252 lines
16 KiB
R
252 lines
16 KiB
R
#A script which attempts to pull in all data, and create a data frame with the maximum revenue values for each facility, year and discount rate. The output can then be used to make figures and graphs
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library(tidyverse)
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library(parallel)
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NCORES <- detectCores()-1
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library(lpSolve) #For solving discrete value maximization for the power plants
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####Manual inputs
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#DISCOUNT_RATE_LIST <- seq(0,1,by=0.0025)
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DISCOUNT_RATE_LIST <- c(0.03,0.0325,0.035,0.0375,0.04,0.045,0.0475,0.05,0.07,0.1)
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SHIPPING_COST_PER_TON <- 1.2874*26000 #Inflation adjusted from New Mexico Report
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CV1 <- 1.3074*(10607030-1060703) #Data from Texas Report, converted from 2018 to Dec 2025 dollars
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#CV2 <- 1.2874*(6984013-1117442) #Data from New Mexico Report, Converted from 2019 to Dec 2025
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CV2 <- 1.2874*(6984013) #Data from New Mexico Report, Converted from 2019 to Dec 2025
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CV2_OP <- 1.2874*1117442
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CV3 <- mean(CV1,CV2) #Average of the two
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###################################Cost results
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#source("Cost_Data_Proc.r")
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CIFS <- rbind(readRDS("Data/Cleaned_Data/Texas_CIFS_Costs.Rds"),readRDS("Data/Cleaned_Data/New_Mexico_CIFS_Costs.Rds"))
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#Adjust for inflation
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CIFS[,-1:-5] <- 1.2874*CIFS[,-1:-5]
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COSTS <- do.call(rbind,lapply(DISCOUNT_RATE_LIST ,function(DISCOUNT){CIFS %>% group_by(Location,Capacity,Cost_Assumption) %>% mutate(NPC=Total/(1+DISCOUNT)^Year) %>% summarize(Discount=DISCOUNT,Costs=sum(NPC))})) %>% ungroup
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TEMP <- COSTS%>% group_by(Location,Cost_Assumption,Discount) %>% summarize(ST_COST=min(Costs),ST_CAPACITY=min(Capacity),M_COST=(max(Costs)-min(Costs))/(max(Capacity)-min(Capacity))) %>% ungroup
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CAPACITY_INCREMENT <- 5000
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#rm(COST_DATA)
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for(i in 1:nrow(TEMP)){
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LOC <- as.character(TEMP[i,"Location"])
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COST_LEVEL <- as.character(TEMP[i,"Cost_Assumption"])
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DISCOUNT <- as.numeric(TEMP[i,"Discount"])
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ST_CAP <- as.numeric(TEMP[i,"ST_CAPACITY"])
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ST_COST <- as.numeric(TEMP[i,"ST_COST"])
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COST_SLOPE <- as.numeric(TEMP[i,]$M_COST)
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CAPACITY <- seq(ST_CAP,160000,by=5000)
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COST <- ST_COST+COST_SLOPE*CAPACITY_INCREMENT*(0:(length(CAPACITY)-1))
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C_RES <- cbind(CAPACITY,COST) %>% as_tibble %>% mutate(Location=LOC,Cost_Assumption=COST_LEVEL,Discount=DISCOUNT) %>% select(Location,Cost_Assumption,Discount,Capacity=CAPACITY,Cost=COST)
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if(!exists("COST_DATA")){COST_DATA <- C_RES}else{COST_DATA<- rbind(COST_DATA,C_RES)}
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}
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saveRDS(COST_DATA ,"Data/Cleaned_Data/All_CIFS_Discounted_Costs.Rds")
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#COST_DATA <- COST_DATA %>% filter(Cost_Assumption=='Average') %>% select(-Cost_Assumption) %>% unique
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COST_DATA <- COST_DATA %>% filter(Cost_Assumption=='High') %>% select(-Cost_Assumption) %>% unique
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##All unique capacity levels that the revenues need to be calculated for
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CAPACITY_LIST <- COST_DATA %>% pull(Capacity) %>% unique
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###
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TOTAL <- read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv") %>% mutate(OP_YEAR=year(Op_Date_Min),CLOSE_YEAR=year(Close_Date_Max))%>% select(Facility,Total_Assemblies,Total_Tons,OP_YEAR,CLOSE_YEAR)
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Duane Arnold
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Nine Mile Point
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Ginna
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FACILITY_LIST <- TOTAL %>% pull(Facility)
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#https://www.nrc.gov/reactors/operating/licensing/renewal/subsequent-license-renewal
