WIP

pik-piam · Aug 28, 2024 · 808fdbb · 808fdbb
1 parent 80bbdf4
commit 808fdbb
Show file tree

Hide file tree

Showing 2 changed files with 67 additions and 32 deletions.
diff --git a/R/calcDiffInvestCosts.R b/R/calcDiffInvestCosts.R
@@ -1,20 +1,23 @@
 #' Aggregated investment cost data for REMIND regions (based on IEA_WEO)
-#' @description Disaggregated investment cost data is aggregated and technologies renamed to REMIND names
-#' @details REMIND does not have a classification of coal power plants e.g., sub-critical. Therefore, countries are given coal plant
-#' costs assuming what type of coal plants are expected to develop there. For other technologies, certain assumptions are taken
+#' @description Disaggregated investment cost data is aggregated and technologies
+#' renamed to REMIND names
+#' @details REMIND does not have a classification of coal power plants e.g., sub-critical.
+#' Therefore, countries are given coal plant costs assuming what type of coal
+#' plants are expected to develop there. For other technologies, certain assumptions are taken
 #' to change to REMIND convention.
-#' @param subtype Investment Costs, I&M Costs, and Efficiency
-#' @return Magpie object with aggregated but diffrentiated investment costs for some technologies.
-#' @author Aman Malik
+#' @param subtype "Invest_Costs" or "Efficiency"
+#' @return Magpie object with aggregated but differentiated investment
+#' costs for some technologies.
+#' @author Aman Malik, Falk Benke
 #' @importFrom dplyr filter
 
 calcDiffInvestCosts <- function(subtype) {
+
   if (subtype == "Invest_Costs") {
-    x <- readSource("IEA_WEO", subtype = "Invest_Costs")
-    x_REN21 <- readSource("REN21", subtype = "investmentCosts")
-    x_IEA_PVPS <- readSource("IEA_PVPS", subtype = "CAPEX")
 
-    x[, , ] <- as.numeric(x[, , ]) # convertng data values into numeric
+    x <- readSource("IEA_WorldEnergyOutlook", subtype = "assumptions")
+    x <- x[, , "Stated Policies"][, , "Capital costs (USD/kW)"] %>%
+      dimReduce()
 
     # Various mapping files used to get needed mappings, for e.g., South Asia
     countries <- toolGetMapping("regionmappingREMIND.csv", where = "mappingfolder", type = "regional")
@@ -29,6 +32,7 @@ calcDiffInvestCosts <- function(subtype) {
       "Coal.Steam Coal - SUPERCRITICAL",
       "Coal.Steam Coal - ULTRASUPERCRITICAL"
     )] <- 0
+
     # For countries in LAM, IND, FSU and MEA use supercritical plant investment costs as standard coal plant costs
     x[c(
       countries$CountryCode[countries$RegionCode == "LAM"], "IND",
@@ -39,11 +43,12 @@ calcDiffInvestCosts <- function(subtype) {
       "Coal.Steam Coal - ULTRASUPERCRITICAL"
     )] <- 0
 
-    # For countries in OECD and CHN use ultrasupercritical investment costs                                                                                                                  ultrasupercritical plants for "Coal.Steam Coal - ULTRASUPERCRITICAL")] <- 0
+    # For countries in OECD and CHN use ultrasupercritical investment costs
     x[c("CHN", "KOR", "MAC", "HKG", countries3$CountryCode[countries3$RegionCode == "OECD"]), , c(
       "Coal.Steam Coal - SUBCRITICAL",
       "Coal.Steam Coal - SUPERCRITICAL"
     )] <- 0
+
     # For remaining countries not covered above, use subcritical plant investment costs
     years <- getYears(x)
     for (y in years) {
@@ -57,7 +62,10 @@ calcDiffInvestCosts <- function(subtype) {
         )
       ] <- 0
     }
-    tech_mapping <- toolGetMapping("comparison.csv", where = "mappingfolder", type = "sectoral") %>% filter(!is.na(!!sym("tech")))
+
+    tech_mapping <- toolGetMapping("techmapping_IEA_WEO_PG_assumptions.csv",
+                                   where = "mrremind", type = "sectoral") %>%
+      filter(!is.na(.data$tech))
 
