R/zchunk_LA120.offshore_wind_reeds_USA.R
In JGCRI/gcamdata: Build the GCAM Input Datasets
Defines functions
module_gcamusa_LA120.offshore_wind_reeds_USA
Documented in
module_gcamusa_LA120.offshore_wind_reeds_USA
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_gcamusa_LA120.offshore_wind_reeds_USA
#'
#' 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
#' @return Depends on \code{command}: either a vector of required inputs,
#' @param ... other optional parameters, depending on command
#' a vector of output names, or (if \code{command} is "MAKE") all
#' the generated outputs: \code{L120.RsrcCurves_EJ_R_offshore_wind_USA}, \code{L120.GridCost_offshore_wind_USA}, \code{L120.RegCapFactor_offshore_wind_USA}.
#' The corresponding file in the
#' original data system was \code{LA120.offshore_wind_reeds_USA.R} (gcam_usa level1).
#' @details Takes in data on US-level offshore wind energy potential and global offshore wind capital cost assumptions and generates tables containing state-level offshore wind data.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select group_by summarise distinct arrange bind_rows
#' @importFrom tidyr gather
#' @importFrom stats optimize
#' @author MB GI AJS March 2019
module_gcamusa_LA120.offshore_wind_reeds_USA <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "gcam-usa/reeds_regions_states",
FILE = "gcam-usa/A20.offshore_wind_class_depth",
FILE = "energy/A20.offshore_wind_depth_cap_cost",
FILE = "gcam-usa/reeds_offshore_wind_curve_capacity",
FILE = "gcam-usa/reeds_offshore_wind_curve_grid_cost",
FILE = "gcam-usa/reeds_offshore_wind_curve_CF_avg",
FILE = "gcam-usa/offshore_wind_potential_missing",
"L113.globaltech_capital_ATB",
"L113.globaltech_OMfixed_ATB"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L120.RsrcCurves_EJ_R_offshore_wind_USA",
"L120.GridCost_offshore_wind_USA",
"L120.RegCapFactor_offshore_wind_USA"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
State <- resource.potential.MW <- depth_class <- distance_to_shore <-
resource.potential.EJ <- fixed.charge.rate <- technology <- socioeconomics.FINAL_HIST_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 <- TRG <- wsc1 <- wsc2 <- wsc3 <- wsc4 <- wsc5 <- renewresource <-
smooth.renewable.subresource <- Wind_Type <- CF <- value <- percent.supply <- P2 <- Q2 <- Region <- rsrc_frac <- state <- NULL # silence package check notes
# Load required inputs
reeds_regions_states <- get_data(all_data, "gcam-usa/reeds_regions_states")
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.offshore_wind_class_depth <- get_data(all_data, "gcam-usa/A20.offshore_wind_class_depth")
A20.offshore_wind_depth_cap_cost <- get_data(all_data, "energy/A20.offshore_wind_depth_cap_cost")
reeds_offshore_wind_curve_capacity <- get_data(all_data, "gcam-usa/reeds_offshore_wind_curve_capacity")
reeds_offshore_wind_curve_grid_cost <- get_data(all_data, "gcam-usa/reeds_offshore_wind_curve_grid_cost")
reeds_offshore_wind_curve_CF_avg <- get_data(all_data, "gcam-usa/reeds_offshore_wind_curve_CF_avg")
offshore_wind_potential_missing <- get_data(all_data, "gcam-usa/offshore_wind_potential_missing")
# -----------------------------------------------------------------------------
# Perform computations
# Assigning average capacity factor by ReEDS region and class to a variable which will then be used to
# come up with an average capacity factor by state and class.
reeds_offshore_wind_curve_CF_avg %>%
left_join_error_no_match(reeds_regions_states, by = c("Wind_Resource_Region" = "Region")) %>%
