R/zchunk_LA120.offshore_wind.R
In JGCRI/gcamdata: Build the GCAM Input Datasets
Defines functions
module_energy_LA120.offshore_wind
Documented in
module_energy_LA120.offshore_wind
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_LA120.offshore_wind
#'
#' Takes in data on country-level offshore wind energy potential and global offshore wind capital
#' cost assumptions and generates tables containing region-level offshore wind data.
#'
#' @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{L120.RsrcCurves_EJ_R_offshore_wind}, \code{L120.TechChange_offshore_wind},
#' \code{L120.GridCost_offshore_wind}, \code{L120.RegCapFactor_offshore_wind}. The corresponding file in the
#' original data system was \code{LA120.offshore_wind.R} (energy level1).
#' @details Takes in data on country-level offshore wind energy potential and global offshore wind
#' capital cost assumptions and generates tables containing region-level offshore wind data.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select group_by summarise distinct arrange bind_rows rename
#' @importFrom tidyr gather
#' @importFrom stats optimize
#' @author MB GI AJS March 2019
module_energy_LA120.offshore_wind <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/iso_GCAM_regID",
FILE = "common/GCAM_region_names",
FILE = "energy/A20.wind_class_CFs",
FILE = "energy/A20.offshore_wind_depth_cap_cost",
FILE = "energy/NREL_offshore_energy",
FILE = "energy/NREL_wind_energy_distance_range",
FILE = "energy/offshore_wind_grid_cost",
FILE = "energy/offshore_wind_potential_scaler",
"L113.globaltech_capital_ATB",
"L113.globaltech_OMfixed_ATB"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L120.RsrcCurves_EJ_R_offshore_wind",
"L120.TechChange_offshore_wind",
"L120.GridCost_offshore_wind",
"L120.RegCapFactor_offshore_wind"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
country_name <- GCAM_region_ID <- wind_class <- resource.potential.PWh <- IAM_country <- depth_class <- distance_to_shore <-
resource.potential.EJ <- fixed.charge.rate <- technology <- X_final_historical_year <- fcr <- OM.fixed <- price <- CFmax <-
supply <- curve.exponent <- mid.price <- base.price <- maxSubResource <- supply_points <- Pvar <- P1 <- Q1 <- sector.name <-
supplysector <- subsector.name <- subsector <- intermittent.technology <- year <- input.capital <- capital.overnight <-
capacity.factor <- capital.overnight.lag <- capital.tech.change.5yr <- kl <- tech.change.5yr <- tech.change <- bin <-
cost <- Wind_Resource_Region <- Wind_Class <- grid.cost <- total <- value <- CF <- percent.supply <- P2 <- Q2 <- resource <-
subresource <- k1 <- k2 <- potential <- region <- rep_dist_for_bin <- share <- cost_per_kW_km <- capital.tech.change.period <-
tech.change.period <- time.change <- reason <- scaler <- NULL # silence package check notes
# Load required inputs
iso_GCAM_regID <- get_data(all_data, "common/iso_GCAM_regID")
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names", strip_attributes = TRUE)
L113.globaltech_capital_ATB <- get_data(all_data, "L113.globaltech_capital_ATB")
L113.globaltech_OMfixed_ATB <- get_data(all_data, "L113.globaltech_OMfixed_ATB")
A20.wind_class_CFs <- get_data(all_data, "energy/A20.wind_class_CFs")
A20.offshore_wind_depth_cap_cost <- get_data(all_data, "energy/A20.offshore_wind_depth_cap_cost")
NREL_offshore_energy <- get_data(all_data, "energy/NREL_offshore_energy")
NREL_wind_energy_distance_range <- get_data(all_data, "energy/NREL_wind_energy_distance_range")
offshore_wind_grid_cost <- get_data(all_data, "energy/offshore_wind_grid_cost")
offshore_wind_potential_scaler <- get_data(all_data, "energy/offshore_wind_potential_scaler")
# -----------------------------------------------------------------------------
# Perform computations
# Map NREL data on resource potential by country to GCAM 32 regions, convert PWh to EJ,
# aggregate by GCAM region/ wind class/ depth class
NREL_offshore_energy %>%
select(-total) %>%
left_join_error_no_match(iso_GCAM_regID %>%
select(country_name, GCAM_region_ID) %>% distinct(),
by = c("IAM_country" = "country_name")) %>%
gather(wind_class, resource.potential.PWh, -IAM_country, -GCAM_region_ID, -depth_class, -distance_to_shore) %>%
mutate(resource.potential.EJ = resource.potential.PWh * 1000 * CONV_TWH_EJ ) %>%
group_by(GCAM_region_ID, wind_class, depth_class) %>%
summarise(resource.potential.EJ = sum(resource.potential.EJ)) %>%
ungroup() %>%
filter(resource.potential.EJ != 0) -> L120.offshore_wind_potential_EJ
L120.offshore_wind_capital <- A20.offshore_wind_depth_cap_cost
L113.globaltech_capital_ATB %>%
filter(technology == "wind_offshore") %>%
select(fixed.charge.rate) -> L120.offshore_wind_fcr
L120.offshore_wind_fcr <- as.numeric(L120.offshore_wind_fcr)
L113.globaltech_OMfixed_ATB %>%
gather_years() %>%
filter(technology == "wind_offshore",
year == max(HISTORICAL_YEARS)) %>%
distinct(value)-> L120.offshore_wind_OMfixed
L120.offshore_wind_OMfixed <- as.numeric(L120.offshore_wind_OMfixed)
# NOTE that the process for calculating supply/ price is different for offshore wind (vs. onshore wind). For offshore wind, we
# (1) calculate the price associated with each wind class, then (2) arrange the dataset by region/ price and calculate
# cumulative resource supply. (Conversely, for onshore wind, we (1) arrange the data by capacity factor and calculate supply,
# then (2) calculate the corresponding prices.) The reason for this difference is that, for offshore wind, price varies with
# both capacity factor AND ocean depth. (For onshore wind, CF is the only major determinant of price.) Thus, arranging by CF
# and calculating cumulative supply would result in an illogical offshore wind supply curve where price fluctuates up and down