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SUBMITTED <-rbind(c(FACILITY_LIST[str_detect(FACILITY_LIST,"Duane*" )],2025),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Nine Mile*" )],2026),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Ginna*" )],2026),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Cooper*" )],2026),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Farley*" )],2027),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Prairie*" )],2027),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Brunswick*" )],2027),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Cook" )],2027),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Hope" )],2027),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Salem" )],2027),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Perry" )],2027),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Millstone" )],2028),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Palisades" )],2028),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Beaver" )],2028),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Callaway" )],2029),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Three Mile Island" )],2029),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Davis-Besse" )],2029),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Wolf" )],2030),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Lucie" )],2021),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Robinson" )],2025),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Hatch" )],2025))
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SUBMITTED <- SUBMITTED %>% as_tibble
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colnames(SUBMITTED ) <- c("Facility","App_Date","Status")
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SUBMITTED <- SUBMITTED%>% mutate(Status="Applied",App_Date=as.numeric(App_Date)) %>% select(Facility,Status,App_Date)
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#Issued
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RENEWED <- rbind(c(FACILITY_LIST[str_detect(FACILITY_LIST,"Turkey" )],"Granted",2018,2033),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Peach" )],"Granted",2019,2034),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Surry" )],"Granted",2020,2033),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"North" )],"Granted",2021,2040),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Monticello" )],"Granted",2024,2030),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Oconee" )],"Granted",2025,2034),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Summer" )],"Granted",2025,2042),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Beach" )],"Granted",2025,2033),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Browns" )],"Granted",2025,2036),
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c(FACILITY_LIST[str_detect(FACILITY_LIST,"Dresden" )],"Granted",2025,2031))
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RENEWED <- RENEWED %>% as_tibble
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colnames(RENEWED) <- c("Facility","Status","App_Date","Op_Date")
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RENEWED <- RENEWED %>% mutate(Op_Date=as.numeric(Op_Date),App_Date=as.numeric(App_Date))
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AVG_LENGTH <- RENEWED %>% mutate(DIFF=Op_Date-App_Date) %>% pull(DIFF) %>% mean %>% round
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SUBMITTED <- SUBMITTED %>% mutate(Op_Date=App_Date+AVG_LENGTH)
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UPDATE <- rbind(RENEWED,SUBMITTED )
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UPDATE
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str_detect()
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TOTAL %>% print(n=50)
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#!!!!!!!!!!!!#####TEST OF VARIABLITY IN CLOSING YEAR, CHANGE OR FORMALIZE LATER
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#TOTAL <- TOTAL %>% mutate(CLOSE_YEAR=ifelse(CLOSE_YEAR>2018,CLOSE_YEAR+20,CLOSE_YEAR))
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###########Series of functions to calculate the gross consumer surplus from a CIFS for each facility, in each year from 1960 to 2082.
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#Function to find the net present revenue of a facility,given a discount rate, and the current year, and year the facility will close. This is the sum of discounted costs that WOULD have taken place if the facility was not built
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VALUE_ADD <- function(r,CURRENT_YEAR,CLOSE_YEAR){
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Years_Until_Close <- max(CLOSE_YEAR-CURRENT_YEAR+1,0)
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VALUES <- (1+r)^-(1:10^4)
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if(Years_Until_Close==0){return(sum(VALUES))} else{return(sum(VALUES[-1:-Years_Until_Close]))}
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}
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#A function to extend the revenues calculations to the closure date of all of the facilities.
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VALUE_ADD_SINGLE_YEAR <- function(r,CURRENT_YEAR,CLOSE_YEARS){return(sapply(CLOSE_YEARS,function(x){VALUE_ADD(r,CURRENT_YEAR,x)}))}
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#A function to extend the calculation of the net present revenues of each facility to all years of interest. That is what is the NPV of building the facility in each year, for each facility.
