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04_allocation.Rmd
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---
title: "04_allocation"
output: html_notebook
---
This notebook assigns hosting capacity to households
```{r}
library(tidyverse)
library(tictoc)
library(reshape2)
library(ggplot2)
library(stringr)
library(tidytext)
library(measurements) # https://cran.r-project.org/web/packages/measurements/measurements.pdf
library(readxl)
library(colorspace)
```
```{r}
SCEtypes = read.csv("circuitfiles/SCE_ICAalldemoclasses.csv", header=T, na.strings="?")
SCE_ICAalldemo = read.csv("circuitfiles/SCE_ICAalldemo.csv", header=T, na.strings="?", colClasses=as.character(SCEtypes$x))
PGEtypes = read.csv("circuitfiles/PGE_ICAalldemoclasses.csv", header=T, na.strings="?")
PGE_ICAalldemo = read.csv("circuitfiles/PGE_ICAalldemo.csv", header=T, na.strings="?", colClasses=as.character(PGEtypes$x))
```
--------------------------------------------------------------------------------------------
REMAINING HOSTING CAPACITY
--------------------------------------------------------------------------------------------
Hosting capacity by household within circuit polygons, scaled by length
```{r}
SCE_ICAalldemo <- SCE_ICAalldemo %>%
mutate(ICL_max_lenWt = ICL_max * Length_Wt,
ICPVOF_max_lenWt = ICPVOF_max * Length_Wt, ICPV_max_lenWt = ICPV_max * Length_Wt,
ICUGOF_max_lenWt = ICUGOF_max * Length_Wt, ICUG_max_lenWt = ICUG_max * Length_Wt) %>%
mutate(ICL_kWphh_len = ICL_max_lenWt / tothh_Cpoly *1000,
ICPVOF_kWphh_len = ICPVOF_max_lenWt / tothh_Cpoly *1000, ICPV_kWphh_len = ICPV_max_lenWt / tothh_Cpoly *1000,
ICUGOF_kWphh_len = ICUGOF_max_lenWt / tothh_Cpoly *1000, ICUG_kWphh_len = ICUG_max_lenWt / tothh_Cpoly *1000)
PGE_ICAalldemo <- PGE_ICAalldemo %>%
mutate(ICL_max_lenWt = ICL_max * Length_Wt,
ICPVOF_max_lenWt = ICPVOF_max * Length_Wt, ICPV_max_lenWt = ICPV_max * Length_Wt,
ICUGOF_max_lenWt = ICUGOF_max * Length_Wt, ICUG_max_lenWt = ICUG_max * Length_Wt) %>%
mutate(ICL_kWphh_len = ICL_max_lenWt / tothh_Cpoly,
ICPVOF_kWphh_len = ICPVOF_max_lenWt / tothh_Cpoly, ICPV_kWphh_len = ICPV_max_lenWt / tothh_Cpoly,
ICUGOF_kWphh_len = ICUGOF_max_lenWt / tothh_Cpoly, ICUG_kWphh_len = ICUG_max_lenWt / tothh_Cpoly)
```
Scaling hosting capacity by number of households; percent of households a circuit segment serves relative to the whole circuit
```{r}
SCE_ICAalldemo <- SCE_ICAalldemo %>%
mutate(ICL_max_hhWt = ICL_max * tothh_pct,
ICPVOF_max_hhWt = ICPVOF_max * tothh_pct, ICPV_max_hhWt = ICPV_max * tothh_pct,
ICUGOF_max_hhWt = ICUGOF_max * tothh_pct, ICUG_max_hhWt = ICUG_max * tothh_pct) %>%
mutate(ICL_kWphh_hh = ICL_max_hhWt / tothh_Cpoly *1000,
ICPVOF_kWphh_hh = ICPVOF_max_hhWt / tothh_Cpoly *1000, ICPV_kWphh_hh = ICPV_max_hhWt / tothh_Cpoly *1000,
ICUGOF_kWphh_hh = ICUGOF_max_hhWt / tothh_Cpoly *1000, ICUG_kWphh_hh = ICUG_max_hhWt / tothh_Cpoly *1000)
PGE_ICAalldemo <- PGE_ICAalldemo %>%
mutate(ICL_max_hhWt = ICL_max * tothh_pct,
ICPVOF_max_hhWt = ICPVOF_max * tothh_pct, ICPV_max_hhWt = ICPV_max * tothh_pct,
ICUGOF_max_hhWt = ICUGOF_max * tothh_pct, ICUG_max_hhWt = ICUG_max * tothh_pct) %>%
mutate(ICL_kWphh_hh = ICL_max_hhWt / tothh_Cpoly,
ICPVOF_kWphh_hh = ICPVOF_max_hhWt / tothh_Cpoly, ICPV_kWphh_hh = ICPV_max_hhWt / tothh_Cpoly,
ICUGOF_kWphh_hh = ICUGOF_max_hhWt / tothh_Cpoly, ICUG_kWphh_hh = ICUG_max_hhWt / tothh_Cpoly)
```
To what extent does each weighting approach under- or over-estimate the total amount of hosting capacity available on a given circuit? To check, we want to see how much lower the overall ICA values are relative to the overall max.
```{r}
# calculating weighted quantities and normalization factors
SCE_ICAcheck <- SCE_ICAalldemo %>%
select(CircuitName, Length_m, Length_m_ctot, Length_Wt,
ICL_max_ctot, ICPVOF_max_ctot, ICPV_max_ctot, ICUGOF_max_ctot, ICUG_max_ctot,
ICL_max_lenWt, ICPVOF_max_lenWt, ICPV_max_lenWt, ICUGOF_max_lenWt, ICUG_max_lenWt,
ICL_max_hhWt, ICPVOF_max_hhWt, ICPV_max_hhWt, ICUGOF_max_hhWt, ICUG_max_hhWt) %>%
group_by(CircuitName) %>%
summarise(Length_m = sum(Length_m, na.rm=TRUE),
Length_m_ctot = mean(Length_m_ctot, na.rm=TRUE),
Length_Wt_ctot = sum(Length_Wt, na.rm=TRUE),
nseg = n(),
ICL_max_ctot = mean(ICL_max_ctot, na.rm=TRUE),
ICPVOF_max_ctot = mean(ICPVOF_max_ctot, na.rm=TRUE), ICPV_max_ctot = mean(ICPV_max_ctot, na.rm=TRUE),
ICUGOF_max_ctot = mean(ICUGOF_max_ctot, na.rm=TRUE), ICUG_max_ctot = mean(ICUG_max_ctot, na.rm=TRUE),
ICL_max_lenWt = sum(ICL_max_lenWt, na.rm=TRUE),
ICPVOF_max_lenWt = sum(ICPVOF_max_lenWt, na.rm=TRUE), ICPV_max_lenWt = sum(ICPV_max_lenWt, na.rm=TRUE),
ICUGOF_max_lenWt = sum(ICUGOF_max_lenWt, na.rm=TRUE), ICUG_max_lenWt = sum(ICUG_max_lenWt, na.rm=TRUE),
ICL_max_hhWt = sum(ICL_max_hhWt, na.rm=TRUE),
ICPVOF_max_hhWt = sum(ICPVOF_max_hhWt, na.rm=TRUE), ICPV_max_hhWt = sum(ICPV_max_hhWt, na.rm=TRUE),
ICUGOF_max_hhWt = sum(ICUGOF_max_hhWt, na.rm=TRUE), ICUG_max_hhWt = sum(ICUG_max_hhWt, na.rm=TRUE)) %>%
mutate(normlen_ICL = ICL_max_lenWt/ICL_max_ctot,
normlen_ICPVOF = ICPVOF_max_lenWt/ICPVOF_max_ctot, normlen_ICPV = ICPV_max_lenWt/ICPV_max_ctot,
normlen_ICUGOF = ICUGOF_max_lenWt/ICUGOF_max_ctot, normlen_ICUG = ICUG_max_lenWt/ICUG_max_ctot) %>%
mutate(normhh_ICL = ICL_max_hhWt/ICL_max_ctot,
normhh_ICPVOF = ICPVOF_max_hhWt/ICPVOF_max_ctot, normhh_ICPV = ICPV_max_hhWt/ICPV_max_ctot,
