diff --git a/Create_PLEXOS_database/1-parse-matpower/.Rhistory b/Create_PLEXOS_database/1-parse-matpower/.Rhistory
deleted file mode 100644
index d8d9691..0000000
--- a/Create_PLEXOS_database/1-parse-matpower/.Rhistory
+++ /dev/null
@@ -1,512 +0,0 @@
-# other.inputs <- "create_other_inputs.R" # old
-other.inputs <- "create_other_inputs_rts2016.R"# new
-output.dir <- "outputs"
-#------------------------------------------------------------------------------|
-#------------------------------------------------------------------------------|
-# nesta opf format parser - based on NESTA v0.6.0
-# -----------------------------------------------
-#
-#   format requirements:
-#       - one function that creates and returns a matlab struct of
-#         tables and strings
-#       - comments that procede tables are two lines.
-#               first: %% data_description
-#               second: % col1  col2    col3, etc
-#
-#       - colnames have no spaces
-#       - function definition has an output, an equal sign, and no arguments
-#       - fields are either one-liner strings or tables that begin with
-#           'strucname.name = [' and end with '];'
-#
-#------------------------------------------------------------------------------|
-#------------------------------------------------------------------------------|
-#------------------------------------------------------------------------------|
-# helper functions ----
-#------------------------------------------------------------------------------|
-func.obj.init <- function(dt) {
-# args: data.table with a column called orig.text
-# use: finds where function is defined
-# result: global environent variables: case.name, struct.name (to be used
-#   for building list of elemnts), and stuct.list (empty list to be
-#   populated as the rest of the file is parsed)
-# find function definition
-funct.decl <- which(grepl("^function.*=", dt$orig.text))
-if (length(funct.decl) == 0) {
-stop(paste("It looks like there is either no function declaration",
-"in this file or that no function declarations define",
-sprintf("an output. Please check your file, %s", file.p)))
-} else if (length(funct.decl) > 1) {
-stop(paste("It looks like there is more than one function definition",
-"in this file. This script cannot handle that at the moment.",
-sprintf("Please check your file, %s", file.p)))
-}
-# initialize global variables to be accessed by other functions
-case.name <- dt[funct.decl, tstrsplit(orig.text, "=")[[2]]]
-case.name <<- gsub(" ", "", case.name)
-struct.name <- dt[funct.decl, tstrsplit(orig.text, "=")[[1]]]
-struct.name <<- gsub("function| ", "", struct.name)
-struct.list <<- list()
-}
-func.find.objs <- function(dt, strct.strng) {
-# find indices for all fields added to the structure
-# assumes has one one-liners and tables.
-#   one.lines: index will be index of the line where everything is defined
-#   tables: begin index is line with table name; row names are one up;
-#       data starts one down
-#           end index is line after last line of data
-#
-file.dt[grepl(paste0("^[^%]*",strct.strng, "\\..*="), orig.text),
-obj.new := TRUE]
-file.dt[grepl("=.*\\[", orig.text) & obj.new == TRUE, tag := "table.beg"]
-file.dt[grepl("\\]", orig.text) & is.na(obj.new), tag := "table.end"]
-file.dt[obj.new & is.na(tag), tag := "one.line"]
-# grab info
-file.dt[,index := .I]
-field.loc <- file.dt[!is.na(tag),.(tag, index)]
-# clean up
-file.dt[,c("obj.new", "index", "tag") := NULL]
-return(field.loc)
-}
-func.build.list <- function(dt, obj.locs) {
-# from already created tables, pull out elements and add them to a list
-# of all objects in the .m file
-if (nrow(obj.locs[!(tag %in% c("one.line", "table.beg", "table.end"))])) {
-stop(paste("Input obj.locs table has at least one tag that isn't",
-"one.line, table.beg, table.end. Don't know how to handle",
-"that. Please check the table"))
-}
-for (i in seq(nrow(obj.locs))) {
-# get object name
-i.str <- dt[obj.locs[i, index], orig.text]
-i.name <- tstrsplit(i.str, "=")[[1]]
-i.name <- gsub(paste0(" |", struct.name, "\\."), "", i.name)
-# get value, which changes based on type
-if (obj.locs[i,tag] == "one.line") {
-i.value <- tstrsplit(i.str, "=")[[2]]
-i.value <- gsub(" |;|\\'", "", i.value)
-struct.list[[i.name]] <- i.value
-} else if (obj.locs[i, tag] == "table.beg") {
-# get and clean table (collapse spaces into tabs, break into table)
-i.table <- dt[obj.locs[i, index+1]:obj.locs[i+1, index-1],
-# tstrsplit(gsub(";","",gsub("\\s+", "\t", orig.text)), "\t| ")]
-tstrsplit(gsub(";","", orig.text), "\t|\\s+")]
-# deal with nans if needed
-for (j in names(i.table)) {
-set(i.table, which(i.table[[j]] == "nan"), j, NA)
-}
-# remove first col if is blank
-if (all(i.table[[1]] == "" | is.na(i.table[[1]]))) {
-i.table[, (1) := NULL] # this is sloppy
-}
-# get prev line, assuming is colnames
-i.colnames <- dt[obj.locs[i, index-1]]
-if (!grepl("%", i.colnames)) {
-message(paste0("couldn't find column names in line above ",
-"table beginning. please check format of table ",
-struct.name, ".", i.name, ". In the mean time, ",
-"labelling columns with generic names."))
-i.colnames <- paste("V", 1:ncol(i.table),
-sep = "", collapse = "\t")
-}
-# set new column nanes
-i.colnames <- unlist(tstrsplit(i.colnames, "\t|\\s+"))
-i.colnames <- i.colnames[!grepl("%", i.colnames)]
-# treat gencost as a special case
-if (i.name == "gencost") {
-# set names for first four (non-variable) columns
-setnames(i.table,
-names(i.table[,.SD, .SDcols = 1:4]),
-c("model", "startup", "shutdown", "n"))
-# determine if is piecewise linear or polynomial
-if (length(unique(i.table$model)) > 1) {
-stop("I don't know how to handle multiple gen cost types yet")
-}
-if (unique(i.table$model) == "1") {
-# name using model "1" scheme (n = number of piecewise
-# linear segments and subsequent cols are named
-# p0, c0,...,p(n-1),c(n-1)
-tab.names <- names(i.table)
-tab.names <- tab.names[grepl("^V", tab.names)]
-if (length(tab.names) != 2 * as.numeric(i.table[,max(n)])) {
-stop(paste0("max2 * (n) is not equal to the number of extra ",
-"columns in gen.cost. Please verify data is",
-"in the right format"))
-}
-new.names.p <- paste0("p", 0:(length(tab.names)/2 - 1))
-new.names.f <- paste0("f", 0:(length(tab.names)/2 - 1))
-new.names <- c(rbind(new.names.p, new.names.f))
-setnames(i.table,
-tab.names,
-new.names)
-}
-if (unique(i.table$model) == "2") {
-# name using model "2" scheme (n = number of coefficients
-# in polynomial and subsequent cols are named c(n-1) - c0)
-tab.names <- names(i.table)
-tab.names <- tab.names[grepl("^V", tab.names)]
-if (length(tab.names) != i.table[,max(n)]) {
-stop(paste0("max(n) is not equal to the number of extra ",
-"columns in gen.cost. Please verify data is",
-"in the right format"))
-}
-new.names <- paste0("c", (length(tab.names) - 1):0)
-setnames(i.table,
-tab.names,
-new.names)
-}
-} else { # any table but gencost
-# adjust name setting if there is an error
-tryCatch(setnames(i.table, colnames(i.table), i.colnames),
-error =  function(e) {
-if (length(i.colnames) < length(colnames(i.table))) {
-message(paste0("not enough new colnames for table ",
-struct.name, ".", i.name,
-". leaving some cols unnameed. ",
-"please check table."))
