R/calcCapacity.R
In pik-piam/mrremind: MadRat REMIND Input Data Package
Defines functions
calcCapacity
Documented in
calcCapacity
#' @title calc Capacity
#' @description provides historical capacity values in TW
#'
#' @param subtype data subtype. Either "capacityByTech" or "capacityByPE"
#' @return magpie object of capacity data
#' @author Renato Rodrigues, Stephen Bi
#' @examples
#'
#' \dontrun{
#' calcOutput("Capacity",subtype="capacityByTech")
#' }
calcCapacity <- function(subtype) {
if ((subtype == "capacityByTech_windoff") | (subtype == "capacityByTech")) {
if (subtype == "capacityByTech_windoff"){
description <- "Historical capacity by technology including offshore wind."
# Use IRENA data for world renewables capacity.
# Year: 2000-2017
# Technologies: "csp", "geohdr", "hydro", "spv", "wind", "windoff"
IRENAcap <- readSource(type="IRENA",subtype="Capacity") # Read IRENA renewables capacity data
IRENAcap <- IRENAcap[,,c("Concentrated solar power",
"Geothermal", "Renewable hydropower",
"Solar photovoltaic",
"Onshore wind energy",
"Offshore wind energy"
)] # selecting data used on REMIND
mapping <- data.frame(IRENA_techs=c("Concentrated solar power",
"Geothermal",
"Renewable hydropower",
"Solar photovoltaic",
"Onshore wind energy",
"Offshore wind energy"),
REMIND_techs=c("csp", "geohdr", "hydro", "spv", "wind", "windoff"),
stringsAsFactors = FALSE)
}
else if (subtype == "capacityByTech"){
description <- "Historical capacity by technology."
# Use IRENA data for world renewables capacity.
# Year: 2000-2017
# Technologies: "csp", "geohdr", "hydro", "spv", "wind"
IRENAcap <- readSource(type="IRENA",subtype="Capacity") # Read IRENA renewables capacity data
# selecting data used on REMIND
IRENAcap <- IRENAcap[,,c("Concentrated solar power", "Geothermal", "Renewable hydropower", "Solar photovoltaic", "Wind")]
mapping <- data.frame(IRENA_techs=c("Concentrated solar power",
"Geothermal", "Renewable hydropower",
"Solar photovoltaic",
"Wind"),
REMIND_techs=c("csp", "geohdr", "hydro", "spv", "wind"),
stringsAsFactors = FALSE)
}
# renaming technologies to REMIND naming convention
IRENAcap <- luscale::rename_dimnames(IRENAcap, dim = 3, query = mapping, from = "IRENA_techs", to="REMIND_techs")
