R/zchunk_LA122.gasproc_refining.R
In rohmin9122/gcam-korea-release: Build the GCAM-Korea Input Datasets
Defines functions
module_energy_LA122.gasproc_refining
Documented in
module_energy_LA122.gasproc_refining
#' module_energy_LA122.gasproc_refining
#'
#' Create gasproc, oil refining and crops inputs, outputs and IO "input-output" coefficients for refining.
#'
#' @param command API command to execute
#' @param ... other optional parameters, depending on command
#' @return Depends on \code{command}: either a vector of required inputs,
#' a vector of output names, or (if \code{command} is "MAKE") all
#' the generated outputs: \code{L122.out_EJ_R_gasproc_F_Yh}, \code{L122.in_EJ_R_gasproc_F_Yh}, \code{L122.IO_R_oilrefining_F_Yh}, \code{L122.out_EJ_R_refining_F_Yh}, \code{L122.in_EJ_R_refining_F_Yh}, \code{L122.in_Mt_R_C_Yh}. The corresponding file in the
#' original data system was \code{LA122.gasproc_refining.R} (energy level1).
#' @details This chunk creates gasproc, oil refining and crops inputs, outputs and IO "input-output" coefficients for refining.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author FF, May 2017
module_energy_LA122.gasproc_refining <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "aglu/FAO/FAO_ag_items_PRODSTAT",
FILE = "energy/calibrated_techs",
FILE = "energy/A_regions",
FILE = "energy/A21.globaltech_coef",
FILE = "energy/A22.globaltech_coef",
"L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L122.out_EJ_R_gasproc_F_Yh",
"L122.in_EJ_R_gasproc_F_Yh",
"L122.IO_R_oilrefining_F_Yh",
"L122.out_EJ_R_refining_F_Yh",
"L122.in_EJ_R_refining_F_Yh",
"L122.in_Mt_R_C_Yh"))
} else if(command == driver.MAKE) {
EcYield_kgm2_hi <- EcYield_kgm2_lo <- GCAM_commodity <- GCAM_region_ID <- GLU <-
Irr_Rfd <- LC_bm2_hi <- LC_bm2_lo <- biodiesel <- biomassOil_tech <- ethanol <- fuel <-
fuel.x <- fuelInput <- gas_coef <- hist_year <- in_value <- landshare_hi <-
landshare_lo <- level <- minicam.energy.input <- passthrough.sector <- sector <-
subsector <- supplysector <- technology <- value <- value.x <- value.y <- valueInput <-
value_ctl_oil <- value_en_bal <- value_en_bal_TPES <- value_en_bal_net_oil <-
value_gtl_oil <- value_gtlctl <- year <- yield <- yieldmult_hi <- yieldmult_lo <- NULL # silence package check notes
all_data <- list(...)[[1]]
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
FAO_ag_items_PRODSTAT <- get_data(all_data, "aglu/FAO/FAO_ag_items_PRODSTAT")
calibrated_techs <- get_data(all_data, "energy/calibrated_techs")
A_regions <- get_data(all_data, "energy/A_regions")
A21.globaltech_coef <- get_data(all_data, "energy/A21.globaltech_coef")
A22.globaltech_coef <- get_data(all_data, "energy/A22.globaltech_coef")
L1011.en_bal_EJ_R_Si_Fi_Yh <- get_data(all_data, "L1011.en_bal_EJ_R_Si_Fi_Yh")
get_data(all_data, "L121.in_EJ_R_unoil_F_Yh") %>%
filter(year %in% HISTORICAL_YEARS) -> # ensure temp data match our current history
L121.in_EJ_R_unoil_F_Yh
# ===================================================
# Perform computations: Will start with refining
# Most of the technologies (in calibrated_techs.csv) have inputs from outputs based on exogenous coefficients (A22.globaltech_coef).
# Extracting those coefficients and interpolating them to 2010.
# Remove "x" year
A22.globaltech_coef %>%
semi_join(select(calibrated_techs, supplysector, subsector, technology), by = c("supplysector", "subsector", "technology")) %>%
left_join(select(calibrated_techs, supplysector, subsector, technology, minicam.energy.input, sector, fuel), by = c("supplysector", "subsector", "technology", "minicam.energy.input")) %>%
gather(hist_year, value, -supplysector, -subsector, -technology, -minicam.energy.input, -sector, -fuel) %>%
mutate(hist_year = as.numeric(hist_year)) %>%
filter(hist_year == min(HISTORICAL_YEARS)) %>%
repeat_add_columns(tibble(year = HISTORICAL_YEARS)) %>%
select(-hist_year) -> L122.globaltech_coef
# BIOMASS LIQUIDS: Ethanol and biodiesel output are equal to regional TPES
# Creating fuel constant for biomass liquids that will be used to filter biofuels from L1011.en_bal_EJ_R_Si_Fi_Yh and to create L122.out_EJ_R_biofuel_Yh
BIOMASS_LIQUIDS <- c("refined biofuels_ethanol", "refined biofuels_FT")