-setnames(i.table,
-colnames(i.table)[seq_along(i.colnames)],
-i.colnames)
-} else {
-message(paste0("too many new colnames for table ",
-struct.name, ".", i.name,
-". exluding extras. ",
-"please check table."))
-setnames(i.table,
-colnames(i.table),
-i.colnames[seq_along(colnames(i.table))])
-}}
-)
-}
-# now that table is created and renames, add to list
-struct.list[[i.name]] <- i.table
-} else if (obj.locs[i, tag] == "table.beg") {
-next
-}
-}
-return(struct.list)
-}
-#------------------------------------------------------------------------------|
-# read in and parse file ----
-#------------------------------------------------------------------------------|
-# read in file
-file.dt <- data.table(orig.text = readLines(file.p))
-# get rid of blanks at beginnings of lines and blank lines
-file.dt[,orig.text := gsub("^\\s+|^\t", "", orig.text)]
-file.dt <- file.dt[orig.text != ""]
-# pull out name, object name
-#   creates vars case.name, struct.name, struct.list
-func.obj.init(file.dt)
-# prep for real parsing
-if (!(exists("case.name")|exists("struct.name")|exists("struct.list"))) {
-stop(paste("At least one of case.name, struct.name, and struct.list",
-"doesn't exist. Please initialize these by running",
-"func.obj.init"))
-}
-# id where new objects are
-field.loc <- func.find.objs(file.dt, struct.name)
-# for all objects, grab and put in list
-struct.list <- func.build.list(file.dt, field.loc)
-#------------------------------------------------------------------------------|
-# reformat for use in scripts ----
-#------------------------------------------------------------------------------|
-# regions
-region.refnode.data <- struct.list$areas[,.(Region = area,
-`Region.Reference Node` = refbus)]
-# nodes
-node.data <- struct.list$bus[,.(Node = bus_i, Voltage = baseKV, Region = area,
-Zone = zone)]
-# node lpf TODO
-node.data <- struct.list$bus[,.(Node = bus_i, Voltage = baseKV, Region = area,
-Zone = zone)]
-# generators
-generator.data <- struct.list$gen[,.(Node = bus, Units = status,
-`Max Capacity` = Pmax)]
-generator.data[,id := 1:.N, by = Node]
-generator.data[,Generator := paste0(Node, "_", id)]
-generator.data[,id := NULL]
-# change synchronous condenser max capacity from NA to 0
-generator.data[is.na(`Max Capacity`), `Max Capacity` := '0']
-gencost <- struct.list$gencost
-# lines
-line.data <- struct.list$branch[,.(`Node From` = fbus, `Node To` = tbus,
-Resistance = r, Reactance = x,
-`Max Flow` = rateA, rateA, rateB, rateC,
-Units = status)]
-line.data[,id := 1:.N, by = .(`Node To`, `Node From`)]
-line.data[,Line := paste0(`Node From`, "_", `Node To`, "_", id)]
-line.data[,id := NULL]
-line.data[,`Min Flow` := -1 * as.numeric(`Max Flow`)]
-setcolorder(line.data,unique(c("Line",names(line.data))))
-all.tabs <- c()
-View(generator.data)
-View(generator.data)
-if (!exists("all.tabs")) all.tabs <- c()
-gen.params = fread('../../RTS_Data/gen_params.csv')
-gen.id = fread('../../RTS_Data/gen_id.csv')
-View(gen.params)
-View(gen.id)
-node.data[, Zone := as.numeric(Region)]
-node.data[, Region := substr(Region, 1, 1)] # just tens digit
-# add to all.tabs
-all.tabs <- c(all.tabs, "node.data")
-#------------------------------------------------------------------------------|
-# add load datafile pointers to nodes that have load on them ----
-#------------------------------------------------------------------------------|
-# hardcode reigonal filepointers
-region.load.da <- data.table(Region = c(1:3),
-Load = "timeseries_data_files\\load\\DA_hourly.csv")
-region.load.rt <- data.table(Region = c(1:3),
-Load = "timeseries_data_files\\load\\RT_5min.csv")
-# add to all.tabs
-all.tabs <- c(all.tabs, "region.load.da", "region.load.rt")
-#------------------------------------------------------------------------------|
-# add load participation factor for load nodes ----
-#------------------------------------------------------------------------------|
-# can leave node load in MW; psse2plexos will normalize
-node.lpf <- struct.list$bus[,.(Node = bus_i, Load = Pd, Status = 1)]
-# add to all.tabs
-all.tabs <- c(all.tabs, "node.lpf")
-gen.fuel <- gen.id[,Generator := paste0(Bus, "_", ID)]
-View(gen.fuel)
-gen.fuel <- gen.fuel[,.(Generator, Type = Unit)]
-View(gen.fuel)
-fuels <- gen.params[,.(Type = Unit, Fuel)]
-gen.fuel <- merge(gen.fuel, fuels, all.x = TRUE, by='Type')
-View(gen.fuel)
-gen.fuel <- gen.id[,Generator := paste0(Bus, "_", ID)]
-gen.fuel <- gen.fuel[,.(Generator, Type = Unit)]
-# fuel type to unit type
-fuels <- gen.params[,.(Type = Unit, Fuel)]
-# fuel type to generator
-gen.fuel <- merge(gen.fuel, fuels, all.x = TRUE, by='Type')
-# leave only Generator and Fuel columns
-gen.fuel[,Type := NULL]
-# add fuel to synchronous condensers
-gen.fuel[is.na(Fuel), Fuel := 'SynchCond']
-# get fuel price (2010$/MMBtu)
-fuel.price <- fread("../../RTS_Data/fuel_prices.csv")
-fuel.price[, Price := round(Price, 3)]
-# add to get written out
-all.tabs <- c(all.tabs, "gen.fuel", "fuel.price")
-#------------------------------------------------------------------------------|
-# generator outages by type ----
-#------------------------------------------------------------------------------|
-# read gen types
-gen.type = gen.id[,Generator := paste0(Bus, "_", ID)]
-gen.type = gen.type[,.(Generator, Unit)]
-# read generator outage info
-gen.outages = gen.params[, .(Unit, `Forced Outage Rate` = Outage, `Mean Time to Repair` = MTTR)]
-# combine gens to outage
-gen.outages = merge(gen.type, gen.outages, all.x=TRUE, by='Unit')
-gen.outages = gen.outages[, .(Generator, `Forced Outage Rate`, `Mean Time to Repair`)]
-# set outage for synchronous condensers to 0
-gen.outages[is.na(`Forced Outage Rate`), ":=" (`Forced Outage Rate` = 0, `Mean Time to Repair` = 0)]
-# add to get written out
-all.tabs <- c(all.tabs, "gen.outages")
-#------------------------------------------------------------------------------|
-# cost data ----
-#------------------------------------------------------------------------------|
