R/zchunk_L224.heat.R
In rohmin9122/gcam-korea-release: Build the GCAM-Korea Input Datasets
Defines functions
module_energy_L224.heat
Documented in
module_energy_L224.heat
#' module_energy_L224.heat
#'
#' Write district heat sector outputs.
#'
#' @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{L224.SectorLogitTables[[ curr_table ]]$data}, \code{L224.Supplysector_heat}, \code{L224.SubsectorLogitTables[[ curr_table ]]$data}, \code{L224.SubsectorLogit_heat}, \code{L224.SubsectorShrwt_heat}, \code{L224.SubsectorShrwtFllt_heat}, \code{L224.SubsectorInterp_heat}, \code{L224.SubsectorInterpTo_heat}, \code{L224.StubTech_heat}, \code{L224.GlobalTechCoef_heat}, \code{L224.GlobalTechCost_heat}, \code{L224.GlobalTechShrwt_heat}, \code{L224.StubTechCalInput_heat}, \code{L224.StubTechSecOut_elec}, \code{L224.StubTechCost_elec}. The corresponding file in the
#' original data system was \code{L224.heat.R} (energy level2).
#' @details This chunk creates level 2 output files for district heat sector. It creates supply sector information,
#' subsector logit exponents, subsector shareweight and interpolation, and stubtech info by writing assumption file
#' information to all model periods and regions that have district heat. It creates global tech coef, costs, and shareweights
#' by interpolating assumptions. From the level 1 heat data, this chunk computes stub tech calibrated inputs, secondary
#' outputs from elec and modified costs.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author JDH August 2017
module_energy_L224.heat <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/calibrated_techs",
FILE = "energy/A_regions",
FILE = "energy/A24.sector",
FILE = "energy/A24.subsector_logit",
FILE = "energy/A24.subsector_shrwt",
FILE = "energy/A24.subsector_interp",
FILE = "energy/A24.globaltech_coef",
FILE = "energy/A24.globaltech_cost",
FILE = "energy/A24.globaltech_shrwt",
"L1231.eff_R_elec_F_tech_Yh",
"L124.in_EJ_R_heat_F_Yh",
"L124.heatoutratio_R_elec_F_tech_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L224.Supplysector_heat",
"L224.SubsectorLogit_heat",
"L224.SubsectorShrwt_heat",
"L224.SubsectorShrwtFllt_heat",
"L224.SubsectorInterp_heat",
"L224.SubsectorInterpTo_heat",
"L224.StubTech_heat",
"L224.GlobalTechCoef_heat",
"L224.GlobalTechCost_heat",
"L224.GlobalTechShrwt_heat",
"L224.StubTechCalInput_heat",
"L224.StubTechSecOut_elec",
"L224.StubTechCost_elec"))
} else if(command == driver.MAKE) {
# Silence package checks
has_district_heat <- region <- year.fillout <- to.value <- efficiency <-
technology <- coef <- subsector <- supplysector <- minicam.energy.input <-
input.cost <- minicam.non.energy.input <- share.weight <- sector <- fuel <-
value <- subs.share.weight <- calibrated.value <- secondary.output.name <-
secondary.output <- stub.technology <- cost_modifier <- year <- NULL
all_data <- list(...)[[1]]
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
calibrated_techs <- get_data(all_data, "energy/calibrated_techs")
A_regions <- get_data(all_data, "energy/A_regions")
A24.sector <- get_data(all_data, "energy/A24.sector")
A24.subsector_logit <- get_data(all_data, "energy/A24.subsector_logit")
A24.subsector_shrwt <- get_data(all_data, "energy/A24.subsector_shrwt")
A24.subsector_interp <- get_data(all_data, "energy/A24.subsector_interp")
A24.globaltech_coef <- get_data(all_data, "energy/A24.globaltech_coef")
A24.globaltech_cost <- get_data(all_data, "energy/A24.globaltech_cost")
A24.globaltech_shrwt <- get_data(all_data, "energy/A24.globaltech_shrwt")
L1231.eff_R_elec_F_tech_Yh <- get_data(all_data, "L1231.eff_R_elec_F_tech_Yh")
L124.in_EJ_R_heat_F_Yh <- get_data(all_data, "L124.in_EJ_R_heat_F_Yh")
L124.heatoutratio_R_elec_F_tech_Yh <- get_data(all_data, "L124.heatoutratio_R_elec_F_tech_Yh")
# Changing input data into long format
A24.globaltech_coef %>%
gather_years(value_col = "coef") -> A24.globaltech_coef
A24.globaltech_cost %>%
gather_years(value_col = "input.cost") -> A24.globaltech_cost
A24.globaltech_shrwt %>%
gather_years(value_col = "share.weight") -> A24.globaltech_shrwt
# ===================================================
# Create list of regions with district heat modeled
A_regions %>%
filter(has_district_heat == 1) %>%
select(region) -> heat_region
# Supply sector information for district heat sectors
A24.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
filter(region %in% heat_region$region) -> L224.Supplysector_heat
# Subsector logit exponents of district heat sectors
A24.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
filter(region %in% heat_region$region) -> L224.SubsectorLogit_heat
# L224.SubsectorShrwt_heat and L224.SubsectorShrwtFllt_heat: Subsector shareweights of district heat sectors
if(any(!is.na(A24.subsector_shrwt$year))) {
A24.subsector_shrwt %>%
