diff --git a/Data_Proc.r b/Data_Proc.r index a19d828..d9ee79d 100644 --- a/Data_Proc.r +++ b/Data_Proc.r @@ -5,7 +5,7 @@ library(parallel) library(lpSolve) #For solving discrete value maximization for the power plants ####Manual inputs #DISCOUNT_RATE_LIST <- seq(0,1,by=0.0025) -DISCOUNT_RATE_LIST <- c(0.03,0.04,0.045,0.05,0.07,0.1) +DISCOUNT_RATE_LIST <- c(0.03,0.05,0.07,0.1) SHIPPING_COST_PER_TON <- 1.2874*26000 #Inflation adjusted from New Mexico Report CV1 <- 1.3074*(10607030-1060703) #Data from Texas Report, converted from 2018 to Dec 2025 dollars CV2 <- 1.2874*(6984013-1117442) #Data from New Mexico Report, Converted from 2019 to Dec 2025 @@ -14,10 +14,11 @@ CV3 <- mean(CV1,CV2) #Average of the two ###################################Cost results #source("Cost_Data_Proc.r") CIFS <- rbind(readRDS("Data/Cleaned_Data/Texas_CIFS_Costs.Rds"),readRDS("Data/Cleaned_Data/New_Mexico_CIFS_Costs.Rds")) +#Adjust for inflation +CIFS[,-1:-5] <- 1.2874*CIFS[,-1:-5] 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 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 - CAPACITY_INCREMENT <- 5000 #rm(COST_DATA) for(i in 1:nrow(TEMP)){ @@ -34,15 +35,18 @@ for(i in 1:nrow(TEMP)){ if(!exists("COST_DATA")){COST_DATA <- C_RES}else{COST_DATA<- rbind(COST_DATA,C_RES)} } saveRDS(COST_DATA ,"Data/Cleaned_Data/All_CIFS_Discounted_Costs.Rds") -COST_DATA <- COST_DATA %>% filter(Cost_Assumption=='Average') %>% select(-Cost_Assumption) %>% unique +#COST_DATA <- COST_DATA %>% filter(Cost_Assumption=='Average') %>% select(-Cost_Assumption) %>% unique +COST_DATA <- COST_DATA %>% filter(Cost_Assumption=='High') %>% select(-Cost_Assumption) %>% unique + ##All unique capacity levels that the revenues need to be calculated for CAPACITY_LIST <- COST_DATA %>% pull(Capacity) %>% unique ### -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 -TOTAL +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) +#!!!!!!!!!!!!#####TEST OF VARIABLITY IN CLOSING YEAR, CHANGE OR FORMALIZE LATER +#TOTAL <- TOTAL %>% mutate(CLOSE_YEAR=ifelse(CLOSE_YEAR>2018,CLOSE_YEAR+20,CLOSE_YEAR)) ###########Series of functions to calculate the gross consumer surplus from a CIFS for each facility, in each year from 1960 to 2082. @@ -72,7 +76,7 @@ MULTI_DISCOUNT_RATE_NPV <- function(DISCOUNT_INCREMENT,DATA=TOTAL,YEARS=1960:208 TOTAL_VALUE_METRICS <- MULTI_DISCOUNT_RATE_NPV(DISCOUNT_RATE_LIST ,DOLLARS_SAVED_PER_YEAR=CV2) -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 +#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 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) #Function to find the maximum Revenue BUT it does not take dollars just relative savings @@ -96,13 +100,21 @@ YEARLY_RESULTS <- function(DATA,DISCOUNT,CAPACITY,SHIPPING_COSTS=0){ 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))) Rev_No_Shipping 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 +TEST TEST <- TEST %>% mutate(Current_Profit=Profit/((1+Discount)^(Year-2026))) %>% ungroup TEST <- TEST %>% group_by(Year,Location,Discount) %>% filter(Current_Profit==max(Current_Profit)) %>% ungroup TEST %>% select(Profit,Current_Profit,Year,Discount) %>% tail -TEST %>% group_by(Location,Discount) %>% filter(Current_Profit==max(Current_Profit)) +TEST %>% group_by(Location,Discount) %>% filter(Current_Profit==max(Current_Profit)) %>% print(n=100) TEST %>% group_by(Location,Discount) %>% filter(Discount==0.04) %>% select(Year,Capacity,Profit,Current_Profit) %>% mutate(Delay_Profit=(lead(Current_Profit)-Current_Profit)/10^6) %>% print(n=70) -ggplot(TEST ,aes(x=Year,y=Current_Profit/10^9,color=Capacity))+geom_line()+facet_wrap(Discount~Location) +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)) + +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)) + +ggplot(TEST %>% filter(Year>2000) ,aes(x=Year,y=Current_Profit/10^9,color=Capacity))+geom_line()+facet_wrap(Discount~Location,ncol=2,scales = "free_y")+scale_x_continuous(breaks=seq(1960,2083,by=5)) + + + ggplot(TEST ,aes(x=Year,y=Current_Profit/10^9,color=Capacity))+geom_line()+facet_wrap(Discount~Location) diff --git a/Single_Price_Data_Proc.r b/Single_Price_Data_Proc.r new file mode 100644 index 0000000..16badf9 --- /dev/null +++ b/Single_Price_Data_Proc.r @@ -0,0 +1,168 @@ +#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 +library(tidyverse) +library(parallel) + NCORES <- detectCores()-1 +library(lpSolve) #For solving discrete value maximization for the power plants +####Manual inputs +#DISCOUNT_RATE_LIST <- seq(0,1,by=0.0025) +DISCOUNT_RATE_LIST <- c(0.03,0.05,0.07,0.1) +SHIPPING_COST_PER_TON <- 1.2874*26000 #Inflation adjusted from New Mexico Report +CV1 <- 1.3074*(10607030-1060703) #Data from Texas Report, converted from 2018 to Dec 2025 dollars +CV2 <- 1.2874*(6984013-1117442) #Data from New Mexico Report, Converted from 2019 to Dec 2025 +CV3 <- mean(CV1,CV2) #Average of the two + +###################################Cost results +#source("Cost_Data_Proc.r") +CIFS <- rbind(readRDS("Data/Cleaned_Data/Texas_CIFS_Costs.Rds"),readRDS("Data/Cleaned_Data/New_Mexico_CIFS_Costs.Rds")) +#Adjust for inflation +CIFS[,-1:-5] <- 1.2874*CIFS[,-1:-5] +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 + +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 +CAPACITY_INCREMENT <- 5000 +#rm(COST_DATA) +for(i in 1:nrow(TEMP)){ + LOC <- as.character(TEMP[i,"Location"]) + COST_LEVEL <- as.character(TEMP[i,"Cost_Assumption"]) + DISCOUNT <- as.numeric(TEMP[i,"Discount"]) + ST_CAP <- as.numeric(TEMP[i,"ST_CAPACITY"]) + ST_COST <- as.numeric(TEMP[i,"ST_COST"]) + COST_SLOPE <- as.numeric(TEMP[i,]$M_COST) + CAPACITY <- seq(ST_CAP,160000,by=5000) + COST <- ST_COST+COST_SLOPE*CAPACITY_INCREMENT*(0:(length(CAPACITY)-1)) + + 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) + if(!exists("COST_DATA")){COST_DATA <- C_RES}else{COST_DATA<- rbind(COST_DATA,C_RES)} +} +saveRDS(COST_DATA ,"Data/Cleaned_Data/All_CIFS_Discounted_Costs.Rds") +#COST_DATA <- COST_DATA %>% filter(Cost_Assumption=='Average') %>% select(-Cost_Assumption) %>% unique +COST_DATA <- COST_DATA %>% filter(Cost_Assumption=='High') %>% select(-Cost_Assumption) %>% unique + + + +##All unique capacity levels that the revenues need to be calculated for +CAPACITY_LIST <- COST_DATA %>% pull(Capacity) %>% unique + +### +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) +#!!!!!!!!!!!!#####TEST OF VARIABLITY IN CLOSING YEAR, CHANGE OR FORMALIZE LATER +#TOTAL <- TOTAL %>% mutate(CLOSE_YEAR=ifelse(CLOSE_YEAR>2018,CLOSE_YEAR+20,CLOSE_YEAR)) + +###########Series of functions to calculate the gross consumer surplus from a CIFS for each facility, in each year from 1960 to 2082. + +#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 +VALUE_ADD <- function(r,CURRENT_YEAR,CLOSE_YEAR){ + Years_Until_Close <- max(CLOSE_YEAR-CURRENT_YEAR+1,0) + VALUES <- (1+r)^-(1:10^4) + if(Years_Until_Close==0){return(sum(VALUES))} else{return(sum(VALUES[-1:-Years_Until_Close]))} +} +#A function to extend the revenues calculations to the closure date of all of the facilities. +VALUE_ADD_SINGLE_YEAR <- function(r,CURRENT_YEAR,CLOSE_YEARS){return(sapply(CLOSE_YEARS,function(x){VALUE_ADD(r,CURRENT_YEAR,x)}))} +#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. +NPV_CALC <- function(r,DATA=TOTAL,YEARS=1960:2083){ + Facility <- pull(DATA,Facility) + RES <- cbind(Facility,do.call(cbind,lapply(YEARS,function(x){VALUE_ADD_SINGLE_YEAR(r,x,DATA$CLOSE_YEAR)}))) +colnames(RES) <- c("Facility",YEARS) + 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) + return(RES) +} +#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 +MULTI_DISCOUNT_RATE_NPV <- function(DISCOUNT_INCREMENT,DATA=TOTAL,YEARS=1960:2083,DOLLARS_SAVED_PER_YEAR){ + NCORES <- detectCores()-1 + RES <- mclapply(DISCOUNT_INCREMENT,NPV_CALC,mc.cores = NCORES) + RES <- do.call(rbind,RES) %>% mutate(Revenue=Revenue*DOLLARS_SAVED_PER_YEAR) + return(RES) +} + + +TOTAL_VALUE_METRICS <- MULTI_DISCOUNT_RATE_NPV(DISCOUNT_RATE_LIST ,DOLLARS_SAVED_PER_YEAR=CV2) +#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 +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) + +#Function to find the maximum Revenue BUT it does not take dollars just relative savings +TBL <- TOTAL_VALUE_METRICS +YEAR <- 2020 +CIFS_SIZE=40000 +SHIPPING_COST <- 0 + +MAX_REV <- function(YEAR,TBL,DISCOUNT,CIFS_SIZE,SHIPPING_COST=0){ + TBL <- TBL %>% filter(Year==YEAR,Discount==DISCOUNT,Revenue/Total_Tons>SHIPPING_COST)%>% mutate(Marginal=Revenue/Total_Tons) + VALUES <- TBL %>% pull(Marginal) %>% unique + RESULT <- 0 + for(i in 1:length(VALUES)){ + VOL <- pull(TBL[TBL$Marginal<=VALUES[i],],Total_Tons) + REV <- VALUES[i]*VOL + C_MAX_VALUE <- as.numeric(lp(direction = "max", objective.in = REV, const.mat = matrix(VOL, nrow = 1),const.dir = "<=", const.rhs = CIFS_SIZE, all.bin = TRUE)[11]) + C_MAX_VALUE + RESULT <- max(RESULT,C_MAX_VALUE) + } + return(RESULT) + } + +#Calculate all results for the start year to the end year. Discount rate, and capacities are fixed. +YEARLY_RESULTS <- function(DATA,DISCOUNT,CAPACITY,SHIPPING_COSTS=0){ + RES <- cbind(1960:2083,sapply(1960:2083, function(x){MAX_REV(YEAR=x,TBL=DATA,DISCOUNT,CAPACITY,SHIPPING_COSTS)})) %>% as_tibble + colnames(RES) <- c("Year","Revenue") + RES <- RES %>% mutate(Discount=DISCOUNT,Capacity=CAPACITY) %>% select(Year,Discount,Capacity,Revenue) %>% mutate(Shipping_Cost_Cuttoff=SHIPPING_COSTS) + return(RES) +} +#Calculate and individual facilities rate, for all discount rates, and all years +TOTAL_VALUE_METRICS + 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))) +Rev_No_Shipping +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 +TEST +TEST <- TEST %>% mutate(Current_Profit=Profit/((1+Discount)^(Year-2026))) %>% ungroup +TEST <- TEST %>% group_by(Year,Location,Discount) %>% filter(Current_Profit==max(Current_Profit)) %>% ungroup +TEST %>% select(Profit,Current_Profit,Year,Discount) %>% tail +TEST %>% group_by(Location,Discount) %>% filter(Current_Profit==max(Current_Profit)) %>% print(n=100) +TEST %>% group_by(Location,Discount) %>% filter(Discount==0.04) %>% select(Year,Capacity,Profit,Current_Profit) %>% mutate(Delay_Profit=(lead(Current_Profit)-Current_Profit)/10^6) %>% print(n=70) + +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)) + +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)) + +ggplot(TEST %>% filter(Year>2000) ,aes(x=Year,y=Current_Profit/10^9,color=Capacity))+geom_line()+facet_wrap(Discount~Location,ncol=2,scales = "free_y")+scale_x_continuous(breaks=seq(1960,2083,by=5)) + + + + +ggplot(TEST ,aes(x=Year,y=Current_Profit/10^9,color=Capacity))+geom_line()+facet_wrap(Discount~Location) + + + + +TEST2 <- TEST %>% filter(Discount==0.05,Location=='Texas') + +ggplot(TEST2,aes(x=Year,y=Marginal,group=as.factor(Capacity),color=as.factor(Capacity)))+geom_line() + + #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. + 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))) + +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 +Revenue_Results +#################################################### +COSTS <- rbind(COSTS,EXTENDED_CAPACITY_NEW_MEXICO) +COST_DATA %>% pull(Cost_Assumption) %>% unique +saveRDS(COSTS,"Data/Cleaned_Data/All_CIFS_Discounted_Costs.Rds") +COSTS %>% group_by(Location,Capac +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()) +#CIFS_Data <- +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 +TEMP <- CIFS_Data %>% filter(Discount==0.05) %>% mutate(Time_Benefit=lead(Profit)-Profit,Op_Cost=Profit*Discount,Marginal=Time_Benefit-Op_Cost) + +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) +((-3504542954)-(-3530861857))-0.05*(-3530861857 ) +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 +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) + +############# +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)) + +ggplot(CIFS_Data ,aes(x=Year,y=Marginal,group=Capacity,color=as.factor(Capacity)))+geom_line()+facet_grid(~Discount) + + + + +dir.create("./Results",showWarnings=FALSE) +saveRDS(TOTAL_VALUE_METRICS,"./Results/Storage_Values_by_Facility_and_Variable_Discounts.Rds")