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Lavinia Baumstark authoredLavinia Baumstark authored
reportEsubs.R 11.50 KiB
# | (C) 2006-2019 Potsdam Institute for Climate Impact Research (PIK)
# | authors, and contributors see CITATION.cff file. This file is part
# | of REMIND and licensed under AGPL-3.0-or-later. Under Section 7 of
# | AGPL-3.0, you are granted additional permissions described in the
# | REMIND License Exception, version 1.0 (see LICENSE file).
# | Contact: remind@pik-potsdam.de
#------------------------------------------------------------------------------
#----------------------------# PREPARATION ------------------------------------
#------------------------------------------------------------------------------
library(tidyverse)
library(quitte)
library(lucode)
library(grid)
library(gridExtra)
options(digits = 2)
if(!exists("source_include")) {
#Define arguments that can be read from command line
output_folder <- "dummy"
readArgs("output_folder")
scenario<-"remind17_6013_SSP2-tax20-Noaff-CT-rem-5"
output_folder <- "remind17_6013_SSP2-tax20-Noaff-CT-rem-5"
} else {
output_folder <- outputdir
}
scenario <- getScenNames(outputdir)
#----------------------------------------------------------------------------
#---------------- FUNCTIONS ------------------------------------------------
#---------------------------------------------------------------------------
computeEnergyDemand = function(.df, discount){
tmp_result = NULL
for (per in getPeriods(.df)){
.tmp = .df %>% filter(period == per)
#take the efficiencies from the considered period
eff_k = .tmp[.tmp$index == "remind" & .tmp$variable == kap_in & .tmp$parameter == "eff" , "value"]
eff_fe = .tmp[.tmp$index == "remind" & .tmp$variable == en_in & .tmp$parameter == "eff", "value"]
effGr_k = .tmp[.tmp$index == "remind" & .tmp$variable == kap_in & .tmp$parameter == "effGr" , "value"]
effGr_fe = .tmp[.tmp$index == "remind" & .tmp$variable == en_in & .tmp$parameter == "effGr", "value"]
xi_k = .tmp[.tmp$index == "remind" & .tmp$variable == kap_in & .tmp$parameter == "xi", "value"]
xi_fe = .tmp[.tmp$index == "remind" & .tmp$variable == en_in & .tmp$parameter == "xi", "value"]
p_k = .tmp[.tmp$index == "remind" & .tmp$variable == kap_in & .tmp$parameter == "price_Noscale", "value"]
p_fe = .tmp[.tmp$index == "remind" & .tmp$variable == en_in & .tmp$parameter == "price_Noscale", "value"]
V_en_in = .tmp[.tmp$index == "remind" & .tmp$variable == en_in & .tmp$parameter == "quantity", "value"]
V_kap_in = .tmp[.tmp$index == "remind" & .tmp$variable == kap_in & .tmp$parameter == "quantity", "value"]
V_out = .tmp[.tmp$index == "remind" & .tmp$variable == out & .tmp$parameter == "quantity", "value"]
rho = .tmp[.tmp$index == "remind" & .tmp$variable == out & .tmp$parameter == "rho", "value"]
alpha = xi_fe^(1/rho) * eff_fe * effGr_fe
beta = xi_k^(1/rho) * eff_k * effGr_k
disc = discount %>% filter(region == regi, period == per, variable == kap_in) %>% mutate(variable = "discount") %>% spread(variable,value)
disc = disc %>% mutate(REM_en = V_en_in,
p_fe = p_fe,
p_k = p_k,
p_EG = p_k -discount,
energyDemand = 1/alpha * V_out / (1 + (p_k/p_fe * alpha /beta)^(rho/(rho-1)))^(1/rho),
energyDemand_EG = 1/alpha * V_out / (1 + (p_EG/p_fe * alpha /beta)^(rho/(rho-1)))^(1/rho),
E_K = energyDemand / V_kap_in,
ChangeP_K = (p_EG / p_k -1) * 100,
changeED = (energyDemand_EG / energyDemand - 1) *100 )
tmp_result = rbind(tmp_result,disc)
}
return(tmp_result)
}
computeISO = function(.df,includePrices=T){