normhh_ICUGOF = ICUGOF_max_hhWt/ICUGOF_max_ctot, normhh_ICUG = ICUG_max_hhWt/ICUG_max_ctot) %>%
mutate(normlen_ICL = ifelse((is.na(normlen_ICL)|is.infinite(normlen_ICL)|normlen_ICL==0), 1, normlen_ICL),
normlen_ICPVOF = ifelse((is.na(normlen_ICPVOF)|is.infinite(normlen_ICPVOF)|normlen_ICPVOF==0), 1, normlen_ICPVOF),
normlen_ICPV = ifelse((is.na(normlen_ICPV)|is.infinite(normlen_ICPV)|normlen_ICPV==0), 1, normlen_ICPV),
normlen_ICUGOF = ifelse((is.na(normlen_ICUGOF)|is.infinite(normlen_ICUGOF)|normlen_ICUGOF==0), 1, normlen_ICUGOF),
normlen_ICUG = ifelse((is.na(normlen_ICUG)|is.infinite(normlen_ICUG)|normlen_ICUG==0), 1, normlen_ICUG)) %>%
mutate(normhh_ICL = ifelse((is.na(normhh_ICL)|is.infinite(normhh_ICL)|normhh_ICL==0), 1, normhh_ICL),
normhh_ICPVOF = ifelse((is.na(normhh_ICPVOF)|is.infinite(normhh_ICPVOF)|normhh_ICPVOF==0), 1, normhh_ICPVOF),
normhh_ICPV = ifelse((is.na(normhh_ICPV)|is.infinite(normhh_ICPV)|normhh_ICPV==0), 1, normhh_ICPV),
normhh_ICUGOF = ifelse((is.na(normhh_ICUGOF)|is.infinite(normhh_ICUGOF)|normhh_ICUGOF==0), 1, normhh_ICUGOF),
normhh_ICUG = ifelse((is.na(normhh_ICUG)|is.infinite(normhh_ICUG)|normhh_ICUG==0), 1, normhh_ICUG))
# merging normalization factors back into main file
SCE_ICAalldemo <- merge(SCE_ICAalldemo, SCE_ICAcheck[, c("CircuitName", "normlen_ICL", "normlen_ICPVOF", "normlen_ICPV", "normlen_ICUGOF", "normlen_ICUG", "normhh_ICL", "normhh_ICPVOF", "normhh_ICPV", "normhh_ICUGOF", "normhh_ICUG")], by=c("CircuitName"), all.x=TRUE, suffixes = c("",""))
# calculating normalized quantities
SCE_ICAalldemo <- SCE_ICAalldemo %>%
mutate(ICL_max_lenWt_n = ICL_max_lenWt/normlen_ICL,
ICPVOF_max_lenWt_n = ICPVOF_max_lenWt/normlen_ICPVOF, ICPV_max_lenWt_n = ICPV_max_lenWt/normlen_ICPV,
ICUGOF_max_lenWt_n = ICUGOF_max_lenWt/normlen_ICUGOF, ICUG_max_lenWt_n = ICUG_max_lenWt/normlen_ICUG,
ICL_max_hhWt_n = ICL_max_hhWt/normhh_ICL,
ICPVOF_max_hhWt_n = ICPVOF_max_hhWt/normhh_ICPVOF, ICPV_max_hhWt_n = ICPV_max_hhWt/normhh_ICPV,
ICUGOF_max_hhWt_n = ICUGOF_max_hhWt/normhh_ICUGOF, ICUG_max_hhWt_n = ICUG_max_hhWt/normhh_ICUG) %>%
mutate(ICL_max_lenWt_nadj = ifelse(abs(ICL_max_lenWt_n)<abs(ICL_max), ICL_max_lenWt_n, ICL_max),
ICPVOF_max_lenWt_nadj = ifelse(abs(ICPVOF_max_lenWt_n)<abs(ICPVOF_max), ICPVOF_max_lenWt_n, ICPVOF_max),
ICPV_max_lenWt_nadj = ifelse(abs(ICPV_max_lenWt_n)<abs(ICPV_max), ICPV_max_lenWt_n, ICPV_max),
ICUGOF_max_lenWt_nadj = ifelse(abs(ICUGOF_max_lenWt_n)<abs(ICUGOF_max), ICUGOF_max_lenWt_n, ICUGOF_max),
ICUG_max_lenWt_nadj = ifelse(abs(ICUG_max_lenWt_n)<abs(ICUG_max), ICUG_max_lenWt_n, ICUG_max),
ICL_max_hhWt_nadj = ifelse(abs(ICL_max_hhWt_n)<abs(ICL_max), ICL_max_hhWt_n, ICL_max),
ICPVOF_max_hhWt_nadj = ifelse(abs(ICPVOF_max_hhWt_n)<abs(ICPVOF_max), ICPVOF_max_hhWt_n, ICPVOF_max),
ICPV_max_hhWt_nadj = ifelse(abs(ICPV_max_hhWt_n)<abs(ICPV_max), ICPV_max_hhWt_n, ICPV_max),
ICUGOF_max_hhWt_nadj = ifelse(abs(ICUGOF_max_hhWt_n)<abs(ICUGOF_max), ICUGOF_max_hhWt_n, ICUGOF_max),
ICUG_max_hhWt_nadj = ifelse(abs(ICUG_max_hhWt_n)<abs(ICUG_max), ICUG_max_hhWt_n, ICUG_max))
# checking overall values
SCE_ICAcheck2 <- SCE_ICAalldemo %>%
select(CircuitName, ends_with("_max_lenWt_n"), ends_with("_max_lenWt_nadj"),
ends_with("_max_hhWt_n"), ends_with("_max_hhWt_nadj")) %>%
group_by(CircuitName) %>%
summarise(ICL_max_lenWt_n = sum(ICL_max_lenWt_n, na.rm=TRUE),
ICPVOF_max_lenWt_n = sum(ICPVOF_max_lenWt_n, na.rm=TRUE), ICPV_max_lenWt_n = sum(ICPV_max_lenWt_n, na.rm=TRUE),
ICUGOF_max_lenWt_n = sum(ICUGOF_max_lenWt_n, na.rm=TRUE), ICUG_max_lenWt_n = sum(ICUG_max_lenWt_n, na.rm=TRUE),
ICL_max_lenWt_nadj = sum(ICL_max_lenWt_nadj, na.rm=TRUE),
ICPVOF_max_lenWt_nadj = sum(ICPVOF_max_lenWt_nadj, na.rm=TRUE),
ICPV_max_lenWt_nadj = sum(ICPV_max_lenWt_nadj, na.rm=TRUE),
ICUGOF_max_lenWt_nadj = sum(ICUGOF_max_lenWt_nadj, na.rm=TRUE),
ICUG_max_lenWt_nadj = sum(ICUG_max_lenWt_nadj, na.rm=TRUE),
ICL_max_hhWt_n = sum(ICL_max_hhWt_n, na.rm=TRUE),
ICPVOF_max_hhWt_n = sum(ICPVOF_max_hhWt_n, na.rm=TRUE), ICPV_max_hhWt_n = sum(ICPV_max_hhWt_n, na.rm=TRUE),
ICUGOF_max_hhWt_n = sum(ICUGOF_max_hhWt_n, na.rm=TRUE), ICUG_max_hhWt_n = sum(ICUG_max_hhWt_n, na.rm=TRUE),
ICL_max_hhWt_nadj = sum(ICL_max_hhWt_nadj, na.rm=TRUE),
ICPVOF_max_hhWt_nadj = sum(ICPVOF_max_hhWt_nadj, na.rm=TRUE),
ICPV_max_hhWt_nadj = sum(ICPV_max_hhWt_nadj, na.rm=TRUE),
ICUGOF_max_hhWt_nadj = sum(ICUGOF_max_hhWt_nadj, na.rm=TRUE),
ICUG_max_hhWt_nadj = sum(ICUG_max_hhWt_nadj, na.rm=TRUE))
SCE_ICAcheck <- merge(SCE_ICAcheck, SCE_ICAcheck2, by=c("CircuitName"), all.x=TRUE)
rm(SCE_ICAcheck2)
SCE_ICAcheck <- SCE_ICAcheck %>%
melt( id=c("CircuitName","Length_m","Length_m_ctot","Length_Wt_ctot","nseg",
"normlen_ICL","normlen_ICPVOF","normlen_ICPV","normlen_ICUGOF","normlen_ICUG",
"normhh_ICL","normhh_ICPVOF","normhh_ICPV","normhh_ICUGOF","normhh_ICUG")) %>%
mutate(DER = factor(recode(substr(variable,1,6),
'ICL_ma'="ICL", 'ICPVOF'="ICPVOF", 'ICPV_m'="ICPV", 'ICUGOF'="ICUGOF", 'ICUG_m'="ICUG"),
levels=c("ICPV","ICPVOF","ICUG","ICUGOF","ICL")),
type = factor(recode(str_sub(variable,-8,-1), 'max_ctot'="ctot",
'ax_lenWt'="lenWt", '_lenWt_n'="lenWt_n",
'max_hhWt'="hhWt", 'x_hhWt_n'="hhWt_n",
'nWt_nadj'="lenWt_nadj", 'hWt_nadj'="hhWt_nadj")))
ggplot(SCE_ICAcheck, aes(x=type, y=value)) + geom_boxplot() + facet_grid(~DER) +
