193 lines
12 KiB
R
193 lines
12 KiB
R
#####Packages
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library(tidyverse) #Cleaning data
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library(fixest) #Estimating a model of birth rates, to provide variance in the birth rate Monte Carlo using a fixed effect model.
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library(forecast) #Fore ARIMA migration simulations
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library(parallel)
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library(uuid) #To add a index to each batch
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####If the preliminary data needs to be reloaded run the supplied bash script to download, process, and generate all needed data sets for the Monte Carlo population Simulation. Otherwise skip this step to save time
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RELOAD_DATA <- FALSE
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if(RELOAD_DATA){system("bash Prelim_Process.sh")}
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#Load custom functions needed for the simulation
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source("Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r")
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source("Scripts/Load_Custom_Functions/Birth_Simulation_Functions.r")
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source("Scripts/Load_Custom_Functions/Increment_Data_Year.r")
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source("Scripts/Load_Custom_Functions/Single_Age_Mortality_Trend_Simulation.r")
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source("Scripts/Load_Custom_Functions/Induced_Migration_Functions.r")
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#######Preliminary Model Inputs
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YEARS_AHEAD <- 41
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ST_YEAR <- 2025
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################################Load Data
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DEMO <- readRDS("Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
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BIRTH_MOD <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Birth_Regression.Rds")
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#Must add region as a factor with multiple levels for predict to work. Seems to check for multiple levels although that is not needed econometrics.
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BIRTH_DATA <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Regression_Data/Birth_Simulation_Key_Starting_Points.Rds") %>% mutate(Region=factor(Region)) %>% filter(KEM==1,Year==2023) %>% mutate(Year=2024)
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MIGRATION_ARIMA <- readRDS("Data/Intermediate_Inputs/Migration_ARIMA_Models/Kemmerer_Diamondville_Net_Migration_ARIMA.Rds")
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MIGRATION_ODDS <- readRDS("Data/Intermediate_Inputs/Migration_Trends/Migration_Age_Probability_Zero_to_85.Rds")
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####
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OPERATORS <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Operating_Worker_Related_Migration.Rds")
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CONSTRUCTION <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Construction_Related_Migration.Rds")
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OPERATOR_LIN_MIGRATION <- OPERATORS %>% pull("Operator_Emp_Migrated")
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CONSTRUCTION_LIN_MIGRATION <- CONSTRUCTION %>% pull("Construction_Emp_Migrated")
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INDUCED_MIGRATION_MULTIPLIERS <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Induced_Jobs.Rds")
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##############
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#Data for death rate trends
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SINGLE_AGE_MODS <- readRDS("Data/Intermediate_Inputs/Mortality_Regression_Data/Single_Sex_Age_Time_Series_Regression.Rds")
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BOUNDS <- readRDS("Data/Intermediate_Inputs/Mortality_Regression_Data/Single_Sex_Age_Bounds_for_Predictions.Rds")