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "TPES") %>%
filter(fuel %in% BIOMASS_LIQUIDS) -> L122.out_EJ_R_biofuel_Yh
# Create ethanol sector for biomass from A_regions. This will be "row binded" with biodiesel sector below.
L122.out_EJ_R_biofuel_Yh %>%
filter(fuel == "refined biofuels_ethanol") %>%
left_join_error_no_match(select(A_regions, GCAM_region_ID, ethanol), by = "GCAM_region_ID") %>%
select(-sector) %>%
rename(sector = ethanol) -> BIOMASS_LIQUIDS_Ethanol
# Biodiesel sector and then binded with ethanol sector to consolidate everything into L122.out_EJ_R_biofuel_Yh
L122.out_EJ_R_biofuel_Yh %>%
filter(fuel == "refined biofuels_FT") %>%
left_join_error_no_match(select(A_regions, GCAM_region_ID, biodiesel), by = "GCAM_region_ID") %>%
select(-sector) %>%
rename(sector = biodiesel) %>%
bind_rows(BIOMASS_LIQUIDS_Ethanol) -> L122.out_EJ_R_biofuel_Yh
# Add fuels to appropiate sectors and regions in L122.out_EJ_R_biofuel_Yh from calibrated_techs
calibrated_techs %>%
select(sector, fuel) %>%
distinct(sector, .keep_all = TRUE) %>%
right_join(L122.out_EJ_R_biofuel_Yh, by = "sector") %>%
select(GCAM_region_ID, sector, fuel = fuel.x, year, value) -> L122.out_EJ_R_biofuel_Yh
# Inputs to biofuel production are region-specific
# Because some have multiple inputs, repeat coefficient table by number of regions and then subset only the applicable combinations
L122.globaltech_coef %>%
filter(sector %in% L122.out_EJ_R_biofuel_Yh$sector) %>%
repeat_add_columns(tibble(GCAM_region_ID = A_regions$GCAM_region_ID)) -> L122.biofuel_coef_repR
# Subset L122.biofuel_coef_repR based on A_regions by region and sector (ethanol)
L122.biofuel_coef_repR %>%
# Using semi_join to keep the sector = ethanol in L122.biofuel_coef_repR based on A_regions
semi_join(select(A_regions, GCAM_region_ID, sector = ethanol), by = c("sector", "GCAM_region_ID")) -> L122.biofuel_coef_repR_Ethanol
# Subset L122.biofuel_coef_repR based on A_regions by region and sector (biodisiel). Then adding ethanol sector (L122.biofuel_coef_repR_Ethanol)
L122.biofuel_coef_repR %>%
# Using semi_join to keep the sector = biodiesel in L122.biofuel_coef_repR based on A_regions
semi_join(select(A_regions, GCAM_region_ID, sector = biodiesel), by = c("sector", "GCAM_region_ID")) %>%
bind_rows(L122.biofuel_coef_repR_Ethanol) -> L122.biofuel_coef_R
# Build table of inputs to biofuel production (IO coefs times output)
L122.biofuel_coef_R %>%
rename(fuelInput = fuel, valueInput = value) %>%
left_join(L122.out_EJ_R_biofuel_Yh, by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value = valueInput * value) %>%
select(-valueInput, -fuel) %>%
rename(fuel = fuelInput) %>%
select(GCAM_region_ID, sector, fuel, year, value) -> L122.in_EJ_R_biofuel_F_Yh
# GAS AND COAL TO LIQUIDS
# Create L122.out_EJ_R_gtlctl_Yh from L1011.en_bal_EJ_R_Si_Fi_Yh for gas to liquids (gtl) and coal to liquids (ctl) sectors
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "out_gtl" | sector == "out_ctl") %>%
mutate(sector = if_else(sector == "out_gtl", "gtl", "ctl")) -> L122.out_EJ_R_gtlctl_Yh
# GTL and CTL inputs (L122.in_EJ_R_gtlctl_F_Yh): derived as output times exogenous input-output coefficients
# Interpolate gas processing IO coefs to all historical years and match in the fuel name
L122.globaltech_coef %>%
filter(sector == "gtl"| sector == "ctl") -> L122.gtlctl_coef
L122.out_EJ_R_gtlctl_Yh %>%
rename(valueInput = value) %>%
left_join(L122.gtlctl_coef, by = c("sector", "fuel", "year")) %>%
mutate(value = valueInput * value) %>%
select(GCAM_region_ID, sector, fuel, year, value) -> L122.in_EJ_R_gtlctl_F_Yh
# CRUDE OIL REFINING
# Copied from original text:
# NOTE*1: This is complicated. The outputs of CTL and GTL have the same fuel name as the output of oil refining,
# so need to be deducted from TPES in order to calculate regional output of oil refining.
# In contrast, biofuels are assigned different names, so they are not in the TPES of refined liquids.
# Create tibble with appropriate sector and fuels for oil refining (output) for L122.out_EJ_R_oilrefining_Yh and add historical years
tibble(GCAM_region_ID = GCAM_region_names$GCAM_region_ID, sector = "oil refining", fuel = "oil")%>%
repeat_add_columns(tibble(year = HISTORICAL_YEARS)) -> L122.out_EJ_R_oilrefining_Yh
# Create en_bal_TPES_OIL, en_bal_oil, ctl_OIL, and gtlctl_oil to adjust the outputs of CTL and GTL given the same fuel names of the oil refining outputs (as mentioned in the note above)
# Get output for refined liquids for oil refining (TPES) sector
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "TPES") %>%
filter(fuel == "refined liquids") %>%
select(GCAM_region_ID,sector, year, value_en_bal_TPES = value) %>%
mutate(sector = "oil refining") -> en_bal_TPES_OIL
# Output for refined liquids for net_oil refining sector
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "net_oil refining") %>%
filter(fuel == "refined liquids") %>%
select(GCAM_region_ID, sector, year, value_en_bal_net_oil = value) %>%
mutate(sector = "oil refining") %>%
left_join_error_no_match(en_bal_TPES_OIL, by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value_en_bal = value_en_bal_TPES - value_en_bal_net_oil) %>%
select(-value_en_bal_TPES, -value_en_bal_net_oil) -> en_bal_oil
# Output for coal for CTL sector
L122.out_EJ_R_gtlctl_Yh %>%
filter(sector == "ctl", fuel == "coal") %>%
select(GCAM_region_ID, sector, year, value_ctl_oil = value) %>%
mutate(sector = "oil refining") -> ctl_OIL
# Output for coal for GTL sector
L122.out_EJ_R_gtlctl_Yh %>%
filter(sector == "gtl", fuel == "gas") %>%
select(GCAM_region_ID, sector, year, value_gtl_oil = value) %>%
mutate(sector = "oil refining") %>%
left_join_error_no_match(ctl_OIL, by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value_gtlctl = value_ctl_oil + value_gtl_oil) %>%
select(-value_ctl_oil, -value_gtl_oil) -> gtlctl_oil
# Final adjustments to fuel outputs for CTL and GTL to tackle the Note (NOTE*1) made above
# left_join_error_no_match could be used here since it has some problems with the timeshifting test
L122.out_EJ_R_oilrefining_Yh %>%
left_join(en_bal_oil, by = c("GCAM_region_ID", "sector", "year")) %>%
left_join(gtlctl_oil, by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value = value_en_bal - value_gtlctl) %>%
select(-value_en_bal, -value_gtlctl) -> L122.out_EJ_R_oilrefining_Yh
