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Fixed some data

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This commit is contained in:
Alex 2026-01-29 17:03:26 -07:00
parent b816c060f3
commit 2d2de480df
4 changed files with 67 additions and 47 deletions
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27
Analysis.r
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@ -14,9 +14,19 @@ SUPPLY_RESULTS_WITH_SHIPPING <- list() #This shows the amount availble to ship t
SHIPPING_COST <- 1.2874*26000 #Inflation adjusted from New Mexico Report SHIPPING_COST <- 1.2874*26000 #Inflation adjusted from New Mexico Report
TEST <- RES$Per_5%>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% mutate(Marginal_Value=Revenue/Total_Tons) %>% select(Year,Facility,Marginal_Value,Total_Tons) %>% group_by(Year) %>% arrange(Year,desc(Marginal_Value)) %>% mutate(Q=cumsum(Total_Tons)) %>% mutate(IN=Q<10000) %>% mutate(MIN=ZZMIN
TEST %>% filter(IN) %>% filter(Marginal_Value==min(Marginal_Value)) %>% print(n=100)
TEST %>% print(n=30)
TEST %>% arrange(Year,Marginal_Value)
TEST
TEST %>% filter(IN) %>% filter(Marginal_Value )
TEST <- RES[[1]]%>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% select(Facility,Year,Revenue,Total_Tons) %>% mutate(Marginal=Revenue/Total_Tons) %>% group_by(Year) %>% arrange(Marginal) %>% mutate(IN=cumsum(Total_Tons)<10000)
TEST
for(i in 1:LENGTH){ for(i in 1:LENGTH){
RES[[i]] <- RES[[i]] %>% filter(!(Facility %in% c("Palo Verde","Vogtle"))) #The shipping cost is higher than the marginal value per SNF at these locations in the lat period of study 2083. RES[[i]] <- RES[[i]] %>% filter(!(Facility %in% c("Palo Verde","Vogtle"))) #The shipping cost is higher than the marginal value per SNF at these locations in the lat period of study 2083.
RES[[i]]$Revenue <-CV2*as.numeric(RES[[i]]$Revenue) RES[[i]]$Revenue <-CV1*as.numeric(RES[[i]]$Revenue)
RES[[i]]$Year<-as.numeric(RES[[i]]$Year) RES[[i]]$Year<-as.numeric(RES[[i]]$Year)
DISCOUNT <- as.numeric(gsub("Per_","",names(RES)[i]))/100 DISCOUNT <- as.numeric(gsub("Per_","",names(RES)[i]))/100
SUPPLY_RESULTS[[i]] <- RES[[i]] %>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% mutate(Marginal_Value=Revenue/Total_Tons) %>% select(Year,Marginal_Value,Total_Tons) %>% group_by(Year) %>% arrange(Year,desc(Marginal_Value)) %>% mutate(Q=cumsum(Total_Tons),Discount=factor(percent(DISCOUNT,1),levels=c("3%","5%","7%","10%"))) SUPPLY_RESULTS[[i]] <- RES[[i]] %>% left_join(read_csv("Data/Raw_Data/Curie_Spent_Fuel_Site_Totals.csv")) %>% mutate(Marginal_Value=Revenue/Total_Tons) %>% select(Year,Marginal_Value,Total_Tons) %>% group_by(Year) %>% arrange(Year,desc(Marginal_Value)) %>% mutate(Q=cumsum(Total_Tons),Discount=factor(percent(DISCOUNT,1),levels=c("3%","5%","7%","10%")))
@ -72,9 +82,11 @@ DEMAND_CURVE_FACET
CIFS <- rbind(readRDS("Data/Cleaned_Data/Texas_CIFS_Costs.Rds"),readRDS("Data/Cleaned_Data/New_Mexico_CIFS_Costs.Rds")) CIFS <- rbind(readRDS("Data/Cleaned_Data/Texas_CIFS_Costs.Rds"),readRDS("Data/Cleaned_Data/New_Mexico_CIFS_Costs.Rds"))
NPC <- CIFS %>% select(Year,Location,Phase,Capacity,Cost_Assumption,Total) %>% group_by(Year,Location,Cost_Assumption,Total,Phase,Capacity) %>% summarize(Total=mean(Total)) %>% arrange(Location,Phase,Year) %>% mutate(Cost_3=Total/(1+0.03)^Year,Cost_5=Total/(1+0.05)^Year,Cost_7=Total/(1+0.07)^Year,Cost_10=Total/(1+0.1)^Year) %>% ungroup %>% group_by(Location,Phase,Capacity,Cost_Assumption) %>% summarize('3%'=sum(Cost_3),'5%'=sum(Cost_5),"7%"=sum(Cost_7),"10%"=sum(Cost_10)) %>% pivot_longer(c(-Phase,-Location,-Cost_Assumption,-Capacity),names_to="Discount",values_to="CIFS_Cost") NPC <- CIFS %>% select(Year,Location,Phase,Capacity,Cost_Assumption,Total) %>% group_by(Year,Location,Cost_Assumption,Total,Phase,Capacity) %>% summarize(Total=mean(Total)) %>% arrange(Location,Phase,Year) %>% mutate(Cost_3=Total/(1+0.03)^Year,Cost_5=Total/(1+0.05)^Year,Cost_7=Total/(1+0.07)^Year,Cost_10=Total/(1+0.1)^Year) %>% ungroup %>% group_by(Location,Phase,Capacity,Cost_Assumption) %>% summarize('3%'=sum(Cost_3),'5%'=sum(Cost_5),"7%"=sum(Cost_7),"10%"=sum(Cost_10)) %>% pivot_longer(c(-Phase,-Location,-Cost_Assumption,-Capacity),names_to="Discount",values_to="CIFS_Cost")