# as supply increases.
L120.offshore_wind_potential_EJ %>%
left_join_error_no_match(A20.wind_class_CFs, by = c("wind_class")) %>%
left_join_error_no_match(L120.offshore_wind_capital, by = c("depth_class")) %>%
mutate(fcr = L120.offshore_wind_fcr,
OM.fixed = L120.offshore_wind_OMfixed) %>%
mutate(price = fcr * capital.overnight / CF / CONV_YEAR_HOURS / CONV_KWH_GJ +
OM.fixed / CF / CONV_YEAR_HOURS / CONV_KWH_GJ) %>%
group_by(GCAM_region_ID) %>%
mutate(CFmax = max(CF)) %>%
arrange(GCAM_region_ID, price) %>%
mutate(supply = cumsum(resource.potential.EJ)) %>%
ungroup() %>%
select(GCAM_region_ID, price, supply, CFmax) -> L120.offshore_wind_matrix
# Assigning additional resource to the USA because the global dataset does not include any resource for Alaska,
# even though initial estimates suggest that its resource potential could be very large.
# Each supply point is simply increased by 5% (specified in offshore_wind_potential_scaler); thus, Alaska's resource is assumed to be a representative
# sample of the total USA resource.
L120.offshore_wind_matrix %>%
left_join_error_no_match(offshore_wind_potential_scaler %>%
select(-reason),
by = "GCAM_region_ID") %>%
mutate(supply = supply * scaler) -> L120.offshore_wind_matrix
# Calculate maxSubResource, base.price, and Pvar.
# base.price represents the minimum cost of generating electricity from the resource.
# base.price comprises of the cost of generating power at the most optimal location.
# Pvar represents costs that are expected to increase from base.price as deployment increases.
# This models the increase in costs as more optimal locations are used first.
L120.offshore_wind_matrix %>%
group_by(GCAM_region_ID) %>%
arrange(GCAM_region_ID, price) %>%
mutate(base.price = min(price),
Pvar = price - base.price,
maxSubResource = round(max(supply), energy.DIGITS_MAX_SUB_RESOURCE)) %>%
ungroup() -> L120.offshore_wind_curve
# Approximate mid-price using first supply points that are less than (p1, Q1) and greater than (p2,Q2) 50% of maxSubResource.
# Using these points, the mid-price can be estimated as:
# mid.price = ((P2-P1)*maxSubResource + 2*Q2*P1 - 2*Q1*P2)/(2*(Q2-Q1))
# This assumes that the curve is largely linear between the two points above.
# Calculating P1 and Q1
L120.offshore_wind_curve %>%
mutate(percent.supply = supply/maxSubResource) %>%
group_by(GCAM_region_ID) %>%
filter(percent.supply <= energy.WIND_CURVE_MIDPOINT) %>%
# filter for highest price point below 50% of total resource
filter(Pvar == max(Pvar)) %>%
ungroup() %>%
select(GCAM_region_ID, P1 = Pvar, Q1 = supply, maxSubResource) -> L120.mid.price_1
# Calculating P2 and Q2
L120.offshore_wind_curve %>%
mutate(percent.supply = supply/maxSubResource) %>%
group_by(GCAM_region_ID) %>%
filter(percent.supply >= energy.WIND_CURVE_MIDPOINT) %>%
# filter for lowest price point above 50% of total resource
filter(Pvar == min(Pvar)) %>%
ungroup() %>%
select(GCAM_region_ID, P2 = Pvar, Q2 = supply) -> L120.mid.price_2
# Calculating mid.price
L120.mid.price_1 %>%
left_join_error_no_match(L120.mid.price_2, by = "GCAM_region_ID") %>%
mutate(mid.price = round(((P2 - P1) * maxSubResource + 2 * Q2 * P1 - 2 * Q1 * P2) / (2 * (Q2 - Q1)),
energy.DIGITS_MAX_SUB_RESOURCE)) %>%
select(GCAM_region_ID, mid.price) -> L120.mid.price
L120.offshore_wind_curve %>%
left_join_error_no_match(L120.mid.price, by = c("GCAM_region_ID")) -> L120.offshore_wind_curve