theme(axis.text.x = element_text(angle=90, size=8)) + labs(x="",y="Hosting capacity (MW) per circuit")
ggsave("SCE_ICAnormalization.png", path = "figures/", width=16, height=8)
b <- SCE_ICAcheck %>% filter(type=="ctot" | type=="hhWt" | type=="hhWt_n" | type=="hhWt_nadj") %>%
ggplot( aes(x=type, y=value)) + geom_boxplot() + facet_grid(~DER, labeller=labeller(DER=labsDER)) + theme_light() +
theme(axis.text.x = element_text(angle=90, size=8)) + labs(x="",y="Hosting capacity (MW) per circuit")
b
ggsave("SCE_ICAnormhh.png", path = "figures/", width=5, height=4)
```
```{r}
# calculating weighted quantities and normalization factors
PGE_ICAcheck <- PGE_ICAalldemo %>%
select(CircuitName, Length_m, Length_m_ctot, Length_Wt,
ICL_max_ctot, ICPVOF_max_ctot, ICPV_max_ctot, ICUGOF_max_ctot, ICUG_max_ctot,
ICL_max_lenWt, ICPVOF_max_lenWt, ICPV_max_lenWt, ICUGOF_max_lenWt, ICUG_max_lenWt,
ICL_max_hhWt, ICPVOF_max_hhWt, ICPV_max_hhWt, ICUGOF_max_hhWt, ICUG_max_hhWt) %>%
group_by(CircuitName) %>%
summarise(Length_m = sum(Length_m, na.rm=TRUE),
Length_m_ctot = mean(Length_m_ctot, na.rm=TRUE),
Length_Wt_ctot = sum(Length_Wt, na.rm=TRUE),
nseg = n(),
ICL_max_ctot = mean(ICL_max_ctot, na.rm=TRUE),
ICPVOF_max_ctot = mean(ICPVOF_max_ctot, na.rm=TRUE), ICPV_max_ctot = mean(ICPV_max_ctot, na.rm=TRUE),
ICUGOF_max_ctot = mean(ICUGOF_max_ctot, na.rm=TRUE), ICUG_max_ctot = mean(ICUG_max_ctot, na.rm=TRUE),
ICL_max_lenWt = sum(ICL_max_lenWt, na.rm=TRUE),
ICPVOF_max_lenWt = sum(ICPVOF_max_lenWt, na.rm=TRUE), ICPV_max_lenWt = sum(ICPV_max_lenWt, na.rm=TRUE),
ICUGOF_max_lenWt = sum(ICUGOF_max_lenWt, na.rm=TRUE), ICUG_max_lenWt = sum(ICUG_max_lenWt, na.rm=TRUE),
ICL_max_hhWt = sum(ICL_max_hhWt, na.rm=TRUE),
ICPVOF_max_hhWt = sum(ICPVOF_max_hhWt, na.rm=TRUE), ICPV_max_hhWt = sum(ICPV_max_hhWt, na.rm=TRUE),
ICUGOF_max_hhWt = sum(ICUGOF_max_hhWt, na.rm=TRUE), ICUG_max_hhWt = sum(ICUG_max_hhWt, na.rm=TRUE)) %>%
mutate(normlen_ICL = ICL_max_lenWt/ICL_max_ctot,
normlen_ICPVOF = ICPVOF_max_lenWt/ICPVOF_max_ctot, normlen_ICPV = ICPV_max_lenWt/ICPV_max_ctot,
normlen_ICUGOF = ICUGOF_max_lenWt/ICUGOF_max_ctot, normlen_ICUG = ICUG_max_lenWt/ICUG_max_ctot) %>%
mutate(normhh_ICL = ICL_max_hhWt/ICL_max_ctot,
normhh_ICPVOF = ICPVOF_max_hhWt/ICPVOF_max_ctot, normhh_ICPV = ICPV_max_hhWt/ICPV_max_ctot,
normhh_ICUGOF = ICUGOF_max_hhWt/ICUGOF_max_ctot, normhh_ICUG = ICUG_max_hhWt/ICUG_max_ctot) %>%
mutate(normlen_ICL = ifelse((is.na(normlen_ICL)|is.infinite(normlen_ICL)|normlen_ICL==0), 1, normlen_ICL),
normlen_ICPVOF = ifelse((is.na(normlen_ICPVOF)|is.infinite(normlen_ICPVOF)|normlen_ICPVOF==0), 1, normlen_ICPVOF),
normlen_ICPV = ifelse((is.na(normlen_ICPV)|is.infinite(normlen_ICPV)|normlen_ICPV==0), 1, normlen_ICPV),
normlen_ICUGOF = ifelse((is.na(normlen_ICUGOF)|is.infinite(normlen_ICUGOF)|normlen_ICUGOF==0), 1, normlen_ICUGOF),
normlen_ICUG = ifelse((is.na(normlen_ICUG)|is.infinite(normlen_ICUG)|normlen_ICUG==0), 1, normlen_ICUG)) %>%
mutate(normhh_ICL = ifelse((is.na(normhh_ICL)|is.infinite(normhh_ICL)|normhh_ICL==0), 1, normhh_ICL),
normhh_ICPVOF = ifelse((is.na(normhh_ICPVOF)|is.infinite(normhh_ICPVOF)|normhh_ICPVOF==0), 1, normhh_ICPVOF),
normhh_ICPV = ifelse((is.na(normhh_ICPV)|is.infinite(normhh_ICPV)|normhh_ICPV==0), 1, normhh_ICPV),
normhh_ICUGOF = ifelse((is.na(normhh_ICUGOF)|is.infinite(normhh_ICUGOF)|normhh_ICUGOF==0), 1, normhh_ICUGOF),
normhh_ICUG = ifelse((is.na(normhh_ICUG)|is.infinite(normhh_ICUG)|normhh_ICUG==0), 1, normhh_ICUG))
# merging normalization factors back into main file
PGE_ICAalldemo <- merge(PGE_ICAalldemo, PGE_ICAcheck[, c("CircuitName", "normlen_ICL", "normlen_ICPVOF", "normlen_ICPV", "normlen_ICUGOF", "normlen_ICUG", "normhh_ICL", "normhh_ICPVOF", "normhh_ICPV", "normhh_ICUGOF", "normhh_ICUG")], by=c("CircuitName"), all.x=TRUE, suffixes = c("",""))
# calculating normalized quantities
PGE_ICAalldemo <- PGE_ICAalldemo %>%
mutate(ICL_max_lenWt_n = ICL_max_lenWt/normlen_ICL,
ICPVOF_max_lenWt_n = ICPVOF_max_lenWt/normlen_ICPVOF, ICPV_max_lenWt_n = ICPV_max_lenWt/normlen_ICPV,
ICUGOF_max_lenWt_n = ICUGOF_max_lenWt/normlen_ICUGOF, ICUG_max_lenWt_n = ICUG_max_lenWt/normlen_ICUG,
ICL_max_hhWt_n = ICL_max_hhWt/normhh_ICL,
ICPVOF_max_hhWt_n = ICPVOF_max_hhWt/normhh_ICPVOF, ICPV_max_hhWt_n = ICPV_max_hhWt/normhh_ICPV,
ICUGOF_max_hhWt_n = ICUGOF_max_hhWt/normhh_ICUGOF, ICUG_max_hhWt_n = ICUG_max_hhWt/normhh_ICUG) %>%
mutate(ICL_max_lenWt_nadj = ifelse(abs(ICL_max_lenWt_n)<abs(ICL_max), ICL_max_lenWt_n, ICL_max),
ICPVOF_max_lenWt_nadj = ifelse(abs(ICPVOF_max_lenWt_n)<abs(ICPVOF_max), ICPVOF_max_lenWt_n, ICPVOF_max),
ICPV_max_lenWt_nadj = ifelse(abs(ICPV_max_lenWt_n)<abs(ICPV_max), ICPV_max_lenWt_n, ICPV_max),
ICUGOF_max_lenWt_nadj = ifelse(abs(ICUGOF_max_lenWt_n)<abs(ICUGOF_max), ICUGOF_max_lenWt_n, ICUGOF_max),
ICUG_max_lenWt_nadj = ifelse(abs(ICUG_max_lenWt_n)<abs(ICUG_max), ICUG_max_lenWt_n, ICUG_max),
ICL_max_hhWt_nadj = ifelse(abs(ICL_max_hhWt_n)<abs(ICL_max), ICL_max_hhWt_n, ICL_max),
ICPVOF_max_hhWt_nadj = ifelse(abs(ICPVOF_max_hhWt_n)<abs(ICPVOF_max), ICPVOF_max_hhWt_n, ICPVOF_max),
ICPV_max_hhWt_nadj = ifelse(abs(ICPV_max_hhWt_n)<abs(ICPV_max), ICPV_max_hhWt_n, ICPV_max),
ICUGOF_max_hhWt_nadj = ifelse(abs(ICUGOF_max_hhWt_n)<abs(ICUGOF_max), ICUGOF_max_hhWt_n, ICUGOF_max),
ICUG_max_hhWt_nadj = ifelse(abs(ICUG_max_hhWt_n)<abs(ICUG_max), ICUG_max_hhWt_n, ICUG_max))