select(State, Wind_Class = TRG, CF) %>%
group_by(State, Wind_Class) %>%
summarise(CF = mean(CF)) %>%
ungroup() -> L120.offshore_wind_CF
# We first calculate the resource potential in EJ in each ReEDS region and class using the
# potential in MW with the average capacity factor for each region and class.
# We then aggregate this to the state-level.
reeds_offshore_wind_curve_capacity %>%
mutate (resource.potential.MW = wsc1 + wsc2 + wsc3 + wsc4 + wsc5) %>%
select(Wind_Resource_Region, Wind_Class, resource.potential.MW) %>%
left_join_error_no_match(reeds_offshore_wind_curve_CF_avg, by = c("Wind_Resource_Region", "Wind_Class" = "TRG")) %>%
mutate(resource.potential.EJ = resource.potential.MW * CONV_YEAR_HOURS * CF * CONV_MWH_EJ) %>%
left_join_error_no_match(reeds_regions_states, by = c("Wind_Resource_Region" = "Region")) %>%
select(State, Wind_Class, resource.potential.EJ) %>%
group_by(State, Wind_Class) %>%
summarise(resource.potential.EJ = sum(resource.potential.EJ)) %>%
ungroup() -> L120.offshore_wind_potential_EJ
A20.offshore_wind_class_depth %>%
left_join_error_no_match(A20.offshore_wind_depth_cap_cost, by = c("depth_class")) %>%
select(Wind_Class, capital.overnight) -> L2231.offshore_wind_capital
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(L120.offshore_wind_CF, by = c("State", "Wind_Class")) %>%
left_join_error_no_match(L2231.offshore_wind_capital, by = "Wind_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(State) %>%
mutate(CFmax = max(CF)) %>%
arrange(State, price) %>%
mutate(supply = cumsum(resource.potential.EJ)) %>%
ungroup() %>%
select(State, price, supply, CFmax) -> L120.offshore_wind_matrix
# Assigning resource to states missing from the ReEDS dataset (currently only Alaska).
# Alaska is assigned 5% of each supply point (this assumption is defined in offshore_wind_potential_missing).
# Note that this approach assumes that a missing state's resource is a representative sample of the total USA resource.
L120.CFmax.average <- L120.offshore_wind_matrix %>%
summarise(CFmax = mean(CFmax))
L120.CFmax.average <- as.numeric(L120.CFmax.average)
L120.offshore_wind_potential_EJ %>%
left_join_error_no_match(L120.offshore_wind_CF, by = c("State", "Wind_Class")) %>%
left_join_error_no_match(L2231.offshore_wind_capital, by = c("Wind_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) %>%
select(-State) %>%
arrange(price) %>%
repeat_add_columns(offshore_wind_potential_missing) %>%
mutate(resource.potential.EJ = resource.potential.EJ * rsrc_frac,
supply = cumsum(resource.potential.EJ),
CFmax = L120.CFmax.average) %>%
select(State = state, price, supply, CFmax) -> L120.offshore_wind_matrix_missing
L120.offshore_wind_matrix %>%
bind_rows(L120.offshore_wind_matrix_missing) -> 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(State) %>%
arrange(State, 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(State) %>%
filter(percent.supply <= energy.WIND_CURVE_MIDPOINT) %>%
# filter for highest price point below 50% of total resource
filter(Pvar == max(Pvar)) %>%
ungroup() %>%
select(State, P1 = Pvar, Q1 = supply, maxSubResource) -> L120.mid.price_1
# Calculating P2 and Q2
L120.offshore_wind_curve %>%
mutate(percent.supply = supply/maxSubResource) %>%
group_by(State) %>%
filter(percent.supply >= energy.WIND_CURVE_MIDPOINT) %>%
# filter for lowest price point above 50% of total resource
filter(Pvar == min(Pvar)) %>%
ungroup() %>%
select(State, 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 = "State") %>%
mutate(mid.price = round(((P2 - P1) * maxSubResource + (2 * Q2 * P1) - (2 * Q1 * P2)) / (2 * (Q2 - Q1)),
