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MarketPositionFunctions.R
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MarketPositionFunctions.R
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#setwd('D:/Users/jason.west/Clean Energy Transfer Fund Pty Ltd/Rob Ashdown - Revenue Gx Model/Active Strategy/Dashboard')
##############################################################################
##############################################################################
#
# tryCatch(
# library(odbc),
# error = function(e) {
# install.packages("odbc", repos = "http://cran.rstudio.com/")
# library(odbc)
# }
# )
#
# tryCatch(
# library(dplyr),
# error = function(e) {
# install.packages("dplyr", repos = "http://cran.rstudio.com/")
# library(dplyr)
# }
# )
#
# tryCatch(
# library(dbplyr),
# error = function(e) {
# install.packages("dbplyr", repos = "http://cran.rstudio.com/")
# library(dbplyr)
# }
# )
#
# tryCatch(
# library(sqldf),
# error = function(e) {
# install.packages("sqldf", repos = "http://cran.rstudio.com/")
# library(sqldf)
# }
# )
#
# tryCatch(
# library(lubridate),
# error = function(e) {
# install.packages("lubridate", repos = "http://cran.rstudio.com/")
# library(lubridate)
# }
# )
#
# tryCatch(
# library(tidyverse),
# error = function(e) {
# install.packages("tidyverse", repos = "http://cran.rstudio.com/")
# library(tidyverse)
# }
# )
#
# tryCatch(
# library(rio),
# error = function(e) {
# install.packages("rio", repos = "http://cran.rstudio.com/")
# library(rio)
# }
# )
#
# tryCatch(
# library(reshape2),
# error = function(e) {
# install.packages("reshape2", repos = "http://cran.rstudio.com/")
# library(reshape2)
# }
# )
#
# tryCatch(
# library(data.table),
# error = function(e) {
# install.packages("data.table", repos = "http://cran.rstudio.com/")
# library(data.table)
# }
# )
############################################################
############################################################
## Packages listed in case already loaded
library(odbc)
library(dplyr)
library(dbplyr)
library(sqldf)
library(tidyverse)
library(rio)
#library(reshape2)
library(lubridate)
library(data.table)
##############################################################################
##############################################################################
# set this as the date from which MMS data is used to compute PnL
last.settle<-'2022-04-01 10:00:00'
# Connection code....enter password if prompted
dwConnect <- function() {
con <- DBI::dbConnect(
odbc::odbc(),
Driver = "SQL Server",
Server = "172.17.133.92",
Database = "aemosql",
UID = "aemosqluser",
PWD = "LVtsFxCup17Ckk7",
#rstudioapi::askForPassword("aemosqluser"), #pw = LVtsFxCup17Ckk7
Port = 1433
)
return(con)
}
##############################################################################
##############################################################################
windsp <- function() {
df <- file.info(list.files("//172.17.135.233/share/Speedwell_ERA5_FTP_Daily_Import/Daily Updates", full.names = T))
windfile <- rownames(df)[which.max(df$mtime)]
windspeed.load <- read.csv(windfile, header=TRUE)
windspeed.load <- as.data.table(windspeed.load)
windspeed.load <- windspeed.load[,.(SRC_ID,DATE,HOUR,HOURLY_WIND100M_SPEED)]
WS <- dcast(windspeed.load, DATE+HOUR ~ SRC_ID)
WS <- as.data.table(WS)
old <- c("DATE","HOUR","GR100_-37.250x143.750","GR100_-37.750x142.750",
"GR100_-38.250x142.000","GR101_-38.000x142.250")
new <- c("Date","Hour","Loc1","Loc2","Loc3","Loc4")
WS <- setnames(WS,old,new)
WS$Loc1 <- as.numeric(WS$Loc1)
WS$Loc2 <- as.numeric(WS$Loc2)
WS$Loc3 <- as.numeric(WS$Loc3)
WS$Loc4 <- as.numeric(WS$Loc4)
WS[, AverageWS := (Loc1+Loc2+Loc3+Loc4)/4]
WS[, Date := ymd(WS[["Date"]])]
WS$newdate<-as.POSIXct(WS$Date, tz="UTC", format = "%Y-%m-%d %H:%M:%S")
#WS$hr<-as.POSIXct(WS$Hour, origin = "1960-01-01", format = "%Y-%m-%d", tz="UTC")
WS$newdate<-WS$newdate-3600*10 # reset times
WS$newdate1 <- WS$newdate+WS$Hour*3600+14*3600
WS.trimmed<-WS[,.(newdate1,AverageWS)]
WS.trimmed <- setnames(WS.trimmed, c("date_time","WindSpeed"))
WS.trimmed<-na.omit(WS.trimmed)
windspeed.hr<-read.csv('//172.17.135.233/share/Dashboards/PowerBI/windspeed.csv')
windspeed.hr <- as.data.table(windspeed.hr)
windspeed.hr[,(1):=NULL] # delete first column
windspeed.hr[, datetime := ymd_hms(windspeed.hr[["date_time"]])]#,tz = "Australia/Brisbane")] # set date format
windspeed.hr[, date_time := NULL]
windspeed.hr<-windspeed.hr[!duplicated(windspeed.hr), ]
old <- c("WindSpeed","datetime")
new <- c("WindSpeed","date_time")
windspeed.hr <- setnames(windspeed.hr,old,new)
setcolorder(windspeed.hr, c("date_time", "WindSpeed"))
WS.full<-merge.data.table(windspeed.hr,WS.trimmed, all=TRUE)
WS.full<-WS.full[!duplicated(WS.full), ]
WS.final<-unique(WS.full, by = "date_time")
write.csv(WS.final,'//172.17.135.233/share/Dashboards/PowerBI/windspeed.csv')
return(WS.final)
}
###################################################################
###################################################################
##############################################################################
