Goa Results Visualizations

This data was gathered for Kenya.

Libraries

library(tidyverse)
library(ggpmisc) #annotations
library(RColorBrewer)
library(ggcorrplot)
library(ggpubr)
library(grid)
library(gridExtra)

#library(forecast)
library(readxl)

source("my_theme.R")

## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Year Vector

year_vector <-  c(2019, 2025, 2030, 2035, 2040, 2045, 2050)

Scenario Name

sc_bau <- "Bau"
sc_nm <- "RE"

Power Sector

bau_PbT <- read.csv("./results/bau_PbT.csv")
bau_PbT["Scenario"] = sc_bau 

re_PbT <- read.csv("./results/re_PbT.csv")
re_PbT["Scenario"] = sc_nm

PbT = rbind(bau_PbT, re_PbT)

High resolution

# List of keys
keys <- c("re_PbT_72", "re_PbT_144", "re_PbT_288")  

for (i in seq_along(keys)) {

  # Assign the name of the data frame using the current key
  assign(keys[i], read.csv(paste0("./results/", keys[i], ".csv")))
}

P_tech_names <- c("Small hydro", "Solar-PV Agriculture", "Solar-PV Commercial",
                  "Solar-PV Residential", "Solar RPO", "Solar-ground+float",
                  "Wind Goa", "Wind RPO", "Wind Tech", "Solar Tech", "Biomass PP",
                  "RE-Imports", "IEX-Imports", "Nuclear", "Diesel", "CCGT", 
                  "Import Coal PP", "Coal PP")

#
#2019
#
# bau_PbT <- read.csv("./results/bau_PbT.csv")
# 
# bau_PbT <- bau_PbT %>% filter(!YEAR==2019)
# 
# 
# re_PbT <- read.csv("./results/re_PbT.csv")
# new3 <- re_PbT %>% filter(YEAR==2019)
# 
# bau_PbT <- rbind(bau_PbT, new3)
# 
# bau_PbT["Scenario"] = sc_bau 
# re_PbT["Scenario"] = sc_nm
# 
# PbT = rbind(bau_PbT, re_PbT)

PbT_f <-  PbT %>%
    filter(YEAR %in% year_vector) %>%
    group_by(YEAR, Tech_name, Scenario) %>% 
    summarise(VALUE=sum(VALUE)) %>% 
    mutate(Tech_name = factor(Tech_name, levels=P_tech_names)) %>% 
    ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name)) +
    geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 0.4)+
   scale_fill_viridis_d(direction = -1) +
   labs(fill = "", x="", y = "Generation [PJ]") +
   facet_grid(.~YEAR) +
   rh_theme +
  
   guides(fill = guide_legend(ncol = 7))  +
   theme(axis.text.x = element_text(angle = 90, size = 5), 
         legend.margin = margin(t=-5, b=0, unit='mm'))

## `summarise()` has grouped output by 'YEAR', 'Tech_name'. You can override using
## the `.groups` argument.

PbT_f

PbT %>% filter(YEAR %in% year_vector) %>%
    group_by(YEAR, Tech_name, Scenario) %>% 
    summarise(VALUE=sum(VALUE)) %>% filter(YEAR==2019) %>% filter(Scenario=='Bau')

## `summarise()` has grouped output by 'YEAR', 'Tech_name'. You can override using
## the `.groups` argument.

# Coal contribution
PbT %>% filter(YEAR==2050) %>% filter(Tech_name=='Import Coal PP') %>% group_by(Scenario) %>% summarise(sum(VALUE))

PbT %>% filter(YEAR==2050) %>% group_by(Tech_name,Scenario) %>% summarise(sum(VALUE))

## `summarise()` has grouped output by 'Tech_name'. You can override using the
## `.groups` argument.

PbT %>% filter(YEAR==2050) %>% filter(Scenario=='RE') %>% group_by(Tech_name,Scenario) %>% summarise(sum(VALUE))