IRENAcap <- IRENAcap * 1E-06 # converting MW to TW
# overwriting Russia and Japan capacities for wind and spv to avoid REMIND convergence problems
# (this is a temporary solution that should be removed once the bounds in REMIND are reworked)
# IRENAcap["JPN",2010,"wind"] <- 0.0012
# IRENAcap["RUS",2010,"spv"] <- 5e-06
# IRENAcap["RUS",2015,"wind"] <- 2e-05
# IRENAcap["RUS",2015,"spv"] <- 2e-05
#
# Use Openmod capacity values updated by the LIMES team for the European countries.
# Year: 2015
# Technologies: "tnrs","ngcc","ngt","dot"
Openmodcap <- readSource(type="Openmod") # Read Openmod capacities
# selecting data used on REMIND "BAL"
Openmodcap <- Openmodcap[c("FIN","NOR","SWE","EST","LVA","LTU","DNK","GBR","IRL","NLD","POL",
"DEU","BEL","LUX","CZE","SVK","AUT","CHE","HUN","ROU","SVN","FRA",
"HRV","BGR","ITA","ESP","PRT","GRC"),,c("tnr","ngcc","ngt","oil")]
mapping <- data.frame( Openmod_techs=c("tnr","ngcc","ngt","oil"),
REMIND_techs=c("tnrs","ngcc","ngt","dot"), stringsAsFactors = FALSE)
# renaming technologies to REMIND naming convention
Openmodcap <- luscale::rename_dimnames(Openmodcap, dim = 3, query = mapping, from = "Openmod_techs", to="REMIND_techs")
Openmodcap <- Openmodcap * 1E-03 # converting GW to TW
# Use WEO 2017 data to additional countries: "USA","BRA","RUS","CHN","IND","JPN"
# Year: 2015
# Technologies: "tnrs","dot"
WEOcap <- readSource(type="IEA_WEO",subtype="Capacity") # Read IEA WEO capacities
WEOcap <- WEOcap[c("USA","BRA","RUS","CHN","IND","JPN"),2015,c("Nuclear","Oil")] # selecting data used on REMIND
mapping <- data.frame( WEO_techs=c("Nuclear","Oil"),
REMIND_techs=c("tnrs","dot"), stringsAsFactors = FALSE)
# renaming technologies to REMIND naming convention
WEOcap <- luscale::rename_dimnames(WEOcap, dim = 3, query = mapping, from = "WEO_techs", to="REMIND_techs")
WEOcap <- WEOcap * 1E-03 # converting GW to TW
# ***CG: fix CHA gas power capacities: 97 GW by September 2020 (Oxford Institute for Energy Studies:
# Natural gas in China’s power sector: Challenges and the road ahead
# (https://www.oxfordenergy.org/wpcms/wp-content/uploads/2020/12/Insight-80-Natural-gas-in-Chinas-power-sector.pdf)
# ~50% is peaking (= ngt), the other 50 is called cogeneration but contains ngcc and gaschp
# *** for 2018-2022, take 90GW, 90GW*0.5=50GW ngt, the rest is split between ngcc and gaschp 70:30 (from IEA EB energy output)
CHA.2020.GasData <- as.magpie(
tribble(
~region, ~year, ~data, ~value,
"CHN", 2010, "gaschp", 0.004,
"CHN", 2015, "gaschp", 0.011,
"CHN", 2020, "gaschp", 0.014,
"CHN", 2010, "ngcc", 0.009,
"CHN", 2015, "ngcc", 0.025,
"CHN", 2020, "ngcc", 0.032,
"CHN", 2010, "ngt", 0.013,
"CHN", 2015, "ngt", 0.036,
"CHN", 2020, "ngt", 0.045))
# RP: add upper bound for USA PV in 2025, as current forecast by Wood Mackenzie Solar Market Insight Report 2022 sees ~ 265 GW DC in 2025 in
# bullish scenario. So it would be less in GW_AC, but REMIND corrects for lower model CF than real world (in USA) by upscaling capacity
# so it should be roughly ok as upper bound. (don't use as lower bound!)
USA.2025.PVData <- as.magpie(
tribble(
~region, ~year, ~data, ~value,
"USA", 2025, "spv", 0.265))
# merge IRENA, Openmod and WEO capacities data
output <- new.magpie(cells_and_regions=unique(c(getRegions(IRENAcap),getRegions(Openmodcap), getRegions(WEOcap), getRegions(CHA.2020.GasData), getRegions(USA.2025.PVData) )),
years = unique(c(getYears(IRENAcap),getYears(Openmodcap),getYears(WEOcap), getYears(CHA.2020.GasData), getYears(USA.2025.PVData))),
names = unique(c(getNames(IRENAcap),getNames(Openmodcap),getNames(WEOcap), getNames(CHA.2020.GasData), getNames(USA.2025.PVData))),
fill=0)
output[getRegions(IRENAcap),getYears(IRENAcap),getNames(IRENAcap)] <- IRENAcap[getRegions(IRENAcap),
getYears(IRENAcap),
getNames(IRENAcap)]
output[getRegions(Openmodcap),getYears(Openmodcap),getNames(Openmodcap)] <- Openmodcap[getRegions(Openmodcap),
getYears(Openmodcap),
getNames(Openmodcap)]
output[getRegions(WEOcap),getYears(WEOcap),getNames(WEOcap)] <- WEOcap[getRegions(WEOcap),
getYears(WEOcap),
getNames(WEOcap)]
output[getRegions(CHA.2020.GasData),getYears(CHA.2020.GasData), getNames(CHA.2020.GasData)] <- CHA.2020.GasData
output[getRegions(USA.2025.PVData),getYears(USA.2025.PVData), getNames(USA.2025.PVData)] <- USA.2025.PVData
output[is.na(output)] <- 0 #set NA to 0
output <- toolCountryFill(output,fill=0,verbosity=2) # fill missing countries
}
else if (grepl("capacityByPE", subtype)) {
# Pe -> peoil, pegas, pecoal, peur, pegeo, pehyd, pewin, pesol, pebiolc, pebios, pebioil
description <- "Historical capacity by primary energy."