energy.DIGITS_MAX_SUB_RESOURCE)) %>%
select(State, mid.price) -> L120.mid.price
# NOTE: Five states (AL, DE, IN, MS, NH) are dropped from L120.mid.price_1 because their lowest supply point contains
# greater than 50% of the state's supply. (AL, MS, & NH have only one supply point.)
L120.offshore_wind_matrix %>%
anti_join(L120.mid.price, by = c("State")) -> L120.dropped.states
# To prevent states with offshore wind resource from being dropped:
# 1) add P,Q point of 99% of base.price, 1% of supply at base.price and recalculate L120.offshore_wind_curve parameters
L120.dropped.states %>%
bind_rows(L120.dropped.states %>%
group_by(State) %>%
# select the lowest price point for each state
filter(price == min(price)) %>%
ungroup() %>%
# add a data point with price = 99% of base.price & supply = 1% of supply at base.price
# so that we can calculate a mid.price for states where the first point on the
# resource curve contains > 50% of the total resource
mutate(price = price * 0.99,
supply = supply * 0.01)) %>%
group_by(State) %>%
arrange(State, price) %>%
mutate(base.price = min(price),
Pvar = price - base.price,
maxSubResource = round(max(supply), energy.DIGITS_MAX_SUB_RESOURCE)) %>%
ungroup() -> L120.offshore_wind_curve_adj
# 2) calculate mid.price as above
L120.offshore_wind_curve_adj %>%
mutate(percent.supply = supply/maxSubResource) %>%
group_by(State) %>%
filter(percent.supply <= energy.WIND_CURVE_MIDPOINT) %>%
# filter for highest price point below 50% of total resource
filter(Pvar == max(Pvar)) %>%
ungroup() %>%
select(State, P1 = Pvar, Q1 = supply, maxSubResource) -> L120.mid.price_1_adj
L120.offshore_wind_curve_adj %>%
mutate(percent.supply = supply/maxSubResource) %>%
group_by(State) %>%
filter(percent.supply >= energy.WIND_CURVE_MIDPOINT) %>%
# filter for lowest price point above 50% of total resource
filter(Pvar == min(Pvar)) %>%
ungroup() %>%
select(State, P2 = Pvar, Q2 = supply) -> L120.mid.price_2_adj
L120.mid.price_1_adj %>%
left_join_error_no_match(L120.mid.price_2_adj, by = "State") %>%
mutate(mid.price = round(((P2 - P1) * maxSubResource + (2 * Q2 * P1) - (2 * Q1 * P2)) / (2 * (Q2 - Q1)),
energy.DIGITS_MAX_SUB_RESOURCE)) %>%
select(State, mid.price) -> L120.mid.price_adj
# Remove dropped states from L120.offshore_wind_curve, and bind in revised data points
L120.offshore_wind_curve %>%
anti_join(L120.offshore_wind_curve_adj, by = c("State")) %>%
bind_rows(L120.offshore_wind_curve_adj) -> L120.offshore_wind_curve
# Bind in mid.price data for dropped states
L120.mid.price %>%
bind_rows(L120.mid.price_adj) -> L120.mid.price
# Add mid.price to offshore_wind_curve
L120.offshore_wind_curve %>%
left_join_error_no_match(L120.mid.price, by = c("State")) %>%
arrange(State) -> L120.offshore_wind_curve
# Dropping regions with maxSubResource < .001 (i.e. NH, 0.00048 EJ), to avoid potential solution errors
L120.offshore_wind_curve %>%
filter(maxSubResource > energy.WIND_MIN_POTENTIAL) -> L120.offshore_wind_curve
# Defining variables to be used later. Note that the ReEDS data includes information for the 48 contiguous states only.
# For now, since this script creates add-on files, we'll assume that the existing curves in the remaining states are good enough.
states_list <- unique(L120.offshore_wind_curve$State)
L120.curve.exponent <- tibble()
for (L120.state in states_list) {
L120.offshore_wind_curve %>%
filter(State == L120.state) -> L120.offshore_wind_curve_state
L120.offshore_wind_curve_state %>%
select(price, supply) -> L120.supply_points_state
L120.error_min_curve.exp <- optimize(f = smooth_res_curve_approx_error, interval=c(1.0,15.0),
L120.offshore_wind_curve_state$mid.price,
L120.offshore_wind_curve_state$base.price,
L120.offshore_wind_curve_state$maxSubResource,
L120.offshore_wind_curve_state )
L120.offshore_wind_curve_state$curve.exponent <- round(L120.error_min_curve.exp$minimum,energy.DIGITS_MAX_SUB_RESOURCE)
L120.offshore_wind_curve_state %>%
distinct(State, curve.exponent) -> L120.curve.exponent_state
L120.curve.exponent %>%
bind_rows(L120.curve.exponent_state) -> L120.curve.exponent
}
L120.offshore_wind_curve %>%
left_join_error_no_match(L120.curve.exponent, by = "State") -> L120.offshore_wind_curve
# Prepare cost curve for output
L120.offshore_wind_curve %>%
distinct(State, maxSubResource, mid.price, curve.exponent) %>%
mutate(renewresource = "offshore wind resource",
smooth.renewable.subresource = "offshore wind resource") %>%
select(region = State, renewresource, smooth.renewable.subresource,
maxSubResource, mid.price, curve.exponent) -> L120.RsrcCurves_EJ_R_offshore_wind_USA
# Grid connection costs are read in as fixed non-energy cost adders (in $/GJ) that vary by state. Our starting data consists
# of grid connection costs in $/MW by ReEDS region and wind class. This data also categorizes the connection cost into five
# bins in each region and class. Using this data, we obtain a grid connection cost in $/GJ for each region/ class/ bin data
# point as FCR * (grid connection cost in $/MW) / (CONV_YEAR_HOURS*CF*MWH_GJ). Costs are then obtained for a state by averaging.