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MAX_MALE <- BOUNDS %>% filter(Sex=='Male') %>% pull(MAX_RATE)
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MIN_MALE <- BOUNDS %>% filter(Sex=='Male') %>% pull(MIN_RATE)
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MAX_FEMALE <- BOUNDS %>% filter(Sex=='Female') %>% pull(MAX_RATE)
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MIN_FEMALE <- BOUNDS %>% filter(Sex=='Female') %>% pull(MIN_RATE)
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MIN_GAP <- BOUNDS %>% filter(Sex=='Male') %>% pull(MIN_MALE_FEMALE_GAP)
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MAX_GAP <- BOUNDS %>% filter(Sex=='Male') %>% pull(MAX_MALE_FEMALE_GAP)
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BASELINE_AGE_ADJUST_MEN <- readRDS("Data/Cleaned_Data/Mortality_Data/RDS/Single_Sex_Age_Population_in_2000.Rds") %>% filter(Sex=='Male') %>% pull(Percent_of_Population)
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BASELINE_AGE_ADJUST_WOMEN <- readRDS("Data/Cleaned_Data/Mortality_Data/RDS/Single_Sex_Age_Population_in_2000.Rds") %>% filter(Sex=='Female') %>% pull(Percent_of_Population)
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#Adjust to just women popualtion (Not all population percent
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BASELINE_AGE_ADJUST_WOMEN <- BASELINE_AGE_ADJUST_WOMEN/sum(BASELINE_AGE_ADJUST_WOMEN )
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BASELINE_AGE_ADJUST_MEN <- BASELINE_AGE_ADJUST_MEN/ sum(BASELINE_AGE_ADJUST_MEN )
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MOD_MEN_ALL <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_US_Men_Mortality_by_Age.Rds")
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MOD_WOMEN_ALL <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_US_Women_Mortality_by_Age.Rds")
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MOD_LIN_MEN <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_Lincoln_Men_Mortality_by_Age.Rds")
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MOD_LIN_WOMEN <- readRDS("Data/Intermediate_Inputs/Age_Mortality_ARIMA_Models/ARIMA_Lincoln_Women_Mortality_by_Age.Rds")
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XREG <- cbind(rep(0.0001,YEARS_AHEAD+1),rep(0.0001,YEARS_AHEAD+1)) #Empty data set to simulate in the future
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XREG <- ts(XREG,start=ST_YEAR,frequency=1)
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SIMULATE_MORTALITY_RATE_TRENDS <- function(){
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SIMULATED_MORTALITY_DATA_SET <- MAKE_EMPTY(ST_YEAR,ST_YEAR+YEARS_AHEAD,MOD_LIN_MEN,MOD_LIN_WOMEN,MOD_MEN_ALL,MOD_WOMEN_ALL,XREG)
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MORTALITY_SIMULATION <- AGE_DIST(SINGLE_AGE_MODS,SIMULATED_MORTALITY_DATA_SET ,MAX_MALE,MAX_FEMALE,MIN_MALE,MIN_FEMALE,MAX_GAP,MIN_GAP,BASELINE_AGE_ADJUST_MEN,BASELINE_AGE_ADJUST_WOMEN)
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return(MORTALITY_SIMULATION )
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}
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#####################START YEAR BY SIMULATIONS
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#CURRENT_YEARS_AHEAD=1;CURRENT_SIM_NUM <- 1;MORTALITY_SIMULATION <- SIMULATE_MORTALITY_RATE_TRENDS()
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SINGLE_YEAR_SIM <- function(DEMO,BIRTH_DATA,CURRENT_YEARS_AHEAD,MORTALITY_SIMULATION,NET_MIGRATION){
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ORIG_DEMO <- DEMO
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DEMO <- DEMOGRAPHICS_AFTER_MIGRATION(DEMO, NET_MIGRATION,MIGRATION_ODDS )
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TOTAL_MIGRATION <- sum(DEMO-ORIG_DEMO)
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BIRTH_DATA$Year <- BIRTH_DATA$Year+1
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BIRTH_DATA$Lag_Two_Births <- BIRTH_DATA$Lag_Births
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BIRTH_DATA$Lag_Births <- BIRTH_DATA$Births
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BIRTH_DATA$Births <- NA
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##We grab one year earlier than the window because they are one year older this year. Because the ages are from 0-85, row 18 is year 17, but one year is added making it 18 years in the current year. The birth windows are 18-28 for women and 18-30 for men.