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NPV_CALC <- function(r,DATA=TOTAL,YEARS=1960:2083){
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Facility <- pull(DATA,Facility)
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RES <- cbind(Facility,do.call(cbind,lapply(YEARS,function(x){VALUE_ADD_SINGLE_YEAR(r,x,DATA$CLOSE_YEAR)})))
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colnames(RES) <- c("Facility",YEARS)
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RES <- as_tibble(RES) %>% pivot_longer(cols=-Facility,names_to="Year",values_to="Revenue") %>% arrange(Year) %>% mutate(Year=parse_number(Year),Revenue=parse_number(Revenue),Discount=r)
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return(RES)
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}
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#A function which returns a list, of net present revenue calculation tables (facility by year) for a range of possible discount rates. This allows for the results to be quickly looked up, when we want to adjust the time value of money. These results combine costs savings to calculate NPV
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MULTI_DISCOUNT_RATE_NPV <- function(DISCOUNT_INCREMENT,DATA=TOTAL,YEARS=1960:2083,DOLLARS_SAVED_PER_YEAR){
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NCORES <- detectCores()-1
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RES <- mclapply(DISCOUNT_INCREMENT,NPV_CALC,mc.cores = NCORES)
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RES <- do.call(rbind,RES) %>% mutate(Revenue=Revenue*DOLLARS_SAVED_PER_YEAR)
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return(RES)
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}
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TOTAL_VALUE_METRICS <- MULTI_DISCOUNT_RATE_NPV(DISCOUNT_RATE_LIST ,DOLLARS_SAVED_PER_YEAR=CV2)
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#TOTAL_VALUE_METRICS <- TOTAL_VALUE_METRICS %>% filter(!(Facility %in% c("Palo Verde","Vogtle")))#These facilities always have a negative NPV by the end date 2083
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TOTAL_VALUE_METRICS <- TOTAL_VALUE_METRICS %>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% select(Year,Facility,Discount,Total_Tons,Revenue)
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#Function to find the maximum Revenue BUT it does not take dollars just relative savings
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MAX_REV <- function(YEAR,TBL,DISCOUNT,CIFS_SIZE,SHIPPING_COST=0){
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TBL <- TBL %>% filter(Year==YEAR,Discount==DISCOUNT,Revenue/Total_Tons>SHIPPING_COST)
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REV <- TBL %>% pull(Revenue)
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VOL <- TBL %>% pull(Total_Tons)
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RES <- lp(direction = "max", objective.in = REV, const.mat = matrix(VOL, nrow = 1),const.dir = "<=", const.rhs = CIFS_SIZE, all.bin = TRUE)
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return(RES[[11]])
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}
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#Calculate all results for the start year to the end year. Discount rate, and capacities are fixed.
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YEARLY_RESULTS <- function(DATA,DISCOUNT,CAPACITY,SHIPPING_COSTS=0){
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RES <- cbind(1960:2083,sapply(1960:2083, function(x){MAX_REV(YEAR=x,TBL=DATA,DISCOUNT,CAPACITY,SHIPPING_COSTS)})) %>% as_tibble
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colnames(RES) <- c("Year","Revenue")
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RES <- RES %>% mutate(Discount=DISCOUNT,Capacity=CAPACITY) %>% select(Year,Discount,Capacity,Revenue) %>% mutate(Shipping_Cost_Cuttoff=SHIPPING_COSTS)
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return(RES)
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}
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#Calculate and individual facilities rate, for all discount rates, and all years
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Rev_No_Shipping <- do.call(rbind,mclapply(CAPACITY_LIST,function(CAPACITY){do.call(rbind,lapply(DISCOUNT_RATE_LIST,function(x){YEARLY_RESULTS(TOTAL_VALUE_METRICS,x,CAPACITY,0)}))},mc.cores=min(length(CAPACITY_LIST),NCORES)))
