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Commit 19085a04 authored by Marianna Rottoli's avatar Marianna Rottoli
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Reporting just loads EDGE output. Iterative scripts saves all necessary files.

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1 merge request!79EDGE-T Validation Output - Fix
This commit is part of merge request !79. Comments created here will be created in the context of that merge request.
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scripts/iterative/EDGE_transport.R
+ 19
− 4
View file @ 19085a04
......@@ -200,7 +200,7 @@ mj_km_data = vintages[["mj_km_data"]]
EDGE2CESmap <- fread(mapspath("mapping_CESnodes_EDGE.csv"))
shares_intensity_demand <- shares_intensity_and_demand(
shares_int_dem <- shares_intensity_and_demand(
logit_shares=shares,
MJ_km_base=mj_km_data,
EDGE2CESmap=EDGE2CESmap,
......@@ -209,9 +209,24 @@ shares_intensity_demand <- shares_intensity_and_demand(
REMIND2ISO_MAPPING=REMIND2ISO_MAPPING,
demand_input = if (opt$reporting) ES_demand)
demByTech <- shares_intensity_demand[["demand"]] ##in [-]
intensity <- shares_intensity_demand[["demandI"]] ##in million pkm/EJ
norm_demand <- shares_intensity_demand$demandF_plot_pkm ## total demand is 1, required for costs
demByTech <- shares_int_dem[["demand"]] ##in [-]
intensity <- shares_int_dem[["demandI"]] ##in million pkm/EJ
norm_demand <- shares_int_dem[["demandF_plot_pkm"]] ## total demand is 1, required for costs
if (opt$reporting) {
saveRDS(vintages[["vintcomp"]], file = datapath("vintcomp.RDS"))
saveRDS(vintages[["newcomp"]], file = datapath("newcomp.RDS"))
saveRDS(shares, file = datapath("shares.RDS"))
saveRDS(logit_data$EF_shares, file = datapath("EF_shares.RDS"))
saveRDS(logit_data$mj_km_data, file = datapath("mj_km_data.RDS"))
saveRDS(logit_data$inconv_cost, file=datapath("inco_costs.RDS"))
saveRDS(shares_int_dem$demandF_plot_EJ,
file=datapath("demandF_plot_EJ.RDS"))
saveRDS(shares_int_dem$demandF_plot_pkm,
datapath("demandF_plot_pkm.RDS"))
saveRDS(logit_data$annual_sales, file = datapath("annual_sales.RDS"))
}
demand_BEV=calc_num_vehicles(
norm_dem_BEV = norm_demand[
......
scripts/output/single/notebook_templates/EDGETransportReport.Rmd
+ 26
− 146
View file @ 19085a04
......@@ -34,7 +34,7 @@ cols <- c("NG" = "#d11141",
"FCEV" = "#00aedb")
datapath <- function(fname){
file.path("./input_EDGE/", fname)
file.path("./EDGE-T/", fname)
}
mapspath <- function(fname, scenariopath=""){
......@@ -43,143 +43,23 @@ mapspath <- function(fname, scenariopath=""){
## Load mappings
EDGE2CESmap <- fread(mapspath("mapping_CESnodes_EDGE.csv"))
REMIND2ISO_MAPPING <- fread("../../config/regionmappingH12.csv")[, .(iso = CountryCode,
region = RegionCode)]
REMIND2ISO_MAPPING <- fread("../../config/regionmappingH12.csv")[, .(iso = CountryCode, region = RegionCode)]
EDGE2teESmap <- fread(mapspath("mapping_EDGE_REMIND_transport_categories.csv"))
REMINDyears <- c(1990,
seq(2005, 2060, by = 5),
seq(2070, 2110, by = 10),
2130, 2150)
years <- c(1990,
seq(2005, 2060, by = 5),
seq(2070, 2110, by = 10),
2130, 2150)
load("config.Rdata")
EDGE_scenario <- cfg$gms$cm_EDGEtr_scen
## load EDGE settings and apply them
settingsEDGE = readRDS(paste0(output_folder, "settingsEDGE.RDS"))
selfmarket_taxes <<- as.logical(settingsEDGE[settings == "selfmarket_taxes", value])
selfmarket_policypush <<- as.logical(settingsEDGE[settings == "selfmarket_policypush", value])
selfmarket_acceptancy <<- as.logical(settingsEDGE[settings == "selfmarket_acceptancy", value])
techswitch <<- settingsEDGE[settings == "techswitch", value]
enhancedtech <<- as.logical(settingsEDGE[settings == "enhancedtech", value])
rebates_febates <<- as.logical(settingsEDGE[settings == "rebates_febates", value])
