R/zchunk_LB1233.elec_water_USA.R
In JGCRI/gcamdata: Build the GCAM Input Datasets
Defines functions
module_gcamusa_LB1233.elec_water_USA
Documented in
module_gcamusa_LB1233.elec_water_USA
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_gcamusa_LB1233.elec_water_USA
#'
#' Compute water withdrawals/consumption by state, fuel, technology, and cooling system type.
#'
#' @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{L1233.out_EJ_state_elec_F_tech_cool}, \code{L1233.share_sR_elec_F_tech_cool}. The corresponding file in the
#' original data system was \code{LB1233.Elec_water.R} (gcam-usa level1).
#' @details Compute water withdrawals/consumption by state, fuel, technology, and cooling system type.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author ST October 2017, NTG May 2020
module_gcamusa_LB1233.elec_water_USA <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "gcam-usa/states_subregions",
FILE = "gcam-usa/UCS_tech_names",
FILE = "gcam-usa/UCS_water_types",
FILE = "gcam-usa/A23.elecS_tech_mapping_cool",
FILE = "gcam-usa/UCS_Database",
FILE = "gcam-usa/usa_seawater_states_basins",
"L1231.out_EJ_state_elec_F_tech"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L1233.out_EJ_state_elec_F_tech_cool",
"L1233.share_sR_elec_F_tech_cool"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
State <- `Reported Water Source (Type)` <- reported_water_source <- `Estimated Generation (MWh)` <-
`Technology Code` <- cooling_system <- water_type <- technology <- state <-
state <- sector <- fuel <- out_MWh <- out_MWh_ <- `First Year of Operation` <-
grid_region <- share <- sum_share_tech <- share_nat <- value <-
year <- water_withdrawals <- water_consumption <- out_MWh.sea <- `Generation Technology` <-
out_MWh_sea <- share_nat.sea <- Electric.sector <- Electric.sector.technology <- NULL
# Load required inputs
states_subregions <- get_data(all_data, "gcam-usa/states_subregions")
UCS_tech_names <- get_data(all_data, "gcam-usa/UCS_tech_names")
UCS_water_types <- get_data(all_data, "gcam-usa/UCS_water_types")
A23.elecS_tech_mapping_cool <- get_data(all_data, "gcam-usa/A23.elecS_tech_mapping_cool")
UCS_Database <- get_data(all_data, "gcam-usa/UCS_Database")
usa_seawater_states_basins <- get_data(all_data, "gcam-usa/usa_seawater_states_basins")
L1231.out_EJ_state_elec_F_tech <- get_data(all_data, "L1231.out_EJ_state_elec_F_tech")
# ===================================================
`Generation Technology` <- NULL # silence package check notes
# Define unique states and basins that have access to seawater that will
# allow for seawate cooling
seawater_states_basins <- unique(usa_seawater_states_basins$seawater_region)
