R/zchunk_L270.limits.R

In rohmin9122/gcam-korea-release: Build the GCAM-Korea Input Datasets

#' module_energy_L270.limits
#'
#' Generate GCAM policy constraints which limit model behavior in some way.  In
#' particular limit the fraction of liquid feedstocks and inputs to electricity
#' generation which can come from sources other than crude oil.  Constrain the
#' total amount of subsidy as a fraction of GDP which an economy is will to give
#' to have net negative emissions.
#'
#' @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{L270.CreditOutput}, \code{L270.CreditInput_elec}, \code{L270.CreditInput_feedstocks}, \code{L270.CreditMkt}, \code{L270.CTaxInput}, \code{L270.NegEmissFinalDemand}, \code{L270.NegEmissFinalDemand_SPA}, \code{L270.NegEmissBudgetMaxPrice}, \code{paste0("L270.NegEmissBudget_", c("GCAM3", paste0("SSP", 1:5), paste0("gSSP", 1:5), paste0("spa", 1:5))) )}. The corresponding file in the
#' original data system was \code{L270.limits.R} (energy level2).
#' @details Generate GCAM policy constraints which enforce limits to liquid feedstocks
#' and the amount of subsidies given for net negative emissions.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @importFrom magrittr %<>%
#' @author PLP March 2018
module_energy_L270.limits <- function(command, ...) {
  if(command == driver.DECLARE_INPUTS) {
    return(c(FILE = "common/GCAM_region_names",
             FILE = "energy/A23.globaltech_eff",
             "L102.gdp_mil90usd_GCAM3_R_Y",
             "L102.gdp_mil90usd_Scen_R_Y",
             "L221.GlobalTechCoef_en"))
  } else if(command == driver.DECLARE_OUTPUTS) {
    return(c("L270.CreditOutput",
             "L270.CreditInput_elec",
             "L270.CreditInput_feedstocks",
             "L270.CreditMkt",
             "L270.CTaxInput",
             "L270.NegEmissFinalDemand",
             "L270.NegEmissFinalDemand_SPA",
             "L270.NegEmissBudgetMaxPrice",
             # TODO: might just be easier to keep the scenarios in a single
             # table here and split when making XMLs but to match the old
             # data system we will split here
             paste0("L270.NegEmissBudget_", c("GCAM3", paste0("SSP", 1:5), paste0("gSSP", 1:5), paste0("spa", 1:5)) )))
  } else if(command == driver.MAKE) {

    value <- subsector <- supplysector <- year <- GCAM_region_ID <- sector.name <-
      region <- scenario <- constraint <- . <- NULL # silence package check notes

    all_data <- list(...)[[1]]

    # Load required inputs
    GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
    A23.globaltech_eff <- get_data(all_data, "energy/A23.globaltech_eff")
    L102.gdp_mil90usd_GCAM3_R_Y <- get_data(all_data, "L102.gdp_mil90usd_GCAM3_R_Y")
    L102.gdp_mil90usd_Scen_R_Y <- get_data(all_data, "L102.gdp_mil90usd_Scen_R_Y")
    L102.gdp_mil90usd_Scen_R_Y <- get_data(all_data, "L102.gdp_mil90usd_Scen_R_Y")
    L221.GlobalTechCoef_en <- get_data(all_data, "L221.GlobalTechCoef_en")

    # Limit bioliquids for feedstocks and electricity
    # Note: we do this by requiring a certain fraction of inputs to those technologies to come from oil
    # L270.CreditOutput: Secondary output of oil credits
    tibble(sector.name = "refining",
           subsector.name = "oil refining",
           technology = "oil refining") %>%
      repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
      mutate(res.secondary.output = "oil-credits",
             output.ratio = 1.0) ->
      L270.CreditOutput

    # L270.CreditInput_elec: minicam-energy-input of oil credits for electricity techs
    A23.globaltech_eff %>%
      fill_exp_decay_extrapolate(MODEL_YEARS) %>%
      mutate(value = round(value, energy.DIGITS_EFFICIENCY)) %>%
      filter(subsector == "refined liquids") %>%
      mutate(minicam.energy.input = "oil-credits",
             # note we are converting the efficiency to a coefficient here
             coefficient = energy.OILFRACT_ELEC / value) %>%
      select(-value) %>%
      rename(sector.name = supplysector,
             subsector.name = subsector) ->
      L270.CreditInput_elec

    # L270.CreditInput_feedstocks: minicam-energy-input of oil credits for feedstock techs
    tibble(sector.name = "industrial feedstocks",
           subsector.name = "refined liquids",
           technology = "refined liquids") %>%
      repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
      mutate(minicam.energy.input = "oil-credits",
             coefficient = energy.OILFRACT_FEEDSTOCKS) ->
      L270.CreditInput_feedstocks

