cost_effectiveness_defense_2.R
In chintanp/dcfcoster:
# This file does calculations for cost-effectiveness
library(magrittr)
# Database settings -------------------------------------------------------
if (!DBI::dbCanConnect(
RPostgres::Postgres(),
host = Sys.getenv("MAIN_HOST"),
dbname = Sys.getenv("MAIN_DB"),
user = Sys.getenv("MAIN_USER"),
password = Sys.getenv("MAIN_PWD"),
port = Sys.getenv("MAIN_PORT")
)) {
lg$log(level = "fatal",
msg = "Cannot connect to database",
"ip" = ipify::get_ip())
# Exit if DB cannot connect
stop("Cannot connect to database")
}
main_con <- DBI::dbConnect(
RPostgres::Postgres(),
host = Sys.getenv("MAIN_HOST"),
dbname = Sys.getenv("MAIN_DB"),
user = Sys.getenv("MAIN_USER"),
password = Sys.getenv("MAIN_PWD"),
port = Sys.getenv("MAIN_PORT")
)
## Locations of with max CS
basecase_aid <- 484
sql_be_cs = glue::glue(
"select count(cs_id) as count, analysis_id, evse_id, be.latitude, be.longitude
from evse_charging_session ecs
left join built_evse be on
'b' || cast(be.bevse_id as text) = ecs.evse_id
where ecs.analysis_id = {basecase_aid}
group by ecs.analysis_id, ecs.evse_id, be.latitude, be.longitude
order by count desc
limit 5;
"
)
be_cs <- DBI::dbGetQuery(main_con, sql_be_cs)
## Stats for the sets with new and upgrades
set_id <- c(125, 126, 127, 128)
sql_count_stats = glue::glue(
"select count(ets.veh_id) as sim_count,
ets.analysis_id,
coalesce(ne.count, 0) as nevse_count,
ef.fin_count,
ef.evmt,
es.str_count,
ecs.cs_count,
ew.wait_count,
ar.set_id,
ew.wait_duration
from evtrip_scenarios ets
left join (select count(nevse_id), analysis_id from new_evses group by analysis_id) as ne
on ets.analysis_id = ne.analysis_id
join (select count(veh_id) as fin_count, sum(distance_travelled) as evmt, analysis_id
from ev_finished
group by analysis_id) as ef
on ets.analysis_id = ef.analysis_id
join (select count(veh_id) as str_count, analysis_id from ev_stranded group by analysis_id) as es
on ets.analysis_id = es.analysis_id
join (select count(cs_id) as cs_count, analysis_id from evse_charging_session group by analysis_id) as ecs
on ets.analysis_id = ecs.analysis_id
join (select count(wait_id) as wait_count, analysis_id, coalesce(sum(extract(minutes from (wait_end_time::timestamp - wait_start_time::timestamp))),
0) as wait_duration from evse_evs_waiting group by analysis_id) as ew
on ets.analysis_id = ew.analysis_id
join (select analysis_id, set_id from analysis_record where set_id IN ({glue::glue_collapse(set_id, ', ')})) as ar ON ets.analysis_id = ar.analysis_id
group by ets.analysis_id, nevse_count, ef.fin_count, es.str_count, ef.evmt, ecs.cs_count, ew.wait_count, ar.set_id, ew.wait_duration
order by ets.analysis_id desc
;"
)
count_stats <- DBI::dbGetQuery(main_con, sql_count_stats)
count_stats$new_up_set_id <- NA
count_stats <- count_stats %>% dplyr::mutate(new_up_set_id =
dplyr::case_when(
671 <= analysis_id & analysis_id <= 675 ~ 0,
676 <= analysis_id & analysis_id <= 680 ~ 1,
681 <= analysis_id & analysis_id <= 685 ~ 2,
686 <= analysis_id & analysis_id <= 690 ~ 3,
691 <= analysis_id & analysis_id <= 695 ~ 4,
696 <= analysis_id & analysis_id <= 700 ~ 5,
