rebase with new version

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '1390130'
+ValidationKey: '1236714'
 AutocreateReadme: yes
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'

.pre-commit-config.yaml

@@ -15,7 +15,7 @@ repos:
     -   id: mixed-line-ending
 
 -   repo: https://github.com/lorenzwalthert/precommit
-    rev: 7910e0323d7213f34275a7a562b9ef0fde8ce1b9  # frozen: v0.4.2
+    rev: bae853d82da476eee0e0a57960ee6b741a3b3fb7  # frozen: v0.4.3
     hooks:
     -   id: parsable-R
     -   id: deps-in-desc

CITATION.cff

@@ -2,14 +2,14 @@ cff-version: 1.2.0
 message: If you use this software, please cite it using the metadata from this file.
 type: software
 title: 'mredgebuildings: Prepare data to be used by the EDGE-Buildings model'
-version: 0.7.0
-date-released: '2024-05-16'
+version: 0.6.2
+date-released: '2024-08-12'
 abstract: Prepare data to be used by the EDGE-Buildings model.
 authors:
 - family-names: Hasse
   given-names: Robin
   email: [email protected]
-  orcid: 0000-0003-1818-3186
+  orcid: https://orcid.org/0000-0003-1818-3186
 - family-names: Führlich
   given-names: Pascal
   email: [email protected]

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: mredgebuildings
 Title: Prepare data to be used by the EDGE-Buildings model
-Version: 0.7.0
-Date: 2024-05-16
+Version: 0.6.2
+Date: 2024-08-12
 Authors@R: c(
     person("Robin", "Hasse", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-1818-3186")),
@@ -20,7 +20,7 @@ Depends:
     mrcommons (>= 1.35.0),
     mrremind (>= 0.175.4)
 Imports:
-    brick (>= 0.1.3),
+    brick (>= 0.5.0),
     dplyr,
     GDPuc,
     ncdf4,

R/calcCarrierPrices.R

@@ -61,16 +61,9 @@ calcCarrierPrices <- function() {
 
   # map ECEMF carriers
   # assumption: liquids and gases remain fossil
-  carrierMap <- inline.data.frame(
-    "carrierECEMF;       carrier",
-    "Gases - Fossil Gas; gases",
-    "Liquids - Oil;      liquids",
-    "Liquids - Biomass;  NA",
-    "Electricity;        elec",
-    "Hydrogen;           NA",
-    "Solids - Coal;      coal",
-    "Solids - Biomass;   biomod"
-  )
+  carrierMap <- toolGetMapping("carrierMapping_ECEMF.csv",
+                               type = "sectoral",
+                               where = "mredgebuildings")
 
   # all considered price components
   # (drop CO2 price as this is a scenario assumption)
@@ -186,7 +179,17 @@ calcCarrierPrices <- function() {
 
   # Combine data ---------------------------------------------------------------
 
-  data <- rbind(prices, emi) %>%
+  data <- rbind(prices, emi)
+
+  # all carriers included?
+  carrier <- unique(getBrickMapping("heatingSystem.csv")["carrier"])
+  data <- data %>%
+    right_join(carrier, by = "carrier")
+  if (any(is.na(data))) {
+    stop("Incomplete mapping of energy carriers.")
+  }
+
+  data <- data  %>%
     as.quitte() %>%
     as.magpie()
 

R/calcCostRenovation.R

@@ -40,7 +40,7 @@ calcCostRenovation <- function(energyLadder = FALSE) {
   ## Heating mark down ====
 
   # decrease installation cost if there is no technology switch
-  sameHsFactor <- 0.9
+  sameHsFactor <- 0.2
 
 
 
@@ -137,7 +137,7 @@ calcCostRenovation <- function(energyLadder = FALSE) {
     as.quitte(na.rm = TRUE) %>%
     select("region", "typ", "vin", "bs", capacity = "value")
 
-  # heating system installation cost: USD/kW -> USD/m2
+  # heating system purchasing cost: USD/kW -> USD/m2
   heatingCost <- calcOutput("HeatingSystem",
                             subtype = "Purchasing cost",
                             aggregate = FALSE) %>%
@@ -157,19 +157,38 @@ calcCostRenovation <- function(energyLadder = FALSE) {
                              .data[["hsr"]])) %>%
     left_join(heatingCost, by = c(hs.final = "hs"),
               relationship = "many-to-many") %>%
-    mutate(heatingCost = ifelse(.data[["hs"]] == .data[["hsr"]],
-                                sameHsFactor * .data[["heatingCost"]],
-                                ifelse(.data[["hsr"]] == "0",
-                                       0,
-                                       .data[["heatingCost"]]))) %>%
+    mutate(heatingCost = ifelse(.data[["hsr"]] == "0",
+                                0,
+                                .data[["heatingCost"]])) %>%
     select(-"hs.final")
 