filter(!is.na(year)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwt"]], GCAM_region_names = GCAM_region_names ) %>%
filter(region %in% heat_region$region) -> L224.SubsectorShrwt_heat
}
if(any(!is.na(A24.subsector_shrwt$year.fillout))) {
A24.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names = GCAM_region_names ) %>%
filter(region %in% heat_region$region) -> L224.SubsectorShrwtFllt_heat
}
# Subsector shareweight interpolation of district heat sectors
if(any(is.na(A24.subsector_interp$to.value))) {
A24.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]], GCAM_region_names = GCAM_region_names) %>%
filter(region %in% heat_region$region) -> L224.SubsectorInterp_heat
}
if(any(!is.na(A24.subsector_interp$to.value))) {
A24.subsector_interp %>%
filter(!is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterpTo"]], GCAM_region_names = GCAM_region_names) %>%
filter(region %in% heat_region$region) -> L224.SubsectorInterpTo_heat
}
# Identification of stub technologies of district heat
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A24.globaltech_shrwt %>%
select(supplysector, subsector, technology) %>%
distinct %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names = GCAM_region_names) %>%
rename(stub.technology = technology) %>%
filter(region %in% heat_region$region) -> L224.StubTech_heat
# Coefficients of global technologies
# L224.GlobalTechCoef_heat: Energy inputs and coefficients of global technologies for district heat
A24.globaltech_coef %>%
select(supplysector, subsector, technology, minicam.energy.input) %>%
distinct %>%
# Interpolate to all years
repeat_add_columns(tibble(year = c(HISTORICAL_YEARS, MODEL_FUTURE_YEARS))) %>%
left_join(A24.globaltech_coef, by = c("supplysector", "subsector", "technology", "minicam.energy.input", "year")) %>%
mutate(coef = round(approx_fun(year, value = coef, rule = 1), energy.DIGITS_COEFFICIENT)) %>%
filter(year %in% MODEL_YEARS) %>%
select(sector.name = supplysector, subsector.name = subsector, technology, minicam.energy.input, year, coefficient = coef) -> L224.GlobalTechCoef_heat
# Costs of global technologies
# L224.GlobalTechCost_heat: Costs of global technologies for district heat
A24.globaltech_cost %>%
select(supplysector, subsector, technology, minicam.non.energy.input) %>%
distinct %>%
# Interpolate to all years
repeat_add_columns(tibble(year = c(HISTORICAL_YEARS, MODEL_FUTURE_YEARS))) %>%
left_join(A24.globaltech_cost, by = c("supplysector", "subsector", "technology", "minicam.non.energy.input", "year")) %>%
group_by(supplysector, subsector, technology, minicam.non.energy.input) %>%
mutate(input.cost = round(approx_fun(year, value = input.cost, rule = 1), energy.DIGITS_COST)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
select(sector.name = supplysector, subsector.name = subsector, technology, minicam.non.energy.input, year, input.cost) -> L224.GlobalTechCost_heat
# Shareweights of global technologies
# L224.GlobalTechShrwt_heat: Shareweights of global technologies for district heat
A24.globaltech_shrwt %>%
select(supplysector, subsector, technology) %>%
distinct %>%
# Interpolate to all years
repeat_add_columns(tibble(year = c(HISTORICAL_YEARS, MODEL_FUTURE_YEARS))) %>%
left_join(A24.globaltech_shrwt, by = c("supplysector", "subsector", "technology", "year")) %>%
mutate(share.weight = approx_fun(year, value = share.weight, rule = 1)) %>%
filter(year %in% MODEL_YEARS) %>%
select(sector.name = supplysector, subsector.name = subsector, technology, year, share.weight) -> L224.GlobalTechShrwt_heat
# Calibration and region-specific data, calibrated input to district heat
L124.in_EJ_R_heat_F_Yh %>%
left_join(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join(calibrated_techs %>%
select(sector, fuel, supplysector, subsector, technology, minicam.energy.input) %>%
distinct, by = c("sector", "fuel")) %>%
rename(stub.technology = technology) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
filter(region %in% heat_region$region) %>%
select(LEVEL2_DATA_NAMES[["StubTechYr"]], "minicam.energy.input", "value") %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT),
year.share.weight = year,
subs.share.weight = if_else(calibrated.value == 0, 0, 1),
share.weight = subs.share.weight) %>%
select(-value) -> L224.StubTechCalInput_heat
# Secondary output of heat, applied to electricity generation technologies
# NOTE: This is complicated. Initially tried using historical information for all model periods that fall within historical time
# (i.e. not just the model base years). However for regions like the FSU where historical periods often have very low output of heat
# from the district heat sector, and most heat as a secondary output from the electricity sector, the secondary output heat can easily
# exceed the demands from the end-use sectors, causing model solution failure. For this reason, the convention applied here is to
