R/convertIEA_WEO.R
In pik-piam/moinput: Model Operations Input Data Library
Defines functions
convertIEA_WEO
#' Converts IEA World Energy Outlook data
#'
#' @param x MAgPIE object to be converted
#' @param subtype data subtype. Either "Capacity", "Generation", "Emissions", "Investment Costs", "O&M Costs" or "Efficiency"
#' @return magpie object of the WEO data on generation (TWh), capacities (GW), emissions (Mt CO2) or disaggregated investment cost as magpie object
#' @author Renato Rodrigues and Aman Malik
#' @seealso \code{\link{readSource}}
#' @examples
#'
#' \dontrun{ a <- convertWEO(x,subtype="Capacity")
#' }
#'
#' @importFrom tidyr gather
#' @importFrom readxl read_excel
convertIEA_WEO <- function(x,subtype){
if ((subtype=="Invest_Costs") || (subtype=="O&M_Costs") || (subtype=="Efficiency")) {
# x <- readSource("IEA_WEO",subtype = "Invest_Costs",convert = FALSE)
# x[,,] <- as.numeric(x[,,])
# # mapping of all countries and their respective regions
all_c <- toolGetMapping("regionmappingH12.csv",where = "mappingfolder",type = "regional")
# seperating hydro case
x_hydro <- x[unique(all_c$RegionCode),,"Hydro_2.hydro"]
x <- x[unique(all_c$RegionCode),,"Hydro_2.hydro",invert=T]
eu_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="EUR"],years = getYears(x),names=getNames(x))
neu_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="NEU"],years = getYears(x),names=getNames(x))
africa_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="SSA"],years = getYears(x),names = getNames(x))
mea_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="MEA"],years = getYears(x),names = getNames(x))
asia_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="OAS"],years = getYears(x),names = getNames(x))
caz_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="CAZ"],years = getYears(x),names = getNames(x))
sc_america_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="LAM"],years = getYears(x),names = getNames(x))
# All countries from the database which have zero investment costs get some numbers
# So, for e.g., Brazil and Middle East have zero for all the CCS related investment costs. They get their value from the
# average of all other regions/countries for the same technology.
x[c("Brazil","Middle East"),,"CCS",pmatch=TRUE] <- dimSums(x[c("Brazil","Middle East"),,invert=TRUE][,,"CCS"],na.rm = TRUE,dim=1)/
nregions(x[c("Brazil","Middle East"),,invert=T])
x["Brazil",,"Renewables.Geothermal"] <- dimSums(x["Brazil",,,invert=TRUE][,,"Renewables.Geothermal"],dim = 1)/
nregions(x["Brazil",,invert=T])
x["Brazil",,"Renewables.Marine"] <- dimSums(x["Brazil",,,invert=TRUE][,,"Renewables.Marine"],dim=1)/
nregions(x["Brazil",,invert=T])
x[c("Japan","Russia"),,"Renewables.Concentrating solar power"] <- dimSums(x[c("Japan","Russia"),,,invert=TRUE][,,"Renewables.Concentrating solar power"],dim=1)/
nregions(x[c("Japan","Russia"),,invert=T])
for (n in getRegions(eu_costs)){# all EU28 countries get "Europe" investment costs
eu_costs[n,,] <- as.numeric(x["Europe",,])
}
for (n in getRegions(neu_costs)){# all non-EU european countries get average of "Europe" and "RUS" investment costs
neu_costs[n,,] <- 0.5*as.numeric(x["Europe",,])+0.5*as.numeric(x["Russia",,])
}
for (n in getRegions(caz_costs)){# all CAZ countries get "OECD" average investment costs
caz_costs[n,,] <- 0.34*as.numeric(x["Europe",,])+0.33*as.numeric(x["United States",,])+0.33*as.numeric(x["Japan",,])