## `summarise()` has grouped output by 'Tech_name'. You can override using the
## `.groups` argument.

PbT %>% filter(YEAR==2050) %>% filter(Scenario=='Bau') %>% group_by(Tech_name,Scenario) %>% summarise(sum(VALUE))

## `summarise()` has grouped output by 'Tech_name'. You can override using the
## `.groups` argument.

Prod by Tech

Figure

ggsave(PbT_f, filename = './figures/Pro_b_Tech.jpg', width = 12, height = 8, units = 'cm' )
ggsave(PbT_f, filename = './figures/Pro_b_Tech.pdf', width = 12, height = 8, units = 'cm' )

Prod by Tech Time slice

Figure

the common.legend=TRUE argument takes the legend from the first plot in the series.

PbT_ts_f <- PbT %>%
  filter(YEAR ==2045) %>%
  filter(Scenario=='RE') %>% 
  mutate(Tech_name = factor(Tech_name, levels=P_tech_names)) %>% 
  ggplot(aes(x=TIMESLICE, y=VALUE, fill=Tech_name)) +
  geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 0.4)+
  scale_fill_viridis_d(direction = -1) +
  labs(title="12 TS",fill = "", x="", y = "") +
  rh_theme +
  theme(legend.margin = margin(t=-5, b=0, unit='mm'),
        plot.title = element_text(hjust=0, vjust = -3, size = 5),
        legend.text = element_text(size = 4, face = "plain"))+
  scale_x_continuous(breaks=seq(0,12,1)) +
  guides(fill = guide_legend(ncol = 6))
PbT_ts_f

High resolution

#get common legend
leg <- get_legend(PbT_ts_f)

PbT_72 <- plot_PbT(re_PbT_72, 2045, "72 TS", 6, lw=0.01, width = 1) +
  scale_x_continuous(breaks=seq(0,72,6))
  
  
PbT_144 <- plot_PbT(re_PbT_144, 2045, "144 TS", 6, lw=0.01, width = 1) +
   scale_x_continuous(breaks=seq(0,144,12))
  

PbT_288 <- plot_PbT(re_PbT_288, 2045, "288 TS", 6, lw=0.001, width = 1) +
  scale_x_continuous(breaks=seq(0,288,24))

PbT_288

PbT_ts_f1 <- PbT_ts_f + geom_bar(stat = "identity",
                    linewidth = 0.001,
                    width = 0.4)

Figure PbT HR 2045

annotate figure:

http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/#annotate-the-arranged-figure

empty <- ggplot()+theme_void()
gap <- -0.15

PbT_HR <- ggarrange(PbT_ts_f1, empty,
                    PbT_72, empty,
                    PbT_144, empty,
                    PbT_288, 
                    #leg,
                    nrow = 7,
                    #legend = "bottom",
                    labels = c('E', '', 'F', '', 'G', '', 'H', ''
                               ),
                    font.label=list(size=6),
                    align = "v",
                    #common.legend=TRUE,
                    legend = 'none',
                    heights = c(1,gap,
                                1,gap,
                                1,gap,
                                1
                                #,0.15
                                ))
PbT_HR <- annotate_figure(PbT_HR,
                left = text_grob('Generation by Timeslice 2045 [PJ]', size=6, rot = 90))
PbT_HR

# ggsave(PbT_HR, filename = './figures/PbT_HR.jpg', width = 12, height = 8.5, units = 'cm' )
# ggsave(PbT_HR, filename = './figures/PbT_HR.pdf', width = 12, height = 8.5, units = 'cm' )