# Oil refining: input of oil is equal to TPES, and input of other fuels is from net refinery energy use
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "net_oil refining") %>%
filter(fuel == "refined liquids") %>%
left_join_error_no_match(select(filter(L1011.en_bal_EJ_R_Si_Fi_Yh, sector == "TPES", fuel == "refined liquids"), -sector), by = c("GCAM_region_ID", "fuel", "year")) %>%
select(-value.x) %>%
rename(value = value.y) %>%
bind_rows(filter(L1011.en_bal_EJ_R_Si_Fi_Yh, sector == "net_oil refining", fuel!= "refined liquids")) %>%
mutate(sector = "oil refining",
fuel = if_else(fuel == "refined liquids", "oil", fuel)) -> L122.in_EJ_R_oilrefining_F_Yh
# Calculate region- and fuel-specific coefficients of crude oil refining
L122.in_EJ_R_oilrefining_F_Yh %>%
left_join(select(L122.out_EJ_R_oilrefining_Yh, -fuel), by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value = value.x / value.y) %>%
select(-value.x, -value.y) -> L122.IO_R_oilrefining_F_Yh
# Combine all calibrated refinery input and output tables
bind_rows(L122.out_EJ_R_oilrefining_Yh, L122.out_EJ_R_gtlctl_Yh, L122.out_EJ_R_biofuel_Yh) -> L122.out_EJ_R_refining_F_Yh
bind_rows(L122.in_EJ_R_oilrefining_F_Yh, L122.in_EJ_R_gtlctl_F_Yh, L122.in_EJ_R_biofuel_F_Yh) -> L122.in_EJ_R_refining_F_Yh
# Extra final step: calculate and create the derived crop inputs to the various first-generation biofuel technologies, for the AGLU processing
L122.in_EJ_R_biofuel_F_Yh %>%
left_join(select(calibrated_techs, sector, fuel, passthrough.sector = minicam.energy.input), by = c("sector", "fuel")) %>%
filter(passthrough.sector %in% A21.globaltech_coef$supplysector) -> L122.in_EJ_R_1stgenbio_F_Yh
A21.globaltech_coef %>%
select(passthrough.sector = supplysector, minicam.energy.input) %>%
distinct(passthrough.sector, .keep_all = TRUE) %>%
right_join(L122.in_EJ_R_1stgenbio_F_Yh, by = "passthrough.sector") %>%
rename(GCAM_commodity = minicam.energy.input) -> L122.in_EJ_R_1stgenbio_F_Yh
# Crop inputs to biodiesel are region-specific
L122.in_EJ_R_1stgenbio_F_Yh %>%
filter(passthrough.sector == "regional biomassOil") %>%
left_join(select(A_regions, GCAM_region_ID, biomassOil_tech), by = "GCAM_region_ID") %>%
select(-GCAM_commodity) %>%
rename(GCAM_commodity = biomassOil_tech) %>%
bind_rows(filter(L122.in_EJ_R_1stgenbio_F_Yh, passthrough.sector != "regional biomassOil")) %>%
filter(GCAM_commodity %in% FAO_ag_items_PRODSTAT$GCAM_commodity) %>%
select(GCAM_region_ID, sector, fuel, passthrough.sector, GCAM_commodity, year, value) -> L122.in_EJ_R_1stgenbio_F_Yh
# Interpolate coefs (using repeat_add_columns) to all historical periods, and then multiply by the input quantities
# Filter 1971 since the value associated to this year is the same till 2100, but all historical years are missing. Therefore filter
# 1971 and the repeat the corresponding value (repeat_add_columns) for historical years
A21.globaltech_coef %>%
gather(hist_year, value, -supplysector, -subsector, -technology, -minicam.energy.input) %>%
filter(hist_year == min(HISTORICAL_YEARS)) %>%
repeat_add_columns(tibble(year = HISTORICAL_YEARS)) %>%
select(-hist_year) -> L121.globaltech_coef
# Multiply by input quantities by coefficients in L121.globaltech_coef
L122.in_EJ_R_1stgenbio_F_Yh %>%
select(GCAM_region_ID, GCAM_commodity, year, in_value = value) %>%
left_join(select(L121.globaltech_coef, GCAM_commodity = minicam.energy.input, year, value), by = c("GCAM_commodity", "year")) %>%
mutate(value = in_value * value) %>%
select(-in_value) -> L122.in_Mt_R_C_Yh
# GAS PROCESSING
# Note: Gas processing input-output coefficients are exogenous
# L122.gasproc_coef created by repeating 1971 coef (repeat_add_columns) for historical years based on A22.globaltech_coef.
# Match with the correct technologies based on calibrated_techs
A22.globaltech_coef %>%
filter(supplysector == "gas processing") %>%
gather(hist_year, value, -supplysector, -subsector, -technology, -minicam.energy.input) %>%
# Filter 1971 since the value associated to this year is the same till 2100, but all historical years are missing. Therefore filter
# 1971 and the repeat the corresponding value (repeat_add_columns) for historical years
filter(hist_year == min(HISTORICAL_YEARS)) %>%
repeat_add_columns(tibble(year = HISTORICAL_YEARS)) %>%
select(-hist_year) %>%
left_join(select(calibrated_techs, supplysector, subsector, technology, sector, fuel), by = c("supplysector", "subsector", "technology")) -> L122.gasproc_coef
# Gas processing output from biomass gasification is equal to regional TPES
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "TPES" , fuel == "gasified biomass") %>%
mutate(sector = "gas processing", fuel = "biomass") -> L122.out_EJ_R_gasproc_bio_Yh
# Gas processing output from coal gasification is calculated from the input of coal
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "in_gas processing", fuel == "coal") %>%
mutate(sector = "gas processing") -> L122.in_EJ_R_gasproc_coal_Yh
# Calulate output of gas (L122.out_EJ_R_gasproc_coal_Yh) based on coefficients and inputs.
# Output gas is obtain by dividing the input gas by coefficients from L122.gasproc_coef (gas_coef)
L122.in_EJ_R_gasproc_coal_Yh %>%
left_join(select(L122.gasproc_coef, sector, fuel, gas_coef = value, year), by = c("sector", "fuel", "year")) %>%
mutate(value = value/gas_coef) %>%
select(-gas_coef) -> L122.out_EJ_R_gasproc_coal_Yh
# Natural gas is equal to regional TPES minus upstream use of natural gas (e.g. GTL). Procedure and assumptiosn are explained below
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "TPES", fuel == "gas") %>%
mutate(sector = "gas processing") -> L122.out_EJ_R_gasproc_gas_Yh
# Note: The following code and their reason is given in "NOTE2" (pasted from original code) below
# NOTE2 (copied from original code): This is complicated. Several of the "upstream" energy users--in GCAM 3.0, unconventional oil production and gas-to-liquids--are assumed
# to use natural gas upstream of the processing and T&D infrastructure. If this is the case, then these sectors' consumption of gas should not
# be included in the gas processing and pipeline sectors. However this may be changed in the future, and for this reason the following
# method is designed to work if future users re-set the energy inputs to these technologies from "regional natural gas" to "wholesale gas".