701 <= analysis_id & analysis_id <= 705 ~ 6,
706 <= analysis_id & analysis_id <= 710 ~ 7,
711 <= analysis_id & analysis_id <= 715 ~ 8,
716 <= analysis_id & analysis_id <= 720 ~ 9,
721 <= analysis_id & analysis_id <= 725 ~ 10,
726 <= analysis_id & analysis_id <= 730 ~ 11,
731 <= analysis_id & analysis_id <= 735 ~ 12,
736 <= analysis_id & analysis_id <= 740 ~ 13,
741 <= analysis_id & analysis_id <= 745 ~ 14,
746 <= analysis_id & analysis_id <= 750 ~ 15,
751 <= analysis_id & analysis_id <= 755 ~ 16,
756 <= analysis_id & analysis_id <= 760 ~ 17,
761 <= analysis_id & analysis_id <= 765 ~ 18,
766 <= analysis_id & analysis_id <= 770 ~ 19,
771 <= analysis_id & analysis_id <= 775 ~ 20,
776 <= analysis_id & analysis_id <= 780 ~ 21,
781 <= analysis_id & analysis_id <= 785 ~ 22,
786 <= analysis_id & analysis_id <= 790 ~ 23,
791 <= analysis_id & analysis_id <= 795 ~ 24,
796 <= analysis_id & analysis_id <= 800 ~ 25,
801 <= analysis_id & analysis_id <= 805 ~ 26,
806 <= analysis_id & analysis_id <= 810 ~ 27,
811 <= analysis_id & analysis_id <= 815 ~ 28,
816 <= analysis_id & analysis_id <= 820 ~ 29,
821 <= analysis_id & analysis_id <= 825 ~ 30
))
count_stats_set_avg <-
count_stats %>% dplyr::group_by(new_up_set_id) %>% dplyr::summarise(mean_evmt = mean(evmt), mean_wait_duration = mean(wait_duration))
count_stats_set_avg$delta_evmt <-
count_stats_set_avg$mean_evmt - count_stats_set_avg$mean_evmt[count_stats_set_avg$new_up_set_id == 0]
count_stats_set_avg$type <-
c(
'basecase',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new'
)
count_stats_set_avg$dcfc_count <-
c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
count_stats_set_avg$total_cost <- 0
for (i in rownames(count_stats_set_avg)) {
type <- count_stats_set_avg[i, "type"]
dcfc_count <- count_stats_set_avg[i, "dcfc_count"]
total_cost <- 0
print(type)
print(dcfc_count)
for (evse_id in be_cs$evse_id)
{
evse_id <- substr(evse_id, start = 2, stop = nchar(evse_id))
print(evse_id)
cost <-
dcfcoster::dcfc_cost(type = type,
new_plug_count = dcfc_count,
evse_id = evse_id)
print(cost)
total_cost <- total_cost + cost
}
print(total_cost)
count_stats_set_avg[i, "total_cost"] <- total_cost
}
count_stats_set_avg$cost_eff <-
count_stats_set_avg$delta_evmt / count_stats_set_avg$total_cost
write.csv(count_stats_set_avg, "results_ws2.csv")
# ## Locations of with max WS
# basecase_aid <- 484
# sql_be_ws = glue::glue(
# "select count(wait_id) as count, analysis_id, evse_id, be.latitude, be.longitude
# from evse_evs_waiting ews
# left join built_evse be on
# 'b' || cast(be.bevse_id as text) = ews.evse_id
# where ews.analysis_id = {basecase_aid}
# group by ews.analysis_id, ews.evse_id, be.latitude, be.longitude
# order by count desc
# limit 5;
# "
# )
# be_ws <- DBI::dbGetQuery(main_con, sql_be_ws)
#
# ## Stats for the sets with new and upgrades
# set_id <- c(102, 104, 105, 106, 107, 108, 109)
# sql_count_stats_ws = glue::glue(
# "select count(ets.veh_id) as sim_count,
# ets.analysis_id,
# coalesce(ne.count, 0) as nevse_count,
# ef.fin_count,
# ef.evmt,
# es.str_count,
# ecs.cs_count,
# ew.wait_count,
# ar.set_id
