R/zchunk_L225.hydrogen.R
In bpbond/gcamdata: Build the GCAM Input Datasets
Defines functions
module_energy_L225.hydrogen
Documented in
module_energy_L225.hydrogen
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_L225.hydrogen
#'
#' Provides supply sector information, subsector information, technology information for hydrogen sectors.
#'
#' @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{L225.Supplysector_h2}, \code{L225.SubsectorLogit_h2}, \code{L225.SubsectorShrwtFllt_h2}, \code{L225.StubTech_h2}, \code{L225.GlobalTechCoef_h2}, \code{L225.GlobalTechCost_h2}, \code{L225.GlobalTechShrwt_h2}, \code{L225.PrimaryRenewKeyword_h2}, \code{L225.GlobalTechCapture_h2}, \code{L225.StubTechCost_h2}, \code{L225.GlobalTechProfitShutdown_h2}, \code{L225.GlobalTechSCurve_h2}. The corresponding file in the
#' original data system was \code{L225.hydrogen.R} (energy level2).
#' @details Provides supply sector information, subsector information, technology information for hydrogen sectors.
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange filter group_by mutate select
#' @importFrom tidyr complete nesting
#' @author LF Augest 2017
module_energy_L225.hydrogen <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/A25.sector",
FILE = "energy/A25.subsector_logit",
FILE = "energy/A25.subsector_shrwt",
FILE = "energy/A25.globaltech_cost",
FILE = "energy/A25.globaltech_coef",
FILE = "energy/A25.globaltech_retirement",
FILE = "energy/A25.globaltech_shrwt",
FILE = "energy/A25.globaltech_keyword",
FILE = "energy/A25.globaltech_co2capture",
"L125.globaltech_coef",
"L125.globaltech_cost",
"L125.Electrolyzer_IdleRatio_Params",
"L223.StubTechCapFactor_elec",
"L223.GlobalIntTechCapital_elec",
"L223.GlobalIntTechOMfixed_elec"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L225.Supplysector_h2",
"L225.SectorUseTrialMarket_h2",
"L225.SubsectorLogit_h2",
"L225.SubsectorShrwtFllt_h2",
"L225.StubTech_h2",
"L225.GlobalTechCoef_h2",
"L225.GlobalTechCost_h2",
"L225.GlobalTechShrwt_h2",
"L225.PrimaryRenewKeyword_h2",
"L225.AvgFossilEffKeyword_h2",
"L225.GlobalTechCapture_h2",
"L225.GlobalTechInputPMult_h2",
"L225.GlobalTechProfitShutdown_h2",
"L225.GlobalTechSCurve_h2",
"L225.StubTechCost_h2"))
} else if(command == driver.MAKE) {
# Silencing package checks
region <- coefficient <- cost <- price.unit.conversion <- sector.name <- subsector.name <-
stub.technology <- capacity.factor <- IdleRatio <- `2040` <- `2015` <- `2050` <-
intermittent.technology <- capital.overnight <- fixed.charge.rate <- OM.fixed <-
cost_75USD_kW_yr <- kWh_elec_per_kgH2 <- output_kgh2_d <- cost_75USD_kgH2 <- NULL
all_data <- list(...)[[1]]
year.fillout <- technology <- year <- efficiency <- supplysector <- value <-
subsector <- minicam.energy.input <- input.cost <- minicam.non.energy.input <-
share.weight <- primary.renewable <- average.fossil.efficiency <-
remove.fraction <- NULL # silence package check notes
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
A25.sector <- get_data(all_data, "energy/A25.sector", strip_attributes = TRUE)
A25.subsector_logit <- get_data(all_data, "energy/A25.subsector_logit", strip_attributes = TRUE)
A25.subsector_shrwt <- get_data(all_data, "energy/A25.subsector_shrwt", strip_attributes = TRUE)
A25.globaltech_coef <- get_data(all_data, "energy/A25.globaltech_coef", strip_attributes = TRUE)
A25.globaltech_cost <- get_data(all_data, "energy/A25.globaltech_cost", strip_attributes = TRUE)
A25.globaltech_shrwt <- get_data(all_data, "energy/A25.globaltech_shrwt", strip_attributes = TRUE)
A25.globaltech_keyword <- get_data(all_data, "energy/A25.globaltech_keyword", strip_attributes = TRUE)
A25.globaltech_retirement <- get_data(all_data, "energy/A25.globaltech_retirement", strip_attributes = TRUE)
A25.globaltech_co2capture <- get_data(all_data, "energy/A25.globaltech_co2capture", strip_attributes = TRUE)
L125.globaltech_coef <- get_data(all_data, "L125.globaltech_coef", strip_attributes = TRUE)
L125.globaltech_cost <- get_data(all_data, "L125.globaltech_cost", strip_attributes = TRUE)
L125.Electrolyzer_IdleRatio_Params <- get_data(all_data, "L125.Electrolyzer_IdleRatio_Params", strip_attributes = TRUE)
L223.GlobalIntTechCapital_elec <- get_data(all_data, "L223.GlobalIntTechCapital_elec", strip_attributes = TRUE)
L223.GlobalIntTechOMfixed_elec <- get_data(all_data, "L223.GlobalIntTechOMfixed_elec", strip_attributes = TRUE)
L223.StubTechCapFactor_elec <- get_data(all_data, "L223.StubTechCapFactor_elec", strip_attributes = TRUE)
# ===================================================
# 1. Build tables for CSVs
# 1a. Supply sector information
# L225.Supplysector_h2: Supply sector information for hydrogen sectors
A25.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L225.Supplysector_h2