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BIRTH_DATA$Min_Birth_Group <- min(sum(DEMO[18:30,1]),sum(DEMO[18:28,2]))
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NEW_BORNS <- BIRTH_SIM(BIRTH_MOD,BIRTH_DATA)
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TOTAL_BIRTHS <- sum(NEW_BORNS)
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BIRTH_DATA[,"Births"] <- TOTAL_BIRTHS
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DEMO <- INCREMENT_AGES(DEMO,NEW_BORNS)
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MORTALITY_SIMULATION
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MALE_DEATHS <- sapply(1:86,function(x){rbinom(1,DEMO[x,1],MORTALITY_SIMULATION[[1]][x,CURRENT_YEARS_AHEAD])})
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FEMALE_DEATHS <- sapply(1:86,function(x){rbinom(1,DEMO[x,2],MORTALITY_SIMULATION[[2]][x,CURRENT_YEARS_AHEAD])})
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MALE_DEATHS <- ifelse(MALE_DEATHS>=DEMO[,1],DEMO[,1],MALE_DEATHS)
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FEMALE_DEATHS <- ifelse(FEMALE_DEATHS>=DEMO[,1],DEMO[,1],FEMALE_DEATHS)
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TOTAL_DEATHS <- sum(MALE_DEATHS+FEMALE_DEATHS)
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DEMO[,"Num_Male"] <- DEMO[,"Num_Male"] -MALE_DEATHS
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DEMO[,"Num_Female"] <- DEMO[,"Num_Female"] -FEMALE_DEATHS
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#List of values needed for the next run or for reporting a result
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TOTAL_POP <- sum(DEMO)
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return(list(DEMO,BIRTH_DATA,c(TOTAL_POP,TOTAL_BIRTHS,TOTAL_DEATHS,TOTAL_MIGRATION)))
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}
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MIGRATION_ARIMA_MODEL <- MIGRATION_ARIMA
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#DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_TOTAL,CONSTRUCTION_TOTAL,MIGRATION_MULTIPLIERS
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CONSTRUCTION_MIGRATION <- CONSTRUCTION_LIN_MIGRATION
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MIGRATION_MULTIPLIERS <- INDUCED_MIGRATION_MULTIPLIERS
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OPERATOR_MIGRATION <- OPERATOR_LIN_MIGRATION
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SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_MIGRATION,CONSTRUCTION_MIGRATION,MIGRATION_MULTIPLIERS ){
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TERRA_POWER_EFFECT <- rep(0,YEARS_AHEAD)
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OTHER_PROJECT_EFFECTS_PERM <- rep(0,YEARS_AHEAD)
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OTHER_PROJECT_EFFECTS_TEMP <- rep(0,YEARS_AHEAD)
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OTHER_PROJECT_EFFECTS_TEMP_POP_ADDED <- rep(0,YEARS_AHEAD)
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OPERATOR_MIGRATION <- LOCAL_WORK_ADJ(OPERATOR_MIGRATION ,0.85) #Assume between 85%-100% operators live in Kemmerer
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CONSTRUCTION_MIGRATION <- LOCAL_WORK_ADJ(CONSTRUCTION_MIGRATION,0.41) #Assume between 41%-100% operators live in Kemmerer
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CONSTRUCTION_MIGRATION[7] <- CONSTRUCTION_MIGRATION[7] - sum(CONSTRUCTION_MIGRATION )
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CONSTRUCTION_POPULATION_ADDED <- cumsum(CONSTRUCTION_MIGRATION)
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ADDED_FROM_BASELINE <- runif(1) #The percentage of the possible growth in industries like restaurants to add compared to the Kemmerer IMPLAN model which understates possible structural growth
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PERMANENT_TERRAPOWER_MIGRATION <- INDUCED_SIMULATION(CONSTRUCTION_MIGRATION,OPERATOR_MIGRATION,MIGRATION_MULTIPLIERS,ADDED_FROM_BASELINE)+OPERATOR_MIGRATION
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###############NOTE NEED TO USE THIS AT END TO ADJUST THE RESULTS WHILE LEAVING THE DEMOGRAPHIC MATRIX