tmp_result = NULL
for (per in getPeriods(.df)){
.tmp = .df %>% filter(period == per)
#take the efficiencies from the considered period
eff_k = .tmp[.tmp$index == "remind" & .tmp$variable == kap_in & .tmp$parameter == "eff" , "value"]
eff_fe = .tmp[.tmp$index == "remind" & .tmp$variable == en_in & .tmp$parameter == "eff", "value"]
effGr_k = .tmp[.tmp$index == "remind" & .tmp$variable == kap_in & .tmp$parameter == "effGr" , "value"]
effGr_fe = .tmp[.tmp$index == "remind" & .tmp$variable == en_in & .tmp$parameter == "effGr", "value"]
xi_k = .tmp[.tmp$index == "remind" & .tmp$variable == kap_in & .tmp$parameter == "xi", "value"]
xi_fe = .tmp[.tmp$index == "remind" & .tmp$variable == en_in & .tmp$parameter == "xi", "value"]
p_k = .tmp[.tmp$index == "remind" & .tmp$variable == kap_in & .tmp$parameter == "price", "value"]
p_fe = .tmp[.tmp$index == "remind" & .tmp$variable == en_in & .tmp$parameter == "price", "value"]
V_en_in = .tmp[.tmp$index == "remind" & .tmp$variable == en_in & .tmp$parameter == "quantity", "value"]
V_kap_in = .tmp[.tmp$index == "remind" & .tmp$variable == kap_in & .tmp$parameter == "quantity", "value"]
#take the quantities of the 2015 period and compute the ISO going through the 2015 point
# which correspond to the 2015, 2050 or 2100 efficiencies
.tmp_2015 = .df %>% filter(period == 2015,
parameter == "quantity")
.tmp_2015 = .tmp_2015 %>% spread(variable,value)
#change the period to the period considered
.tmp_per = .tmp_2015 %>% mutate(period = per)
#In case the period is not the estimation period, adjust the output
#to compute the isoquant that goes through the initial point
if (per > 2015){
V_en_in_2015 = .tmp_per[.tmp_per$index == "remind", en_in]
V_kap_in_2015 = .tmp_per[.tmp_per$index == "remind", kap_in]
V_out = (xi_fe*(eff_fe * effGr_fe * V_en_in_2015 )^rho
+ xi_k*(eff_k * effGr_k * V_kap_in_2015)^rho)^(1/rho)
.tmp_per[[out]] = V_out
ratio_inputs = V_kap_in / V_en_in
V_en_adj = V_out / (xi_fe * (eff_fe * effGr_fe)^rho
+xi_k * (eff_k * effGr_k * ratio_inputs)^rho) ^(1/rho)
V_kap_adj = ratio_inputs * V_en_adj
tmp_adj = head(.tmp_per, 1)
tmp_adj["period"] = per
tmp_adj["region"] = regi
tmp_adj["parameter"] = "quantity"
tmp_adj[out] = V_out
tmp_adj[kap_in] = V_kap_adj
tmp_adj[en_in] = V_en_adj
tmp_adj["iso"] = V_en_adj
tmp_adj["index"] = "remind_adjusted"
tmp_adj$prices = NA
tmp_result = rbind(tmp_result,tmp_adj)
}
.tmp_per = .tmp_per %>% mutate_(iso = lazyeval::interp(~ (1/(eff_fe*effGr_fe))
*((1/xi_fe)
*(out^rho
- xi_k * (eff_k * effGr_k * kap_in)^rho)
)^(1/rho),
out = as.name(out), kap_in = as.name(kap_in)
))
if(includePrices){
.tmp_per = .tmp_per %>% mutate_(
prices = lazyeval::interp(~ out/p_fe - p_k/p_fe*kap_in,
out = as.name(out), kap_in = as.name(kap_in)
),
prices = lazyeval::interp(~ ifelse (prices < 0 | prices > 1.05*max(en_in),
NA,
prices), prices = as.name("prices"),
en_in = as.name(en_in))
)
if (per != 2015){
.tmp_per$prices = NA
}
}
tmp_result = rbind(tmp_result,.tmp_per)
}
return(tmp_result)
}
#------------------------------------------------------------------------------
#----------------------------# READ INPUT DATA --------------------------------
#------------------------------------------------------------------------------
filename<-"capital_unit.csv"
if (file.exists(filename)) {
df = read.csv(filename)
}else if (file.exists(path(output_folder,filename))) {
df = read.csv(path(output_folder,filename))
} else {
stop("No capital_unit.csv file found - please perform postprocessing first!")