# In the future, we might think about a separate state-level curve for grid connection costs.
reeds_offshore_wind_curve_grid_cost %>%
select(-Wind_Type) %>%
gather(bin, cost, -Wind_Resource_Region, -Wind_Class) %>%
filter(cost != 0) %>%
left_join_error_no_match(reeds_offshore_wind_curve_CF_avg, by =c("Wind_Resource_Region", "Wind_Class" = "TRG")) %>%
mutate(fcr = L120.offshore_wind_fcr,
grid.cost = fcr * cost / (CONV_YEAR_HOURS * CF * CONV_MWH_GJ) * gdp_deflator(1975,2013)) %>%
left_join_error_no_match(reeds_regions_states %>%
select(Region, State),
by = c("Wind_Resource_Region" = "Region")) %>%
group_by(State) %>%
summarise(grid.cost = round(mean(grid.cost), energy.DIGITS_COST)) %>%
ungroup() %>%
filter(State %in% states_list) -> L120.GridCost_offshore_wind_USA
# Assinging Alaska USA-maximum grid connection cost
L120.GridCost_offshore_wind_USA %>%
bind_rows(L120.GridCost_offshore_wind_USA %>%
summarise(grid.cost = max(grid.cost)) %>%
mutate(State = "AK")) -> L120.GridCost_offshore_wind_USA
L120.offshore_wind_curve %>%
distinct(State, CFmax) %>%
filter(!is.na(CFmax)) -> L120.RegCapFactor_offshore_wind_USA
# -----------------------------------------------------------------------------
# Produce outputs
L120.RsrcCurves_EJ_R_offshore_wind_USA %>%
add_title("Offshore wind resource curve USA") %>%
add_units("maxSubResource: EJ; mid.price: 1975$/GJ") %>%
add_comments("Offshore wind resource curve by states") %>%
add_precursors("gcam-usa/reeds_regions_states",
"L113.globaltech_capital_ATB",
"L113.globaltech_OMfixed_ATB",
"gcam-usa/A20.offshore_wind_class_depth",
"energy/A20.offshore_wind_depth_cap_cost",
"gcam-usa/reeds_offshore_wind_curve_capacity",
"gcam-usa/reeds_offshore_wind_curve_grid_cost",
"gcam-usa/reeds_offshore_wind_curve_CF_avg",
"gcam-usa/offshore_wind_potential_missing") ->
L120.RsrcCurves_EJ_R_offshore_wind_USA
L120.GridCost_offshore_wind_USA %>%
add_title("Grid connectivity cost adder for offshore wind in USA") %>%
add_units("$1975/GJ") %>%
add_comments("Adder by States") %>%
add_precursors( "gcam-usa/reeds_offshore_wind_curve_grid_cost",
"gcam-usa/reeds_regions_states",
"gcam-usa/reeds_offshore_wind_curve_CF_avg") ->
L120.GridCost_offshore_wind_USA
L120.RegCapFactor_offshore_wind_USA %>%
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_USA") ->
L120.RegCapFactor_offshore_wind_USA
return_data(L120.RsrcCurves_EJ_R_offshore_wind_USA, L120.GridCost_offshore_wind_USA, L120.RegCapFactor_offshore_wind_USA)
} else {
stop("Unknown command")
}
}
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Defines functions module_gcamusa_LA120.offshore_wind_reeds_USA
Documented in module_gcamusa_LA120.offshore_wind_reeds_USA
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_gcamusa_LA120.offshore_wind_reeds_USA
#'
#' 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
#' @return Depends on \code{command}: either a vector of required inputs,
#' @param ... other optional parameters, depending on command
#' a vector of output names, or (if \code{command} is "MAKE") all
#' the generated outputs: \code{L120.RsrcCurves_EJ_R_offshore_wind_USA}, \code{L120.GridCost_offshore_wind_USA}, \code{L120.RegCapFactor_offshore_wind_USA}.