+  # heating system installation cost (independent of capacity)
+  # rough numbers from BDEW Heizkostenvergleich cost/floor
+  installationCost <- inline.data.frame(
+    "typ; installationCost",
+    "SFH; 30",
+    "MFH; 10",
+    "Com; 6"
+  )
+  heatingCost <- heatingCost %>%
+    left_join(installationCost, by = "typ") %>%
+    mutate(installationCost = .data[["installationCost"]] *
+             ifelse(.data[["hsr"]] == "0",
+                    0,
+                    ifelse(.data[["hsr"]] == "reel",
+                           0.1,
+                           ifelse(.data[["hs"]] == .data[["hsr"]],
+                                  sameHsFactor,
+                                  1))),
+           heatingCost = .data[["heatingCost"]] + .data[["installationCost"]]) %>%
+    select(-"installationCost")
+
   # mark up for installation of central heating system (piping, etc.)
   # https://www.checkatrade.com/blog/cost-guides/central-heating-installation-cost/
   heatingCost <- heatingCost %>%
     mutate(heatingCost = .data[["heatingCost"]] +
              ifelse(.data[["hs"]] == "reel" & !.data[["hsr"]] %in% c("0", "reel"),
-                    3000 / 150 * 1.21, # units GBP/m2 * USD/GBP
+                    3000 / 150 * 1.21, # units: GBP / m2 * USD/GBP # nolint: commented_code_linter.
                     0))
 
   ## Total ====

R/calcHeatingCapacity.R

@@ -33,7 +33,7 @@ calcHeatingCapacity <- function(swissFormular = FALSE) {
     heatCap <- ueDem / flh
   } else {
     # reference heating capacity from online tool (SFH, after 1994)
-    heatCapRef <- 0.060  # in kW/m2
+    heatCapRef <- 0.100  # in kW/m2
 
 
     # heating degree days

R/calcHeatingSystem.R

@@ -7,6 +7,7 @@
 #'
 #' @author Robin Hasse
 #'
+#' @importFrom brick getBrickMapping
 #' @importFrom madrat toolGetMapping readSource toolCountryFill
 #' @importFrom magclass as.magpie getSets<- setNames mbind dimSums
 #'   time_interpolate
@@ -21,14 +22,22 @@ calcHeatingSystem <- function(subtype = c("Purchasing cost", "Efficiency")) {
 
   subtype <- match.arg(subtype)
 
+  # all heating technologies
+  hsMap <- getBrickMapping("heatingSystem.csv")
+
   # map heating technologies
-  hsMap <- toolGetMapping("technologyMapping_EU_ReferenceScenario.csv",
-                          type = "sectoral", where = "mredgebuildings",
-                          returnPathOnly = TRUE) %>%
+  euRefMap <- toolGetMapping("technologyMapping_EU_ReferenceScenario.csv",
+                             type = "sectoral", where = "mredgebuildings",
+                             returnPathOnly = TRUE) %>%
     read.csv(comment.char = "") %>%
     select(-"comment") %>%
     pivot_longer(matches("^weight"), names_to = "typ", values_to = "weight") %>%
-    mutate(typ = sub("^weight(.*)$", "\\1", .data[["typ"]]))
+    mutate(typ = sub("^weight(.*)$", "\\1", .data[["typ"]])) %>%
+    right_join(unique(hsMap["hs"]), by = c(technologyBRICK = "hs"))
+
+  if (any(is.na(euRefMap))) {
+    stop("Incomplete mapping of heating technologies.")
+  }
 
   # map building types to subsectors
   typMap <- inline.data.frame(
@@ -53,7 +62,7 @@ calcHeatingSystem <- function(subtype = c("Purchasing cost", "Efficiency")) {
            .data[["level"]] %in% c("Current", "central")) %>%
     left_join(typMap, by = "subsector", relationship = "many-to-many") %>%
     left_join(periodMap, by = "pointintime") %>%
-    left_join(hsMap, by = c("typ", "technology")) %>%
+    left_join(euRefMap, by = c("typ", "technology"), relationship = "many-to-many") %>%
     select("region", "period", hs = "technologyBRICK", "typ", "unit", "value",
            "weight") %>%
     filter(!is.na(.data[["hs"]])) %>%
@@ -65,6 +74,21 @@ calcHeatingSystem <- function(subtype = c("Purchasing cost", "Efficiency")) {
 