TEMP1 <- NPC%>% filter(Location=='Texas') %>% select(-CIFS_Cost) %>% mutate(Phase="Extended",Capacity=10^5) %>% unique
TEMP2 <- NPC %>% filter(Location=='Texas') %>% group_by(Location,Discount,Cost_Assumption) %>% summarize(CAP_CHANGE=max(Capacity)-min(Capacity),ST_COST=min(CIFS_Cost),END_COST=max(CIFS_Cost),SLOPE=(END_COST-ST_COST)/CAP_CHANGE,CIFS_Cost=ST_COST+SLOPE*(10^5-5000)) %>% select(Location,Discount,CIFS_Cost,Cost_Assumption)
TEMP <- TEMP1 %>% left_join(TEMP2)
NPC <- rbind(NPC,TEMP )
NPC <- NPC %>% filter(Cost_Assumption=="Average") %>% select(-Cost_Assumption) NPC <- NPC %>% filter(Cost_Assumption=="Average") %>% select(-Cost_Assumption)
NPC
KEY_DATA
RETURN_DATA <- KEY_DATA %>% left_join(NPC) %>% filter(Q<=Capacity) %>% group_by(Year,Discount,Location,Phase,Capacity,CIFS_Cost) %>% summarize(Q=sum(Total_Tons),Revenue=sum(Marginal_Value*Total_Tons),P=Revenue/Q,Profit=sum(Revenue-CIFS_Cost)) %>% ungroup RETURN_DATA <- KEY_DATA %>% left_join(NPC) %>% filter(Q<=Capacity) %>% group_by(Year,Discount,Location,Phase,Capacity,CIFS_Cost) %>% summarize(Q=sum(Total_Tons),Revenue=sum(Marginal_Value*Total_Tons),P=Revenue/Q,Profit=sum(Revenue-CIFS_Cost)) %>% ungroup
@ -88,9 +100,16 @@ RETURN_DATA <- RETURN_DATA %>% group_by(Discount,Location,Phase,Capacity,CIFS_C
#RETURN_DATA #RETURN_DATA
#RETURN_DATA <- RETURN_DATA %>% mutate(size=ifelse(Phase=="Partial","5,000 MTU Capacity (Phase 1)","40,000 MTU Capacity (Full Build Out)")) #RETURN_DATA <- RETURN_DATA %>% mutate(size=ifelse(Phase=="Partial","5,000 MTU Capacity (Phase 1)","40,000 MTU Capacity (Full Build Out)"))
RETURN_DATA %>% filter(Phase=="Extended")
RETURN_DATA %>% pull(Capacity) %>% unique
RETURN_DATA %>% group_by(Year,Capacity,Discount) %>% filter(n()>1) %>% arrange(Year,Capacity)
png("Revenue.png",height=6,width=11,units="in",res=900)
ggplot(RETURN_DATA %>% filter(Discount=='5%'),aes(x=Year,y=Profit/10^9,color=Location))+geom_point()+scale_x_continuous(breaks=seq(1960,2085,by=5))
dev.off()
FOC_PLOT <- ggplot(RETURN_DATA,aes(x=Year,y=FOC/10^6,group=Discount,color=Discount))+facet_wrap(~Capacity,ncol=2)+scale_x_continuous(breaks=seq(1960,2083,by=10))+geom_point(size=0.5)+geom_step(linewidth=0.75)+ geom_hline(yintercept = 0, color = "black", linetype = "solid", size = 1)+theme(text = element_text(size = 16),legend.position = "top")+ylab("Million Dollars") FOC_PLOT <- ggplot(RETURN_DATA,aes(x=Year,y=FOC/10^6,group=Discount,color=Discount))+facet_wrap(~Capacity,ncol=1,scales="free")+scale_x_continuous(breaks=seq(1960,2083,by=10))+geom_point(size=0.5)+geom_step(linewidth=0.75)+ geom_hline(yintercept = 0, color = "black", linetype = "solid", size = 1)+theme(text = element_text(size = 16),legend.position = "top")+ylab("Million Dollars")png("FOC_PLOT.png",width=8,height=18, units="in",res=1800)
FOC_PLOT FOC_PLOT
dev.off()
DEMAND_CURVE_YEARS+geom_segment(aes(x = 0, y = C, xend = 5, yend = C),color="orange")+geom_segment(aes(x = 5, y = C, xend = 5, yend = M),color="orange")+geom_segment(aes(x = 5, y = M, xend = 140, yend = M),color="orange",arrow = arrow(length = unit(0.4, "cm"))) DEMAND_CURVE_YEARS+geom_segment(aes(x = 0, y = C, xend = 5, yend = C),color="orange")+geom_segment(aes(x = 5, y = C, xend = 5, yend = M),color="orange")+geom_segment(aes(x = 5, y = M, xend = 140, yend = M),color="orange",arrow = arrow(length = unit(0.4, "cm")))

