R/calcDiffInvestCosts.R
In pik-piam/moinput: Model Operations Input Data Library
Defines functions
calcDiffInvestCosts
#' Aggregated investment cost data for REMIND regions (based on IEA_WEO)
#' @description Disaggregated investment cost data is aggregated and technologies renamed to REMIND names
#' @details REMIND does not have a classification of coal power plants e.g., sub-critical. Therefore, countries are given coal plant
#' costs assuming what type of coal plants are expected to develop there. For other technologies, certain assumptions are taken
#' to change to REMIND convention.
#' @param subtype Investment Costs, I&M Costs, and Efficiency
#' @return Magpie object with aggregated but diffrentiated investment costs for some technologies.
#' @author Aman Malik
#' @importFrom magclass new.magpie
calcDiffInvestCosts <- function(subtype){
if (subtype=="Invest_Costs"){
x <- readSource("IEA_WEO",subtype="Invest_Costs")
x_REN21 <- readSource("REN21",subtype="investmentCosts")
x_IEA_PVPS <- readSource("IEA_PVPS", subtype="CAPEX")
# x <- readSource("IEA_WEO")# reading data end of convert function
x[,,] <- as.numeric(x[,,]) # convertng data values into numeric
# Various mapping files used to get needed mappings, for e.g., South Asia
countries <- toolGetMapping("regionmappingREMIND.csv",where = "mappingfolder",type = "regional")
countries2 <- toolGetMapping("regionmappingMAgPIE.csv",where = "mappingfolder",type = "regional")
countries3 <- toolGetMapping("regionmappingSSP.csv",where = "mappingfolder",type = "regional")
# For countries in Africa and South Asia (except India) use subcritical plant investment costs as coal plant costs
x[c(countries$CountryCode[countries$RegionCode=="AFR"],
countries2$CountryCode[countries2$RegionCode=="SAS"][-4]),,c("Coal.Steam Coal - SUPERCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
# For countries in LAM, IND, FSU and MEA use supercritical plant investment costs as standard coal plant costs
x[c(countries$CountryCode[countries$RegionCode=="LAM"],"IND",
countries2$CountryCode[countries2$RegionCode=="FSU"],
countries$CountryCode[countries$RegionCode=="MEA"]),,c("Coal.Steam Coal - SUBCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
# For countries in OECD and CHN use ultrasupercritical investment costs ultrasupercritical plants for "Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
x[c("CHN","KOR","MAC","HKG", countries3$CountryCode[countries3$RegionCode=="OECD"]),,c("Coal.Steam Coal - SUBCRITICAL",
"Coal.Steam Coal - SUPERCRITICAL")] <- 0
# For remaining countries not covered above, use subcritical plant investment costs
years <- getYears(x)
for (y in years){
x[getRegions(x)[x[,y,"Coal.Steam Coal - SUBCRITICAL"]!=0 &
x[,y,"Coal.Steam Coal - SUPERCRITICAL"]!=0 &
x[,y,"Coal.Steam Coal - ULTRASUPERCRITICAL"]!=0],y,
c("Coal.Steam Coal - SUPERCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
}
tech_mapping <- toolGetMapping("comparison.csv",where = "mappingfolder",type = "sectoral")
techs <- unique(tech_mapping$tech[!is.na(tech_mapping$tech)]) # remove all names with no corresponding REMIND name
# list of original technologies with a REMIND tech mapping
techs2 <- unique(tech_mapping$IEA[!is.na(tech_mapping$tech)])