# checking overall values
PGE_ICAcheck2 <- PGE_ICAalldemo %>%
select(CircuitName, ends_with("_max_lenWt_n"), ends_with("_max_lenWt_nadj"),
ends_with("_max_hhWt_n"), ends_with("_max_hhWt_nadj")) %>%
group_by(CircuitName) %>%
summarise(ICL_max_lenWt_n = sum(ICL_max_lenWt_n, na.rm=TRUE),
ICPVOF_max_lenWt_n = sum(ICPVOF_max_lenWt_n, na.rm=TRUE), ICPV_max_lenWt_n = sum(ICPV_max_lenWt_n, na.rm=TRUE),
ICUGOF_max_lenWt_n = sum(ICUGOF_max_lenWt_n, na.rm=TRUE), ICUG_max_lenWt_n = sum(ICUG_max_lenWt_n, na.rm=TRUE),
ICL_max_lenWt_nadj = sum(ICL_max_lenWt_nadj, na.rm=TRUE),
ICPVOF_max_lenWt_nadj = sum(ICPVOF_max_lenWt_nadj, na.rm=TRUE),
ICPV_max_lenWt_nadj = sum(ICPV_max_lenWt_nadj, na.rm=TRUE),
ICUGOF_max_lenWt_nadj = sum(ICUGOF_max_lenWt_nadj, na.rm=TRUE),
ICUG_max_lenWt_nadj = sum(ICUG_max_lenWt_nadj, na.rm=TRUE),
ICL_max_hhWt_n = sum(ICL_max_hhWt_n, na.rm=TRUE),
ICPVOF_max_hhWt_n = sum(ICPVOF_max_hhWt_n, na.rm=TRUE), ICPV_max_hhWt_n = sum(ICPV_max_hhWt_n, na.rm=TRUE),
ICUGOF_max_hhWt_n = sum(ICUGOF_max_hhWt_n, na.rm=TRUE), ICUG_max_hhWt_n = sum(ICUG_max_hhWt_n, na.rm=TRUE),
ICL_max_hhWt_nadj = sum(ICL_max_hhWt_nadj, na.rm=TRUE),
ICPVOF_max_hhWt_nadj = sum(ICPVOF_max_hhWt_nadj, na.rm=TRUE),
ICPV_max_hhWt_nadj = sum(ICPV_max_hhWt_nadj, na.rm=TRUE),
ICUGOF_max_hhWt_nadj = sum(ICUGOF_max_hhWt_nadj, na.rm=TRUE),
ICUG_max_hhWt_nadj = sum(ICUG_max_hhWt_nadj, na.rm=TRUE))
PGE_ICAcheck <- merge(PGE_ICAcheck, PGE_ICAcheck2, by=c("CircuitName"), all.x=TRUE)
rm(PGE_ICAcheck2)
PGE_ICAcheck <- PGE_ICAcheck %>%
melt( id=c("CircuitName","Length_m","Length_m_ctot","Length_Wt_ctot","nseg",
"normlen_ICL","normlen_ICPVOF","normlen_ICPV","normlen_ICUGOF","normlen_ICUG",
"normhh_ICL","normhh_ICPVOF","normhh_ICPV","normhh_ICUGOF","normhh_ICUG")) %>%
mutate(DER = factor(recode(substr(variable,1,6),
'ICL_ma'="ICL", 'ICPVOF'="ICPVOF", 'ICPV_m'="ICPV", 'ICUGOF'="ICUGOF", 'ICUG_m'="ICUG"),
levels=c("ICPV","ICPVOF","ICUG","ICUGOF","ICL")),
type = factor(recode(str_sub(variable,-8,-1), 'max_ctot'="ctot",
'ax_lenWt'="lenWt", '_lenWt_n'="lenWt_n",
'max_hhWt'="hhWt", 'x_hhWt_n'="hhWt_n",
'nWt_nadj'="lenWt_nadj", 'hWt_nadj'="hhWt_nadj")))
ggplot(PGE_ICAcheck, aes(x=type, y=value/1000)) + geom_boxplot() + facet_grid(~DER) +
theme(axis.text.x = element_text(angle = 90, size=8)) + labs(x="",y="Hosting capacity (MW) per circuit")
ggsave("PGE_ICAnormalization.png", path = "figures/", width=16, height=8)
a <- PGE_ICAcheck %>% filter(type=="ctot" | type=="hhWt" | type=="hhWt_n" | type=="hhWt_nadj") %>%
ggplot( aes(x=type, y=value/1000)) + geom_boxplot() + facet_grid(~DER, labeller=labeller(DER=labsDER)) + theme_light() +
theme(axis.text.x = element_text(angle = 90, size=8)) + labs(x="",y="Hosting capacity (MW) per circuit")
a
ggsave("PGE_ICAnormhh.png", path = "figures/", width=5, height=4)
```
Supplementary Figure S7
```{r}
rbind(mutate(filter(PGE_ICAcheck, type=="ctot" | type=="hhWt" | type=="hhWt_n" | type=="hhWt_nadj"), IOU="PGE", value=value/1000),
mutate(filter(SCE_ICAcheck, type=="ctot" | type=="hhWt" | type=="hhWt_n" | type=="hhWt_nadj"), IOU="SCE")) %>%
ggplot( aes(x=type, y=value)) + geom_boxplot() +
facet_grid(IOU~DER, scales="free_y", labeller=labeller(IOU=labsIOU, DER=labsDER)) + theme_light() +
theme(axis.text.x = element_text(angle=90, size=8)) + labs(x="Normalization method",y="Hosting capacity (MW) per circuit")
#plot_grid(a, b, labels=c('a','b'), label_size=12)
ggsave("IOU_normhh.png", path="figures/", width=8.5, height=5)
ggsave("IOU_normhh.pdf", path="figures/", width=8.5, height=5)
```
Decision: We will base per-household kW calculations on the normalized & adjusted population-scaled values.
```{r}
SCE_ICAalldemo <- SCE_ICAalldemo %>%
mutate(ICL_kWphh = ICL_max_hhWt_nadj / tothh_Cpoly *1000,
ICPVOF_kWphh = ICPVOF_max_hhWt_nadj / tothh_Cpoly *1000,
ICPV_kWphh = ICPV_max_hhWt_nadj / tothh_Cpoly *1000,
ICUGOF_kWphh = ICUGOF_max_hhWt_nadj / tothh_Cpoly *1000,
ICUG_kWphh = ICUG_max_hhWt_nadj / tothh_Cpoly *1000) %>%
mutate(ICL_kWphh_cap = ifelse(ICL_kWphh>=50, 50, ifelse(ICL_kWphh<=-10, -10, ICL_kWphh)),
ICPVOF_kWphh_cap = ifelse(ICPVOF_kWphh>=50, 50, ifelse(ICPVOF_kWphh<=-10, -10, ICPVOF_kWphh)),
ICPV_kWphh_cap = ifelse(ICPV_kWphh>=50, 50, ifelse(ICPV_kWphh<=-10, -10, ICPV_kWphh)),
ICUGOF_kWphh_cap = ifelse(ICUGOF_kWphh>=50, 50, ifelse(ICUGOF_kWphh<=-10, -10, ICUGOF_kWphh)),
ICUG_kWphh_cap = ifelse(ICUG_kWphh>=50, 50, ifelse(ICUG_kWphh<=-10, -10, ICUG_kWphh)))
PGE_ICAalldemo <- PGE_ICAalldemo %>%
mutate(ICL_kWphh = ICL_max_hhWt_nadj / tothh_Cpoly,
ICPVOF_kWphh = ICPVOF_max_hhWt_nadj / tothh_Cpoly,
ICPV_kWphh = ICPV_max_hhWt_nadj / tothh_Cpoly,
ICUGOF_kWphh = ICUGOF_max_hhWt_nadj / tothh_Cpoly,
ICUG_kWphh = ICUG_max_hhWt_nadj / tothh_Cpoly) %>%
mutate(ICL_kWphh_cap = ifelse(ICL_kWphh>=50, 50, ifelse(ICL_kWphh<=-10, -10, ICL_kWphh)),
ICPVOF_kWphh_cap = ifelse(ICPVOF_kWphh>=50, 50, ifelse(ICPVOF_kWphh<=-10, -10, ICPVOF_kWphh)),
ICPV_kWphh_cap = ifelse(ICPV_kWphh>=50, 50, ifelse(ICPV_kWphh<=-10, -10, ICPV_kWphh)),
ICUGOF_kWphh_cap = ifelse(ICUGOF_kWphh>=50, 50, ifelse(ICUGOF_kWphh<=-10, -10, ICUGOF_kWphh)),
ICUG_kWphh_cap = ifelse(ICUG_kWphh>=50, 50, ifelse(ICUG_kWphh<=-10, -10, ICUG_kWphh)))
```
Check: is the hosting capacity on any circuit/block group combo greater than its allowed value?