     # create new magpie object with names of corresponding REMIND technologies
     x_new <- new.magpie(getRegions(x), names = unique(tech_mapping$tech), years = getYears(x), fill = 0)
@@ -66,30 +74,32 @@ calcDiffInvestCosts <- function(subtype) {
     x_new[, , "pc"] <- x[, , "Coal.Steam Coal - SUBCRITICAL"] + x[, , "Coal.Steam Coal - SUPERCRITICAL"] +
       x[, , "Coal.Steam Coal - ULTRASUPERCRITICAL"]
 
-    # for "spv" take 1/4 of costs from small-scale and 3/4 costs from large scale (utility scale)ildings
-    x_new[, , "spv"] <- 0.75 * x[, , "Renewables.Solar photovoltaics - Large scale"] + 0.25 * x[, , "Renewables.Solar photovoltaics - Buildings"]
+    # for "spv" take 1/4 of costs from small-scale buildings and 3/4 costs from large scale (utility scale)
+    x_new[, , "spv"] <- 0.75 * x[, , "Renewables.Solar photovoltaics - Large scale unit"] +
+      0.25 * x[, , "Renewables.Solar photovoltaics - Buildings"]
 
-    # same for hydro - removed when hydro values are fed from "private" data source
-    # x_new[,,"hydro"] <- 0.75*x[,,"Renewables.Hydropower - large-scale"] + 0.25*x[,,"Renewables.Hydropower - small-scale"]
-    x_new[, , "hydro"] <- x[, , "Renewables.Hydropower - large-scale"]
-    # and Biomass CHP
-    x_new[, , "biochp"] <- 0.75 * x[, , "Renewables.Biomass CHP Medium"] + 0.25 * x[, , "Renewables.Biomass CHP Small"]
+    # same for hydro
+    x_new[, , "hydro"] <- 0.75 * x[, , "Renewables.Hydropower - large-scale unit"] +
+      0.25 * x[, , "Renewables.Hydropower - small-scale unit"]
 
-    # for rest of technologies, simply match
-    further_techs_to_map <- filter(tech_mapping, !(!!sym("tech") %in% c("pc", "spv", "hydro", "biochp")))
+    # for rest of technologies, simply match (they all have a 1 to 1 relationship)
+    further_techs_to_map <- filter(tech_mapping, !(.data$tech %in% c("pc", "spv", "hydro")))
     x_new[, , further_techs_to_map$tech] <- as.numeric(x[, , further_techs_to_map$IEA])
 
-    x_new <- time_interpolate(x_new, c(2025, 2035), integrate_interpolated_years = T)
-    # overwrite investmetn costs vor renewables with data form REN21
+    x_new <- time_interpolate(x_new, c(2025, 2035, 2040), integrate_interpolated_years = TRUE)
+
+    # overwrite investment costs for renewables with data form REN21
+
+    x_REN21 <- readSource("REN21", subtype = "investmentCosts")
 
     x_REN21_wa <- collapseNames(x_REN21[, , "wa"]) # use weighted average
     getNames(x_REN21_wa) <- gsub("hydropower", "hydro", getNames(x_REN21_wa))
     getNames(x_REN21_wa) <- gsub("Solar PV", "spv", getNames(x_REN21_wa))
     getNames(x_REN21_wa) <- gsub("wind-on", "wind", getNames(x_REN21_wa))
+
     x_REN21_wa <- x_REN21_wa[, , c("Biopower", "Geothermal Power", "wind-off", "csp"), invert = TRUE]
-    # use REN21 data for all time steps for hydro
-    # x_new[,,"hydro"] <- x_REN21_wa[,,"hydro"]
-    # use REN21 data only for 2015 for spv,csp and wind; all other time steps are 0
+
+    # use REN21 data only for 2015 for spv, csp and wind; all other time steps are 0
     x_new[, , c("spv", "wind")] <- 0
     x_new[, 2015, c("spv", "wind")] <- x_REN21_wa[, , c("spv", "wind")]
     x_new["JPN", 2015, "wind"] <- x_REN21["JPN", , "wind-on.max"]
@@ -147,14 +157,15 @@ calcDiffInvestCosts <- function(subtype) {
     x_adj_iso <- toolAggregate(x_adj, regmapping)
 