   switch(subtype,
     `Purchasing cost` = {
+      # until here, H2 boilers are just condensing gas boilers. We add
+      # 2500€/unit for the conversion to 100% H2 readiness (cf. ISE
+      # Heizkostenvergleich) and assume 10kW/unit capacity
+      # (cf. BDEW Heizkostenvergleich). The mark up reduces linearly to half its
+      # value until 2050. The value is deflated (1.39) to be consistent.
+      data <- data.frame(period = c(2020, 2050),
+                         hs = "h2bo",
+                         h2MarkUp = c(250, 125) / 1.39) %>%
+        interpolate_missing_periods(unique(data[["period"]]),
+                                    value = "h2MarkUp",
+                                    expand.values = TRUE) %>%
+        right_join(data, by = c("period", "hs")) %>%
+        mutate(value = .data[["value"]] + replace_na(.data[["h2MarkUp"]], 0)) %>%
+        select(-"h2MarkUp")
+
       # unit conversion EUR/kW -> USD/kW
       usd2eur <- usd2eur()
       data <- data %>%

R/calcLifetimeParams.R

@@ -13,10 +13,12 @@
 #'
 #' @author Robin Hasse
 #'
+#' @importFrom brick getBrickMapping
 #' @importFrom madrat readSource calcOutput
 #' @importFrom magclass add_dimension as.magpie mselect getSets mbind
 #' @importFrom quitte inline.data.frame
 #' @importFrom dplyr .data %>% mutate filter everything
+#'   right_join
 #' @importFrom tidyr pivot_longer
 #' @export
 
@@ -62,27 +64,29 @@ calcLifetimeParams <- function(subtype) {
       # delay is approximated by adding it to the scale
       params <- inline.data.frame(
         "subsector;   hs;   scale; shape",
-        "residential; ehp1; 16.88;     2" # Residential Air-Source Heat Pumps
+        "Res;         ehp1; 16.88;     2" # Residential Air-Source Heat Pumps
       )
 
       # technologies given with typical ranges
       ranges <- inline.data.frame(
         "subsector;   hs;   from; to",
-        "residential; biom;   12; 25", # Residential Cordwood / Wood Pallet Stoves
-        "residential; gabo;   20; 30", # Residential Gas-Fired Boilers
-        "residential; reel;   15; 30", # Residential Electric Resistance (Unit) Heaters
-        "residential; libo;   18; 28"  # Residential Oil-Fired Boilers
+        "Res;         biom;   12; 25", # Residential Cordwood / Wood Pallet Stoves
+        "Res;         h2bo;   20; 30", # Residential Gas-Fired Boilers
+        "Res;         gabo;   20; 30", # Residential Gas-Fired Boilers
+        "Res;         reel;   15; 30", # Residential Electric Resistance (Unit) Heaters
+        "Res;         libo;   18; 28"  # Residential Oil-Fired Boilers
       )
 
       # technologies given only with central estimate
       central <- inline.data.frame(
-        "subsector;     hs;  mean",
-        "commercial;  ehp1;    21", # Commercial Rooftop Heat Pumps
-        "commercial;  gabo;    25", # Commercial Gas-Fired Boilers
-        "commercial;  reel;    18", # Commercial Electric Resistance Heaters
-        "commercial;  libo;    25", # Commercial Oil-Fired Boilers
-        "residential; dihe;    30", # value assumed, not from EIA
-        "commercial;  dihe;    30"  # value assumed, not from EIA
+        "subsector; hs;    mean",
+        "Com;       ehp1;    21", # Commercial Rooftop Heat Pumps
+        "Com;       h2bo;    25", # Commercial Gas-Fired Boilers
+        "Com;       gabo;    25", # Commercial Gas-Fired Boilers
+        "Com;       reel;    18", # Commercial Electric Resistance Heaters
+        "Com;       libo;    25", # Commercial Oil-Fired Boilers
+        "Res;       dihe;    30", # value assumed, not from EIA
+        "Com;       dihe;    30"  # value assumed, not from EIA
       )
 
 
@@ -147,23 +151,30 @@ calcLifetimeParams <- function(subtype) {
       # assume residential biomass value for commercial
       params <- params %>%
         rbind(params %>%
-                filter(.data[["subsector"]] == "residential",
+                filter(.data[["subsector"]] == "Res",
                        .data[["hs"]] == "biom") %>%
-                mutate(subsector = "commercial"))
+                mutate(subsector = "Com"))
 
       # assume biomass values for coal
       params <- params %>%
         rbind(params %>%
                 filter(.data[["hs"]] == "biom") %>%
                 mutate(hs = "sobo"))
 
+      # all technologies included?
+      hs <- getBrickMapping("heatingSystem.csv")
+      params <- params %>%
+        right_join(hs["hs"], by = "hs")
+      if (any(is.na(params))) {
+        stop("Incomplete mapping of heating technologies.")
+      }
+
+
 