-#
-gen.cost.data <- cbind(generator.data[,.(Generator)],
-struct.list$gencost)
-# remove unneeded cols
-model <- gen.cost.data[1,model]
-gen.cost.data[,c("model", "startup", "shutdown", "n") := NULL]
-if (model == 1) {
-# assumes heat rates are in $/hr and calculated using a dummy fuel price
-# of $1/MMBtu
-# converts from $/hr to MMBtu/MWh by diving by load point
-# ($/hr * 1 MMBtu/$1 * 1 hr/[loadpoint MW * hr = MWh])
-# reset names to be load point and heat rate for plexos
-names.to.convert <- names(gen.cost.data)
-names.to.convert <- names.to.convert[names.to.convert != "Generator"]
-names.converted <- gsub("p", "Load Point", names.to.convert)
-names.converted <- gsub("f", "Heat Rate", names.converted)
-setnames(gen.cost.data, names.to.convert, names.converted)
-# names are almost plexos names, but have band id numbers at end
-# now, melt down, get band id, convert to real MMBtu/MWh
-# there is probably a fancier/more efficient way to do this
-gen.cost.data <- melt(gen.cost.data, id.vars = "Generator",
-variable.factor = FALSE, value.factor = FALSE)
-gen.cost.data[, Band := as.numeric(substr(variable,
-nchar(variable),
-nchar(variable))) + 1]
-gen.cost.data[,variable := substr(variable, 1, nchar(variable) - 1)]
-# recast to get all Load Point and Heat Rate back as column names
-gen.cost.data[,value := as.numeric(value)]
-gen.cost.data <- data.table(dcast(gen.cost.data, Generator+Band~variable))
-gen.cost.data = merge(gen.cost.data,
-merge(gen.fuel,
-fuel.price,by='Fuel')[,.(Generator,Price)],
-by='Generator')
-# heat rate is in $/hr, assuming fuel is $1/MMBtu. Convert to MMBtu/MWh
-gen.cost.data[, `Heat Rate` := `Heat Rate`/(`Load Point`*`Price`)][,Price:=NULL]
-# TODO HMMM SOMETHING IS WRONG WITH THESE HEAT RATE NUMBERS
-} else if (model == 2) {
-# polynomial function coefficients
-# reset names to be plexos properties
-setnames(gen.cost.data, "c0", "Heat Rate Base")
-# reset numbering of c to index from 1
-names.to.convert <- names(gen.cost.data)
-names.converted <- names.to.convert[grepl("^c", names.to.convert)]
-names.converted <- gsub("c", "", names.converted)
-names.converted <- as.character(as.numeric(names.converted) + 1)
-names.converted <- paste("Heat Rate Incr", names.converted)
-setnames(gen.cost.data,
-names.to.convert,
-names.converted)
-} else {
-stop("heat rate model is not 1 or 2. not sure how to treat this data.")
-}
-# Round values to 1 decimal place
-gen.cost.data[, c('Heat Rate', 'Load Point') := list(round(`Heat Rate`, 1), round(`Load Point`, 1))]
-all.tabs <- c(all.tabs, "gen.cost.data")
-#------------------------------------------------------------------------------|
-# start costs ----
-#------------------------------------------------------------------------------|
-# requires that fuel.price and gen.fuel tables have been read in (see section
-# "fuels and fuel price")
-gen.startshut <- cbind(generator.data[,.(Generator)],
-struct.list$gencost[,.(startup, shutdown)])
-# start and shutdown costs are given in MMBtu. convert this to $ by
-# adding gen fuel type and fuel prices, then multiplying by fuel price
-gen.startshut <- merge(gen.startshut, gen.fuel, by = "Generator", all.x = TRUE)
-gen.startshut <- merge(gen.startshut, fuel.price, by = "Fuel", all.x = TRUE)
-gen.startshut[, Price := as.numeric(Price)]
-# Use Oil/Steam price to calculate coal gen start and shutdown costs
-gen.startshut[Fuel == "Coal/Steam", Price := fuel.price[Fuel == "Oil/Steam", Price]]
-# calculate and add start and shutdown costs based on input heat and fuel price
-gen.startshut[, `Start Cost` := Price * as.numeric(startup)]
-gen.startshut[, `Shutdown Cost` := Price * as.numeric(shutdown)]
-# save only plexos property columns
-gen.startshut <- gen.startshut[,.(Generator, `Start Cost`, `Shutdown Cost`)]
-# round values to integers
-gen.startshut[, c('Start Cost', 'Shutdown Cost') := list(round(`Start Cost`), round(`Shutdown Cost`))]
-all.tabs <- c(all.tabs, "gen.startshut")
-#------------------------------------------------------------------------------|
-# attach VG ----
-#------------------------------------------------------------------------------|
-# two things: 1. pass through filepointerts to VG rating files (these files
-# are created manually based on site selection) 2. add a new generator for all
-# the VG gens
-# assumes generator.data has already been made
-# assumes gen.fuel is already made
-# read in profiles to read out again (inefficient but at least treated the
-# same as all other property files...)
-gen.da.vg <- fread("../../RTS_Data/vg_gens_DA.csv")
-gen.rt.vg <- fread("../../RTS_Data/vg_gens_RT.csv")
-gen.csp <- fread("../../RTS_Data/csp_gens.csv")
-storage.csp <- fread("../../RTS_Data/storage.csp.csv")
-storage.props <- fread("../../RTS_Data/storage.props.csv")
-storage.props.rt <- fread("../../RTS_Data/storage.props.rt.csv")
-# get vg max cap and add to total generator.table
-vg.gens <- fread("../../RTS_Data/vg_gens_maxMW.csv", colClasses = "character")
-# add node
-vg.gens[,Node := tstrsplit(Generator, "_")[[1]]]
-vg.gens[, `Min Stable Level` := "0"]
-generator.data <- merge(generator.data,
-vg.gens,
-by = c("Generator", "Max Capacity", "Node"),
-all = TRUE)
-# add units since don't have this in mpc file
-generator.data[, Units := "1"]
-# get rid of some PV/Wind units
-disappear.units <- c("101_pv","101_pv_2","101_pv_3","101_pv_4","102_pv","103_pv","104_pv",
-"119_pv","310_pv","310_pv_2","312_pv","314_pv","314_pv_2","314_pv_3",
-"314_pv_4","319_pv","324_pv","324_pv_2","324_pv_3","118_rtpv","118_rtpv_8",
-"308_rtpv","313_rtpv_11","320_rtpv","320_rtpv_2","320_rtpv_3","320_rtpv_4",
-"320_rtpv_5","320_rtpv_6","314_pv_5","303_wind","317_wind", "212_csp")
-generator.data[Generator %in% disappear.units, Units:="0"]
-# add fuel types to these gens
-vg.gen.fuel <- vg.gens[,.(Generator)]
-vg.gen.fuel[grepl("_pv", Generator), Fuel := "PV"]
-vg.gen.fuel[grepl("_csp", Generator), Fuel := "CSP"]
-vg.gen.fuel[grepl("_rtpv", Generator), Fuel := "RTPV"]