Figure PbT HR 2025

PbT2_ts_f <- PbT %>%
  filter(YEAR ==2025) %>%
  filter(Scenario=='RE') %>% 
  mutate(Tech_name = factor(Tech_name, levels=P_tech_names)) %>% 
  ggplot(aes(x=TIMESLICE, y=VALUE, fill=Tech_name)) +
  geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 0.4)+
  scale_fill_viridis_d(direction = -1) +
  labs(title="12 TS",fill = "", x="", y = "") +
  rh_theme +
  theme(legend.margin = margin(t=-5, b=0, unit='mm'),
        plot.title = element_text(hjust=0, vjust = -3, size = 5))+
  scale_x_continuous(breaks=seq(0,12,1)) +
  guides(fill = guide_legend(ncol = 6))

leg2 <- get_legend(PbT2_ts_f)

PbT2_72 <- plot_PbT(re_PbT_72, 2025, "72 TS", 6, lw=0.01, width = 1) +
  scale_x_continuous(breaks=seq(0,72,6))
  
  
PbT2_144 <- plot_PbT(re_PbT_144, 2025, "144 TS", 6, lw=0.01, width = 1) +
   scale_x_continuous(breaks=seq(0,144,12))
  

PbT2_288 <- plot_PbT(re_PbT_288, 2025, "288 TS", 6, lw=0.01, width = 1) +
  scale_x_continuous(breaks=seq(0,288,24))

PbT2_ts_f1 <- PbT2_ts_f + geom_bar(stat = "identity",
                    linewidth = 0.001,
                    width = 0.4)

PbT_HR2 <- ggarrange(PbT2_ts_f1, empty,
                    PbT2_72, empty,
                    PbT2_144, empty,
                    PbT2_288, 
                    #leg2, 
                    nrow = 7,
                    #legend = "bottom",
                    labels = c('A', '', 'B', '', 'C', '', 'D', ''),
                    font.label=list(size=6),
                    align = "v",
                    #common.legend=TRUE,
                    legend='none',
                    heights = c(1,gap,
                                1,gap,
                                1,gap,
                                1
                                #,0.15
                                ))
PbT_HR2 <- annotate_figure(PbT_HR2,
                left = text_grob('Generation by Timeslice 2025 [PJ]',
                                 size=6, rot = 90))
PbT_HR2

# ggsave(PbT_HR2, filename = './figures/PbT_HR2.jpg', width = 6, height = 8.5, units = 'cm' )
# ggsave(PbT_HR2, filename = './figures/PbT_HR2.pdf', width = 6, height = 8.5, units = 'cm' )

Figure PbT HR 2025-2045

PbT_HiRes <- ggarrange(
  ggarrange(PbT_HR2, PbT_HR, nrow = 1, widths = c(1,1),
            font.label=list(size=6), align = "h"),
  ggarrange(leg, widths = c(2), font.label=list(size=6)),
  nrow = 2,
  heights = c(1, 0.075)
)
PbT_HiRes

ggsave(PbT_HiRes, filename = './figures/Figure_11thesis.jpg', width = 16, height = 10, units = 'cm' )
ggsave(PbT_HiRes, filename = './figures/Figure_11thesis.pdf', width = 16, height = 10, units = 'cm' )

PbT %>% filter(YEAR %in% c(2025,2050)) %>% filter(grepl("Residential", Tech_name)) %>% group_by(YEAR, Scenario) %>% 
    summarise(VALUE=sum(VALUE))

## `summarise()` has grouped output by 'YEAR'. You can override using the
## `.groups` argument.

#Pro_b_Tech <- ggarrange(PbT_f, PbT_ts_f,
#                   nrow = 2, heights = c(1.5,1),
#                   labels = c('A','B'), font.label=list(size=6), 
#                   common.legend = TRUE, legend='bottom')
#Pro_b_Tech

#ggsave(Pro_b_Tech, filename = './figures/Pro_b_Tech.jpg', width = 12, height = 8, units = 'cm' )
#ggsave(Pro_b_Tech, filename = './figures/Pro_b_Tech.pdf', width = 12, height = 8, units = 'cm' )