# inital processing of UCS database
UCS_Database %>%
rename(state = State,
out_MWh = `Estimated Generation (MWh)`,
Technology.Code = `Technology Code`,
reported_water_source = `Reported Water Source (Type)`) %>%
# filter out entries with no technology detail
filter(!is.na(`Generation Technology`)) %>%
# reset Michigan and Wisconsin power plant seawater use to surface water
mutate(reported_water_source = if_else(!(state %in% seawater_states_basins) &
reported_water_source == gcamusa.UCS_WATER_TYPE_OCEAN,
gcamusa.UCS_WATER_TYPE_SURFACE,
reported_water_source)) %>%
# introduce tech and water type names
left_join_error_no_match(UCS_tech_names, by = "Technology.Code") %>%
left_join_error_no_match(UCS_water_types %>%
rename(reported_water_source = `Reported Water Source (Type)`),
by = "reported_water_source") %>%
# set water type to "none" for techs with no cooling system...
mutate(water_type = if_else(cooling_system == gcamusa.ELEC_COOLING_SYSTEM_NONE, gcamusa.ELEC_COOLING_SYSTEM_NONE, water_type),
# ...and change hydro and PV to fresh
water_type = if_else(technology %in% gcamusa.ELEC_TECHS_NO_COOLING_FRESH, gcamusa.WATER_TYPE_FRESH, water_type),
cooling_system = if_else(water_type == gcamusa.WATER_TYPE_SEAWATER, gcamusa.WATER_TYPE_SEAWATER, cooling_system)) %>%
# Filter out all non hydro / PV / wind technologies which do not use cooling systems and binary recirculation which we do not model
filter((!grepl(gcamusa.ELEC_COOLING_SYSTEM_NONE, cooling_system) | technology %in% gcamusa.ELEC_TECHS_NO_COOLING),
!grepl(gcamusa.ELEC_COOLING_SYSTEM_BINARY, cooling_system)) ->
USC_db_adj
# aggregate and compute shares for states
USC_db_adj %>%
group_by(state, sector, fuel, technology) %>%
summarise(out_MWh_ = sum(out_MWh)) %>%
ungroup() ->
L1233.out_MWh_state_elec_F_tech
USC_db_adj %>%
group_by(state, sector, fuel, technology, cooling_system, water_type) %>%
summarise(out_MWh = sum(out_MWh)) %>%
ungroup() %>%
left_join_error_no_match(L1233.out_MWh_state_elec_F_tech,
by = c("state", "sector", "fuel", "technology")) %>%
mutate(share = out_MWh / out_MWh_) %>%
select(-out_MWh_) ->
L1233.out_MWh_state_elec_F_tech_cool
# aggregate and compute shares for Federeal Energy Regulatory Commission (FERC) subregions; ...
# ... filtering for power plants built this century for contempory output estimates
USC_db_adj %>%
filter(`First Year of Operation` >= gcamusa.UCS_WATER_FIRST_YEAR) %>%
left_join_error_no_match(select(states_subregions, state, grid_region), by = "state") ->
USC_db_adj_2000s
USC_db_adj_2000s %>%
group_by(grid_region, sector, fuel, technology) %>%
summarise(out_MWh_ = sum(out_MWh)) %>%
ungroup() ->
L1233.out_MWh_sR_elec_F_tech # all technologies
USC_db_adj_2000s %>%
group_by(grid_region, sector, fuel, technology, cooling_system, water_type) %>%
summarise(out_MWh = sum(out_MWh)) %>%
ungroup() %>%
left_join_error_no_match(L1233.out_MWh_sR_elec_F_tech,
by = c("grid_region", "sector", "fuel", "technology")) %>%
mutate(share = out_MWh / out_MWh_) %>%
select(-out_MWh_) ->
L1233.out_MWh_sR_elec_F_tech_cool # cooling technologies
# calculate national averages, to be used as default values where data are missing
# we separate states with and without seawater availability to calculate separate shares for these states.
# therefore we have a national avg for non seawater states and for seawater states
L1233.out_MWh_state_elec_F_tech %>%
filter(state %in% seawater_states_basins) %>%
group_by(sector, fuel, technology) %>%
summarise(out_MWh_sea = sum(out_MWh_)) %>%
ungroup() ->
L1233.out_MWh_USA_elec_F_tech_sea
L1233.out_MWh_state_elec_F_tech %>%
filter(!(state %in% seawater_states_basins)) %>%
group_by(sector, fuel, technology) %>%
summarise(out_MWh_ = sum(out_MWh_)) %>%
ungroup() ->
L1233.out_MWh_USA_elec_F_tech
L1233.out_MWh_USA_elec_F_tech_sea %>%
