R/convertREN21.R
In pik-piam/moinput: Model Operations Input Data Library
Defines functions
convertREN21
#' Policy targets for REN21
#' @description This code aggregates and homogenises different types of renewable energy targets into total installed capacity targets (in GW).
#' @details Policy database accessible in "inputdata/sources/REN21/README"
#' @param x MAgPIE object to be converted
#' @param subtype Only "Capacity" asof now
#' @return Magpie object with Total Installed Capacity targets. The target years differ depending upon the database.
#' @importFrom R.utils isZero
#' @author Aman Malik
convertREN21 <- function(x,subtype){
if(subtype == "Capacity"){
x <- readSource("REN21",subtype="Capacity",convert = F)
x <- magpiesort(x) # sorting years chronologically and region names alphabetically
x[is.na(x)] <- 0 # Converting all NAs to zero
getRegions(x) <- toolCountry2isocode(getRegions(x))# Country names to ISO3 code
# 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 2015
cf_realworld <- hist_gen[,2015,"Hydropower"]/(8760*hist_cap[,2015,"Hydropower"])
cf_realworld[is.na(cf_realworld)] <- 0
getNames(cf_realworld) <- "Hydro"
# renaming variable names from IRENA historical database
getNames(hist_cap)[c(2,7,8,10,11,12)] <- c("Hydro","Wind_ON","Wind_OFF","SolarPV",
"SolarCSP","Biomass")
x_tmp <- x # creating working copy(for modifications) and preserving original
# Combining wind (on and offshore) as no distinction made in REMIND
x_tmp <- add_columns(x_tmp ,addnm = "Wind", dim = 3.2)
x_tmp[,,"Wind"] <- x_tmp[,,"Wind_ON"] + x_tmp[,,"Wind_OFF"]
# Selecting relevant technologies
techs <- c("SolarPV","SolarCSP","Wind","Hydro","Biomass","Geothermal")
# 1. If targets are given in non-model year. e.g., 2022, 2032, then targets are
# extrapolated linearly.
input_yr <- getYears(x,as.integer = TRUE)
input_yr <- input_yr[input_yr%%5!=0]# years not multiple of 5/non-model years
regions <- getRegions(x_tmp)
tech_n <- getNames(x_tmp[,,techs])
for (r in regions){
for (t in tech_n){
for ( i in input_yr){
if (i>2015 & i<2020 & x_tmp[r,i,t]!=0)
# x_tmp[,2020,] <- as.vector(x[,i,])*1.1
x_tmp[r,2020,t] <- setYears(x_tmp[r,i,t])*(1+(2020-i)*0.05)
if (i>2020 & i<2025 & x_tmp[r,i,t]!=0)
x_tmp[r,2025,t] <- setYears(x_tmp[r,i,t])*(1+(2025-i)*0.05)
if (i>2025 & i<2030 & x_tmp[r,i,t]!=0)
x_tmp[r,2030,t] <- setYears(x_tmp[r,i,t])*(1+(2030-i)*0.05)
if (i>2030 & i<2035 & x_tmp[r,i,t]!=0)
x_tmp[r,2035,t] <- setYears(x_tmp[r,i,t])*(1+(2035-i)*0.05)
if (i>2035 & i<2040 & x_tmp[r,i,t]!=0)
x_tmp[r,2040,t] <- setYears(x_tmp[r,i,t])*(1+(2040-i)*0.05)
}
}
}
# Selecting relevant model years
target_years <- c(2020,2025,2030,2035,2040)
x_tmp <- x_tmp[,target_years,] # only take model years
# Creating new magpie object containing only capacity targets
x_new <- new.magpie(getRegions(x_tmp),target_years,techs)