# create new magpie object with names of corresponding REMIND technologies
x_new <- new.magpie(getRegions(x),names = techs,years = getYears(x))
x_new[is.na(x_new)] <- 0
# for "pc" add all types of coal plants so each country has one value of "pc"
x_new[,,"pc"] <- x[,,"Coal.Steam Coal - SUBCRITICAL"] + x[,,"Coal.Steam Coal - SUPERCRITICAL"] +
x[,,"Coal.Steam Coal - ULTRASUPERCRITICAL"]
# for "spv" take 1/4 of costs from small-scale and 3/4 costs from large scale (utility scale)ildings
x_new[,,"spv"] <- 0.75*x[,,"Renewables.Solar photovoltaics - Large scale"] + 0.25*x[,,"Renewables.Solar photovoltaics - Buildings"]
# same for hydro - removed when hydro values are fed from "private" data source
#x_new[,,"hydro"] <- 0.75*x[,,"Renewables.Hydropower - large-scale"] + 0.25*x[,,"Renewables.Hydropower - small-scale"]
x_new[,,"hydro"] <- x[,,"Renewables.Hydropower - large-scale"]
# and Biomass CHP
x_new[,,"biochp"] <- 0.75*x[,,"Renewables.Biomass CHP Medium"] + 0.25*x[,,"Renewables.Biomass CHP Small"]
# for rest of technologies, simply match
x_new[,,techs[-c(1,13,15,16)]] <- as.numeric(x[,,getNames(x,dim=2)[getNames(x,dim=2) %in% techs2[-c(1:3,15,16,18:21)]]])
x_new <- time_interpolate(x_new,c(2025,2035),integrate_interpolated_years = T)
# overwrite investmetn costs vor renewables with data form REN21
x_REN21_wa <- collapseNames(x_REN21[,,"wa"]) # use weighted average
getNames(x_REN21_wa) <- gsub("hydropower","hydro",getNames(x_REN21_wa))
getNames(x_REN21_wa) <- gsub("Solar PV","spv",getNames(x_REN21_wa))
getNames(x_REN21_wa) <- gsub("wind-on","wind",getNames(x_REN21_wa))
x_REN21_wa <- x_REN21_wa[,,c("Biopower","Geothermal Power", "wind-off", "csp"),invert=TRUE]
# use REN21 data for all time steps for hydro
# x_new[,,"hydro"] <- x_REN21_wa[,,"hydro"]
# use REN21 data only for 2015 for spv,csp and wind; all other time steps are 0
x_new[,,c("spv","wind")] <- 0
x_new[,2015,c("spv","wind")] <- x_REN21_wa[,,c("spv","wind")]
x_new["JPN",2015,"wind"] <- x_REN21["JPN",,"wind-on.max"]
x_new["JPN",2015,"spv"] <- 2000 # in USD/KW, source attached in input folder IEA_WEO
# (National_Survey_Report_of_PV_Power_Applications_in_Japan_-_2017.pdf)
x_new["JPN",2015,"hydro"] <- 2400 # in USD/KW, source is the 2016 WEO numbers - they seem more reliable here than the Oceania data of <2000USD/kW
# as Japan is not substantially expanding hydro even at high electricity prices
### Australia ###
# for wind 2015: take average of Europe and USA from REN21 (not from "Oceania" as before)
x_new["AUS",2015,c("wind")] <- setNames(dimSums(x_REN21[c("USA","FRA"), ,c("wind-on.wa")], dim = 1)/2, c("wind"))
# for solar pv 2015: take IRENA number for large-scale solar investment cost by 2016
# (neglect that rooftop is a bit more expensive)
# source: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Jan/IRENA_2017_Power_Costs_2018.pdf
x_new["AUS",2015,"spv"] <- 1400 # in USD/kW
### RP/FS: add PV investment cost for 2020
# based on IEA PVPS data from 2018,
# some regions manually adjusted
# add some manual adjustments to IEA PVPS data for 2020 PV investment cost input data
regmapping <- toolGetMapping("regionmappingH12.csv",where = "mappingfolder",type = "regional")
x_adj <- new.magpie(unique(regmapping$RegionCode), years = "y2020",fill = NA)