left_join_error_no_match(L1233.out_MWh_USA_elec_F_tech,
by = c("sector", "fuel", "technology")) %>%
replace_na(list(out_MWh_sea = 0, out_MWh_=0)) ->
L1233.out_MWh_USA_elec_F_tech
L1233.out_MWh_state_elec_F_tech_cool %>%
filter(state %in% seawater_states_basins) %>%
group_by(sector, fuel, technology, cooling_system, water_type) %>%
summarise(out_MWh.sea = sum(out_MWh)) %>%
ungroup() -> L1233.out_MWh_state_elec_F_tech_cool_sea
L1233.out_MWh_state_elec_F_tech_cool %>%
filter(!(state %in% seawater_states_basins)) %>%
group_by(sector, fuel, technology, cooling_system, water_type) %>%
summarise(out_MWh = sum(out_MWh)) %>%
ungroup() -> L1233.out_MWh_state_elec_F_tech_cool_nosea
L1233.out_MWh_state_elec_F_tech_cool_sea %>%
# sea and nosea tables have different tech / cooling system / water type combinations
# all of which need to be reflected in final table. Thus, full_join() is used, and
# NA values are replaced with zeros below
full_join(L1233.out_MWh_state_elec_F_tech_cool_nosea,
by = c("sector", "fuel", "technology", "cooling_system", "water_type")) %>%
replace_na(list(out_MWh = 0, out_MWh.sea=0)) %>%
left_join_error_no_match(L1233.out_MWh_USA_elec_F_tech,
by = c("sector", "fuel", "technology")) %>%
mutate(share_nat.sea = out_MWh.sea / out_MWh_sea, share_nat = out_MWh / out_MWh_) %>%
select(-out_MWh_, -out_MWh, -out_MWh_sea, -out_MWh.sea) ->
L1233.out_MWh_USA_elec_F_tech_cool
# get all possible combinations of power plants, cooling system types, and water types in all states
L1233.out_MWh_state_elec_F_tech_cool %>%
select(-out_MWh, -share, -state) %>%
unique ->
L1233.elec_tech_cool
L1233.elec_tech_cool %>%
repeat_add_columns(tibble(state = gcamusa.STATES)) %>%
left_join_keep_first_only(L1233.out_MWh_state_elec_F_tech_cool,
by = c("state", "sector", "fuel", "technology", "cooling_system", "water_type")) %>%
# ^^ lots of NAs induced in the above join... most should be zeros;
# where technologies have all zeros, replace with national averages
replace_na(list(share = 0)) %>%
select(-out_MWh) %>%
left_join_error_no_match(L1233.out_MWh_USA_elec_F_tech_cool,
by = c("sector", "fuel", "technology", "cooling_system", "water_type")) %>%
# ^^ brings in national average data
group_by(state, sector, fuel, technology) %>%
mutate(sum_share_tech = sum(share)) %>%
ungroup() %>%
mutate(share = if_else(sum_share_tech == 0 & (state %in% seawater_states_basins), share_nat.sea,
if_else(sum_share_tech == 0 & !(state %in% seawater_states_basins), share_nat, share))) %>%
# we want to make sure that states that do not have seawater available (i.e. inland states) do not get the national average added
select(-share_nat, -share_nat.sea, -sum_share_tech) %>%
# multiply through by historical output
left_join(L1231.out_EJ_state_elec_F_tech,
by = c("sector", "fuel", "technology", "state")) %>%
# ^^ non-restricted join used to allow for expanded rows for all historical years
mutate(value = value * share) ->
L1233.out_EJ_state_elec_F_tech_cool
# Add GCAM-USA supplysector names and correct subsector vintages
L1233.out_EJ_state_elec_F_tech_cool %>%
# left_join here as numerous new rows are created due to vintages addition
left_join(A23.elecS_tech_mapping_cool %>%
select(technology, Electric.sector, Electric.sector.technology, water_type),
by = c("technology", "water_type")) %>%
filter(year %in% MODEL_YEARS) %>%
unique() ->
L1233.out_EJ_state_elec_F_tech_cool
# Get shares by FERC subregion for new investment in future periods
L1233.elec_tech_cool %>%
repeat_add_columns(tibble(grid_region = unique(states_subregions$grid_region))) %>%
left_join_keep_first_only(L1233.out_MWh_sR_elec_F_tech_cool,
by = c("grid_region", "sector", "fuel", "technology", "cooling_system", "water_type")) %>%
# ^^ lots of NAs induced in the above join... most should be zeros;
# where technologies have all zeros, replace with national averages
replace_na(list(share = 0)) %>%