## models of ICE are available to consumers?
endogeff <<-TRUE
## load input data from last EDGE run
demand_km <- readRDS(datapath(fname = "demandF_plot_pkm.RDS")) ## detailed energy services demand, million km
demand_ej <- readRDS(datapath(fname = "demandF_plot_EJ.RDS")) ## detailed final energy demand, EJ
vintcomp <- readRDS(datapath(fname = "vintcomp.RDS"))
newcomp <- readRDS(datapath(fname = "newcomp.RDS"))
shares <- readRDS(datapath(fname = "shares.RDS"))
inco_tech <- readRDS(datapath(fname = "inco_costs.RDS"))
EF_shares <- readRDS(datapath(fname = "EF_shares.RDS"))
annual_sales <- readRDS(datapath(fname = "annual_sales.RDS"))
mj_km_data <- readRDS(datapath(fname = "mj_km_data.RDS"))
## save intermediate input for plotting purposes
savetmpinput <<- TRUE
## is learning applied?
setlearning <<- TRUE
## load input data from REMIND
gdx = paste0("fulldata.gdx") ## gdx file
name_mif = list.files(pattern = "REMIND_generic", full.names = F)
name_mif = name_mif[!grepl("withoutPlu", name_mif)]
miffile <- as.data.table(read.quitte(name_mif))
## load input data from EDGE
input_path = paste0("../../modules/35_transport/edge_esm/input/")
inputdata = createRDS(input_path, SSP_scenario = scenario, EDGE_scenario = EDGE_scenario)
vot_data = inputdata$vot_data
sw_data = inputdata$sw_data
inco_data = inputdata$inco_data
logit_params = inputdata$logit_params
int_dat = inputdata$int_dat
nonfuel_costs = inputdata$nonfuel_costs
price_nonmot = inputdata$price_nonmot
## load total energy services demand
ES_demand = readREMINDdemand(gdx, REMIND2ISO_MAPPING, EDGE2teESmap, REMINDyears)
if (setlearning) {
## load non fuel costs based on learning
nonfuel_costs = readRDS(paste0("nonfuel_costs_learning.RDS"))
}
## calculate prices
REMIND_prices <- merge_prices(
gdx = gdx,
REMINDmapping = REMIND2ISO_MAPPING,
REMINDyears = REMINDyears,
intensity_data = int_dat,
nonfuel_costs = nonfuel_costs)
## calculate logit
logit_data <- calculate_logit_inconv_endog(
prices= REMIND_prices[tot_price > 0],
vot_data = vot_data,
inco_data = inco_data,
logit_params = logit_params,
intensity_data = int_dat,
price_nonmot = price_nonmot)
shares <- logit_data[["share_list"]] ## shares of alternatives for each level of the logit function
mj_km_data <- logit_data[["mj_km_data"]] ## energy intensity at a technology level
prices <- logit_data[["prices_list"]] ## prices at each level of the logit function, 1990USD/pkm
sales_LDV <- logit_data[["annual_sales"]] ## annual sales composition of LDVs, %
inco_tech <- logit_data$inconv_cost ## inconvenience cost, 1990USD/pkm
if(savetmpinput){
saveRDS(logit_data$share_list, file = paste0(output_folder, "/share_newvehicles.RDS"))
saveRDS(logit_data$EF_shares, file = paste0(output_folder, "EF_shares.RDS"))
saveRDS(logit_data$mj_km_data, file= paste0(output_folder, "mj_km_data.RDS"))
saveRDS(nonfuel_costs, file=paste0(output_folder, "nonfuel_costs.RDS"))
saveRDS(inco_tech, file=paste0(output_folder, "inco_costs.RDS"))
saveRDS(REMIND_prices, file=paste0(output_folder, "fuel_prices.RDS"))
}
