Working on more result options to pick from

Cost_Data_Proc.r

@@ -5,9 +5,12 @@ read_csv("./Data/Raw_Data/Cost_Tables/Texas/Table_C-3_Undiscounted_Cost_Estimate
 CIFS_TEXAS <- rbind( read_csv("./Data/Raw_Data/Cost_Tables/Texas/Table_C-3_Undiscounted_Cost_Estimates_Phase_1_Low.csv") %>% mutate(Phase='Partial',Cost_Assumption="Low"),read_csv("./Data/Raw_Data/Cost_Tables/Texas/Table_C-4_Undiscounted_Cost_Estimates_Phase_1_High.csv") %>% mutate(Phase='Partial',Cost_Assumption="High"), read_csv("./Data/Raw_Data/Cost_Tables/Texas/Table_C-5_Undiscounted_Cost_Estimates_Full_Low.csv") %>% mutate(Phase='Full',Cost_Assumption="Low"), read_csv("./Data/Raw_Data/Cost_Tables/Texas/Table_C-6_Undiscounted_Cost_Estimates_Full_High.csv") %>% mutate(Phase='Full',Cost_Assumption="High")) %>% mutate(Location="Texas",Capacity=ifelse(Phase=='Partial',5000,8*5000)) %>% select(Year,Location,Phase,Capacity,Cost_Assumption,Total,everything())
 
 CIFS_TEXAS <- rbind(CIFS_TEXAS,CIFS_TEXAS %>% group_by(Year,Location,Phase,Capacity) %>% summarize(Cost_Assumption="Average",Total=mean(Total),Construction=mean(Construction),Transportation_to_CISF=mean(Transportation_to_CISF),Operations=mean(Operations),Transportation_to_Repository=mean(Transportation_to_Repository),Decommissioning=mean(Decommissioning)) %>% ungroup)
+#CIFS_TEXAS  %>%  group_by(Year,Phase,
+
 saveRDS(CIFS_TEXAS,"Data/Cleaned_Data/Texas_CIFS_Costs.Rds") 
 
 CIFS_NEW_MEXICO <- rbind( read_csv("./Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-3_Undiscounted_Cost_Estimates_Phase_1_Low.csv") %>% mutate(Phase='Partial',Cost_Assumption="Low"),read_csv("./Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-4_Undiscounted_Cost_Estimates_Phase_1_High.csv") %>% mutate(Phase='Partial',Cost_Assumption="High"), read_csv("./Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-5_Undiscounted_Cost_Estimates_Full_Low.csv") %>% mutate(Phase='Full',Cost_Assumption="Low"), read_csv("./Data/Raw_Data/Cost_Tables/New_Mexico/Table_C-6_Undiscounted_Cost_Estimates_Full_High.csv") %>% mutate(Phase='Full',Cost_Assumption="High"))%>% mutate(Location="New Mexico",Capacity=ifelse(Phase=='Partial',8680,8680+5000*19))%>% select(Year,Location,Phase,Capacity,Cost_Assumption,Total,everything())
 CIFS_NEW_MEXICO <- rbind(CIFS_NEW_MEXICO,CIFS_NEW_MEXICO %>% group_by(Year,Location,Phase,Capacity) %>% summarize(Cost_Assumption="Average",Total=mean(Total),Construction=mean(Construction),Transportation_to_CISF=mean(Transportation_to_CISF),Operations=mean(Operations),Transportation_to_Repository=mean(Transportation_to_Repository),Decommissioning=mean(Decommissioning)) %>% ungroup)
 saveRDS(CIFS_NEW_MEXICO,"Data/Cleaned_Data/New_Mexico_CIFS_Costs.Rds")
 
+

Data/Raw_Data/Cost_Tables/Texas/Table_C-3_Undiscounted_Cost_Estimates_Phase_1_Low.csv