left_join_error_no_match(L1233.out_MWh_USA_elec_F_tech_cool,
by = c("sector", "fuel", "technology", "cooling_system", "water_type")) %>%
# ^^ brings in national average data
group_by(grid_region, sector, fuel, technology) %>%
mutate(sum_share_tech = sum(share)) %>%
ungroup() %>%
mutate(share = if_else(sum_share_tech == 0, share_nat, share)) %>%
select(-share_nat, -sum_share_tech, -out_MWh) %>%
# join is intended to duplicate rows to introduce load segments
# LJENM produces error, so left_join() is used
left_join(A23.elecS_tech_mapping_cool %>%
select(technology, Electric.sector, Electric.sector.technology),
by = c("technology")) %>%
unique() %>%
select(-sector, -technology) %>%
rename(sector = Electric.sector,
technology = Electric.sector.technology) ->
L1233.share_sR_elec_F_tech_cool
# Rename sector and subsectors based on GCAM-USA set-up
L1233.out_EJ_state_elec_F_tech_cool %>%
select(-sector) %>%
rename(sector = Electric.sector,
subsector = Electric.sector.technology) ->
L1233.out_EJ_state_elec_F_tech_cool
# ===================================================
L1233.out_EJ_state_elec_F_tech_cool %>%
add_title("Electricity output by state / fuel / technology / cooling system / water type") %>%
add_units("EJ") %>%
add_comments("Electricity output by state / fuel / technology / cooling system / water type data source: UCS") %>%
add_legacy_name("L1233.out_EJ_state_elec_F_tech_cool") %>%
add_precursors("gcam-usa/states_subregions",
"gcam-usa/UCS_tech_names",
"gcam-usa/UCS_water_types",
"gcam-usa/UCS_Database",
"gcam-usa/usa_seawater_states_basins",
"L1231.out_EJ_state_elec_F_tech",
"gcam-usa/A23.elecS_tech_mapping_cool") ->
L1233.out_EJ_state_elec_F_tech_cool
L1233.share_sR_elec_F_tech_cool %>%
add_title("Electricity generation shares by grid region / fuel / technology / cooling system / water type") %>%
add_units("Unitless") %>%
add_comments("Electricity generation shares by grid region / fuel / technology / cooling system / water type") %>%
add_legacy_name("L1233.share_sR_elec_F_tech_cool") %>%
add_precursors("gcam-usa/states_subregions",
"gcam-usa/UCS_tech_names",
"gcam-usa/UCS_water_types",
"gcam-usa/UCS_Database",
"gcam-usa/usa_seawater_states_basins",
"gcam-usa/A23.elecS_tech_mapping_cool") ->
L1233.share_sR_elec_F_tech_cool
return_data(L1233.out_EJ_state_elec_F_tech_cool, L1233.share_sR_elec_F_tech_cool)
} else {
stop("Unknown command")
}
}
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Defines functions module_gcamusa_LB1233.elec_water_USA
Documented in module_gcamusa_LB1233.elec_water_USA
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_gcamusa_LB1233.elec_water_USA
#'
#' Compute water withdrawals/consumption by state, fuel, technology, and cooling system type.
#'
#' @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{L1233.out_EJ_state_elec_F_tech_cool}, \code{L1233.share_sR_elec_F_tech_cool}. The corresponding file in the
#' original data system was \code{LB1233.Elec_water.R} (gcam-usa level1).
#' @details Compute water withdrawals/consumption by state, fuel, technology, and cooling system type.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author ST October 2017, NTG May 2020
module_gcamusa_LB1233.elec_water_USA <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "gcam-usa/states_subregions",
FILE = "gcam-usa/UCS_tech_names",
FILE = "gcam-usa/UCS_water_types",
FILE = "gcam-usa/A23.elecS_tech_mapping_cool",
FILE = "gcam-usa/UCS_Database",
FILE = "gcam-usa/usa_seawater_states_basins",
"L1231.out_EJ_state_elec_F_tech"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L1233.out_EJ_state_elec_F_tech_cool",
"L1233.share_sR_elec_F_tech_cool"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