-vg.gen.fuel[grepl("_wind", Generator), Fuel := "Wind"]
-gen.fuel <- rbind(gen.fuel, vg.gen.fuel)
-# add these to all.tabs to be written out at the end
-all.tabs <- c(all.tabs, "gen.da.vg", "gen.rt.vg", "gen.csp", "storage.csp", "storage.props", "storage.props.rt")
-#------------------------------------------------------------------------------|
-# min gen ----
-#------------------------------------------------------------------------------|
-# have to match min gen to individual unit, because these are by size and fuel
-gen.mingen <- merge(gen.id[, .(Generator, Unit, PG)],
-gen.params[,.(Unit, MinGen)],
-by = "Unit",
-all.x = TRUE)
-# keep only relevant columns
-gen.mingen <- gen.mingen[,.(Generator, `Min Stable Level` = MinGen)]
-gen.mingen.rtpv <- gen.fuel[Fuel=='RTPV']
-gen.mingen.rtpv[, Fuel := NULL]
-gen.mingen.rtpv0 <- gen.mingen.rtpv
-gen.mingen.rtpv0[, `Min Stable Level` := 0]
-gen.mingen.rtpv = merge(gen.mingen.rtpv, generator.data[,.(Generator, `Max Capacity`)], by='Generator')
-gen.mingen.rtpv[, `Min Stable Level` := as.numeric(`Max Capacity`)*0.6 ]
-gen.mingen.rtpv[, `Max Capacity` := NULL]
-gen.mingen.rtpv[, `Min Stable Level` := round(`Min Stable Level`)]
-gen.mingen = rbind(gen.mingen, gen.mingen.rtpv)
-# add to all.tabs to be written out
-all.tabs <- c(all.tabs, "gen.mingen", "gen.mingen.rtpv0")
-#------------------------------------------------------------------------------|
-# attach hydro ----
-#------------------------------------------------------------------------------|
-# just like vg profiles, just read in to write out (to keep in same format as
-# everything else and also in case we need to do something with it eventually)
-gen.hydro <- fread("../../RTS_Data/hydro_profiles.csv")
-# add to all.tabs
-all.tabs <- c(all.tabs, "gen.hydro")
-#------------------------------------------------------------------------------|
-eligible.gens <- c("Oil/Steam","Coal/Steam","Oil/CT","NG/CC","NG/CT","PV","Wind", "CSP")
-# add reserve(s) which is x% of load in each region and what gens can provide it
-l.reserve <- c("Spin Up")
-l.is.enabled <- c(-1)
-l.reserve.type <- c(1)
-l.reserve.percent <- c(3.0)
-l.scenario.name <- c("Add Spin Up")
-l.reserve.violation <- c(4000.0)
-l.reserve.timeframe.sec <- c(600.0)
-l.mutually.exclusive <- c(1)
-# add reserve(s) in which risk is defined with data file
-d.reserve <- c("Flex Up","Flex Down","Reg Up","Reg Down")
-d.is.enabled <- c(-1,-1,-1,-1)
-d.reserve.type <- c(1,2,1,2)
-d.scenario.name <- c("Add Flex Reserves","Add Flex Reserves","Add Regulation Reserves","Add Regulation Reserves")
-d.reserve.violation <- c(4100,4100,3900,3900)
-d.reserve.timeframe.sec <- c(1200.0,1200.0,300.0,300.0)
-d.mutually.exclusive <- c(1,1,1,1)
-reserve.data <- data.table('Reserve' = c(l.reserve,d.reserve),
-'Is Enabled' = c(l.is.enabled,d.is.enabled),
-'Type' = c(l.reserve.type,d.reserve.type),
-'Scenario' = c(l.scenario.name,d.scenario.name),
-'Timeframe' = c(l.reserve.timeframe.sec,d.reserve.timeframe.sec),
-'VoRS' = c(l.reserve.violation,d.reserve.violation),
-'Mutually Exclusive' = c(l.mutually.exclusive,d.mutually.exclusive))
-View(reserve.data)
-reserve.generators <- gen.fuel[Fuel %in% eligible.gens,]
-View(reserve.generators)
-reserve.generators <- reserve.generators[,.(Reserve = c(rep(l.reserve,length(Generator)*length(l.reserve)),rep(d.reserve,each = length(Generator))),
-Generator = c(rep(Generator,times = length(l.reserve)+length(d.reserve))))]
-View(reserve.generators)
-reserve.regions <- region.refnode.data[]
-View(reserve.regions)
-reserve.regions <- reserve.regions[,.(Reserve = l.reserve,Region,`Load Risk` = l.reserve.percent)]
-View(reserve.regions)
-reserve.generators <- gen.fuel[Fuel %in% eligible.gens,]
-reserve.generators <- reserve.generators[,.(Reserve = c(rep(l.reserve,length(Generator)*length(l.reserve)),rep(d.reserve,each = length(Generator))),
-Generator = c(rep(Generator,times = length(l.reserve)+length(d.reserve))))]
-reserve.generators <- gen.fuel[Fuel %in% eligible.gens,]
-View(reserve.generators)
-reserve.generators <- reserve.generators[,.(Reserve = c(rep(l.reserve,length(Generator)*length(l.reserve)),rep(d.reserve,each = length(Generator))),
-Generator = c(rep(Generator,times = length(l.reserve)+length(d.reserve))))]
-View(reserve.generators)
-l.reserve
-d.reserve
-reserve.generators <- gen.fuel[Fuel %in% eligible.gens,]
-View(reserve.generators)
-reserve.generators.noCSP <- reserve.generators[!Fuel == 'CSP',]
-reserve.generators <- reserve.generators[,.(Reserve = c(rep(l.reserve,length(Generator)*length(l.reserve)),rep(d.reserve,each = length(Generator))),
-Generator = c(rep(Generator,times = length(l.reserve)+length(d.reserve))))]
-reserve.generators.noCSP <- reserve.generators.noCSP[,.(Reserve = c(rep(l.reserve,length(Generator)*length(l.reserve)),rep(d.reserve,each = length(Generator))),
-Generator = c(rep(Generator,times = length(l.reserve)+length(d.reserve))))]
-View(reserve.generators)
-View(reserve.generators.noCSP)
diff --git a/Create_PLEXOS_database/2-create-excel/.Rhistory b/Create_PLEXOS_database/2-create-excel/.Rhistory
deleted file mode 100644
index b4f2c5a..0000000
--- a/Create_PLEXOS_database/2-create-excel/.Rhistory
+++ /dev/null
@@ -1,70 +0,0 @@
-sessionInfo()
-as.posixct("2024-01-01 00:01:00")
-as.POSIXct("2024-01-01 00:01:00")
-seq(as.POSIXct("2024-01-01 00:01:00"),as.POSIXct("2025-01-01 00:00:00"),by = '5 minute')
-seq(as.POSIXct("2024-01-01 00:01:00"),as.POSIXct("2025-01-01 00:00:00"),by = 'hour')
-seq(as.POSIXct("2024-01-01 01:00:00"),as.POSIXct("2025-01-01 00:00:00"),by = 'hour')
-seq(as.POSIXct("2024-01-01 01:00:00"),as.POSIXct("2025-01-01 00:00:00"),by = 5)
-seq(as.POSIXct("2024-01-01 01:00:00"),as.POSIXct("2025-01-01 00:00:00"),by = 5*60)
-x - seq(as.POSIXct("2024-01-01 01:00:00"),as.POSIXct("2025-01-01 00:00:00"),by = 5*60)
-x = seq(as.POSIXct("2024-01-01 01:00:00"),as.POSIXct("2025-01-01 00:00:00"),by = 5*60)
-x
-hour(x)
-as.hour(x)
-pacman::p_load(lubridate)
-hour(x)
