Added non-linear results. Working to clean code
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Alex
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2d2de480df
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63
Analysis.r
63
Analysis.r
@ -1,6 +1,7 @@
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library(tidyverse)
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library(scales)
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library(RcppRoll)
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library(lpSolve)
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RES <- readRDS("Results/Storage_Values_by_Facility_and_Variable_Discounts.Rds")
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RES_REDUCED_FEE <- RES
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RES_INCREASED_FEE <- RES
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@ -9,36 +10,52 @@ CV1 <- 1.3074*(10607030-1060703) #Data from Texas Report, converted from 2018 to
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CV2 <- 1.2874*(6984013-1117442) #Data from New Mexico Report, Converted from 2019 to Dec 2025
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LENGTH <- length(RES)
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SUPPLY_RESULTS <- list()
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SUPPLY_RESULTS_WITH_SHIPPING <- list() #This shows the amount availble to ship today, the SUPPLY_RESULTS shows the demand to have a CIFS available sometime in the future (present value brought forward with a discount). This SUPPLY_RESULTS_WITH_SHIPPING shows only SNF that has a current Net Present Cost high enough to exceed the shipping costs.
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SHIPPING_COST <- 1.2874*26000 #Inflation adjusted from New Mexico Report
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TEST <- RES$Per_5%>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% mutate(Marginal_Value=Revenue/Total_Tons) %>% select(Year,Facility,Marginal_Value,Total_Tons) %>% group_by(Year) %>% arrange(Year,desc(Marginal_Value)) %>% mutate(Q=cumsum(Total_Tons)) %>% mutate(IN=Q<10000) %>% mutate(MIN=ZZMIN
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TEST %>% filter(IN) %>% filter(Marginal_Value==min(Marginal_Value)) %>% print(n=100)
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TEST %>% print(n=30)
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TEST %>% arrange(Year,Marginal_Value)
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TEST
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TEST %>% filter(IN) %>% filter(Marginal_Value )
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TEST <- RES[[1]]%>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% select(Facility,Year,Revenue,Total_Tons) %>% mutate(Marginal=Revenue/Total_Tons) %>% group_by(Year) %>% arrange(Marginal) %>% mutate(IN=cumsum(Total_Tons)<10000)
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TEST
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for(i in 1:LENGTH){
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RES[[i]] <- RES[[i]] %>% filter(!(Facility %in% c("Palo Verde","Vogtle"))) #The shipping cost is higher than the marginal value per SNF at these locations in the lat period of study 2083.
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RES[[i]]$Revenue <-CV1*as.numeric(RES[[i]]$Revenue)
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RES[[i]]$Year<-as.numeric(RES[[i]]$Year)
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RES[[i]]$Revenue <- CV1*as.numeric(RES[[i]]$Revenue)
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RES[[i]]$Year <- as.numeric(RES[[i]]$Year)
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DISCOUNT <- as.numeric(gsub("Per_","",names(RES)[i]))/100
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SUPPLY_RESULTS[[i]] <- RES[[i]] %>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% mutate(Marginal_Value=Revenue/Total_Tons) %>% select(Year,Marginal_Value,Total_Tons) %>% group_by(Year) %>% arrange(Year,desc(Marginal_Value)) %>% mutate(Q=cumsum(Total_Tons),Discount=factor(percent(DISCOUNT,1),levels=c("3%","5%","7%","10%")))
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SUPPLY_RESULTS_WITH_SHIPPING[[i]] <- RES[[i]] %>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% mutate(Marginal_Value=Revenue/Total_Tons) %>% filter(Marginal_Value>=SHIPPING_COST) %>% select(Year,Marginal_Value,Total_Tons) %>% group_by(Year) %>% arrange(Year,desc(Marginal_Value)) %>% mutate(Q=cumsum(Total_Tons),Discount=factor(percent(DISCOUNT,1),levels=c("3%","5%","7%","10%")))
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RES[[i]] <- RES[[i]] %>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% select(Year,Facility,Total_Tons,Revenue)
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}
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names(SUPPLY_RESULTS) <- names(RES)
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names(SUPPLY_RESULTS_WITH_SHIPPING) <- names(RES)
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KEY_DATA <- do.call(rbind,SUPPLY_RESULTS[c("Per_3","Per_5","Per_7","Per_10")]) %>% ungroup
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SUPPLY_RESULTS_WITH_SHIPPING
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KEY_DATA_WITH_SHIPPING <- do.call(rbind,SUPPLY_RESULTS_WITH_SHIPPING[c("Per_3","Per_5","Per_7","Per_10")]) %>% ungroup
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MAX_REV <- function(YEAR,TBL,CIFS_SIZE,COST=0){
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TBL <- TBL %>% filter(Year==YEAR)
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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-COST, const.mat = matrix(VOL, nrow = 1),const.dir = "<=", const.rhs = CIFS_SIZE, all.bin = TRUE)
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return(sum(RES$objective))
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}
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FACILITY_REVENUE <-function(CAPACITY){OUTCOME <- do.call(cbind,lapply(1:length(RES),function(i){sapply(1960:2083,MAX_REV,TBL=RES[[i]],CIFS_SIZE=CAPACITY)}))
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OUTCOME <- cbind(1960:2083,OUTCOME ) %>% as_tibble
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colnames(OUTCOME) <- c("Year",parse_number(names(RES))/100)
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OUTCOME <- OUTCOME %>% pivot_longer(-Year,names_to="Discount",values_to="Revenue") %>% mutate(Capacity=CAPACITY) %>% select(Year,Capacity,everything())