# Secondary Energy Electricity capacities by primary energy source
# Data for non-RE techs from Ember
# Except coal, which comes from Global Coal Plant Tracker
## Primary Energies: "peur", "pecoal", "pecoal", "pegas", "pegas", "pehyd",
## "pewin", "pewin", "pesol", "pehyd", "pebiolc", "pesol", "peoil"
# mapping <- data.frame( Openmod_techs=c("tnr", "pc", "lpc", "ngcc", "ngt", "hydro", "windon", "windoff", "spv",
# "psp", "biolcigcc", "csp", "oil"), #, "waste", "others"
# REMIND_PE=c("peur", "pecoal", "pecoal", "pegas", "pegas", "pehyd", "pewin", "pewin",
# "pesol", "pehyd", "pebiolc", "pesol", "peoil"), stringsAsFactors = FALSE)
mapping <- data.frame(ember_techs = c("Biomass", "Coal", "Gas", "Oil", "Hydro", "Nuclear", "Solar", "Wind"),
REMIND_PE=c("pebiolc", "pecoal", "pegas", "peoil", "pehyd", "peur", "pesol", "pewin"), stringsAsFactors = FALSE)
embercap <- calcOutput("Ember", subtype = "capacity", aggregate = F)
embercap <- setNames(embercap,
nm = gsub("Cap|Electricity|", "",
gsub(" (GW)", "",
getNames(embercap), fixed = TRUE), fixed = TRUE))
# aggregating primary energies to REMIND naming convention
embercap <- toolAggregate(embercap[,,mapping$ember_techs], rel=mapping, from="ember_techs",
to="REMIND_PE",dim=3.1)
embercap <- embercap * 1E-03 # converting GW to TW
embercap <- embercap[,,c("peur", "pegas", "pebiolc", "pehyd")] #pegas is handled at technology level
# estimating lower bound coal capacity to remaining countries assuming
# (1) capacity factors are given by REMIND pc capacity factor in 2015,
# (2) generation is given by IEA 2015 generation values,
# (3) all 2015 coal capacity is provided by the pc technology.
# SB Use coal capacity data from Global Coal Plant Tracker (GCPT)
# historical coal capacity data
coal_hist <- readSource("GCPT",subtype="historical") * 1e-03
coal_hist <- setNames(coal_hist, nm = "pecoal")
if (grepl("annual", subtype)) {
output <- new.magpie(cells_and_regions=c(getRegions(embercap)),
years = c(min(c(getYears(embercap, as.integer = T), getYears(coal_hist, as.integer = T)))
: max(c(getYears(embercap, as.integer = T), getYears(coal_hist, as.integer = T)))),
names = c("pecoal", "pegas", "pebiolc", "pehyd", "peur"),
fill=0)
output[, intersect(getYears(coal_hist), getYears(output)), "pecoal"] <- coal_hist[, intersect(getYears(coal_hist), getYears(output)),]
output[, intersect(getYears(embercap), getYears(output)), getItems(output, dim = 3) != "pecoal"] <- embercap[, intersect(getYears(embercap), getYears(output)),]
}else {
last_ts <- max(intersect(getYears(coal_hist, as.integer = TRUE), seq(2010, 2050, 5)))
coal_hist <- setNames(coal_hist[,getYears(coal_hist)>="y2007",], nm = "pecoal")
output <- new.magpie(cells_and_regions=c(getRegions(embercap)), years = seq(2010, last_ts, 5),
names = c("pecoal", "pegas", "pebiolc", "pehyd", "peur"), fill=0)