-ts = data.table(Year = year(x),Hour = hour(x),Minute=minute(x),value=0)
-pacman::p_load(lubridate,data.table)
-ts = data.table(Year = year(x),Hour = hour(x),Minute=minute(x),value=0)
-ts
-x = seq(as.POSIXct("2024-01-01 01:00:00"),as.POSIXct("2024-12-31 23:55:00"),by = 5*60)
-ts = data.table(Year = year(x),Hour = hour(x),Minute=minute(x),value=0)
-ts
-write.csv(ts,file='Documents/repos/RTS-GMLC/RTS_Data/timeseries_data_files/hydro/region1_Hydro_RT.csv')
-write.csv(ts,file='Documents/repos/RTS-GMLC/RTS_Data/timeseries_data_files/hydro/region2_Hydro_RT.csv')
-write.csv(ts,file='Documents/repos/RTS-GMLC/RTS_Data/timeseries_data_files/hydro/region3_Hydro_RT.csv')
-write.csv(ts,file='Documents/repos/RTS-GMLC/RTS_Data/timeseries_data_files/hydro/region3_Hydro_RT.csv',row.names = F)
-write.csv(ts,file='Documents/repos/RTS-GMLC/RTS_Data/timeseries_data_files/hydro/region3_Hydro_RT.csv',row.names = F,quote = F)
-ts = data.table(Year = year(x),Hour = hour(x),Minute=minute(x),value=0)
-ts = data.table(Year = year(x),Month = month(x),Day=day(x),Period = (hour(x)-1)*12 + minute(x),value=0)
-ts
-ts = data.table(Year = year(x),Month = month(x),Day=day(x),Period = (hour(x)-1)*12 + minute(x)/5,value=0)
-ts
-5*12*12
-5*12*24
-max(ts$Period)
-ts = data.table(Year = year(x),Month = month(x),Day=day(x),Period = (hour(x))*12 + minute(x)/5,value=0)
-ts
-ts = data.table(Year = year(x),Month = month(x),Day=day(x),Period = ((hour(x)-1)*12 + minute(x))/5,value=0)
-ts
-mintue(x)-1
-minute(x)-1
-minute(x)
-minute(x)/5
-minute(x)/5+1
-(minute(x)/5+1)*(hour(x))
-(minute(x)/5+1)
-(minute(x)/5+1)+(12*hour(x))
-12*24
-ts = data.table(Year = year(x),Month = month(x),Day=day(x),Period = (minute(x)/5+1)+(12*hour(x)),value=0)
-ts
-ts = data.table(Year = year(x),Month = month(x),Day=day(x),Period = (minute(x)/5+1)+(12*(hour(x)-1)),value=0)
-ts
-24*12
-View(ts)
-x = seq(as.POSIXct("2024-01-01 00:00:00"),as.POSIXct("2024-12-31 23:55:00"),by = 5*60)
-ts = data.table(Year = year(x),Month = month(x),Day=day(x),Period = (minute(x)/5+1)+(12*(hour(x))),value=0)
-View(ts)
-write.csv(ts,file='Documents/repos/RTS-GMLC/RTS_Data/timeseries_data_files/hydro/region3_Hydro_RT.csv',row.names = F,quote = F)
-write.csv(ts,file='Documents/repos/RTS-GMLC/RTS_Data/timeseries_data_files/hydro/region2_Hydro_RT.csv',row.names = F,quote = F)
-write.csv(ts,file='Documents/repos/RTS-GMLC/RTS_Data/timeseries_data_files/hydro/region1_Hydro_RT.csv',row.names = F,quote = F)
-setwd('Documents/repos/RTS-GMLC/')
-setwd('Create_PLEXOS_database/')
-source('create_RTS_spreadsheet.R')
-source('1-parse-matpower/parse_matpower.R')
-setwd('../2-create-excel')
-source('run_PSSE2PLEXOS.R')
-prop.table()
-Properties.sheet
-Properties.sheet[property=='Heat Rate']
-Properties.sheet[property=='Heat Rate']
diff --git a/RTS_Data/timeseries_data_files/load/.Rhistory b/RTS_Data/timeseries_data_files/load/.Rhistory
deleted file mode 100644
index 8708cdc..0000000
--- a/RTS_Data/timeseries_data_files/load/.Rhistory
+++ /dev/null
@@ -1,512 +0,0 @@
-library(rplexos)
-db = plexos_open('//plexossql/data/moconnel/CA Flexibility Paper/Mark_runs/html')
-props = query_property(db)
-View(props)
-prices = query_interval(db, 'Region', 'Price')
-View(prices)
-region.prices = dcast(prices, time~name, value.var = 'value')
-library(dplyr)
-region.prices = dcast(prices, time~name, value.var = 'value')
-library(reshape2)
-region.prices = dcast(prices, time~name, value.var = 'value')
-View(region.prices)
-count(region.prices$AESO==0)
-count(region.prices$AESO=0)
-region.prices$AESO=0
-region.prices$AVA==0
-sum(region.prices$AVA==0)
-region.prices = dcast(prices, time~name, value.var = 'value')
-sum(region.prices[,2:44]==0)
-zero.prices.hours[1:43] = 0
-zero.prices.hours = data.frame(1:43)
-View(zero.prices.hours)
-zero.price.hours = data.frame(1:43)
-zero.price.hours = 0
-for ( i in 1:ncol(region.prices)) {
-zero.price.hours[i] = sum(region.prices[,i+1]==0)
-}
-zero.price.hours = data.frame(1:43=0)
-zero.price.hours = list()
-rm(zero.price.hours)
-rm(zero.prices.hours)
-zero.price.hours = list()
-for ( i in 1:ncol(region.prices)) {
-zero.price.hours[i] = sum(region.prices[,i+1]==0)
-}
-ncol(region.prices)
-zero.price.hours = list()
-for ( i in 1:ncol(region.prices)-2) {
-zero.price.hours[i] = sum(region.prices[,i+1]==0)
-}
-zero.price.hours = data.frame(matrix(1,43))
-View(zero.price.hours)
-for ( i in 1:ncol(region.prices)-2) {
-zero.price.hours[i] = sum(region.prices[,i+1]==0)
-}
-ncol(region.prices)-2
-zero.price.hours = data.frame(matrix(1,43))
-for ( i in 1:ncol(region.prices)-1) {
-zero.price.hours[i] = sum(region.prices[,i+1]==0)
-}
-zero.price.hours = data.frame(matrix(1,43)=0)
-zero.price.hours = data.frame(matrix(1,43))
-View(zero.price.hours)
-zero.price.hours[3]
-for ( i in 1:ncol(region.prices)-1) {
-zero.price.hours[i,1] = sum(region.prices[,i+1]==0)
-}
-View(zero.price.hours)
-write.csv(region.prices, 'region_prices.csv', row.names = FALSE)
-getwd()
-View(props)
-dc = read.csv('//plexossql/data/moconnel/RTS-2016 Stuff/Davis_PLEXOSformat.csv')
-View(dc)
-replace = read.csv('//plexossql/data/moconnel/RTS-2016 Stuff/HydroProfiles/DevilCanyon_2024_RTS2016_Hydro.csv')
-View(replace)
-new = rbind(dc[1:3], replace[2])
-new = replace[2]
-new2 = new
-rm(new2)
-rm(new)
-str(replace)
-replace['Year'] = year(replace[1])
-library(lubridate)
-replace['Year'] = year(replace[1])
-replace[1] = as.POSIXct(replace[1])
-replace[1] = as.character(replace[1])
-replace = read.csv('//plexossql/data/moconnel/RTS-2016 Stuff/HydroProfiles/DevilCanyon_2024_RTS2016_Hydro.csv')
-new = replace
-?strptime()
-new[1] = strptime(new[1], '%Y-%m-%d %H:%M')
-new[1] = strptime(as.character(new[1]), '%Y-%m-%d %H:%M')
-test = strptime(as.character(new[1]), '%Y-%m-%d %H:%M')
-test
-as.character(new[1])
-test = as.character(new[1])
-View(new)
-View(replace)
-new= replace
-test = as.character(new[1])
-test
-View(new)
-test = strptime(as.character(new[1]), '%Y-%m-%d %H:%M')
-test
-new
-dates = strptime(as.character(new[,1]), '%Y-%m-%d %H:%M')