# Defining variables to be used later.
region_list <- unique(L120.offshore_wind_curve$GCAM_region_ID)
L120.curve.exponent <- tibble()
for (L120.region in region_list) {
L120.offshore_wind_curve %>%
filter(GCAM_region_ID == L120.region) -> L120.offshore_wind_curve_region
L120.offshore_wind_curve_region %>%
select(price, supply) -> L120.supply_points_region
L120.error_min_curve.exp <- optimize(f = smooth_res_curve_approx_error, interval=c(1.0,15.0),
L120.offshore_wind_curve_region$mid.price,
L120.offshore_wind_curve_region$base.price,
L120.offshore_wind_curve_region$maxSubResource,
L120.offshore_wind_curve_region )
L120.offshore_wind_curve_region$curve.exponent <- round(L120.error_min_curve.exp$minimum,energy.DIGITS_MAX_SUB_RESOURCE)
L120.offshore_wind_curve_region %>%
distinct(GCAM_region_ID, curve.exponent) -> L120.curve.exponent_region
L120.curve.exponent %>%
bind_rows(L120.curve.exponent_region) -> L120.curve.exponent
}
L120.offshore_wind_curve %>%
left_join_error_no_match(L120.curve.exponent, by = "GCAM_region_ID") -> L120.offshore_wind_curve
# Prepare supply curve for output
L120.offshore_wind_curve %>%
mutate(resource = "offshore wind resource",
subresource = "offshore wind resource") %>%
distinct(GCAM_region_ID, resource, subresource, maxSubResource, mid.price, curve.exponent) -> L120.RsrcCurves_EJ_R_offshore_wind
# Technological change in the supply curve is related to assumed improvements in capital cost.
# If capital cost changes from CC to a.CC, then every price point of the curve will scale by a factor a' given as follows:
# a' = (k1.a.CC + k2. OM-fixed) / (k1.CC + k2. OM-fixed) where k1 = FCR/(CONV_YEAR_HOURS*kWh_GJ) and k2 = 1/(CONV_YEAR_HOURS*kWh_GJ)
# Thus, we calculate model input parameter techChange (which is the reduction per year) as 1-a'^(1/5)
# First, calculate capital cost over time for "wind_offshore" technology
L113.globaltech_capital_ATB %>%
# filter for wind and offshore wind technologies; wind is needed because it serves
# as a "shadow technology" for offshore wind in fill_exp_decay_extrapolate function
filter(technology %in% c("wind", "wind_offshore")) %>%
fill_exp_decay_extrapolate(c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
filter(technology == "wind_offshore") %>%
rename(capital.overnight=value, intermittent.technology=technology) -> L120.offshore_wind_cap_cost
# Second, calculate technological change
L120.offshore_wind_cap_cost %>%
filter(intermittent.technology == "wind_offshore") %>%
select(year, capital.overnight) %>%
mutate(capital.tech.change.period = lag(capital.overnight, 1) / capital.overnight,
time.change = year - lag(year),
fixed.charge.rate = L120.offshore_wind_fcr,
OM.fixed = L120.offshore_wind_OMfixed,
k1 = fixed.charge.rate / (CONV_YEAR_HOURS * CONV_KWH_GJ),
k2 = 1 / (CONV_YEAR_HOURS * CONV_KWH_GJ),
tech.change.period = (k1 * capital.tech.change.period * capital.overnight + k2 * OM.fixed) /
(k1 * capital.overnight + k2 * OM.fixed),
tech.change = round(abs(1 - (tech.change.period) ^ ( 1 / time.change)), energy.DIGITS_TECHCHANGE)) %>%
select(year, tech.change) %>%
filter(!is.na(tech.change),
year > max(MODEL_BASE_YEARS)) -> L120.TechChange_offshore_wind
# Creating region-specific capacity factors to be used for levelizing grid connection costs. This is calculated by
# getting the maximum possible capacity factor for a region. Since offshore wind resource utilization is pretty low, it is
# not expected that regions would be able to exhaust the potential offered by the highest capacity class available.
NREL_offshore_energy %>%
select(-depth_class,-distance_to_shore,-total) %>%
inner_join(iso_GCAM_regID %>%
select(country_name, GCAM_region_ID),
by = c("IAM_country" = "country_name")) %>%
left_join_error_no_match(GCAM_region_names, by = c("GCAM_region_ID")) %>%
select(-IAM_country, -GCAM_region_ID) %>%
gather(class,potential,-region) %>%
group_by(region, class) %>%
summarise(potential = sum(potential)) %>%
ungroup() %>%
left_join_error_no_match(A20.wind_class_CFs,
by = c("class" = "wind_class")) %>%
filter(potential != 0) %>%
select(-class, -potential) %>%
group_by(region) %>%
mutate(CF = max(CF)) %>%
ungroup() %>%
unique() -> L120.offshore_wind_CF
# Grid connection costs are read in as fixed non-energy cost adders (in $/GJ). This is calculated using three things:
# 1. the offshore wind $/kW-km cost based on distance cut-offs.
# 2. Average distance from shore of existing and upcoming project for each bin used by NREL to assess wind potential - which are basically midpoints, and
# 3. Share of wind potential at various distance bins.
# The shares and the representative distances for the bins are multiplied to determine the effective average distance to grid for a given region,
# which is then multiplied to the $/kW-km cost to obtain the $/kW grid connection data. GCAM region capacity factors calculated above to convert data into $/kWh
# which is then converted to $/GJ, and the final number is obtained for each region by using the apporpriate FCR and deflator.