# Fill in output with GCPT and Ember data, averaging across each 5 (or 3 or 4) year period
ts_coal <- getYears(coal_hist, as.integer = TRUE)
ts_ember <- getYears(embercap, as.integer = TRUE)
for (yr in getYears(output, as.integer = TRUE)) {
if ((yr+2) %in% ts_coal) { ## Fill in coal separately because data is more recent
output[,yr,"pecoal"] <- dimSums(coal_hist[,(yr-2):(yr+2),],dim=2)/5
}else if ((yr+1) %in% ts_coal) {
output[,yr,"pecoal"] <- dimSums(coal_hist[,(yr-2):(yr+1),],dim=2)/4
}else {
output[,yr,"pecoal"] <- dimSums(coal_hist[,(yr-2):yr,],dim=2)/3
}
if ((yr+2) %in% ts_ember) {
output[,yr,getItems(output,dim=3)!='pecoal'] <- dimSums(embercap[,(yr-2):(yr+2),],dim=2)/5
}else if ((yr+1) %in% ts_ember) {
output[,yr,getItems(output,dim=3)!='pecoal'] <- dimSums(embercap[,(yr-2):(yr+1),],dim=2)/4
}else {
output[,yr,getItems(output,dim=3)!='pecoal'] <- dimSums(embercap[,(yr-2):yr,],dim=2)/3
}
}
}
output <- toolCountryFill(output,fill=0,verbosity=2) # fill missing countries
output <- magclass::add_dimension(output, dim = 3.2, add = "enty", nm = "seel") # add secondary energy dimension
} else if (subtype=="coalPlantTraj") {
output <- readSource("GCPT",subtype="future") * 1e-03
description <- "Coal power project pipeline completion scenarios"
} else {
stop("Not a valid subtype!")
}
# Returning capacity values
return(list(x=output, weight=NULL,
unit="TW",
description=description
))
}
pik-piam/mrremind documentation built on March 30, 2024, 3:37 a.m.
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Defines functions calcCapacity
Documented in calcCapacity
#' @title calc Capacity
#' @description provides historical capacity values in TW
#'
#' @param subtype data subtype. Either "capacityByTech" or "capacityByPE"
#' @return magpie object of capacity data
#' @author Renato Rodrigues, Stephen Bi
#' @examples
#'
#' \dontrun{
#' calcOutput("Capacity",subtype="capacityByTech")
#' }
calcCapacity <- function(subtype) {
if ((subtype == "capacityByTech_windoff") | (subtype == "capacityByTech")) {
if (subtype == "capacityByTech_windoff"){
description <- "Historical capacity by technology including offshore wind."
# Use IRENA data for world renewables capacity.
# Year: 2000-2017
# Technologies: "csp", "geohdr", "hydro", "spv", "wind", "windoff"
IRENAcap <- readSource(type="IRENA",subtype="Capacity") # Read IRENA renewables capacity data
IRENAcap <- IRENAcap[,,c("Concentrated solar power",
"Geothermal", "Renewable hydropower",
"Solar photovoltaic",
"Onshore wind energy",
"Offshore wind energy"
)] # selecting data used on REMIND
mapping <- data.frame(IRENA_techs=c("Concentrated solar power",
"Geothermal",
"Renewable hydropower",
"Solar photovoltaic",
"Onshore wind energy",
"Offshore wind energy"),
REMIND_techs=c("csp", "geohdr", "hydro", "spv", "wind", "windoff"),
stringsAsFactors = FALSE)
}
else if (subtype == "capacityByTech"){
description <- "Historical capacity by technology."