-dates = strptime(as.character(new[,1]), '%Y-%m-%d %H:%M:%S')
-View(dc)
-View(new)
-new[1,1]
-str(new)
-replace = read.csv('//plexossql/data/moconnel/RTS-2016 Stuff/HydroProfiles/DevilCanyon_2024_RTS2016_Hydro.csv', stringsAsFactors = FALSE)
-str(replace)
-new = replace
-str(new)
-new[1] = strptime(new[1], "%Y-%d-%m %H:%M")
-new[1] = strptime(new[1], "%Y-%d-%m %H:%M", tz='')
-new[1] = strptime(new[1], "%m/%d/%Y %H:%M")
-as.character(new[1,1])
-new=replace
-as.character(new[1,1])
-new[1] = strptime(new[1], "%m/%d/%Y %H:%M")
-new = replace
-date = strptime(new[,1], "%m/%d/%Y %H:%M")
-date
-new[,1] = strptime(new[,1], "%m/%d/%Y %H:%M")
-new=replace
-new[1] = date
-View(new)
-new=replace
-date = as.date.frame(date)
-date = as.data.frame(date)
-new[1] = date
-View(new)
-new['Year'] = year(new[1])
-new['Year'] = year(as.POSIXct(new[1]))
-View(new)
-str(new)
-year(date)
-str(date)
-?year
-year(date[1])
-day(new[1,1])
-new['Year'] = year(new[,1])
-new['Month'] = month(new[,1])
-new['Day'] = day(new[,1])
-?select
-library(reshape2)
-?select
-library(dplyr)
-?select
-new = select(new, Year, Month, Day, Value)
-View(date)
-new = rbind(new, date)
-new = cbind(new, date)
-new['Hour'] = hour(date)
-new['Hour'] = hour(date[,1])
-new = select(new, Year, Month, Day, Hour, Value)
-formatted = dcast(new, Year+Month+Day~Hour, value.var = 'Value')
-View(dc)
-View(formatted)
-new['Hour'==0] = 24
-new['Hour'==0]
-new[,'Hour'==0]
-new[,'Hour']
-new[,'Hour']==0
-new[,new$Hour==0]=24
-new[,new$Hour==0]=24
-new[,new$Hour==0]
-new[,'Hour'][new$Hour==0]
-new[,'Hour'][new$Hour==0]=24
-formatted = dcast(new, Year+Month+Day~Hour, value.var = 'Value')
-colnames(formatted)
-formatted$NA = NULL
-formatted['NA'] = NULL
-formatted[367] = NULL
-formatted[367,] = NULL
-formatted = formatted[1:366,]
-View(formatted)
-getwd()
-write.csv(formatted, 'DC.csv', row.names=FALSE)
-View(replace)
-sum(replace[2])
-View(replace)
-sum(replace[2])
-new = replace
-str(new)
-new[,1] = strptime(new[,1], '%m/%d/%Y %H:%M')
-new = replace
-date = strptime(new[,1], '%m/%d/%Y %H:%M')
-date = as.data.frame(strptime(new[,1], '%m/%d/%Y %H:%M'))
-View(date)
-new['Year'] = year(date)
-new['Year'] = year(date[,1])
-new['Month'] = month(date[,1])
-new['Day'] = day(date[,1])
-new['Hour'] = hour(date[,1])
-new = select(new, Year, Month, Day, Hour, Value)
-new = dcast(new, Year+Month+Day~Hour, value.var='Value')
-new = replace
-date = as.data.frame(strptime(new[,1], '%m/%d/%Y %H:%M'))
-new['Year'] = year(date[,1])
-new['Month'] = month(date[,1])
-new['Day'] = day(date[,1])
-new['Hour'] = hour(date[,1])
-new = select(new, Year, Month, Day, Hour, Value)
-new[,'Hour'][new$Hour==0]=24
-sum(new$Value)
-new = dcast(new, Year+Month+Day~Hour, value.var='Value')
-sum(new[,4:27])
-sum(new)
-new = replace
-date = as.data.frame(strptime(new[,1], '%m/%d/%Y %H:%M'))
-new['Year'] = year(date[,1])
-new['Month'] = month(date[,1])
-new['Day'] = day(date[,1])
-new['Hour'] = hour(date[,1])
-new = select(new, Year, Month, Day, Hour, Value)
-new[,'Hour'][new$Hour==0]=24
-is.na(new)
-sum(is.na(new))
-new = replace
-date = as.data.frame(strptime(new[,1], '%m/%d/%Y %H:%M'))
-new['Year'] = year(date[,1])
-new['Month'] = month(date[,1])
-new['Day'] = day(date[,1])
-new['Hour'] = hour(date[,1])
-new = select(new, Year, Month, Day, Hour, Value)
-new = replace
-date = as.data.frame(strptime(new[,1], '%m/%d/%Y %H:%M'))
-new['Year'] = year(date[,1])
-new['Month'] = month(date[,1])
-new['Day'] = day(date[,1])
-new['Hour'] = hour(date[,1])
-new = replace
-date = as.data.frame(strptime(new[,1], '%m/%d/%Y %H:%M'))
-new[1658,]
-new['Year'] = year(date[,1])
-new['Year'] = year(date[,1])
-new[1658,]
-new = replace
-date = as.data.frame(strptime(new[,1], '%m/%d/%Y %H:%M'))
-new['Year'] = year(date[,1])
-new['Month'] = month(date[,1])
-new['Day'] = day(date[,1])
-new['Hour'] = hour(date[,1])
-new[1658,]
-new[1658,'Year']=2024
-new[1658,]
-new[1658,'Year']=2024
-new[1657,'Year']=2024
-new[1657,]
-new[1659,]
-new = replace
-date = as.data.frame(strptime(new[,1], '%m/%d/%Y %H:%M'))
-new['Year'] = year(date[,1])
-new['Month'] = month(date[,1])
-new['Day'] = day(date[,1])
-new['Hour'] = hour(date[,1])
-new[1658,'Year']=2024
-new[1658,'Month']=3
-new[1658,'Day']=10
-new[1658,'Hour']=2
-new = select(new, Year, Month, Day, Hour, Value)
-new[,'Hour'][new$Hour==0]=24
-new = dcast(new, Year+Month+Day~Hour, value.var='Value')
-new = replace
-date = as.data.frame(strptime(new[,1], '%m/%d/%Y %H:%M'))
-new['Year'] = year(date[,1])
-new['Month'] = month(date[,1])
-new['Day'] = day(date[,1])
-new['Hour'] = hour(date[,1])
-new[1658,'Year']=2024
-new[1658,'Month']=3
-new[1658,'Day']=10
-new[1658,'Hour']=2
-new = select(new, Year, Month, Day, Hour, Value)
-new[,'Hour'][new$Hour==0]=24
-hear(new)
-head(new)
-sum(new$Value)
-new = dcast(new, Year+Month+Day~Hour, value.var='Value')
-write.csv(new, 'DC.csv', row.names=FALSE)
-sum(replace[2])
-mingen = read.csv('C:/users/moconnel/documents/RTS-2016/mingen.csv')
-mingen = read.csv('C:\users\moconnel\documents\RTS-2016\mingen.csv')
-mingen = read.csv('C:/users/moconnel/documents/RTS-2016/gen_updates/mingen.csv')
-head(mingen)
-library(dplyr)
-?reshape
-?dcast
-library(reshape2)
-?dcast
-avg_mingen = dcast(mingen, Type~Percent.of.Max.Cap, value.var = 'Percent.of.Max.Cap', fun.aggregate=sum)
-View(avg_mingen)
-avg_mingen = dcast(mingen, Type~Percent.of.Max.Cap, fun.aggregate=sum)
-avg_mingen = dcast(mingen, Type~., value.var='Percent.of.Max.Cap', fun.aggregate=sum)
-View(avg_mingen)
-avg_mingen = dcast(mingen, Type~., value.var='Percent.of.Max.Cap', fun.aggregate=mean)
-View(avg_mingen)
-View(mingen)
-sum_mingen = dcast(mingen, Type~., value.var='MaxCap', fun.aggregate=sum)
-View(sum_mingen)
-View(mingen)
-View(sum_mingen)
-mingen = join(mingen, sum_mingen, by="Type")
-library(plyr)
-?join
-mingen = join(mingen, sum_mingen, by="Type")
-View(mingen)
-View(sum_mingen)
-View(mingen)
-mingen$PercentTotalCap = mingen$MaxCap/mingen$.