# from evtrip_scenarios ets
# left join (select count(nevse_id), analysis_id from new_evses group by analysis_id) as ne
# on ets.analysis_id = ne.analysis_id
# join (select count(veh_id) as fin_count, sum(distance_travelled) as evmt, analysis_id
# from ev_finished
# group by analysis_id) as ef
# on ets.analysis_id = ef.analysis_id
# join (select count(veh_id) as str_count, analysis_id from ev_stranded group by analysis_id) as es
# on ets.analysis_id = es.analysis_id
# join (select count(cs_id) as cs_count, analysis_id from evse_charging_session group by analysis_id) as ecs
# on ets.analysis_id = ecs.analysis_id
# join (select count(wait_id) as wait_count, analysis_id from evse_evs_waiting group by analysis_id) as ew
# on ets.analysis_id = ew.analysis_id
# join (select analysis_id, set_id from analysis_record where set_id IN ({glue::glue_collapse(set_id, ', ')})) as ar ON ets.analysis_id = ar.analysis_id
# group by ets.analysis_id, nevse_count, ef.fin_count, es.str_count, ef.evmt, ecs.cs_count, ew.wait_count, ar.set_id
# order by ets.analysis_id desc
# ;"
# )
#
# count_stats_ws <- DBI::dbGetQuery(main_con, sql_count_stats_ws)
#
# count_stats_ws_set_avg <-
# count_stats_ws %>% dplyr::group_by(set_id) %>% dplyr::summarise(mean_evmt = mean(evmt))
#
# count_stats_ws_set_avg$delta_evmt <-
# count_stats_ws_set_avg$mean_evmt - count_stats_ws_set_avg$mean_evmt[count_stats_ws_set_avg$set_id == 102]
#
# count_stats_ws_set_avg$type <-
# c('basecase', 'upgrade', 'upgrade', 'new', 'new', 'new', 'new')
# count_stats_ws_set_avg$dcfc_count <- c(0, 5, 10, 5, 10, 5, 10)
# count_stats_ws_set_avg$total_cost <- 0
#
# for (i in rownames(count_stats_ws_set_avg)) {
# type <- count_stats_ws_set_avg[i, "type"]
# dcfc_count <- count_stats_ws_set_avg[i, "dcfc_count"]
# total_cost <- 0
# print(type)
# print(dcfc_count)
# for (evse_id in be_ws$evse_id)
# {
# evse_id <- substr(evse_id, start = 2, stop = nchar(evse_id))
# print(evse_id)
# cost <-
# dcfcoster::dcfc_cost(type = type,
# new_plug_count = dcfc_count,
# evse_id = evse_id)
# print(cost)
# total_cost <- total_cost + cost
# }
# print(total_cost)
# count_stats_ws_set_avg[i, "total_cost"] <- total_cost
# }
#
#
# count_stats_ws_set_avg$cost_eff <-
# count_stats_ws_set_avg$delta_evmt / count_stats_ws_set_avg$total_cost
#
#
# write.csv(count_stats_ws_set_avg, "results_ws.csv")
# Plotting
x_labels <-
c(
"New / Upgrade with 1 chargers",
"New / Upgrade with 2 chargers",
"New / Upgrade with 3 chargers",
"New / Upgrade with 4 chargers",
"New / Upgrade with 5 chargers",
"New / Upgrade with 6 chargers",
"New / Upgrade with 7 chargers",
"New / Upgrade with 8 chargers",
"New / Upgrade with 9 chargers",
"New / Upgrade with 10 chargers"
)
ay <- list(
tickfont = list(color = "red"),
overlaying = "y",
side = "right",
title = "Cumulative Wait Duration (min)"
)
fig <-
plotly::plot_ly(
x = x_labels,
y = count_stats_set_avg$cost_eff[2:11] * 1000,
type = 'scatter',
mode = 'lines+markers',
name = 'Upgrade cost effectiveness'
)
fig <-
fig %>% plotly::add_trace(
x = x_labels,
y = count_stats_set_avg$cost_eff[12:21]*1000,
type = 'scatter',