# CAZ: The Australian utility-scale market saw a stong jump upwards in 2018.
# It is likely that the reported prices are a result of this jump, as in contrast,
# rooftop is already well-established and has prices around 1.25$/W.
# Accordingly, we expect that by 2020, the utility-scale solar market will be more
# in equilibrium and have prices below the current rooftop prices
x_adj["CAZ",,] <- 1200
# IND: Other sources for Indian utility-scale prices (IRENA, WEO, REN21) are more in the range of ~800$/kW.
# Also, the reports around failed auctions and non-delivery of projects might indicate that
# the stated prices are below cost.
x_adj["IND",,] <- 700
# JPN: We assume that Japan prices will be downward-influenced by the low prices realized everywhere else in Asia.
x_adj["JPN",,] <- 1500
# LAM: IRENA states utility-scale prices in the range of ~1400-1500$/kW for LAM.
# Recent auctions in individual countries indicate lower prices, but it is unlikely that
# all the countries will immediately achieve the lowest prices realized in some auctions
x_adj["LAM",,] <- 950
# MEA: IRENA states utility-scale prices in the range of ~1250$/kW for MEA
# Recent auctions in individual countries indicate lower prices,
# but it is unlikely that all the countries will immediately achieve
# the lowest prices realized in some auctions
x_adj["MEA",,] <- 850
# REF: Very different costs in IRENA (2300) and REN21 (1300) -
# but unlikely to have higher capital costs than severly space-constrained Japan
x_adj["REF",,] <- 1400
# SSA: IRENA states prices of ~1600$/W, but very likely that prices are currently decreasing through learning
# from the low prices in North African countries.
x_adj["SSA",,] <- 1300
# disaggregate adjustments to iso level
x_adj_iso <- toolAggregate(x_adj, regmapping)
# regions which have not been manually adjusted -> replace by original IEA PVPS data
x_adj_iso[which(is.na(x_adj_iso))] <- x_IEA_PVPS[which(is.na(x_adj_iso))]
# add new 2020 values PV
x_new[,"y2020","spv"] <- x_adj_iso
return(list(x = x_new,weight= x_new ,unit="USD$/KW 2015",description="Investment costs data" ))
}
else if (subtype=="O&M_Costs"){
x <- readSource("IEA_WEO",subtype="Invest_Costs")
} else if (subtype=="Efficiency"){
x <- readSource("IEA_WEO",subtype="Efficiency")
x[,,] <- as.numeric(x[,,]) # convertng data values into numeric
# Various mapping files used to get needed mappings, for e.g., South Asia
countries <- toolGetMapping("regionmappingREMIND.csv",where = "mappingfolder",type = "regional")
countries2 <- toolGetMapping("regionmappingMAgPIE.csv",where = "mappingfolder",type = "regional")
countries3 <- toolGetMapping("regionmappingSSP.csv",where = "mappingfolder",type = "regional")
# For countries in Africa and South Asia (except India) use subcritical plant investment costs as coal plant costs
x[c(countries$CountryCode[countries$RegionCode=="AFR"],
countries2$CountryCode[countries2$RegionCode=="SAS"][-4]),,c("Coal.Steam Coal - SUPERCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
# For countries in LAM, IND, FSU and MEA use supercritical plant investment costs as standard coal plant costs
x[c(countries$CountryCode[countries$RegionCode=="LAM"],"IND",
countries2$CountryCode[countries2$RegionCode=="FSU"],
countries$CountryCode[countries$RegionCode=="MEA"]),,c("Coal.Steam Coal - SUBCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
# For countries in OECD and CHN use ultrasupercritical investment costs ultrasupercritical plants for "Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
x[c("CHN","KOR","MAC","HKG", countries3$CountryCode[countries3$RegionCode=="OECD"]),,c("Coal.Steam Coal - SUBCRITICAL",
"Coal.Steam Coal - SUPERCRITICAL")] <- 0
# For remaining countries not covered above, use subcritical plant investment costs
years <- getYears(x)
for (y in years){
x[getRegions(x)[x[,y,"Coal.Steam Coal - SUBCRITICAL"]!=0 &
x[,y,"Coal.Steam Coal - SUPERCRITICAL"]!=0 &
x[,y,"Coal.Steam Coal - ULTRASUPERCRITICAL"]!=0],y,
c("Coal.Steam Coal - SUPERCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
}
tech_mapping <- toolGetMapping("comparison.csv",where = "mappingfolder",type = "sectoral")
techs <- unique(tech_mapping$tech[!is.na(tech_mapping$tech)]) # remove all names with no corresponding REMIND name
# list of original technologies with a REMIND tech mapping
techs2 <- unique(tech_mapping$IEA[!is.na(tech_mapping$tech)])
# create new magpie object with names of corresponding REMIND technologies
x_new <- new.magpie(getRegions(x),names = techs,years = getYears(x))
x_new[is.na(x_new)] <- 0
# for "pc" add all types of coal plants so each country has one value of "pc"
x_new[,,"pc"] <- x[,,"Coal.Steam Coal - SUBCRITICAL"] + x[,,"Coal.Steam Coal - SUPERCRITICAL"] +
x[,,"Coal.Steam Coal - ULTRASUPERCRITICAL"]
# for rest of technologies, simply match
x_new[,,techs[-c(1,13,15,16)]] <- as.numeric(x[,,getNames(x,dim=2)[getNames(x,dim=2) %in% techs2[-c(1:3,15,16,18:21)]]])
x_new <- time_interpolate(x_new,c(2025,2035),integrate_interpolated_years = T)
return(list(x = x_new,weight= x_new ,unit="NA",description="Efficiency data" ))
}
if (subtype=="Invest_Costs"|subtype=="O&M_Costs"){
return(list(x = x_new,weight= x_new ,unit="USD$/KW 2015",description="Investment costs data" ))
}
}
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Defines functions calcDiffInvestCosts
#' Aggregated investment cost data for REMIND regions (based on IEA_WEO)
#' @description Disaggregated investment cost data is aggregated and technologies renamed to REMIND names
#' @details REMIND does not have a classification of coal power plants e.g., sub-critical. Therefore, countries are given coal plant
#' costs assuming what type of coal plants are expected to develop there. For other technologies, certain assumptions are taken
#' to change to REMIND convention.