-mingen$WeightedMaxCapPercent = mingen$Min.Stable / mingen$MaxCap * mingen$PercentTotalCap
-weighted_mingen = dcast(mingen, Type~., variable.var="WeightedMaxCapPercent", fun.aggregate=sum)
-weighted_mingen = dcast(mingen, Type~., value.var="WeightedMaxCapPercent", fun.aggregate=sum)
-View(weighted_mingen)
-region_lpf = read.csv('region_lpf.csv')
-library(data.table)
-library(dplyr)
-library(reshape2)
-setwd('//nrelqnap01d/PLEXOS/Projects/IM3')
-region_lpf = read.csv('region_lpf.csv')
-head(region_lpf)
-region_lpf_spring = filter(region_lpf, Time=='Spring')
-region_lpf_spring = filter(region_lpf, Time=='2024_Spring')
-region_lpf_autumn = filter(region_lpf, Time=='2024_Autumn')
-head(region_lpf)
-region_lpf_winter = filter(region_lpf, Time=='2024_Winter')
-region_lpf_summer = filter(region_lpf, Time=='2024_Summer')
-View(region_lpf_autumn)
-View(region_lpf_spring)
-View(region_lpf_summer)
-View(region_lpf_winter)
-View(region_lpf_autumn)
-node_zone = read.csv('node_zone.csv')
-View(node_zone)
-View(node_zone)
-View(region_lpf_autumn)
-region_lpf_spring = join(region_lpf_spring, node_zone, by='Node')
-library(plyr)
-region_lpf_spring = join(region_lpf_spring, node_zone, by='Node')
-View(region_lpf_spring)
-region_lpf = read.csv('region_lpf.csv')
-region_lpf_spring = filter(region_lpf, Time=='2024_Spring')
-region_lpf_autumn = filter(region_lpf, Time=='2024_Autumn')
-region_lpf_winter = filter(region_lpf, Time=='2024_Winter')
-region_lpf_summer = filter(region_lpf, Time=='2024_Summer')
-node_zone = read.csv('node_zone.csv')
-region_lpf_spring = join(region_lpf_spring, node_zone, by='Node')
-region_lpf_autumn = join(region_lpf_autumn, node_zone, by='Node')
-region_lpf_winter = join(region_lpf_winter, node_zone, by='Node')
-region_lpf_summer = join(region_lpf_summer, node_zone, by='Node')
-View(region_lpf_autumn)
-region_lpf = read.csv('region_lpf.csv')
-region_lpf = data.table(region_lpf)
-region_lpf_spring = filter(region_lpf, Time=='2024_Spring')
-region_lpf_autumn = filter(region_lpf, Time=='2024_Autumn')
-region_lpf_winter = filter(region_lpf, Time=='2024_Winter')
-region_lpf_summer = filter(region_lpf, Time=='2024_Summer')
-node_zone = read.csv('node_zone.csv')
-node_zone = data.table(node_zone)
-region_lpf_spring = join(region_lpf_spring, node_zone, by='Node')
-region_lpf_autumn = join(region_lpf_autumn, node_zone, by='Node')
-region_lpf_winter = join(region_lpf_winter, node_zone, by='Node')
-region_lpf_summer = join(region_lpf_summer, node_zone, by='Node')
-region_sum = region_lpf_spring[,.(LPF_sum=sum(LPF)), by=.(Region)]
-View(region_sum)
-sum(region_sum$LPF_sum
-)
-region_sum_spring = region_lpf_spring[,.(LPF_sum=sum(LPF)), by=.(Region)]
-region_sum_summer = region_lpf_summer[,.(LPF_sum=sum(LPF)), by=.(Region)]
-region_sum_autumn = region_lpf_autumn[,.(LPF_sum=sum(LPF)), by=.(Region)]
-region_sum_winter = region_lpf_winter[,.(LPF_sum=sum(LPF)), by=.(Region)]
-sum(region_sum_spring$LPF_sum)
-sum(region_sum_summer$LPF_sum)
-sum(region_sum_winter$LPF_sum)
-sum(region_sum_autumn$LPF_sum)
-View(node_zone)
-region_sum_spring = region_sum_spring[,.(region_sum=sum(LPF)), by=.(Region)]
-region_sum_summer = region_sum_summer[,.(region_sum=sum(LPF)), by=.(Region)]
-region_sum_autumn = region_sum_autumn[,.(region_sum=sum(LPF)), by=.(Region)]
-region_sum_winter = region_sum_winter[,.(region_sum=sum(LPF)), by=.(Region)]
-View(region_lpf_spring)
-region_sum_spring[,.(region_sum=sum(LPF)), by=.(Region)]
-View(region_sum_spring)
-str(region_sum_spring)
-rm(region_sum)
-county_sum_spring = region_lpf_spring[,.(LPF_sum=sum(LPF)), by=.(County, State, Country)]
-View(county_sum_spring)
-unique(region_lpf_autumn[,c('County', 'State', 'Country')])
-View(region_lpf_autumn)
-write.csv(region_lpf_autumn, 'test.csv', row.names=FALSE)
-View(county_sum_spring)
-lpf = read.csv('region_lpf.csv')
-lpf = data.table(lpf)
-lpf_spring = filter(lpf, Time=='2024_Spring')
-lpf_autumn = filter(lpf, Time=='2024_Autumn')
-lpf_winter = filter(lpf, Time=='2024_Winter')
-lpf_summer = filter(lpf, Time=='2024_Summer')
-node_zone = read.csv('node_zone.csv')
-node_zone = data.table(node_zone)
-lpf_spring = join(lpf_spring, node_zone, by='Node')
-lpf_autumn = join(lpf_autumn, node_zone, by='Node')
-lpf_winter = join(lpf_winter, node_zone, by='Node')
-lpf_summer = join(lpf_summer, node_zone, by='Node')
-View(lpf_autumn)
-View(lpf_autumn)
-lpf_sum_spring = lpf_spring[,.(LPF_region_sum=sum(LPF)), by=.(Region)]
-View(lpf_sum_spring)
-county_sum_spring = lpf_spring[,.(LPF_county_sum=sum(LPF)), by=.(County, State, Country)]
-lpf = read.csv('region_lpf.csv')
-lpf = data.table(lpf)
-lpf_spring = filter(lpf, Time=='2024_Spring')
-lpf_autumn = filter(lpf, Time=='2024_Autumn')
-lpf_winter = filter(lpf, Time=='2024_Winter')
-lpf_summer = filter(lpf, Time=='2024_Summer')
-node_zone = read.csv('node_zone.csv')
-node_zone = data.table(node_zone)
-lpf_spring = join(lpf_spring, node_zone, by='Node')
-lpf_autumn = join(lpf_autumn, node_zone, by='Node')
-lpf_winter = join(lpf_winter, node_zone, by='Node')
-lpf_summer = join(lpf_summer, node_zone, by='Node')