# use the secondary output of heat from the power sector only in the model base years.
L124.heatoutratio_R_elec_F_tech_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
left_join(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join(calibrated_techs %>%
select(sector, fuel, supplysector, subsector, technology) %>%
distinct, by = c("sector", "fuel", "technology")) %>%
mutate(stub.technology = technology,
secondary.output.name = A24.sector[["supplysector"]]) %>%
select(LEVEL2_DATA_NAMES[["StubTechYr"]], "secondary.output.name", "value") %>%
mutate(secondary.output = round(value, energy.DIGITS_CALOUTPUT)) %>%
select(-value) -> L224.StubTechSecOut_elec
# Calculate cost adjustment, equal to the output of heat multiplied by the heat price (to minimize the distortion of including the secondary output)
L224.StubTechSecOut_elec %>%
select(LEVEL2_DATA_NAMES[["StubTechYr"]], "secondary.output") %>%
mutate(minicam.non.energy.input = "heat plant",
input.cost = round(secondary.output*energy.HEAT_PRICE, energy.DIGITS_COST))-> L224.StubTechCost_elec
# The secondary output of heat from CHP in the electric sector can cause the price of the technologies
# to go very low or negative if the technology cost is not modified to reflect the additional costs of
# CHP systems (as compared with electricity-only systems). Low technology costs can cause unrealistically
# low electricity prices in the calibration year, distorting behavior in future years. In this method,
# costs related to heat production and distribution are backed out from exogenous heat prices and data-derived heat:power ratios.
L1231.eff_R_elec_F_tech_Yh %>%
filter(year %in% MODEL_YEARS) %>%
rename(efficiency = value) %>%
left_join(GCAM_region_names, by = "GCAM_region_ID") %>%
filter(region %in% heat_region$region) %>%
filter(fuel == "gas") %>%
filter(efficiency < energy.DEFAULT_ELECTRIC_EFFICIENCY) %>%
mutate(cost_modifier = energy.GAS_PRICE * (1 / energy.DEFAULT_ELECTRIC_EFFICIENCY - 1 / efficiency)) -> L224.eff_cost_adj_Rh_elec_gas_sc_Y
# Modify the costs
L224.StubTechCost_elec %>%
left_join(L224.eff_cost_adj_Rh_elec_gas_sc_Y %>%
rename(subsector = fuel, stub.technology = technology) %>%
select(region, subsector, stub.technology, year, cost_modifier),
by = c("region", "subsector", "stub.technology", "year")) %>%
mutate(input.cost = if_else(!is.na(cost_modifier), round(pmax(0, input.cost + cost_modifier), energy.DIGITS_COST), input.cost)) %>%
select(-cost_modifier, -secondary.output) -> L224.StubTechCost_elec
# Need to fill out object names for all model time periods
L224.StubTechCost_elec %>%
filter(year == max(year)) %>%
select(-year) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
mutate(input.cost = 0) -> L224.StubTechCost_elec_fut
L224.StubTechCost_elec %>%
bind_rows(L224.StubTechCost_elec_fut) -> L224.StubTechCost_elec
# ===================================================
# Produce outputs
L224.Supplysector_heat %>%
add_title("Supply sector information for district heat sectors") %>%
add_units("N/A") %>%
add_comments("Supply sector info for district heat is written to all regions, ") %>%
add_comments("filtered by which regions have district heat") %>%
add_legacy_name("L224.Supplysector_heat") %>%
add_precursors("energy/A24.sector", "energy/A_regions", "common/GCAM_region_names") ->
L224.Supplysector_heat
L224.SubsectorLogit_heat %>%
add_title("Subsector logit exponents of district heat sectors") %>%
add_units("N/A") %>%
add_comments("Subsector logit exponents for district heat is written to all regions, ") %>%
add_comments("filtered by which regions have district heat") %>%
add_legacy_name("L224.SubsectorLogit_heat") %>%
add_precursors("energy/A24.subsector_logit", "energy/A_regions", "common/GCAM_region_names") ->
L224.SubsectorLogit_heat
if(exists("L224.SubsectorShrwt_heat")) {
L224.SubsectorShrwt_heat %>%
add_title("Subsector shareweights of district heat sectors") %>%
add_units("N/A") %>%
add_comments("If year is not NA: Subsector shareweights for district heat written to all regions, ") %>%
add_comments("filtered by which regions have district heat") %>%
add_legacy_name("L224.SubsectorShrwt_heat") %>%
add_precursors("energy/A24.subsector_shrwt", "energy/A_regions", "common/GCAM_region_names") ->
L224.SubsectorShrwt_heat
} else {
# If year column of A24.subsector_shrwt is all N/A, then a blank tibble is produced (and presumably the following tibble, using year.fillout is made)
missing_data() %>%
add_legacy_name("L224.SubsectorShrwt_heat") ->
L224.SubsectorShrwt_heat
}
if(exists("L224.SubsectorShrwtFllt_heat")) {
L224.SubsectorShrwtFllt_heat %>%
add_title("Subsector shareweights of district heat sectors with fillout year") %>%
add_units("N/A") %>%
add_comments("If year.fillout is not NA: Subsector shareweights for district heat written to all regions, ") %>%
add_comments("filtered by which regions have district heat, uses year.fillout") %>%
add_legacy_name("L224.SubsectorShrwtFllt_heat") %>%
add_precursors("energy/A24.subsector_shrwt", "energy/A_regions", "common/GCAM_region_names") ->
L224.SubsectorShrwtFllt_heat
} else {