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return(OUTCOME)
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}
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REV_RES <- do.call(rbind,lapply(c(8680,8680+5000*19,5000,40000,10000),FACILITY_REVENUE))
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REV_RES %>% mutate(as.numeric(Discount))
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%>% mutate(
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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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COSTS <- do.call(rbind,lapply(seq(0,1,by=0.025),function(DISCOUNT){CIFS %>% group_by(Location,Capacity,Cost_Assumption) %>% mutate(NPC=Total/(1+DISCOUNT)^Year) %>% summarize(Discount=DISCOUNT,Costs=sum(NPC))}))
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RES <- REV_RES %>% left_join(COSTS) %>% mutate(Profit=Revenue-Costs)
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PLOT_DATA <- RES %>% filter(Discount %in% c(0.03,0.05,0.07,0.1)) %>% filter(!is.na(Costs))
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PLOT_DATA
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png("Revenue.png",height=6,width=11,units="in",res=900)
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ggplot(PLOT_DATA,aes(x=Year,y=Profit/10^6,color=Discount))+geom_point()+facet_wrap(~Capacity)
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NPC <- CIFS %>% select(Year,Location,Phase,Capacity,Cost_Assumption,Total) %>% group_by(Year,Location,Cost_Assumption,Total,Phase,Capacity) %>% summarize(Total=mean(Total)) %>% arrange(Location,Phase,Year) %>% mutate(Cost_3=Total/(1+0.03)^Year,Cost_5=Total/(1+0.05)^Year,Cost_7=Total/(1+0.07)^Year,Cost_10=Total/(1+0.1)^Year) %>% ungroup %>% group_by(Location,Phase,Capacity,Cost_Assumption) %>% summarize('3%'=sum(Cost_3),'5%'=sum(Cost_5),"7%"=sum(Cost_7),"10%"=sum(Cost_10)) %>% pivot_longer(c(-Phase,-Location,-Cost_Assumption,-Capacity),names_to="Discount",values_to="CIFS_Cost")
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PLOT_DATA <- OUTCOME %>% filter(Discount %in% c(0.03,0.05,0.1))
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png("Revenue.png",height=6,width=11,units="in",res=900)
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ggplot(PLOT_DATA,aes(x=Year,y=Revenue,color=Discount))+geom_point()
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dev.off()
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KEY_YEARS <- c(1986,2026,2066)
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#Data for reduced and increased fee at 5% and 2026
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55
Data_Proc.r
55
Data_Proc.r
@ -1,14 +1,18 @@
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#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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library(lpSolve) #For solving discrete value maximization for the power plants
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TS <- read_csv("Data/Raw_Data//Curie_Spent_Fuel_Timeline.csv")
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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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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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###########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)^-(1:10^6)
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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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@ -18,17 +22,54 @@ 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))
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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 will need to be combined with costs to calculate NPV
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MULTI_DISCOUNT_RATE_NPV <- function(INCREMENT=0.005,DATA=TOTAL,YEARS=1960:2083){
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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(INCREMENT=0.005,DATA=TOTAL,YEARS=1960:2083,DOLLARS_SAVED_PER_YEAR){
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NCORES <- detectCores()-1
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RES <- mclapply(seq(0,1,by=INCREMENT),NPV_CALC,mc.cores = NCORES)
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names(RES) <- paste0("Per_",100*seq(0,1,by=INCREMENT)) #Name with the discount rate of the given table
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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(INCREMENT=0.0025)
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TOTAL_VALUE_METRICS <- MULTI_DISCOUNT_RATE_NPV(INCREMENT=0.1,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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#TBL <- TOTAL_VALUE_METRICS
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#DICOUNT=0.1
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#CIFS_SIZE=1
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#SHIPPING_COST <- 0
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#YEAR=2026
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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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VOL
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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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SHIPPING_COST_PER_TON <- 1.2874*26000 #Inflation adjusted from New Mexico Report
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#Unique values of discount rates used
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DISCOUNTS <- TOTAL_VALUE_METRICS$Discount %>% unique
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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)
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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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FACILITY_RESULTS <- function(CAPACITY){do.call(rbind,lapply(DISCOUNTS,function(x){YEARLY_RESULTS(TOTAL_VALUE_METRICS,x,CAPACITY)}))}
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lapply(FACILITY_RESULTS()
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FACILITY_RESULTS(10^5)
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MAX_REV(YEAR=2020,TBL=TOTAL_VALUE_METRICS,DISCOUNT=0.1,CIFS_SIZE=100000,SHIPPING_COST_PER_TON )
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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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