}
for (n in getRegions(africa_costs)){# all african countries get "Africa" investment costs
africa_costs[n,,]<- as.numeric(x["Africa",,])
}
for (n in getRegions(mea_costs)){# all OAS countries get average of Indian and Japanese investment costs
mea_costs[n,,] <- as.numeric(x["Middle East",,])
}
for (n in getRegions(asia_costs)){# all OAS countries get average of Indian and Japanese investment costs
asia_costs[n,,] <- 0.5*as.numeric(x["India",,])+0.5*as.numeric(x["Japan",,])
}
for (n in getRegions(sc_america_costs)){
sc_america_costs[n,,] <- as.numeric(x["Brazil",,])# all S. and C. American countries get "Brazilian" investment costs
}
# Extracting proper countries from the region/country list in input data
x_countries <- x[c("Europe","Middle East","Africa"),,,invert=TRUE]
# converting to ISO country names
getRegions(x_countries) <- toolCountry2isocode(getRegions(x_countries))
# one magpie object with all names
x_total <- mbind(eu_costs,neu_costs,caz_costs,africa_costs,mea_costs,asia_costs,
sc_america_costs,x_countries[c("BRA"),,invert=TRUE])
# remaining countries
x_total <- toolCountryFill(x_total,"HKG"="CHN","PRI"="USA","MAC"="CHN")
x_hydro <- toolAggregate(x_hydro,rel = all_c,from = "RegionCode",to = "CountryCode")
# setting 2040 values as 2030 values
x_hydro[,"y2040",] <- setYears(x_hydro[,2030,])
x_total[,,"Renewables.Hydropower - large-scale"] <- x_hydro[,,"Hydro_2.hydro"]
x <- x_total
x[is.na(x)] <- 0
} else if ((subtype=="Capacity") || (subtype=="Generation") || (subtype=="Emissions")) {
# WEO_regions -> c("World", "OECDNAM", "US", "CSAM", "BRAZIL", "EUR", "EU", "Africa", "SAFR", "ME", "EURASIA", "RUS", "ASIAPAC", "CHINA", "INDIA", "JPN", "ASEAN", "OECD", "NonOECD"
H12map <- toolGetMapping("regionmappingH12.csv", type = "regional", where = "mappingfolder")
if (subtype=="Capacity"){ # estimate OAS coal
# Approximate Caspian countries (part of the REF)
weight <- calcOutput("IO",subtype="input",aggregate=FALSE)[,2015,"pecoal.seel.pc"]
caspian <- new.magpie(cells_and_regions = "Caspian",names=getNames(x),years = 2015, fill=as.vector(x["EURASIA",2015,getNames(x)]) - as.vector(x["RUS",2015,getNames(x)]))
caspianMap <- data.frame(CountryCode=c("ARM","AZE","GEO","KAZ","KGZ","TJK","TKM","UZB"),RegionCode="Caspian")
caspianCountries <- toolAggregate(caspian, caspianMap, weight[c("ARM","AZE","GEO","KAZ","KGZ","TJK","TKM","UZB"),,])
# Approximate Latin America countries (minus MEX)
LAM <- new.magpie(cells_and_regions = "LAM",names=getNames(x),years = 2015, fill=as.vector(x["CSAM",2015,getNames(x)])-as.vector(x["BRAZIL",2015,getNames(x)]))
LAMCountryCode <- H12map[which(H12map$RegionCode=="LAM"),]$CountryCode
LAMCountries <- toolAggregate(LAM, H12map[which(H12map$CountryCode %in% LAMCountryCode[! LAMCountryCode %in% c("MEX","BRA")]),], weight[LAMCountryCode[! LAMCountryCode %in% c("MEX","BRA")],,])
# Approximate OAS countries
AUS_Coal <- 20 # Australia coal capacity in 2015 (GW)
NZL_Coal <- 0.55 # New zealand coal capacity in 2015 (GW)
OAStotal <- as.vector(x["ASIAPAC",2015,"Coal"]) - as.vector(x["CHINA",2015,"Coal"]) - as.vector(x["INDIA",2015,"Coal"]) - as.vector(x["JPN",2015,"Coal"]) - AUS_Coal - NZL_Coal
OAS <- new.magpie(cells_and_regions = "OAS",names=c("Coal"),years = 2015, fill = OAStotal)