# First, extract the input names to reg_nat_gas, gas_to_unconv_oil, gas_to_gtl
calibrated_techs %>%
filter(sector == "gas processing", fuel == "gas") %>%
select(minicam.energy.input) -> reg_nat_gas_tibble
reg_nat_gas_tibble$minicam.energy.input -> reg_nat_gas
calibrated_techs %>%
filter(sector == "unconventional oil production", fuel == "gas") %>%
select(minicam.energy.input) -> gas_to_unconv_oil_tibble
gas_to_unconv_oil_tibble$minicam.energy.input -> gas_to_unconv_oil
calibrated_techs %>%
filter(sector == "gtl", fuel == "gas") %>%
select(minicam.energy.input) -> gas_to_gtl_tibble
gas_to_gtl_tibble$minicam.energy.input -> gas_to_gtl
# (Copied from original text)
# Where the input names for unconv oil or GTL are equal to the name of the input to the gas processing sector,
# subtract from the gas processing sector's production
if(gas_to_unconv_oil == reg_nat_gas) {
L122.out_EJ_R_gasproc_gas_Yh %>%
filter(GCAM_region_ID %in% L121.in_EJ_R_unoil_F_Yh$GCAM_region_ID) %>%
left_join(select(L121.in_EJ_R_unoil_F_Yh, GCAM_region_ID, fuel, year, in_value = value), by = c("GCAM_region_ID", "fuel", "year")) %>%
mutate(value = value - in_value) %>%
select(-in_value)%>%
bind_rows(filter(L122.out_EJ_R_gasproc_gas_Yh,!(GCAM_region_ID %in% L121.in_EJ_R_unoil_F_Yh$GCAM_region_ID))) ->
L122.out_EJ_R_gasproc_gas_Yh
}
if(gas_to_gtl == reg_nat_gas) {
L122.out_EJ_R_gasproc_gas_Yh %>%
left_join(select(L122.in_EJ_R_gtlctl_F_Yh, GCAM_region_ID, fuel, year, in_value = value), by = c("GCAM_region_ID", "fuel", "year")) %>%
mutate(value = value - in_value)%>%
select(-in_value) -> L122.out_EJ_R_gasproc_gas_Yh
}
# Combine (rbind) individual fuel tables
bind_rows(L122.out_EJ_R_gasproc_gas_Yh, L122.out_EJ_R_gasproc_bio_Yh, L122.out_EJ_R_gasproc_coal_Yh) -> L122.out_EJ_R_gasproc_F_Yh
# Calculate the inputs to gas processing
L122.out_EJ_R_gasproc_F_Yh %>%
left_join(select(L122.gasproc_coef, sector, fuel, gas_coef = value, year), by = c("sector", "fuel", "year")) %>%
mutate(value = value * gas_coef)%>%
select(-gas_coef) -> L122.in_EJ_R_gasproc_F_Yh
# Create final outputs
L122.out_EJ_R_gasproc_F_Yh %>%
add_title("Outputs of gas processing by GCAM region / fuel / historical year ") %>%
add_units("EJ") %>%
add_comments("Combine individual fuel tables, including L122.out_EJ_R_gasproc_gas_Yh, L122.out_EJ_R_gasproc_bio_Yh, and L122.out_EJ_R_gasproc_coal_Yh") %>%
add_legacy_name("L122.out_EJ_R_gasproc_F_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.out_EJ_R_gasproc_F_Yh
L122.in_EJ_R_gasproc_F_Yh %>%
add_title("Inputs to gas processing by GCAM region / fuel / historical year") %>%
add_units("EJ") %>%
add_comments("Based on outputs and gasproc coefficients. Obtained as L122.out_EJ_R_gasproc_F_Yh times L122.gasproc_coef ") %>%
add_legacy_name("L122.in_EJ_R_gasproc_F_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.in_EJ_R_gasproc_F_Yh
L122.IO_R_oilrefining_F_Yh %>%
add_title("Oil refining input-output coefficients by GCAM region / fuel / historical year") %>%
add_units("Unitless") %>%
add_comments("Obtained by caltulating the ratio inpout/output for oil refining from L122.in_EJ_R_oilrefining_F_Yh and L122.out_EJ_R_oilrefining_Yh") %>%
add_legacy_name("L122.IO_R_oilrefining_F_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.IO_R_oilrefining_F_Yh
L122.out_EJ_R_refining_F_Yh %>%
add_title("Outputs of refining by GCAM region / fuel / historical year") %>%
add_units("EJ") %>%
add_comments("Combines all calibrated refinery output tables, including oil refining, gtl-ctl and biofuels ") %>%
add_legacy_name("L122.out_EJ_R_refining_F_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.out_EJ_R_refining_F_Yh
L122.in_EJ_R_refining_F_Yh %>%
add_title("Inputs to refining by GCAM region / fuel / historical year") %>%
add_units("EJ") %>%
add_comments("Combines all calibrated refinery input tables, including oil refining, gtl-ctl and biofuels") %>%
add_legacy_name("L122.in_EJ_R_refining_F_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.in_EJ_R_refining_F_Yh
L122.in_Mt_R_C_Yh %>%
add_title("Crop inputs to first-generation biofuel production by GCAM region / commodity / historical year") %>%
add_units("Mt") %>%
add_comments("Created by matching 1st generation bio with the global technologies coefficients for existing minicam energy inputs") %>%
add_legacy_name("L122.in_Mt_R_C_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.in_Mt_R_C_Yh
return_data(L122.out_EJ_R_gasproc_F_Yh, L122.in_EJ_R_gasproc_F_Yh, L122.IO_R_oilrefining_F_Yh, L122.out_EJ_R_refining_F_Yh, L122.in_EJ_R_refining_F_Yh, L122.in_Mt_R_C_Yh)
} else {
stop("Unknown command")
}
}
rohmin9122/gcam-korea-release documentation built on Nov. 26, 2020, 8:11 a.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 module_energy_LA122.gasproc_refining
Documented in module_energy_LA122.gasproc_refining
#' module_energy_LA122.gasproc_refining
#'
#' Create gasproc, oil refining and crops inputs, outputs and IO "input-output" coefficients for refining.