##############################################################################
CETFVicWind <- function() {
connDB<-dwConnect()
WF <-
dbGetQuery(
connDB,
"SELECT * FROM dbo.DISPATCH_UNIT_SCADA WHERE (SETTLEMENTDATE > '2021-03-31 23:30:00')
AND (DUID='CHALLHWF' OR DUID='CROWLWF1' OR DUID='MACARTH1'
OR DUID='OAKLAND1' OR DUID='PORTWF' OR DUID='WAUBRAWF' OR DUID='YAMBUKWF'
OR DUID='YSWF1') "
)
WF <- as.data.table(WF)
WF[, date_time := ymd_hms(WF[["SETTLEMENTDATE"]])]#,tz = "Australia/Brisbane")] # set date format
WF[, SETTLEMENTDATE := NULL]
setcolorder(WF, c("date_time", "DUID", "SCADAVALUE"))
WF_temp <- dcast(WF, date_time ~ DUID)
DT <- WF_temp[order(WF_temp$date_time), ]
######################################################################
### Compute half-hour aggregations for SCADA data
cuts <- seq(round(min(DT$date_time), "hours"), max(DT$date_time)+30*60, "30 min")
# # CHALLHWF CROWLWF1 MACARTH1 OAKLAND1 PORTWF WAUBRAWF YAMBUKWF YSWF1
CROWLWF1.hh <- aggregate(DT$CROWLWF1, list(cut(DT$date_time, cuts)), mean)
CHALLHWF.hh <- aggregate(DT$CHALLHWF, list(cut(DT$date_time, cuts)), mean)
MACARTH1.hh <- aggregate(DT$MACARTH1, list(cut(DT$date_time, cuts)), mean)
OAKLAND1.hh <- aggregate(DT$OAKLAND1, list(cut(DT$date_time, cuts)), mean)
PORTWF.hh <- aggregate(DT$PORTWF, list(cut(DT$date_time, cuts)), mean)
WAUBRAWF.hh <- aggregate(DT$WAUBRAWF, list(cut(DT$date_time, cuts)), mean)
YAMBUKWF.hh <- aggregate(DT$YAMBUKWF, list(cut(DT$date_time, cuts)), mean)
YSWF1.hh <- aggregate(DT$YSWF1, list(cut(DT$date_time, cuts)), mean)
CROWLWF1.hh <- as.data.table(CROWLWF1.hh)
CHALLHWF.hh <- as.data.table(CHALLHWF.hh)
MACARTH1.hh <- as.data.table(MACARTH1.hh)
OAKLAND1.hh <- as.data.table(OAKLAND1.hh)
PORTWF.hh <- as.data.table(PORTWF.hh)
WAUBRAWF.hh <- as.data.table(WAUBRAWF.hh)
YAMBUKWF.hh <- as.data.table(YAMBUKWF.hh)
YSWF1.hh <- as.data.table(YSWF1.hh)
CROWLWF1.hh[, date_time := ymd_hms(CROWLWF1.hh[["Group.1"]])]
CHALLHWF.hh[, date_time := ymd_hms(CHALLHWF.hh[["Group.1"]])]
MACARTH1.hh[, date_time := ymd_hms(MACARTH1.hh[["Group.1"]])]
OAKLAND1.hh[, date_time := ymd_hms(OAKLAND1.hh[["Group.1"]])]
PORTWF.hh[, date_time := ymd_hms(PORTWF.hh[["Group.1"]])]
WAUBRAWF.hh[, date_time := ymd_hms(WAUBRAWF.hh[["Group.1"]])]
YAMBUKWF.hh[, date_time := ymd_hms(YAMBUKWF.hh[["Group.1"]])]
YSWF1.hh[, date_time := ymd_hms(YSWF1.hh[["Group.1"]])]
DTT <- cbind(CROWLWF1.hh$date_time,CROWLWF1.hh[,2], CHALLHWF.hh[,2], MACARTH1.hh[,2],
OAKLAND1.hh[,2], PORTWF.hh[,2], WAUBRAWF.hh[,2], YAMBUKWF.hh[,2], YSWF1.hh[,2])
DTT <- as.data.table(DTT)
DTT <- setnames(DTT, c("date_time","CROWLWF1","CHALLHWF","MACARTH1","OAKLAND1","PORTWF","WAUBRAWF","YAMBUKWF","YSWF1"))
return(DTT)
}
##############################################################################
##############################################################################
VicPrices <- function() {
connDB<-dwConnect()
# Regional reference price
pricedat_raw <-
dbGetQuery(
connDB,
"SELECT SETTLEMENTDATE, RRP FROM dbo.TRADINGPRICE WHERE (SETTLEMENTDATE > '2021-03-31 23:30:00')
AND (REGIONID='VIC1')"
)
pricedat_raw <- as.data.table(pricedat_raw)
pricedat_raw[, date_time := ymd_hms(pricedat_raw[["SETTLEMENTDATE"]])]#,tz = "Australia/Brisbane")] # set date format
pricedat_raw[, SETTLEMENTDATE := NULL]
setcolorder(pricedat_raw, c("date_time", "RRP"))
pricedat_raw <-
setNames(pricedat_raw, c("date_time", "Regions_VIC_Price"))
pricedat <- pricedat_raw[order(pricedat_raw$date_time),]
return(pricedat)
}
#############################################################################
#############################################################################
PriceVol <- function() {
# Get WF data from CETF assets
DTT <- CETFVicWind()
# Vic prices
pricedat<-VicPrices()
DT.all <- merge(pricedat, DTT, by = "date_time")
DT.all$CROWLWF1 <- nafill(DT.all$CROWLWF1, "const", fill = 0)
DT.all$CHALLHWF <- nafill(DT.all$CHALLHWF, "const", fill = 0)
DT.all$MACARTH1 <- nafill(DT.all$MACARTH1, "const", fill = 0)
DT.all$OAKLAND1 <- nafill(DT.all$OAKLAND1, "const", fill = 0)
DT.all$PORTWF <- nafill(DT.all$PORTWF, "const", fill = 0)
DT.all$WAUBRAWF <- nafill(DT.all$WAUBRAWF, "const", fill = 0)
DT.all$YAMBUKWF <- nafill(DT.all$YAMBUKWF, "const", fill = 0)
DT.all$YSWF1 <- nafill(DT.all$YSWF1, "const", fill = 0)
#MLF
#Oakland 1.0063
#Macarthur 0.9757
#Challicum 0.9711
#Crowlands 0.9026
#Portland 0.991
#Waubra 0.9228
#Yambuk 1.0079
#Yaloak 0.9711
# CETF Portfolio
DT.all[, Act_Oakland := 1.0063 * 0.881 * OAKLAND1/2]
DT.all[, Act_Macarthur := 0.9757 * 0.119 * MACARTH1/2]
DT.all[, Act_Challicum := 0.9711 * 0.29 * CHALLHWF/2]
DT.all[, Act_Crowlands := 0.9026 * 0.30 * CROWLWF1/2]
DT.all[, Act_Portland := 0.991 * 0.11 * PORTWF/2]
DT.all[, Act_Yambuk := 1.0079 * 0.83 * YAMBUKWF/2]
DT.all[, Act_Yaloak := 0.9711 * 0.40 * YSWF1/2]
DT.all[, Act_Waubra := 0.9228 * 0.26 * WAUBRAWF/2]
DT.all[, Act_AGL := Act_Oakland + Act_Macarthur]
DT.all[, Act_Acciona := Act_Waubra]
DT.all[, Act_PacHydro := Act_Challicum + Act_Crowlands + Act_Portland +
Act_Yambuk + Act_Yaloak]