#' @param subtype Investment Costs, I&M Costs, and Efficiency
#' @return Magpie object with aggregated but diffrentiated investment costs for some technologies.
#' @author Aman Malik
#' @importFrom magclass new.magpie
calcDiffInvestCosts <- function(subtype){
if (subtype=="Invest_Costs"){
x <- readSource("IEA_WEO",subtype="Invest_Costs")
x_REN21 <- readSource("REN21",subtype="investmentCosts")
x_IEA_PVPS <- readSource("IEA_PVPS", subtype="CAPEX")
# x <- readSource("IEA_WEO")# reading data end of convert function
x[,,] <- as.numeric(x[,,]) # convertng data values into numeric
# Various mapping files used to get needed mappings, for e.g., South Asia
countries <- toolGetMapping("regionmappingREMIND.csv",where = "mappingfolder",type = "regional")
countries2 <- toolGetMapping("regionmappingMAgPIE.csv",where = "mappingfolder",type = "regional")
countries3 <- toolGetMapping("regionmappingSSP.csv",where = "mappingfolder",type = "regional")
# For countries in Africa and South Asia (except India) use subcritical plant investment costs as coal plant costs
x[c(countries$CountryCode[countries$RegionCode=="AFR"],
countries2$CountryCode[countries2$RegionCode=="SAS"][-4]),,c("Coal.Steam Coal - SUPERCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
# For countries in LAM, IND, FSU and MEA use supercritical plant investment costs as standard coal plant costs
x[c(countries$CountryCode[countries$RegionCode=="LAM"],"IND",
countries2$CountryCode[countries2$RegionCode=="FSU"],
countries$CountryCode[countries$RegionCode=="MEA"]),,c("Coal.Steam Coal - SUBCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
# For countries in OECD and CHN use ultrasupercritical investment costs ultrasupercritical plants for "Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
x[c("CHN","KOR","MAC","HKG", countries3$CountryCode[countries3$RegionCode=="OECD"]),,c("Coal.Steam Coal - SUBCRITICAL",
"Coal.Steam Coal - SUPERCRITICAL")] <- 0
# For remaining countries not covered above, use subcritical plant investment costs
years <- getYears(x)
for (y in years){
x[getRegions(x)[x[,y,"Coal.Steam Coal - SUBCRITICAL"]!=0 &
x[,y,"Coal.Steam Coal - SUPERCRITICAL"]!=0 &
x[,y,"Coal.Steam Coal - ULTRASUPERCRITICAL"]!=0],y,
c("Coal.Steam Coal - SUPERCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
}
tech_mapping <- toolGetMapping("comparison.csv",where = "mappingfolder",type = "sectoral")
techs <- unique(tech_mapping$tech[!is.na(tech_mapping$tech)]) # remove all names with no corresponding REMIND name
# list of original technologies with a REMIND tech mapping
techs2 <- unique(tech_mapping$IEA[!is.na(tech_mapping$tech)])
# create new magpie object with names of corresponding REMIND technologies
x_new <- new.magpie(getRegions(x),names = techs,years = getYears(x))
x_new[is.na(x_new)] <- 0
# for "pc" add all types of coal plants so each country has one value of "pc"
x_new[,,"pc"] <- x[,,"Coal.Steam Coal - SUBCRITICAL"] + x[,,"Coal.Steam Coal - SUPERCRITICAL"] +