#'
#' @param command API command to execute
#' @param ... other optional parameters, depending on command
#' @return Depends on \code{command}: either a vector of required inputs,
#' a vector of output names, or (if \code{command} is "MAKE") all
#' the generated outputs: \code{L122.out_EJ_R_gasproc_F_Yh}, \code{L122.in_EJ_R_gasproc_F_Yh}, \code{L122.IO_R_oilrefining_F_Yh}, \code{L122.out_EJ_R_refining_F_Yh}, \code{L122.in_EJ_R_refining_F_Yh}, \code{L122.in_Mt_R_C_Yh}. The corresponding file in the
#' original data system was \code{LA122.gasproc_refining.R} (energy level1).
#' @details This chunk creates gasproc, oil refining and crops inputs, outputs and IO "input-output" coefficients for refining.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author FF, May 2017
module_energy_LA122.gasproc_refining <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "aglu/FAO/FAO_ag_items_PRODSTAT",
FILE = "energy/calibrated_techs",
FILE = "energy/A_regions",
FILE = "energy/A21.globaltech_coef",
FILE = "energy/A22.globaltech_coef",
"L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L122.out_EJ_R_gasproc_F_Yh",
"L122.in_EJ_R_gasproc_F_Yh",
"L122.IO_R_oilrefining_F_Yh",
"L122.out_EJ_R_refining_F_Yh",
"L122.in_EJ_R_refining_F_Yh",
"L122.in_Mt_R_C_Yh"))
} else if(command == driver.MAKE) {
EcYield_kgm2_hi <- EcYield_kgm2_lo <- GCAM_commodity <- GCAM_region_ID <- GLU <-
Irr_Rfd <- LC_bm2_hi <- LC_bm2_lo <- biodiesel <- biomassOil_tech <- ethanol <- fuel <-
fuel.x <- fuelInput <- gas_coef <- hist_year <- in_value <- landshare_hi <-
landshare_lo <- level <- minicam.energy.input <- passthrough.sector <- sector <-
subsector <- supplysector <- technology <- value <- value.x <- value.y <- valueInput <-
value_ctl_oil <- value_en_bal <- value_en_bal_TPES <- value_en_bal_net_oil <-
value_gtl_oil <- value_gtlctl <- year <- yield <- yieldmult_hi <- yieldmult_lo <- NULL # silence package check notes
all_data <- list(...)[[1]]
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
FAO_ag_items_PRODSTAT <- get_data(all_data, "aglu/FAO/FAO_ag_items_PRODSTAT")
calibrated_techs <- get_data(all_data, "energy/calibrated_techs")
A_regions <- get_data(all_data, "energy/A_regions")
A21.globaltech_coef <- get_data(all_data, "energy/A21.globaltech_coef")
A22.globaltech_coef <- get_data(all_data, "energy/A22.globaltech_coef")
L1011.en_bal_EJ_R_Si_Fi_Yh <- get_data(all_data, "L1011.en_bal_EJ_R_Si_Fi_Yh")
get_data(all_data, "L121.in_EJ_R_unoil_F_Yh") %>%
filter(year %in% HISTORICAL_YEARS) -> # ensure temp data match our current history
L121.in_EJ_R_unoil_F_Yh
# ===================================================
# Perform computations: Will start with refining
# Most of the technologies (in calibrated_techs.csv) have inputs from outputs based on exogenous coefficients (A22.globaltech_coef).
# Extracting those coefficients and interpolating them to 2010.
# Remove "x" year
A22.globaltech_coef %>%
semi_join(select(calibrated_techs, supplysector, subsector, technology), by = c("supplysector", "subsector", "technology")) %>%
left_join(select(calibrated_techs, supplysector, subsector, technology, minicam.energy.input, sector, fuel), by = c("supplysector", "subsector", "technology", "minicam.energy.input")) %>%
gather(hist_year, value, -supplysector, -subsector, -technology, -minicam.energy.input, -sector, -fuel) %>%
mutate(hist_year = as.numeric(hist_year)) %>%
filter(hist_year == min(HISTORICAL_YEARS)) %>%
repeat_add_columns(tibble(year = HISTORICAL_YEARS)) %>%
select(-hist_year) -> L122.globaltech_coef
# BIOMASS LIQUIDS: Ethanol and biodiesel output are equal to regional TPES
# Creating fuel constant for biomass liquids that will be used to filter biofuels from L1011.en_bal_EJ_R_Si_Fi_Yh and to create L122.out_EJ_R_biofuel_Yh
BIOMASS_LIQUIDS <- c("refined biofuels_ethanol", "refined biofuels_FT")