    # L270.CreditMkt: Market for oil credits
    tibble(region = GCAM_region_names$region,
           policy.portfolio.standard = "oil-credits",
           market = "global",
           policyType = "RES") %>%
      repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
      mutate(constraint = 1,
             price.unit = "1975$/GJ",
             output.unit = "EJ") ->
      L270.CreditMkt

    # Create the negative emissions GDP budget constraint limits

    # Create a usable GDP by region + scenario + year
    L102.gdp_mil90usd_GCAM3_R_Y %>%
      mutate(scenario = "GCAM3") %>%
      bind_rows(L102.gdp_mil90usd_Scen_R_Y) %>%
      left_join_error_no_match(GCAM_region_names, by = c("GCAM_region_ID")) %>%
      filter(year %in% MODEL_YEARS) %>%
      select(-GCAM_region_ID) ->
      GDP_scenario

    # L270.CTaxInput: Create ctax-input for all biomass
    L221.GlobalTechCoef_en %>%
      filter(grepl("(biomass|ethanol)", sector.name)) %>%
      mutate(ctax.input = energy.NEG_EMISS_POLICY_NAME,
             fuel.name = sector.name) ->
      L270.CTaxInput
    L270.CTaxInput <- L270.CTaxInput[, c(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "ctax.input", "fuel.name")]

    # L270.NegEmissFinalDemand: Create negative emissions final demand
    tibble(region = GCAM_region_names$region,
           negative.emissions.final.demand = "CO2",
           policy.name=energy.NEG_EMISS_POLICY_NAME ) ->
      L270.NegEmissFinalDemand

    # L270.NegEmissBudget: Create the budget for paying for net negative emissions
    GDP_scenario %>%
      # no dollar year or unit conversions since emissions already match
      mutate(constraint = value * energy.NEG_EMISS_GDP_BUDGET_PCT,
             policy.portfolio.standard = energy.NEG_EMISS_POLICY_NAME,
             policyType = "tax",
             market = region,
             price.unit = "%",
             output.unit = "mil 1990$") %>%
      select(-value) %>%
      # constrain in only years which could include a carbon price
      filter(year >= 2020) ->
      L270.NegEmissBudget

    # Create a table for max price which gives a hint to the solver the price of
    # this market is 0 <= p <= 1
    tibble(region = GCAM_region_names$region,
           policy.portfolio.standard = energy.NEG_EMISS_POLICY_NAME,
           max.price = 1.0) ->
      L270.NegEmissBudgetMaxPrice

    # split out SSPs so that we can generate SPA policies
    # NOTE: SPA policies *must* be regional no matter the value of NEG_EMISS_MARKT_GLOBAL
    L270.NegEmissBudget %>%
      filter(grepl('^SSP\\d', scenario)) %>%
      mutate(scenario = sub('SSP', 'spa', scenario)) ->
      L270.NegEmissBudget_SPA

    # Copy Final Demand tibble as well to ensure it is also always regional
    L270.NegEmissFinalDemand_SPA <- L270.NegEmissFinalDemand

    if(energy.NEG_EMISS_MARKT_GLOBAL) {
      # when the negative emissions budget is global we need to aggregate
      # the constraint across regions
      L270.NegEmissBudget %>%
        group_by(scenario, year) %>%
        summarize(constraint = sum(constraint)) %>%
        ungroup() %>%
        left_join_error_no_match(select(L270.NegEmissBudget, -constraint), ., by = c("scenario", "year")) %>%
        mutate(market = "global") ->
        L270.NegEmissBudget

      # the negative emissions final demand must only be included in just one region (could be any)
      L270.NegEmissFinalDemand %<>% slice(1)
    }

    # Produce outputs
    L270.CreditOutput %>%
      add_title("Creates the supply of oil credits") %>%
      add_units("Unitless") %>%
      add_comments("Adds a secondary output to oil refining to generate oil-credits") %>%
      add_comments("that are consumed by electricity and feedstocks") %>%
      add_legacy_name("L270.CreditOutput") ->
      L270.CreditOutput

    L270.CreditInput_elec %>%
      add_title("Creates demand of oil credits in electricity") %>%
      add_units("Elec coef * constraint") %>%
      add_comments("Consumes oil-credits limiting the blend of refined") %>%
      add_comments("liquids that can be used generate electricity") %>%
      add_legacy_name("L270.CreditInput_elec") %>%
      add_precursors("energy/A23.globaltech_eff") ->
      L270.CreditInput_elec