}
#------------------------------------------------------------------------------
#----------------------------# Parameters ------------------------------------
#------------------------------------------------------------------------------
cesOut2cesIn = inline.data.frame(
"out;kap_in;en_in",
"uescb;kapsc;fescelb",
"esswb;kaphc;ueswb",
"uealb;kapal;fealelb"
)
fileDisc = "fulldata.gdx"
if (file.exists(fileDisc)) {
discRateImpl = read.gdx(fileDisc, "p21_implicitDiscRateMarg")
}else if (file.exists(path(output_folder,fileDisc))) {
discRateImpl = read.gdx(path(output_folder,fileDisc), "p21_implicitDiscRateMarg")
} else { stop("Could not find fulldata.gdx")}
colnames(discRateImpl) = c("period","region","variable","value")
TWa_2_kWh = 8.76e+12
Trillion_2_non = 1e+12
#------------------------------------------------------------------------------
#----------------------------# Process Data --------------------------------
#------------------------------------------------------------------------------
max_iter = max(df$iteration)
df = df %>% filter(iteration == max_iter) %>% select(-iteration)
#----------------------------------------------------------------------------------------------------------
pdf(path(outputdir,paste0("Esubs calibration report.pdf")),
width = 42 / 2.54, height = 29.7 / 2.54, title = "Esubs calibration report")
for (i in 1:nrow(cesOut2cesIn)){
for (regi in getRegs(df)){
out = getElement(cesOut2cesIn[i,], "out")
kap_in = getElement(cesOut2cesIn[i,], "kap_in")
en_in = getElement(cesOut2cesIn[i,], "en_in")
tmp = df %>% filter(region == regi, variable %in% c(out, kap_in, en_in))
demand_2015 = tmp[tmp$index == "remind" & tmp$variable == out & tmp$parameter == "quantity" & tmp$period == 2015, "value"]
#rescale the output to the level considered in the estimation
output_scale = tmp[tmp$index == "remind" & tmp$variable == out & tmp$parameter == "output_scale", "value"]
scale = output_scale / demand_2015
tmp = tmp %>% mutate(value = ifelse(index == "remind" & parameter == "quantity", value *scale,value))
tmp = tmp %>% mutate(value = ifelse(variable == out & index != "remind" & parameter == "quantity", output_scale, value))
rho = tmp[tmp$index == "remind" & tmp$variable == out & tmp$parameter == "rho" & tmp$period == 2015, "value"]
sigma = 1/(1-rho)
EnerDemand = computeEnergyDemand(tmp, discRateImpl)
tmp = computeISO(tmp,includePrices = T)
tmp$period = as.factor(tmp$period)
# Change units from Trillion$/TWa to $/kWh
tmp[kap_in] = tmp[kap_in] / (TWa_2_kWh / Trillion_2_non)
p = ggplot()+
geom_point(data = tmp %>% filter(index != "remind", period == 2015), aes_string(kap_in,en_in))+
geom_point(data = tmp %>% filter(index == "remind", period == 2015), aes_string(kap_in,en_in), size = 4) +
geom_point(data = tmp %>% filter(index == "0"), aes_string(kap_in,en_in), colour = "green", size = 4) +
geom_point(data = tmp %>% filter(index == "remind_adjusted"), aes_string(kap_in,en_in, colour = "period"), size = 5, shape = 15)+
geom_line(data = tmp %>% filter(!is.na(iso)), aes_string(kap_in,"iso", colour = "period"), size = 1.5)+
geom_line(data = tmp %>% filter(!is.na(prices)), aes_string(kap_in,"prices", colour = "period"), size = 0.5)+
ggtitle(paste0(regi, ". Sigma = ",sigma,"\n Demand = ", demand_2015))+
expand_limits(x = 0, y =0) +
theme(text = element_text(size = 18))
print(p)
grid.newpage()
grid.table((EnerDemand), rows = NULL)
}}
dev.off()