103 lines
6.5 KiB
R
103 lines
6.5 KiB
R
#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 <- c(0.03,0.05,0.07)
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#DISCOUNT_RATE_LIST <- c(0.03,0.0325,0.035,0.0375,0.04,0.045,0.0475,0.05,0.07,0.1)
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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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SHIPPING_COST_PER_TON <- 1.2874*26000 #Inflation adjusted from New Mexico Report
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#The savings per year of having a CIFS at a served reactor (cost to house the CIFS).
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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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RES_DIR <- "./Data/Results/"
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INTERMEDIATE_DIR <- "./Data/Results/Separate_Costs_and_Benefits_Data/"
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#Directory where the individual CIFS project plan cost data can be found.
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#This has data has already been combined with the original files (low,high) and inflation adjusted.
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CIFS_INDIVIDUAL_COST_DATA_DIR <- 'Data/Cleaned_Data/'
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#Create any need save locations
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dir.create(RES_DIR,recursive=TRUE,showWarnings=FALSE)
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dir.create(INTERMEDIATE_DIR,recursive=TRUE,showWarnings=FALSE)
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###################################Cost results
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CIFS <- rbind(readRDS(paste0(CIFS_INDIVIDUAL_COST_DATA_DIR,"Texas_CIFS_Costs.Rds")),readRDS(paste0(CIFS_INDIVIDUAL_COST_DATA_DIR,"New_Mexico_CIFS_Costs.Rds")))
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#Adjust for inflation
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#CIFS[,-1:-5] <- 1.2874*CIFS[,-1:-5]
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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
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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
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CAPACITY_INCREMENT <- 5000
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#rm(COST_DATA)
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for(i in 1:nrow(TEMP)){
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LOC <- as.character(TEMP[i,"Location"])
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COST_LEVEL <- as.character(TEMP[i,"Cost_Assumption"])
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DISCOUNT <- as.numeric(TEMP[i,"Discount"])
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ST_CAP <- as.numeric(TEMP[i,"ST_CAPACITY"])
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ST_COST <- as.numeric(TEMP[i,"ST_COST"])
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COST_SLOPE <- as.numeric(TEMP[i,]$M_COST)
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CAPACITY <- seq(ST_CAP,160000,by=5000)
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COST <- ST_COST+COST_SLOPE*CAPACITY_INCREMENT*(0:(length(CAPACITY)-1))
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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)
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if(!exists("COST_DATA")){COST_DATA <- C_RES}else{COST_DATA<- rbind(COST_DATA,C_RES)}
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}
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saveRDS(COST_DATA ,paste0(INTERMEDIATE_DIR,"All_CIFS_Discounted_Costs.Rds"))
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#COST_DATA <- COST_DATA %>% filter(Cost_Assumption=='Average') %>% select(-Cost_Assumption) %>% unique
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COST_DATA <- COST_DATA %>% filter(Cost_Assumption=='High') %>% select(-Cost_Assumption) %>% unique
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##All unique capacity levels that the revenues need to be calculated for
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CAPACITY_LIST <- COST_DATA %>% pull(Capacity) %>% unique
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###
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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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