# H2 liquid truck has a simultaneity that may benefit from using a trial market here
L225.SectorUseTrialMarket_h2 <- filter(L225.Supplysector_h2, supplysector == "H2 liquid truck") %>%
mutate(supplysector = "trn_freight_road") %>%
select(region, supplysector) %>%
mutate(use.trial.market = 1)
# 1b. Subsector information
# L225.SubsectorLogit_h2: Subsector logit exponents of hydrogen sectors
A25.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L225.SubsectorLogit_h2
# L225.SubsectorShrwtFllt_h2: Subsector shareweights of hydrogen sectors
A25.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names) ->
L225.SubsectorShrwtFllt_h2
# 1c. Technology information
# L225.StubTech_h2: Identification of stub technologies of hydrogen
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A25.globaltech_shrwt %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names) %>%
rename(stub.technology = technology) ->
L225.StubTech_h2
# L225.GlobalTechCoef_h2: Energy inputs coefficients of global technologies for hydrogen
L125.globaltech_coef %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector,
coefficient = value) %>%
mutate(coefficient = round(coefficient,energy.DIGITS_COEFFICIENT))-> L225.GlobalTechCoef_h2
# L225.GlobalTechCost_h2: Costs of global technologies for hydrogen
# Costs of global technologies
L125.globaltech_cost %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector,
input.cost = cost) %>%
mutate(input.cost = round(input.cost,energy.DIGITS_COST))-> L225.GlobalTechCost_h2
# L225.GlobalTechShrwt_h2: Shareweights of global technologies for hydrogen
# Shareweights of global technologies
A25.globaltech_shrwt %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, year) %>%
group_by(supplysector, subsector, technology) %>%
mutate(share.weight = approx_fun(year, value, rule = 1)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(-value) ->
L225.GlobalTechShrwt_h2
A25.globaltech_coef %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input),
year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, minicam.energy.input, year) %>%
group_by(supplysector, subsector, technology, minicam.energy.input) %>%
mutate(coefficient = approx_fun(year, value, rule = 2),
units = 'GJ input / GJ H2') %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector) %>%
anti_join(L125.globaltech_coef, by = c("sector.name", "subsector.name", "technology", "minicam.energy.input")) %>%
select(-value,-efficiency,-price.unit.conversion) ->
L225.GlobalTechCoef_h2_noprod #filter and convert efficiencies to coefficients for only end use and distribution pass-through sectors and technologies
L225.GlobalTechCoef_h2 <- bind_rows(L225.GlobalTechCoef_h2,L225.GlobalTechCoef_h2_noprod) %>%
mutate(minicam.energy.input = if_else(minicam.energy.input == 'elect_td_trn (compression and refrigeration)','elect_td_trn',minicam.energy.input)) %>%
group_by(sector.name,subsector.name,technology,minicam.energy.input,units,year) %>%
summarize(coefficient = sum(coefficient)) %>%
ungroup()
A25.globaltech_coef %>%
filter(!is.na(price.unit.conversion)) %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input),
year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
mutate(price.unit.conversion = approx_fun(year, price.unit.conversion, rule = 2)) %>%
rename(sector.name = supplysector, subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechInputPMult"]]) %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) -> L225.GlobalTechInputPMult
A25.globaltech_cost %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology, minicam.non.energy.input), year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(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, rule = 2), digits = energy.DIGITS_COST),
units = '$1975/GJ H2') %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector) %>%
anti_join(L125.globaltech_cost, by = c("sector.name", "subsector.name", "technology", "minicam.non.energy.input")) %>%
select(-value) ->
L225.GlobalTechCost_h2_noprod #get costs for only end use, distribution pass-through sectors, (global) renewable portion of LCOH (e.g., solar panels), and technologies from A25
L225.GlobalTechCost_h2 <- bind_rows(L225.GlobalTechCost_h2,L225.GlobalTechCost_h2_noprod)
# Estimate the region-specific costs of direct wind and solar electrolysis, based on the capacity factors of the
# electric generation technologies and relationship between capacity factors and NE costs of electrolysis
L125.Electrolyzer_IdleRatio_Params_2015 <- filter(L125.Electrolyzer_IdleRatio_Params, year == 2015)
L125.Electrolyzer_IdleRatio_Params_2040 <- filter(L125.Electrolyzer_IdleRatio_Params, year == 2040)