DT.all[, Act_CETF := Act_Oakland+Act_Macarthur+Act_Challicum+Act_Crowlands+Act_Portland+
Act_Yambuk+Act_Yaloak+Act_Waubra]
# Start calculations
DT.all[,PPA_Mac:=(Regions_VIC_Price-46.3)*Act_Macarthur]
DT.all[,PPA_Oak:=(Regions_VIC_Price-46.3)*Act_Oakland]
DT.all[,PPA_Chall:=(Regions_VIC_Price-43.75)*Act_Challicum]
DT.all[,PPA_Crow:=(Regions_VIC_Price-43.75)*Act_Crowlands]
DT.all[,PPA_Port:=(Regions_VIC_Price-43.75)*Act_Portland]
DT.all[,PPA_Yamb:=(Regions_VIC_Price-43.75)*Act_Yambuk]
DT.all[,PPA_Yal:=(Regions_VIC_Price-43.75)*Act_Yaloak]
# seperate columns for Waubra calcs
DT.all[,Waub_Collar:=ifelse(Regions_VIC_Price*0.898>=405,405,
ifelse(Regions_VIC_Price*0.898<=37.5,37.5,Regions_VIC_Price*0.898))]
DT.all[,Waub_RawP:=(44.5+Waub_Collar)/2]
DT.all[,PPA_Waub:=(ifelse(Regions_VIC_Price<0,0,Regions_VIC_Price)-Waub_RawP)*Act_Waubra]
#PPA by counterpart
DT.all[,PPA_AGL_Act:=PPA_Mac+PPA_Oak]
DT.all[,AGL_Inflow:=Regions_VIC_Price*Act_AGL]
DT.all[,AGL_Outflow:=46.32*Act_AGL]
DT.all[,PPA_Acciona_Act:=PPA_Waub]
DT.all[,Acciona_Inflow:=Regions_VIC_Price*Act_Waubra]
DT.all[,Acciona_Outflow:=44.5*Act_Waubra]
DT.all[,PPA_PH_Act:=PPA_Chall+PPA_Crow+PPA_Port+PPA_Yamb+PPA_Yal]
DT.all[,PH_Inflow:=Regions_VIC_Price*Act_PacHydro]
DT.all[,PH_Outflow:=43.75*Act_PacHydro]
DT.all[,CETF_250_Inflow:=AGL_Inflow+Acciona_Inflow+PH_Inflow]
DT.all[,CETF_250_Outflow:=AGL_Outflow+Acciona_Outflow+PH_Outflow]
DT.all$grouped_time = lubridate::ceiling_date(DT.all$date_time, unit = "hour")
DT = DT.all %>%
group_by(grouped_time) %>%
summarise(Regions_VIC_Price = mean(Regions_VIC_Price),
PPA_Chall = sum(PPA_Chall),
PPA_Crow = sum(PPA_Crow),
PPA_Mac = sum(PPA_Mac),
PPA_Oak = sum(PPA_Oak),
PPA_Port = sum(PPA_Port),
PPA_Waub = sum(PPA_Waub),
PPA_Yamb = sum(PPA_Yamb),
PPA_Yal = sum(PPA_Yal),
Act_Challicum = sum(Act_Challicum),
Act_Crowlands = sum(Act_Crowlands),
Act_Macarthur = sum(Act_Macarthur),
Act_Oakland = sum(Act_Oakland),
Act_Portland = sum(Act_Portland),
Act_Waubra = sum(Act_Waubra),
Act_Yambuk = sum(Act_Yambuk),
Act_Yaloak = sum(Act_Yaloak),
Act_AGL = sum(Act_AGL),
Act_Acciona = sum(Act_Acciona),
Act_PacHydro = sum(Act_PacHydro),
Act_CETF = sum(Act_CETF),
AGL_Inflow = sum(AGL_Inflow),
AGL_Outflow = sum(AGL_Outflow),
Acciona_Inflow = sum(Acciona_Inflow),
PH_Inflow = sum(PH_Inflow),
PH_Outflow = sum(PH_Outflow),
CETF_250_Inflow = sum(CETF_250_Inflow),
CETF_250_Outflow = sum(CETF_250_Outflow))
# Bring windspeed data into the data table
DT<-as.data.table(DT)
DT[, date_time := ymd_hms(DT[["grouped_time"]])]#,tz = "Australia/Brisbane")] # set date format
DT[, grouped_time := NULL]
WindData<-windsp()
DT.full <- merge(DT, WindData, by = "date_time", all = TRUE)
# compute assumed windspeeds for missing data
DT.full[,WindSpeed:=ifelse(is.na(WindSpeed),(Act_CETF+90)/25.5,WindSpeed)]
# assign time periods to data
DT.full[, Day := day(date_time)]
DT.full[, Mth := month(date_time)]
DT.full[, Year := year(date_time)]
DT.full[, Hour := hour(date_time)]
DT.full[, Week := week(date_time)]
#DT.full[, Quart := quarter(date_time,with_year=TRUE,fiscal_start = 1)]
# Blunt Hedge payoffs
layer1 <- 100
layer2 <- 1200
layer3 <- 1600
layer4 <- 2100
layer5 <- 2400
layer6 <- 3000
layer7 <- 3600
layer8 <- 800
# sharp and blunt hedge triggers
bluntp.cap <- 30
sharp.low <- 30
sharp.hi <- 45
#max payouts
bluntcap <- 6000000
sharpcap <- 6000000
# Price below blunt hedge price cap?
DT.full[, bprice := ifelse(Regions_VIC_Price<bluntp.cap,1,0)]
DT.full[, ws6 := ifelse(WindSpeed<6,1,0)]
DT.full[, ws6.7 := ifelse(WindSpeed>6 & WindSpeed<7,1,0)*layer1*bprice]
DT.full[, ws7.8 := ifelse(WindSpeed>7 & WindSpeed<8,1,0)*layer2*bprice]
DT.full[, ws8.9 := ifelse(WindSpeed>8 & WindSpeed<9,1,0)*layer3*bprice]
DT.full[, ws9.10 := ifelse(WindSpeed>9 & WindSpeed<10,1,0)*layer4*bprice]
DT.full[, ws10.11 := ifelse(WindSpeed>10 & WindSpeed<11,1,0)*layer5*bprice]
DT.full[, ws11.12 := ifelse(WindSpeed>11 & WindSpeed<12,1,0)*layer6*bprice]
DT.full[, ws12.13 := ifelse(WindSpeed>12 & WindSpeed<13,1,0)*layer7*bprice]
DT.full[, ws13.14 := ifelse(WindSpeed>13 & WindSpeed<14,1,0)*layer8*bprice]
DT.full[, ws14 := ifelse(WindSpeed>14,1,0)]
DT.full[date_time<'2021-07-01 10:00:00', blunt := (ws6.7+ws7.8+ws8.9+ws9.10+ws10.11+ws11.12+ws12.13+ws13.14)/2] #half price <Jul21
DT.full[date_time>='2021-07-01 10:00:00', blunt := ws6.7+ws7.8+ws8.9+ws9.10+ws10.11+ws11.12+ws12.13+ws13.14]
DT.full[, bluntcum := cumsum(blunt),.(rleid(Year))]
# Now cap the blunt hedge payments if bluntcum > payout cap
DT.full[, bluntpay := ifelse(bluntcum<bluntcap,blunt,0)] # this is the blunt insurance payout
# Sharp hedge (don't forget the premium)
DT.full[, sharp := 0]
DT.full[date_time>='2021-07-01 10:00:00', sharp := ifelse((Regions_VIC_Price<sharp.hi & Regions_VIC_Price>sharp.low & WindSpeed>6.9 & WindSpeed<13.2),(-90+25.5*WindSpeed)*45,0)]
DT.full[date_time>='2021-07-01 10:00:00', sharpflag := ifelse((Regions_VIC_Price<sharp.hi & Regions_VIC_Price>sharp.low & WindSpeed>6.9 & WindSpeed<13.2),1,0)]
DT.full[, sharpcum := cumsum(sharp),.(rleid(Year))]
# Now cap the blunt hedge payments if bluntcum > payout cap
DT.full[, sharppay := ifelse(sharpcum<sharpcap,sharp,0)] # this is the sharp insurance payout