State <- `Reported Water Source (Type)` <- reported_water_source <- `Estimated Generation (MWh)` <-
`Technology Code` <- cooling_system <- water_type <- technology <- state <-
state <- sector <- fuel <- out_MWh <- out_MWh_ <- `First Year of Operation` <-
grid_region <- share <- sum_share_tech <- share_nat <- value <-
year <- water_withdrawals <- water_consumption <- out_MWh.sea <- `Generation Technology` <-
out_MWh_sea <- share_nat.sea <- Electric.sector <- Electric.sector.technology <- NULL
# Load required inputs
states_subregions <- get_data(all_data, "gcam-usa/states_subregions")
UCS_tech_names <- get_data(all_data, "gcam-usa/UCS_tech_names")
UCS_water_types <- get_data(all_data, "gcam-usa/UCS_water_types")
A23.elecS_tech_mapping_cool <- get_data(all_data, "gcam-usa/A23.elecS_tech_mapping_cool")
UCS_Database <- get_data(all_data, "gcam-usa/UCS_Database")
usa_seawater_states_basins <- get_data(all_data, "gcam-usa/usa_seawater_states_basins")
L1231.out_EJ_state_elec_F_tech <- get_data(all_data, "L1231.out_EJ_state_elec_F_tech")
# ===================================================
`Generation Technology` <- NULL # silence package check notes
# Define unique states and basins that have access to seawater that will
# allow for seawate cooling
seawater_states_basins <- unique(usa_seawater_states_basins$seawater_region)
# inital processing of UCS database
UCS_Database %>%
rename(state = State,
out_MWh = `Estimated Generation (MWh)`,
Technology.Code = `Technology Code`,
reported_water_source = `Reported Water Source (Type)`) %>%
# filter out entries with no technology detail
filter(!is.na(`Generation Technology`)) %>%
# reset Michigan and Wisconsin power plant seawater use to surface water
mutate(reported_water_source = if_else(!(state %in% seawater_states_basins) &
reported_water_source == gcamusa.UCS_WATER_TYPE_OCEAN,
gcamusa.UCS_WATER_TYPE_SURFACE,
reported_water_source)) %>%
# introduce tech and water type names
left_join_error_no_match(UCS_tech_names, by = "Technology.Code") %>%
left_join_error_no_match(UCS_water_types %>%
rename(reported_water_source = `Reported Water Source (Type)`),
by = "reported_water_source") %>%
# set water type to "none" for techs with no cooling system...
mutate(water_type = if_else(cooling_system == gcamusa.ELEC_COOLING_SYSTEM_NONE, gcamusa.ELEC_COOLING_SYSTEM_NONE, water_type),
# ...and change hydro and PV to fresh
water_type = if_else(technology %in% gcamusa.ELEC_TECHS_NO_COOLING_FRESH, gcamusa.WATER_TYPE_FRESH, water_type),
cooling_system = if_else(water_type == gcamusa.WATER_TYPE_SEAWATER, gcamusa.WATER_TYPE_SEAWATER, cooling_system)) %>%
# Filter out all non hydro / PV / wind technologies which do not use cooling systems and binary recirculation which we do not model
filter((!grepl(gcamusa.ELEC_COOLING_SYSTEM_NONE, cooling_system) | technology %in% gcamusa.ELEC_TECHS_NO_COOLING),
!grepl(gcamusa.ELEC_COOLING_SYSTEM_BINARY, cooling_system)) ->
USC_db_adj
# aggregate and compute shares for states
USC_db_adj %>%
group_by(state, sector, fuel, technology) %>%
summarise(out_MWh_ = sum(out_MWh)) %>%
ungroup() ->
L1233.out_MWh_state_elec_F_tech
USC_db_adj %>%
group_by(state, sector, fuel, technology, cooling_system, water_type) %>%
summarise(out_MWh = sum(out_MWh)) %>%
ungroup() %>%
left_join_error_no_match(L1233.out_MWh_state_elec_F_tech,
by = c("state", "sector", "fuel", "technology")) %>%
mutate(share = out_MWh / out_MWh_) %>%
select(-out_MWh_) ->
L1233.out_MWh_state_elec_F_tech_cool
# aggregate and compute shares for Federeal Energy Regulatory Commission (FERC) subregions; ...
# ... filtering for power plants built this century for contempory output estimates
USC_db_adj %>%
filter(`First Year of Operation` >= gcamusa.UCS_WATER_FIRST_YEAR) %>%
left_join_error_no_match(select(states_subregions, state, grid_region), by = "state") ->