# Set the fraction of 2050 to 2040 costs. The specific number is from Pat's workbook
Electrolyzer_2050_2040_cost_ratio <- 0.77
# The following block uses the wind+solar capacity factors in each region to estimate the levelized cost of the
# hydrogen electrolyzers. The available cost estimate years are 2015 and 2040. Cost reductions are extrapolated to
# 2050 using the same improvement factor in all regions. The costs assigned to years between 2015 and 2050 are estimated
# with linear interpolation, and outside this window we use fixed extrapolation.
# Electrolyzer non-energy costs replace rather than add to the global default
# values in L225.GlobalTechCost_h2. This is handled in the left_join.
L223.StubTechCapFactor_elec %>%
filter(year == max(MODEL_BASE_YEARS),
stub.technology %in% c("wind", "PV")) %>%
mutate(IdleRatio = pmax(1, 1 / (capacity.factor / energy.ELECTROLYZER_RENEWABLE_CAPACITY_RATIO)),
`2015` = L125.Electrolyzer_IdleRatio_Params_2015$intercept +
IdleRatio * L125.Electrolyzer_IdleRatio_Params_2015$slope,
`2040` = L125.Electrolyzer_IdleRatio_Params_2040$intercept +
IdleRatio * L125.Electrolyzer_IdleRatio_Params_2040$slope,
`2050` = `2040` * Electrolyzer_2050_2040_cost_ratio) %>%
select(region, subsector, `2015`, `2040`, `2050`) %>%
gather_years() %>%
complete(nesting(region, subsector), year = MODEL_YEARS) %>%
group_by(region, subsector) %>%
mutate(minicam.non.energy.input = "electrolyzer",
input.cost = approx_fun(year, value, rule = 2),
input.cost = input.cost * gdp_deflator(1975, 2016) / CONV_GJ_KGH2) %>%
ungroup() %>%
select(-value) %>%
left_join(select(L225.GlobalTechCost_h2, -input.cost),
by = c("subsector" = "subsector.name", "year", "minicam.non.energy.input")) %>%
rename(supplysector = sector.name, stub.technology = technology) %>%
select(LEVEL2_DATA_NAMES[["StubTechCost"]]) ->
L225.StubTechCost_h2_electrolyzer
# Estimate the wind turbine and solar panel related aspects of the costs of direct renewable hydrogen electrolysis
# These are estimated from the capital costs of wind and solar technologies (generic, global), the region-specific
# capacity factors, efficiencies of hydrogen production, and size of the representative hydrogen plant
L225.RenewElec_cost <- L223.GlobalIntTechCapital_elec %>%
filter(intermittent.technology %in% c("wind", "PV")) %>%
left_join(L223.GlobalIntTechOMfixed_elec, by = c("sector.name", "subsector.name", "intermittent.technology", "year")) %>%
mutate(cost_75USD_kW_yr = capital.overnight * fixed.charge.rate + OM.fixed) %>%
select(subsector.name, intermittent.technology, year, cost_75USD_kW_yr)
L225.RenewElec_eff <- filter(L225.GlobalTechCoef_h2,
subsector.name %in% c("solar", "wind") & !grepl("water", minicam.energy.input)) %>%
mutate(kWh_elec_per_kgH2 = coefficient * CONV_GJ_KGH2 / CONV_KWH_GJ) %>%
select(subsector.name, year, kWh_elec_per_kgH2)
L225.RenewElec_cost %>%
left_join_error_no_match(L225.RenewElec_eff, by = c("subsector.name", "year")) %>%
left_join(L223.StubTechCapFactor_elec,
by = c("subsector.name" = "subsector", "intermittent.technology" = "stub.technology", "year")) %>%
mutate(minicam.non.energy.input = if_else(subsector.name == "solar", "solar panels", "wind turbines"),
output_kgh2_d = if_else(subsector.name == "solar", energy.SOLAR_ELECTROLYSIS_KGH2_D, energy.WIND_ELECTROLYSIS_KGH2_D),
cost_75USD_kgH2 = cost_75USD_kW_yr * kWh_elec_per_kgH2 * output_kgh2_d / CONV_DAY_HOURS /
(output_kgh2_d * capacity.factor / CONV_DAYS_YEAR),
input.cost = cost_75USD_kgH2 / CONV_GJ_KGH2) %>%
select(region, subsector.name, year, minicam.non.energy.input, input.cost) %>%
left_join(select(L225.GlobalTechCost_h2, -input.cost), by = c("subsector.name", "year", "minicam.non.energy.input")) %>%
rename(supplysector = sector.name, subsector = subsector.name, stub.technology = technology) %>%
select(LEVEL2_DATA_NAMES[["StubTechCost"]]) ->
L225.StubTechCost_h2_renewables
# Combine the electrolyzer and renewable power generation technologies' levelized non-energy costs into a single table
L225.StubTechCost_h2 <- bind_rows(L225.StubTechCost_h2_electrolyzer, L225.StubTechCost_h2_renewables) %>%
mutate(input.cost = round(input.cost, digits = energy.DIGITS_COST))
# L225.PrimaryRenewKeyword_h2: Keywords of primary renewable electric generation technologies
A25.globaltech_keyword %>%
repeat_add_columns(tibble(year = c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS))) %>%
rename(sector.name = supplysector, subsector.name = subsector) ->
L225.AllKeyword_h2
L225.AllKeyword_h2 %>%
filter(!is.na(primary.renewable)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "primary.renewable") ->
L225.PrimaryRenewKeyword_h2
# L225.AvgFossilEffKeyword_h2: Keywords of fossil/bio electric generation technologies
L225.AllKeyword_h2 %>%
filter(!is.na(average.fossil.efficiency)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "average.fossil.efficiency") ->
L225.AvgFossilEffKeyword_h2
# L225.GlobalTechCapture_h2: CO2 capture fractions from global fertilizer production technologies with CCS
# Note: No need to consider historical periods or intermittent technologies here
A25.globaltech_co2capture %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, year) %>%
group_by(supplysector, subsector, technology) %>%
mutate(remove.fraction = approx_fun(year, value, rule = 1),
remove.fraction = round(remove.fraction,energy.DIGITS_REMOVE.FRACTION)) %>%
ungroup %>%
filter(year %in% MODEL_FUTURE_YEARS) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector, subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "remove.fraction") %>%
mutate(storage.market = energy.CO2.STORAGE.MARKET) ->
L225.GlobalTechCapture_h2
# Set global technology retirement information for all hydrogen sector technologies
# ------------------------------------------------------------------------------------
# Retirement may consist of any of three types of retirement function (phased, s-curve, or none)
# This section checks L225.globaltech_retirement for each of these functions and creates a subset for each option then removes any subsets with 0 rows