@@ -1,42 +1,42 @@
 Year,Construction,Transportation_to_CISF,Operations,Transportation_to_Repository,Decommissioning,Total
-5041229,0,0,155305226
+1,76552618,73711378,5041229,0,0,155305226
-5041229,0,0,213789386
+2,65910317,142837839,5041229,0,0,213789386
-5041229,0,0,19326086
+3,11737391,2547465,5041229,0,0,19326086
-5041229,0,0,53313056
+4,40629430,7642396,5041229,0,0,53313056
-5041229,0,0,53313056
+5,40629430,7642396,5041229,0,0,53313056
-5041229,0,0,53313056
+6,40629430,7642396,5041229,0,0,53313056
-5041229,0,0,5041229
+7,0,0,5041229,0,0,5041229
-5041229,0,0,53313056
+8,40629430,7642396,5041229,0,0,53313056
-5041229,0,0,15768302
+9,9028762,1698310,5041229,0,0,15768302
-5041229,0,0,5041229
+10,0,0,5041229,0,0,5041229
-5041229,0,0,8647444
+11,3606215,0,5041229,0,0,8647444
-5041229,0,0,5041229
+12,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+13,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+14,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+15,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+16,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+17,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+18,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+19,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+20,0,0,5041229,0,0,5041229
-5041229,0,0,22895959
+21,17854730,0,5041229,0,0,22895959
-5041229,0,0,5041229
+22,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+23,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+24,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+25,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+26,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+27,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+28,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+29,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+30,0,0,5041229,0,0,5041229
-5041229,0,0,8647444
+31,3606215,0,5041229,0,0,8647444
-5041229,0,0,5041229
+32,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+33,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+34,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+35,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+36,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+37,0,0,5041229,0,0,5041229
-5041229,0,0,5041229
+38,0,0,5041229,0,0,5041229
-5041229,125682289,0,130723519
+39,0,0,5041229,125682289,0,130723519
-5041229,125682289,0,130723519
+40,0,0,5041229,125682289,0,130723519
-0,0,56740382,56740382
+41,0,0,0,0,56740382,56740382

Data/Raw_Data/Cost_Tables/Texas/test.r

@@ -1,7 +1,8 @@
 library(tidyverse)
 #########CIFS Costs
-DAT <- read_csv("Table_C-3_Undiscounted_Cost_Estimates_Phase_1.csv")
+DAT <- read_csv("Table_C-3_Undiscounted_Cost_Estimates_Phase_1_Low.csv")
 rowSums(DAT[,2:6])-DAT$Total
+sum(DAT[,2:6])
 
 DAT <- read_csv("Table_C-4_Undiscounted_Cost_Estimates_Phase_1_High.csv")
 rowSums(DAT[,2:6])-DAT$Total

Data_Proc.r

@@ -1,12 +1,49 @@
 #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
-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) 
+#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) 
+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"))
+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
+
+
+##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 
+
 ###########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 
@@ -26,48 +63,81 @@ colnames(RES) <- c("Facility",YEARS)
 	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(INCREMENT=0.005,DATA=TOTAL,YEARS=1960:2083,DOLLARS_SAVED_PER_YEAR){
+MULTI_DISCOUNT_RATE_NPV <- function(DISCOUNT_INCREMENT,DATA=TOTAL,YEARS=1960:2083,DOLLARS_SAVED_PER_YEAR){
 	NCORES <- detectCores()-1
-	RES <- mclapply(seq(0,1,by=INCREMENT),NPV_CALC,mc.cores = NCORES)
+	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(INCREMENT=0.1,DOLLARS_SAVED_PER_YEAR=CV2)
+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)
-#TBL <- TOTAL_VALUE_METRICS 
+
-#DICOUNT=0.1
-#CIFS_SIZE=1
-#SHIPPING_COST <- 0
-#YEAR=2026
 #Function to find the maximum Revenue BUT it does not take dollars just relative savings 
 MAX_REV <- function(YEAR,TBL,DISCOUNT,CIFS_SIZE,SHIPPING_COST=0){
 		TBL <- TBL %>% filter(Year==YEAR,Discount==DISCOUNT,Revenue/Total_Tons>SHIPPING_COST)
 		REV <- TBL %>% pull(Revenue)
 		VOL <- TBL %>% pull(Total_Tons)
-		VOL
 		RES <-	lp(direction = "max", objective.in = REV, const.mat = matrix(VOL, nrow = 1),const.dir = "<=", const.rhs = CIFS_SIZE, all.bin = TRUE)
 		 return(RES[[11]])
 	}
 
-SHIPPING_COST_PER_TON <- 1.2874*26000 #Inflation adjusted from New Mexico Report
-#Unique values of discount rates used
-DISCOUNTS <- TOTAL_VALUE_METRICS$Discount %>% unique
 #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)
+	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
-FACILITY_RESULTS <- function(CAPACITY){do.call(rbind,lapply(DISCOUNTS,function(x){YEARLY_RESULTS(TOTAL_VALUE_METRICS,x,CAPACITY)}))}
-lapply(FACILITY_RESULTS()
-FACILITY_RESULTS(10^5)
 
-	MAX_REV(YEAR=2020,TBL=TOTAL_VALUE_METRICS,DISCOUNT=0.1,CIFS_SIZE=100000,SHIPPING_COST_PER_TON )
+	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 %>%  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(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 ,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)
+