#' The corresponding file in the
#' original data system was \code{LA120.offshore_wind_reeds_USA.R} (gcam_usa level1).
#' @details Takes in data on US-level offshore wind energy potential and global offshore wind capital cost assumptions and generates tables containing state-level offshore wind data.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select group_by summarise distinct arrange bind_rows
#' @importFrom tidyr gather
#' @importFrom stats optimize
#' @author MB GI AJS March 2019
module_gcamusa_LA120.offshore_wind_reeds_USA <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "gcam-usa/reeds_regions_states",
FILE = "gcam-usa/A20.offshore_wind_class_depth",
FILE = "energy/A20.offshore_wind_depth_cap_cost",
FILE = "gcam-usa/reeds_offshore_wind_curve_capacity",
FILE = "gcam-usa/reeds_offshore_wind_curve_grid_cost",
FILE = "gcam-usa/reeds_offshore_wind_curve_CF_avg",
FILE = "gcam-usa/offshore_wind_potential_missing",
"L113.globaltech_capital_ATB",
"L113.globaltech_OMfixed_ATB"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L120.RsrcCurves_EJ_R_offshore_wind_USA",
"L120.GridCost_offshore_wind_USA",
"L120.RegCapFactor_offshore_wind_USA"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
State <- resource.potential.MW <- depth_class <- distance_to_shore <-
resource.potential.EJ <- fixed.charge.rate <- technology <- socioeconomics.FINAL_HIST_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 <- TRG <- wsc1 <- wsc2 <- wsc3 <- wsc4 <- wsc5 <- renewresource <-
smooth.renewable.subresource <- Wind_Type <- CF <- value <- percent.supply <- P2 <- Q2 <- Region <- rsrc_frac <- state <- NULL # silence package check notes
# Load required inputs
reeds_regions_states <- get_data(all_data, "gcam-usa/reeds_regions_states")
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.offshore_wind_class_depth <- get_data(all_data, "gcam-usa/A20.offshore_wind_class_depth")
A20.offshore_wind_depth_cap_cost <- get_data(all_data, "energy/A20.offshore_wind_depth_cap_cost")
reeds_offshore_wind_curve_capacity <- get_data(all_data, "gcam-usa/reeds_offshore_wind_curve_capacity")
reeds_offshore_wind_curve_grid_cost <- get_data(all_data, "gcam-usa/reeds_offshore_wind_curve_grid_cost")
reeds_offshore_wind_curve_CF_avg <- get_data(all_data, "gcam-usa/reeds_offshore_wind_curve_CF_avg")
offshore_wind_potential_missing <- get_data(all_data, "gcam-usa/offshore_wind_potential_missing")
# -----------------------------------------------------------------------------
# Perform computations
# Assigning average capacity factor by ReEDS region and class to a variable which will then be used to
# come up with an average capacity factor by state and class.
reeds_offshore_wind_curve_CF_avg %>%
left_join_error_no_match(reeds_regions_states, by = c("Wind_Resource_Region" = "Region")) %>%