OASCountries <- toolAggregate(OAS, H12map[which(H12map$RegionCode=="OAS"),], weight[H12map[which(H12map$RegionCode=="OAS"),]$CountryCode,,])
}
# get other regions directly from WEO data
otherCap <- x[c("US", "BRAZIL", "RUS", "CHINA", "INDIA", "JPN"),2015,] # countries only
getRegions(otherCap) <- toolCountry2isocode(getRegions(otherCap), mapping = c("US"="USA", "RUS"="RUS", "JPN" = "JPN"))
#merge data
if (subtype=="Capacity"){
reg <- unique(c(getRegions(otherCap),getRegions(OASCountries),getRegions(caspianCountries),getRegions(LAMCountries),"MEX"))
names <- unique(c(getNames(otherCap),getNames(OASCountries),getNames(caspianCountries),getNames(LAMCountries)))
years <- unique(c(getYears(otherCap),getYears(OASCountries),getYears(caspianCountries),getYears(LAMCountries)))
out <- new.magpie(cells_and_regions = reg, names = names, years = years)
out[getRegions(otherCap),,] <- otherCap[getRegions(otherCap),,]
out[getRegions(OASCountries),,"Coal"] <- OASCountries[getRegions(OASCountries),,"Coal"]
out[getRegions(caspianCountries),,"Coal"] <- caspianCountries[getRegions(caspianCountries),,"Coal"]
out[getRegions(LAMCountries),,"Coal"] <- LAMCountries[getRegions(LAMCountries),,"Coal"]
out["MEX",,"Coal"] <- 5.378 # Mexico coal capacity in 2015 (GW)
} else {
out <- otherCap
}
# fill countries with no data
out <- toolCountryFill(out,fill=0,verbosity=0)
# replace NAs with 0
out[is.na(out)] <- 0
x <- out
}
return(x)
}
pik-piam/moinput documentation built on June 9, 2020, 12:23 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions convertIEA_WEO
#' Converts IEA World Energy Outlook data
#'
#' @param x MAgPIE object to be converted
#' @param subtype data subtype. Either "Capacity", "Generation", "Emissions", "Investment Costs", "O&M Costs" or "Efficiency"
#' @return magpie object of the WEO data on generation (TWh), capacities (GW), emissions (Mt CO2) or disaggregated investment cost as magpie object
#' @author Renato Rodrigues and Aman Malik
#' @seealso \code{\link{readSource}}
#' @examples
#'
#' \dontrun{ a <- convertWEO(x,subtype="Capacity")
#' }
#'
#' @importFrom tidyr gather
#' @importFrom readxl read_excel
convertIEA_WEO <- function(x,subtype){
if ((subtype=="Invest_Costs") || (subtype=="O&M_Costs") || (subtype=="Efficiency")) {
# x <- readSource("IEA_WEO",subtype = "Invest_Costs",convert = FALSE)
# x[,,] <- as.numeric(x[,,])
# # mapping of all countries and their respective regions
all_c <- toolGetMapping("regionmappingH12.csv",where = "mappingfolder",type = "regional")
# seperating hydro case
x_hydro <- x[unique(all_c$RegionCode),,"Hydro_2.hydro"]
x <- x[unique(all_c$RegionCode),,"Hydro_2.hydro",invert=T]
eu_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="EUR"],years = getYears(x),names=getNames(x))
neu_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="NEU"],years = getYears(x),names=getNames(x))
africa_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="SSA"],years = getYears(x),names = getNames(x))
mea_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="MEA"],years = getYears(x),names = getNames(x))
asia_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="OAS"],years = getYears(x),names = getNames(x))
caz_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="CAZ"],years = getYears(x),names = getNames(x))
sc_america_costs <- new.magpie(all_c$CountryCode[all_c$RegionCode=="LAM"],years = getYears(x),names = getNames(x))