mode = 'lines+markers',
name = 'New - 1 cost effectiveness'
)
fig <-
fig %>% plotly::add_trace(
x = x_labels,
y = count_stats_set_avg$cost_eff[22:31]*1000,
type = 'scatter',
mode = 'lines+markers',
name = 'New - 2 cost effectiveness'
)
fig <-
fig %>% plotly::layout(
title = "Cost effectiveness of new/upgrade for various cases",
xaxis = list(
title = "Scenario Description",
categoryorder = "array",
type = 'category',
categoryarray = x_labels
),
yaxis = list(title = "Cost Effectiveness (in evmt/k$)")
)
fig
fig2 <-
plotly::plot_ly(
x = x_labels,
y = count_stats_set_avg$mean_wait_duration[2:11],
type = 'scatter',
mode = 'lines+markers',
name = 'Upgrade wait durations'
)
fig2 <-
fig2 %>% plotly::add_trace(
x = x_labels,
y = count_stats_set_avg$mean_wait_duration[12:21],
type = 'scatter',
mode = 'lines+markers',
name = 'New - 1 wait duration'
)
fig2 <-
fig2 %>% plotly::add_trace(
x = x_labels,
y = count_stats_set_avg$mean_wait_duration[22:31],
type = 'scatter',
mode = 'lines+markers',
name = 'New - 2 wait duration'
)
fig2 <-
fig2 %>% plotly::add_trace(
x = x_labels,
y = count_stats_set_avg$mean_wait_duration[1:1],
type = 'scatter',
mode = 'lines+markers',
name = 'Basecase'
)
fig2 <-
fig2 %>% plotly::layout(
title = "Cumulative wait duration of new/upgrade for various cases",
xaxis = list(
title = "Scenario Description",
categoryorder = "array",
type = 'category',
categoryarray = x_labels
),
yaxis = list(title = "Cumulative Wait Duration (min)")
)
fig2
chintanp/dcfcoster documentation built on Dec. 19, 2021, 3:58 p.m.
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# This file does calculations for cost-effectiveness
library(magrittr)
# Database settings -------------------------------------------------------
if (!DBI::dbCanConnect(
RPostgres::Postgres(),
host = Sys.getenv("MAIN_HOST"),
dbname = Sys.getenv("MAIN_DB"),
user = Sys.getenv("MAIN_USER"),
password = Sys.getenv("MAIN_PWD"),
port = Sys.getenv("MAIN_PORT")
)) {
lg$log(level = "fatal",
msg = "Cannot connect to database",
"ip" = ipify::get_ip())
# Exit if DB cannot connect
stop("Cannot connect to database")
}
main_con <- DBI::dbConnect(
RPostgres::Postgres(),
host = Sys.getenv("MAIN_HOST"),
dbname = Sys.getenv("MAIN_DB"),
user = Sys.getenv("MAIN_USER"),
password = Sys.getenv("MAIN_PWD"),
port = Sys.getenv("MAIN_PORT")
)
## Locations of with max CS
basecase_aid <- 484
sql_be_cs = glue::glue(
"select count(cs_id) as count, analysis_id, evse_id, be.latitude, be.longitude
from evse_charging_session ecs
left join built_evse be on
'b' || cast(be.bevse_id as text) = ecs.evse_id
where ecs.analysis_id = {basecase_aid}
group by ecs.analysis_id, ecs.evse_id, be.latitude, be.longitude
order by count desc
limit 5;
"
)
be_cs <- DBI::dbGetQuery(main_con, sql_be_cs)
## Stats for the sets with new and upgrades
set_id <- c(125, 126, 127, 128)
sql_count_stats = glue::glue(
"select count(ets.veh_id) as sim_count,
ets.analysis_id,
coalesce(ne.count, 0) as nevse_count,
ef.fin_count,
ef.evmt,
es.str_count,
ecs.cs_count,
ew.wait_count,
ar.set_id,
ew.wait_duration
from evtrip_scenarios ets