3
Cost_Data_Proc.r
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@ -1,9 +1,10 @@
library(tidyverse) library(tidyverse)
######CIFS costs ######CIFS costs
dir.create("./Data/Cleaned_Data",recursive=TRUE,showWarnings=FALSE)
read_csv("./Data/Raw_Data/Cost_Tables/Texas/Table_C-3_Undiscounted_Cost_Estimates_Phase_1_Low.csv")
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( 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 <- 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)
saveRDS(CIFS_TEXAS,"Data/Cleaned_Data/Texas_CIFS_Costs.Rds") 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( 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())

82
Data/Raw_Data/Cost_Tables/Texas/Table_C-3_Undiscounted_Cost_Estimates_Phase_1_Low.csv
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@ -1,42 +1,42 @@
Year,Construction,Transportation_to_CISF,Operations,Transportation_to_Repository,Decommissioning,Total Year,Construction,Transportation_to_CISF,Operations,Transportation_to_Repository,Decommissioning,Total
1,76552618,73711378,5041229,0,0,155305226 5041229,0,0,155305226
2,65910317,142837839,5041229,0,0,213789386 5041229,0,0,213789386
3,11737391,2547465,5041229,0,0,19326086 5041229,0,0,19326086
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 5041229,0,0,53313056
7,0,0,5041229,0,0,5041229 5041229,0,0,5041229
8,40629430,7642396,5041229,0,0,53313056 5041229,0,0,53313056
9,9028762,1698310,5041229,0,0,15768302 5041229,0,0,15768302
10,0,0,5041229,0,0,5041229 5041229,0,0,5041229
11,3606215,0,5041229,0,0,8647444 5041229,0,0,8647444
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 5041229,0,0,5041229
21,17854730,0,5041229,0,0,22895959 5041229,0,0,22895959
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 5041229,0,0,5041229
31,3606215,0,5041229,0,0,8647444 5041229,0,0,8647444
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 5041229,0,0,5041229
39,0,0,5041229,125682289,0,130723519 5041229,125682289,0,130723519
40,0,0,5041229,125682289,0,130723519 5041229,125682289,0,130723519
41,0,0,0,0,56740382,56740382 0,0,56740382,56740382

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

2
Data/Raw_Data/Cost_Tables/Texas/Table_C-4_Undiscounted_Cost_Estimates_Phase_1_High.csv
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@ -38,5 +38,5 @@ Year,Construction,Transportation_to_CISF,Operations,Transportation_to_Repository
37,0,0,12170532,0,0,12170532 37,0,0,12170532,0,0,12170532
38,0,0,12170532,0,0,12170532 38,0,0,12170532,0,0,12170532
39,0,0,12170532,125682289,0,137852821 39,0,0,12170532,125682289,0,137852821
40,0,0,12170532,0,125682289,137852821 40,0,0,12170532,125682289,0,137852821
41,0,0,0,0,56740382,56740382 41,0,0,0,0,56740382,56740382

1 Year Construction Transportation_to_CISF Operations Transportation_to_Repository Decommissioning Total
38 37 0 0 12170532 0 0 12170532
39 38 0 0 12170532 0 0 12170532
40 39 0 0 12170532 125682289 0 137852821
41 40 0 0 12170532 0 125682289 125682289 0 137852821
42 41 0 0 0 0 56740382 56740382