Working on timing optimization

Optimal_Deployments.r

@@ -1,4 +1,5 @@
 library(tidyverse)
+library(lpSolve)
 ##Value from table C-2 of Holtec report, cost categories
 	#"Initial Annual Estimated Costs and 2019 Constant Dollar Values for the Various Activities for the Proposed CISF and the No-Action Alternative"
 
@@ -8,21 +9,31 @@ library(tidyverse)
 #PHASE_SIZE <- 5000
 #ST_CAP <- 8680
 #TRANSPORT_COST_RATIO <- 155462880/5000 # or 269883561/8680 since transportation cost scale linearly
-#VOLUME <- 10^5/2+480
+#VOLUME <- 10 
 #TRANS_CONSTRAINT <- (ST_CAP+5000*19)/19
 #PHASES_ADDED <- max(ceiling((VOLUME-ST_CAP)/PHASE_SIZE),0) #Make sure added phases are not negative
-
+#STARTING_VOLUME=8680
+#PHASE_CONST_COST=103399272
+#DISCOUNT <- 0.05
+#VOLUME <- 8660+5000
+#PHASES_ADDED <- 1
+#DECOM_COST_PER_TON =24822656/5000
+#155462880/10^9
 OPTIMAL_COST <- function(PHASES_ADDED,VOLUME,CURRENT_YEAR,DISCOUNT=0.05,STARTING_VOLUME=8680,PHASE_SIZE=5000,END_YEAR=40,PHASE_CONST_COST=103399272,TRANSPORT_COST_RATIO=155462880/5000,TRANS_CONSTRAINT =(8680+5000*19)/19,DECOM_COST_PER_TON =24822656/5000,INFLATION_ADJUST=1.2874){
 	ADDED_VOLUME=max(VOLUME-STARTING_VOLUME,0)
+ADDED_VOLUME
+STARTING_VOLUME
+VOLUME
 if(ADDED_VOLUME/PHASE_SIZE>PHASES_ADDED){stop("Not enough capacity for the requested volume")}
 	###Construction Cost to cover supplied SNF volume
 	CONSTRUCT_COST <- PHASES_ADDED*PHASE_CONST_COST
 	#Cost to transport SNF from reactors to the CIFS in the current year and to repository at end of project period
 	SHIPPING_TIME<- VOLUME/TRANS_CONSTRAINT 
+	VOLUME/TRANS_CONSTRAINT 
 	SHIPPING_YEARS <- ceiling(SHIPPING_TIME )
 	if(SHIPPING_YEARS>END_YEAR-CURRENT_YEAR){stop("Not enough time to ship the requested SNF volume")}
-	AT_CAPACITY_VOLUME <- TRANS_CONSTRAINT*floor(SHIPPING_TIME )
+	AT_CAPACITY_VOLUME <- TRANS_CONSTRAINT
-	UNDER_CAPACITY_VOLUME <- VOLUME-AT_CAPACITY_VOLUME
+	UNDER_CAPACITY_VOLUME <- VOLUME-(SHIPPING_YEARS-1)*AT_CAPACITY_VOLUME
 	SHIPPING_SCHEDULE_OUT <- rep(AT_CAPACITY_VOLUME,SHIPPING_YEARS)
 	if(UNDER_CAPACITY_VOLUME!=0){SHIPPING_SCHEDULE_OUT[1] <- UNDER_CAPACITY_VOLUME}
 	SHIPPING_SCHEDULE_OUT <- TRANSPORT_COST_RATIO*SHIPPING_SCHEDULE_OUT
@@ -40,7 +51,6 @@ if(ADDED_VOLUME/PHASE_SIZE>PHASES_ADDED){stop("Not enough capacity for the reque
 	TOTAL_COST <- TOTAL_COST*INFLATION_ADJUST
 	return(TOTAL_COST)
 }
-OPTIMAL_COST(20,10^4,20)
 
 CHECK_FEASIBLE_SHIPPING <- function(VOLUME,ST_YEAR){
 RESULT <- try(OPTIMAL_COST(10^5,VOLUME,ST_YEAR),silent=TRUE)
@@ -60,11 +70,6 @@ FIND_FEASIBLE_LIMIT <- function(STARTING_TIME){
 }
 SHIPPING_CAPACITY_LIMITS <- cbind(1:40,sapply(1:40,FIND_FEASIBLE_LIMIT)) %>% as_tibble %>% rename(Year=V1,Max_Capacity=V2) 
 SHIPPING_CAPACITY_LIMITS  %>% print(n=100)
-REACTOR_VALUES <- readRDS("Data/Cleaned_Data/Reactor_Values.Rds")
-C_VALUES <- REACTOR_VALUES  %>% filter(Year==2026+40,Discount==0.05)
-C_VALUES
-TBL <- CURRENT
-CIFS_SIZE <- ST_CAP
 MAX_REV <- function(TBL,CIFS_SIZE){
 #		TBL <- TBL %>% filter(Year==YEAR,Discount==DISCOUNT,Revenue/Total_Tons>SHIPPING_COST)
 		REV <- TBL %>% pull(Revenue)
@@ -72,11 +77,7 @@ MAX_REV <- function(TBL,CIFS_SIZE){
 		RES <-	lp(direction = "max", objective.in = REV, const.mat = matrix(VOL, nrow = 1,byrow=TRUE),const.dir = "<=", const.rhs = CIFS_SIZE, all.bin = TRUE)
 		 return(RES)
 	}
-ST_CAP <- 8680
-PHASE_SIZE <- 5000
 