left join (select count(nevse_id), analysis_id from new_evses group by analysis_id) as ne
on ets.analysis_id = ne.analysis_id
join (select count(veh_id) as fin_count, sum(distance_travelled) as evmt, analysis_id
from ev_finished
group by analysis_id) as ef
on ets.analysis_id = ef.analysis_id
join (select count(veh_id) as str_count, analysis_id from ev_stranded group by analysis_id) as es
on ets.analysis_id = es.analysis_id
join (select count(cs_id) as cs_count, analysis_id from evse_charging_session group by analysis_id) as ecs
on ets.analysis_id = ecs.analysis_id
join (select count(wait_id) as wait_count, analysis_id, coalesce(sum(extract(minutes from (wait_end_time::timestamp - wait_start_time::timestamp))),
0) as wait_duration from evse_evs_waiting group by analysis_id) as ew
on ets.analysis_id = ew.analysis_id
join (select analysis_id, set_id from analysis_record where set_id IN ({glue::glue_collapse(set_id, ', ')})) as ar ON ets.analysis_id = ar.analysis_id
group by ets.analysis_id, nevse_count, ef.fin_count, es.str_count, ef.evmt, ecs.cs_count, ew.wait_count, ar.set_id, ew.wait_duration
order by ets.analysis_id desc
;"
)
count_stats <- DBI::dbGetQuery(main_con, sql_count_stats)
count_stats$new_up_set_id <- NA
count_stats <- count_stats %>% dplyr::mutate(new_up_set_id =
dplyr::case_when(
671 <= analysis_id & analysis_id <= 675 ~ 0,
676 <= analysis_id & analysis_id <= 680 ~ 1,
681 <= analysis_id & analysis_id <= 685 ~ 2,
686 <= analysis_id & analysis_id <= 690 ~ 3,
691 <= analysis_id & analysis_id <= 695 ~ 4,
696 <= analysis_id & analysis_id <= 700 ~ 5,
701 <= analysis_id & analysis_id <= 705 ~ 6,
706 <= analysis_id & analysis_id <= 710 ~ 7,
711 <= analysis_id & analysis_id <= 715 ~ 8,
716 <= analysis_id & analysis_id <= 720 ~ 9,
721 <= analysis_id & analysis_id <= 725 ~ 10,
726 <= analysis_id & analysis_id <= 730 ~ 11,
731 <= analysis_id & analysis_id <= 735 ~ 12,
736 <= analysis_id & analysis_id <= 740 ~ 13,
741 <= analysis_id & analysis_id <= 745 ~ 14,
746 <= analysis_id & analysis_id <= 750 ~ 15,
751 <= analysis_id & analysis_id <= 755 ~ 16,
756 <= analysis_id & analysis_id <= 760 ~ 17,
761 <= analysis_id & analysis_id <= 765 ~ 18,
766 <= analysis_id & analysis_id <= 770 ~ 19,
771 <= analysis_id & analysis_id <= 775 ~ 20,
776 <= analysis_id & analysis_id <= 780 ~ 21,
781 <= analysis_id & analysis_id <= 785 ~ 22,
786 <= analysis_id & analysis_id <= 790 ~ 23,
791 <= analysis_id & analysis_id <= 795 ~ 24,
796 <= analysis_id & analysis_id <= 800 ~ 25,
801 <= analysis_id & analysis_id <= 805 ~ 26,
806 <= analysis_id & analysis_id <= 810 ~ 27,
811 <= analysis_id & analysis_id <= 815 ~ 28,
816 <= analysis_id & analysis_id <= 820 ~ 29,
821 <= analysis_id & analysis_id <= 825 ~ 30
))
count_stats_set_avg <-
count_stats %>% dplyr::group_by(new_up_set_id) %>% dplyr::summarise(mean_evmt = mean(evmt), mean_wait_duration = mean(wait_duration))
count_stats_set_avg$delta_evmt <-
count_stats_set_avg$mean_evmt - count_stats_set_avg$mean_evmt[count_stats_set_avg$new_up_set_id == 0]
count_stats_set_avg$type <-
c(
'basecase',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'upgrade',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new',