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saveRDS(Rev_No_Shipping,"Data/Cleaned_Data/Model_Estimates.Rds")
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TEST <- Rev_No_Shipping %>% left_join(COST_DATA) %>% mutate(Profit=Revenue-Cost) %>% group_by(Discount,Capacity,Location) %>% mutate(Time_Benefit=(1-Discount)*(lead(Profit)-Profit),Op_Cost=Discount*Profit,Marginal=Time_Benefit-Op_Cost) %>% unique
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TEST <- TEST %>% mutate(Current_Profit=Profit/((1+Discount)^(Year-2026))) %>% ungroup
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TEST <- TEST %>% group_by(Year,Location,Discount) %>% filter(Current_Profit==max(Current_Profit)) %>% ungroup
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TEST %>% select(Profit,Current_Profit,Year,Discount) %>% tail
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TEST %>% group_by(Location,Discount) %>% filter(Current_Profit==max(Current_Profit)) %>% print(n=100) %>%
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TEMP <- TEST %>% group_by(Location,Discount) %>% select(Year,Capacity,Profit,Current_Profit) %>% mutate(Delay_Profit=(lead(Current_Profit)-Current_Profit)/10^6)
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TEMP %>% filter(Year>=2025) %>% summarize(Optimal_Year=sum(ifelse(Current_Profit==max(Current_Profit),Year,0)),Max_Profit=max(Current_Profit),Current_Profit=sum(ifelse(Year==2026,Current_Profit,0)),Yearly_Loss=(Max_Profit-Current_Profit)/(2026-Optimal_Year)/10^6,Next_Years_Loss=(Max_Profit-sum(ifelse(Year==(Optimal_Year+1),Current_Profit,0)))/10^6)
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TEST %>% group_by(Location,Discount,
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ggplot(TEST %>% filter(Discount==0.03) ,aes(x=Year,y=Current_Profit/10^9,color=Capacity))+geom_line()+facet_wrap(Discount~Location,ncol=1)+scale_x_continuous(breaks=seq(1960,2083,by=5))
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ggplot(TEST %>% filter(Discount==0.05) ,aes(x=Year,y=Current_Profit/10^9,color=Capacity))+geom_line()+facet_wrap(Discount~Location,ncol=1)+scale_x_continuous(breaks=seq(1960,2083,by=5))
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ggplot(TEST %>% filter(Discount %in% c(0.03,0.05,0.07) ,Year>1970) ,aes(x=Year,y=Current_Profit/10^9,color=Capacity))+geom_line()+facet_wrap(Discount~Location,ncol=2)+scale_x_continuous(breaks=seq(1960,2083,by=5))
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ggplot(TEST %>% filter(Discount %in% c(0.05) ,Year>1970) ,aes(x=Year,y=Profit/10^9,color=Location))+geom_line()+scale_x_continuous(breaks=seq(1960,2083,by=5))
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ggplot(TEST %>% filter(Discount %in% c(0.03,0.05,0.07),Year>1970,Year<2050) ,aes(x=Year,y=Marginal/10^6))+ geom_area(aes(y=ifelse(Marginal>0,Marginal/10^6,0)),fill="lightgreen",alpha=0.6)+ geom_area(aes(y=ifelse(Marginal<0,Marginal/10^6,0)),fill="firebrick2",alpha=0.5) +facet_wrap(Discount~Location,ncol=2,scales = "free_y")+scale_x_continuous(breaks=seq(1960,2083,by=5))+scale_y_continuous(breaks=seq(-300,50,by=50))+geom_hline(yintercept=0)+theme_bw()+ylab("Profit Change (Million Dollars)")
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+geom_line()+facet_wrap(Discount~Location,ncol=2,scales = "free_y")+scale_x_continuous(breaks=seq(1960,2083,by=5))+geom_hline(yintercept=0)+theme_bw()+geom_area(fill="green")
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ggplot(TEST %>% filter(Year>2000) ,aes(x=Year,y=Marginal/10^9,color=Capacity))+geom_line()+facet_wrap(Discount~Location,ncol=2,scales = "free_y")+scale_x_continuous(breaks=seq(1960,2083,by=5))
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ggplot(TEST ,aes(x=Year,y=Current_Profit/10^9,color=Capacity))+geom_line()+facet_wrap(Location~Discount)
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TEST2 <- TEST %>% filter(Discount==0.05,Location=='Texas')
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ggplot(TEST2,aes(x=Year,y=Marginal,group=as.factor(Capacity),color=as.factor(Capacity)))+geom_line()
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#Calculate the net present revenue of all facilities that can CURRENTLY be shipped to the site with a profit. Note that the total Revenue is more important because most of the sites will be willing to pay for the right to CIFS storage in the future even if the shipping costs are too high presently. This result is used to show what might be the current ideal facility size, even if future expansion is expected to maximize profit.