-RES <-	
-	names(RES)
 PROFIT_EST <- function(ADDED_PHASES,ST_YEAR,YEARS_AHEAD,DATA=REACTOR_VALUES,DISCOUNT_RATE=0.05,ST_CAP=8680,PHASE_SIZE=5000){
 	CURRENT <-  DATA%>% filter(Year==ST_YEAR+YEARS_AHEAD,Discount==DISCOUNT_RATE)
 	RES <- MAX_REV(CURRENT,ST_CAP+PHASE_SIZE*ADDED_PHASES)
@@ -85,19 +86,54 @@ PROFIT_EST <- function(ADDED_PHASES,ST_YEAR,YEARS_AHEAD,DATA=REACTOR_VALUES,DISC
 	PROFIT <- rbind(REVENUE,OPTIMAL_COST(ADDED_PHASES,TONS_STORED,YEARS_AHEAD))
 	return(PROFIT)
 }
-MAX_REV(CURRENT,ST_CAP+PHASE_SIZE*1)
 
-OPTIMAL_COST(0,TONS_STORED,YEARS_AHEAD)
+REACTOR_VALUES <- readRDS("Data/Cleaned_Data/Reactor_Values.Rds")
-t(sapply(0:3,PROFIT_EST,ST_YEAR=2026,YEARS_AHEAD=15)/10^6) %>% as_tibble  %>% rename(Rev=V1,Cost=V2) %>% mutate(Profit=Rev-Cost)
+#ADDED <- 1
+#ADD_RES  <-  MAX_REV(CURRENT,8680+5000*ADDED)
+#REACTOR_DATA <- CURRENT
+#STARTING_YEAR <- 2026
+#YEARS_AHEAD=20
+#STARTING_CAP=8680
+#SINGLE_PHASE_CAP=5000
+#ADDED_UNITS <- 1
+#Find the optimal profit and cost, plus if the capacity constraint of an addtion in binding.
+ADDITION_CHECK <- function(REACTOR_DATA,ADDED_UNITS,YEARS_AHEAD,Discount_Rate=0.05,STARTING_CAP=8680,SINGLE_PHASE_CAP=5000){
+	OPTIM_GUESS <- MAX_REV(REACTOR_DATA,STARTING_CAP+SINGLE_PHASE_CAP*ADDED_UNITS)
+	SELECTED_REACTORS <- REACTOR_DATA[which(ADD_RES$solution==1),]%>% mutate(MARGINAL_VALUE=Revenue/Total_Tons)
+	FOUND_VOLUME <- OPTIM_GUESS$constraint[76]
+	LOWEST_VALUE_REACTOR <-SELECTED_REACTORS[SELECTED_REACTORS$MARGINAL_VALUE== min(SELECTED_REACTORS$MARGINAL_VALUE),]
+	MARGINAL_VALUE <- LOWEST_VALUE_REACTOR$MARGINAL_VALUE
+	FULL_COST_AT_CAPACITY <- OPTIMAL_COST(ADDED_UNITS,FOUND_VOLUME,YEARS_AHEAD)
+	MARGINAL_COST <- FULL_COST_AT_CAPACITY -OPTIMAL_COST(ADDED_UNITS,FOUND_VOLUME-1,YEARS_AHEAD)
+	BOUNDED <- MARGINAL_VALUE>MARGINAL_COST
+if(BOUNDED){
+	FOUND_VOLUME <- FOUND_VOLUME-STARTING_CAP
+	State <- "Capacity Constrainted"
+	Optimal_Rev <- OPTIM_GUESS$objval
+	Optimal_Cost <- FULL_COST_AT_CAPACITY
+}
+else {
+	HIGHEST_VALUE_REACTOR <-SELECTED_REACTORS[SELECTED_REACTORS$MARGINAL_VALUE== max(SELECTED_REACTORS$MARGINAL_VALUE),]
+	MARGINAL_VALUE <- unique(HIGHEST_VALUE_REACTOR$MARGINAL_VALUE)
+	if(MARGINAL_VALUE>=MARGINAL_COST){
+		State <- "Not a Profitable Phase" 
+		FOUND_VOLUME <- 0
+		OPTIM_STARTING <- MAX_REV(REACTOR_DATA,STARTING_CAP)
+		Optimal_Rev <- OPTIM_STARTING$objval
+		Optimal_Cost <-  OPTIMAL_COST(0,STARTING_CAP,YEARS_AHEAD)
+	}else{
+		State <- "No Binding Constraints" 
+		FOUND_VOLUME <- NA 
+		Optimal_Rev <- NA 
+	 Optimal_Cost <- NA
 