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TERRA_POWER_EFFECT[1:7] <- TERRA_POWER_EFFECT[1:7]+CONSTRUCTION_MIGRATION
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#Dry Pine Migration
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DRY_PINE <- round(runif(1,0.15,0.45)*c(126,176,-302)*1.15)
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DRY_PINE[3] <- DRY_PINE[3]-sum(DRY_PINE)
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OTHER_PROJECT_EFFECTS_TEMP[2:4] <- DRY_PINE
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OTHER_PROJECT_EFFECTS_TEMP_POP_ADDED[2:4] <- cumsum(DRY_PINE)
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#Data Center Simulation
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if(runif(1)>0.67){
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MW_CAPACITY <- runif(1,20,120)
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PERM_WORKERS <- MW_CAPACITY/8+30 #Seems to be a good ration 32.5 workers at 20 capacity and 45 at max this is a heurstic I came up with
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OTHER_PROJECT_EFFECTS_PERM
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DATA_CENTER_START_YEAR <- which.max(runif(YEARS_AHEAD-6))+3
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PERM_DATA_CENTER_OP <- PERM_WORKERS*(1+(2.515334+19.52286*ADDED_FROM_BASELINE)/40) #Add induced
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#Estimate construction workers on data center
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AVG_CONST_COST_PER_MW <- (10.4+13.2)/2
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RELATIVE_COST_DRAW <- runif(1,10.4*0.75,13.2*1.25)/AVG_CONST_COST_PER_MW #Draw from the range of reported construction costs per MW of capacity in Cheyene. See "DATA CENTER DEVELOPMENT COST GUIDE"
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PERCENT_IN_KEMMERER <- runif(1,0.41,1) #What percent of the in migration will live in Kemmerer vs other areas. Taken from TerraPower assumptions
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POP_PER_YEAR <- (1.15*46.02*MW_CAPACITY)/3 #MW capacity ranges from 18 to 120 based on historic Sabre projects. IMPLAN estimates 46.02 total jobs, per MW of capacity (using average cost per MWh). 1.15 accounts for 5% of workers bringing families
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TEMP_DATA_MIGRATION <- round(PERCENT_IN_KEMMERER*RELATIVE_COST_DRAW*(POP_PER_YEAR))
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OTHER_PROJECT_EFFECTS_TEMP[DATA_CENTER_START_YEAR+3] <- OTHER_PROJECT_EFFECTS_TEMP[DATA_CENTER_START_YEAR+3]+TEMP_DATA_MIGRATION #Enter
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OTHER_PROJECT_EFFECTS_TEMP[DATA_CENTER_START_YEAR+6] <- OTHER_PROJECT_EFFECTS_TEMP[DATA_CENTER_START_YEAR+6]-TEMP_DATA_MIGRATION #Leave
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OTHER_PROJECT_EFFECTS_TEMP_POP_ADDED[(DATA_CENTER_START_YEAR+3):(DATA_CENTER_START_YEAR+5)]<- OTHER_PROJECT_EFFECTS_TEMP_POP_ADDED[(DATA_CENTER_START_YEAR+3):(DATA_CENTER_START_YEAR+5)]+TEMP_DATA_MIGRATION
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OTHER_PROJECT_EFFECTS_PERM[DATA_CENTER_START_YEAR+6] <- OTHER_PROJECT_EFFECTS_PERM[DATA_CENTER_START_YEAR+3]+PERM_DATA_CENTER_OP
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}
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MIGRATION_SIM_VALUES <- round(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA_MODEL))+TERRA_POWER_EFFECT+OTHER_PROJECT_EFFECTS_PERM)
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#The runif applies a downshift ranging from the historic decline rate all the way to the Lincoln rate applied in the model
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FINAL_REPORT_VALUES <- matrix(NA,ncol=6,nrow=YEARS_AHEAD)
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colnames(FINAL_REPORT_VALUES ) <- c("Sim_UUID","Year","Population","Births","Deaths","Net_Migration")
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FINAL_REPORT_VALUES[,1] <- UUIDgenerate()
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for(i in 1:YEARS_AHEAD){
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C_YEAR <- ST_YEAR+i-1