x_new[is.na(x_new)] <- 0 # replacing NAs with zero
# Capacity factors in REMIND. From : calcOutput("Capacityfactor"...)
cf_biomass <- 0.75
cf_geothermal <- 0.8
cf_hydro <- cf_realworld
# Initialising all capacities for all model years to 2015 capacities.
# except when Base year is mentioned and except for Hydro which will have generation targets
for (t in target_years){
for (r in getRegions(x_tmp)){
if (x_tmp[r,t,"AC-Absolute.Base year"]!=0)
x_new[r,t,techs] <- setYears(hist_cap[r,x_tmp[r,t,"AC-Absolute.Base year"],techs])
else
x_new[r,t,techs] <- setYears(hist_cap[r,2015,techs])
}
}
# special case for hydro.
x_new[,,"Hydro"] <- setYears(hist_gen[getRegions(x_new),2015,"Hydropower"])
x_current <- x_new
x_new_abs <- x_new
x_new_abs[] <- 0
x_new_prod_bg <- x_new
x_new_prod_bg[] <- 0
x_new_tic <- x_new
x_new_tic[] <- 0
x_new_prod_sswh <- x_new
x_new_prod_sswh[] <- 0
# Converting additional capacity targets to absolute capacity targets.
x_new_abs[,,"Biomass"] <- x_current[,,"Biomass"] + x_tmp[,,"AC-Absolute.Biomass",drop=TRUE]
x_new_abs[,,c("Wind","SolarPV","SolarCSP")] <- x_current[,,c("Wind","SolarPV","SolarCSP")] +
x_tmp[,,c("AC-Absolute.Wind","AC-Absolute.SolarPV","AC-Absolute.SolarCSP"),drop=TRUE]
# For hydro converting to additional generation targets
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 geothermal and biomass
# pmax takes the higher value from existing capacity and new capacity derived (from production)
x_new_prod_bg[,,"Biomass"] <- pmax(x_current[,,"Biomass"],x_tmp[,,c("Production-Absolute.Biomass")]/(8760*cf_biomass))
x_new_prod_bg[,,"Geothermal"] <- pmax(x_current[,,"Geothermal"],x_tmp[,,c("Production-Absolute.Geothermal")]/(8760*cf_geothermal))
# Total installed capacity Targets for all technologies except Hydro
x_new_tic[,,c("Wind","SolarPV","SolarCSP","Biomass")] <- pmax(x_current[,,c("Wind","SolarPV","SolarCSP","Biomass")],x_tmp[,,techs][,,c("Wind","SolarPV","SolarCSP","Biomass")][,,"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),,]*8760))
# Converting Production targets to capacity targets for solar, 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)
data_solar <- calcOutput("Solar")
names_solarPV <- paste0("SolarPV.",getNames(collapseNames((mselect(data_solar,type=c("nur","maxprod"),technology="spv")),collapsedim = 2)))
names_solarCSP <- paste0("SolarCSP.",getNames(collapseNames((mselect(data_solar,type=c("nur","maxprod"),technology="csp")),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_solarPV,names_solarCSP,names_hydro,names_wind))
data_combined[,,"Hydro"] <- data_hydro
data_combined[,,"Wind"] <- data_wind_sorted
# hard-coding values for countries with solar pv and csp generation data as country level maxprod
# and nur value for solar are yet not available.
data_combined[c("KOR","MKD"),,"SolarPV"][,,"maxprod"] <- as.vector(data_solar[c("JPN","EUR"),,"maxprod"][,,"spv"])
data_combined[c("KOR","MKD"),,"SolarPV"][,,"nur"] <- as.vector(data_solar[c("JPN","EUR"),,"nur"][,,"spv"])
data_combined[c("KOR","MKD"),,"SolarCSP"][,,"maxprod"] <- as.vector(data_solar[c("JPN","EUR"),,"maxprod"][,,"csp"])
data_combined[c("KOR","MKD"),,"SolarCSP"][,,"nur"] <- as.vector(data_solar[c("JPN","EUR"),,"nur"][,,"csp"])
data_combined <- data_combined[getRegions(x_tmp),,]# only interested in limited dataset
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
}
# Production/Generation targets are converted into capacity targets by allocating
# production to certain capacity factors based on maxprod.
# final <- numeric(length(getRegions(x_tmp)))
final <- numeric(length(getRegions(x)))
names(final) <- getRegions(x)
tmp_target <- numeric(10)
x_tmp[,,"Production-Absolute.Hydro"] <- pmax(x_tmp[,,"Production-Absolute.Hydro"],x_new_tic[,,"Hydro"],x_new_abs[,,"Hydro"])
# 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("SolarPV","SolarCSP","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)){
#Only start loop if Production targets are non-zero and if the maxprod for that
#country can absorb the targets set.