```{r}
nrow(SCE_ICAalldemo %>% filter(ICL_max_lenWt_n > ICL_max) %>% select(CircuitName, starts_with("ICL_")))
nrow(SCE_ICAalldemo %>% filter(ICPVOF_max_lenWt_n > ICPVOF_max) %>% select(CircuitName, starts_with("ICPVOF_")))
nrow(SCE_ICAalldemo %>% filter(ICPV_max_lenWt_n > ICPV_max) %>% select(CircuitName, starts_with("ICPV_")))
nrow(SCE_ICAalldemo %>% filter(ICUGOF_max_lenWt_n > ICUGOF_max) %>% select(CircuitName, starts_with("ICUGOF_")))
nrow(SCE_ICAalldemo %>% filter(ICUG_max_lenWt_n > ICUG_max) %>%
#filter(CircuitName=="ACALA") %>%
select(CircuitName, starts_with("ICUG_")))
nrow(SCE_ICAalldemo %>% filter(ICL_max_hhWt_n > ICL_max) %>%
select(CircuitName, Length_Wt, tothh_pct, starts_with("ICL_"), normlen_ICL, normhh_ICL))
nrow(SCE_ICAalldemo %>% filter(ICPVOF_max_hhWt_n > ICPVOF_max) %>%
select(CircuitName, Length_Wt, tothh_pct, starts_with("ICPVOF_"), normlen_ICPVOF, normhh_ICPVOF))
nrow(SCE_ICAalldemo %>% filter(ICPV_max_hhWt_n > ICPV_max) %>%
select(CircuitName, Length_Wt, tothh_pct, starts_with("ICPV_"), normlen_ICPV, normhh_ICPV))
nrow(SCE_ICAalldemo %>% filter(ICUGOF_max_hhWt_n > ICUGOF_max) %>%
select(CircuitName, Length_Wt, tothh_pct, starts_with("ICUGOF_"), normlen_ICUGOF, normhh_ICUGOF))
nrow(SCE_ICAalldemo %>% filter(ICUG_max_hhWt_n > ICUG_max) %>%
select(CircuitName, Length_Wt, tothh_pct, starts_with("ICUG_"), normlen_ICUG, normhh_ICUG))
```
```{r}
nrow(PGE_ICAalldemo %>% filter(ICL_max_lenWt_n > ICL_max) %>% select(CircuitName, starts_with("ICL_")))
nrow(PGE_ICAalldemo %>% filter(ICPVOF_max_lenWt_n > ICPVOF_max) %>% select(CircuitName, starts_with("ICPVOF_")))
nrow(PGE_ICAalldemo %>% filter(ICPV_max_lenWt_n > ICPV_max) %>% select(CircuitName, starts_with("ICPV_")))
nrow(PGE_ICAalldemo %>% filter(ICUGOF_max_lenWt_n > ICUGOF_max) %>% select(CircuitName, starts_with("ICUGOF_")))
nrow(PGE_ICAalldemo %>% filter(ICUG_max_lenWt_n > ICUG_max) %>%
#filter(CircuitName=="ACALA") %>%
select(CircuitName, starts_with("ICUG_")))
nrow(PGE_ICAalldemo %>% filter(ICL_max_hhWt_n > ICL_max) %>%
select(CircuitName, Length_Wt, tothh_pct, starts_with("ICL_"), normlen_ICL, normhh_ICL))
nrow(PGE_ICAalldemo %>% filter(ICPVOF_max_hhWt_n > ICPVOF_max) %>%
select(CircuitName, Length_Wt, tothh_pct, starts_with("ICPVOF_"), normlen_ICPVOF, normhh_ICPVOF))
nrow(PGE_ICAalldemo %>% filter(ICPV_max_hhWt_n > ICPV_max) %>%
select(CircuitName, Length_Wt, tothh_pct, starts_with("ICPV_"), normlen_ICPV, normhh_ICPV))
nrow(PGE_ICAalldemo %>% filter(ICUGOF_max_hhWt_n > ICUGOF_max) %>%
select(CircuitName, Length_Wt, tothh_pct, starts_with("ICUGOF_"), normlen_ICUGOF, normhh_ICUGOF))
nrow(PGE_ICAalldemo %>% filter(ICUG_max_hhWt_n > ICUG_max) %>%
select(CircuitName, Length_Wt, tothh_pct, starts_with("ICUG_"), normlen_ICUG, normhh_ICUG))
```
--------------------------------------------------------------------------------------------
EXISTING HOSTING CAPACITY
--------------------------------------------------------------------------------------------
Creating variables that incorporate DG that is already on the circuit
We will use "ExistDG" for this, weighted by...
...just the percentage of households served by that circuit polygon
...for PG&E, we first scale by the percentage of households on the circuit served by lines with ICA info
```{r}
SCE_ICAalldemo <- SCE_ICAalldemo %>%
mutate(existdg_kWphh = (ExistDG*1000*tothh_pct)/tothh_Cpoly) %>%
mutate(ICL_e_kWphh = ICL_kWphh,
ICPVOF_e_kWphh = ICPVOF_kWphh + existdg_kWphh, ICPV_e_kWphh = ICPV_kWphh + existdg_kWphh,
ICUGOF_e_kWphh = ICUGOF_kWphh + existdg_kWphh, ICUG_e_kWphh = ICUG_kWphh + existdg_kWphh) %>%
mutate(ICL_e_kWphh_cap = ifelse(ICL_e_kWphh>=50, 50, ifelse(ICL_e_kWphh<=-10, -10, ICL_e_kWphh)),
ICPVOF_e_kWphh_cap = ifelse(ICPVOF_e_kWphh>=50, 50, ifelse(ICPVOF_e_kWphh<=-10, -10, ICPVOF_e_kWphh)),
ICPV_e_kWphh_cap = ifelse(ICPV_e_kWphh>=50, 50, ifelse(ICPV_e_kWphh<=-10, -10, ICPV_e_kWphh)),
ICUGOF_e_kWphh_cap = ifelse(ICUGOF_e_kWphh>=50, 50, ifelse(ICUGOF_e_kWphh<=-10, -10, ICUGOF_e_kWphh)),
ICUG_e_kWphh_cap = ifelse(ICUG_e_kWphh>=50, 50, ifelse(ICUG_e_kWphh<=-10, -10, ICUG_e_kWphh)))
PGE_ICAalldemo <- PGE_ICAalldemo %>%
mutate(existdg_kWphh = (ExistDG*tothhICA_pct*tothh_pct)/tothh_Cpoly) %>%
mutate(ICL_e_kWphh = ICL_kWphh,
ICPVOF_e_kWphh = ICPVOF_kWphh + existdg_kWphh, ICPV_e_kWphh = ICPV_kWphh + existdg_kWphh,
ICUGOF_e_kWphh = ICUGOF_kWphh + existdg_kWphh, ICUG_e_kWphh = ICUG_kWphh + existdg_kWphh) %>%
mutate(ICL_e_kWphh_cap = ifelse(ICL_e_kWphh>=50, 50, ifelse(ICL_e_kWphh<=-10, -10, ICL_e_kWphh)),
ICPVOF_e_kWphh_cap = ifelse(ICPVOF_e_kWphh>=50, 50, ifelse(ICPVOF_e_kWphh<=-10, -10, ICPVOF_e_kWphh)),
ICPV_e_kWphh_cap = ifelse(ICPV_e_kWphh>=50, 50, ifelse(ICPV_e_kWphh<=-10, -10, ICPV_e_kWphh)),
ICUGOF_e_kWphh_cap = ifelse(ICUGOF_e_kWphh>=50, 50, ifelse(ICUGOF_e_kWphh<=-10, -10, ICUGOF_e_kWphh)),
ICUG_e_kWphh_cap = ifelse(ICUG_e_kWphh>=50, 50, ifelse(ICUG_e_kWphh<=-10, -10, ICUG_e_kWphh)))
```
---> CODE TO NEXT ARROW WAS TESTING OTHER ALLOCATION METHODS, CAN OMIT
...the relative amount of solar by zip code (z) from tracking the sun data
Multiple zip codes may be matched to one block group. To join existing PV capacity by zip code data, we will take the average of the PV_kWpm2 of the matched zip codes and multiply them by the area of the circuit polygon.
