"Production-Absolute.Hydro"] <- pmax(x_tmp[,,"Production-Absolute.Hydro"],x_new[,,"Hydro"])
x_tmp[,,"Production-Absolute.Hydro"] <- pmax(x_tmp[,,"Production-Absolute.Hydro"],x_new_tic[,,"Hydro"],x_new_abs[,,"Hydro"])
x_tmp[is.na(x_tmp)] <- 0
# For all countries which have non-zero generation values but zero or negative maxprod(),
# replace x_tmp[,,"Production-Absolute.Hydro]==0
#
for (r in names(final)){
if(any(x_tmp[r,,"Production-Absolute.Hydro"]!=0) &
all(data_combined[r,,"Hydro.maxprod"]==0)|any(data_combined[r,,"Hydro.maxprod"]<0) )
x_tmp[r,,"Production-Absolute.Hydro"] <- 0
}
for (t in c("Solar","Wind","Hydro")){
data_sel <- data_combined[,,t]
data_in_use <- data_sel[,,"maxprod"]/data_sel[,,"nur"]
for (y in target_years){
final[] <-0
for (r in names(final)){
tmp_target <- numeric(10)
name <- paste0(t,".maxprod")
name2 <- paste0("Production-Absolute.",t)
if (!isZero(x_tmp[,,"Production-Absolute"][,,t])[r,y,] &
dimSums(data_combined[r,,name],na.rm=T) > max(x_tmp[r,,name2])){
# extracting the first non-zero location of maxprod
name <- paste0(t,".maxprod")
loc <- min(which(!R.utils::isZero(data_combined[r,,name,pmatch=TRUE])))
tmp_target[1] <- x_tmp[r,y,"Production-Absolute"][,,t]
if (data_sel[r,,"maxprod"][,,loc] > tmp_target[1]){
final[r] <- tmp_target[1]/(8760*data_sel[r,,"nur"][,,loc])
} else {tmp_target[2] <- tmp_target[1] - data_sel[r,,"maxprod"][,,loc]
if(data_sel[r,,"maxprod"][,,loc+1] > tmp_target[2]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + tmp_target[1]/data_sel[r,,"nur"][,,loc+1])
} else {tmp_target[3] <- tmp_target[2] - data_sel[r,,"maxprod"][,,loc+1]
if(data_sel[r,,"maxprod"][,,loc+2] > tmp_target[3]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1]
+ tmp_target[2]/data_sel[r,,"nur"][,,loc+2])
} else {tmp_target[4] <- tmp_target[3] - data_sel[r,,"maxprod"][,,loc+2]
if(data_sel[r,,"maxprod"][,,loc+3] > tmp_target[4]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
tmp_target[3]/data_sel[r,,"nur"][,,loc+3])
final[r] <- tmp_target[1]
} else {tmp_target[5] <- tmp_target[4] - data_sel[r,,"maxprod"][,,loc+3]
if(data_sel[r,,"maxprod"][loc+4] > tmp_target[5]){
final[r] <-(1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
data_in_use[r,,][,,loc+3] + tmp_target[4]/data_sel[r,,"nur"][,,loc+4])
} else {tmp_target[6] <- tmp_target[5] - data_sel[r,,"maxprod"][,,loc+4]
if(data_sel[r,,"maxprod"][loc+5] > tmp_target[6]){
final[r] <- (1/8760)*(data_in_use[r,,][,,loc] + data_in_use[r,,][,,loc+1] + data_in_use[r,,][,,loc+2] +
data_in_use[r,,][,,loc+3] + data_in_use[r,,][,,loc+4] +
tmp_target[5]/data_sel[r,,"nur"][,,loc+5])
}
}
}
}
}
}
}
}
x_new_prod_swh[,y,t] <-final
}
}
x_new_gen <- mbind(x_new_prod_swh[,,c("Solar","Wind","Hydro")],x_new_prod_nb[,,c("Biomass","Nuclear")])
x_new[,,c("Solar","Wind","Hydro","Biomass","Nuclear")] <- pmax(x_new_abs[,,c("Solar","Wind","Hydro","Biomass","Nuclear")],
x_new_gen[,,c("Solar","Wind","Hydro","Biomass","Nuclear")],
x_new_tic[,,c("Solar","Wind","Hydro","Biomass","Nuclear")])
# x_new[,,c("Solar","Wind")] <- pmax(x_new_copy[,,c("Solar","Wind")],x_new[,,c("Solar","Wind")])
x_new[,,"Hydro"] <- x_new_gen[,,"Hydro"]
#x_new[,,c("Solar","Wind")] <- pmax(x_new_copy[,,c("Solar","Wind")],x_new[,,c("Solar","Wind")])
#x_new[,,"Hydro"] <- x_new_copy[,,"Hydro"]
# # Making sure that targets in subsequent years are always same or greater than the proceeding year
for (r in getRegions(x_tmp)){
for (t in tech){
for(i in c(2020,2025,2030)){
if(x_new[r,i+5,t] < setYears(x_new[r,i,t])){
x_new[r,i+5,t] <- setYears(x_new[r,i,t])
}else{
x_new[r,i,t] <- x_new[r,i,t]
}
}
}
}
# countries not in the database
rest_regions <- getRegions(hist_cap)[!(getRegions(hist_cap) %in% getRegions(x_new))]
x_other <- new.magpie(rest_regions,target_years,tech)
x_other[,,c("Wind","Solar")] <- setYears(hist_cap[rest_regions,2015,c("Solar","Wind")])
x_other[,,"Nuclear"] <- 0
x_other[,,"Biomass"] <- setYears(hist_cap[rest_regions,2015,"Bioenergy"])
x_other[,,"Hydro"] <- setYears(hist_cap[rest_regions,2015,"Hydropower"])*setYears(cf_hydro[rest_regions,,])
x_final <- magpiesort(mbind(x_new,x_other))
x_final[is.na(x_final)] <- 0
x <- x_final
getNames(x) <- c("wind","spv","hydro","tnrs","bioigcc")
}
x <- toolCountryFill(x,fill = 0)
return(x)
}
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