# All of these options have different headers, and all are allowed.
# Also, technologies that have an additional shutdown rate as a function of their profitability are also defined.
# Replace years and prepare assumptions into correct format
A25.globaltech_retirement %>%
set_years() %>%
mutate(year = as.integer(year)) %>%
rename(sector.name = supplysector, subsector.name = subsector) ->
L225.globaltech_retirement_base
# Copies base year retirement information into all future years and appends back onto itself
L225.globaltech_retirement_base %>%
filter(year == min(MODEL_FUTURE_YEARS)) %>%
select(-year) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
bind_rows(filter(L225.globaltech_retirement_base, year == max(MODEL_BASE_YEARS))) ->
L225.globaltech_retirement
# S-CURVE RETIREMENT
# Subsets the S-Curve retirement function
L225.globaltech_retirement %>%
filter(!is.na(L225.globaltech_retirement$half.life)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "lifetime", "steepness", "half.life") ->
L225.GlobalTechSCurve_h2
# PROFIT-BASED SHUTDOWN PARAMETERS
# Subsets any technologies with a shutdown parameter based on profitability
L225.globaltech_retirement %>%
filter(!is.na(L225.globaltech_retirement$median.shutdown.point)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "median.shutdown.point", "profit.shutdown.steepness") ->
L225.GlobalTechProfitShutdown_h2
# ===================================================
# Produce outputs
L225.Supplysector_h2 %>%
add_title("Supply sector information for hydrogen sectors") %>%
add_units("Unitless") %>%
add_comments("Expand sector information for all GCAM regions") %>%
add_legacy_name("L225.Supplysector_h2") %>%
add_precursors("common/GCAM_region_names", "energy/A25.sector") ->
L225.Supplysector_h2
L225.SectorUseTrialMarket_h2 %>%
add_title("Supply sector trial markets") %>%
add_units("Boolean") %>%
add_comments("Read in to help solver deal with simultaneities") %>%
add_precursors("common/GCAM_region_names", "energy/A25.sector") ->
L225.SectorUseTrialMarket_h2
L225.SubsectorLogit_h2 %>%
add_title("Subsector logit exponents of hydrogen sectors") %>%
add_units("Unitless") %>%
add_comments("Expand subsector logit exponents for all GCAM regions") %>%
add_legacy_name("L225.SubsectorLogit_h2") %>%
add_precursors("common/GCAM_region_names", "energy/A25.subsector_logit") ->
L225.SubsectorLogit_h2
L225.SubsectorShrwtFllt_h2 %>%
add_title("Subsector shareweights of hydrogen sectors") %>%
add_units("Unitless") %>%
add_comments("Expand Subsector shareweights for all GCAM regions") %>%
add_legacy_name("L225.SubsectorShrwtFllt_h2") %>%
add_precursors("common/GCAM_region_names", "energy/A25.subsector_shrwt") ->
L225.SubsectorShrwtFllt_h2
L225.StubTech_h2 %>%
add_title("Identification of stub technologies of hydrogen") %>%
add_units("NA") %>%
add_comments("Expand stub technologies information for all GCAM regions") %>%
add_comments("assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)") %>%
add_legacy_name("L225.StubTech_h2") %>%
add_precursors("common/GCAM_region_names", "energy/A25.globaltech_shrwt") ->
L225.StubTech_h2
L225.GlobalTechCoef_h2 %>%
add_title("Energy inputs and efficiencies of global technologies for hydrogen") %>%
add_units("Unitless") %>%
add_comments("Interpolated orginal data into all model years") %>%
add_precursors("L125.globaltech_coef",'energy/A25.globaltech_coef') -> L225.GlobalTechCoef_h2
L225.GlobalTechCost_h2 %>%
add_title("Costs of global technologies for hydrogen") %>%
add_units("$1975 / GJ H2") %>%
add_comments("Interpolated orginal data into all model years") %>%
add_precursors("L125.globaltech_cost",'energy/A25.globaltech_cost') -> L225.GlobalTechCost_h2
L225.GlobalTechShrwt_h2 %>%
add_title("Shareweights of global technologies for hydrogen") %>%
add_units("Unitless") %>%
add_comments("Interpolated orginal data into all model years") %>%
add_legacy_name("L225.GlobalTechShrwt_h2") %>%
add_precursors("energy/A25.globaltech_shrwt") ->
L225.GlobalTechShrwt_h2
L225.PrimaryRenewKeyword_h2 %>%
add_title("Keywords of primary renewable electric generation technologies") %>%
add_units("NA") %>%
add_comments("Identify Keywords of primary renewable electric generation technologies for all model years") %>%
add_comments("can be multiple lines") %>%
add_legacy_name("L225.PrimaryRenewKeyword_h2") %>%
add_precursors("energy/A25.globaltech_keyword") ->
L225.PrimaryRenewKeyword_h2
L225.AvgFossilEffKeyword_h2 %>%
add_title("Keywords of fossil/bio electric generation technologies") %>%
add_units("NA") %>%
add_comments("Identify Keywords of fossil/bio electric generation technologies for all model years") %>%
add_legacy_name("L225.AvgFossilEffKeyword_h2") %>%
add_precursors("energy/A25.globaltech_keyword") ->
L225.AvgFossilEffKeyword_h2
L225.GlobalTechCapture_h2 %>%
add_title("CO2 capture fractions from global fertilizer production technologies with CCS") %>%
add_units("Unitless") %>%
add_comments("Interpolated orginal data into all model years") %>%
add_legacy_name("L225.GlobalTechCapture_h2") %>%
add_precursors("energy/A25.globaltech_co2capture")->
L225.GlobalTechCapture_h2
L225.GlobalTechInputPMult %>%
add_title("Price conversion from transportation technologies") %>%
add_comments("converts from $1990/tkm to $1975$/EJ") %>%
add_units("Unitless") ->
L225.GlobalTechInputPMult_h2
L225.GlobalTechSCurve_h2 %>%
add_title("Global tech lifetime for techs with s-curve retirement function") %>%
add_units("Lifetime in years, half-life in years") %>%
add_comments("Filters for any technology that uses an S-curve retirement function") %>%
add_legacy_name("L225.GlobalTechSCurve_h2") %>%
add_precursors("energy/A25.globaltech_retirement") ->
L225.GlobalTechSCurve_h2
L225.GlobalTechProfitShutdown_h2 %>%
add_title("Global tech profit shutdown decider and parameters") %>%
add_units("Unitless, used to determine shape of the function defining the relationship between shutdown rate and profitability") %>%
add_comments("Filters for any technologies that use a profit-based shutdown parameter") %>%
add_legacy_name("L225.GlobalTechProfitShutdown_h2") %>%
add_precursors("energy/A25.globaltech_retirement") ->
L225.GlobalTechProfitShutdown_h2
L225.StubTechCost_h2 %>%
add_title("Regional hydrogen production costs") %>%
add_units("$1975/GJ") %>%
add_comments("LCOH for the electrolyzer and renewables providing electricity.") %>%
add_precursors("L125.Electrolyzer_IdleRatio_Params",
"L223.StubTechCapFactor_elec",
"L223.GlobalIntTechCapital_elec",
"L223.GlobalIntTechOMfixed_elec") ->
L225.StubTechCost_h2
return_data(L225.Supplysector_h2, L225.SectorUseTrialMarket_h2, L225.SubsectorLogit_h2, L225.StubTech_h2,
L225.GlobalTechCoef_h2, L225.GlobalTechCost_h2, L225.GlobalTechShrwt_h2,
L225.PrimaryRenewKeyword_h2, L225.AvgFossilEffKeyword_h2,
L225.GlobalTechCapture_h2, L225.SubsectorShrwtFllt_h2,
L225.GlobalTechInputPMult_h2,
L225.GlobalTechSCurve_h2, L225.GlobalTechProfitShutdown_h2, L225.StubTechCost_h2)
} else {
stop("Unknown command")
}
}
bpbond/gcamdata documentation built on March 22, 2023, 4:52 a.m.