       ### map to building types ####
-      typMap <- c(
-        SFH = "residential",
-        MFH = "residential",
-        Com = "commercial"
-      )
+      typMap <- getBrickMapping("buildingType.csv")
+      typMap <- stats::setNames(typMap[["subsector"]], typMap[["typ"]])
+
       params <- params %>%
         pivot_longer(c("scale", "shape"), names_to = "variable") %>%
         as.magpie(datacol = "value")
@@ -218,7 +229,10 @@ calcLifetimeParams <- function(subtype) {
   # weight: FE demand
   feBuildings <- calcOutput("FEdemandBuildings", aggregate = FALSE) %>%
     mselect(period = "y2020", collapseNames = TRUE)
-  if (!"typ" %in% getSets(params)) {
+  if ("typ" %in% getSets(params)) {
+    feBuildings <- feBuildings %>%
+      mselect(typ = getItems(params, "typ"))
+  } else {
     feBuildings <- dimSums(feBuildings)
   }
 

R/usd2eur.R

@@ -9,7 +9,7 @@
 
 usd2eur <- function(year = 2020) {
   GDPuc:::wb_wdi %>% # nolint: undesirable_operator_linter.
-    filter(.data[["year"]] == year, .data[["iso3c"]] == "DEU") %>%
+    filter(.data[["year"]] == !!year, .data[["iso3c"]] == "DEU") %>%
     getElement("MER (LCU per US$)") %>%
     mean()
 }

README.md

@@ -1,6 +1,6 @@
 # Prepare data to be used by the EDGE-Buildings model
 
-R package **mredgebuildings**, version **0.7.0**
+R package **mredgebuildings**, version **0.6.2**
 
 [![CRAN status](https://www.r-pkg.org/badges/version/mredgebuildings)](https://cran.r-project.org/package=mredgebuildings)  [![R build status](https://github.com/pik-piam/mredgebuildings/workflows/check/badge.svg)](https://github.com/pik-piam/mredgebuildings/actions) [![codecov](https://codecov.io/gh/pik-piam/mredgebuildings/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/mredgebuildings) [![r-universe](https://pik-piam.r-universe.dev/badges/mredgebuildings)](https://pik-piam.r-universe.dev/builds)
 
@@ -38,7 +38,7 @@ In case of questions / problems please contact Robin Hasse <robin.hasse@pik-pots
 
 To cite package **mredgebuildings** in publications use:
 
-Hasse R, Führlich P, Levesque A, Tockhorn H (2024). _mredgebuildings: Prepare data to be used by the EDGE-Buildings model_. R package version 0.7.0, <https://github.com/pik-piam/mredgebuildings>.
+Hasse R, Führlich P, Levesque A, Tockhorn H (2024). _mredgebuildings: Prepare data to be used by the EDGE-Buildings model_. R package version 0.6.2, <https://github.com/pik-piam/mredgebuildings>.
 
 A BibTeX entry for LaTeX users is
 
@@ -47,7 +47,11 @@ A BibTeX entry for LaTeX users is
   title = {mredgebuildings: Prepare data to be used by the EDGE-Buildings model},
   author = {Robin Hasse and Pascal Führlich and Antoine Levesque and Hagen Tockhorn},
   year = {2024},
+<<<<<<< HEAD
   note = {R package version 0.7.0},
+=======
+  note = {R package version 0.6.2},
+>>>>>>> 8aff157d9945bce34154e6bab672c915009a6bf7
   url = {https://github.com/pik-piam/mredgebuildings},
 }
 ```

inst/extdata/sectoral/carrierMapping_ECEMF.csv

@@ -0,0 +1,8 @@
+carrierECEMF,carrier
+Gases - Fossil Gas,gases
+Liquids - Oil,liquids
+Liquids - Biomass,NA
+Electricity,elec
+Hydrogen,h2
+Solids - Coal,coal
+Solids - Biomass,biomod

inst/extdata/sectoral/technologyMapping_EU_ReferenceScenario.csv

@@ -1,6 +1,7 @@
 technology,technologyBRICK,weightSFH,weightMFH,weightCom,comment
 Boilers gas (non-condensing),gabo,0.1,0.07,0.05,
 Boilers gas condensing*,gabo,0.8,0.67,0.45,
+Boilers gas condensing*,h2bo,1,0.75,0,
 Boilers oil (non-condensing),libo,0.8,0.8,0.8,
 Boilers oil condensing*,libo,0.2,0.2,0.2,
 Wood stoves or boiler pellets,biom,1,1,1,
@@ -16,4 +17,5 @@ CHP fuel cell**,NA,,,,
 District heating,dihe,1,1,1,
 Large scale gas boilers (non-condensing),gabo,0,0.03,0.05,
 Large scale gas boilers condensing,gabo,0,0.23,0.45,
+Large scale gas boilers condensing,h2bo,0,0.25,1,
 Wood stoves or boiler pellets,sobo,1,1,1,we assume similarity between wood stoves and coal heaters