# All countries from the database which have zero investment costs get some numbers
# So, for e.g., Brazil and Middle East have zero for all the CCS related investment costs. They get their value from the
# average of all other regions/countries for the same technology.
x[c("Brazil","Middle East"),,"CCS",pmatch=TRUE] <- dimSums(x[c("Brazil","Middle East"),,invert=TRUE][,,"CCS"],na.rm = TRUE,dim=1)/
nregions(x[c("Brazil","Middle East"),,invert=T])
x["Brazil",,"Renewables.Geothermal"] <- dimSums(x["Brazil",,,invert=TRUE][,,"Renewables.Geothermal"],dim = 1)/
nregions(x["Brazil",,invert=T])
x["Brazil",,"Renewables.Marine"] <- dimSums(x["Brazil",,,invert=TRUE][,,"Renewables.Marine"],dim=1)/
nregions(x["Brazil",,invert=T])
x[c("Japan","Russia"),,"Renewables.Concentrating solar power"] <- dimSums(x[c("Japan","Russia"),,,invert=TRUE][,,"Renewables.Concentrating solar power"],dim=1)/
nregions(x[c("Japan","Russia"),,invert=T])
for (n in getRegions(eu_costs)){# all EU28 countries get "Europe" investment costs
eu_costs[n,,] <- as.numeric(x["Europe",,])
}
for (n in getRegions(neu_costs)){# all non-EU european countries get average of "Europe" and "RUS" investment costs
neu_costs[n,,] <- 0.5*as.numeric(x["Europe",,])+0.5*as.numeric(x["Russia",,])
}
for (n in getRegions(caz_costs)){# all CAZ countries get "OECD" average investment costs
caz_costs[n,,] <- 0.34*as.numeric(x["Europe",,])+0.33*as.numeric(x["United States",,])+0.33*as.numeric(x["Japan",,])
}
for (n in getRegions(africa_costs)){# all african countries get "Africa" investment costs
africa_costs[n,,]<- as.numeric(x["Africa",,])
}
for (n in getRegions(mea_costs)){# all OAS countries get average of Indian and Japanese investment costs
mea_costs[n,,] <- as.numeric(x["Middle East",,])
}
for (n in getRegions(asia_costs)){# all OAS countries get average of Indian and Japanese investment costs
asia_costs[n,,] <- 0.5*as.numeric(x["India",,])+0.5*as.numeric(x["Japan",,])
}
for (n in getRegions(sc_america_costs)){
sc_america_costs[n,,] <- as.numeric(x["Brazil",,])# all S. and C. American countries get "Brazilian" investment costs
}
# Extracting proper countries from the region/country list in input data
x_countries <- x[c("Europe","Middle East","Africa"),,,invert=TRUE]
# converting to ISO country names
getRegions(x_countries) <- toolCountry2isocode(getRegions(x_countries))
# one magpie object with all names
x_total <- mbind(eu_costs,neu_costs,caz_costs,africa_costs,mea_costs,asia_costs,
sc_america_costs,x_countries[c("BRA"),,invert=TRUE])
# remaining countries
x_total <- toolCountryFill(x_total,"HKG"="CHN","PRI"="USA","MAC"="CHN")
x_hydro <- toolAggregate(x_hydro,rel = all_c,from = "RegionCode",to = "CountryCode")
# setting 2040 values as 2030 values
x_hydro[,"y2040",] <- setYears(x_hydro[,2030,])
x_total[,,"Renewables.Hydropower - large-scale"] <- x_hydro[,,"Hydro_2.hydro"]
x <- x_total
x[is.na(x)] <- 0
} else if ((subtype=="Capacity") || (subtype=="Generation") || (subtype=="Emissions")) {