```{r}
coltypes = read.csv("../../data/solardeployment/TrackingTheSun/tts19zipCAtypes.csv", header=T, na.strings="?")
tts19zipCA = read.csv("../../data/solardeployment/TrackingTheSun/tts19zipCA.csv", header=T, na.strings="?",
colClasses=as.character(coltypes$x))
zcta = read.csv("../geography/tl_2010_06_zcta510/tl_2010_06_zcta510_CalAlbers.csv", header=T, na.strings="?")
tts19zipCA <- tts19zipCA %>% mutate(zip = as.integer(zip)) %>%
merge(zcta[,c("ZCTA5CE10","zcta_m2")], by.x=c("zip"), by.y=c("ZCTA5CE10"), all.x=TRUE) %>%
mutate(PV_kWpkm2 = System.Size / zcta_m2*1000000,
zip = as.character(zip),
IOU = as.character(recode(Utility.Service.Territory, 'Pacific Gas and Electric'="PGE",
'Southern California Edison'="SCE", 'San Diego Gas and Electric'="SDGE",
'Sacramento Municipal Utility District'="SMUD", 'Los Angeles Department of Water & Power'="LADWP")))
```
```{r}
SCE_zip <- SCE_ICAaccess[,c("CircuitName","GEOID10","ZCTA","zip_ct")] %>%
mutate(IOU = "SCE") %>%
mutate(nzip = nchar(ZCTA)/5) %>%
mutate(zip = ifelse(nzip>=1,ZCTA,zip_ct)) %>%
mutate(nzip = nchar(zip)/5) %>%
mutate(zip1 = substr(zip,1,5), zip2 = ifelse(nzip>1, substr(zip,6,10), NA),
zip3 = ifelse(nzip>2, substr(zip,11,15), NA), zip4 = ifelse(nzip>3, substr(zip,16,20), NA),
zip5 = ifelse(nzip>4, substr(zip,21,25), NA), zip6 = ifelse(nzip>5, substr(zip,26,30), NA),
zip7 = ifelse(nzip>6, substr(zip,31,35), NA)) %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip1","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_1"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip2","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_2"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip3","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_3"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip4","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_4"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip5","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_5"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip6","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_6"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip7","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_7"="PV_kWpkm2") %>%
mutate(PV_kWpkm2 = rowMeans(select(., c("PV_kWpkm2_1", "PV_kWpkm2_2", "PV_kWpkm2_3", "PV_kWpkm2_4",
"PV_kWpkm2_5", "PV_kWpkm2_6", "PV_kWpkm2_7")), na.rm=TRUE))
summary(as.factor(SCE_zip$nzip))
```
```{r}
PGE_zip <- PGE_ICAaccess[,c("CircuitName","GEOID10","ZCTA","zip_ct")] %>%
mutate(IOU = "PGE") %>%
mutate(nzip = nchar(ZCTA)/5) %>%
mutate(zip = ifelse(nzip>=1,ZCTA,zip_ct)) %>%
mutate(nzip = nchar(zip)/5) %>%
mutate(zip1 = substr(zip,1,5), zip2 = ifelse(nzip>1, substr(zip,6,10), NA),
zip3 = ifelse(nzip>2, substr(zip,11,15), NA), zip4 = ifelse(nzip>3, substr(zip,16,20), NA),
zip5 = ifelse(nzip>4, substr(zip,21,25), NA), zip6 = ifelse(nzip>5, substr(zip,26,30), NA),
zip7 = ifelse(nzip>6, substr(zip,31,35), NA)) %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip1","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_1"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip2","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_2"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip3","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_3"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip4","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_4"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip5","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_5"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip6","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_6"="PV_kWpkm2") %>%
merge(tts19zipCA[,c("zip","IOU","PV_kWpkm2")], by.x=c("zip7","IOU"), by.y=c("zip","IOU"), all.x=TRUE) %>%
dplyr::rename("PV_kWpkm2_7"="PV_kWpkm2") %>%
mutate(PV_kWpkm2 = rowMeans(select(., c("PV_kWpkm2_1", "PV_kWpkm2_2", "PV_kWpkm2_3", "PV_kWpkm2_4",
"PV_kWpkm2_5", "PV_kWpkm2_6", "PV_kWpkm2_7")), na.rm=TRUE))
summary(as.factor(PGE_zip$nzip))
```
```{r}
SCE_ICAalldemo <- SCE_ICAalldemo %>%
merge(SCE_zip[,c("CircuitName","GEOID10","PV_kWpkm2")], by=c("CircuitName","GEOID10"), all.x=TRUE) %>%
mutate(PV_kW = PV_kWpkm2/1000000 * CpolyA)
SCE_solar <- SCE_ICAalldemo[,c("CircuitName","GEOID10","PV_kW")] %>%
group_by(CircuitName) %>% summarise(PV_kW_ctot = sum(PV_kW, na.rm=TRUE))
SCE_ctotpoly = read.csv("circuitfiles/SCE_ctotpoly.csv", header=T, na.strings="?")
SCE_ctotpoly <- SCE_ctotpoly %>% merge(SCE_solar, by=c("CircuitName"), all.x=TRUE)
SCE_ICAalldemo <- SCE_ICAalldemo %>% merge(SCE_solar, by=c("CircuitName"), all.x=TRUE) %>%
mutate(PV_kW_pct = PV_kW / PV_kW_ctot) %>%
mutate(#ICL_e_kWphh = ICL_kWphh + (ExistDG*1000*tothh_pct)/tothh_Cpoly,
ICPVOF_z_kWphh = ICPVOF_kWphh + (ExistDG*1000*PV_kW_pct)/tothh_Cpoly,
ICPV_z_kWphh = ICPV_kWphh + (ExistDG*1000*PV_kW_pct)/tothh_Cpoly,
ICUGOF_z_kWphh = ICUGOF_kWphh + (ExistDG*1000*PV_kW_pct)/tothh_Cpoly,
ICUG_z_kWphh = ICUG_kWphh + (ExistDG*1000*PV_kW_pct)/tothh_Cpoly) %>%
mutate(#ICL_e_kWphh_cap = ifelse(ICL_e_kWphh>=50, 50, ifelse(ICL_e_kWphh<=-10, -10, ICL_e_kWphh)),
ICPVOF_z_kWphh_cap = ifelse(ICPVOF_z_kWphh>=50, 50, ifelse(ICPVOF_z_kWphh<=-10, -10, ICPVOF_z_kWphh)),
ICPV_z_kWphh_cap = ifelse(ICPV_z_kWphh>=50, 50, ifelse(ICPV_z_kWphh<=-10, -10, ICPV_z_kWphh)),
ICUGOF_z_kWphh_cap = ifelse(ICUGOF_z_kWphh>=50, 50, ifelse(ICUGOF_z_kWphh<=-10, -10, ICUGOF_z_kWphh)),
ICUG_z_kWphh_cap = ifelse(ICUG_z_kWphh>=50, 50, ifelse(ICUG_z_kWphh<=-10, -10, ICUG_z_kWphh)))
summary(SCE_ICAalldemo$PV_kW_pct)
```
```{r}
PGE_ICAalldemo <- PGE_ICAalldemo %>%
merge(PGE_zip[,c("CircuitName","GEOID10","PV_kWpkm2")], by=c("CircuitName","GEOID10"), all.x=TRUE) %>%
mutate(PV_kW = PV_kWpkm2/1000000 * CpolyA)
PGE_solar <- PGE_ICAalldemo[,c("CircuitName","GEOID10","PV_kW")] %>%
group_by(CircuitName) %>% summarise(PV_kW_ctot = sum(PV_kW, na.rm=TRUE))
PGE_ctotpoly = read.csv("circuitfiles/PGE_ctotpoly.csv", header=T, na.strings="?")