     # regions which have not been manually adjusted -> replace by original IEA PVPS data
+    x_IEA_PVPS <- readSource("IEA_PVPS", subtype = "CAPEX")
     x_adj_iso[which(is.na(x_adj_iso))] <- x_IEA_PVPS[which(is.na(x_adj_iso))]
     # add new 2020 values PV
     x_new[, "y2020", "spv"] <- x_adj_iso
 
     return(list(x = x_new, weight = x_new, unit = "USD$/KW 2015", description = "Investment costs data"))
-  } else if (subtype == "O&M_Costs") {
-    x <- readSource("IEA_WEO", subtype = "Invest_Costs")
+
   } else if (subtype == "Efficiency") {
+
     x <- readSource("IEA_WEO", subtype = "Efficiency")
     x[, , ] <- as.numeric(x[, , ]) # convertng data values into numeric
 
@@ -212,12 +223,9 @@ calcDiffInvestCosts <- function(subtype) {
     further_techs_to_map <- filter(tech_mapping, !(!!sym("tech") %in% c("pc", "spv", "hydro", "biochp")))
     x_new[, , further_techs_to_map$tech] <- as.numeric(x[, , further_techs_to_map$IEA])
 
-    x_new <- time_interpolate(x_new, c(2025, 2035), integrate_interpolated_years = T)
+    x_new <- time_interpolate(x_new, c(2025, 2035), integrate_interpolated_years = TRUE)
 
     return(list(x = x_new, weight = x_new, unit = "NA", description = "Efficiency data"))
   }
 
-  if (subtype == "Invest_Costs" | subtype == "O&M_Costs") {
-    return(list(x = x_new, weight = x_new, unit = "USD$/KW 2015", description = "Investment costs data"))
-  }
 }
diff --git a/inst/extdata/sectoral/techmapping_IEA_WEO_PG_assumptions.csv b/inst/extdata/sectoral/techmapping_IEA_WEO_PG_assumptions.csv
@@ -0,0 +1,27 @@
+IEA,tech,Comments
+Steam Coal - SUBCRITICAL,pc,"Africa, South Asia"
+Steam Coal - SUPERCRITICAL,pc,"LAM, IND, FSU, MEA"
+Steam Coal - ULTRASUPERCRITICAL,pc,"China, OECD/IEA"
+IGCC,igcc,
+CCGT,ngcc,
+Gas turbine,ngt,
+CCGT - CHP,gaschp,
+Fuel cell (distributed electricity generation),NA,Not in REMIND
+Coal + CCS,pcc,
+Oxyfuel + CCS,pco,
+IGCC + CCS,igccc,
+CCGT + CCS,ngccc,
+Nuclear,tnrs,
+Bioenergy - Large scale unit,NA,
+Bioenergy - Cofiring,NA,Not in REMIND
+Bioenergy - Medium-scale CHP,biochp,
+Bioenergy + CCUS,NA,
+Geothermal,geohdr,
+Hydropower - large-scale unit,hydro,
+Hydropower - small-scale unit,hydro,
+Solar photovoltaics - Large scale unit,spv,
+Solar photovoltaics - Buildings,spv,
+Concentrating solar power,csp,
+Marine,NA,Not in REMIND
+Wind onshore,wind,
+Wind offshore,NA,Not in REMIND