blunthedgecost <- 2370000
sharphedgecost <- 660000*4
DT.full[, blunthedgeprem := blunthedgecost/(24*365)]
DT.full[, sharphedgeprem := sharphedgecost/(24*365)]
# construct P_L and cumulative PL
DT.full[, P_L:=PPA_Mac+PPA_Oak+blunt]
DT.full[date_time>='2021-07-01' & date_time<'2021-07-04',
P_L:=PPA_Mac+PPA_Oak+PPA_Chall+PPA_Crow+PPA_Port+PPA_Yamb+PPA_Yal+blunt+sharp]
DT.full[date_time>='2021-07-04', P_L:=PPA_Mac+PPA_Oak+PPA_Chall+
PPA_Crow+PPA_Port+PPA_Yamb+PPA_Yal+PPA_Waub+blunt+sharp]
DT.full[,Cum_PL := cumsum(P_L)]
return(DT.full)
}
ReconPL <- function() {
# Get WF data from CETF assets
DTT <- CETFVicWind()
# Vic prices
pricedat<-VicPrices()
DT.all <- merge(pricedat, DTT, by = "date_time")
DT.all$CROWLWF1 <- nafill(DT.all$CROWLWF1, "const", fill = 0)
DT.all$CHALLHWF <- nafill(DT.all$CHALLHWF, "const", fill = 0)
DT.all$MACARTH1 <- nafill(DT.all$MACARTH1, "const", fill = 0)
DT.all$OAKLAND1 <- nafill(DT.all$OAKLAND1, "const", fill = 0)
DT.all$PORTWF <- nafill(DT.all$PORTWF, "const", fill = 0)
DT.all$WAUBRAWF <- nafill(DT.all$WAUBRAWF, "const", fill = 0)
DT.all$YAMBUKWF <- nafill(DT.all$YAMBUKWF, "const", fill = 0)
DT.all$YSWF1 <- nafill(DT.all$YSWF1, "const", fill = 0)
#MLF
#Oakland 1.0063
#Macarthur 0.9757
#Challicum 0.9711
#Crowlands 0.9026
#Portland 0.991
#Waubra 0.9228
#Yambuk 1.0079
#Yaloak 0.9711
# CETF Portfolio
DT.all[, Act_Oakland := 1.0063 * 0.881 * OAKLAND1/2]
DT.all[, Act_Macarthur := 0.9757 * 0.119 * MACARTH1/2]
DT.all[, Act_Challicum := 0.9711 * 0.29 * CHALLHWF/2]
DT.all[, Act_Crowlands := 0.9026 * 0.30 * CROWLWF1/2]
DT.all[, Act_Portland := 0.991 * 0.11 * PORTWF/2]
DT.all[, Act_Yambuk := 1.0079 * 0.83 * YAMBUKWF/2]
DT.all[, Act_Yaloak := 0.9711 * 0.40 * YSWF1/2]
DT.all[, Act_Waubra := 0.9228 * 0.26 * WAUBRAWF/2]
DT.all[, Act_AGL := Act_Oakland + Act_Macarthur]
DT.all[, Act_Acciona := Act_Waubra]
DT.all[, Act_PacHydro := Act_Challicum + Act_Crowlands + Act_Portland +
Act_Yambuk + Act_Yaloak]
DT.all[, Act_CETF := Act_Oakland+Act_Macarthur+Act_Challicum+Act_Crowlands+Act_Portland+
Act_Yambuk+Act_Yaloak+Act_Waubra]
# Start calculations
DT.all[,PPA_Mac:=(Regions_VIC_Price-46.3)*Act_Macarthur]
DT.all[,PPA_Oak:=(Regions_VIC_Price-46.3)*Act_Oakland]
DT.all[,PPA_Chall:=(Regions_VIC_Price-43.75)*Act_Challicum]
DT.all[,PPA_Crow:=(Regions_VIC_Price-43.75)*Act_Crowlands]
DT.all[,PPA_Port:=(Regions_VIC_Price-43.75)*Act_Portland]
DT.all[,PPA_Yamb:=(Regions_VIC_Price-43.75)*Act_Yambuk]
DT.all[,PPA_Yal:=(Regions_VIC_Price-43.75)*Act_Yaloak]
# seperate columns for Waubra calcs
DT.all[,Waub_Collar:=ifelse(Regions_VIC_Price*0.898>=405,405,
ifelse(Regions_VIC_Price*0.898<=37.5,37.5,Regions_VIC_Price*0.898))]
DT.all[,Waub_RawP:=(44.5+Waub_Collar)/2]
DT.all[,PPA_Waub:=(ifelse(Regions_VIC_Price<0,0,Regions_VIC_Price)-Waub_RawP)*Act_Waubra]
#PPA by counterpart
DT.all[,PPA_AGL_Act:=PPA_Mac+PPA_Oak]
DT.all[,AGL_Inflow:=Regions_VIC_Price*Act_AGL]
DT.all[,AGL_Outflow:=46.32*Act_AGL]
DT.all[,PPA_Acciona_Act:=PPA_Waub]
DT.all[,Acciona_Inflow:=Regions_VIC_Price*Act_Waubra]
DT.all[,Acciona_Outflow:=44.5*Act_Waubra]
DT.all[,PPA_PH_Act:=PPA_Chall+PPA_Crow+PPA_Port+PPA_Yamb+PPA_Yal]
DT.all[,PH_Inflow:=Regions_VIC_Price*Act_PacHydro]
DT.all[,PH_Outflow:=43.75*Act_PacHydro]
DT.all[,CETF_250_Inflow:=AGL_Inflow+Acciona_Inflow+PH_Inflow]
DT.all[,CETF_250_Outflow:=AGL_Outflow+Acciona_Outflow+PH_Outflow]
DT.all$grouped_time = lubridate::ceiling_date(DT.all$date_time, unit = "hour")
#detach("package:reshape2", unload = TRUE)
DT = DT.all %>%
group_by(grouped_time) %>%
summarise(Regions_VIC_Price = mean(Regions_VIC_Price),
PPA_Chall = sum(PPA_Chall),
PPA_Crow = sum(PPA_Crow),
PPA_Mac = sum(PPA_Mac),
PPA_Oak = sum(PPA_Oak),
PPA_Port = sum(PPA_Port),
PPA_Waub = sum(PPA_Waub),
PPA_Yamb = sum(PPA_Yamb),
PPA_Yal = sum(PPA_Yal),
Act_Challicum = sum(Act_Challicum),
Act_Crowlands = sum(Act_Crowlands),
Act_Macarthur = sum(Act_Macarthur),
Act_Oakland = sum(Act_Oakland),
Act_Portland = sum(Act_Portland),
Act_Waubra = sum(Act_Waubra),
Act_Yambuk = sum(Act_Yambuk),
Act_Yaloak = sum(Act_Yaloak),
Act_AGL = sum(Act_AGL),
Act_Acciona = sum(Act_Acciona),
Act_PacHydro = sum(Act_PacHydro),
Act_CETF = sum(Act_CETF),
AGL_Inflow = sum(AGL_Inflow),
AGL_Outflow = sum(AGL_Outflow),
Acciona_Inflow = sum(Acciona_Inflow),
PH_Inflow = sum(PH_Inflow),
PH_Outflow = sum(PH_Outflow),
CETF_250_Inflow = sum(CETF_250_Inflow),
CETF_250_Outflow = sum(CETF_250_Outflow))
# Bring windspeed data into the data table
DT<-as.data.table(DT)
DT[, date_time := ymd_hms(DT[["grouped_time"]])]#,tz = "Australia/Brisbane")] # set date format
DT[, grouped_time := NULL]
WindData<-windsp()
DT.full <- merge(DT, WindData, by = "date_time", all = TRUE)
# compute assumed windspeeds for missing data
DT.full[,WindSpeed:=ifelse(is.na(WindSpeed),(Act_CETF+90)/25.5,WindSpeed)]
# assign time periods to data
DT.full[, Day := day(date_time)]
DT.full[, Mth := month(date_time)]
DT.full[, Year := year(date_time)]
DT.full[, Hour := hour(date_time)]
DT.full[, Week := week(date_time)]
#DT.full[, Quart := quarter(date_time,with_year=TRUE,fiscal_start = 1)]
