Working on timing optimization
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Alex
parent
3cecb59560
commit
71af838e03
@ -1,4 +1,5 @@
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library(tidyverse)
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library(lpSolve)
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##Value from table C-2 of Holtec report, cost categories
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#"Initial Annual Estimated Costs and 2019 Constant Dollar Values for the Various Activities for the Proposed CISF and the No-Action Alternative"
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@ -8,21 +9,31 @@ library(tidyverse)
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#PHASE_SIZE <- 5000
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#ST_CAP <- 8680
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#TRANSPORT_COST_RATIO <- 155462880/5000 # or 269883561/8680 since transportation cost scale linearly
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#VOLUME <- 10^5/2+480
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#VOLUME <- 10
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#TRANS_CONSTRAINT <- (ST_CAP+5000*19)/19
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#PHASES_ADDED <- max(ceiling((VOLUME-ST_CAP)/PHASE_SIZE),0) #Make sure added phases are not negative
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#STARTING_VOLUME=8680
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#PHASE_CONST_COST=103399272
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#DISCOUNT <- 0.05
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#VOLUME <- 8660+5000
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#PHASES_ADDED <- 1
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#DECOM_COST_PER_TON =24822656/5000
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#155462880/10^9
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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){
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ADDED_VOLUME=max(VOLUME-STARTING_VOLUME,0)
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ADDED_VOLUME
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STARTING_VOLUME
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VOLUME
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if(ADDED_VOLUME/PHASE_SIZE>PHASES_ADDED){stop("Not enough capacity for the requested volume")}
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###Construction Cost to cover supplied SNF volume
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CONSTRUCT_COST <- PHASES_ADDED*PHASE_CONST_COST
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#Cost to transport SNF from reactors to the CIFS in the current year and to repository at end of project period
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SHIPPING_TIME<- VOLUME/TRANS_CONSTRAINT
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VOLUME/TRANS_CONSTRAINT
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SHIPPING_YEARS <- ceiling(SHIPPING_TIME )
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if(SHIPPING_YEARS>END_YEAR-CURRENT_YEAR){stop("Not enough time to ship the requested SNF volume")}
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AT_CAPACITY_VOLUME <- TRANS_CONSTRAINT*floor(SHIPPING_TIME )
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UNDER_CAPACITY_VOLUME <- VOLUME-AT_CAPACITY_VOLUME
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AT_CAPACITY_VOLUME <- TRANS_CONSTRAINT
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UNDER_CAPACITY_VOLUME <- VOLUME-(SHIPPING_YEARS-1)*AT_CAPACITY_VOLUME
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SHIPPING_SCHEDULE_OUT <- rep(AT_CAPACITY_VOLUME,SHIPPING_YEARS)
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if(UNDER_CAPACITY_VOLUME!=0){SHIPPING_SCHEDULE_OUT[1] <- UNDER_CAPACITY_VOLUME}
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SHIPPING_SCHEDULE_OUT <- TRANSPORT_COST_RATIO*SHIPPING_SCHEDULE_OUT
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@ -40,7 +51,6 @@ if(ADDED_VOLUME/PHASE_SIZE>PHASES_ADDED){stop("Not enough capacity for the reque
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TOTAL_COST <- TOTAL_COST*INFLATION_ADJUST
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return(TOTAL_COST)
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}
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OPTIMAL_COST(20,10^4,20)
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CHECK_FEASIBLE_SHIPPING <- function(VOLUME,ST_YEAR){
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RESULT <- try(OPTIMAL_COST(10^5,VOLUME,ST_YEAR),silent=TRUE)
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@ -60,11 +70,6 @@ FIND_FEASIBLE_LIMIT <- function(STARTING_TIME){
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}
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SHIPPING_CAPACITY_LIMITS <- cbind(1:40,sapply(1:40,FIND_FEASIBLE_LIMIT)) %>% as_tibble %>% rename(Year=V1,Max_Capacity=V2)
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SHIPPING_CAPACITY_LIMITS %>% print(n=100)
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REACTOR_VALUES <- readRDS("Data/Cleaned_Data/Reactor_Values.Rds")
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C_VALUES <- REACTOR_VALUES %>% filter(Year==2026+40,Discount==0.05)
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C_VALUES
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TBL <- CURRENT
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CIFS_SIZE <- ST_CAP
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MAX_REV <- function(TBL,CIFS_SIZE){
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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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@ -72,11 +77,7 @@ MAX_REV <- function(TBL,CIFS_SIZE){
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RES <- lp(direction = "max", objective.in = REV, const.mat = matrix(VOL, nrow = 1,byrow=TRUE),const.dir = "<=", const.rhs = CIFS_SIZE, all.bin = TRUE)
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return(RES)
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}
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ST_CAP <- 8680
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PHASE_SIZE <- 5000
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RES <-
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names(RES)
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PROFIT_EST <- function(ADDED_PHASES,ST_YEAR,YEARS_AHEAD,DATA=REACTOR_VALUES,DISCOUNT_RATE=0.05,ST_CAP=8680,PHASE_SIZE=5000){
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CURRENT <- DATA%>% filter(Year==ST_YEAR+YEARS_AHEAD,Discount==DISCOUNT_RATE)
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RES <- MAX_REV(CURRENT,ST_CAP+PHASE_SIZE*ADDED_PHASES)
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@ -85,19 +86,54 @@ PROFIT_EST <- function(ADDED_PHASES,ST_YEAR,YEARS_AHEAD,DATA=REACTOR_VALUES,DISC
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PROFIT <- rbind(REVENUE,OPTIMAL_COST(ADDED_PHASES,TONS_STORED,YEARS_AHEAD))
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return(PROFIT)
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}
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MAX_REV(CURRENT,ST_CAP+PHASE_SIZE*1)
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OPTIMAL_COST(0,TONS_STORED,YEARS_AHEAD)
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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)
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REACTOR_VALUES <- readRDS("Data/Cleaned_Data/Reactor_Values.Rds")
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#ADDED <- 1
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#ADD_RES <- MAX_REV(CURRENT,8680+5000*ADDED)
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#REACTOR_DATA <- CURRENT
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#STARTING_YEAR <- 2026
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#YEARS_AHEAD=20
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#STARTING_CAP=8680
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#SINGLE_PHASE_CAP=5000
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#ADDED_UNITS <- 1
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#Find the optimal profit and cost, plus if the capacity constraint of an addtion in binding.