# First, get share of potential by each distance bin for each GCAM region
NREL_offshore_energy %>%
select(IAM_country, distance_to_shore, total) %>%
inner_join(iso_GCAM_regID %>%
select(country_name, GCAM_region_ID),
by = c("IAM_country" = "country_name")) %>%
left_join_error_no_match(GCAM_region_names, by = c("GCAM_region_ID")) %>%
select(-IAM_country, -GCAM_region_ID) %>%
group_by(region, distance_to_shore) %>%
summarise(total = sum(total)) %>%
ungroup() %>%
group_by(region) %>%
mutate(share = total / sum(total)) %>%
ungroup() -> L120.offshore_wind_potential_share
# Then, generate bins for each cost point using representative distances from the shore
NREL_wind_energy_distance_range %>%
filter(technology == "offshore_wind") %>%
mutate(max = max * CONV_NMILE_KM) -> NREL_wind_energy_distance_range
# Add representative distances and cost to the potential share tables and calculate
# average cost per kW by region.
L120.offshore_wind_potential_share %>%
left_join_error_no_match(offshore_wind_grid_cost %>%
select(-technology),
by = c("distance_to_shore" = "distance_bin")) %>%
mutate(cost = share * cost_per_kW_km * rep_dist_for_bin) %>%
group_by(region) %>%
summarise(cost = sum(cost)) %>%
ungroup() -> L120.offshore_wind_cost_per_kW
# Now to convert 2013 $/kW cost to 1975 $/GJ cost
L120.offshore_wind_cost_per_kW %>%
left_join_error_no_match(L120.offshore_wind_CF %>%
select(region, CF) %>%
unique(),
by = "region") %>%
mutate(fcr = L120.offshore_wind_fcr,
grid.cost = fcr * cost / (CONV_YEAR_HOURS * CF* CONV_KWH_GJ) * gdp_deflator(1975, 2013)) -> L120.grid.cost
# Set grid connection cost for all regions
GCAM_region_names %>%
select(region) %>%
left_join_error_no_match(L120.grid.cost %>%
select(region, grid.cost),
by = "region") -> L120.GridCost_offshore_wind
L120.offshore_wind_curve %>%
distinct(GCAM_region_ID, CFmax) %>%
filter(!is.na(CFmax)) -> L120.RegCapFactor_offshore_wind
# -----------------------------------------------------------------------------
# Produce outputs
L120.RsrcCurves_EJ_R_offshore_wind %>%
add_title("Offshore wind resource curve") %>%
add_units("EJ") %>%
add_comments("Offshore wind resource curve by region") %>%
add_precursors("common/iso_GCAM_regID", "common/GCAM_region_names", "energy/NREL_offshore_energy",
"energy/A20.wind_class_CFs", "L113.globaltech_capital_ATB",
"L113.globaltech_OMfixed_ATB", "energy/A20.offshore_wind_depth_cap_cost",
"energy/offshore_wind_potential_scaler") ->
L120.RsrcCurves_EJ_R_offshore_wind
L120.TechChange_offshore_wind %>%
add_title("Technological Change Offshore Wind") %>%
add_units("Unitless") %>%
add_comments("Technological Change associated with offshore wind by year") %>%
add_precursors("L113.globaltech_capital_ATB", "L113.globaltech_OMfixed_ATB") ->
L120.TechChange_offshore_wind
L120.GridCost_offshore_wind %>%
add_title("Grid connectivity cost adder for offshore wind") %>%
add_units("$1975/GJ") %>%
add_comments("Adder by GCAM Region") %>%
add_precursors("common/iso_GCAM_regID", "common/GCAM_region_names", "energy/NREL_offshore_energy",
"energy/A20.wind_class_CFs", "L113.globaltech_capital_ATB",
"energy/offshore_wind_grid_cost", "energy/NREL_wind_energy_distance_range") ->
L120.GridCost_offshore_wind
L120.RegCapFactor_offshore_wind %>%
add_title("Region-specific capacity factors for offshore wind") %>%
add_units("Unitless") %>%
add_comments("Region-specific maximum capacity factor") %>%
same_precursors_as(L120.RsrcCurves_EJ_R_offshore_wind) ->
L120.RegCapFactor_offshore_wind
return_data(L120.RsrcCurves_EJ_R_offshore_wind,
L120.TechChange_offshore_wind,
L120.GridCost_offshore_wind,
L120.RegCapFactor_offshore_wind)
} else {
stop("Unknown command")
}
}
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Defines functions module_energy_LA120.offshore_wind
Documented in module_energy_LA120.offshore_wind
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_LA120.offshore_wind
#'
#' Takes in data on country-level offshore wind energy potential and global offshore wind capital
#' cost assumptions and generates tables containing region-level offshore wind data.