x[,,"Coal.Steam Coal - ULTRASUPERCRITICAL"]
# for "spv" take 1/4 of costs from small-scale and 3/4 costs from large scale (utility scale)ildings
x_new[,,"spv"] <- 0.75*x[,,"Renewables.Solar photovoltaics - Large scale"] + 0.25*x[,,"Renewables.Solar photovoltaics - Buildings"]
# same for hydro - removed when hydro values are fed from "private" data source
#x_new[,,"hydro"] <- 0.75*x[,,"Renewables.Hydropower - large-scale"] + 0.25*x[,,"Renewables.Hydropower - small-scale"]
x_new[,,"hydro"] <- x[,,"Renewables.Hydropower - large-scale"]
# and Biomass CHP
x_new[,,"biochp"] <- 0.75*x[,,"Renewables.Biomass CHP Medium"] + 0.25*x[,,"Renewables.Biomass CHP Small"]
# for rest of technologies, simply match
x_new[,,techs[-c(1,13,15,16)]] <- as.numeric(x[,,getNames(x,dim=2)[getNames(x,dim=2) %in% techs2[-c(1:3,15,16,18:21)]]])
x_new <- time_interpolate(x_new,c(2025,2035),integrate_interpolated_years = T)
# overwrite investmetn costs vor renewables with data form REN21
x_REN21_wa <- collapseNames(x_REN21[,,"wa"]) # use weighted average
getNames(x_REN21_wa) <- gsub("hydropower","hydro",getNames(x_REN21_wa))
getNames(x_REN21_wa) <- gsub("Solar PV","spv",getNames(x_REN21_wa))
getNames(x_REN21_wa) <- gsub("wind-on","wind",getNames(x_REN21_wa))
x_REN21_wa <- x_REN21_wa[,,c("Biopower","Geothermal Power", "wind-off", "csp"),invert=TRUE]
# use REN21 data for all time steps for hydro
# x_new[,,"hydro"] <- x_REN21_wa[,,"hydro"]
# use REN21 data only for 2015 for spv,csp and wind; all other time steps are 0
x_new[,,c("spv","wind")] <- 0
x_new[,2015,c("spv","wind")] <- x_REN21_wa[,,c("spv","wind")]
x_new["JPN",2015,"wind"] <- x_REN21["JPN",,"wind-on.max"]
x_new["JPN",2015,"spv"] <- 2000 # in USD/KW, source attached in input folder IEA_WEO
# (National_Survey_Report_of_PV_Power_Applications_in_Japan_-_2017.pdf)
x_new["JPN",2015,"hydro"] <- 2400 # in USD/KW, source is the 2016 WEO numbers - they seem more reliable here than the Oceania data of <2000USD/kW
# as Japan is not substantially expanding hydro even at high electricity prices
### Australia ###
# for wind 2015: take average of Europe and USA from REN21 (not from "Oceania" as before)
x_new["AUS",2015,c("wind")] <- setNames(dimSums(x_REN21[c("USA","FRA"), ,c("wind-on.wa")], dim = 1)/2, c("wind"))
# for solar pv 2015: take IRENA number for large-scale solar investment cost by 2016
# (neglect that rooftop is a bit more expensive)
# source: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Jan/IRENA_2017_Power_Costs_2018.pdf
x_new["AUS",2015,"spv"] <- 1400 # in USD/kW
### RP/FS: add PV investment cost for 2020
# based on IEA PVPS data from 2018,
# some regions manually adjusted
# add some manual adjustments to IEA PVPS data for 2020 PV investment cost input data
regmapping <- toolGetMapping("regionmappingH12.csv",where = "mappingfolder",type = "regional")
x_adj <- new.magpie(unique(regmapping$RegionCode), years = "y2020",fill = NA)