select(State, Wind_Class = TRG, CF) %>%
group_by(State, Wind_Class) %>%
summarise(CF = mean(CF)) %>%
ungroup() -> L120.offshore_wind_CF
# We first calculate the resource potential in EJ in each ReEDS region and class using the
# potential in MW with the average capacity factor for each region and class.
# We then aggregate this to the state-level.
reeds_offshore_wind_curve_capacity %>%
mutate (resource.potential.MW = wsc1 + wsc2 + wsc3 + wsc4 + wsc5) %>%
select(Wind_Resource_Region, Wind_Class, resource.potential.MW) %>%
left_join_error_no_match(reeds_offshore_wind_curve_CF_avg, by = c("Wind_Resource_Region", "Wind_Class" = "TRG")) %>%
mutate(resource.potential.EJ = resource.potential.MW * CONV_YEAR_HOURS * CF * CONV_MWH_EJ) %>%
left_join_error_no_match(reeds_regions_states, by = c("Wind_Resource_Region" = "Region")) %>%
select(State, Wind_Class, resource.potential.EJ) %>%
group_by(State, Wind_Class) %>%
summarise(resource.potential.EJ = sum(resource.potential.EJ)) %>%
ungroup() -> L120.offshore_wind_potential_EJ
A20.offshore_wind_class_depth %>%
left_join_error_no_match(A20.offshore_wind_depth_cap_cost, by = c("depth_class")) %>%
select(Wind_Class, capital.overnight) -> L2231.offshore_wind_capital
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(L120.offshore_wind_CF, by = c("State", "Wind_Class")) %>%
left_join_error_no_match(L2231.offshore_wind_capital, by = "Wind_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(State) %>%
mutate(CFmax = max(CF)) %>%
arrange(State, price) %>%
mutate(supply = cumsum(resource.potential.EJ)) %>%
ungroup() %>%
select(State, price, supply, CFmax) -> L120.offshore_wind_matrix
# Assigning resource to states missing from the ReEDS dataset (currently only Alaska).
# Alaska is assigned 5% of each supply point (this assumption is defined in offshore_wind_potential_missing).
# Note that this approach assumes that a missing state's resource is a representative sample of the total USA resource.
L120.CFmax.average <- L120.offshore_wind_matrix %>%
summarise(CFmax = mean(CFmax))
L120.CFmax.average <- as.numeric(L120.CFmax.average)
L120.offshore_wind_potential_EJ %>%
left_join_error_no_match(L120.offshore_wind_CF, by = c("State", "Wind_Class")) %>%
left_join_error_no_match(L2231.offshore_wind_capital, by = c("Wind_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) %>%
select(-State) %>%
arrange(price) %>%
repeat_add_columns(offshore_wind_potential_missing) %>%
mutate(resource.potential.EJ = resource.potential.EJ * rsrc_frac,
supply = cumsum(resource.potential.EJ),
CFmax = L120.CFmax.average) %>%
select(State = state, price, supply, CFmax) -> L120.offshore_wind_matrix_missing
L120.offshore_wind_matrix %>%
bind_rows(L120.offshore_wind_matrix_missing) -> 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(State) %>%
arrange(State, 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(State) %>%
filter(percent.supply <= energy.WIND_CURVE_MIDPOINT) %>%
# filter for highest price point below 50% of total resource
filter(Pvar == max(Pvar)) %>%
ungroup() %>%
select(State, P1 = Pvar, Q1 = supply, maxSubResource) -> L120.mid.price_1
# Calculating P2 and Q2
L120.offshore_wind_curve %>%
mutate(percent.supply = supply/maxSubResource) %>%
group_by(State) %>%
filter(percent.supply >= energy.WIND_CURVE_MIDPOINT) %>%
# filter for lowest price point above 50% of total resource
filter(Pvar == min(Pvar)) %>%
ungroup() %>%
select(State, 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 = "State") %>%
mutate(mid.price = round(((P2 - P1) * maxSubResource + (2 * Q2 * P1) - (2 * Q1 * P2)) / (2 * (Q2 - Q1)),