# If year.fillout column of A24.subsector_shrwt is all N/A, then a blank tibble is produced
missing_data() %>%
add_legacy_name("L224.SubsectorShrwtFllt_heat") ->
L224.SubsectorShrwtFllt_heat
}
if(exists("L224.SubsectorInterp_heat")) {
L224.SubsectorInterp_heat %>%
add_title("Subsector shareweight interpolation of district heat sectors") %>%
add_units("units") %>%
add_comments("Interpolated data from A24.subsector_interp, ") %>%
add_comments("filtered by which regions have district heat") %>%
add_legacy_name("L224.SubsectorInterp_heat") %>%
add_precursors("energy/A24.subsector_interp", "energy/A_regions", "common/GCAM_region_names") ->
L224.SubsectorInterp_heat
} else {
# If interp.to column of A24.subsector_interp contains no N/A values, then a blank tibble is produced
missing_data() %>%
add_legacy_name("L224.SubsectorInterp_heat") ->
L224.SubsectorInterp_heat
}
if(exists("L224.SubsectorInterpTo_heat")) {
L224.SubsectorInterpTo_heat %>%
add_title("Subsector shareweight interpolation of district heat sectors using to.year") %>%
add_units("units") %>%
add_comments("Interpolated data from A24.subsector_interp, ") %>%
add_comments("filtered by which regions have district heat") %>%
add_legacy_name("L224.SubsectorInterpTo_heat") %>%
add_precursors("energy/A24.subsector_interp", "energy/A_regions", "common/GCAM_region_names") ->
L224.SubsectorInterpTo_heat
} else {
# If interp.to column of A24.subsector_interp contains N/A values, then a blank tibble is produced
missing_data() %>%
add_legacy_name("L224.SubsectorInterpTo_heat") ->
L224.SubsectorInterpTo_heat
}
L224.StubTech_heat %>%
add_title("Identification of stub technologies of district heat") %>%
add_units("N/A") %>%
add_comments("A24.globaltech_shrwt written to all regions, filtered regions with district heat") %>%
add_legacy_name("L224.StubTech_heat") %>%
add_precursors("energy/A24.globaltech_shrwt", "energy/A_regions", "common/GCAM_region_names") ->
L224.StubTech_heat
L224.GlobalTechCoef_heat %>%
add_title("Energy inputs and coefficients of global technologies for district heat") %>%
add_units("unitless") %>%
add_comments("A24.globaltech_coef interpolated to all model years for sector district heat") %>%
add_legacy_name("L224.GlobalTechCoef_heat") %>%
add_precursors("energy/A24.globaltech_coef") ->
L224.GlobalTechCoef_heat
L224.GlobalTechCost_heat %>%
add_title("Costs of global technologies for district heat") %>%
add_units("1975$/GJ") %>%
add_comments("A24.globaltech_cost interpolated to all model years for sector district heat") %>%
add_legacy_name("L224.GlobalTechCost_heat") %>%
add_precursors("energy/A24.globaltech_cost") ->
L224.GlobalTechCost_heat
L224.GlobalTechShrwt_heat %>%
add_title("Shareweights of global technologies for district heat") %>%
add_units("unitless") %>%
add_comments("A24.globaltech_shrwt interpolated to all model years for sector district heat") %>%
add_comments("can be multiple lines") %>%
add_legacy_name("L224.GlobalTechShrwt_heat") %>%
add_precursors("energy/A24.globaltech_shrwt") ->
L224.GlobalTechShrwt_heat
L224.StubTechCalInput_heat %>%
add_title("Calibrated input to district heat") %>%
add_units("EJ/yr") %>%
add_comments("L124.in_EJ_R_heat_F_Yh and calibrated_techs are joined, shareweights assigned") %>%
add_comments("as 0 if the calibrated value is 0 and 1 if it is not 0") %>%
add_legacy_name("L224.StubTechCalInput_heat") %>%
add_precursors("L124.in_EJ_R_heat_F_Yh", "energy/calibrated_techs", "energy/A_regions", "common/GCAM_region_names") ->
L224.StubTechCalInput_heat
L224.StubTechSecOut_elec %>%
add_title("Secondary output of district heat from electricity technologies") %>%
add_units("EJ") %>%
add_comments("L124.heatoutratio_R_elec_F_tech_Yh used to determine secondary output heat from elec, ") %>%
add_comments("filtering for only model base years") %>%
add_legacy_name("L224.StubTechSecOut_elec") %>%
add_precursors("L124.heatoutratio_R_elec_F_tech_Yh", "energy/calibrated_techs", "energy/A24.sector", "common/GCAM_region_names") ->
L224.StubTechSecOut_elec
L224.StubTechCost_elec %>%
add_title("Stubtech costs with secondary output heat") %>%
add_units("1975$/GJ") %>%
add_comments("From L224.StubTechSecOut_elec calculate cost adjustment, equal to the output of heat multiplied by the heat price") %>%
add_comments("modify costs for technologies with efficiencies below default, apply to all model periods") %>%
add_legacy_name("L224.StubTechCost_elec") %>%
add_precursors("L124.heatoutratio_R_elec_F_tech_Yh", "energy/calibrated_techs", "energy/A24.sector", "energy/A_regions", "L1231.eff_R_elec_F_tech_Yh", "common/GCAM_region_names") ->
L224.StubTechCost_elec
return_data(L224.Supplysector_heat, L224.SubsectorLogit_heat, L224.SubsectorShrwt_heat, L224.SubsectorShrwtFllt_heat, L224.SubsectorInterp_heat, L224.SubsectorInterpTo_heat, L224.StubTech_heat, L224.GlobalTechCoef_heat, L224.GlobalTechCost_heat, L224.GlobalTechShrwt_heat, L224.StubTechCalInput_heat, L224.StubTechSecOut_elec, L224.StubTechCost_elec)
} else {
stop("Unknown command")
}
}
rohmin9122/gcam-korea-release documentation built on Nov. 26, 2020, 8:11 a.m.