#'
#' @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{L120.RsrcCurves_EJ_R_offshore_wind}, \code{L120.TechChange_offshore_wind},
#' \code{L120.GridCost_offshore_wind}, \code{L120.RegCapFactor_offshore_wind}. The corresponding file in the
#' original data system was \code{LA120.offshore_wind.R} (energy level1).
#' @details Takes in data on country-level offshore wind energy potential and global offshore wind
#' capital cost assumptions and generates tables containing region-level offshore wind data.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select group_by summarise distinct arrange bind_rows rename
#' @importFrom tidyr gather
#' @importFrom stats optimize
#' @author MB GI AJS March 2019
module_energy_LA120.offshore_wind <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/iso_GCAM_regID",
FILE = "common/GCAM_region_names",
FILE = "energy/A20.wind_class_CFs",
FILE = "energy/A20.offshore_wind_depth_cap_cost",
FILE = "energy/NREL_offshore_energy",
FILE = "energy/NREL_wind_energy_distance_range",
FILE = "energy/offshore_wind_grid_cost",
FILE = "energy/offshore_wind_potential_scaler",
"L113.globaltech_capital_ATB",
"L113.globaltech_OMfixed_ATB"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L120.RsrcCurves_EJ_R_offshore_wind",
"L120.TechChange_offshore_wind",
"L120.GridCost_offshore_wind",
"L120.RegCapFactor_offshore_wind"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
country_name <- GCAM_region_ID <- wind_class <- resource.potential.PWh <- IAM_country <- depth_class <- distance_to_shore <-
resource.potential.EJ <- fixed.charge.rate <- technology <- X_final_historical_year <- fcr <- OM.fixed <- price <- CFmax <-
supply <- curve.exponent <- mid.price <- base.price <- maxSubResource <- supply_points <- Pvar <- P1 <- Q1 <- sector.name <-
supplysector <- subsector.name <- subsector <- intermittent.technology <- year <- input.capital <- capital.overnight <-
capacity.factor <- capital.overnight.lag <- capital.tech.change.5yr <- kl <- tech.change.5yr <- tech.change <- bin <-
cost <- Wind_Resource_Region <- Wind_Class <- grid.cost <- total <- value <- CF <- percent.supply <- P2 <- Q2 <- resource <-
subresource <- k1 <- k2 <- potential <- region <- rep_dist_for_bin <- share <- cost_per_kW_km <- capital.tech.change.period <-
tech.change.period <- time.change <- reason <- scaler <- NULL # silence package check notes
# Load required inputs
iso_GCAM_regID <- get_data(all_data, "common/iso_GCAM_regID")
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names", strip_attributes = TRUE)
L113.globaltech_capital_ATB <- get_data(all_data, "L113.globaltech_capital_ATB")
L113.globaltech_OMfixed_ATB <- get_data(all_data, "L113.globaltech_OMfixed_ATB")
A20.wind_class_CFs <- get_data(all_data, "energy/A20.wind_class_CFs")
A20.offshore_wind_depth_cap_cost <- get_data(all_data, "energy/A20.offshore_wind_depth_cap_cost")
NREL_offshore_energy <- get_data(all_data, "energy/NREL_offshore_energy")
NREL_wind_energy_distance_range <- get_data(all_data, "energy/NREL_wind_energy_distance_range")
offshore_wind_grid_cost <- get_data(all_data, "energy/offshore_wind_grid_cost")
offshore_wind_potential_scaler <- get_data(all_data, "energy/offshore_wind_potential_scaler")
# -----------------------------------------------------------------------------
# Perform computations
# Map NREL data on resource potential by country to GCAM 32 regions, convert PWh to EJ,
# aggregate by GCAM region/ wind class/ depth class
NREL_offshore_energy %>%
select(-total) %>%
left_join_error_no_match(iso_GCAM_regID %>%
select(country_name, GCAM_region_ID) %>% distinct(),
by = c("IAM_country" = "country_name")) %>%
gather(wind_class, resource.potential.PWh, -IAM_country, -GCAM_region_ID, -depth_class, -distance_to_shore) %>%
mutate(resource.potential.EJ = resource.potential.PWh * 1000 * CONV_TWH_EJ ) %>%
group_by(GCAM_region_ID, wind_class, depth_class) %>%
summarise(resource.potential.EJ = sum(resource.potential.EJ)) %>%
ungroup() %>%
filter(resource.potential.EJ != 0) -> L120.offshore_wind_potential_EJ
L120.offshore_wind_capital <- A20.offshore_wind_depth_cap_cost
L113.globaltech_capital_ATB %>%
filter(technology == "wind_offshore") %>%
select(fixed.charge.rate) -> L120.offshore_wind_fcr
L120.offshore_wind_fcr <- as.numeric(L120.offshore_wind_fcr)
L113.globaltech_OMfixed_ATB %>%
gather_years() %>%
filter(technology == "wind_offshore",
year == max(HISTORICAL_YEARS)) %>%
distinct(value)-> L120.offshore_wind_OMfixed
L120.offshore_wind_OMfixed <- as.numeric(L120.offshore_wind_OMfixed)
# NOTE that the process for calculating supply/ price is different for offshore wind (vs. onshore wind). For offshore wind, we
# (1) calculate the price associated with each wind class, then (2) arrange the dataset by region/ price and calculate
# cumulative resource supply. (Conversely, for onshore wind, we (1) arrange the data by capacity factor and calculate supply,
# then (2) calculate the corresponding prices.) The reason for this difference is that, for offshore wind, price varies with
# both capacity factor AND ocean depth. (For onshore wind, CF is the only major determinant of price.) Thus, arranging by CF
# and calculating cumulative supply would result in an illogical offshore wind supply curve where price fluctuates up and down