PGE_ctotpoly <- PGE_ctotpoly %>% merge(PGE_solar, by=c("CircuitName"), all.x=TRUE)
PGE_ICAalldemo <- PGE_ICAalldemo %>% merge(PGE_solar, by=c("CircuitName"), all.x=TRUE) %>%
mutate(PV_kW_pct = PV_kW / PV_kW_ctot) %>%
mutate(#ICL_e_kWphh = ICL_kWphh + (ExistDG*tothh_pct)/tothh_Cpoly,
ICPVOF_z_kWphh = ICPVOF_kWphh + (ExistDG*PV_kW_pct)/tothh_Cpoly,
ICPV_z_kWphh = ICPV_kWphh + (ExistDG*PV_kW_pct)/tothh_Cpoly,
ICUGOF_z_kWphh = ICUGOF_kWphh + (ExistDG*PV_kW_pct)/tothh_Cpoly,
ICUG_z_kWphh = ICUG_kWphh + (ExistDG*PV_kW_pct)/tothh_Cpoly) %>%
mutate(#ICL_e_kWphh_cap = ifelse(ICL_e_kWphh>=50, 50, ifelse(ICL_e_kWphh<=-10, -10, ICL_e_kWphh)),
ICPVOF_z_kWphh_cap = ifelse(ICPVOF_z_kWphh>=50, 50, ifelse(ICPVOF_z_kWphh<=-10, -10, ICPVOF_z_kWphh)),
ICPV_z_kWphh_cap = ifelse(ICPV_z_kWphh>=50, 50, ifelse(ICPV_z_kWphh<=-10, -10, ICPV_z_kWphh)),
ICUGOF_z_kWphh_cap = ifelse(ICUGOF_z_kWphh>=50, 50, ifelse(ICUGOF_z_kWphh<=-10, -10, ICUGOF_z_kWphh)),
ICUG_z_kWphh_cap = ifelse(ICUG_z_kWphh>=50, 50, ifelse(ICUG_z_kWphh<=-10, -10, ICUG_z_kWphh)))
summary(PGE_ICAalldemo$PV_kW_pct)
```
...the relative amount of solar potential by census tract (t) from project sunroof data
```{r}
ps = read.csv("../../data/solardeployment/ProjectSunroof/projsunroof_kWbyct.csv", header=T, na.strings="?",
colClasses=c("character","factor","numeric"))
ct = read.csv("../geography/tl_2010_06_tract10/tl_2010_06_tract10_CalAlbers.csv", header=T, na.strings="?",
colClasses=c(rep("integer",4),"character","numeric",rep("factor",3),rep("numeric",5)))
ps <- ps %>% subset(state_name=="California") %>%
merge(ct[,c("GEOID10","Area")], by.x=c("region_name"), by.y=c("GEOID10"), all.x=TRUE) %>%
mutate(state_name = as.character(state_name),
PV_kWpkm2 = kw_total/Area*1000000)
```
```{r}
SCE_ICAalldemo <- SCE_ICAalldemo %>%
merge(ps[,c("region_name","PV_kWpkm2")], by.x=c("GEOIDCT"), by.y=c("region_name"), all.x=TRUE, suffixes=c("","_t")) %>%
mutate(PV_kW = PV_kWpkm2_t/1000000 * CpolyA)
SCE_solar <- SCE_ICAalldemo[,c("CircuitName","GEOID10","PV_kW")] %>%
group_by(CircuitName) %>% summarise(PV_kW_ctot = sum(PV_kW, na.rm=TRUE))
SCE_ctotpoly <- SCE_ctotpoly %>% merge(SCE_solar, by=c("CircuitName"), all.x=TRUE, suffixes=c("","_t"))
SCE_ICAalldemo <- SCE_ICAalldemo %>% merge(SCE_solar, by=c("CircuitName"), all.x=TRUE, suffixes=c("","_t")) %>%
mutate(PV_kW_pct = PV_kW / PV_kW_ctot_t) %>%
mutate(#ICL_e_kWphh = ICL_kWphh + (ExistDG*1000*tothh_pct)/tothh_Cpoly,
ICPVOF_t_kWphh = ICPVOF_kWphh + (ExistDG*1000*PV_kW_pct)/tothh_Cpoly,
ICPV_t_kWphh = ICPV_kWphh + (ExistDG*1000*PV_kW_pct)/tothh_Cpoly,
ICUGOF_t_kWphh = ICUGOF_kWphh + (ExistDG*1000*PV_kW_pct)/tothh_Cpoly,
ICUG_t_kWphh = ICUG_kWphh + (ExistDG*1000*PV_kW_pct)/tothh_Cpoly) %>%
mutate(#ICL_e_kWphh_cap = ifelse(ICL_e_kWphh>=50, 50, ifelse(ICL_e_kWphh<=-10, -10, ICL_e_kWphh)),
ICPVOF_t_kWphh_cap = ifelse(ICPVOF_t_kWphh>=50, 50, ifelse(ICPVOF_t_kWphh<=-10, -10, ICPVOF_t_kWphh)),
ICPV_t_kWphh_cap = ifelse(ICPV_t_kWphh>=50, 50, ifelse(ICPV_t_kWphh<=-10, -10, ICPV_t_kWphh)),
ICUGOF_t_kWphh_cap = ifelse(ICUGOF_t_kWphh>=50, 50, ifelse(ICUGOF_t_kWphh<=-10, -10, ICUGOF_t_kWphh)),
ICUG_t_kWphh_cap = ifelse(ICUG_t_kWphh>=50, 50, ifelse(ICUG_t_kWphh<=-10, -10, ICUG_t_kWphh)))
summary(SCE_ICAalldemo$PV_kW_pct)
```
```{r}
PGE_ICAalldemo <- PGE_ICAalldemo %>%
merge(ps[,c("region_name","PV_kWpkm2")], by.x=c("GEOIDCT"), by.y=c("region_name"), all.x=TRUE, suffixes=c("","_t")) %>%
mutate(PV_kW = PV_kWpkm2_t/1000000 * CpolyA)
PGE_solar <- PGE_ICAalldemo[,c("CircuitName","GEOID10","PV_kW")] %>%
group_by(CircuitName) %>% summarise(PV_kW_ctot = sum(PV_kW, na.rm=TRUE))
PGE_ctotpoly <- PGE_ctotpoly %>% merge(PGE_solar, by=c("CircuitName"), all.x=TRUE, suffixes=c("","_t"))
PGE_ICAalldemo <- PGE_ICAalldemo %>% merge(PGE_solar, by=c("CircuitName"), all.x=TRUE, suffixes=c("","_t")) %>%
mutate(PV_kW_pct = PV_kW / PV_kW_ctot_t) %>%
mutate(#ICL_e_kWphh = ICL_kWphh + (ExistDG*1000*tothh_pct)/tothh_Cpoly,
ICPVOF_t_kWphh = ICPVOF_kWphh + (ExistDG*PV_kW_pct)/tothh_Cpoly,
ICPV_t_kWphh = ICPV_kWphh + (ExistDG*PV_kW_pct)/tothh_Cpoly,
ICUGOF_t_kWphh = ICUGOF_kWphh + (ExistDG*PV_kW_pct)/tothh_Cpoly,
ICUG_t_kWphh = ICUG_kWphh + (ExistDG*PV_kW_pct)/tothh_Cpoly) %>%
mutate(#ICL_e_kWphh_cap = ifelse(ICL_e_kWphh>=50, 50, ifelse(ICL_e_kWphh<=-10, -10, ICL_e_kWphh)),
ICPVOF_t_kWphh_cap = ifelse(ICPVOF_t_kWphh>=50, 50, ifelse(ICPVOF_t_kWphh<=-10, -10, ICPVOF_t_kWphh)),
ICPV_t_kWphh_cap = ifelse(ICPV_t_kWphh>=50, 50, ifelse(ICPV_t_kWphh<=-10, -10, ICPV_t_kWphh)),
ICUGOF_t_kWphh_cap = ifelse(ICUGOF_t_kWphh>=50, 50, ifelse(ICUGOF_t_kWphh<=-10, -10, ICUGOF_t_kWphh)),
ICUG_t_kWphh_cap = ifelse(ICUG_t_kWphh>=50, 50, ifelse(ICUG_t_kWphh<=-10, -10, ICUG_t_kWphh)))
summary(PGE_ICAalldemo$PV_kW_pct)
```
...the relative amount of residential PV panel area from DeepSolar
```{r}
dsCA = read.csv("../../data/solardeployment/DeepSolar/deepsolarCA.csv", header=T, na.strings="?",
colClasses=c(rep("character",3),rep("numeric",8)))
```
```{r}
SCE_ICAalldemo <- SCE_ICAalldemo %>%
merge(dsCA[,c("fips","PVresm2pkm2")], by.x=c("GEOIDCT"), by.y=c("fips"), all.x=TRUE, suffixes=c("","_d")) %>%
mutate(PVresm2_cpoly = PVresm2pkm2*CpolyA/1000000)
SCE_solar <- SCE_ICAalldemo[,c("CircuitName","GEOID10","PVresm2_cpoly")] %>%
group_by(CircuitName) %>% summarise(PVresm2_ctot = sum(PVresm2_cpoly, na.rm=TRUE))
SCE_ctotpoly <- SCE_ctotpoly %>% merge(SCE_solar, by=c("CircuitName"), all.x=TRUE, suffixes=c("","_d"))
SCE_ICAalldemo <- SCE_ICAalldemo %>% merge(SCE_solar, by=c("CircuitName"), all.x=TRUE, suffixes=c("","_d")) %>%
mutate(PVres_pct = ifelse(PVresm2_ctot==0, tothh_pct, PVresm2_cpoly/PVresm2_ctot)) %>%
mutate(#ICL_e_kWphh = ICL_kWphh + (ExistDG*1000*tothh_pct)/tothh_Cpoly,
ICPVOF_d_kWphh = ICPVOF_kWphh + (ExistDG*1000*PVres_pct)/tothh_Cpoly,
ICPV_d_kWphh = ICPV_kWphh + (ExistDG*1000*PVres_pct)/tothh_Cpoly,
ICUGOF_d_kWphh = ICUGOF_kWphh + (ExistDG*1000*PVres_pct)/tothh_Cpoly,
ICUG_d_kWphh = ICUG_kWphh + (ExistDG*1000*PVres_pct)/tothh_Cpoly) %>%
mutate(#ICL_e_kWphh_cap = ifelse(ICL_e_kWphh>=50, 50, ifelse(ICL_e_kWphh<=-10, -10, ICL_e_kWphh)),
ICPVOF_d_kWphh_cap = ifelse(ICPVOF_d_kWphh>=50, 50, ifelse(ICPVOF_d_kWphh<=-10, -10, ICPVOF_d_kWphh)),