New Capacity

bau_accnc <- read.csv("./results/bau_accnc.csv")
bau_accnc["Scenario"] = sc_bau 

re_accnc <- read.csv("./results/re_accnc.csv")
re_accnc["Scenario"] = sc_nm

AccNc = rbind(bau_accnc, re_accnc)

AccNc %>% filter(YEAR==2050)

Figure

rpo <- c("Wind Tech", "Solar Tech", "Solar RPO", "Wind RPO")

AccNc_f <-  AccNc %>%
    filter(YEAR %in% year_vector) %>%
    group_by(YEAR, Tech_name, Scenario) %>% 
    summarise(VALUE=sum(VALUE)) %>% 
    mutate(Tech_name = factor(Tech_name, levels=P_tech_names)) %>% 
    filter(VALUE >0) %>% 
    filter(!Tech_name %in% rpo) %>% 
    ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name)) +
    geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 0.4)+
   scale_fill_viridis_d(direction = -1) +
   labs(fill = "", x="", y = "Goa \n New Capacity [GW]") +
   facet_grid(.~YEAR) +
   theme_bw() + #+
   rh_theme +
   guides(fill = guide_legend(ncol = 3)) +
   theme(axis.text.x = element_text(angle = 90))

## `summarise()` has grouped output by 'YEAR', 'Tech_name'. You can override using
## the `.groups` argument.

AccNc_f

New cap-ren

Figure

AccNc_f2 <-  AccNc %>%
    filter(YEAR %in% year_vector) %>%
    group_by(YEAR, Tech_name, Scenario) %>% 
    summarise(VALUE=sum(VALUE)) %>% 
    mutate(Tech_name = factor(Tech_name, levels=P_tech_names)) %>% 
    filter(Scenario=='RE') %>% 
    filter(Tech_name %in% rpo) %>% 
    ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name)) +
    geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 0.4)+
   scale_fill_viridis_d() +
   labs(fill = "", x="", y = "New Capacity [GW]") +
   facet_grid(.~YEAR) +
   theme_bw() + #+
   rh_theme +
   theme(axis.text.x = element_text(angle = 90))

## `summarise()` has grouped output by 'YEAR', 'Tech_name'. You can override using
## the `.groups` argument.

AccNc_f2

# Pro_Accnc_Tech <- ggarrange(AccNc_f, AccNc_f2, ncol=2,
#                             labels = c('A', 'B'), font.label=list(size=6),
#                             align = "h",
#                             legend = "bottom")

Figure new cap(2) + generation(1)

Pro_Accnc_Tech <- ggarrange(
  ggarrange(AccNc_f, AccNc_f2, nrow = 1,
             widths = c(1,1), legend = "bottom", labels = c('A', 'B'),
             font.label=list(size=6), align = "h"),
  ggarrange(PbT_f, nrow = 1,
            widths = c(2), legend = "bottom", labels = c('C'),
            font.label=list(size=6)),
  nrow = 2)
Pro_Accnc_Tech

ggsave(Pro_Accnc_Tech, filename = './figures/Figure_07.jpg', width = 12, height = 8, units = 'cm' )
ggsave(Pro_Accnc_Tech, filename = './figures/Figure_07.pdf', width = 12, height = 8, units = 'cm' )

New Capacity HighResolution

AccNc_12 <- AccNc %>% 
  filter(Scenario=='RE') %>% 
  mutate(TS = '12 TS') %>% 
  select(-Scenario)

  
# List of keys
AccNc_72 = read.csv("./results/re_accnc_72.csv")
AccNc_72$TS = '72 TS'

AccNc_144 = read.csv("./results/re_accnc_144.csv")
AccNc_144$TS = '144 TS'

AccNc_288 = read.csv("./results/re_accnc_288.csv")
AccNc_288$TS = '288 TS'



AccNC_HR <- rbind(AccNc_12, AccNc_72, AccNc_144, AccNc_288)

hr_list <- c('12 TS', '72 TS',
             '144 TS', '288 TS')