-RES$solution
+	}
-names(RES)
-length(RES$objective)
-
-CURRENT[RES[[9]],]
-CURRENT[-RES[[9]],]
-
-REACTOR_VALUES %>% filter(Year=[RES[[9]],]
 
+}
+	Profit <- Optimal_Rev-Optimal_Cost
+	return(c(ADDED_UNITS,State,FOUND_VOLUME,Profit,Optimal_Rev,Optimal_Cost))
+}
+#Note for self: By running addition's from 1 to 20 (Roughly) at the same number of years ahead the number of 5000 unit addtions which maximizes profit in that year can be found. It looks like at least 22 units will be built which is enough for the whole US, but the timing of addtions needs to be worked out by backwards induction using the years.
+ADDITION_CHECK(CURRENT,22,1)
 
 

Scripts/Reactor_Clean.r

@@ -0,0 +1,68 @@
+#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
+	#Range of discount rates to calculate in the model. Each facility will have each rate calculated, so more values slows the results but allows for more discount rates to be reported in the findings.
+DISCOUNT_RATE_LIST <- seq(0.01,0.15,by=0.0025)
+	#The cost per ton of shipping uranium, used to see what can be shipped on day one of the project.
+CV <- 1.2874*(6984013) #Data from New Mexico Report, Converted from 2019 to  Dec 2025
+	#Locations to save results
+SAVE_DIR <- "./Data/Cleaned_Data/"
+#Create any need save locations
+	dir.create(SAVE_DIR,recursive=TRUE,showWarnings=FALSE)
+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)
+FACILITY_LIST <- TOTAL %>% pull(Facility)
+#https://www.nrc.gov/reactors/operating/licensing/renewal/subsequent-license-renewal
+SUBMITTED <-rbind(c(FACILITY_LIST[str_detect(FACILITY_LIST,"Duane*" )],2025),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Nine Mile*" )],2026),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Ginna*" )],2026),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Cooper*" )],2026),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Farley*" )],2027),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Prairie*" )],2027),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Brunswick*" )],2027),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Cook" )],2027),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Hope" )],2027),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Salem" )],2027),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Perry" )],2027),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Millstone" )],2028),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Palisades" )],2028),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Beaver" )],2028),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Callaway" )],2029),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Three Mile Island" )],2029),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Davis-Besse" )],2029),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Wolf" )],2030),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Lucie" )],2021),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Robinson" )],2025),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Hatch" )],2025))
+SUBMITTED <- SUBMITTED %>% as_tibble 
+colnames(SUBMITTED ) <- c("Facility","App_Date","Status") 
+SUBMITTED <- SUBMITTED%>% mutate(Status="Applied",App_Date=as.numeric(App_Date)) %>% select(Facility,Status,App_Date)
+
+#Issued
+RENEWED <- rbind(c(FACILITY_LIST[str_detect(FACILITY_LIST,"Turkey" )],"Granted",2018,2033),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Peach" )],"Granted",2019,2034),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Surry" )],"Granted",2020,2033),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"North" )],"Granted",2021,2040),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Monticello" )],"Granted",2024,2030),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Oconee" )],"Granted",2025,2034),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Summer" )],"Granted",2025,2042),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Beach" )],"Granted",2025,2033),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Browns" )],"Granted",2025,2036),
+c(FACILITY_LIST[str_detect(FACILITY_LIST,"Dresden" )],"Granted",2025,2031))
+RENEWED <- RENEWED  %>% as_tibble
+colnames(RENEWED) <- c("Facility","Status","App_Date","Op_Date")
+RENEWED <- RENEWED %>% mutate(Op_Date=as.numeric(Op_Date),App_Date=as.numeric(App_Date))
+AVG_LENGTH <- RENEWED %>% mutate(DIFF=Op_Date-App_Date) %>% pull(DIFF) %>% mean %>% round
+SUBMITTED <- SUBMITTED %>% mutate(Op_Date=App_Date+AVG_LENGTH)
+UPDATE <- rbind(RENEWED,SUBMITTED )
+TOTAL_ORIG <- TOTAL
+TOTAL <- TOTAL %>% left_join(UPDATE) %>% mutate(CLOSE_YEAR=ifelse(Op_Date>CLOSE_YEAR & !is.na(Status),Op_Date,CLOSE_YEAR)) %>% select(-Status,-App_Date,-Op_Date)
+source("./Scripts/Functions/NPV_Functions.r")
+TOTAL_VALUE_METRICS <- MULTI_DISCOUNT_RATE_NPV(DISCOUNT_RATE_LIST,TOTAL ,DOLLARS_SAVED_PER_YEAR=CV)%>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% select(Year,Facility,Discount,Total_Tons,Revenue)
+TOTAL_VALUE_METRICS_ORIG <- MULTI_DISCOUNT_RATE_NPV(DISCOUNT_RATE_LIST,TOTAL_ORIG ,DOLLARS_SAVED_PER_YEAR=CV) %>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% select(Year,Facility,Discount,Total_Tons,Revenue)
+TOTAL_VALUE_METRICS_ORIG 
+saveRDS(TOTAL_VALUE_METRICS ,paste0(SAVE_DIR,"Reactor_Values.Rds"))
+
+