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ADDITION_CHECK <- function(REACTOR_DATA,ADDED_UNITS,YEARS_AHEAD,Discount_Rate=0.05,STARTING_CAP=8680,SINGLE_PHASE_CAP=5000){
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OPTIM_GUESS <- MAX_REV(REACTOR_DATA,STARTING_CAP+SINGLE_PHASE_CAP*ADDED_UNITS)
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SELECTED_REACTORS <- REACTOR_DATA[which(ADD_RES$solution==1),]%>% mutate(MARGINAL_VALUE=Revenue/Total_Tons)
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FOUND_VOLUME <- OPTIM_GUESS$constraint[76]
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LOWEST_VALUE_REACTOR <-SELECTED_REACTORS[SELECTED_REACTORS$MARGINAL_VALUE== min(SELECTED_REACTORS$MARGINAL_VALUE),]
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MARGINAL_VALUE <- LOWEST_VALUE_REACTOR$MARGINAL_VALUE
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FULL_COST_AT_CAPACITY <- OPTIMAL_COST(ADDED_UNITS,FOUND_VOLUME,YEARS_AHEAD)
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MARGINAL_COST <- FULL_COST_AT_CAPACITY -OPTIMAL_COST(ADDED_UNITS,FOUND_VOLUME-1,YEARS_AHEAD)
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BOUNDED <- MARGINAL_VALUE>MARGINAL_COST
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if(BOUNDED){
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FOUND_VOLUME <- FOUND_VOLUME-STARTING_CAP
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State <- "Capacity Constrainted"
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Optimal_Rev <- OPTIM_GUESS$objval
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Optimal_Cost <- FULL_COST_AT_CAPACITY
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}
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else {
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HIGHEST_VALUE_REACTOR <-SELECTED_REACTORS[SELECTED_REACTORS$MARGINAL_VALUE== max(SELECTED_REACTORS$MARGINAL_VALUE),]
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MARGINAL_VALUE <- unique(HIGHEST_VALUE_REACTOR$MARGINAL_VALUE)
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if(MARGINAL_VALUE>=MARGINAL_COST){
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State <- "Not a Profitable Phase"
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FOUND_VOLUME <- 0
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OPTIM_STARTING <- MAX_REV(REACTOR_DATA,STARTING_CAP)
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Optimal_Rev <- OPTIM_STARTING$objval
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Optimal_Cost <- OPTIMAL_COST(0,STARTING_CAP,YEARS_AHEAD)
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}else{
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State <- "No Binding Constraints"
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FOUND_VOLUME <- NA
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Optimal_Rev <- NA
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Optimal_Cost <- NA
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RES$solution
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names(RES)
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length(RES$objective)
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CURRENT[RES[[9]],]
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CURRENT[-RES[[9]],]
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REACTOR_VALUES %>% filter(Year=[RES[[9]],]
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}
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}
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Profit <- Optimal_Rev-Optimal_Cost
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return(c(ADDED_UNITS,State,FOUND_VOLUME,Profit,Optimal_Rev,Optimal_Cost))
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}
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#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.
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ADDITION_CHECK(CURRENT,22,1)
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68
Scripts/Reactor_Clean.r
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68
Scripts/Reactor_Clean.r
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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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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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#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.
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DISCOUNT_RATE_LIST <- seq(0.01,0.15,by=0.0025)
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#The cost per ton of shipping uranium, used to see what can be shipped on day one of the project.
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CV <- 1.2874*(6984013) #Data from New Mexico Report, Converted from 2019 to Dec 2025
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#Locations to save results
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SAVE_DIR <- "./Data/Cleaned_Data/"
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#Create any need save locations
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dir.create(SAVE_DIR,recursive=TRUE,showWarnings=FALSE)
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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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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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TOTAL_ORIG <- TOTAL
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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)
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source("./Scripts/Functions/NPV_Functions.r")
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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)
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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)
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TOTAL_VALUE_METRICS_ORIG
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saveRDS(TOTAL_VALUE_METRICS ,paste0(SAVE_DIR,"Reactor_Values.Rds"))
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