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Defines functions module_energy_L224.heat
Documented in module_energy_L224.heat
#' module_energy_L224.heat
#'
#' Write district heat sector outputs.
#'
#' @param command API command to execute
#' @param ... other optional parameters, depending on command
#' @return Depends on \code{command}: either a vector of required inputs,
#' a vector of output names, or (if \code{command} is "MAKE") all
#' the generated outputs: \code{L224.SectorLogitTables[[ curr_table ]]$data}, \code{L224.Supplysector_heat}, \code{L224.SubsectorLogitTables[[ curr_table ]]$data}, \code{L224.SubsectorLogit_heat}, \code{L224.SubsectorShrwt_heat}, \code{L224.SubsectorShrwtFllt_heat}, \code{L224.SubsectorInterp_heat}, \code{L224.SubsectorInterpTo_heat}, \code{L224.StubTech_heat}, \code{L224.GlobalTechCoef_heat}, \code{L224.GlobalTechCost_heat}, \code{L224.GlobalTechShrwt_heat}, \code{L224.StubTechCalInput_heat}, \code{L224.StubTechSecOut_elec}, \code{L224.StubTechCost_elec}. The corresponding file in the
#' original data system was \code{L224.heat.R} (energy level2).
#' @details This chunk creates level 2 output files for district heat sector. It creates supply sector information,
#' subsector logit exponents, subsector shareweight and interpolation, and stubtech info by writing assumption file
#' information to all model periods and regions that have district heat. It creates global tech coef, costs, and shareweights
#' by interpolating assumptions. From the level 1 heat data, this chunk computes stub tech calibrated inputs, secondary
#' outputs from elec and modified costs.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author JDH August 2017
module_energy_L224.heat <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/calibrated_techs",
FILE = "energy/A_regions",
FILE = "energy/A24.sector",
FILE = "energy/A24.subsector_logit",
FILE = "energy/A24.subsector_shrwt",
FILE = "energy/A24.subsector_interp",
FILE = "energy/A24.globaltech_coef",
FILE = "energy/A24.globaltech_cost",
FILE = "energy/A24.globaltech_shrwt",
"L1231.eff_R_elec_F_tech_Yh",
"L124.in_EJ_R_heat_F_Yh",
"L124.heatoutratio_R_elec_F_tech_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L224.Supplysector_heat",
"L224.SubsectorLogit_heat",
"L224.SubsectorShrwt_heat",
"L224.SubsectorShrwtFllt_heat",
"L224.SubsectorInterp_heat",
"L224.SubsectorInterpTo_heat",
"L224.StubTech_heat",
"L224.GlobalTechCoef_heat",
"L224.GlobalTechCost_heat",
"L224.GlobalTechShrwt_heat",
"L224.StubTechCalInput_heat",
"L224.StubTechSecOut_elec",
"L224.StubTechCost_elec"))
} else if(command == driver.MAKE) {
# Silence package checks
has_district_heat <- region <- year.fillout <- to.value <- efficiency <-
technology <- coef <- subsector <- supplysector <- minicam.energy.input <-
input.cost <- minicam.non.energy.input <- share.weight <- sector <- fuel <-
value <- subs.share.weight <- calibrated.value <- secondary.output.name <-
secondary.output <- stub.technology <- cost_modifier <- year <- NULL
all_data <- list(...)[[1]]
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
calibrated_techs <- get_data(all_data, "energy/calibrated_techs")
A_regions <- get_data(all_data, "energy/A_regions")
A24.sector <- get_data(all_data, "energy/A24.sector")
A24.subsector_logit <- get_data(all_data, "energy/A24.subsector_logit")
A24.subsector_shrwt <- get_data(all_data, "energy/A24.subsector_shrwt")
A24.subsector_interp <- get_data(all_data, "energy/A24.subsector_interp")
A24.globaltech_coef <- get_data(all_data, "energy/A24.globaltech_coef")
A24.globaltech_cost <- get_data(all_data, "energy/A24.globaltech_cost")
A24.globaltech_shrwt <- get_data(all_data, "energy/A24.globaltech_shrwt")
L1231.eff_R_elec_F_tech_Yh <- get_data(all_data, "L1231.eff_R_elec_F_tech_Yh")
L124.in_EJ_R_heat_F_Yh <- get_data(all_data, "L124.in_EJ_R_heat_F_Yh")
L124.heatoutratio_R_elec_F_tech_Yh <- get_data(all_data, "L124.heatoutratio_R_elec_F_tech_Yh")
# Changing input data into long format
A24.globaltech_coef %>%
gather_years(value_col = "coef") -> A24.globaltech_coef
A24.globaltech_cost %>%
gather_years(value_col = "input.cost") -> A24.globaltech_cost
A24.globaltech_shrwt %>%
gather_years(value_col = "share.weight") -> A24.globaltech_shrwt
# ===================================================
# Create list of regions with district heat modeled
A_regions %>%
filter(has_district_heat == 1) %>%
select(region) -> heat_region
# Supply sector information for district heat sectors
A24.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
filter(region %in% heat_region$region) -> L224.Supplysector_heat
# Subsector logit exponents of district heat sectors
A24.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
filter(region %in% heat_region$region) -> L224.SubsectorLogit_heat
# L224.SubsectorShrwt_heat and L224.SubsectorShrwtFllt_heat: Subsector shareweights of district heat sectors
if(any(!is.na(A24.subsector_shrwt$year))) {