name <- paste0(t,".maxprod")
name2 <- paste0("Production-Absolute.",t)
if (!isZero(x_tmp[,,"Production-Absolute"][,,t])[r,y,] &
dimSums(data_combined[r,,name]) > max(x_tmp[r,,name2])){
# extracting the first non-zero location of maxprod
# name <- paste0(t,".maxprod")
loc <- min(which(!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])
} else {tmp_target[7] <- tmp_target[6] - data_sel[r,,"maxprod"][,,loc+5]
if(data_sel[r,,"maxprod"][loc+6] > tmp_target[7]){
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] + data_in_use[r,,][,,loc+5]
+ tmp_target[6]/data_sel[r,,"nur"][,,loc+6])
} else {tmp_target[8] <- tmp_target[7] - data_sel[r,,"maxprod"][,,loc+6]
if(data_sel[r,,"maxprod"][loc+7] > tmp_target[8]){
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] + data_in_use[r,,][,,loc+5]
+ data_in_use[r,,][,,loc+6]+ tmp_target[7]/data_sel[r,,"nur"][,,loc+7])
}
}
}
}
}
}
}
}
}
}
x_new_prod_sswh[,y,t] <-final
}
}
x_new_gen <- mbind(x_new_prod_sswh[,,c("SolarPV","SolarCSP","Wind","Hydro")],x_new_prod_bg[,,c("Biomass","Geothermal")])
#x_new[,,c("SolarPV","SolarCSP","Wind")] <- pmax(x_new_copy[,,c("SolarPV","SolarCSP","Wind")],
#x_new[,,c("SolarPV","SolarCSP","Wind")])
x_new[,,c("SolarPV","SolarCSP","Wind","Hydro","Biomass","Geothermal")] <- pmax(x_new_abs[,,c("SolarPV","SolarCSP","Wind","Hydro","Biomass","Geothermal")],
x_new_gen[,,c("SolarPV","SolarCSP","Wind","Hydro","Biomass","Geothermal")],
x_new_tic[,,c("SolarPV","SolarCSP","Wind","Hydro","Biomass","Geothermal")])
# for hydro revert to capacity targets from previous for loop
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 regions){
for (t in techs){
for(i in c(2020,2025,2030,2035)){
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])
}
}
}
}
# countries not in the REN21 capacity targets database
rest_regions <- getRegions(hist_cap)[!(getRegions(hist_cap) %in% getRegions(x_new))]
x_other <- new.magpie(rest_regions,target_years,techs)
# for all other countries not in database, targets for all model years are historical capacities
x_other[,,c("Wind","SolarPV","SolarCSP","Biomass","Geothermal")] <- setYears(hist_cap[rest_regions,2015,c("Wind","SolarPV","SolarCSP","Biomass","Geothermal")])
x_other[,,"Hydro"] <- setYears(hist_cap[rest_regions,2015,"Hydro"])*setYears(cf_hydro[rest_regions,,])
x_final <- magpiesort(mbind(x_new,x_other))
x <- x_final
x[is.na(x)] <- 0
getNames(x) <- c("spv","csp","wind","hydro","biochp","geohdr") #renaming to REMIND convention
} else if (subtype == "investmentCosts") {
# save data of specific countries
x_country <- x[c("China","India","United States"),,]
# translate country names into ISO-codes
getRegions(x_country) <- toolCountry2isocode(getRegions(x_country))
# delete those countries from x
x <- x[c("China","India","United States"),,,invert=TRUE]
# split up regional data into countries
map <- read.csv("regionmappingREN2Country.csv",sep=";")
x <- toolAggregate(x,map,weight=NULL)
# overwrite country data
x[getRegions(x_country),,] <- x_country
}
return (x)
}
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Defines functions convertREN21
#' Policy targets for REN21
#' @description This code aggregates and homogenises different types of renewable energy targets into total installed capacity targets (in GW).