    L270.CreditInput_feedstocks %>%
      add_title("Creates demand of oil credits in feedstocks") %>%
      add_units("% constraint") %>%
      add_comments("Consumes oil-credits limiting the blend of refined") %>%
      add_comments("liquids that can be used generate electricity") %>%
      add_legacy_name("L270.CreditInput_feedstocks") ->
      L270.CreditInput_feedstocks

    L270.CreditMkt %>%
      add_title("Sets up the oil-credits RES market") %>%
      add_units("NA") %>%
      add_comments("Boiler plate and units for creating the actual") %>%
      add_comments("market for balancing oil-credits") %>%
      add_legacy_name("L270.CreditMkt") %>%
      add_precursors("common/GCAM_region_names") ->
      L270.CreditMkt

    L270.CTaxInput %>%
      add_title("Creates the ctax-input limiting the amount of negative emissions") %>%
      add_units("NA") %>%
      add_comments("Add ctax-input to all of the bio-energy supply sectors") %>%
      add_comments("by using L221.GlobalTechCoef_en filtered by biomass|ethanol") %>%
      add_legacy_name("L270.CTaxInput") %>%
      add_precursors("L221.GlobalTechCoef_en") ->
      L270.CTaxInput

    L270.NegEmissFinalDemand %>%
      add_title("Creates a negative emissions final demand") %>%
      add_units("NA") %>%
      add_comments("Mostly just a tag to create the final demand object") %>%
      add_comments("that will check the net subsidy being paid") %>%
      add_comments("Note if we are using a global market this tag should only") %>%
      add_comments("be read into a single region (doesn't matter which)") %>%
      add_legacy_name("L270.NegEmissFinalDemand") %>%
      add_precursors("common/GCAM_region_names") ->
      L270.NegEmissFinalDemand

    L270.NegEmissFinalDemand_SPA %>%
      same_attributes_as(L270.NegEmissFinalDemand, copy_strict_flags = FALSE) %>%
      add_title("Creates a negative emissions final demand for SPA policies") %>%
      add_comments("Note this is essentially the same as L270.NegEmissFinalDemand except always") %>%
      add_comments("gauranteeded to be for regional markets since SPAs are all regional") %>%
      add_legacy_name("L270.NegEmissFinalDemand_SPA") ->
      L270.NegEmissFinalDemand_SPA

    L270.NegEmissBudgetMaxPrice %>%
      add_title("A hint for the solver for what the max price of this market is") %>%
      add_units("%") %>%
      add_comments("This value is just used to give the solver a hint of the") %>%
      add_comments("range of prices which are valid.  For the negative emissions") %>%
      add_comments("budget constraint the price is a fraction from 0 to 1") %>%
      add_legacy_name("L270.NegEmissBudgetMaxPrice") %>%
      add_precursors("common/GCAM_region_names") ->
      L270.NegEmissBudgetMaxPrice

    ret_data <- c("L270.CreditOutput", "L270.CreditInput_elec", "L270.CreditInput_feedstocks", "L270.CreditMkt", "L270.CTaxInput", "L270.NegEmissFinalDemand", "L270.NegEmissFinalDemand_SPA", "L270.NegEmissBudgetMaxPrice")
    # We will generate a bunch of tibbles for the negative emissions budgets for each scenario
    # and use assign() to save them to variables with names as L270.NegEmissBudget_[SCENARIO]
    # Note that since the call to assign() is in the for loop we must explicitly set the
    # environment to just outside of the loop:
    curr_env <- environment()
    # Put the SPA budgets in with the rest of the scenarios so that we can export them all in bulk
    L270.NegEmissBudget %<>% bind_rows(L270.NegEmissBudget_SPA)
    for(scen in unique(L270.NegEmissBudget$scenario)) {
      curr_data_name <- paste0("L270.NegEmissBudget_", scen)
      L270.NegEmissBudget %>%
        filter(scenario == scen) %>%
        select(-scenario) %>%
        add_title(paste0("The negative emissions budget in scenario ", scen)) %>%
        add_units("mil 1990$") %>%
        add_comments("The budget a market is willing to subsidize negative emissions") %>%
        add_legacy_name(curr_data_name) %>%
        add_precursors(if_else(scen == "GCAM3", "L102.gdp_mil90usd_GCAM3_R_Y", "L102.gdp_mil90usd_Scen_R_Y")) %>%
        assign(curr_data_name, ., envir = curr_env)

      ret_data <- c(ret_data, curr_data_name)
    }

    # Call return_data but we need to jump through some hoops since we generated some of the
    # tibbles from the scenarios so we will generate the call to return_data
    ret_data %>%
      paste(collapse = ", ") %>%
      paste0("return_data(", ., ")") %>%
      parse(text = .) %>%
      eval()
  } else {
    stop("Unknown command")
  }
}