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "TPES") %>%
filter(fuel %in% BIOMASS_LIQUIDS) -> L122.out_EJ_R_biofuel_Yh
# Create ethanol sector for biomass from A_regions. This will be "row binded" with biodiesel sector below.
L122.out_EJ_R_biofuel_Yh %>%
filter(fuel == "refined biofuels_ethanol") %>%
left_join_error_no_match(select(A_regions, GCAM_region_ID, ethanol), by = "GCAM_region_ID") %>%
select(-sector) %>%
rename(sector = ethanol) -> BIOMASS_LIQUIDS_Ethanol
# Biodiesel sector and then binded with ethanol sector to consolidate everything into L122.out_EJ_R_biofuel_Yh
L122.out_EJ_R_biofuel_Yh %>%
filter(fuel == "refined biofuels_FT") %>%
left_join_error_no_match(select(A_regions, GCAM_region_ID, biodiesel), by = "GCAM_region_ID") %>%
select(-sector) %>%
rename(sector = biodiesel) %>%
bind_rows(BIOMASS_LIQUIDS_Ethanol) -> L122.out_EJ_R_biofuel_Yh
# Add fuels to appropiate sectors and regions in L122.out_EJ_R_biofuel_Yh from calibrated_techs
calibrated_techs %>%
select(sector, fuel) %>%
distinct(sector, .keep_all = TRUE) %>%
right_join(L122.out_EJ_R_biofuel_Yh, by = "sector") %>%
select(GCAM_region_ID, sector, fuel = fuel.x, year, value) -> L122.out_EJ_R_biofuel_Yh
# Inputs to biofuel production are region-specific
# Because some have multiple inputs, repeat coefficient table by number of regions and then subset only the applicable combinations
L122.globaltech_coef %>%
filter(sector %in% L122.out_EJ_R_biofuel_Yh$sector) %>%
repeat_add_columns(tibble(GCAM_region_ID = A_regions$GCAM_region_ID)) -> L122.biofuel_coef_repR
# Subset L122.biofuel_coef_repR based on A_regions by region and sector (ethanol)
L122.biofuel_coef_repR %>%
# Using semi_join to keep the sector = ethanol in L122.biofuel_coef_repR based on A_regions
semi_join(select(A_regions, GCAM_region_ID, sector = ethanol), by = c("sector", "GCAM_region_ID")) -> L122.biofuel_coef_repR_Ethanol
# Subset L122.biofuel_coef_repR based on A_regions by region and sector (biodisiel). Then adding ethanol sector (L122.biofuel_coef_repR_Ethanol)
L122.biofuel_coef_repR %>%
# Using semi_join to keep the sector = biodiesel in L122.biofuel_coef_repR based on A_regions
semi_join(select(A_regions, GCAM_region_ID, sector = biodiesel), by = c("sector", "GCAM_region_ID")) %>%
bind_rows(L122.biofuel_coef_repR_Ethanol) -> L122.biofuel_coef_R
# Build table of inputs to biofuel production (IO coefs times output)
L122.biofuel_coef_R %>%
rename(fuelInput = fuel, valueInput = value) %>%
left_join(L122.out_EJ_R_biofuel_Yh, by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value = valueInput * value) %>%
select(-valueInput, -fuel) %>%
rename(fuel = fuelInput) %>%
select(GCAM_region_ID, sector, fuel, year, value) -> L122.in_EJ_R_biofuel_F_Yh
# GAS AND COAL TO LIQUIDS
# Create L122.out_EJ_R_gtlctl_Yh from L1011.en_bal_EJ_R_Si_Fi_Yh for gas to liquids (gtl) and coal to liquids (ctl) sectors
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "out_gtl" | sector == "out_ctl") %>%
mutate(sector = if_else(sector == "out_gtl", "gtl", "ctl")) -> L122.out_EJ_R_gtlctl_Yh
# GTL and CTL inputs (L122.in_EJ_R_gtlctl_F_Yh): derived as output times exogenous input-output coefficients
# Interpolate gas processing IO coefs to all historical years and match in the fuel name
L122.globaltech_coef %>%
filter(sector == "gtl"| sector == "ctl") -> L122.gtlctl_coef
L122.out_EJ_R_gtlctl_Yh %>%
rename(valueInput = value) %>%
left_join(L122.gtlctl_coef, by = c("sector", "fuel", "year")) %>%
mutate(value = valueInput * value) %>%
select(GCAM_region_ID, sector, fuel, year, value) -> L122.in_EJ_R_gtlctl_F_Yh
# CRUDE OIL REFINING
# Copied from original text:
# NOTE*1: This is complicated. The outputs of CTL and GTL have the same fuel name as the output of oil refining,
# so need to be deducted from TPES in order to calculate regional output of oil refining.
# In contrast, biofuels are assigned different names, so they are not in the TPES of refined liquids.
# Create tibble with appropriate sector and fuels for oil refining (output) for L122.out_EJ_R_oilrefining_Yh and add historical years
tibble(GCAM_region_ID = GCAM_region_names$GCAM_region_ID, sector = "oil refining", fuel = "oil")%>%
repeat_add_columns(tibble(year = HISTORICAL_YEARS)) -> L122.out_EJ_R_oilrefining_Yh
# Create en_bal_TPES_OIL, en_bal_oil, ctl_OIL, and gtlctl_oil to adjust the outputs of CTL and GTL given the same fuel names of the oil refining outputs (as mentioned in the note above)
# Get output for refined liquids for oil refining (TPES) sector
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "TPES") %>%
filter(fuel == "refined liquids") %>%
select(GCAM_region_ID,sector, year, value_en_bal_TPES = value) %>%
mutate(sector = "oil refining") -> en_bal_TPES_OIL
# Output for refined liquids for net_oil refining sector
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "net_oil refining") %>%
filter(fuel == "refined liquids") %>%
select(GCAM_region_ID, sector, year, value_en_bal_net_oil = value) %>%
mutate(sector = "oil refining") %>%
left_join_error_no_match(en_bal_TPES_OIL, by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value_en_bal = value_en_bal_TPES - value_en_bal_net_oil) %>%
select(-value_en_bal_TPES, -value_en_bal_net_oil) -> en_bal_oil
# Output for coal for CTL sector
L122.out_EJ_R_gtlctl_Yh %>%
filter(sector == "ctl", fuel == "coal") %>%
select(GCAM_region_ID, sector, year, value_ctl_oil = value) %>%
mutate(sector = "oil refining") -> ctl_OIL
# Output for coal for GTL sector
L122.out_EJ_R_gtlctl_Yh %>%
filter(sector == "gtl", fuel == "gas") %>%
select(GCAM_region_ID, sector, year, value_gtl_oil = value) %>%
mutate(sector = "oil refining") %>%
left_join_error_no_match(ctl_OIL, by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value_gtlctl = value_ctl_oil + value_gtl_oil) %>%
select(-value_ctl_oil, -value_gtl_oil) -> gtlctl_oil
# Final adjustments to fuel outputs for CTL and GTL to tackle the Note (NOTE*1) made above
# left_join_error_no_match could be used here since it has some problems with the timeshifting test
L122.out_EJ_R_oilrefining_Yh %>%
left_join(en_bal_oil, by = c("GCAM_region_ID", "sector", "year")) %>%
left_join(gtlctl_oil, by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value = value_en_bal - value_gtlctl) %>%
select(-value_en_bal, -value_gtlctl) -> L122.out_EJ_R_oilrefining_Yh