A24.subsector_shrwt %>%
filter(!is.na(year)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwt"]], GCAM_region_names = GCAM_region_names ) %>%
filter(region %in% heat_region$region) -> L224.SubsectorShrwt_heat
}
if(any(!is.na(A24.subsector_shrwt$year.fillout))) {
A24.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names = GCAM_region_names ) %>%
filter(region %in% heat_region$region) -> L224.SubsectorShrwtFllt_heat
}
# Subsector shareweight interpolation of district heat sectors
if(any(is.na(A24.subsector_interp$to.value))) {
A24.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]], GCAM_region_names = GCAM_region_names) %>%
filter(region %in% heat_region$region) -> L224.SubsectorInterp_heat
}
if(any(!is.na(A24.subsector_interp$to.value))) {
A24.subsector_interp %>%
filter(!is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterpTo"]], GCAM_region_names = GCAM_region_names) %>%
filter(region %in% heat_region$region) -> L224.SubsectorInterpTo_heat
}
# Identification of stub technologies of district heat
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A24.globaltech_shrwt %>%
select(supplysector, subsector, technology) %>%
distinct %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names = GCAM_region_names) %>%
rename(stub.technology = technology) %>%
filter(region %in% heat_region$region) -> L224.StubTech_heat
# Coefficients of global technologies
# L224.GlobalTechCoef_heat: Energy inputs and coefficients of global technologies for district heat
A24.globaltech_coef %>%
select(supplysector, subsector, technology, minicam.energy.input) %>%
distinct %>%
# Interpolate to all years
repeat_add_columns(tibble(year = c(HISTORICAL_YEARS, MODEL_FUTURE_YEARS))) %>%
left_join(A24.globaltech_coef, by = c("supplysector", "subsector", "technology", "minicam.energy.input", "year")) %>%
mutate(coef = round(approx_fun(year, value = coef, rule = 1), energy.DIGITS_COEFFICIENT)) %>%
filter(year %in% MODEL_YEARS) %>%
select(sector.name = supplysector, subsector.name = subsector, technology, minicam.energy.input, year, coefficient = coef) -> L224.GlobalTechCoef_heat
# Costs of global technologies
# L224.GlobalTechCost_heat: Costs of global technologies for district heat
A24.globaltech_cost %>%
select(supplysector, subsector, technology, minicam.non.energy.input) %>%
distinct %>%
# Interpolate to all years
repeat_add_columns(tibble(year = c(HISTORICAL_YEARS, MODEL_FUTURE_YEARS))) %>%
left_join(A24.globaltech_cost, by = c("supplysector", "subsector", "technology", "minicam.non.energy.input", "year")) %>%
group_by(supplysector, subsector, technology, minicam.non.energy.input) %>%
mutate(input.cost = round(approx_fun(year, value = input.cost, rule = 1), energy.DIGITS_COST)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
select(sector.name = supplysector, subsector.name = subsector, technology, minicam.non.energy.input, year, input.cost) -> L224.GlobalTechCost_heat
# Shareweights of global technologies
# L224.GlobalTechShrwt_heat: Shareweights of global technologies for district heat
A24.globaltech_shrwt %>%
select(supplysector, subsector, technology) %>%
distinct %>%
# Interpolate to all years
repeat_add_columns(tibble(year = c(HISTORICAL_YEARS, MODEL_FUTURE_YEARS))) %>%
left_join(A24.globaltech_shrwt, by = c("supplysector", "subsector", "technology", "year")) %>%
mutate(share.weight = approx_fun(year, value = share.weight, rule = 1)) %>%
filter(year %in% MODEL_YEARS) %>%
select(sector.name = supplysector, subsector.name = subsector, technology, year, share.weight) -> L224.GlobalTechShrwt_heat
# Calibration and region-specific data, calibrated input to district heat
L124.in_EJ_R_heat_F_Yh %>%
left_join(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join(calibrated_techs %>%
select(sector, fuel, supplysector, subsector, technology, minicam.energy.input) %>%
distinct, by = c("sector", "fuel")) %>%
rename(stub.technology = technology) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
filter(region %in% heat_region$region) %>%
select(LEVEL2_DATA_NAMES[["StubTechYr"]], "minicam.energy.input", "value") %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT),
year.share.weight = year,
subs.share.weight = if_else(calibrated.value == 0, 0, 1),
share.weight = subs.share.weight) %>%
select(-value) -> L224.StubTechCalInput_heat
# Secondary output of heat, applied to electricity generation technologies
# NOTE: This is complicated. Initially tried using historical information for all model periods that fall within historical time
# (i.e. not just the model base years). However for regions like the FSU where historical periods often have very low output of heat
# from the district heat sector, and most heat as a secondary output from the electricity sector, the secondary output heat can easily
# exceed the demands from the end-use sectors, causing model solution failure. For this reason, the convention applied here is to