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Defines functions module_energy_L225.hydrogen
Documented in module_energy_L225.hydrogen
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_L225.hydrogen
#'
#' Provides supply sector information, subsector information, technology information for hydrogen sectors.
#'
#' @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{L225.Supplysector_h2}, \code{L225.SubsectorLogit_h2}, \code{L225.SubsectorShrwtFllt_h2}, \code{L225.StubTech_h2}, \code{L225.GlobalTechCoef_h2}, \code{L225.GlobalTechCost_h2}, \code{L225.GlobalTechShrwt_h2}, \code{L225.PrimaryRenewKeyword_h2}, \code{L225.GlobalTechCapture_h2}, \code{L225.StubTechCost_h2}, \code{L225.GlobalTechProfitShutdown_h2}, \code{L225.GlobalTechSCurve_h2}. The corresponding file in the
#' original data system was \code{L225.hydrogen.R} (energy level2).
#' @details Provides supply sector information, subsector information, technology information for hydrogen sectors.
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange filter group_by mutate select
#' @importFrom tidyr complete nesting
#' @author LF Augest 2017
module_energy_L225.hydrogen <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/A25.sector",
FILE = "energy/A25.subsector_logit",
FILE = "energy/A25.subsector_shrwt",
FILE = "energy/A25.globaltech_cost",
FILE = "energy/A25.globaltech_coef",
FILE = "energy/A25.globaltech_retirement",
FILE = "energy/A25.globaltech_shrwt",
FILE = "energy/A25.globaltech_keyword",
FILE = "energy/A25.globaltech_co2capture",
"L125.globaltech_coef",
"L125.globaltech_cost",
"L125.Electrolyzer_IdleRatio_Params",
"L223.StubTechCapFactor_elec",
"L223.GlobalIntTechCapital_elec",
"L223.GlobalIntTechOMfixed_elec"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L225.Supplysector_h2",
"L225.SectorUseTrialMarket_h2",
"L225.SubsectorLogit_h2",
"L225.SubsectorShrwtFllt_h2",
"L225.StubTech_h2",
"L225.GlobalTechCoef_h2",
"L225.GlobalTechCost_h2",
"L225.GlobalTechShrwt_h2",
"L225.PrimaryRenewKeyword_h2",
"L225.AvgFossilEffKeyword_h2",
"L225.GlobalTechCapture_h2",
"L225.GlobalTechInputPMult_h2",
"L225.GlobalTechProfitShutdown_h2",
"L225.GlobalTechSCurve_h2",
"L225.StubTechCost_h2"))
} else if(command == driver.MAKE) {
# Silencing package checks
region <- coefficient <- cost <- price.unit.conversion <- sector.name <- subsector.name <-
stub.technology <- capacity.factor <- IdleRatio <- `2040` <- `2015` <- `2050` <-
intermittent.technology <- capital.overnight <- fixed.charge.rate <- OM.fixed <-
cost_75USD_kW_yr <- kWh_elec_per_kgH2 <- output_kgh2_d <- cost_75USD_kgH2 <- NULL
all_data <- list(...)[[1]]
year.fillout <- technology <- year <- efficiency <- supplysector <- value <-
subsector <- minicam.energy.input <- input.cost <- minicam.non.energy.input <-
share.weight <- primary.renewable <- average.fossil.efficiency <-
remove.fraction <- NULL # silence package check notes
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
A25.sector <- get_data(all_data, "energy/A25.sector", strip_attributes = TRUE)
A25.subsector_logit <- get_data(all_data, "energy/A25.subsector_logit", strip_attributes = TRUE)
A25.subsector_shrwt <- get_data(all_data, "energy/A25.subsector_shrwt", strip_attributes = TRUE)
A25.globaltech_coef <- get_data(all_data, "energy/A25.globaltech_coef", strip_attributes = TRUE)
A25.globaltech_cost <- get_data(all_data, "energy/A25.globaltech_cost", strip_attributes = TRUE)
A25.globaltech_shrwt <- get_data(all_data, "energy/A25.globaltech_shrwt", strip_attributes = TRUE)
A25.globaltech_keyword <- get_data(all_data, "energy/A25.globaltech_keyword", strip_attributes = TRUE)
A25.globaltech_retirement <- get_data(all_data, "energy/A25.globaltech_retirement", strip_attributes = TRUE)
A25.globaltech_co2capture <- get_data(all_data, "energy/A25.globaltech_co2capture", strip_attributes = TRUE)
L125.globaltech_coef <- get_data(all_data, "L125.globaltech_coef", strip_attributes = TRUE)
L125.globaltech_cost <- get_data(all_data, "L125.globaltech_cost", strip_attributes = TRUE)
L125.Electrolyzer_IdleRatio_Params <- get_data(all_data, "L125.Electrolyzer_IdleRatio_Params", strip_attributes = TRUE)
L223.GlobalIntTechCapital_elec <- get_data(all_data, "L223.GlobalIntTechCapital_elec", strip_attributes = TRUE)
L223.GlobalIntTechOMfixed_elec <- get_data(all_data, "L223.GlobalIntTechOMfixed_elec", strip_attributes = TRUE)
L223.StubTechCapFactor_elec <- get_data(all_data, "L223.StubTechCapFactor_elec", strip_attributes = TRUE)
# ===================================================
# 1. Build tables for CSVs
# 1a. Supply sector information
# L225.Supplysector_h2: Supply sector information for hydrogen sectors
A25.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L225.Supplysector_h2
