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calcCapacity.R
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calcCapacity.R
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#' @title calc Capacity
#' @description provides historical capacity values in TW
#'
#' @param subtype data subtype. Either "capacityByTech" or "capacityByPE"
#' @return magpie object of capacity data
#' @importFrom dplyr tribble
#' @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 <- madrat::toolAggregate(IRENAcap, dim = 3, rel = mapping, from = "IRENA_techs", to = "REMIND_techs")
IRENAcap <- IRENAcap * 1E-06 # converting MW to TW
# 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 <- madrat::toolAggregate(Openmodcap, dim = 3, rel = 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 <- madrat::toolAggregate(WEOcap, dim = 3, rel = 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 = FALSE)
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 = TRUE), getYears(coal_hist, as.integer = TRUE)))
:max(c(getYears(embercap, as.integer = TRUE), getYears(coal_hist, as.integer = TRUE)))),
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 {
stop("Not a valid subtype!")
}
# Returning capacity values
return(list(x = output, weight = NULL,
unit = "TW",
description = description
))
}