# Blunt Hedge payoffs
layer1 <- 100
layer2 <- 1200
layer3 <- 1600
layer4 <- 2100
layer5 <- 2400
layer6 <- 3000
layer7 <- 3600
layer8 <- 800
# sharp and blunt hedge triggers
bluntp.cap <- 30
sharp.low <- 30
sharp.hi <- 45
#max payouts
bluntcap <- 6000000
sharpcap <- 6000000
# Price below blunt hedge price cap?
DT.full[, bprice := ifelse(Regions_VIC_Price<bluntp.cap,1,0)]
DT.full[, ws6 := ifelse(WindSpeed<6,1,0)]
DT.full[, ws6.7 := ifelse(WindSpeed>6 & WindSpeed<7,1,0)*layer1*bprice]
DT.full[, ws7.8 := ifelse(WindSpeed>7 & WindSpeed<8,1,0)*layer2*bprice]
DT.full[, ws8.9 := ifelse(WindSpeed>8 & WindSpeed<9,1,0)*layer3*bprice]
DT.full[, ws9.10 := ifelse(WindSpeed>9 & WindSpeed<10,1,0)*layer4*bprice]
DT.full[, ws10.11 := ifelse(WindSpeed>10 & WindSpeed<11,1,0)*layer5*bprice]
DT.full[, ws11.12 := ifelse(WindSpeed>11 & WindSpeed<12,1,0)*layer6*bprice]
DT.full[, ws12.13 := ifelse(WindSpeed>12 & WindSpeed<13,1,0)*layer7*bprice]
DT.full[, ws13.14 := ifelse(WindSpeed>13 & WindSpeed<14,1,0)*layer8*bprice]
DT.full[, ws14 := ifelse(WindSpeed>14,1,0)]
DT.full[date_time<'2021-07-01 10:00:00', blunt := (ws6.7+ws7.8+ws8.9+ws9.10+ws10.11+ws11.12+ws12.13+ws13.14)/2] #half price <Jul21
DT.full[date_time>='2021-07-01 10:00:00', blunt := ws6.7+ws7.8+ws8.9+ws9.10+ws10.11+ws11.12+ws12.13+ws13.14]
DT.full[, bluntcum := cumsum(blunt),.(rleid(Year))]
# Now cap the blunt hedge payments if bluntcum > payout cap
DT.full[, bluntpay := ifelse(bluntcum<bluntcap,blunt,0)] # this is the blunt insurance payout
# Sharp hedge (don't forget the premium)
DT.full[, sharp := 0]
DT.full[date_time>='2021-07-01 10:00:00', sharp := ifelse((Regions_VIC_Price<sharp.hi & Regions_VIC_Price>sharp.low & WindSpeed>6.9 & WindSpeed<13.2),(-90+25.5*WindSpeed)*45,0)]
DT.full[date_time>='2021-07-01 10:00:00', sharpflag := ifelse((Regions_VIC_Price<sharp.hi & Regions_VIC_Price>sharp.low & WindSpeed>6.9 & WindSpeed<13.2),1,0)]
DT.full[, sharpcum := cumsum(sharp),.(rleid(Year))]
# Now cap the blunt hedge payments if bluntcum > payout cap
DT.full[, sharppay := ifelse(sharpcum<sharpcap,sharp,0)] # this is the sharp insurance payout
blunthedgecost <- 2370000
sharphedgecost <- 660000*4
DT.full[, blunthedgeprem := blunthedgecost/(24*365)]
DT.full[, sharphedgeprem := sharphedgecost/(24*365)]
# construct P_L and cumulative PL
DT.full[, P_L:=PPA_Mac+PPA_Oak+blunt]
DT.full[date_time>='2021-07-01' & date_time<'2021-07-04',
P_L:=PPA_Mac+PPA_Oak+PPA_Chall+PPA_Crow+PPA_Port+PPA_Yamb+PPA_Yal+blunt+sharp]
DT.full[date_time>='2021-07-04', P_L:=PPA_Mac+PPA_Oak+PPA_Chall+
PPA_Crow+PPA_Port+PPA_Yamb+PPA_Yal+PPA_Waub+blunt+sharp]
DT.full[,Cum_PL := cumsum(P_L)]
# replace already settled payments
settles<-read.csv('//172.17.135.233/share/Dashboards/PowerBI/Settlements.csv')
settles<-as.data.table(settles)
colnames(settles)<-c("date_time","PPA","Blunt","Sharp")
settles$date_time<-mdy(settles$date_time)
settles$date_time<-as.POSIXct(settles$date_time)
# ignore data prior to last settlement date
DT.last <- DT.full[!(date_time<last.settle),]
DT.last[,PPA:=PPA_Chall+PPA_Crow+PPA_Mac+PPA_Oak+PPA_Port+PPA_Waub+PPA_Yal+PPA_Yamb]
DT.last <- DT.last[,.(date_time,PPA,blunt,sharp)]
colnames(DT.last)<-c("date_time","PPA","Blunt","Sharp")
DT.settled <- merge(DT.last, settles, by = "date_time", all = TRUE)
DT.settled[,PPA:=as.numeric(ifelse(is.na(PPA.x),PPA.y,PPA.x))]
DT.settled[,Blunt:=as.numeric(ifelse(is.na(Blunt.x),Blunt.y,Blunt.x))]
DT.settled[,Sharp:=as.numeric(ifelse(is.na(Sharp.x),Sharp.y,Sharp.x))]
DT.settled[,PPA.x:=NULL]
DT.settled[,PPA.y:=NULL]
DT.settled[,Blunt.x:=NULL]
DT.settled[,Blunt.y:=NULL]
DT.settled[,Sharp.x:=NULL]
DT.settled[,Sharp.y:=NULL]
DT.settled[,PnL:=PPA+Blunt+Sharp]
return(DT.settled)
}
## Run above functions to view data
EStatesPrices <- function() {
connDB <- dwConnect()
pricedat_raw <-
dbGetQuery(
connDB,
"SELECT SETTLEMENTDATE, RRP, REGIONID FROM dbo.TRADINGPRICE WHERE (SETTLEMENTDATE > '2014-12-31 23:30:00')
AND (REGIONID='VIC1' OR REGIONID='NSW1' OR REGIONID='QLD1' OR REGIONID='SA1')"
)
pricedat_raw <- as.data.table(pricedat_raw)
pricedat_raw[, date_time := ymd_hms(pricedat_raw[["SETTLEMENTDATE"]])]#,tz = "Australia/Brisbane")] # set date format
pricedat_raw[, SETTLEMENTDATE := NULL]
setcolorder(pricedat_raw, c("date_time", "REGIONID", "RRP"))
price_temp <- dcast(pricedat_raw, date_time ~ REGIONID)