# use the secondary output of heat from the power sector only in the model base years.
L124.heatoutratio_R_elec_F_tech_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
left_join(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join(calibrated_techs %>%
select(sector, fuel, supplysector, subsector, technology) %>%
distinct, by = c("sector", "fuel", "technology")) %>%
mutate(stub.technology = technology,
secondary.output.name = A24.sector[["supplysector"]]) %>%
select(LEVEL2_DATA_NAMES[["StubTechYr"]], "secondary.output.name", "value") %>%
mutate(secondary.output = round(value, energy.DIGITS_CALOUTPUT)) %>%
select(-value) -> L224.StubTechSecOut_elec
# Calculate cost adjustment, equal to the output of heat multiplied by the heat price (to minimize the distortion of including the secondary output)
L224.StubTechSecOut_elec %>%
select(LEVEL2_DATA_NAMES[["StubTechYr"]], "secondary.output") %>%
mutate(minicam.non.energy.input = "heat plant",
input.cost = round(secondary.output*energy.HEAT_PRICE, energy.DIGITS_COST))-> L224.StubTechCost_elec
# The secondary output of heat from CHP in the electric sector can cause the price of the technologies
# to go very low or negative if the technology cost is not modified to reflect the additional costs of
# CHP systems (as compared with electricity-only systems). Low technology costs can cause unrealistically
# low electricity prices in the calibration year, distorting behavior in future years. In this method,
# costs related to heat production and distribution are backed out from exogenous heat prices and data-derived heat:power ratios.
L1231.eff_R_elec_F_tech_Yh %>%
filter(year %in% MODEL_YEARS) %>%
rename(efficiency = value) %>%
left_join(GCAM_region_names, by = "GCAM_region_ID") %>%
filter(region %in% heat_region$region) %>%
filter(fuel == "gas") %>%
filter(efficiency < energy.DEFAULT_ELECTRIC_EFFICIENCY) %>%
mutate(cost_modifier = energy.GAS_PRICE * (1 / energy.DEFAULT_ELECTRIC_EFFICIENCY - 1 / efficiency)) -> L224.eff_cost_adj_Rh_elec_gas_sc_Y
# Modify the costs
L224.StubTechCost_elec %>%
left_join(L224.eff_cost_adj_Rh_elec_gas_sc_Y %>%
rename(subsector = fuel, stub.technology = technology) %>%
select(region, subsector, stub.technology, year, cost_modifier),
by = c("region", "subsector", "stub.technology", "year")) %>%
mutate(input.cost = if_else(!is.na(cost_modifier), round(pmax(0, input.cost + cost_modifier), energy.DIGITS_COST), input.cost)) %>%
select(-cost_modifier, -secondary.output) -> L224.StubTechCost_elec
# Need to fill out object names for all model time periods
L224.StubTechCost_elec %>%
filter(year == max(year)) %>%
select(-year) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
mutate(input.cost = 0) -> L224.StubTechCost_elec_fut
L224.StubTechCost_elec %>%
bind_rows(L224.StubTechCost_elec_fut) -> L224.StubTechCost_elec
# ===================================================
# Produce outputs
L224.Supplysector_heat %>%
add_title("Supply sector information for district heat sectors") %>%
add_units("N/A") %>%
add_comments("Supply sector info for district heat is written to all regions, ") %>%
add_comments("filtered by which regions have district heat") %>%
add_legacy_name("L224.Supplysector_heat") %>%
add_precursors("energy/A24.sector", "energy/A_regions", "common/GCAM_region_names") ->
L224.Supplysector_heat
L224.SubsectorLogit_heat %>%
add_title("Subsector logit exponents of district heat sectors") %>%
add_units("N/A") %>%
add_comments("Subsector logit exponents for district heat is written to all regions, ") %>%
add_comments("filtered by which regions have district heat") %>%
add_legacy_name("L224.SubsectorLogit_heat") %>%
add_precursors("energy/A24.subsector_logit", "energy/A_regions", "common/GCAM_region_names") ->
L224.SubsectorLogit_heat
if(exists("L224.SubsectorShrwt_heat")) {
L224.SubsectorShrwt_heat %>%
add_title("Subsector shareweights of district heat sectors") %>%
add_units("N/A") %>%
add_comments("If year is not NA: Subsector shareweights for district heat written to all regions, ") %>%
add_comments("filtered by which regions have district heat") %>%
add_legacy_name("L224.SubsectorShrwt_heat") %>%
add_precursors("energy/A24.subsector_shrwt", "energy/A_regions", "common/GCAM_region_names") ->
L224.SubsectorShrwt_heat
} else {
# If year column of A24.subsector_shrwt is all N/A, then a blank tibble is produced (and presumably the following tibble, using year.fillout is made)
missing_data() %>%
add_legacy_name("L224.SubsectorShrwt_heat") ->
L224.SubsectorShrwt_heat
}
if(exists("L224.SubsectorShrwtFllt_heat")) {
L224.SubsectorShrwtFllt_heat %>%
add_title("Subsector shareweights of district heat sectors with fillout year") %>%
add_units("N/A") %>%
add_comments("If year.fillout is not NA: Subsector shareweights for district heat written to all regions, ") %>%
add_comments("filtered by which regions have district heat, uses year.fillout") %>%
add_legacy_name("L224.SubsectorShrwtFllt_heat") %>%
add_precursors("energy/A24.subsector_shrwt", "energy/A_regions", "common/GCAM_region_names") ->
L224.SubsectorShrwtFllt_heat