'new'
)
count_stats_set_avg$dcfc_count <-
c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
count_stats_set_avg$total_cost <- 0
for (i in rownames(count_stats_set_avg)) {
type <- count_stats_set_avg[i, "type"]
dcfc_count <- count_stats_set_avg[i, "dcfc_count"]
total_cost <- 0
print(type)
print(dcfc_count)
for (evse_id in be_cs$evse_id)
{
evse_id <- substr(evse_id, start = 2, stop = nchar(evse_id))
print(evse_id)
cost <-
dcfcoster::dcfc_cost(type = type,
new_plug_count = dcfc_count,
evse_id = evse_id)
print(cost)
total_cost <- total_cost + cost
}
print(total_cost)
count_stats_set_avg[i, "total_cost"] <- total_cost
}
count_stats_set_avg$cost_eff <-
count_stats_set_avg$delta_evmt / count_stats_set_avg$total_cost
write.csv(count_stats_set_avg, "results_ws2.csv")
# ## Locations of with max WS
# basecase_aid <- 484
# sql_be_ws = glue::glue(
# "select count(wait_id) as count, analysis_id, evse_id, be.latitude, be.longitude
# from evse_evs_waiting ews
# left join built_evse be on
# 'b' || cast(be.bevse_id as text) = ews.evse_id
# where ews.analysis_id = {basecase_aid}
# group by ews.analysis_id, ews.evse_id, be.latitude, be.longitude
# order by count desc
# limit 5;
# "
# )
# be_ws <- DBI::dbGetQuery(main_con, sql_be_ws)
#
# ## Stats for the sets with new and upgrades
# set_id <- c(102, 104, 105, 106, 107, 108, 109)
# sql_count_stats_ws = glue::glue(
# "select count(ets.veh_id) as sim_count,
# ets.analysis_id,
# coalesce(ne.count, 0) as nevse_count,
# ef.fin_count,
# ef.evmt,
# es.str_count,
# ecs.cs_count,
# ew.wait_count,
# ar.set_id
# from evtrip_scenarios ets
# left join (select count(nevse_id), analysis_id from new_evses group by analysis_id) as ne
# on ets.analysis_id = ne.analysis_id
# join (select count(veh_id) as fin_count, sum(distance_travelled) as evmt, analysis_id
# from ev_finished
# group by analysis_id) as ef
# on ets.analysis_id = ef.analysis_id
# join (select count(veh_id) as str_count, analysis_id from ev_stranded group by analysis_id) as es
# on ets.analysis_id = es.analysis_id
# join (select count(cs_id) as cs_count, analysis_id from evse_charging_session group by analysis_id) as ecs
# on ets.analysis_id = ecs.analysis_id
# join (select count(wait_id) as wait_count, analysis_id from evse_evs_waiting group by analysis_id) as ew
# on ets.analysis_id = ew.analysis_id
# join (select analysis_id, set_id from analysis_record where set_id IN ({glue::glue_collapse(set_id, ', ')})) as ar ON ets.analysis_id = ar.analysis_id
# group by ets.analysis_id, nevse_count, ef.fin_count, es.str_count, ef.evmt, ecs.cs_count, ew.wait_count, ar.set_id
# order by ets.analysis_id desc
# ;"
# )
#
# count_stats_ws <- DBI::dbGetQuery(main_con, sql_count_stats_ws)
#
# count_stats_ws_set_avg <-
# count_stats_ws %>% dplyr::group_by(set_id) %>% dplyr::summarise(mean_evmt = mean(evmt))
#
# count_stats_ws_set_avg$delta_evmt <-
# count_stats_ws_set_avg$mean_evmt - count_stats_ws_set_avg$mean_evmt[count_stats_ws_set_avg$set_id == 102]
#
# count_stats_ws_set_avg$type <-
# c('basecase', 'upgrade', 'upgrade', 'new', 'new', 'new', 'new')