# as supply increases.
L120.offshore_wind_potential_EJ %>%
left_join_error_no_match(A20.wind_class_CFs, by = c("wind_class")) %>%
left_join_error_no_match(L120.offshore_wind_capital, by = c("depth_class")) %>%
mutate(fcr = L120.offshore_wind_fcr,
OM.fixed = L120.offshore_wind_OMfixed) %>%
mutate(price = fcr * capital.overnight / CF / CONV_YEAR_HOURS / CONV_KWH_GJ +
OM.fixed / CF / CONV_YEAR_HOURS / CONV_KWH_GJ) %>%
group_by(GCAM_region_ID) %>%
mutate(CFmax = max(CF)) %>%
arrange(GCAM_region_ID, price) %>%
mutate(supply = cumsum(resource.potential.EJ)) %>%
ungroup() %>%
select(GCAM_region_ID, price, supply, CFmax) -> L120.offshore_wind_matrix
# Assigning additional resource to the USA because the global dataset does not include any resource for Alaska,
# even though initial estimates suggest that its resource potential could be very large.
# Each supply point is simply increased by 5% (specified in offshore_wind_potential_scaler); thus, Alaska's resource is assumed to be a representative
# sample of the total USA resource.
L120.offshore_wind_matrix %>%
left_join_error_no_match(offshore_wind_potential_scaler %>%
select(-reason),
by = "GCAM_region_ID") %>%
mutate(supply = supply * scaler) -> L120.offshore_wind_matrix
# Calculate maxSubResource, base.price, and Pvar.
# base.price represents the minimum cost of generating electricity from the resource.
# base.price comprises of the cost of generating power at the most optimal location.
# Pvar represents costs that are expected to increase from base.price as deployment increases.
# This models the increase in costs as more optimal locations are used first.
L120.offshore_wind_matrix %>%
group_by(GCAM_region_ID) %>%
arrange(GCAM_region_ID, price) %>%
mutate(base.price = min(price),
Pvar = price - base.price,
maxSubResource = round(max(supply), energy.DIGITS_MAX_SUB_RESOURCE)) %>%
ungroup() -> L120.offshore_wind_curve
# Approximate mid-price using first supply points that are less than (p1, Q1) and greater than (p2,Q2) 50% of maxSubResource.
# Using these points, the mid-price can be estimated as:
# mid.price = ((P2-P1)*maxSubResource + 2*Q2*P1 - 2*Q1*P2)/(2*(Q2-Q1))
# This assumes that the curve is largely linear between the two points above.
# Calculating P1 and Q1
L120.offshore_wind_curve %>%
mutate(percent.supply = supply/maxSubResource) %>%
group_by(GCAM_region_ID) %>%
filter(percent.supply <= energy.WIND_CURVE_MIDPOINT) %>%
# filter for highest price point below 50% of total resource
filter(Pvar == max(Pvar)) %>%
ungroup() %>%
select(GCAM_region_ID, P1 = Pvar, Q1 = supply, maxSubResource) -> L120.mid.price_1
# Calculating P2 and Q2
L120.offshore_wind_curve %>%
mutate(percent.supply = supply/maxSubResource) %>%
group_by(GCAM_region_ID) %>%
filter(percent.supply >= energy.WIND_CURVE_MIDPOINT) %>%
# filter for lowest price point above 50% of total resource
filter(Pvar == min(Pvar)) %>%
ungroup() %>%
select(GCAM_region_ID, P2 = Pvar, Q2 = supply) -> L120.mid.price_2
# Calculating mid.price
L120.mid.price_1 %>%
left_join_error_no_match(L120.mid.price_2, by = "GCAM_region_ID") %>%
mutate(mid.price = round(((P2 - P1) * maxSubResource + 2 * Q2 * P1 - 2 * Q1 * P2) / (2 * (Q2 - Q1)),
energy.DIGITS_MAX_SUB_RESOURCE)) %>%
select(GCAM_region_ID, mid.price) -> L120.mid.price
L120.offshore_wind_curve %>%
left_join_error_no_match(L120.mid.price, by = c("GCAM_region_ID")) -> L120.offshore_wind_curve