# Oil refining: input of oil is equal to TPES, and input of other fuels is from net refinery energy use
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "net_oil refining") %>%
filter(fuel == "refined liquids") %>%
left_join_error_no_match(select(filter(L1011.en_bal_EJ_R_Si_Fi_Yh, sector == "TPES", fuel == "refined liquids"), -sector), by = c("GCAM_region_ID", "fuel", "year")) %>%
select(-value.x) %>%
rename(value = value.y) %>%
bind_rows(filter(L1011.en_bal_EJ_R_Si_Fi_Yh, sector == "net_oil refining", fuel!= "refined liquids")) %>%
mutate(sector = "oil refining",
fuel = if_else(fuel == "refined liquids", "oil", fuel)) -> L122.in_EJ_R_oilrefining_F_Yh
# Calculate region- and fuel-specific coefficients of crude oil refining
L122.in_EJ_R_oilrefining_F_Yh %>%
left_join(select(L122.out_EJ_R_oilrefining_Yh, -fuel), by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value = value.x / value.y) %>%
select(-value.x, -value.y) -> L122.IO_R_oilrefining_F_Yh
# Combine all calibrated refinery input and output tables
bind_rows(L122.out_EJ_R_oilrefining_Yh, L122.out_EJ_R_gtlctl_Yh, L122.out_EJ_R_biofuel_Yh) -> L122.out_EJ_R_refining_F_Yh
bind_rows(L122.in_EJ_R_oilrefining_F_Yh, L122.in_EJ_R_gtlctl_F_Yh, L122.in_EJ_R_biofuel_F_Yh) -> L122.in_EJ_R_refining_F_Yh
# Extra final step: calculate and create the derived crop inputs to the various first-generation biofuel technologies, for the AGLU processing
L122.in_EJ_R_biofuel_F_Yh %>%
left_join(select(calibrated_techs, sector, fuel, passthrough.sector = minicam.energy.input), by = c("sector", "fuel")) %>%
filter(passthrough.sector %in% A21.globaltech_coef$supplysector) -> L122.in_EJ_R_1stgenbio_F_Yh
A21.globaltech_coef %>%
select(passthrough.sector = supplysector, minicam.energy.input) %>%
distinct(passthrough.sector, .keep_all = TRUE) %>%
right_join(L122.in_EJ_R_1stgenbio_F_Yh, by = "passthrough.sector") %>%
rename(GCAM_commodity = minicam.energy.input) -> L122.in_EJ_R_1stgenbio_F_Yh
# Crop inputs to biodiesel are region-specific
L122.in_EJ_R_1stgenbio_F_Yh %>%
filter(passthrough.sector == "regional biomassOil") %>%
left_join(select(A_regions, GCAM_region_ID, biomassOil_tech), by = "GCAM_region_ID") %>%
select(-GCAM_commodity) %>%
rename(GCAM_commodity = biomassOil_tech) %>%
bind_rows(filter(L122.in_EJ_R_1stgenbio_F_Yh, passthrough.sector != "regional biomassOil")) %>%
filter(GCAM_commodity %in% FAO_ag_items_PRODSTAT$GCAM_commodity) %>%
select(GCAM_region_ID, sector, fuel, passthrough.sector, GCAM_commodity, year, value) -> L122.in_EJ_R_1stgenbio_F_Yh
# Interpolate coefs (using repeat_add_columns) to all historical periods, and then multiply by the input quantities
# Filter 1971 since the value associated to this year is the same till 2100, but all historical years are missing. Therefore filter
# 1971 and the repeat the corresponding value (repeat_add_columns) for historical years
A21.globaltech_coef %>%
gather(hist_year, value, -supplysector, -subsector, -technology, -minicam.energy.input) %>%
filter(hist_year == min(HISTORICAL_YEARS)) %>%
repeat_add_columns(tibble(year = HISTORICAL_YEARS)) %>%
select(-hist_year) -> L121.globaltech_coef
# Multiply by input quantities by coefficients in L121.globaltech_coef
L122.in_EJ_R_1stgenbio_F_Yh %>%
select(GCAM_region_ID, GCAM_commodity, year, in_value = value) %>%
left_join(select(L121.globaltech_coef, GCAM_commodity = minicam.energy.input, year, value), by = c("GCAM_commodity", "year")) %>%
mutate(value = in_value * value) %>%
select(-in_value) -> L122.in_Mt_R_C_Yh
# GAS PROCESSING
# Note: Gas processing input-output coefficients are exogenous
# L122.gasproc_coef created by repeating 1971 coef (repeat_add_columns) for historical years based on A22.globaltech_coef.
# Match with the correct technologies based on calibrated_techs
A22.globaltech_coef %>%
filter(supplysector == "gas processing") %>%
gather(hist_year, value, -supplysector, -subsector, -technology, -minicam.energy.input) %>%
# Filter 1971 since the value associated to this year is the same till 2100, but all historical years are missing. Therefore filter
# 1971 and the repeat the corresponding value (repeat_add_columns) for historical years
filter(hist_year == min(HISTORICAL_YEARS)) %>%
repeat_add_columns(tibble(year = HISTORICAL_YEARS)) %>%
select(-hist_year) %>%
left_join(select(calibrated_techs, supplysector, subsector, technology, sector, fuel), by = c("supplysector", "subsector", "technology")) -> L122.gasproc_coef
# Gas processing output from biomass gasification is equal to regional TPES
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "TPES" , fuel == "gasified biomass") %>%
mutate(sector = "gas processing", fuel = "biomass") -> L122.out_EJ_R_gasproc_bio_Yh
# Gas processing output from coal gasification is calculated from the input of coal
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "in_gas processing", fuel == "coal") %>%
mutate(sector = "gas processing") -> L122.in_EJ_R_gasproc_coal_Yh
# Calulate output of gas (L122.out_EJ_R_gasproc_coal_Yh) based on coefficients and inputs.
# Output gas is obtain by dividing the input gas by coefficients from L122.gasproc_coef (gas_coef)
L122.in_EJ_R_gasproc_coal_Yh %>%
left_join(select(L122.gasproc_coef, sector, fuel, gas_coef = value, year), by = c("sector", "fuel", "year")) %>%
mutate(value = value/gas_coef) %>%
select(-gas_coef) -> L122.out_EJ_R_gasproc_coal_Yh
# Natural gas is equal to regional TPES minus upstream use of natural gas (e.g. GTL). Procedure and assumptiosn are explained below
L1011.en_bal_EJ_R_Si_Fi_Yh %>%
filter(sector == "TPES", fuel == "gas") %>%
mutate(sector = "gas processing") -> L122.out_EJ_R_gasproc_gas_Yh
# Note: The following code and their reason is given in "NOTE2" (pasted from original code) below
# NOTE2 (copied from original code): This is complicated. Several of the "upstream" energy users--in GCAM 3.0, unconventional oil production and gas-to-liquids--are assumed
# to use natural gas upstream of the processing and T&D infrastructure. If this is the case, then these sectors' consumption of gas should not
# be included in the gas processing and pipeline sectors. However this may be changed in the future, and for this reason the following
# method is designed to work if future users re-set the energy inputs to these technologies from "regional natural gas" to "wholesale gas".