# H2 liquid truck has a simultaneity that may benefit from using a trial market here
L225.SectorUseTrialMarket_h2 <- filter(L225.Supplysector_h2, supplysector == "H2 liquid truck") %>%
mutate(supplysector = "trn_freight_road") %>%
select(region, supplysector) %>%
mutate(use.trial.market = 1)
# 1b. Subsector information
# L225.SubsectorLogit_h2: Subsector logit exponents of hydrogen sectors
A25.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L225.SubsectorLogit_h2
# L225.SubsectorShrwtFllt_h2: Subsector shareweights of hydrogen sectors
A25.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names) ->
L225.SubsectorShrwtFllt_h2
# 1c. Technology information
# L225.StubTech_h2: Identification of stub technologies of hydrogen
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A25.globaltech_shrwt %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names) %>%
rename(stub.technology = technology) ->
L225.StubTech_h2
# L225.GlobalTechCoef_h2: Energy inputs coefficients of global technologies for hydrogen
L125.globaltech_coef %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector,
coefficient = value) %>%
mutate(coefficient = round(coefficient,energy.DIGITS_COEFFICIENT))-> L225.GlobalTechCoef_h2
# L225.GlobalTechCost_h2: Costs of global technologies for hydrogen
# Costs of global technologies
L125.globaltech_cost %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector,
input.cost = cost) %>%
mutate(input.cost = round(input.cost,energy.DIGITS_COST))-> L225.GlobalTechCost_h2
# L225.GlobalTechShrwt_h2: Shareweights of global technologies for hydrogen
# Shareweights of global technologies
A25.globaltech_shrwt %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, year) %>%
group_by(supplysector, subsector, technology) %>%
mutate(share.weight = approx_fun(year, value, rule = 1)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(-value) ->
L225.GlobalTechShrwt_h2
A25.globaltech_coef %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input),
year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, minicam.energy.input, year) %>%
group_by(supplysector, subsector, technology, minicam.energy.input) %>%
mutate(coefficient = approx_fun(year, value, rule = 2),
units = 'GJ input / GJ H2') %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector) %>%
anti_join(L125.globaltech_coef, by = c("sector.name", "subsector.name", "technology", "minicam.energy.input")) %>%
select(-value,-efficiency,-price.unit.conversion) ->
L225.GlobalTechCoef_h2_noprod #filter and convert efficiencies to coefficients for only end use and distribution pass-through sectors and technologies
L225.GlobalTechCoef_h2 <- bind_rows(L225.GlobalTechCoef_h2,L225.GlobalTechCoef_h2_noprod) %>%
mutate(minicam.energy.input = if_else(minicam.energy.input == 'elect_td_trn (compression and refrigeration)','elect_td_trn',minicam.energy.input)) %>%
group_by(sector.name,subsector.name,technology,minicam.energy.input,units,year) %>%
summarize(coefficient = sum(coefficient)) %>%
ungroup()
A25.globaltech_coef %>%
filter(!is.na(price.unit.conversion)) %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input),
year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
mutate(price.unit.conversion = approx_fun(year, price.unit.conversion, rule = 2)) %>%
rename(sector.name = supplysector, subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechInputPMult"]]) %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) -> L225.GlobalTechInputPMult
A25.globaltech_cost %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology, minicam.non.energy.input), year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(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, rule = 2), digits = energy.DIGITS_COST),
units = '$1975/GJ H2') %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector) %>%
anti_join(L125.globaltech_cost, by = c("sector.name", "subsector.name", "technology", "minicam.non.energy.input")) %>%
select(-value) ->
L225.GlobalTechCost_h2_noprod #get costs for only end use, distribution pass-through sectors, (global) renewable portion of LCOH (e.g., solar panels), and technologies from A25
L225.GlobalTechCost_h2 <- bind_rows(L225.GlobalTechCost_h2,L225.GlobalTechCost_h2_noprod)
# Estimate the region-specific costs of direct wind and solar electrolysis, based on the capacity factors of the
# electric generation technologies and relationship between capacity factors and NE costs of electrolysis
L125.Electrolyzer_IdleRatio_Params_2015 <- filter(L125.Electrolyzer_IdleRatio_Params, year == 2015)
L125.Electrolyzer_IdleRatio_Params_2040 <- filter(L125.Electrolyzer_IdleRatio_Params, year == 2040)