## calculate vintages (new shares, prices, intensity)
vintages = calcVint(shares = shares,
totdem_regr = ES_demand[sector == "trn_pass"],
prices = prices,
mj_km_data = mj_km_data,
years = years)
shares$FV_shares = vintages[["shares"]]$FV_shares ## the shares need to be updated with the vintages calculations
prices = vintages[["prices"]] ## prices as well
mj_km_data = vintages[["mj_km_data"]] ## ... and energy intensity as well
vintcomp = vintages[["vintcomp"]] ## composition of vintages
newcomp = vintages[["newcomp"]] ## composition of new additions
if (savetmpinput) {
saveRDS(vintages, file=paste0(output_folder, fname = "vintages.RDS"))
}
## calculate energy intensity and FE demand at a REMIND-region level for the desired level of aggregation
res <- shares_intensity_and_demand(
logit_shares=shares,
MJ_km_base=mj_km_data,
REMIND2ISO_MAPPING=REMIND2ISO_MAPPING,
EDGE2CESmap=EDGE2CESmap,
REMINDyears=REMINDyears,
demand_input = ES_demand)
if(savetmpinput){
saveRDS(res$demandF_plot_EJ, file=paste0(output_folder, "demandF_plot_EJ.RDS"))
saveRDS(res$demandF_plot_pkm, file=paste0(output_folder, "demandF_plot_pkm.RDS"))
}
demand_km <- res$demandF_plot_pkm ## detailed energy services demand, million km
demand_ej <- res$demandF_plot_EJ ## detailed final energy demand, EJ
sharesVS1 <- shares$VS1_shares ## shares at vehicle type level
sharesFV <- shares$FV_shares ## shares at fuel type level
```
# LDVs vintages
......@@ -187,15 +67,15 @@ sharesFV <- shares$FV_shares ## shares at fuel type level
```{r, echo=FALSE, warning=FALSE}
plotVint = function(vintcomp, newcomp, sharesVS1){
vintcomp = vintcomp[,.(totdem, iso, subsector_L1, year, technology,vehicle_type, sector, sharetech_vint, EDGE_scenario)]
newcomp = newcomp[,.(iso, subsector_L1, year, technology,vehicle_type, sector, sharetech_new, EDGE_scenario)]
vintcomp = vintcomp[,.(totdem, iso, subsector_L1, year, technology,vehicle_type, sector, sharetech_vint)]
newcomp = newcomp[,.(iso, subsector_L1, year, technology,vehicle_type, sector, sharetech_new)]
allfleet = merge(newcomp, vintcomp, all =TRUE, by = c("iso", "sector", "subsector_L1", "vehicle_type", "technology", "year", "EDGE_scenario"))
allfleet = merge(newcomp, vintcomp, all =TRUE, by = c("iso", "sector", "subsector_L1", "vehicle_type", "technology", "year"))
allfleet = merge(allfleet, sharesVS1[,.(shareVS1 = share, iso, year, vehicle_type, subsector_L1)], all.x=TRUE, by = c("iso", "year", "vehicle_type", "subsector_L1"))
allfleet[,vintdem:=totdem*sharetech_vint*shareVS1]
allfleet[,newdem:=totdem*sharetech_new*shareVS1]
allfleet=melt(allfleet, id.vars = c("iso", "sector", "subsector_L1", "vehicle_type", "technology",
"year", "EDGE_scenario"), measure.vars = c("vintdem", "newdem"))
"year"), measure.vars = c("vintdem", "newdem"))
allfleet[,alpha:=ifelse(variable == "vintdem", 0, 1)]
load_factor = 2
......@@ -232,7 +112,7 @@ plotVint = function(vintcomp, newcomp, sharesVS1){
}
p = plotVint(vintcomp, newcomp, sharesVS1)
p = plotVint(vintcomp, newcomp, shares$VS1_shares)
p