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C_RES <-SINGLE_YEAR_SIM(DEMO,BIRTH_DATA,i,SIMULATE_MORTALITY_RATE_TRENDS(),MIGRATION_SIM_VALUES[i])
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DEMO <- C_RES[[1]]
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BIRTH_DATA <- C_RES[[2]]
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FINAL_REPORT_VALUES[i,-1] <- c(C_YEAR,C_RES[[3]])
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}
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FINAL_REPORT_VALUES[1:7,3] <- as.numeric(FINAL_REPORT_VALUES[1:7,3])+CONSTRUCTION_POPULATION_ADDED
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FINAL_REPORT_VALUES[1:7,6] <- as.numeric(FINAL_REPORT_VALUES[1:7,6]) +CONSTRUCTION_MIGRATION
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FINAL_REPORT_VALUES[,6] <- as.numeric(FINAL_REPORT_VALUES[,6]) +OTHER_PROJECT_EFFECTS_TEMP[1:nrow(FINAL_REPORT_VALUES)]
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FINAL_REPORT_VALUES[,3] <- as.numeric(FINAL_REPORT_VALUES[,3]) +OTHER_PROJECT_EFFECTS_TEMP_POP_ADDED[1:nrow(FINAL_REPORT_VALUES)]
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return(FINAL_REPORT_VALUES)
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}
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if(!exists("RES_DIR")){RES_DIR<- "./Results/"}
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if(!exists("RES_SIM_DIR")){RES_SIM_DIR <- paste0(RES_DIR,"Simulations/")}
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dir.create(RES_SIM_DIR, recursive = TRUE, showWarnings = FALSE)
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NCORES <- detectCores()-1
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BATCH_SIZE <- NCORES*10
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TOTAL_SIMULATIONS <- 10^5
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N_RUNS <-ceiling(TOTAL_SIMULATIONS/BATCH_SIZE )
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SIM_RES_FILE <- paste0(RES_SIM_DIR,"Kemmerer_2024_Upper_Bound_With_Data_Center_Simulation.csv")
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NEW_RES_FILE <- !file.exists(SIM_RES_FILE)
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OPERATOR_LIN_MIGRATION <- OPERATORS %>% pull("Operator_Emp_Migrated")
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CONSTRUCTION_LIN_MIGRATION <- CONSTRUCTION %>% pull("Construction_Emp_Migrated")
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for(i in 1:N_RUNS){
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# MIGRATION_MATRIX <- simulate(nsim=BATCH_SIZE,MIGRATION_ARIMA,n=YEARS_AHEAD)
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MIGRATION_MATRIX <- do.call(cbind, mclapply(1:BATCH_SIZE,function(x)(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA) )),mc.cores = detectCores()-1))
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# rownames(MIGRATION_MATRIX) <- ST_YEAR:(ST_YEAR+YEARS_AHEAD-1)
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# colnames(MIGRATION_MATRIX) <- 1:BATCH_SIZE
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#SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA,OPERATOR_LIN_MIGRATION,CONSTRUCTION_LIN_MIGRATION,INDUCED_MIGRATION_MULTIPLIERS)
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try(RES <- do.call(rbind,mclapply(1:BATCH_SIZE,function(x){SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA,OPERATOR_LIN_MIGRATION,CONSTRUCTION_LIN_MIGRATION,INDUCED_MIGRATION_MULTIPLIERS)},mc.cores=NCORES)))
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if(exists("RES")){
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RES <- as.data.frame(RES)
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RES[,-1] <- as.numeric(as.matrix(RES[,-1]))
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if(NEW_RES_FILE & i==1){write_csv(RES,SIM_RES_FILE)}else {write_csv(RES,SIM_RES_FILE,col_names=FALSE,append=TRUE)}
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rm(RES)
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}
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}
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