-region_sum_spring = lpf_spring[,.(LPF_region_sum=sum(LPF)), by=.(Region)]
-region_sum_summer = lpf_summer[,.(LPF_region_sum=sum(LPF)), by=.(Region)]
-region_sum_autumn = lpf_autumn[,.(LPF_region_sum=sum(LPF)), by=.(Region)]
-region_sum_winter = lpf_winter[,.(LPF_region_sum=sum(LPF)), by=.(Region)]
-county_sum_spring = lpf_spring[,.(LPF_county_sum=sum(LPF)), by=.(County, State, Country)]
-county_sum_summer = lpf_summer[,.(LPF_county_sum=sum(LPF)), by=.(County, State, Country)]
-county_sum_autumn = lpf_autumn[,.(LPF_county_sum=sum(LPF)), by=.(County, State, Country)]
-county_sum_winter = lpf_winter[,.(LPF_county_sum=sum(LPF)), by=.(County, State, Country)]
-View(county_sum_autumn)
-View(region_sum_autumn)
-lpf = read.csv('region_lpf.csv')
-lpf = data.table(lpf)
-lpf_spring = filter(lpf, Time=='2024_Spring')
-lpf_autumn = filter(lpf, Time=='2024_Autumn')
-lpf_winter = filter(lpf, Time=='2024_Winter')
-lpf_summer = filter(lpf, Time=='2024_Summer')
-node_zone = read.csv('node_zone.csv')
-node_zone = data.table(node_zone)
-lpf_spring = join(lpf_spring, node_zone, by='Node')
-lpf_autumn = join(lpf_autumn, node_zone, by='Node')
-lpf_winter = join(lpf_winter, node_zone, by='Node')
-lpf_summer = join(lpf_summer, node_zone, by='Node')
-region_sum_spring = lpf_spring[,.(LPF_region_sum=sum(LPF)), by=.(Region)]
-region_sum_summer = lpf_summer[,.(LPF_region_sum=sum(LPF)), by=.(Region)]
-region_sum_autumn = lpf_autumn[,.(LPF_region_sum=sum(LPF)), by=.(Region)]
-region_sum_winter = lpf_winter[,.(LPF_region_sum=sum(LPF)), by=.(Region)]
-county_sum_spring = lpf_spring[,.(LPF_county_sum=sum(LPF)), by=.(County, State, Country)]
-county_sum_summer = lpf_summer[,.(LPF_county_sum=sum(LPF)), by=.(County, State, Country)]
-county_sum_autumn = lpf_autumn[,.(LPF_county_sum=sum(LPF)), by=.(County, State, Country)]
-county_sum_winter = lpf_winter[,.(LPF_county_sum=sum(LPF)), by=.(County, State, Country)]
-View(county_sum_autumn)
-lpf_spring = filter(lpf, Time=='2024_Spring')
-lpf_spring = join(lpf_spring, node_zone, by='Node')
-lpf_spring = lpf_spring[,.(LPF_region_sum=sum(LPF)), by=.(Region)]
-lpf_spring = lpf_spring[,.(LPF_county_sum=sum(LPF)), by=.(County, State, Country)]
-lpf_spring = filter(lpf, Time=='2024_Spring')
-lpf_spring = join(lpf_spring, node_zone, by='Node')
-lpf_spring = lpf_spring[,.(LPF_region_sum:=sum(LPF)), by=.(Region)]
-lpf_spring = lpf_spring[, LPF_region_sum := sum(LPF), by=.(Region)]
-View(region_sum_spring)
-lpf_spring = filter(lpf, Time=='2024_Spring')
-lpf_spring = join(lpf_spring, node_zone, by='Node')
-lpf_spring_test = lpf_spring[,LPF_region_sum := sum(LPF), by=Region]
-View(lpf_spring_test)
-View(region_sum_autumn)
-View(lpf_spring_test)
-View(region_sum_autumn)
-lpf_spring_test = lpf_spring[,LPF_region_sum = sum(LPF), by=Region]
-lpf_spring_test = lpf_spring[, .(LPF_region_sum = sum(LPF)), by=Region]
-View(lpf_spring_test)
-lpf_spring_test = join(lpf_spring, lpf_spring[, .(LPF_region_sum = sum(LPF)), by=Region], by='Node')
-lpf_spring = filter(lpf, Time=='2024_Spring')
-lpf_spring = join(lpf_spring, node_zone, by='Node')
-lpf_spring_test = join(lpf_spring, lpf_spring[, .(LPF_region_sum = sum(LPF)), by=Region], by='Node')
-lpf_spring[, .(LPF_region_sum = sum(LPF)), by=Region]
-View(lpf_spring)
-View(lpf_spring)
-lpf_spring_test = join(lpf_spring, lpf_spring[, .(LPF_region_sum = sum(LPF)), by=Region], by='Region')
-View(lpf_spring_test)
-lpf_spring[, .(LPF_county_sum = sum(LPF)), by=.(County, State, Country)]
-lpf_spring[, .(LPF_county_sum = sum(LPF)), by=.(County, State, Country)]
-View(lpf_spring)
-lpf_spring = join(lpf_spring, lpf_spring[, .(LPF_county_sum = sum(LPF)), by=.(County, State, Country)], by=c('County', 'State', 'Country'))
-View(lpf_spring)
-f
-lpf_spring = join(lpf_spring, node_zone, by='Node')
-lpf_spring = filter(lpf, Time=='2024_Spring')
-lpf_spring = join(lpf_spring, node_zone, by='Node')
-lpf_spring = join(lpf_spring, lpf_spring[, .(LPF_region_sum = sum(LPF)), by=Region], by='Region')
-lpf_spring = join(lpf_spring, lpf_spring[, .(LPF_county_sum = sum(LPF)), by=.(County, State, Country)], by=c('County', 'State', 'Country'))
-View(lpf_spring)
-View(county_sum_autumn)
-library(data.table)
-setwd(dirname(sys.frame(1)$ofile))
-setwd('C:/users/moconnel/documents/RTS-2016')
-setwd('RTS2016_test_plexos_database/')
-setwd('data_files/load')
-da.reg1 <- fread("Melted_APS_2020.csv")
-da.reg1[,c("V1", "Hour", "Minutes") := NULL]
-View(da.reg1)
-da.reg1[,Period := 1:24, by = .(Year, Month, Day)]
-View(da.reg1)
-setnames(da.reg1, "Load", "1")
-View(da.reg1)
-da.reg2 <- fread("Melted_NEVP_2020.csv")
-da.reg3 <- fread("Melted_LDWP_2020.csv")
-da.reg2[,c("V1", "Hour", "Minutes") := NULL]
-da.reg2[,Period := 1:24, by = .(Year, Month, Day)]
-setnames(da.reg2, "Load", "2")
-da.reg3[,c("V1", "Hour", "Minutes") := NULL]
-da.reg3[,Period := 1:24, by = .(Year, Month, Day)]
-setnames(da.reg3, "Load", "3")
-# DA: combine
-da.load <- Reduce(function(...) merge(..., all = TRUE),
-list(da.reg1, da.reg2, da.reg3))
-View(da.reg1)
-View(da.reg2)
-View(da.reg3)
-da.load <- Reduce(function(...) merge(..., all = TRUE),
-list(da.reg1, da.reg2, da.reg3))