ICPV_d_kWphh_cap = ifelse(ICPV_d_kWphh>=50, 50, ifelse(ICPV_d_kWphh<=-10, -10, ICPV_d_kWphh)),
ICUGOF_d_kWphh_cap = ifelse(ICUGOF_d_kWphh>=50, 50, ifelse(ICUGOF_d_kWphh<=-10, -10, ICUGOF_d_kWphh)),
ICUG_d_kWphh_cap = ifelse(ICUG_d_kWphh>=50, 50, ifelse(ICUG_d_kWphh<=-10, -10, ICUG_d_kWphh)))
summary(SCE_ICAalldemo$PVres_pct)
```
```{r}
PGE_ICAalldemo <- PGE_ICAalldemo %>%
merge(dsCA[,c("fips","PVresm2pkm2")], by.x=c("GEOIDCT"), by.y=c("fips"), all.x=TRUE, suffixes=c("","_d")) %>%
mutate(PVresm2_cpoly = PVresm2pkm2*CpolyA/1000000)
PGE_solar <- PGE_ICAalldemo[,c("CircuitName","GEOID10","PVresm2_cpoly")] %>%
group_by(CircuitName) %>% summarise(PVresm2_ctot = sum(PVresm2_cpoly, na.rm=TRUE))
PGE_ctotpoly <- PGE_ctotpoly %>% merge(PGE_solar, by=c("CircuitName"), all.x=TRUE, suffixes=c("","_d"))
PGE_ICAalldemo <- PGE_ICAalldemo %>% merge(PGE_solar, by=c("CircuitName"), all.x=TRUE, suffixes=c("","_d")) %>%
mutate(PVres_pct = ifelse(PVresm2_ctot==0, tothh_pct, PVresm2_cpoly/PVresm2_ctot)) %>%
mutate(#ICL_e_kWphh = ICL_kWphh + (ExistDG*1000*tothh_pct)/tothh_Cpoly,
ICPVOF_d_kWphh = ICPVOF_kWphh + (ExistDG*PVres_pct)/tothh_Cpoly,
ICPV_d_kWphh = ICPV_kWphh + (ExistDG*PVres_pct)/tothh_Cpoly,
ICUGOF_d_kWphh = ICUGOF_kWphh + (ExistDG*PVres_pct)/tothh_Cpoly,
ICUG_d_kWphh = ICUG_kWphh + (ExistDG*PVres_pct)/tothh_Cpoly) %>%
mutate(#ICL_e_kWphh_cap = ifelse(ICL_e_kWphh>=50, 50, ifelse(ICL_e_kWphh<=-10, -10, ICL_e_kWphh)),
ICPVOF_d_kWphh_cap = ifelse(ICPVOF_d_kWphh>=50, 50, ifelse(ICPVOF_d_kWphh<=-10, -10, ICPVOF_d_kWphh)),
ICPV_d_kWphh_cap = ifelse(ICPV_d_kWphh>=50, 50, ifelse(ICPV_d_kWphh<=-10, -10, ICPV_d_kWphh)),
ICUGOF_d_kWphh_cap = ifelse(ICUGOF_d_kWphh>=50, 50, ifelse(ICUGOF_d_kWphh<=-10, -10, ICUGOF_d_kWphh)),
ICUG_d_kWphh_cap = ifelse(ICUG_d_kWphh>=50, 50, ifelse(ICUG_d_kWphh<=-10, -10, ICUG_d_kWphh)))
summary(PGE_ICAalldemo$PVres_pct)
```
---> END OF CODE THAT CAN BE OMITTED
Conclusion: We will find in 05_access.Rmd that scaling our allocations of existing generation within a circuit by the locations of existing distributed generation (PV) by zip code and census tract does not substantially impact our results. So, we will just use the percentage of households served by a given circuit polygon to scale existing generation.
Cleaning up infinite values:
```{r}
SCE_ICAalldemo[mapply(is.infinite, SCE_ICAalldemo)] <- NA
PGE_ICAalldemo[mapply(is.infinite, PGE_ICAalldemo)] <- NA
```
Example circuit for writeup (Supplementary Table S4)
```{r}
SCE_ICAalldemo %>% filter(CircuitName=="ACADEMY") %>%
mutate(Length_Wt = Length_Wt*100, tothh_pct = tothh_pct*100) %>%
#mutate_if(is.numeric, round) %>%
select(ICPV_max, ICPV_max_ctot, ExistDG, Length_m, Length_Wt, tothh_Cpoly, tothh_pct, starts_with("ICPV_max_hh"), normhh_ICPV, ICPV_kWphh, ICPV_e_kWphh, existdg_kWphh) #%>%
#colSums(na.rm = TRUE, dims = 1)
```
--------------------------------------------------------------------------------------------
EXPORTING
--------------------------------------------------------------------------------------------
Rename & subset to remove unrealistic population totals:
```{r}
PGE_ICAalldemo <- PGE_ICAalldemo %>% dplyr::rename("SAIDI5yravg"="SAIDI3a5yravg")
SCE_ICAalldemo_real = subset(SCE_ICAalldemo, tothh_Cpoly >= 1)
PGE_ICAalldemo_real = subset(PGE_ICAalldemo, tothh_Cpoly >= 1)
SCE_ICAalldemo <- SCE_ICAalldemo %>% mutate(Cpoly = paste0(GEOID10," ",CircuitName))
PGE_ICAalldemo <- PGE_ICAalldemo %>% mutate(Cpoly = paste0(GEOID10," ",CircuitName))
```
Exporting data
```{r}
write.csv(SCE_ICAalldemo, file="circuitfiles/SCE_ICAalldemoalloc.csv", row.names=FALSE)
write.csv(SCE_ICAalldemo_real, file="circuitfiles/SCE_ICAalldemorealalloc.csv", row.names=FALSE)
write.csv(PGE_ICAalldemo, file="circuitfiles/PGE_ICAalldemoalloc.csv", row.names=FALSE)
write.csv(PGE_ICAalldemo_real, file="circuitfiles/PGE_ICAalldemorealalloc.csv", row.names=FALSE)
PGEtypes <- sapply(PGE_ICAalldemo,class)
SCEtypes <- sapply(SCE_ICAalldemo,class)
write.csv(PGEtypes, file="circuitfiles/PGE_ICAalldemoallocclasses.csv", row.names=FALSE)
write.csv(SCEtypes, file="circuitfiles/SCE_ICAalldemoallocclasses.csv", row.names=FALSE)
```
Cleaned-up files to use in random forests and linear and logistic regression runs
```{r}
idanddepfeatures = c("CircuitName","GEOID10","ICL_kWphh","ICPVOF_kWphh","ICPV_kWphh","ICUGOF_kWphh","ICUG_kWphh",
"ICL_kWphh_cap","ICPVOF_kWphh_cap","ICPV_kWphh_cap","ICUGOF_kWphh_cap","ICUG_kWphh_cap",
"ICPVOF_e_kWphh","ICPV_e_kWphh","ICUGOF_e_kWphh","ICUG_e_kWphh",
"ICPVOF_e_kWphh_cap","ICPV_e_kWphh_cap","ICUGOF_e_kWphh_cap","ICUG_e_kWphh_cap")
infrfeatures = c("CircVolt_kV","Length_m","Length_m_ctot") #"ExistDG","QueueDG",
servgeofeatures = c("ResCust","Res_pct","Com_pct","Ind_pct","Agr_pct","Oth_pct",
"tothh_Cpoly","tothh_ctot","tothh_pct","tothh_perkm","SAIDI5yravg","urbanheat_pctl","ghi_kWhpm2day")
demofeatures = c("tothh", "racediversity", "black_pct", "asian_pct", "nlxwhite_pct", "latinx_pct",
"inc50kbelow_pct", "inc150kplus_pct", "medhhinc", "edavgyrs",
"ownerocc_pct", "singleunit_pct", "unitsavg", "medyrbuilt", "hhdensity_fctr", "hhdensity_hhsqkm",
"polexposure_pctl", "polenvt_pctl", "popsens_pctl", "lingisolation_pctl", "sb535disad")
write.csv(SCE_ICAalldemo[, c("Cpoly", idanddepfeatures, infrfeatures, "Phase_max", "Phase_min", "CLSmax", "CLSmin",
#"ProjLoad_MW", "CurrPen_Pct", "MxRemGen_MW", "x15PenCp_MW", "DelivNote",
servgeofeatures, demofeatures)],
file="circuitfiles/SCE_ICAalldemotrees.csv", row.names=FALSE)
write.csv(SCE_ICAalldemo_real[, c(idanddepfeatures, infrfeatures, "Phase_max", "Phase_min", "CLSmax", "CLSmin",
#"ProjLoad_MW", "CurrPen_Pct", "MxRemGen_MW", "x15PenCp_MW", "DelivNote",
servgeofeatures, demofeatures)],
file="circuitfiles/SCE_ICAalldemotrees_real.csv", row.names=FALSE)
write.csv(PGE_ICAalldemo[, c("Cpoly", idanddepfeatures, infrfeatures, "ICA_pct", servgeofeatures, demofeatures)],
file="circuitfiles/PGE_ICAalldemotrees.csv", row.names=FALSE)
write.csv(PGE_ICAalldemo_real[, c(idanddepfeatures, infrfeatures, "ICA_pct", servgeofeatures, demofeatures)],
file="circuitfiles/PGE_ICAalldemotrees_real.csv", row.names=FALSE)
```