energy.DIGITS_MAX_SUB_RESOURCE)) %>%
select(State, mid.price) -> L120.mid.price
# NOTE: Five states (AL, DE, IN, MS, NH) are dropped from L120.mid.price_1 because their lowest supply point contains
# greater than 50% of the state's supply. (AL, MS, & NH have only one supply point.)
L120.offshore_wind_matrix %>%
anti_join(L120.mid.price, by = c("State")) -> L120.dropped.states
# To prevent states with offshore wind resource from being dropped:
# 1) add P,Q point of 99% of base.price, 1% of supply at base.price and recalculate L120.offshore_wind_curve parameters
L120.dropped.states %>%
bind_rows(L120.dropped.states %>%
group_by(State) %>%
# select the lowest price point for each state
filter(price == min(price)) %>%
ungroup() %>%
# add a data point with price = 99% of base.price & supply = 1% of supply at base.price
# so that we can calculate a mid.price for states where the first point on the
# resource curve contains > 50% of the total resource
mutate(price = price * 0.99,
supply = supply * 0.01)) %>%
group_by(State) %>%
arrange(State, price) %>%
mutate(base.price = min(price),
Pvar = price - base.price,
maxSubResource = round(max(supply), energy.DIGITS_MAX_SUB_RESOURCE)) %>%
ungroup() -> L120.offshore_wind_curve_adj
# 2) calculate mid.price as above
L120.offshore_wind_curve_adj %>%
mutate(percent.supply = supply/maxSubResource) %>%
group_by(State) %>%
filter(percent.supply <= energy.WIND_CURVE_MIDPOINT) %>%
# filter for highest price point below 50% of total resource
filter(Pvar == max(Pvar)) %>%
ungroup() %>%
select(State, P1 = Pvar, Q1 = supply, maxSubResource) -> L120.mid.price_1_adj
L120.offshore_wind_curve_adj %>%
mutate(percent.supply = supply/maxSubResource) %>%
group_by(State) %>%
filter(percent.supply >= energy.WIND_CURVE_MIDPOINT) %>%
# filter for lowest price point above 50% of total resource
filter(Pvar == min(Pvar)) %>%
ungroup() %>%
select(State, P2 = Pvar, Q2 = supply) -> L120.mid.price_2_adj
L120.mid.price_1_adj %>%
left_join_error_no_match(L120.mid.price_2_adj, by = "State") %>%
mutate(mid.price = round(((P2 - P1) * maxSubResource + (2 * Q2 * P1) - (2 * Q1 * P2)) / (2 * (Q2 - Q1)),
energy.DIGITS_MAX_SUB_RESOURCE)) %>%
select(State, mid.price) -> L120.mid.price_adj
# Remove dropped states from L120.offshore_wind_curve, and bind in revised data points
L120.offshore_wind_curve %>%
anti_join(L120.offshore_wind_curve_adj, by = c("State")) %>%
bind_rows(L120.offshore_wind_curve_adj) -> L120.offshore_wind_curve
# Bind in mid.price data for dropped states
L120.mid.price %>%
bind_rows(L120.mid.price_adj) -> L120.mid.price
# Add mid.price to offshore_wind_curve
L120.offshore_wind_curve %>%
left_join_error_no_match(L120.mid.price, by = c("State")) %>%
arrange(State) -> L120.offshore_wind_curve
# Dropping regions with maxSubResource < .001 (i.e. NH, 0.00048 EJ), to avoid potential solution errors
L120.offshore_wind_curve %>%
filter(maxSubResource > energy.WIND_MIN_POTENTIAL) -> L120.offshore_wind_curve
# Defining variables to be used later. Note that the ReEDS data includes information for the 48 contiguous states only.
# For now, since this script creates add-on files, we'll assume that the existing curves in the remaining states are good enough.
states_list <- unique(L120.offshore_wind_curve$State)
L120.curve.exponent <- tibble()
for (L120.state in states_list) {
L120.offshore_wind_curve %>%
filter(State == L120.state) -> L120.offshore_wind_curve_state
L120.offshore_wind_curve_state %>%
select(price, supply) -> L120.supply_points_state
L120.error_min_curve.exp <- optimize(f = smooth_res_curve_approx_error, interval=c(1.0,15.0),
L120.offshore_wind_curve_state$mid.price,
L120.offshore_wind_curve_state$base.price,
L120.offshore_wind_curve_state$maxSubResource,
L120.offshore_wind_curve_state )
L120.offshore_wind_curve_state$curve.exponent <- round(L120.error_min_curve.exp$minimum,energy.DIGITS_MAX_SUB_RESOURCE)
L120.offshore_wind_curve_state %>%
distinct(State, curve.exponent) -> L120.curve.exponent_state
L120.curve.exponent %>%
bind_rows(L120.curve.exponent_state) -> L120.curve.exponent
}
L120.offshore_wind_curve %>%
left_join_error_no_match(L120.curve.exponent, by = "State") -> L120.offshore_wind_curve
# Prepare cost curve for output
L120.offshore_wind_curve %>%
distinct(State, maxSubResource, mid.price, curve.exponent) %>%
mutate(renewresource = "offshore wind resource",
smooth.renewable.subresource = "offshore wind resource") %>%
select(region = State, renewresource, smooth.renewable.subresource,
maxSubResource, mid.price, curve.exponent) -> L120.RsrcCurves_EJ_R_offshore_wind_USA
# Grid connection costs are read in as fixed non-energy cost adders (in $/GJ) that vary by state. Our starting data consists
# of grid connection costs in $/MW by ReEDS region and wind class. This data also categorizes the connection cost into five
# bins in each region and class. Using this data, we obtain a grid connection cost in $/GJ for each region/ class/ bin data
# point as FCR * (grid connection cost in $/MW) / (CONV_YEAR_HOURS*CF*MWH_GJ). Costs are then obtained for a state by averaging.