pricedat <- price_temp[order(price_temp$date_time), ]
pricedat <- as.data.table(pricedat)
pricedat[, Day := day(date_time)]
pricedat[, Mth := month(date_time)]
pricedat[, Year := year(date_time)]
pricedat[, Hour := hour(date_time)]
pricedat[, Week := week(date_time)]
#pricedat[, Quart := quarter(date_time, with_year = TRUE, fiscal_start = 1)]
#pricedat[, days := ymd_hms(pricedat_raw[["date_time"]])]
nsw <- pricedat[, .(mean(NSW1)), by = .(Day, Mth, Year)]
qld <- pricedat[, .(mean(QLD1)), by = .(Day, Mth, Year)]
sa <- pricedat[, .(mean(SA1)), by = .(Day, Mth, Year)]
return(pricedat)
}
VicWindDWAP <- function() {
connDB <- dwConnect()
WF <- dbGetQuery(
connDB,
"SELECT * FROM dbo.DISPATCH_UNIT_SCADA WHERE (SETTLEMENTDATE > '2021-03-31 23:30:00')
AND (DUID='ARWF1' OR DUID='BALDHWF1' OR DUID='BULGANA1' OR DUID='CHALLHWF'
OR DUID='CHYTWF1' OR DUID='CROWLWF1' OR DUID='DUNDWF1' OR DUID='DUNDWF2'
OR DUID='DUNDWF3' OR DUID='ELAINWF1' OR DUID='KIATAWF1' OR DUID='MACARTH1'
OR DUID='MERCER01' OR DUID='MLWF1' OR DUID='MTGELWF1' OR DUID='MUWAWF1' OR
DUID='OAKLAND1' OR DUID='PORTWF' OR DUID='SALTCRK1' OR DUID='WAUBRAWF'
OR DUID='YAMBUKWF' OR DUID='YENDWF1' OR DUID='YSWF1')"
)
WF <- as.data.table(WF)
WF[, date_time := ymd_hms(WF[["SETTLEMENTDATE"]])]#,tz = "Australia/Brisbane")] # set date format
WF[, SETTLEMENTDATE := NULL]
setcolorder(WF, c("date_time", "DUID", "SCADAVALUE"))
WF_temp <- dcast(WF, date_time ~ DUID)
DT <- WF_temp[order(WF_temp$date_time), ]
################################################################################
### Compute half-hour aggregations for SCADA data
cuts <-
seq(round(min(DT$date_time), "hours"), max(DT$date_time) + 30 * 60, "30 min")
#
# # CHALLHWF CROWLWF1 MACARTH1 OAKLAND1 PORTWF WAUBRAWF YAMBUKWF YSWF1
ARWF1.hh <- aggregate(DT$ARWF1, list(cut(DT$date_time, cuts)), mean)
BALDHWF1.hh <-
aggregate(DT$BALDHWF1, list(cut(DT$date_time, cuts)), mean)
BULGANA1.hh <-
aggregate(DT$BULGANA1, list(cut(DT$date_time, cuts)), mean)
CHALLHWF.hh <-
aggregate(DT$CHALLHWF, list(cut(DT$date_time, cuts)), mean)
CHYTWF1.hh <-
aggregate(DT$CHYTWF1, list(cut(DT$date_time, cuts)), mean)
CROWLWF1.hh <-
aggregate(DT$CROWLWF1, list(cut(DT$date_time, cuts)), mean)
DUNDWF1.hh <-
aggregate(DT$DUNDWF1, list(cut(DT$date_time, cuts)), mean)
DUNDWF2.hh <-
aggregate(DT$DUNDWF2, list(cut(DT$date_time, cuts)), mean)
DUNDWF3.hh <-
aggregate(DT$DUNDWF3, list(cut(DT$date_time, cuts)), mean)
ELAINWF1.hh <-
aggregate(DT$ELAINWF1, list(cut(DT$date_time, cuts)), mean)
KIATAWF1.hh <-
aggregate(DT$KIATAWF1, list(cut(DT$date_time, cuts)), mean)
MACARTH1.hh <-
aggregate(DT$MACARTH1, list(cut(DT$date_time, cuts)), mean)
MERCER01.hh <-
aggregate(DT$MERCER01, list(cut(DT$date_time, cuts)), mean)
MLWF1.hh <- aggregate(DT$MLWF1, list(cut(DT$date_time, cuts)), mean)
MTGELWF1.hh <-
aggregate(DT$MTGELWF1, list(cut(DT$date_time, cuts)), mean)
MUWAWF1.hh <-
aggregate(DT$MUWAWF1, list(cut(DT$date_time, cuts)), mean)
OAKLAND1.hh <-
aggregate(DT$OAKLAND1, list(cut(DT$date_time, cuts)), mean)
PORTWF.hh <-
aggregate(DT$PORTWF, list(cut(DT$date_time, cuts)), mean)
SALTCRK1.hh <-
aggregate(DT$SALTCRK1, list(cut(DT$date_time, cuts)), mean)
WAUBRAWF.hh <-
aggregate(DT$WAUBRAWF, list(cut(DT$date_time, cuts)), mean)
YAMBUKWF.hh <-
aggregate(DT$YAMBUKWF, list(cut(DT$date_time, cuts)), mean)
YENDWF1.hh <-
aggregate(DT$YENDWF1, list(cut(DT$date_time, cuts)), mean)
YSWF1.hh <- aggregate(DT$YSWF1, list(cut(DT$date_time, cuts)), mean)
ARWF1.hh <- as.data.table(ARWF1.hh)
BALDHWF1.hh <- as.data.table(BALDHWF1.hh)
BULGANA1.hh <- as.data.table(BULGANA1.hh)
CHALLHWF.hh <- as.data.table(CHALLHWF.hh)
CHYTWF1.hh <- as.data.table(CHYTWF1.hh)
CROWLWF1.hh <- as.data.table(CROWLWF1.hh)
DUNDWF1.hh <- as.data.table(DUNDWF1.hh)
DUNDWF2.hh <- as.data.table(DUNDWF2.hh)
DUNDWF3.hh <- as.data.table(DUNDWF3.hh)
ELAINWF1.hh <- as.data.table(ELAINWF1.hh)
KIATAWF1.hh <- as.data.table(KIATAWF1.hh)
MACARTH1.hh <- as.data.table(MACARTH1.hh)
MERCER01.hh <- as.data.table(MERCER01.hh)
MLWF1.hh <- as.data.table(MLWF1.hh)
MTGELWF1.hh <- as.data.table(MTGELWF1.hh)
MUWAWF1.hh <- as.data.table(MUWAWF1.hh)
OAKLAND1.hh <- as.data.table(OAKLAND1.hh)
PORTWF.hh <- as.data.table(PORTWF.hh)
SALTCRK1.hh <- as.data.table(SALTCRK1.hh)
WAUBRAWF.hh <- as.data.table(WAUBRAWF.hh)
YAMBUKWF.hh <- as.data.table(YAMBUKWF.hh)