# Defining variables to be used later.
region_list <- unique(L120.offshore_wind_curve$GCAM_region_ID)
L120.curve.exponent <- tibble()
for (L120.region in region_list) {
L120.offshore_wind_curve %>%
filter(GCAM_region_ID == L120.region) -> L120.offshore_wind_curve_region
L120.offshore_wind_curve_region %>%
select(price, supply) -> L120.supply_points_region
L120.error_min_curve.exp <- optimize(f = smooth_res_curve_approx_error, interval=c(1.0,15.0),
L120.offshore_wind_curve_region$mid.price,
L120.offshore_wind_curve_region$base.price,
L120.offshore_wind_curve_region$maxSubResource,
L120.offshore_wind_curve_region )
L120.offshore_wind_curve_region$curve.exponent <- round(L120.error_min_curve.exp$minimum,energy.DIGITS_MAX_SUB_RESOURCE)
L120.offshore_wind_curve_region %>%
distinct(GCAM_region_ID, curve.exponent) -> L120.curve.exponent_region
L120.curve.exponent %>%
bind_rows(L120.curve.exponent_region) -> L120.curve.exponent
}
L120.offshore_wind_curve %>%
left_join_error_no_match(L120.curve.exponent, by = "GCAM_region_ID") -> L120.offshore_wind_curve
# Prepare supply curve for output
L120.offshore_wind_curve %>%
mutate(resource = "offshore wind resource",
subresource = "offshore wind resource") %>%
distinct(GCAM_region_ID, resource, subresource, maxSubResource, mid.price, curve.exponent) -> L120.RsrcCurves_EJ_R_offshore_wind
# Technological change in the supply curve is related to assumed improvements in capital cost.
# If capital cost changes from CC to a.CC, then every price point of the curve will scale by a factor a' given as follows:
# a' = (k1.a.CC + k2. OM-fixed) / (k1.CC + k2. OM-fixed) where k1 = FCR/(CONV_YEAR_HOURS*kWh_GJ) and k2 = 1/(CONV_YEAR_HOURS*kWh_GJ)
# Thus, we calculate model input parameter techChange (which is the reduction per year) as 1-a'^(1/5)
# First, calculate capital cost over time for "wind_offshore" technology
L113.globaltech_capital_ATB %>%
# filter for wind and offshore wind technologies; wind is needed because it serves
# as a "shadow technology" for offshore wind in fill_exp_decay_extrapolate function
filter(technology %in% c("wind", "wind_offshore")) %>%
fill_exp_decay_extrapolate(c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
filter(technology == "wind_offshore") %>%
rename(capital.overnight=value, intermittent.technology=technology) -> L120.offshore_wind_cap_cost
# Second, calculate technological change
L120.offshore_wind_cap_cost %>%
filter(intermittent.technology == "wind_offshore") %>%
select(year, capital.overnight) %>%
mutate(capital.tech.change.period = lag(capital.overnight, 1) / capital.overnight,
time.change = year - lag(year),
fixed.charge.rate = L120.offshore_wind_fcr,
OM.fixed = L120.offshore_wind_OMfixed,
k1 = fixed.charge.rate / (CONV_YEAR_HOURS * CONV_KWH_GJ),
k2 = 1 / (CONV_YEAR_HOURS * CONV_KWH_GJ),
tech.change.period = (k1 * capital.tech.change.period * capital.overnight + k2 * OM.fixed) /
(k1 * capital.overnight + k2 * OM.fixed),
tech.change = round(abs(1 - (tech.change.period) ^ ( 1 / time.change)), energy.DIGITS_TECHCHANGE)) %>%
select(year, tech.change) %>%
filter(!is.na(tech.change),
year > max(MODEL_BASE_YEARS)) -> L120.TechChange_offshore_wind
# Creating region-specific capacity factors to be used for levelizing grid connection costs. This is calculated by
# getting the maximum possible capacity factor for a region. Since offshore wind resource utilization is pretty low, it is
# not expected that regions would be able to exhaust the potential offered by the highest capacity class available.
NREL_offshore_energy %>%
select(-depth_class,-distance_to_shore,-total) %>%
inner_join(iso_GCAM_regID %>%
select(country_name, GCAM_region_ID),
by = c("IAM_country" = "country_name")) %>%
left_join_error_no_match(GCAM_region_names, by = c("GCAM_region_ID")) %>%
select(-IAM_country, -GCAM_region_ID) %>%
gather(class,potential,-region) %>%
group_by(region, class) %>%
summarise(potential = sum(potential)) %>%
ungroup() %>%
left_join_error_no_match(A20.wind_class_CFs,
by = c("class" = "wind_class")) %>%
filter(potential != 0) %>%
select(-class, -potential) %>%
group_by(region) %>%
mutate(CF = max(CF)) %>%
ungroup() %>%
unique() -> L120.offshore_wind_CF
# Grid connection costs are read in as fixed non-energy cost adders (in $/GJ). This is calculated using three things:
# 1. the offshore wind $/kW-km cost based on distance cut-offs.
# 2. Average distance from shore of existing and upcoming project for each bin used by NREL to assess wind potential - which are basically midpoints, and
# 3. Share of wind potential at various distance bins.
# The shares and the representative distances for the bins are multiplied to determine the effective average distance to grid for a given region,
# which is then multiplied to the $/kW-km cost to obtain the $/kW grid connection data. GCAM region capacity factors calculated above to convert data into $/kWh
# which is then converted to $/GJ, and the final number is obtained for each region by using the apporpriate FCR and deflator.