# Set the fraction of 2050 to 2040 costs. The specific number is from Pat's workbook
Electrolyzer_2050_2040_cost_ratio <- 0.77
# The following block uses the wind+solar capacity factors in each region to estimate the levelized cost of the
# hydrogen electrolyzers. The available cost estimate years are 2015 and 2040. Cost reductions are extrapolated to
# 2050 using the same improvement factor in all regions. The costs assigned to years between 2015 and 2050 are estimated
# with linear interpolation, and outside this window we use fixed extrapolation.
# Electrolyzer non-energy costs replace rather than add to the global default
# values in L225.GlobalTechCost_h2. This is handled in the left_join.
L223.StubTechCapFactor_elec %>%
filter(year == max(MODEL_BASE_YEARS),
stub.technology %in% c("wind", "PV")) %>%
mutate(IdleRatio = pmax(1, 1 / (capacity.factor / energy.ELECTROLYZER_RENEWABLE_CAPACITY_RATIO)),
`2015` = L125.Electrolyzer_IdleRatio_Params_2015$intercept +
IdleRatio * L125.Electrolyzer_IdleRatio_Params_2015$slope,
`2040` = L125.Electrolyzer_IdleRatio_Params_2040$intercept +
IdleRatio * L125.Electrolyzer_IdleRatio_Params_2040$slope,
`2050` = `2040` * Electrolyzer_2050_2040_cost_ratio) %>%
select(region, subsector, `2015`, `2040`, `2050`) %>%
gather_years() %>%
complete(nesting(region, subsector), year = MODEL_YEARS) %>%
group_by(region, subsector) %>%
mutate(minicam.non.energy.input = "electrolyzer",
input.cost = approx_fun(year, value, rule = 2),
input.cost = input.cost * gdp_deflator(1975, 2016) / CONV_GJ_KGH2) %>%
ungroup() %>%
select(-value) %>%
left_join(select(L225.GlobalTechCost_h2, -input.cost),
by = c("subsector" = "subsector.name", "year", "minicam.non.energy.input")) %>%
rename(supplysector = sector.name, stub.technology = technology) %>%
select(LEVEL2_DATA_NAMES[["StubTechCost"]]) ->
L225.StubTechCost_h2_electrolyzer
# Estimate the wind turbine and solar panel related aspects of the costs of direct renewable hydrogen electrolysis
# These are estimated from the capital costs of wind and solar technologies (generic, global), the region-specific
# capacity factors, efficiencies of hydrogen production, and size of the representative hydrogen plant
L225.RenewElec_cost <- L223.GlobalIntTechCapital_elec %>%
filter(intermittent.technology %in% c("wind", "PV")) %>%
left_join(L223.GlobalIntTechOMfixed_elec, by = c("sector.name", "subsector.name", "intermittent.technology", "year")) %>%
mutate(cost_75USD_kW_yr = capital.overnight * fixed.charge.rate + OM.fixed) %>%
select(subsector.name, intermittent.technology, year, cost_75USD_kW_yr)
L225.RenewElec_eff <- filter(L225.GlobalTechCoef_h2,
subsector.name %in% c("solar", "wind") & !grepl("water", minicam.energy.input)) %>%
mutate(kWh_elec_per_kgH2 = coefficient * CONV_GJ_KGH2 / CONV_KWH_GJ) %>%
select(subsector.name, year, kWh_elec_per_kgH2)
L225.RenewElec_cost %>%
left_join_error_no_match(L225.RenewElec_eff, by = c("subsector.name", "year")) %>%
left_join(L223.StubTechCapFactor_elec,
by = c("subsector.name" = "subsector", "intermittent.technology" = "stub.technology", "year")) %>%
mutate(minicam.non.energy.input = if_else(subsector.name == "solar", "solar panels", "wind turbines"),
output_kgh2_d = if_else(subsector.name == "solar", energy.SOLAR_ELECTROLYSIS_KGH2_D, energy.WIND_ELECTROLYSIS_KGH2_D),
cost_75USD_kgH2 = cost_75USD_kW_yr * kWh_elec_per_kgH2 * output_kgh2_d / CONV_DAY_HOURS /
(output_kgh2_d * capacity.factor / CONV_DAYS_YEAR),
input.cost = cost_75USD_kgH2 / CONV_GJ_KGH2) %>%
select(region, subsector.name, year, minicam.non.energy.input, input.cost) %>%
left_join(select(L225.GlobalTechCost_h2, -input.cost), by = c("subsector.name", "year", "minicam.non.energy.input")) %>%
rename(supplysector = sector.name, subsector = subsector.name, stub.technology = technology) %>%
select(LEVEL2_DATA_NAMES[["StubTechCost"]]) ->
L225.StubTechCost_h2_renewables
# Combine the electrolyzer and renewable power generation technologies' levelized non-energy costs into a single table
L225.StubTechCost_h2 <- bind_rows(L225.StubTechCost_h2_electrolyzer, L225.StubTechCost_h2_renewables) %>%
mutate(input.cost = round(input.cost, digits = energy.DIGITS_COST))
# L225.PrimaryRenewKeyword_h2: Keywords of primary renewable electric generation technologies
A25.globaltech_keyword %>%
repeat_add_columns(tibble(year = c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS))) %>%
rename(sector.name = supplysector, subsector.name = subsector) ->
L225.AllKeyword_h2
L225.AllKeyword_h2 %>%
filter(!is.na(primary.renewable)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "primary.renewable") ->
L225.PrimaryRenewKeyword_h2
# L225.AvgFossilEffKeyword_h2: Keywords of fossil/bio electric generation technologies
L225.AllKeyword_h2 %>%
filter(!is.na(average.fossil.efficiency)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "average.fossil.efficiency") ->
L225.AvgFossilEffKeyword_h2
# L225.GlobalTechCapture_h2: CO2 capture fractions from global fertilizer production technologies with CCS
# Note: No need to consider historical periods or intermittent technologies here
A25.globaltech_co2capture %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, year) %>%
group_by(supplysector, subsector, technology) %>%
mutate(remove.fraction = approx_fun(year, value, rule = 1),
remove.fraction = round(remove.fraction,energy.DIGITS_REMOVE.FRACTION)) %>%
ungroup %>%
filter(year %in% MODEL_FUTURE_YEARS) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector, subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "remove.fraction") %>%
mutate(storage.market = energy.CO2.STORAGE.MARKET) ->
L225.GlobalTechCapture_h2
# Set global technology retirement information for all hydrogen sector technologies
# ------------------------------------------------------------------------------------
# Retirement may consist of any of three types of retirement function (phased, s-curve, or none)
# This section checks L225.globaltech_retirement for each of these functions and creates a subset for each option then removes any subsets with 0 rows