# Use IRENA data for world renewables capacity.
# Year: 2000-2017
# Technologies: "csp", "geohdr", "hydro", "spv", "wind"
IRENAcap <- readSource(type="IRENA",subtype="Capacity") # Read IRENA renewables capacity data
# selecting data used on REMIND
IRENAcap <- IRENAcap[,,c("Concentrated solar power", "Geothermal", "Renewable hydropower", "Solar photovoltaic", "Wind")]
mapping <- data.frame(IRENA_techs=c("Concentrated solar power",
"Geothermal", "Renewable hydropower",
"Solar photovoltaic",
"Wind"),
REMIND_techs=c("csp", "geohdr", "hydro", "spv", "wind"),
stringsAsFactors = FALSE)
}
# renaming technologies to REMIND naming convention
IRENAcap <- luscale::rename_dimnames(IRENAcap, dim = 3, query = mapping, from = "IRENA_techs", to="REMIND_techs")
IRENAcap <- IRENAcap * 1E-06 # converting MW to TW
# overwriting Russia and Japan capacities for wind and spv to avoid REMIND convergence problems
# (this is a temporary solution that should be removed once the bounds in REMIND are reworked)
# IRENAcap["JPN",2010,"wind"] <- 0.0012
# IRENAcap["RUS",2010,"spv"] <- 5e-06
# IRENAcap["RUS",2015,"wind"] <- 2e-05
# IRENAcap["RUS",2015,"spv"] <- 2e-05
#
# Use Openmod capacity values updated by the LIMES team for the European countries.
# Year: 2015
# Technologies: "tnrs","ngcc","ngt","dot"
Openmodcap <- readSource(type="Openmod") # Read Openmod capacities
# selecting data used on REMIND "BAL"
Openmodcap <- Openmodcap[c("FIN","NOR","SWE","EST","LVA","LTU","DNK","GBR","IRL","NLD","POL",
"DEU","BEL","LUX","CZE","SVK","AUT","CHE","HUN","ROU","SVN","FRA",
"HRV","BGR","ITA","ESP","PRT","GRC"),,c("tnr","ngcc","ngt","oil")]
mapping <- data.frame( Openmod_techs=c("tnr","ngcc","ngt","oil"),
REMIND_techs=c("tnrs","ngcc","ngt","dot"), stringsAsFactors = FALSE)
# renaming technologies to REMIND naming convention
Openmodcap <- luscale::rename_dimnames(Openmodcap, dim = 3, query = mapping, from = "Openmod_techs", to="REMIND_techs")
Openmodcap <- Openmodcap * 1E-03 # converting GW to TW
# Use WEO 2017 data to additional countries: "USA","BRA","RUS","CHN","IND","JPN"
# Year: 2015
# Technologies: "tnrs","dot"
WEOcap <- readSource(type="IEA_WEO",subtype="Capacity") # Read IEA WEO capacities
WEOcap <- WEOcap[c("USA","BRA","RUS","CHN","IND","JPN"),2015,c("Nuclear","Oil")] # selecting data used on REMIND
mapping <- data.frame( WEO_techs=c("Nuclear","Oil"),
REMIND_techs=c("tnrs","dot"), stringsAsFactors = FALSE)
# renaming technologies to REMIND naming convention
WEOcap <- luscale::rename_dimnames(WEOcap, dim = 3, query = mapping, from = "WEO_techs", to="REMIND_techs")
WEOcap <- WEOcap * 1E-03 # converting GW to TW
# ***CG: fix CHA gas power capacities: 97 GW by September 2020 (Oxford Institute for Energy Studies:
# Natural gas in China’s power sector: Challenges and the road ahead
# (https://www.oxfordenergy.org/wpcms/wp-content/uploads/2020/12/Insight-80-Natural-gas-in-Chinas-power-sector.pdf)
# ~50% is peaking (= ngt), the other 50 is called cogeneration but contains ngcc and gaschp
# *** for 2018-2022, take 90GW, 90GW*0.5=50GW ngt, the rest is split between ngcc and gaschp 70:30 (from IEA EB energy output)
CHA.2020.GasData <- as.magpie(
tribble(
~region, ~year, ~data, ~value,
"CHN", 2010, "gaschp", 0.004,
"CHN", 2015, "gaschp", 0.011,
"CHN", 2020, "gaschp", 0.014,
"CHN", 2010, "ngcc", 0.009,
"CHN", 2015, "ngcc", 0.025,
"CHN", 2020, "ngcc", 0.032,
"CHN", 2010, "ngt", 0.013,
"CHN", 2015, "ngt", 0.036,
"CHN", 2020, "ngt", 0.045))
# RP: add upper bound for USA PV in 2025, as current forecast by Wood Mackenzie Solar Market Insight Report 2022 sees ~ 265 GW DC in 2025 in
# bullish scenario. So it would be less in GW_AC, but REMIND corrects for lower model CF than real world (in USA) by upscaling capacity