......@@ -290,18 +170,18 @@ intcompplotf = function(EF_shares, FV_shares, VS1_shares){
}
intcompplotf(logit_data$EF_shares, sharesFV, sharesVS1)
intcompplotf(EF_shares, shares$FV_shares, shares$VS1_shares)
```
# Sales of LDVs
```{r, echo=FALSE, warning=FALSE}
salesplot = function(sales_LDV){
sales_LDV = unique(sales_LDV[,c("iso","year", "technology", "shareFS1")])
sales_LDV <- sales_LDV[,.(shareFS1=sum(shareFS1)),by=c("iso","technology","year")]
salesplot = function(annual_sales){
annual_sales = unique(annual_sales[,c("iso","year", "technology", "shareFS1")])
annual_sales <- annual_sales[,.(shareFS1=sum(shareFS1)),by=c("iso","technology","year")]
p = ggplot()+
geom_bar(data = sales_LDV[year<=2050 & year>=2015 & iso == iso_plot], aes(x=as.numeric(as.character(year)),y=shareFS1, group = technology, fill = technology), position = position_stack(), stat = "identity")+
geom_bar(data = annual_sales[year<=2050 & year>=2015 & iso == iso_plot], aes(x=as.numeric(as.character(year)),y=shareFS1, group = technology, fill = technology), position = position_stack(), stat = "identity")+
theme_minimal()+
scale_fill_manual("Technology", values = cols)+
expand_limits(y = c(0,1))+
......@@ -315,7 +195,7 @@ salesplot = function(sales_LDV){
}
salesplot(sales_LDV)
salesplot(annual_sales)
```
# Final energy demand
......@@ -491,7 +371,7 @@ demandpkmplotf(demand_km)
```{r, echo=FALSE, warning=FALSE}
CO2km_intensity_newsalesplotf = function(shares_LDV, mj_km_data, sharesVS1, shares_source_liquids){
CO2km_intensity_newsalesplotf = function(annual_sales, mj_km_data, sharesVS1, shares_source_liquids){
shares_source_liquids[, technology := ifelse(variable %in% c("FE|Transport|Liquids|Oil", "FE|Transport|Liquids|Coal"), "Oil", "Biodiesel")]
shares_source_liquids = shares_source_liquids[,.(value = sum(value)), by = c("model","scenario","region", "period", "unit","technology")]
shares_source_liquids = shares_source_liquids[region != "World"]
......@@ -525,7 +405,7 @@ CO2km_intensity_newsalesplotf = function(shares_LDV, mj_km_data, sharesVS1, shar
emi_fuel[is.na(gCO2_MJ) & !technology %in% c("Liquids", "NG"), gCO2_MJ := 0]
emi_fuel[, gCO2_km := MJ_km * gCO2_MJ]
totalemi = merge(emi_fuel, shares_LDV, all.y = TRUE, by = c("iso", "year", "technology", "vehicle_type", "subsector_L1"), all.x = TRUE)
totalemi = merge(emi_fuel, annual_sales, all.y = TRUE, by = c("iso", "year", "technology", "vehicle_type", "subsector_L1"), all.x = TRUE)
totalemi = totalemi[!is.na(share) & !is.na(gCO2_km)]
totalemi[, gCO2_km_ave := gCO2_km*share]
......@@ -555,5 +435,5 @@ CO2km_intensity_newsalesplotf = function(shares_LDV, mj_km_data, sharesVS1, shar
}
shares_source_liquids = miffile[variable %in% c("FE|Transport|Liquids|Biomass", "FE|Transport|Liquids|Coal", "FE|Transport|Liquids|Oil"),]
CO2km_intensity_newsalesplotf(sales_LDV, mj_km_data, sharesVS1 = vintages$shares$VS1_shares, shares_source_liquids)
CO2km_intensity_newsalesplotf(annual_sales, mj_km_data, sharesVS1 = shares$VS1_shares, shares_source_liquids)
```
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