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specify.R
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specify.R
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# Copyright 2019 Energy Modelling Lab ApS
# Copyright 2020 Olexandr Balyk
# Copyright 2021 University College Cork
#
# This file is part of TIMES-TS-Tool.
#
# TIMES-TS-Tool is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# TIMES-TS-Tool is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with TIMES-TS-Tool. If not, see <https://www.gnu.org/licenses/>.
library(openxlsx)
library(dplyr)
library(tidyr)
source("read.R")
# Collect all the processes that need subannual ts data specified in a data frame
create_main_dict <- function(mopath){
#TODO: Read regions from SystemSettings
defaultYear <- 2018
windOnshore <- c("P-RNW-WIN-ON*")
newWindOffshore <- c("P-RNW-WIN-OF*")
solar <- c("P-RNW-SOL-*")
tidal <- c("P-RNW*TID*")
wave <- c("P-RNW*WAV*")
single_region <- "IE"
county_polyfill <- "National"
county_regions <- c("IE-CW","IE-D","IE-KE","IE-KK","IE-LS",
"IE-LD","IE-LH","IE-MH","IE-OY","IE-WH","IE-WX","IE-WW",
"IE-CE","IE-CO","IE-KY","IE-LK","IE-TA","IE-WD","IE-G",
"IE-LM","IE-MO","IE-RN","IE-SO","IE-CN","IE-DL","IE-MN")
industry_demands <- c("ICAF","ICON","IEOE","IFAB","IMAE","IMAP","INEM","IOMA",
"IONM","IPX4","IRAP","ITAP","ITEM","IWAP")
# Services ----
## Commercial Services ----
srv_cs_dem <- expand.grid(Pset_PN=NA, Cset_CN="SRV-CS",
Region=c(single_region,county_polyfill),
Year=defaultYear, Attribute="COM_FR",
Transformation="none_shr", Value = NA,
TS_Level="DayNite", Other_Indexes=NA, Pset_CI=NA,
Cset_set=NA, Cset_CD=NA, Target_Sheet="SRV_CS_DEM",
Serie="Private_Service")
## Public Services ----
srv_pu_dem <- expand.grid(Pset_PN=NA, Cset_CN="SRV-PU",
Region=c(single_region,county_polyfill),
Year=defaultYear, Attribute="COM_FR",
Transformation="none_shr", Value = NA,
TS_Level="DayNite", Other_Indexes=NA, Pset_CI=NA,
Cset_set=NA, Cset_CD=NA, Target_Sheet="SRV_PU_DEM",
Serie="Public_Service")
## Data Centres ----
### Data Centres demand variation ----
srv_dcs_dem <- expand.grid(Pset_PN=NA, Cset_CN="SRVDCE-CS",
Region=c(single_region,county_polyfill),
Year=defaultYear, Attribute="COM_FR",
Transformation="none_shr", Value = NA,
TS_Level="DayNite", Other_Indexes=NA, Pset_CI=NA,
Cset_set=NA, Cset_CD=NA, Target_Sheet="DCs",
Serie="temp_2018_dublin_demand")
### Data Centres excess heat profile ----
srv_dcs_eh <- expand.grid(Pset_PN="S-DCE-CS", Other_Indexes="SRVHET-DC-LT",
Region=c(single_region,county_polyfill),
Attribute="VDA_FLOP", Value=NA,
Transformation="none_avg", TS_Level="DayNite",
Target_Sheet="DCs", Cset_set=NA, Cset_CD=NA,
Year=defaultYear, Cset_CN=NA, Pset_CI=NA,
Serie="temp_2018_dublin_excess_heat")
### Data centres electricity for cooling profile ----
srv_dcs_ec <- expand.grid(Pset_PN="S-DCE-CS", Other_Indexes="SRVELC-DC-C",
Region=c(single_region,county_polyfill),
Attribute="VDA_FLOP", Value=NA,
Transformation="none_avg", TS_Level="DayNite",
Target_Sheet="DCs", Cset_set=NA, Cset_CD=NA,
Year=defaultYear, Cset_CN=NA, Pset_CI=NA,
Serie = "temp_2018_dublin_cooling")
## Solar profile ----
srv_sol_afcs <- expand.grid(Region=c(single_region,county_polyfill),
Serie="solar", Attribute="NCAP_AFCS",
Year=defaultYear, Other_Indexes="SRVSOL",
TS_Level="DayNite", Target_Sheet="SRVSOL",
Transformation="none_avg", Cset_set=NA,
Cset_CD=NA, Cset_CN=NA, Pset_PN="S*",
Pset_CI="SRVSOL", Value=NA)
# Residential ----
## Solar profile ----
rsd_sol_afcs <- expand.grid(Region=c(single_region,county_polyfill),
Serie="solar", Attribute="NCAP_AFCS",
Year=defaultYear, Other_Indexes="RSDSOL",
TS_Level="DayNite", Target_Sheet="RSDSOL",