USC_db_adj_2000s
USC_db_adj_2000s %>%
group_by(grid_region, sector, fuel, technology) %>%
summarise(out_MWh_ = sum(out_MWh)) %>%
ungroup() ->
L1233.out_MWh_sR_elec_F_tech # all technologies
USC_db_adj_2000s %>%
group_by(grid_region, sector, fuel, technology, cooling_system, water_type) %>%
summarise(out_MWh = sum(out_MWh)) %>%
ungroup() %>%
left_join_error_no_match(L1233.out_MWh_sR_elec_F_tech,
by = c("grid_region", "sector", "fuel", "technology")) %>%
mutate(share = out_MWh / out_MWh_) %>%
select(-out_MWh_) ->
L1233.out_MWh_sR_elec_F_tech_cool # cooling technologies
# calculate national averages, to be used as default values where data are missing
# we separate states with and without seawater availability to calculate separate shares for these states.
# therefore we have a national avg for non seawater states and for seawater states
L1233.out_MWh_state_elec_F_tech %>%
filter(state %in% seawater_states_basins) %>%
group_by(sector, fuel, technology) %>%
summarise(out_MWh_sea = sum(out_MWh_)) %>%
ungroup() ->
L1233.out_MWh_USA_elec_F_tech_sea
L1233.out_MWh_state_elec_F_tech %>%
filter(!(state %in% seawater_states_basins)) %>%
group_by(sector, fuel, technology) %>%
summarise(out_MWh_ = sum(out_MWh_)) %>%
ungroup() ->
L1233.out_MWh_USA_elec_F_tech
L1233.out_MWh_USA_elec_F_tech_sea %>%
left_join_error_no_match(L1233.out_MWh_USA_elec_F_tech,
by = c("sector", "fuel", "technology")) %>%
replace_na(list(out_MWh_sea = 0, out_MWh_=0)) ->
L1233.out_MWh_USA_elec_F_tech
L1233.out_MWh_state_elec_F_tech_cool %>%
filter(state %in% seawater_states_basins) %>%
group_by(sector, fuel, technology, cooling_system, water_type) %>%
summarise(out_MWh.sea = sum(out_MWh)) %>%
ungroup() -> L1233.out_MWh_state_elec_F_tech_cool_sea
L1233.out_MWh_state_elec_F_tech_cool %>%
filter(!(state %in% seawater_states_basins)) %>%
group_by(sector, fuel, technology, cooling_system, water_type) %>%
summarise(out_MWh = sum(out_MWh)) %>%
ungroup() -> L1233.out_MWh_state_elec_F_tech_cool_nosea
L1233.out_MWh_state_elec_F_tech_cool_sea %>%
# sea and nosea tables have different tech / cooling system / water type combinations
# all of which need to be reflected in final table. Thus, full_join() is used, and
# NA values are replaced with zeros below
full_join(L1233.out_MWh_state_elec_F_tech_cool_nosea,
by = c("sector", "fuel", "technology", "cooling_system", "water_type")) %>%
replace_na(list(out_MWh = 0, out_MWh.sea=0)) %>%
left_join_error_no_match(L1233.out_MWh_USA_elec_F_tech,
by = c("sector", "fuel", "technology")) %>%
mutate(share_nat.sea = out_MWh.sea / out_MWh_sea, share_nat = out_MWh / out_MWh_) %>%
select(-out_MWh_, -out_MWh, -out_MWh_sea, -out_MWh.sea) ->
L1233.out_MWh_USA_elec_F_tech_cool
# get all possible combinations of power plants, cooling system types, and water types in all states
L1233.out_MWh_state_elec_F_tech_cool %>%
select(-out_MWh, -share, -state) %>%
unique ->
L1233.elec_tech_cool
L1233.elec_tech_cool %>%
repeat_add_columns(tibble(state = gcamusa.STATES)) %>%
left_join_keep_first_only(L1233.out_MWh_state_elec_F_tech_cool,
by = c("state", "sector", "fuel", "technology", "cooling_system", "water_type")) %>%
# ^^ lots of NAs induced in the above join... most should be zeros;
# where technologies have all zeros, replace with national averages
replace_na(list(share = 0)) %>%
select(-out_MWh) %>%
left_join_error_no_match(L1233.out_MWh_USA_elec_F_tech_cool,
by = c("sector", "fuel", "technology", "cooling_system", "water_type")) %>%
# ^^ brings in national average data
group_by(state, sector, fuel, technology) %>%
mutate(sum_share_tech = sum(share)) %>%
ungroup() %>%
mutate(share = if_else(sum_share_tech == 0 & (state %in% seawater_states_basins), share_nat.sea,
if_else(sum_share_tech == 0 & !(state %in% seawater_states_basins), share_nat, share))) %>%