# All of these options have different headers, and all are allowed.
# Also, technologies that have an additional shutdown rate as a function of their profitability are also defined.
# Replace years and prepare assumptions into correct format
A25.globaltech_retirement %>%
set_years() %>%
mutate(year = as.integer(year)) %>%
rename(sector.name = supplysector, subsector.name = subsector) ->
L225.globaltech_retirement_base
# Copies base year retirement information into all future years and appends back onto itself
L225.globaltech_retirement_base %>%
filter(year == min(MODEL_FUTURE_YEARS)) %>%
select(-year) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
bind_rows(filter(L225.globaltech_retirement_base, year == max(MODEL_BASE_YEARS))) ->
L225.globaltech_retirement
# S-CURVE RETIREMENT
# Subsets the S-Curve retirement function
L225.globaltech_retirement %>%
filter(!is.na(L225.globaltech_retirement$half.life)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "lifetime", "steepness", "half.life") ->
L225.GlobalTechSCurve_h2
# PROFIT-BASED SHUTDOWN PARAMETERS
# Subsets any technologies with a shutdown parameter based on profitability
L225.globaltech_retirement %>%
filter(!is.na(L225.globaltech_retirement$median.shutdown.point)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "median.shutdown.point", "profit.shutdown.steepness") ->
L225.GlobalTechProfitShutdown_h2
# ===================================================
# Produce outputs
L225.Supplysector_h2 %>%
add_title("Supply sector information for hydrogen sectors") %>%
add_units("Unitless") %>%
add_comments("Expand sector information for all GCAM regions") %>%
add_legacy_name("L225.Supplysector_h2") %>%
add_precursors("common/GCAM_region_names", "energy/A25.sector") ->
L225.Supplysector_h2
L225.SectorUseTrialMarket_h2 %>%
add_title("Supply sector trial markets") %>%
add_units("Boolean") %>%
add_comments("Read in to help solver deal with simultaneities") %>%
add_precursors("common/GCAM_region_names", "energy/A25.sector") ->
L225.SectorUseTrialMarket_h2
L225.SubsectorLogit_h2 %>%
add_title("Subsector logit exponents of hydrogen sectors") %>%
add_units("Unitless") %>%
add_comments("Expand subsector logit exponents for all GCAM regions") %>%
add_legacy_name("L225.SubsectorLogit_h2") %>%
add_precursors("common/GCAM_region_names", "energy/A25.subsector_logit") ->
L225.SubsectorLogit_h2
L225.SubsectorShrwtFllt_h2 %>%
add_title("Subsector shareweights of hydrogen sectors") %>%
add_units("Unitless") %>%
add_comments("Expand Subsector shareweights for all GCAM regions") %>%
add_legacy_name("L225.SubsectorShrwtFllt_h2") %>%
add_precursors("common/GCAM_region_names", "energy/A25.subsector_shrwt") ->
L225.SubsectorShrwtFllt_h2
L225.StubTech_h2 %>%
add_title("Identification of stub technologies of hydrogen") %>%
add_units("NA") %>%
add_comments("Expand stub technologies information for all GCAM regions") %>%
add_comments("assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)") %>%
add_legacy_name("L225.StubTech_h2") %>%
add_precursors("common/GCAM_region_names", "energy/A25.globaltech_shrwt") ->
L225.StubTech_h2
L225.GlobalTechCoef_h2 %>%
add_title("Energy inputs and efficiencies of global technologies for hydrogen") %>%
add_units("Unitless") %>%
add_comments("Interpolated orginal data into all model years") %>%
add_precursors("L125.globaltech_coef",'energy/A25.globaltech_coef') -> L225.GlobalTechCoef_h2
L225.GlobalTechCost_h2 %>%
add_title("Costs of global technologies for hydrogen") %>%
add_units("$1975 / GJ H2") %>%
add_comments("Interpolated orginal data into all model years") %>%
add_precursors("L125.globaltech_cost",'energy/A25.globaltech_cost') -> L225.GlobalTechCost_h2
L225.GlobalTechShrwt_h2 %>%
add_title("Shareweights of global technologies for hydrogen") %>%
add_units("Unitless") %>%
add_comments("Interpolated orginal data into all model years") %>%
add_legacy_name("L225.GlobalTechShrwt_h2") %>%
add_precursors("energy/A25.globaltech_shrwt") ->
L225.GlobalTechShrwt_h2
L225.PrimaryRenewKeyword_h2 %>%
add_title("Keywords of primary renewable electric generation technologies") %>%
add_units("NA") %>%
add_comments("Identify Keywords of primary renewable electric generation technologies for all model years") %>%
add_comments("can be multiple lines") %>%
add_legacy_name("L225.PrimaryRenewKeyword_h2") %>%
add_precursors("energy/A25.globaltech_keyword") ->
L225.PrimaryRenewKeyword_h2
L225.AvgFossilEffKeyword_h2 %>%
add_title("Keywords of fossil/bio electric generation technologies") %>%
add_units("NA") %>%
add_comments("Identify Keywords of fossil/bio electric generation technologies for all model years") %>%
add_legacy_name("L225.AvgFossilEffKeyword_h2") %>%
add_precursors("energy/A25.globaltech_keyword") ->
L225.AvgFossilEffKeyword_h2
L225.GlobalTechCapture_h2 %>%
add_title("CO2 capture fractions from global fertilizer production technologies with CCS") %>%
add_units("Unitless") %>%
add_comments("Interpolated orginal data into all model years") %>%
add_legacy_name("L225.GlobalTechCapture_h2") %>%
add_precursors("energy/A25.globaltech_co2capture")->
L225.GlobalTechCapture_h2
L225.GlobalTechInputPMult %>%
add_title("Price conversion from transportation technologies") %>%
add_comments("converts from $1990/tkm to $1975$/EJ") %>%
add_units("Unitless") ->
L225.GlobalTechInputPMult_h2
L225.GlobalTechSCurve_h2 %>%
add_title("Global tech lifetime for techs with s-curve retirement function") %>%
add_units("Lifetime in years, half-life in years") %>%
add_comments("Filters for any technology that uses an S-curve retirement function") %>%
add_legacy_name("L225.GlobalTechSCurve_h2") %>%
add_precursors("energy/A25.globaltech_retirement") ->
L225.GlobalTechSCurve_h2
L225.GlobalTechProfitShutdown_h2 %>%
add_title("Global tech profit shutdown decider and parameters") %>%
add_units("Unitless, used to determine shape of the function defining the relationship between shutdown rate and profitability") %>%
add_comments("Filters for any technologies that use a profit-based shutdown parameter") %>%
add_legacy_name("L225.GlobalTechProfitShutdown_h2") %>%
add_precursors("energy/A25.globaltech_retirement") ->
L225.GlobalTechProfitShutdown_h2
L225.StubTechCost_h2 %>%
add_title("Regional hydrogen production costs") %>%
add_units("$1975/GJ") %>%
add_comments("LCOH for the electrolyzer and renewables providing electricity.") %>%
add_precursors("L125.Electrolyzer_IdleRatio_Params",
"L223.StubTechCapFactor_elec",
"L223.GlobalIntTechCapital_elec",
"L223.GlobalIntTechOMfixed_elec") ->
L225.StubTechCost_h2
return_data(L225.Supplysector_h2, L225.SectorUseTrialMarket_h2, L225.SubsectorLogit_h2, L225.StubTech_h2,
L225.GlobalTechCoef_h2, L225.GlobalTechCost_h2, L225.GlobalTechShrwt_h2,
L225.PrimaryRenewKeyword_h2, L225.AvgFossilEffKeyword_h2,
L225.GlobalTechCapture_h2, L225.SubsectorShrwtFllt_h2,
L225.GlobalTechInputPMult_h2,
L225.GlobalTechSCurve_h2, L225.GlobalTechProfitShutdown_h2, L225.StubTechCost_h2)
} else {
stop("Unknown command")
}
}
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