# First, extract the input names to reg_nat_gas, gas_to_unconv_oil, gas_to_gtl
calibrated_techs %>%
filter(sector == "gas processing", fuel == "gas") %>%
select(minicam.energy.input) -> reg_nat_gas_tibble
reg_nat_gas_tibble$minicam.energy.input -> reg_nat_gas
calibrated_techs %>%
filter(sector == "unconventional oil production", fuel == "gas") %>%
select(minicam.energy.input) -> gas_to_unconv_oil_tibble
gas_to_unconv_oil_tibble$minicam.energy.input -> gas_to_unconv_oil
calibrated_techs %>%
filter(sector == "gtl", fuel == "gas") %>%
select(minicam.energy.input) -> gas_to_gtl_tibble
gas_to_gtl_tibble$minicam.energy.input -> gas_to_gtl
# (Copied from original text)
# Where the input names for unconv oil or GTL are equal to the name of the input to the gas processing sector,
# subtract from the gas processing sector's production
if(gas_to_unconv_oil == reg_nat_gas) {
L122.out_EJ_R_gasproc_gas_Yh %>%
filter(GCAM_region_ID %in% L121.in_EJ_R_unoil_F_Yh$GCAM_region_ID) %>%
left_join(select(L121.in_EJ_R_unoil_F_Yh, GCAM_region_ID, fuel, year, in_value = value), by = c("GCAM_region_ID", "fuel", "year")) %>%
mutate(value = value - in_value) %>%
select(-in_value)%>%
bind_rows(filter(L122.out_EJ_R_gasproc_gas_Yh,!(GCAM_region_ID %in% L121.in_EJ_R_unoil_F_Yh$GCAM_region_ID))) ->
L122.out_EJ_R_gasproc_gas_Yh
}
if(gas_to_gtl == reg_nat_gas) {
L122.out_EJ_R_gasproc_gas_Yh %>%
left_join(select(L122.in_EJ_R_gtlctl_F_Yh, GCAM_region_ID, fuel, year, in_value = value), by = c("GCAM_region_ID", "fuel", "year")) %>%
mutate(value = value - in_value)%>%
select(-in_value) -> L122.out_EJ_R_gasproc_gas_Yh
}
# Combine (rbind) individual fuel tables
bind_rows(L122.out_EJ_R_gasproc_gas_Yh, L122.out_EJ_R_gasproc_bio_Yh, L122.out_EJ_R_gasproc_coal_Yh) -> L122.out_EJ_R_gasproc_F_Yh
# Calculate the inputs to gas processing
L122.out_EJ_R_gasproc_F_Yh %>%
left_join(select(L122.gasproc_coef, sector, fuel, gas_coef = value, year), by = c("sector", "fuel", "year")) %>%
mutate(value = value * gas_coef)%>%
select(-gas_coef) -> L122.in_EJ_R_gasproc_F_Yh
# Create final outputs
L122.out_EJ_R_gasproc_F_Yh %>%
add_title("Outputs of gas processing by GCAM region / fuel / historical year ") %>%
add_units("EJ") %>%
add_comments("Combine individual fuel tables, including L122.out_EJ_R_gasproc_gas_Yh, L122.out_EJ_R_gasproc_bio_Yh, and L122.out_EJ_R_gasproc_coal_Yh") %>%
add_legacy_name("L122.out_EJ_R_gasproc_F_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.out_EJ_R_gasproc_F_Yh
L122.in_EJ_R_gasproc_F_Yh %>%
add_title("Inputs to gas processing by GCAM region / fuel / historical year") %>%
add_units("EJ") %>%
add_comments("Based on outputs and gasproc coefficients. Obtained as L122.out_EJ_R_gasproc_F_Yh times L122.gasproc_coef ") %>%
add_legacy_name("L122.in_EJ_R_gasproc_F_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.in_EJ_R_gasproc_F_Yh
L122.IO_R_oilrefining_F_Yh %>%
add_title("Oil refining input-output coefficients by GCAM region / fuel / historical year") %>%
add_units("Unitless") %>%
add_comments("Obtained by caltulating the ratio inpout/output for oil refining from L122.in_EJ_R_oilrefining_F_Yh and L122.out_EJ_R_oilrefining_Yh") %>%
add_legacy_name("L122.IO_R_oilrefining_F_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.IO_R_oilrefining_F_Yh
L122.out_EJ_R_refining_F_Yh %>%
add_title("Outputs of refining by GCAM region / fuel / historical year") %>%
add_units("EJ") %>%
add_comments("Combines all calibrated refinery output tables, including oil refining, gtl-ctl and biofuels ") %>%
add_legacy_name("L122.out_EJ_R_refining_F_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.out_EJ_R_refining_F_Yh
L122.in_EJ_R_refining_F_Yh %>%
add_title("Inputs to refining by GCAM region / fuel / historical year") %>%
add_units("EJ") %>%
add_comments("Combines all calibrated refinery input tables, including oil refining, gtl-ctl and biofuels") %>%
add_legacy_name("L122.in_EJ_R_refining_F_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.in_EJ_R_refining_F_Yh
L122.in_Mt_R_C_Yh %>%
add_title("Crop inputs to first-generation biofuel production by GCAM region / commodity / historical year") %>%
add_units("Mt") %>%
add_comments("Created by matching 1st generation bio with the global technologies coefficients for existing minicam energy inputs") %>%
add_legacy_name("L122.in_Mt_R_C_Yh") %>%
add_precursors("common/GCAM_region_names", "aglu/FAO/FAO_ag_items_PRODSTAT", "energy/calibrated_techs",
"energy/A_regions", "energy/A21.globaltech_coef", "energy/A22.globaltech_coef", "L1011.en_bal_EJ_R_Si_Fi_Yh",
"L121.in_EJ_R_unoil_F_Yh") ->
L122.in_Mt_R_C_Yh
return_data(L122.out_EJ_R_gasproc_F_Yh, L122.in_EJ_R_gasproc_F_Yh, L122.IO_R_oilrefining_F_Yh, L122.out_EJ_R_refining_F_Yh, L122.in_EJ_R_refining_F_Yh, L122.in_Mt_R_C_Yh)
} else {
stop("Unknown command")
}
}
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.