# First, get share of potential by each distance bin for each GCAM region
NREL_offshore_energy %>%
select(IAM_country, distance_to_shore, total) %>%
inner_join(iso_GCAM_regID %>%
select(country_name, GCAM_region_ID),
by = c("IAM_country" = "country_name")) %>%
left_join_error_no_match(GCAM_region_names, by = c("GCAM_region_ID")) %>%
select(-IAM_country, -GCAM_region_ID) %>%
group_by(region, distance_to_shore) %>%
summarise(total = sum(total)) %>%
ungroup() %>%
group_by(region) %>%
mutate(share = total / sum(total)) %>%
ungroup() -> L120.offshore_wind_potential_share
# Then, generate bins for each cost point using representative distances from the shore
NREL_wind_energy_distance_range %>%
filter(technology == "offshore_wind") %>%
mutate(max = max * CONV_NMILE_KM) -> NREL_wind_energy_distance_range
# Add representative distances and cost to the potential share tables and calculate
# average cost per kW by region.
L120.offshore_wind_potential_share %>%
left_join_error_no_match(offshore_wind_grid_cost %>%
select(-technology),
by = c("distance_to_shore" = "distance_bin")) %>%
mutate(cost = share * cost_per_kW_km * rep_dist_for_bin) %>%
group_by(region) %>%
summarise(cost = sum(cost)) %>%
ungroup() -> L120.offshore_wind_cost_per_kW
# Now to convert 2013 $/kW cost to 1975 $/GJ cost
L120.offshore_wind_cost_per_kW %>%
left_join_error_no_match(L120.offshore_wind_CF %>%
select(region, CF) %>%
unique(),
by = "region") %>%
mutate(fcr = L120.offshore_wind_fcr,
grid.cost = fcr * cost / (CONV_YEAR_HOURS * CF* CONV_KWH_GJ) * gdp_deflator(1975, 2013)) -> L120.grid.cost
# Set grid connection cost for all regions
GCAM_region_names %>%
select(region) %>%
left_join_error_no_match(L120.grid.cost %>%
select(region, grid.cost),
by = "region") -> L120.GridCost_offshore_wind
L120.offshore_wind_curve %>%
distinct(GCAM_region_ID, CFmax) %>%
filter(!is.na(CFmax)) -> L120.RegCapFactor_offshore_wind
# -----------------------------------------------------------------------------
# Produce outputs
L120.RsrcCurves_EJ_R_offshore_wind %>%
add_title("Offshore wind resource curve") %>%
add_units("EJ") %>%
add_comments("Offshore wind resource curve by region") %>%
add_precursors("common/iso_GCAM_regID", "common/GCAM_region_names", "energy/NREL_offshore_energy",
"energy/A20.wind_class_CFs", "L113.globaltech_capital_ATB",
"L113.globaltech_OMfixed_ATB", "energy/A20.offshore_wind_depth_cap_cost",
"energy/offshore_wind_potential_scaler") ->
L120.RsrcCurves_EJ_R_offshore_wind
L120.TechChange_offshore_wind %>%
add_title("Technological Change Offshore Wind") %>%
add_units("Unitless") %>%
add_comments("Technological Change associated with offshore wind by year") %>%
add_precursors("L113.globaltech_capital_ATB", "L113.globaltech_OMfixed_ATB") ->
L120.TechChange_offshore_wind
L120.GridCost_offshore_wind %>%
add_title("Grid connectivity cost adder for offshore wind") %>%
add_units("$1975/GJ") %>%
add_comments("Adder by GCAM Region") %>%
add_precursors("common/iso_GCAM_regID", "common/GCAM_region_names", "energy/NREL_offshore_energy",
"energy/A20.wind_class_CFs", "L113.globaltech_capital_ATB",
"energy/offshore_wind_grid_cost", "energy/NREL_wind_energy_distance_range") ->
L120.GridCost_offshore_wind
L120.RegCapFactor_offshore_wind %>%
add_title("Region-specific capacity factors for offshore wind") %>%
add_units("Unitless") %>%
add_comments("Region-specific maximum capacity factor") %>%
same_precursors_as(L120.RsrcCurves_EJ_R_offshore_wind) ->
L120.RegCapFactor_offshore_wind
return_data(L120.RsrcCurves_EJ_R_offshore_wind,
L120.TechChange_offshore_wind,
L120.GridCost_offshore_wind,
L120.RegCapFactor_offshore_wind)
} else {
stop("Unknown command")
}
}
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