# WEO_regions -> c("World", "OECDNAM", "US", "CSAM", "BRAZIL", "EUR", "EU", "Africa", "SAFR", "ME", "EURASIA", "RUS", "ASIAPAC", "CHINA", "INDIA", "JPN", "ASEAN", "OECD", "NonOECD"
H12map <- toolGetMapping("regionmappingH12.csv", type = "regional", where = "mappingfolder")
if (subtype=="Capacity"){ # estimate OAS coal
# Approximate Caspian countries (part of the REF)
weight <- calcOutput("IO",subtype="input",aggregate=FALSE)[,2015,"pecoal.seel.pc"]
caspian <- new.magpie(cells_and_regions = "Caspian",names=getNames(x),years = 2015, fill=as.vector(x["EURASIA",2015,getNames(x)]) - as.vector(x["RUS",2015,getNames(x)]))
caspianMap <- data.frame(CountryCode=c("ARM","AZE","GEO","KAZ","KGZ","TJK","TKM","UZB"),RegionCode="Caspian")
caspianCountries <- toolAggregate(caspian, caspianMap, weight[c("ARM","AZE","GEO","KAZ","KGZ","TJK","TKM","UZB"),,])
# Approximate Latin America countries (minus MEX)
LAM <- new.magpie(cells_and_regions = "LAM",names=getNames(x),years = 2015, fill=as.vector(x["CSAM",2015,getNames(x)])-as.vector(x["BRAZIL",2015,getNames(x)]))
LAMCountryCode <- H12map[which(H12map$RegionCode=="LAM"),]$CountryCode
LAMCountries <- toolAggregate(LAM, H12map[which(H12map$CountryCode %in% LAMCountryCode[! LAMCountryCode %in% c("MEX","BRA")]),], weight[LAMCountryCode[! LAMCountryCode %in% c("MEX","BRA")],,])
# Approximate OAS countries
AUS_Coal <- 20 # Australia coal capacity in 2015 (GW)
NZL_Coal <- 0.55 # New zealand coal capacity in 2015 (GW)
OAStotal <- as.vector(x["ASIAPAC",2015,"Coal"]) - as.vector(x["CHINA",2015,"Coal"]) - as.vector(x["INDIA",2015,"Coal"]) - as.vector(x["JPN",2015,"Coal"]) - AUS_Coal - NZL_Coal
OAS <- new.magpie(cells_and_regions = "OAS",names=c("Coal"),years = 2015, fill = OAStotal)
OASCountries <- toolAggregate(OAS, H12map[which(H12map$RegionCode=="OAS"),], weight[H12map[which(H12map$RegionCode=="OAS"),]$CountryCode,,])
}
# get other regions directly from WEO data
otherCap <- x[c("US", "BRAZIL", "RUS", "CHINA", "INDIA", "JPN"),2015,] # countries only
getRegions(otherCap) <- toolCountry2isocode(getRegions(otherCap), mapping = c("US"="USA", "RUS"="RUS", "JPN" = "JPN"))
#merge data
if (subtype=="Capacity"){
reg <- unique(c(getRegions(otherCap),getRegions(OASCountries),getRegions(caspianCountries),getRegions(LAMCountries),"MEX"))
names <- unique(c(getNames(otherCap),getNames(OASCountries),getNames(caspianCountries),getNames(LAMCountries)))
years <- unique(c(getYears(otherCap),getYears(OASCountries),getYears(caspianCountries),getYears(LAMCountries)))
out <- new.magpie(cells_and_regions = reg, names = names, years = years)
out[getRegions(otherCap),,] <- otherCap[getRegions(otherCap),,]
out[getRegions(OASCountries),,"Coal"] <- OASCountries[getRegions(OASCountries),,"Coal"]
out[getRegions(caspianCountries),,"Coal"] <- caspianCountries[getRegions(caspianCountries),,"Coal"]
out[getRegions(LAMCountries),,"Coal"] <- LAMCountries[getRegions(LAMCountries),,"Coal"]
out["MEX",,"Coal"] <- 5.378 # Mexico coal capacity in 2015 (GW)
} else {
out <- otherCap
}
# fill countries with no data
out <- toolCountryFill(out,fill=0,verbosity=0)
# replace NAs with 0
out[is.na(out)] <- 0
x <- out
}
return(x)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.