#' @details Policy database accessible in "inputdata/sources/REN21/README"
#' @param x MAgPIE object to be converted
#' @param subtype Only "Capacity" asof now
#' @return Magpie object with Total Installed Capacity targets. The target years differ depending upon the database.
#' @importFrom R.utils isZero
#' @author Aman Malik
convertREN21 <- function(x,subtype){
if(subtype == "Capacity"){
x <- readSource("REN21",subtype="Capacity",convert = F)
x <- magpiesort(x) # sorting years chronologically and region names alphabetically
x[is.na(x)] <- 0 # Converting all NAs to zero
getRegions(x) <- toolCountry2isocode(getRegions(x))# Country names to ISO3 code
# 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 2015
cf_realworld <- hist_gen[,2015,"Hydropower"]/(8760*hist_cap[,2015,"Hydropower"])
cf_realworld[is.na(cf_realworld)] <- 0
getNames(cf_realworld) <- "Hydro"
# renaming variable names from IRENA historical database
getNames(hist_cap)[c(2,7,8,10,11,12)] <- c("Hydro","Wind_ON","Wind_OFF","SolarPV",
"SolarCSP","Biomass")
x_tmp <- x # creating working copy(for modifications) and preserving original
# Combining wind (on and offshore) as no distinction made in REMIND
x_tmp <- add_columns(x_tmp ,addnm = "Wind", dim = 3.2)
x_tmp[,,"Wind"] <- x_tmp[,,"Wind_ON"] + x_tmp[,,"Wind_OFF"]
# Selecting relevant technologies
techs <- c("SolarPV","SolarCSP","Wind","Hydro","Biomass","Geothermal")
# 1. If targets are given in non-model year. e.g., 2022, 2032, then targets are
# extrapolated linearly.
input_yr <- getYears(x,as.integer = TRUE)
input_yr <- input_yr[input_yr%%5!=0]# years not multiple of 5/non-model years
regions <- getRegions(x_tmp)
tech_n <- getNames(x_tmp[,,techs])
for (r in regions){
for (t in tech_n){
for ( i in input_yr){
if (i>2015 & i<2020 & x_tmp[r,i,t]!=0)
# x_tmp[,2020,] <- as.vector(x[,i,])*1.1
x_tmp[r,2020,t] <- setYears(x_tmp[r,i,t])*(1+(2020-i)*0.05)
if (i>2020 & i<2025 & x_tmp[r,i,t]!=0)
x_tmp[r,2025,t] <- setYears(x_tmp[r,i,t])*(1+(2025-i)*0.05)
if (i>2025 & i<2030 & x_tmp[r,i,t]!=0)
x_tmp[r,2030,t] <- setYears(x_tmp[r,i,t])*(1+(2030-i)*0.05)
if (i>2030 & i<2035 & x_tmp[r,i,t]!=0)
x_tmp[r,2035,t] <- setYears(x_tmp[r,i,t])*(1+(2035-i)*0.05)
if (i>2035 & i<2040 & x_tmp[r,i,t]!=0)
x_tmp[r,2040,t] <- setYears(x_tmp[r,i,t])*(1+(2040-i)*0.05)
}
}
}
# Selecting relevant model years
target_years <- c(2020,2025,2030,2035,2040)
x_tmp <- x_tmp[,target_years,] # only take model years
# Creating new magpie object containing only capacity targets
x_new <- new.magpie(getRegions(x_tmp),target_years,techs)