YENDWF1.hh <- as.data.table(YENDWF1.hh)
YSWF1.hh <- as.data.table(YSWF1.hh)
ARWF1.hh[, date_time := ymd_hms(ARWF1.hh[["Group.1"]])]
BALDHWF1.hh[, date_time := ymd_hms(BALDHWF1.hh[["Group.1"]])]
BULGANA1.hh[, date_time := ymd_hms(BULGANA1.hh[["Group.1"]])]
CHALLHWF.hh[, date_time := ymd_hms(CHALLHWF.hh[["Group.1"]])]
CHYTWF1.hh[, date_time := ymd_hms(CHYTWF1.hh[["Group.1"]])]
CROWLWF1.hh[, date_time := ymd_hms(CROWLWF1.hh[["Group.1"]])]
DUNDWF1.hh[, date_time := ymd_hms(DUNDWF1.hh[["Group.1"]])]
DUNDWF2.hh[, date_time := ymd_hms(DUNDWF2.hh[["Group.1"]])]
DUNDWF3.hh[, date_time := ymd_hms(DUNDWF3.hh[["Group.1"]])]
ELAINWF1.hh[, date_time := ymd_hms(ELAINWF1.hh[["Group.1"]])]
KIATAWF1.hh[, date_time := ymd_hms(KIATAWF1.hh[["Group.1"]])]
MACARTH1.hh[, date_time := ymd_hms(MACARTH1.hh[["Group.1"]])]
MERCER01.hh[, date_time := ymd_hms(MERCER01.hh[["Group.1"]])]
MLWF1.hh[, date_time := ymd_hms(MLWF1.hh[["Group.1"]])]
MTGELWF1.hh[, date_time := ymd_hms(MTGELWF1.hh[["Group.1"]])]
MUWAWF1.hh[, date_time := ymd_hms(MUWAWF1.hh[["Group.1"]])]
OAKLAND1.hh[, date_time := ymd_hms(OAKLAND1.hh[["Group.1"]])]
PORTWF.hh[, date_time := ymd_hms(PORTWF.hh[["Group.1"]])]
SALTCRK1.hh[, date_time := ymd_hms(SALTCRK1.hh[["Group.1"]])]
WAUBRAWF.hh[, date_time := ymd_hms(WAUBRAWF.hh[["Group.1"]])]
YAMBUKWF.hh[, date_time := ymd_hms(YAMBUKWF.hh[["Group.1"]])]
YENDWF1.hh[, date_time := ymd_hms(YENDWF1.hh[["Group.1"]])]
YSWF1.hh[, date_time := ymd_hms(YSWF1.hh[["Group.1"]])]
DTT <-
cbind(
ARWF1.hh$date_time,
ARWF1.hh[, 2],
BALDHWF1.hh[, 2],
BULGANA1.hh[, 2],
CHALLHWF.hh[, 2],
CHYTWF1.hh[, 2],
CROWLWF1.hh[, 2],
DUNDWF1.hh[, 2],
DUNDWF2.hh[, 2],
DUNDWF3.hh[, 2],
ELAINWF1.hh[, 2],
KIATAWF1.hh[, 2],
MACARTH1.hh[, 2],
MERCER01.hh[, 2],
MLWF1.hh[, 2],
MTGELWF1.hh[, 2],
MUWAWF1.hh[, 2],
OAKLAND1.hh[, 2],
PORTWF.hh[, 2],
SALTCRK1.hh[, 2],
WAUBRAWF.hh[, 2],
YAMBUKWF.hh[, 2],
YENDWF1.hh[, 2],
YSWF1.hh[, 2]
)
DTT <- as.data.table(DTT)
# 23 wind farms in Victoria
#ARWF1 BALDHWF1 BULGANA1 CHALLHWF CHYTWF1 CROWLWF1 DUNDWF1 DUNDWF2 DUNDWF3
#ELAINWF1 KIATAWF1 MACARTH1 MERCER01 MLWF1 MTGELWF1 MUWAWF1 OAKLAND1
#PORTWF SALTCRK1 WAUBRAWF YAMBUKWF YENDWF1 YSWF1
DTT <-
setnames(
DTT,
c(
"date_time",
"ARWF1",
"BALDHWF1",
"BULGANA1",
"CHALLHWF",
"CHYTWF1",
"CROWLWF1",
"DUNDWF1",
"DUNDWF2",
"DUNDWF3",
"ELAINWF1",
"KIATAWF1",
"MACARTH1",
"MERCER01",
"MLWF1",
"MTGELWF1",
"MUWAWF1",
"OAKLAND1",
"PORTWF",
"SALTCRK1",
"WAUBRAWF",
"YAMBUKWF",
"YENDWF1",
"YSWF1"
)
)
################################################################################################
# Regional reference prices
pricedat<-EStatesPrices()
#############################################################################################
# Merge price and volumes
DT.all <- merge(pricedat, DTT, by = "date_time")
DT.all <- as.data.table(DT.all)
#ARWF1 BALDHWF1 BULGANA1 CHALLHWF CHYTWF1 CROWLWF1 DUNDWF1 DUNDWF2 DUNDWF3
#ELAINWF1 KIATAWF1 MACARTH1 MERCER01 MLWF1 MTGELWF1 MUWAWF1 OAKLAND1
#PORTWF SALTCRK1 WAUBRAWF YAMBUKWF YENDWF1 YSWF1
DT.all$ARWF1 <- nafill(DT.all$ARWF1, "const", fill = 0)
DT.all$BALDHWF1 <- nafill(DT.all$BALDHWF1, "const", fill = 0)
DT.all$BULGANA1 <- nafill(DT.all$BULGANA1, "const", fill = 0)
DT.all$CHALLHWF <- nafill(DT.all$CHALLHWF, "const", fill = 0)
DT.all$CHYTWF1 <- nafill(DT.all$CHYTWF1, "const", fill = 0)
DT.all$CROWLWF1 <- nafill(DT.all$CROWLWF1, "const", fill = 0)
DT.all$DUNDWF1 <- nafill(DT.all$DUNDWF1, "const", fill = 0)
DT.all$DUNDWF2 <- nafill(DT.all$DUNDWF2, "const", fill = 0)
DT.all$DUNDWF3 <- nafill(DT.all$DUNDWF3, "const", fill = 0)
DT.all$ELAINWF1 <- nafill(DT.all$ELAINWF1, "const", fill = 0)
DT.all$KIATAWF1 <- nafill(DT.all$KIATAWF1, "const", fill = 0)
DT.all$MACARTH1 <- nafill(DT.all$MACARTH1, "const", fill = 0)
DT.all$MERCER01 <- nafill(DT.all$MERCER01, "const", fill = 0)
DT.all$MLWF1 <- nafill(DT.all$MLWF1, "const", fill = 0)
DT.all$MTGELWF1 <- nafill(DT.all$MTGELWF1, "const", fill = 0)
DT.all$MUWAWF1 <- nafill(DT.all$MUWAWF1, "const", fill = 0)
DT.all$OAKLAND1 <- nafill(DT.all$OAKLAND1, "const", fill = 0)
DT.all$PORTWF <- nafill(DT.all$PORTWF, "const", fill = 0)
DT.all$SALTCRK1 <- nafill(DT.all$SALTCRK1, "const", fill = 0)
DT.all$WAUBRAWF <- nafill(DT.all$WAUBRAWF, "const", fill = 0)
DT.all$YAMBUKWF <- nafill(DT.all$YAMBUKWF, "const", fill = 0)
DT.all$YENDWF1 <- nafill(DT.all$YENDWF1, "const", fill = 0)