Transformation="none_avg", Cset_set=NA,
Cset_CD=NA, Cset_CN=NA, Pset_PN="R*",
Pset_CI="RSDSOL", Value=NA)
## Space heating demand profile ----
rsd_sh_com_fr <- data.frame(c(single_region,county_polyfill)) %>%
rename(Region=1) %>%
mutate(Serie="Space_Heating",
Attribute="COM_FR",Year=defaultYear, Other_Indexes=NA, Pset_CI=NA,
TS_Level="DayNite",Target_Sheet="RSD_SH",Transformation="none_shr",
Cset_set=NA,Cset_CD=NA,Cset_CN="RSDSH*",Pset_PN=NA,Value=NA)
## Retrofit energy savings profile ----
rsd_rtft_af <- data.frame(c(single_region,county_polyfill)) %>%
rename(Region=1) %>%
mutate(Serie="Heat_Savings", Pset_CI=NA,
Attribute="NCAP_AF",Year=defaultYear, Other_Indexes=NA,
TS_Level="DayNite",Target_Sheet="RSD_RTFT",Transformation="none_sbd",
Cset_set=NA,Cset_CD=NA,Cset_CN=NA,Pset_PN="R-RTFT*",Value=NA)
## Appliances demand profile ----
rsd_oe_dem <- expand.grid(Pset_PN=NA, Cset_CN="RSDOE", Pset_CI=NA,
Region=c(single_region,county_polyfill),
Year=defaultYear, Attribute="COM_FR",
Transformation="none_shr", Value = NA,
TS_Level="DayNite", Other_Indexes=NA,
Cset_set=NA, Cset_CD=NA, Target_Sheet="RSD_OE_DEM",
Serie="Appliances")
# Power sector ----
## Wind, solar, wave and tidal profiles ----
pwr_techs_af <- data.frame(c(windOnshore,newWindOffshore,
solar,wave,tidal)) %>%
rename(Pset_PN=1) %>%
merge(data.frame(c(single_region,county_polyfill))) %>%
rename(Region=2) %>%
mutate(
Value=1,
Serie=ifelse(Pset_PN %in% c(windOnshore),"onshore",NA),
Serie=ifelse(Pset_PN %in% newWindOffshore,"offshore",Serie),
Serie=ifelse(Pset_PN %in% c(solar),"solar",Serie),
Serie=ifelse(Pset_PN %in% c(wave),"wave",Serie),
Serie=ifelse(Pset_PN %in% c(tidal),"tidal",Serie),
Attribute="NCAP_AF",Year=defaultYear, Other_Indexes=NA, Pset_CI=NA,
TS_Level="DayNite",Target_Sheet="PWR_AF",Transformation="mult_avg",
Cset_set=NA,Cset_CD=NA,Cset_CN=NA)
# Transport ----
## Transport demand profile ----
tra_dem <- data.frame(c(single_region,county_regions)) %>%
rename(Region=1) %>%
mutate(
Serie=ifelse(Region %in% c("IE"),"transport_national",NA),
Serie=ifelse(Region %in% c("IE-D"),"transport_dublin",Serie),
Serie=ifelse(Region %in% c("IE-KE","IE-MH","IE-WW"),"transport_greater_dublin",Serie),
Serie=ifelse(Region %in% c("IE-CW","IE-KK","IE-LS","IE-LD","IE-LH","IE-OY",
"IE-WH","IE-WX","IE-CE","IE-KY","IE-TA","IE-LM",
"IE-MO","IE-RN","IE-SO","IE-CN","IE-DL","IE-MN"),
"transport_average1",Serie),
Serie=ifelse(Region %in% c("IE-CO","IE-LK","IE-WD","IE-G"),
"transport_average2",Serie),
Attribute="COM_FR",Year=defaultYear, Other_Indexes=NA, Pset_CI=NA,
TS_Level="DayNite",Target_Sheet="TRA_DEM",Transformation="none_shr",
Cset_set="DEM",Cset_CD="Transport Demand*",Pset_PN=NA,Value=NA,Cset_CN=NA)
# Industry ----
## Industry demand profile ----
ind_dem <- expand.grid(Pset_PN=NA, Other_Indexes=NA, Attribute="COM_FR",
Region=c(single_region,county_polyfill), Value=NA,
Transformation="none_shr", TS_Level="DayNite",
Target_Sheet="IND_DEM", Cset_set=NA, Cset_CD=NA,
Year=defaultYear, Pset_CI=NA, Cset_CN=industry_demands) %>%
mutate(Serie=ifelse(Cset_set == "ICAF", "Chemical", NA),
Serie=ifelse(Cset_set == "ICON", "Construction", Serie),
Serie=ifelse(Cset_set == "IFAB", "Food", Serie),
Serie=ifelse(Cset_set == "IMAP", "Metal", Serie),
Serie=ifelse(Cset_set == "IONM", "Cement", Serie),
Serie=ifelse(Cset_set %in% c("IEOE","IMAE","IOMA","INEM","IPX4","IRAP",
"ITAP","ITEM","IWAP"), "Other_Commodity",
Serie))
# Create main dictionary ----
main_dict <- rbind(
rsd_sol_afcs,
rsd_sh_com_fr,
rsd_rtft_af,
rsd_oe_dem,
pwr_techs_af,
tra_dem,
srv_cs_dem,
srv_pu_dem,
srv_dcs_dem,
srv_dcs_eh,
srv_dcs_ec,
srv_sol_afcs,
ind_dem
)
return(main_dict)
}