# so it should be roughly ok as upper bound. (don't use as lower bound!)
USA.2025.PVData <- as.magpie(
tribble(
~region, ~year, ~data, ~value,
"USA", 2025, "spv", 0.265))
# merge IRENA, Openmod and WEO capacities data
output <- new.magpie(cells_and_regions=unique(c(getRegions(IRENAcap),getRegions(Openmodcap), getRegions(WEOcap), getRegions(CHA.2020.GasData), getRegions(USA.2025.PVData) )),
years = unique(c(getYears(IRENAcap),getYears(Openmodcap),getYears(WEOcap), getYears(CHA.2020.GasData), getYears(USA.2025.PVData))),
names = unique(c(getNames(IRENAcap),getNames(Openmodcap),getNames(WEOcap), getNames(CHA.2020.GasData), getNames(USA.2025.PVData))),
fill=0)
output[getRegions(IRENAcap),getYears(IRENAcap),getNames(IRENAcap)] <- IRENAcap[getRegions(IRENAcap),
getYears(IRENAcap),
getNames(IRENAcap)]
output[getRegions(Openmodcap),getYears(Openmodcap),getNames(Openmodcap)] <- Openmodcap[getRegions(Openmodcap),
getYears(Openmodcap),
getNames(Openmodcap)]
output[getRegions(WEOcap),getYears(WEOcap),getNames(WEOcap)] <- WEOcap[getRegions(WEOcap),
getYears(WEOcap),
getNames(WEOcap)]
output[getRegions(CHA.2020.GasData),getYears(CHA.2020.GasData), getNames(CHA.2020.GasData)] <- CHA.2020.GasData
output[getRegions(USA.2025.PVData),getYears(USA.2025.PVData), getNames(USA.2025.PVData)] <- USA.2025.PVData
output[is.na(output)] <- 0 #set NA to 0
output <- toolCountryFill(output,fill=0,verbosity=2) # fill missing countries
}
else if (grepl("capacityByPE", subtype)) {
# Pe -> peoil, pegas, pecoal, peur, pegeo, pehyd, pewin, pesol, pebiolc, pebios, pebioil
description <- "Historical capacity by primary energy."
# Secondary Energy Electricity capacities by primary energy source
# Data for non-RE techs from Ember
# Except coal, which comes from Global Coal Plant Tracker
## Primary Energies: "peur", "pecoal", "pecoal", "pegas", "pegas", "pehyd",
## "pewin", "pewin", "pesol", "pehyd", "pebiolc", "pesol", "peoil"
# mapping <- data.frame( Openmod_techs=c("tnr", "pc", "lpc", "ngcc", "ngt", "hydro", "windon", "windoff", "spv",
# "psp", "biolcigcc", "csp", "oil"), #, "waste", "others"
# REMIND_PE=c("peur", "pecoal", "pecoal", "pegas", "pegas", "pehyd", "pewin", "pewin",
# "pesol", "pehyd", "pebiolc", "pesol", "peoil"), stringsAsFactors = FALSE)
mapping <- data.frame(ember_techs = c("Biomass", "Coal", "Gas", "Oil", "Hydro", "Nuclear", "Solar", "Wind"),
REMIND_PE=c("pebiolc", "pecoal", "pegas", "peoil", "pehyd", "peur", "pesol", "pewin"), stringsAsFactors = FALSE)
embercap <- calcOutput("Ember", subtype = "capacity", aggregate = F)
embercap <- setNames(embercap,
nm = gsub("Cap|Electricity|", "",
gsub(" (GW)", "",
getNames(embercap), fixed = TRUE), fixed = TRUE))
# aggregating primary energies to REMIND naming convention
embercap <- toolAggregate(embercap[,,mapping$ember_techs], rel=mapping, from="ember_techs",
to="REMIND_PE",dim=3.1)
embercap <- embercap * 1E-03 # converting GW to TW
embercap <- embercap[,,c("peur", "pegas", "pebiolc", "pehyd")] #pegas is handled at technology level