DT.all$YSWF1 <- nafill(DT.all$YSWF1, "const", fill = 0)
#ARWF1 BALDHWF1 BULGANA1 CHALLHWF CHYTWF1 CROWLWF1 DUNDWF1 DUNDWF2 DUNDWF3
#ELAINWF1 KIATAWF1 MACARTH1 MERCER01 MLWF1 MTGELWF1 MUWAWF1 OAKLAND1
#PORTWF SALTCRK1 WAUBRAWF YAMBUKWF YENDWF1 YSWF1
# price x gen for each gen
DT.all[, ARWF1.genp := ARWF1 * VIC1]
DT.all[, BALDHWF1.genp := BALDHWF1 * VIC1]
DT.all[, BULGANA1.genp := BULGANA1 * VIC1]
DT.all[, CHALLHWF.genp := CHALLHWF * VIC1]
DT.all[, CHYTWF1.genp := CHYTWF1 * VIC1]
DT.all[, CROWLWF1.genp := CROWLWF1 * VIC1]
DT.all[, DUNDWF1.genp := DUNDWF1 * VIC1]
DT.all[, DUNDWF2.genp := DUNDWF2 * VIC1]
DT.all[, DUNDWF3.genp := DUNDWF3 * VIC1]
DT.all[, ELAINWF1.genp := ELAINWF1 * VIC1]
DT.all[, KIATAWF1.genp := KIATAWF1 * VIC1]
DT.all[, MACARTH1.genp := MACARTH1 * VIC1]
DT.all[, MERCER01.genp := MERCER01 * VIC1]
DT.all[, MLWF1.genp := MLWF1 * VIC1]
DT.all[, MTGELWF1.genp := MTGELWF1 * VIC1]
DT.all[, MUWAWF1.genp := MUWAWF1 * VIC1]
DT.all[, OAKLAND1.genp := OAKLAND1 * VIC1]
DT.all[, PORTWF.genp := PORTWF * VIC1]
DT.all[, SALTCRK1.genp := SALTCRK1 * VIC1]
DT.all[, WAUBRAWF.genp := WAUBRAWF * VIC1]
DT.all[, YAMBUKWF.genp := YAMBUKWF * VIC1]
DT.all[, YENDWF1.genp := YENDWF1 * VIC1]
DT.all[, YSWF1.genp := YSWF1 * VIC1]
# CETF Portfolio
DT.all[, CETF := 0.29 * CHALLHWF + 0.30 * CROWLWF1 + 0.119 * MACARTH1 +
0.881 * OAKLAND1 + 0.11 * PORTWF + 0.26 * WAUBRAWF + 0.83 * YAMBUKWF + 0.40 *
YSWF1]
DT.all[, AGL := 0.881 * OAKLAND1 + 0.119 * MACARTH1]
DT.all[, PacHydro := 0.29 * CHALLHWF + 0.30 * CROWLWF1 + 0.11 * PORTWF +
0.83 * YAMBUKWF + 0.40 * YSWF1]
DT.all[, Naive := 1 / 23 * (
ARWF1 + BALDHWF1 + BULGANA1 + CHALLHWF + CHYTWF1 + CROWLWF1 + DUNDWF1 +
DUNDWF2 + DUNDWF3 + ELAINWF1 + KIATAWF1 + MACARTH1 +
MERCER01 + MLWF1 + MTGELWF1 +
MUWAWF1 + OAKLAND1 + PORTWF + SALTCRK1 + WAUBRAWF +
YAMBUKWF + YENDWF1 + YSWF1
)]
DT.all[, CETF.100mw := AGL * VIC1]
DT.all[, CETF.PacHydro := PacHydro * VIC1]
DT.all[, CETF.250mw := CETF * VIC1]
DT.all[, VicWind := Naive * VIC1]
DT.all[, Day := day(date_time)]
DT.all[, Mth := month(date_time)]
DT.all[, Year := year(date_time)]
DT.all[, Hour := hour(date_time)]
DT.all[, Week := week(date_time)]
#DT.all[, Quart := quarter(date_time, with_year = TRUE, fiscal_start = 1)]
dwap.100 <- DT.all[, .(sum(CETF.100mw) / sum(AGL)), by = .(Mth, Year)]
dwap.250 <- DT.all[, .(sum(CETF.250mw) / sum(CETF)), by = .(Mth, Year)]
dwap.PacHydro <-
DT.all[, .(sum(CETF.PacHydro) / sum(PacHydro)), by = .(Mth, Year)]
dwap.VicWind <- DT.all[, .(sum(VicWind) / sum(Naive)), by = .(Mth, Year)]
#dwap.with.blunt <- DT.all[, .(sum(CETF.100mw)+387]
dwap.ARWF1 <- DT.all[, .(sum(ARWF1.genp) / sum(ARWF1)), by = .(Mth, Year)]
dwap.BALDHWF1 <-
DT.all[, .(sum(BALDHWF1.genp) / sum(BALDHWF1)), by = .(Mth, Year)]
dwap.BULGANA1 <-
DT.all[, .(sum(BULGANA1.genp) / sum(BULGANA1)), by = .(Mth, Year)]
dwap.CHALLHWF <-
DT.all[, .(sum(CHALLHWF.genp) / sum(CHALLHWF)), by = .(Mth, Year)]
dwap.CHYTWF1 <-
DT.all[, .(sum(CHYTWF1.genp) / sum(CHYTWF1)), by = .(Mth, Year)]
dwap.CROWLWF1 <-
DT.all[, .(sum(CROWLWF1.genp) / sum(CROWLWF1)), by = .(Mth, Year)]
dwap.DUNDWF1 <-
DT.all[, .(sum(DUNDWF1.genp) / sum(DUNDWF1)), by = .(Mth, Year)]
dwap.DUNDWF2 <-
DT.all[, .(sum(DUNDWF2.genp) / sum(DUNDWF2)), by = .(Mth, Year)]
dwap.DUNDWF3 <-
DT.all[, .(sum(DUNDWF3.genp) / sum(DUNDWF3)), by = .(Mth, Year)]
dwap.ELAINWF1 <-
DT.all[, .(sum(ELAINWF1.genp) / sum(ELAINWF1)), by = .(Mth, Year)]
dwap.KIATAWF1 <-
DT.all[, .(sum(KIATAWF1.genp) / sum(KIATAWF1)), by = .(Mth, Year)]
dwap.MACARTH1 <-
DT.all[, .(sum(MACARTH1.genp) / sum(MACARTH1)), by = .(Mth, Year)]
dwap.MERCER01 <-
DT.all[, .(sum(MERCER01.genp) / sum(MERCER01)), by = .(Mth, Year)]
dwap.MLWF1 <- DT.all[, .(sum(MLWF1.genp) / sum(MLWF1)), by = .(Mth, Year)]
dwap.MTGELWF1 <-
DT.all[, .(sum(MTGELWF1.genp) / sum(MTGELWF1)), by = .(Mth, Year)]
dwap.MUWAWF1 <-
DT.all[, .(sum(MUWAWF1.genp) / sum(MUWAWF1)), by = .(Mth, Year)]
dwap.OAKLAND1 <-
DT.all[, .(sum(OAKLAND1.genp) / sum(OAKLAND1)), by = .(Mth, Year)]
dwap.PORTWF <- DT.all[, .(sum(PORTWF.genp) / sum(PORTWF)), by = .(Mth, Year)]
dwap.SALTCRK1 <-
DT.all[, .(sum(SALTCRK1.genp) / sum(SALTCRK1)), by = .(Mth, Year)]
dwap.WAUBRAWF <-
DT.all[, .(sum(WAUBRAWF.genp) / sum(WAUBRAWF)), by = .(Mth, Year)]
dwap.YAMBUKWF <-
DT.all[, .(sum(YAMBUKWF.genp) / sum(YAMBUKWF)), by = .(Mth, Year)]
dwap.YENDWF1 <-