x_new[is.na(x_new)] <- 0 # replacing NAs with zero
# Capacity factors in REMIND. From : calcOutput("Capacityfactor"...)
cf_biomass <- 0.75
cf_geothermal <- 0.8
cf_hydro <- cf_realworld
# Initialising all capacities for all model years to 2015 capacities.
# except when Base year is mentioned and except for Hydro which will have generation targets
for (t in target_years){
for (r in getRegions(x_tmp)){
if (x_tmp[r,t,"AC-Absolute.Base year"]!=0)
x_new[r,t,techs] <- setYears(hist_cap[r,x_tmp[r,t,"AC-Absolute.Base year"],techs])
else
x_new[r,t,techs] <- setYears(hist_cap[r,2015,techs])
}
}
# special case for hydro.
x_new[,,"Hydro"] <- setYears(hist_gen[getRegions(x_new),2015,"Hydropower"])
x_current <- x_new
x_new_abs <- x_new
x_new_abs[] <- 0
x_new_prod_bg <- x_new
x_new_prod_bg[] <- 0
x_new_tic <- x_new
x_new_tic[] <- 0
x_new_prod_sswh <- x_new
x_new_prod_sswh[] <- 0
# Converting additional capacity targets to absolute capacity targets.
x_new_abs[,,"Biomass"] <- x_current[,,"Biomass"] + x_tmp[,,"AC-Absolute.Biomass",drop=TRUE]
x_new_abs[,,c("Wind","SolarPV","SolarCSP")] <- x_current[,,c("Wind","SolarPV","SolarCSP")] +
x_tmp[,,c("AC-Absolute.Wind","AC-Absolute.SolarPV","AC-Absolute.SolarCSP"),drop=TRUE]
# For hydro converting to additional generation targets
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 geothermal and biomass
# pmax takes the higher value from existing capacity and new capacity derived (from production)
x_new_prod_bg[,,"Biomass"] <- pmax(x_current[,,"Biomass"],x_tmp[,,c("Production-Absolute.Biomass")]/(8760*cf_biomass))
x_new_prod_bg[,,"Geothermal"] <- pmax(x_current[,,"Geothermal"],x_tmp[,,c("Production-Absolute.Geothermal")]/(8760*cf_geothermal))
# Total installed capacity Targets for all technologies except Hydro
x_new_tic[,,c("Wind","SolarPV","SolarCSP","Biomass")] <- pmax(x_current[,,c("Wind","SolarPV","SolarCSP","Biomass")],x_tmp[,,techs][,,c("Wind","SolarPV","SolarCSP","Biomass")][,,"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),,]*8760))
# Converting Production targets to capacity targets for solar, 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)
data_solar <- calcOutput("Solar")
names_solarPV <- paste0("SolarPV.",getNames(collapseNames((mselect(data_solar,type=c("nur","maxprod"),technology="spv")),collapsedim = 2)))
names_solarCSP <- paste0("SolarCSP.",getNames(collapseNames((mselect(data_solar,type=c("nur","maxprod"),technology="csp")),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_solarPV,names_solarCSP,names_hydro,names_wind))
data_combined[,,"Hydro"] <- data_hydro
data_combined[,,"Wind"] <- data_wind_sorted
# hard-coding values for countries with solar pv and csp generation data as country level maxprod
# and nur value for solar are yet not available.
data_combined[c("KOR","MKD"),,"SolarPV"][,,"maxprod"] <- as.vector(data_solar[c("JPN","EUR"),,"maxprod"][,,"spv"])
data_combined[c("KOR","MKD"),,"SolarPV"][,,"nur"] <- as.vector(data_solar[c("JPN","EUR"),,"nur"][,,"spv"])
data_combined[c("KOR","MKD"),,"SolarCSP"][,,"maxprod"] <- as.vector(data_solar[c("JPN","EUR"),,"maxprod"][,,"csp"])
data_combined[c("KOR","MKD"),,"SolarCSP"][,,"nur"] <- as.vector(data_solar[c("JPN","EUR"),,"nur"][,,"csp"])
data_combined <- data_combined[getRegions(x_tmp),,]# only interested in limited dataset
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
}
# Production/Generation targets are converted into capacity targets by allocating
# production to certain capacity factors based on maxprod.
# final <- numeric(length(getRegions(x_tmp)))
final <- numeric(length(getRegions(x)))
names(final) <- getRegions(x)
tmp_target <- numeric(10)
x_tmp[,,"Production-Absolute.Hydro"] <- pmax(x_tmp[,,"Production-Absolute.Hydro"],x_new_tic[,,"Hydro"],x_new_abs[,,"Hydro"])
# 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("SolarPV","SolarCSP","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)){
#Only start loop if Production targets are non-zero and if the maxprod for that
#country can absorb the targets set.