} else {
# If year.fillout column of A24.subsector_shrwt is all N/A, then a blank tibble is produced
missing_data() %>%
add_legacy_name("L224.SubsectorShrwtFllt_heat") ->
L224.SubsectorShrwtFllt_heat
}
if(exists("L224.SubsectorInterp_heat")) {
L224.SubsectorInterp_heat %>%
add_title("Subsector shareweight interpolation of district heat sectors") %>%
add_units("units") %>%
add_comments("Interpolated data from A24.subsector_interp, ") %>%
add_comments("filtered by which regions have district heat") %>%
add_legacy_name("L224.SubsectorInterp_heat") %>%
add_precursors("energy/A24.subsector_interp", "energy/A_regions", "common/GCAM_region_names") ->
L224.SubsectorInterp_heat
} else {
# If interp.to column of A24.subsector_interp contains no N/A values, then a blank tibble is produced
missing_data() %>%
add_legacy_name("L224.SubsectorInterp_heat") ->
L224.SubsectorInterp_heat
}
if(exists("L224.SubsectorInterpTo_heat")) {
L224.SubsectorInterpTo_heat %>%
add_title("Subsector shareweight interpolation of district heat sectors using to.year") %>%
add_units("units") %>%
add_comments("Interpolated data from A24.subsector_interp, ") %>%
add_comments("filtered by which regions have district heat") %>%
add_legacy_name("L224.SubsectorInterpTo_heat") %>%
add_precursors("energy/A24.subsector_interp", "energy/A_regions", "common/GCAM_region_names") ->
L224.SubsectorInterpTo_heat
} else {
# If interp.to column of A24.subsector_interp contains N/A values, then a blank tibble is produced
missing_data() %>%
add_legacy_name("L224.SubsectorInterpTo_heat") ->
L224.SubsectorInterpTo_heat
}
L224.StubTech_heat %>%
add_title("Identification of stub technologies of district heat") %>%
add_units("N/A") %>%
add_comments("A24.globaltech_shrwt written to all regions, filtered regions with district heat") %>%
add_legacy_name("L224.StubTech_heat") %>%
add_precursors("energy/A24.globaltech_shrwt", "energy/A_regions", "common/GCAM_region_names") ->
L224.StubTech_heat
L224.GlobalTechCoef_heat %>%
add_title("Energy inputs and coefficients of global technologies for district heat") %>%
add_units("unitless") %>%
add_comments("A24.globaltech_coef interpolated to all model years for sector district heat") %>%
add_legacy_name("L224.GlobalTechCoef_heat") %>%
add_precursors("energy/A24.globaltech_coef") ->
L224.GlobalTechCoef_heat
L224.GlobalTechCost_heat %>%
add_title("Costs of global technologies for district heat") %>%
add_units("1975$/GJ") %>%
add_comments("A24.globaltech_cost interpolated to all model years for sector district heat") %>%
add_legacy_name("L224.GlobalTechCost_heat") %>%
add_precursors("energy/A24.globaltech_cost") ->
L224.GlobalTechCost_heat
L224.GlobalTechShrwt_heat %>%
add_title("Shareweights of global technologies for district heat") %>%
add_units("unitless") %>%
add_comments("A24.globaltech_shrwt interpolated to all model years for sector district heat") %>%
add_comments("can be multiple lines") %>%
add_legacy_name("L224.GlobalTechShrwt_heat") %>%
add_precursors("energy/A24.globaltech_shrwt") ->
L224.GlobalTechShrwt_heat
L224.StubTechCalInput_heat %>%
add_title("Calibrated input to district heat") %>%
add_units("EJ/yr") %>%
add_comments("L124.in_EJ_R_heat_F_Yh and calibrated_techs are joined, shareweights assigned") %>%
add_comments("as 0 if the calibrated value is 0 and 1 if it is not 0") %>%
add_legacy_name("L224.StubTechCalInput_heat") %>%
add_precursors("L124.in_EJ_R_heat_F_Yh", "energy/calibrated_techs", "energy/A_regions", "common/GCAM_region_names") ->
L224.StubTechCalInput_heat
L224.StubTechSecOut_elec %>%
add_title("Secondary output of district heat from electricity technologies") %>%
add_units("EJ") %>%
add_comments("L124.heatoutratio_R_elec_F_tech_Yh used to determine secondary output heat from elec, ") %>%
add_comments("filtering for only model base years") %>%
add_legacy_name("L224.StubTechSecOut_elec") %>%
add_precursors("L124.heatoutratio_R_elec_F_tech_Yh", "energy/calibrated_techs", "energy/A24.sector", "common/GCAM_region_names") ->
L224.StubTechSecOut_elec
L224.StubTechCost_elec %>%
add_title("Stubtech costs with secondary output heat") %>%
add_units("1975$/GJ") %>%
add_comments("From L224.StubTechSecOut_elec calculate cost adjustment, equal to the output of heat multiplied by the heat price") %>%
add_comments("modify costs for technologies with efficiencies below default, apply to all model periods") %>%
add_legacy_name("L224.StubTechCost_elec") %>%
add_precursors("L124.heatoutratio_R_elec_F_tech_Yh", "energy/calibrated_techs", "energy/A24.sector", "energy/A_regions", "L1231.eff_R_elec_F_tech_Yh", "common/GCAM_region_names") ->
L224.StubTechCost_elec
return_data(L224.Supplysector_heat, L224.SubsectorLogit_heat, L224.SubsectorShrwt_heat, L224.SubsectorShrwtFllt_heat, L224.SubsectorInterp_heat, L224.SubsectorInterpTo_heat, L224.StubTech_heat, L224.GlobalTechCoef_heat, L224.GlobalTechCost_heat, L224.GlobalTechShrwt_heat, L224.StubTechCalInput_heat, L224.StubTechSecOut_elec, L224.StubTechCost_elec)
} else {
stop("Unknown command")
}
}
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