# count_stats_ws_set_avg$dcfc_count <- c(0, 5, 10, 5, 10, 5, 10)
# count_stats_ws_set_avg$total_cost <- 0
#
# for (i in rownames(count_stats_ws_set_avg)) {
# type <- count_stats_ws_set_avg[i, "type"]
# dcfc_count <- count_stats_ws_set_avg[i, "dcfc_count"]
# total_cost <- 0
# print(type)
# print(dcfc_count)
# for (evse_id in be_ws$evse_id)
# {
# evse_id <- substr(evse_id, start = 2, stop = nchar(evse_id))
# print(evse_id)
# cost <-
# dcfcoster::dcfc_cost(type = type,
# new_plug_count = dcfc_count,
# evse_id = evse_id)
# print(cost)
# total_cost <- total_cost + cost
# }
# print(total_cost)
# count_stats_ws_set_avg[i, "total_cost"] <- total_cost
# }
#
#
# count_stats_ws_set_avg$cost_eff <-
# count_stats_ws_set_avg$delta_evmt / count_stats_ws_set_avg$total_cost
#
#
# write.csv(count_stats_ws_set_avg, "results_ws.csv")
# Plotting
x_labels <-
c(
"New / Upgrade with 1 chargers",
"New / Upgrade with 2 chargers",
"New / Upgrade with 3 chargers",
"New / Upgrade with 4 chargers",
"New / Upgrade with 5 chargers",
"New / Upgrade with 6 chargers",
"New / Upgrade with 7 chargers",
"New / Upgrade with 8 chargers",
"New / Upgrade with 9 chargers",
"New / Upgrade with 10 chargers"
)
ay <- list(
tickfont = list(color = "red"),
overlaying = "y",
side = "right",
title = "Cumulative Wait Duration (min)"
)
fig <-
plotly::plot_ly(
x = x_labels,
y = count_stats_set_avg$cost_eff[2:11] * 1000,
type = 'scatter',
mode = 'lines+markers',
name = 'Upgrade cost effectiveness'
)
fig <-
fig %>% plotly::add_trace(
x = x_labels,
y = count_stats_set_avg$cost_eff[12:21]*1000,
type = 'scatter',
mode = 'lines+markers',
name = 'New - 1 cost effectiveness'
)
fig <-
fig %>% plotly::add_trace(
x = x_labels,
y = count_stats_set_avg$cost_eff[22:31]*1000,
type = 'scatter',
mode = 'lines+markers',
name = 'New - 2 cost effectiveness'
)
fig <-
fig %>% plotly::layout(
title = "Cost effectiveness of new/upgrade for various cases",
xaxis = list(
title = "Scenario Description",
categoryorder = "array",
type = 'category',
categoryarray = x_labels
),
yaxis = list(title = "Cost Effectiveness (in evmt/k$)")
)
fig
fig2 <-
plotly::plot_ly(
x = x_labels,
y = count_stats_set_avg$mean_wait_duration[2:11],
type = 'scatter',
mode = 'lines+markers',
name = 'Upgrade wait durations'
)
fig2 <-
fig2 %>% plotly::add_trace(
x = x_labels,
y = count_stats_set_avg$mean_wait_duration[12:21],
type = 'scatter',
mode = 'lines+markers',
name = 'New - 1 wait duration'
)
fig2 <-
fig2 %>% plotly::add_trace(
x = x_labels,
y = count_stats_set_avg$mean_wait_duration[22:31],
type = 'scatter',
mode = 'lines+markers',
name = 'New - 2 wait duration'
)
fig2 <-
fig2 %>% plotly::add_trace(
x = x_labels,
y = count_stats_set_avg$mean_wait_duration[1:1],
type = 'scatter',
mode = 'lines+markers',
name = 'Basecase'
)
fig2 <-
fig2 %>% plotly::layout(
title = "Cumulative wait duration of new/upgrade for various cases",
xaxis = list(
title = "Scenario Description",
categoryorder = "array",
type = 'category',
categoryarray = x_labels
),
yaxis = list(title = "Cumulative Wait Duration (min)")
)
fig2
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