name <- paste0(t,".maxprod")
name2 <- paste0("Production-Absolute.",t)
if (!isZero(x_tmp[,,"Production-Absolute"][,,t])[r,y,] &
dimSums(data_combined[r,,name]) > max(x_tmp[r,,name2])){
# extracting the first non-zero location of maxprod
# name <- paste0(t,".maxprod")
loc <- min(which(!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])
} else {tmp_target[7] <- tmp_target[6] - data_sel[r,,"maxprod"][,,loc+5]
if(data_sel[r,,"maxprod"][loc+6] > tmp_target[7]){
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] + data_in_use[r,,][,,loc+5]
+ tmp_target[6]/data_sel[r,,"nur"][,,loc+6])
} else {tmp_target[8] <- tmp_target[7] - data_sel[r,,"maxprod"][,,loc+6]
if(data_sel[r,,"maxprod"][loc+7] > tmp_target[8]){
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] + data_in_use[r,,][,,loc+5]
+ data_in_use[r,,][,,loc+6]+ tmp_target[7]/data_sel[r,,"nur"][,,loc+7])
}
}
}
}
}
}
}
}
}
}
x_new_prod_sswh[,y,t] <-final
}
}
x_new_gen <- mbind(x_new_prod_sswh[,,c("SolarPV","SolarCSP","Wind","Hydro")],x_new_prod_bg[,,c("Biomass","Geothermal")])
#x_new[,,c("SolarPV","SolarCSP","Wind")] <- pmax(x_new_copy[,,c("SolarPV","SolarCSP","Wind")],
#x_new[,,c("SolarPV","SolarCSP","Wind")])
x_new[,,c("SolarPV","SolarCSP","Wind","Hydro","Biomass","Geothermal")] <- pmax(x_new_abs[,,c("SolarPV","SolarCSP","Wind","Hydro","Biomass","Geothermal")],
x_new_gen[,,c("SolarPV","SolarCSP","Wind","Hydro","Biomass","Geothermal")],
x_new_tic[,,c("SolarPV","SolarCSP","Wind","Hydro","Biomass","Geothermal")])
# for hydro revert to capacity targets from previous for loop
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 regions){
for (t in techs){
for(i in c(2020,2025,2030,2035)){
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])
}
}
}
}
# countries not in the REN21 capacity targets database
rest_regions <- getRegions(hist_cap)[!(getRegions(hist_cap) %in% getRegions(x_new))]
x_other <- new.magpie(rest_regions,target_years,techs)
# for all other countries not in database, targets for all model years are historical capacities
x_other[,,c("Wind","SolarPV","SolarCSP","Biomass","Geothermal")] <- setYears(hist_cap[rest_regions,2015,c("Wind","SolarPV","SolarCSP","Biomass","Geothermal")])
x_other[,,"Hydro"] <- setYears(hist_cap[rest_regions,2015,"Hydro"])*setYears(cf_hydro[rest_regions,,])
x_final <- magpiesort(mbind(x_new,x_other))
x <- x_final
x[is.na(x)] <- 0
getNames(x) <- c("spv","csp","wind","hydro","biochp","geohdr") #renaming to REMIND convention
} else if (subtype == "investmentCosts") {
# save data of specific countries
x_country <- x[c("China","India","United States"),,]
# translate country names into ISO-codes
getRegions(x_country) <- toolCountry2isocode(getRegions(x_country))
# delete those countries from x
x <- x[c("China","India","United States"),,,invert=TRUE]
# split up regional data into countries
map <- read.csv("regionmappingREN2Country.csv",sep=";")
x <- toolAggregate(x,map,weight=NULL)
# overwrite country data
x[getRegions(x_country),,] <- x_country
}
return (x)
}
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