# CAZ: The Australian utility-scale market saw a stong jump upwards in 2018.
# It is likely that the reported prices are a result of this jump, as in contrast,
# rooftop is already well-established and has prices around 1.25$/W.
# Accordingly, we expect that by 2020, the utility-scale solar market will be more
# in equilibrium and have prices below the current rooftop prices
x_adj["CAZ",,] <- 1200
# IND: Other sources for Indian utility-scale prices (IRENA, WEO, REN21) are more in the range of ~800$/kW.
# Also, the reports around failed auctions and non-delivery of projects might indicate that
# the stated prices are below cost.
x_adj["IND",,] <- 700
# JPN: We assume that Japan prices will be downward-influenced by the low prices realized everywhere else in Asia.
x_adj["JPN",,] <- 1500
# LAM: IRENA states utility-scale prices in the range of ~1400-1500$/kW for LAM.
# Recent auctions in individual countries indicate lower prices, but it is unlikely that
# all the countries will immediately achieve the lowest prices realized in some auctions
x_adj["LAM",,] <- 950
# MEA: IRENA states utility-scale prices in the range of ~1250$/kW for MEA
# Recent auctions in individual countries indicate lower prices,
# but it is unlikely that all the countries will immediately achieve
# the lowest prices realized in some auctions
x_adj["MEA",,] <- 850
# REF: Very different costs in IRENA (2300) and REN21 (1300) -
# but unlikely to have higher capital costs than severly space-constrained Japan
x_adj["REF",,] <- 1400
# SSA: IRENA states prices of ~1600$/W, but very likely that prices are currently decreasing through learning
# from the low prices in North African countries.
x_adj["SSA",,] <- 1300
# disaggregate adjustments to iso level
x_adj_iso <- toolAggregate(x_adj, regmapping)
# regions which have not been manually adjusted -> replace by original IEA PVPS data
x_adj_iso[which(is.na(x_adj_iso))] <- x_IEA_PVPS[which(is.na(x_adj_iso))]
# add new 2020 values PV
x_new[,"y2020","spv"] <- x_adj_iso
return(list(x = x_new,weight= x_new ,unit="USD$/KW 2015",description="Investment costs data" ))
}
else if (subtype=="O&M_Costs"){
x <- readSource("IEA_WEO",subtype="Invest_Costs")
} else if (subtype=="Efficiency"){
x <- readSource("IEA_WEO",subtype="Efficiency")
x[,,] <- as.numeric(x[,,]) # convertng data values into numeric
# Various mapping files used to get needed mappings, for e.g., South Asia
countries <- toolGetMapping("regionmappingREMIND.csv",where = "mappingfolder",type = "regional")
countries2 <- toolGetMapping("regionmappingMAgPIE.csv",where = "mappingfolder",type = "regional")
countries3 <- toolGetMapping("regionmappingSSP.csv",where = "mappingfolder",type = "regional")
# For countries in Africa and South Asia (except India) use subcritical plant investment costs as coal plant costs
x[c(countries$CountryCode[countries$RegionCode=="AFR"],
countries2$CountryCode[countries2$RegionCode=="SAS"][-4]),,c("Coal.Steam Coal - SUPERCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
# For countries in LAM, IND, FSU and MEA use supercritical plant investment costs as standard coal plant costs
x[c(countries$CountryCode[countries$RegionCode=="LAM"],"IND",
countries2$CountryCode[countries2$RegionCode=="FSU"],
countries$CountryCode[countries$RegionCode=="MEA"]),,c("Coal.Steam Coal - SUBCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
# For countries in OECD and CHN use ultrasupercritical investment costs ultrasupercritical plants for "Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
x[c("CHN","KOR","MAC","HKG", countries3$CountryCode[countries3$RegionCode=="OECD"]),,c("Coal.Steam Coal - SUBCRITICAL",
"Coal.Steam Coal - SUPERCRITICAL")] <- 0
# For remaining countries not covered above, use subcritical plant investment costs
years <- getYears(x)
for (y in years){
x[getRegions(x)[x[,y,"Coal.Steam Coal - SUBCRITICAL"]!=0 &
x[,y,"Coal.Steam Coal - SUPERCRITICAL"]!=0 &
x[,y,"Coal.Steam Coal - ULTRASUPERCRITICAL"]!=0],y,
c("Coal.Steam Coal - SUPERCRITICAL",
"Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
}
tech_mapping <- toolGetMapping("comparison.csv",where = "mappingfolder",type = "sectoral")
techs <- unique(tech_mapping$tech[!is.na(tech_mapping$tech)]) # remove all names with no corresponding REMIND name
# list of original technologies with a REMIND tech mapping
techs2 <- unique(tech_mapping$IEA[!is.na(tech_mapping$tech)])
# create new magpie object with names of corresponding REMIND technologies
x_new <- new.magpie(getRegions(x),names = techs,years = getYears(x))
x_new[is.na(x_new)] <- 0
# for "pc" add all types of coal plants so each country has one value of "pc"
x_new[,,"pc"] <- x[,,"Coal.Steam Coal - SUBCRITICAL"] + x[,,"Coal.Steam Coal - SUPERCRITICAL"] +
x[,,"Coal.Steam Coal - ULTRASUPERCRITICAL"]
# for rest of technologies, simply match
x_new[,,techs[-c(1,13,15,16)]] <- as.numeric(x[,,getNames(x,dim=2)[getNames(x,dim=2) %in% techs2[-c(1:3,15,16,18:21)]]])
x_new <- time_interpolate(x_new,c(2025,2035),integrate_interpolated_years = T)
return(list(x = x_new,weight= x_new ,unit="NA",description="Efficiency data" ))
}
if (subtype=="Invest_Costs"|subtype=="O&M_Costs"){
return(list(x = x_new,weight= x_new ,unit="USD$/KW 2015",description="Investment costs data" ))
}
}
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