# estimating lower bound coal capacity to remaining countries assuming
# (1) capacity factors are given by REMIND pc capacity factor in 2015,
# (2) generation is given by IEA 2015 generation values,
# (3) all 2015 coal capacity is provided by the pc technology.
# SB Use coal capacity data from Global Coal Plant Tracker (GCPT)
# historical coal capacity data
coal_hist <- readSource("GCPT",subtype="historical") * 1e-03
coal_hist <- setNames(coal_hist, nm = "pecoal")
if (grepl("annual", subtype)) {
output <- new.magpie(cells_and_regions=c(getRegions(embercap)),
years = c(min(c(getYears(embercap, as.integer = T), getYears(coal_hist, as.integer = T)))
: max(c(getYears(embercap, as.integer = T), getYears(coal_hist, as.integer = T)))),
names = c("pecoal", "pegas", "pebiolc", "pehyd", "peur"),
fill=0)
output[, intersect(getYears(coal_hist), getYears(output)), "pecoal"] <- coal_hist[, intersect(getYears(coal_hist), getYears(output)),]
output[, intersect(getYears(embercap), getYears(output)), getItems(output, dim = 3) != "pecoal"] <- embercap[, intersect(getYears(embercap), getYears(output)),]
}else {
last_ts <- max(intersect(getYears(coal_hist, as.integer = TRUE), seq(2010, 2050, 5)))
coal_hist <- setNames(coal_hist[,getYears(coal_hist)>="y2007",], nm = "pecoal")
output <- new.magpie(cells_and_regions=c(getRegions(embercap)), years = seq(2010, last_ts, 5),
names = c("pecoal", "pegas", "pebiolc", "pehyd", "peur"), fill=0)
# Fill in output with GCPT and Ember data, averaging across each 5 (or 3 or 4) year period
ts_coal <- getYears(coal_hist, as.integer = TRUE)
ts_ember <- getYears(embercap, as.integer = TRUE)
for (yr in getYears(output, as.integer = TRUE)) {
if ((yr+2) %in% ts_coal) { ## Fill in coal separately because data is more recent
output[,yr,"pecoal"] <- dimSums(coal_hist[,(yr-2):(yr+2),],dim=2)/5
}else if ((yr+1) %in% ts_coal) {
output[,yr,"pecoal"] <- dimSums(coal_hist[,(yr-2):(yr+1),],dim=2)/4
}else {
output[,yr,"pecoal"] <- dimSums(coal_hist[,(yr-2):yr,],dim=2)/3
}
if ((yr+2) %in% ts_ember) {
output[,yr,getItems(output,dim=3)!='pecoal'] <- dimSums(embercap[,(yr-2):(yr+2),],dim=2)/5
}else if ((yr+1) %in% ts_ember) {
output[,yr,getItems(output,dim=3)!='pecoal'] <- dimSums(embercap[,(yr-2):(yr+1),],dim=2)/4
}else {
output[,yr,getItems(output,dim=3)!='pecoal'] <- dimSums(embercap[,(yr-2):yr,],dim=2)/3
}
}
}
output <- toolCountryFill(output,fill=0,verbosity=2) # fill missing countries
output <- magclass::add_dimension(output, dim = 3.2, add = "enty", nm = "seel") # add secondary energy dimension
} else if (subtype=="coalPlantTraj") {
output <- readSource("GCPT",subtype="future") * 1e-03
description <- "Coal power project pipeline completion scenarios"
} else {
stop("Not a valid subtype!")
}
# Returning capacity values
return(list(x=output, weight=NULL,
unit="TW",
description=description
))
}
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