# In the future, we might think about a separate state-level curve for grid connection costs.
reeds_offshore_wind_curve_grid_cost %>%
select(-Wind_Type) %>%
gather(bin, cost, -Wind_Resource_Region, -Wind_Class) %>%
filter(cost != 0) %>%
left_join_error_no_match(reeds_offshore_wind_curve_CF_avg, by =c("Wind_Resource_Region", "Wind_Class" = "TRG")) %>%
mutate(fcr = L120.offshore_wind_fcr,
grid.cost = fcr * cost / (CONV_YEAR_HOURS * CF * CONV_MWH_GJ) * gdp_deflator(1975,2013)) %>%
left_join_error_no_match(reeds_regions_states %>%
select(Region, State),
by = c("Wind_Resource_Region" = "Region")) %>%
group_by(State) %>%
summarise(grid.cost = round(mean(grid.cost), energy.DIGITS_COST)) %>%
ungroup() %>%
filter(State %in% states_list) -> L120.GridCost_offshore_wind_USA
# Assinging Alaska USA-maximum grid connection cost
L120.GridCost_offshore_wind_USA %>%
bind_rows(L120.GridCost_offshore_wind_USA %>%
summarise(grid.cost = max(grid.cost)) %>%
mutate(State = "AK")) -> L120.GridCost_offshore_wind_USA
L120.offshore_wind_curve %>%
distinct(State, CFmax) %>%
filter(!is.na(CFmax)) -> L120.RegCapFactor_offshore_wind_USA
# -----------------------------------------------------------------------------
# Produce outputs
L120.RsrcCurves_EJ_R_offshore_wind_USA %>%
add_title("Offshore wind resource curve USA") %>%
add_units("maxSubResource: EJ; mid.price: 1975$/GJ") %>%
add_comments("Offshore wind resource curve by states") %>%
add_precursors("gcam-usa/reeds_regions_states",
"L113.globaltech_capital_ATB",
"L113.globaltech_OMfixed_ATB",
"gcam-usa/A20.offshore_wind_class_depth",
"energy/A20.offshore_wind_depth_cap_cost",
"gcam-usa/reeds_offshore_wind_curve_capacity",
"gcam-usa/reeds_offshore_wind_curve_grid_cost",
"gcam-usa/reeds_offshore_wind_curve_CF_avg",
"gcam-usa/offshore_wind_potential_missing") ->
L120.RsrcCurves_EJ_R_offshore_wind_USA
L120.GridCost_offshore_wind_USA %>%
add_title("Grid connectivity cost adder for offshore wind in USA") %>%
add_units("$1975/GJ") %>%
add_comments("Adder by States") %>%
add_precursors( "gcam-usa/reeds_offshore_wind_curve_grid_cost",
"gcam-usa/reeds_regions_states",
"gcam-usa/reeds_offshore_wind_curve_CF_avg") ->
L120.GridCost_offshore_wind_USA
L120.RegCapFactor_offshore_wind_USA %>%
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_USA") ->
L120.RegCapFactor_offshore_wind_USA
return_data(L120.RsrcCurves_EJ_R_offshore_wind_USA, L120.GridCost_offshore_wind_USA, L120.RegCapFactor_offshore_wind_USA)
} else {
stop("Unknown command")
}
}
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