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Rev_Shipping <- do.call(rbind,mclapply(CAPACITY_LIST,function(CAPACITY){do.call(rbind,lapply(DISCOUNT_RATE_LIST,function(x){YEARLY_RESULTS(TOTAL_VALUE_METRICS,x,CAPACITY,SHIPPING_COST_PER_TON )}))},mc.cores=min(length(CAPACITY_LIST),NCORES)))
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Revenue_Results <- rbind(Rev_No_Shipping,Rev_Shipping) %>% mutate(Type=ifelse(Shipping_Cost_Cuttoff==0,"Revenue","Revenue_Shipping")) %>% pivot_wider(values_from=Revenue,names_from=Type) %>% select(-Shipping_Cost_Cuttoff) %>% group_by(Year,Discount,Capacity) %>% summarize(Revenue=mean(Revenue,na.rm=TRUE),Revenue_Shipping=mean(Revenue_Shipping,na.rm=TRUE)) %>% ungroup
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Revenue_Results
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####################################################
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COSTS <- rbind(COSTS,EXTENDED_CAPACITY_NEW_MEXICO)
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COST_DATA %>% pull(Cost_Assumption) %>% unique
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saveRDS(COSTS,"Data/Cleaned_Data/All_CIFS_Discounted_Costs.Rds")
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COSTS %>% group_by(Location,Capac
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CIFS_Data <- Revenue_Results %>% left_join(COSTS) %>% mutate(Profit=Revenue-Costs,Profit_Shipping=Revenue_Shipping-Costs) %>% select(Year,Location,Capacity,Discount,Revenue,Costs,Profit,Revenue_Shipping,Profit_Shipping,everything())
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#CIFS_Data <-
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CIFS_Data <- CIFS_Data %>% group_by(Location,Capacity,Discount) %>% arrange(Location,Capacity,Discount,Year) %>% mutate(Time_Benefit=lead(Profit)-Profit,Op_Cost=Profit*Discount,Marginal=Time_Benefit-Op_Cost) %>% ungroup
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TEMP <- CIFS_Data %>% filter(Discount==0.05) %>% mutate(Time_Benefit=lead(Profit)-Profit,Op_Cost=Profit*Discount,Marginal=Time_Benefit-Op_Cost)
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CIFS_Data <- CIFS_Data %>% group_by(Location,Phase,Capacity,Discount) %>% mutate(Time_Benefit=(1-Discount)*(lead(Profit)-Profit),Op_Cost=Profit*Discount,Marginal=Time_Benefit-Op_Cost)
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((-3504542954)-(-3530861857))-0.05*(-3530861857 )
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STARTING_YEARS <- CIFS_Data %>% group_by(Location,Phase,Capacity,Discount) %>% mutate(PROFITABLE=Profit>0 & Marginal<=0) %>% filter(PROFITABLE) %>% filter(Year==min(Year)) %>% select(Location,Phase,Capacity,Discount,Start_Year=Year,Profit) %>% ungroup
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CIFS_Data %>% mutate(Current_Profit=Profit/((1+Discount)^(Year-2026))) %>% group_by(Location,Discount,Capacity) %>% filter(Current_Profit==max(Current_Profit)) %>% select(Start_Year=Year,Location,Phase,Capacity,Current_Profit) %>% ungroup %>% arrange(Discount,Location,desc(Current_Profit)) %>% select(Discount,Location,Phase,Start_Year,Current_Profit)
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#############
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ggplot(CIFS_Data ,aes(x=Year,y=Marginal/10^6,group=Capacity,color=as.factor(Capacity)))+geom_line()+facet_wrap(~Discount,ncol=1)+scale_x_continuous(breaks=seq(1960,2083,by=5))
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ggplot(CIFS_Data ,aes(x=Year,y=Marginal,group=Capacity,color=as.factor(Capacity)))+geom_line()+facet_grid(~Discount)
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dir.create("./Results",showWarnings=FALSE)
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saveRDS(TOTAL_VALUE_METRICS,"./Results/Storage_Values_by_Facility_and_Variable_Discounts.Rds")
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