# we want to make sure that states that do not have seawater available (i.e. inland states) do not get the national average added
select(-share_nat, -share_nat.sea, -sum_share_tech) %>%
# multiply through by historical output
left_join(L1231.out_EJ_state_elec_F_tech,
by = c("sector", "fuel", "technology", "state")) %>%
# ^^ non-restricted join used to allow for expanded rows for all historical years
mutate(value = value * share) ->
L1233.out_EJ_state_elec_F_tech_cool
# Add GCAM-USA supplysector names and correct subsector vintages
L1233.out_EJ_state_elec_F_tech_cool %>%
# left_join here as numerous new rows are created due to vintages addition
left_join(A23.elecS_tech_mapping_cool %>%
select(technology, Electric.sector, Electric.sector.technology, water_type),
by = c("technology", "water_type")) %>%
filter(year %in% MODEL_YEARS) %>%
unique() ->
L1233.out_EJ_state_elec_F_tech_cool
# Get shares by FERC subregion for new investment in future periods
L1233.elec_tech_cool %>%
repeat_add_columns(tibble(grid_region = unique(states_subregions$grid_region))) %>%
left_join_keep_first_only(L1233.out_MWh_sR_elec_F_tech_cool,
by = c("grid_region", "sector", "fuel", "technology", "cooling_system", "water_type")) %>%
# ^^ lots of NAs induced in the above join... most should be zeros;
# where technologies have all zeros, replace with national averages
replace_na(list(share = 0)) %>%
left_join_error_no_match(L1233.out_MWh_USA_elec_F_tech_cool,
by = c("sector", "fuel", "technology", "cooling_system", "water_type")) %>%
# ^^ brings in national average data
group_by(grid_region, sector, fuel, technology) %>%
mutate(sum_share_tech = sum(share)) %>%
ungroup() %>%
mutate(share = if_else(sum_share_tech == 0, share_nat, share)) %>%
select(-share_nat, -sum_share_tech, -out_MWh) %>%
# join is intended to duplicate rows to introduce load segments
# LJENM produces error, so left_join() is used
left_join(A23.elecS_tech_mapping_cool %>%
select(technology, Electric.sector, Electric.sector.technology),
by = c("technology")) %>%
unique() %>%
select(-sector, -technology) %>%
rename(sector = Electric.sector,
technology = Electric.sector.technology) ->
L1233.share_sR_elec_F_tech_cool
# Rename sector and subsectors based on GCAM-USA set-up
L1233.out_EJ_state_elec_F_tech_cool %>%
select(-sector) %>%
rename(sector = Electric.sector,
subsector = Electric.sector.technology) ->
L1233.out_EJ_state_elec_F_tech_cool
# ===================================================
L1233.out_EJ_state_elec_F_tech_cool %>%
add_title("Electricity output by state / fuel / technology / cooling system / water type") %>%
add_units("EJ") %>%
add_comments("Electricity output by state / fuel / technology / cooling system / water type data source: UCS") %>%
add_legacy_name("L1233.out_EJ_state_elec_F_tech_cool") %>%
add_precursors("gcam-usa/states_subregions",
"gcam-usa/UCS_tech_names",
"gcam-usa/UCS_water_types",
"gcam-usa/UCS_Database",
"gcam-usa/usa_seawater_states_basins",
"L1231.out_EJ_state_elec_F_tech",
"gcam-usa/A23.elecS_tech_mapping_cool") ->
L1233.out_EJ_state_elec_F_tech_cool
L1233.share_sR_elec_F_tech_cool %>%
add_title("Electricity generation shares by grid region / fuel / technology / cooling system / water type") %>%
add_units("Unitless") %>%
add_comments("Electricity generation shares by grid region / fuel / technology / cooling system / water type") %>%
add_legacy_name("L1233.share_sR_elec_F_tech_cool") %>%
add_precursors("gcam-usa/states_subregions",
"gcam-usa/UCS_tech_names",
"gcam-usa/UCS_water_types",
"gcam-usa/UCS_Database",
"gcam-usa/usa_seawater_states_basins",
"gcam-usa/A23.elecS_tech_mapping_cool") ->
L1233.share_sR_elec_F_tech_cool
return_data(L1233.out_EJ_state_elec_F_tech_cool, L1233.share_sR_elec_F_tech_cool)
} else {
stop("Unknown command")
}
}
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