AccNC_HR_Goa <-  AccNC_HR %>%
  filter(YEAR %in% year_vector) %>%
  filter(VALUE >0) %>% 
  mutate(Tech_name = factor(Tech_name, levels=P_tech_names)) %>% 
  mutate(TS = factor(TS, levels=hr_list)) %>% 
  droplevels() %>% 
  filter(!Tech_name %in% rpo) %>% 
  ggplot(aes(x=YEAR, y=VALUE, fill=Tech_name)) +
  geom_bar(stat = "identity", colour= "black",
           linewidth = 0.05, width = 1)+
  scale_fill_viridis_d(direction = -1) +
  labs(fill = "", x="", y = "Goa Renewable Capacity [GW]") +
  facet_grid(TS~.) +
  rh_theme +
  guides(fill = guide_legend(ncol = 3)) +
  theme(axis.text.x = element_text(angle = 0))
AccNC_HR_Goa

 AccNC_HR %>% 
   filter(!Tech_name %in% rpo) %>% 
   filter(YEAR==2050) %>% 
   filter(VALUE >0) %>% 
   group_by(Tech_name) %>% 
   summarise(VALUE=sum(VALUE))

AccNC_HR_out <- AccNC_HR %>% 
    filter(YEAR %in% year_vector) %>%
    filter(VALUE >0) %>% 
    mutate(Tech_name = factor(Tech_name, levels=P_tech_names)) %>% 
    mutate(TS = factor(TS, levels=hr_list)) %>% 
    droplevels() %>% 
    filter(Tech_name %in% rpo) %>% 
    ggplot(aes(x=YEAR, y=VALUE, fill=Tech_name)) +
    geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 1)+
   scale_fill_viridis_d(direction = 1) +
   labs(fill = "", x="", y = "Renewable Capacity Addition [GW]") +
   facet_grid(TS~.) +
   rh_theme +
   guides(fill = guide_legend(ncol = 3)) +
   theme(axis.text.x = element_text(angle = 0))

AccNC_HR_out

AccNc_HR <- ggarrange(AccNC_HR_Goa, AccNC_HR_out,
                      nrow = 1,
                      widths = c(1,1), legend = "bottom",
                      labels = c('A', 'B'),
                      font.label=list(size=6),
                      align = "h", common.legend=FALSE)
#AccNc_HR <- annotate_figure(PbT_HR, left = text_grob('Generation by Timeslice [PJ]', size=6, rot = 90))
AccNc_HR

ggsave(AccNc_HR, filename = './figures/AccNc_HR.jpg', width = 12, height = 6, units = 'cm' )
ggsave(AccNc_HR, filename = './figures/AccNc_HR.pdf', width = 12, height = 6, units = 'cm' )

 AccNC_HR %>% 
   filter(Tech_name %in% rpo) %>% 
   filter(YEAR==2050) %>% 
   filter(VALUE >0) %>% 
   group_by(Tech_name, 
            TS) %>% 
   summarise(VALUE=sum(VALUE))

## `summarise()` has grouped output by 'Tech_name'. You can override using the
## `.groups` argument.

AccNC_HR %>%
    filter(YEAR %in% c(2050)) %>%
    group_by(YEAR, Tech_name, TS) %>% 
    summarise(VALUE=sum(VALUE))

## `summarise()` has grouped output by 'YEAR', 'Tech_name'. You can override using
## the `.groups` argument.

Final Energy

by Sector

bau_fe <- read.csv("./results/bau_fe.csv")
bau_fe["Scenario"] = sc_bau 

re_fe <- read.csv("./results/re_fe.csv")
re_fe["Scenario"] = sc_nm

fe_sec = rbind(bau_fe, re_fe)

# #2019
# 
# bau_fe <- read.csv("./results/bau_fe.csv")
# 
# bau_fe <- bau_fe %>% filter(!YEAR==2019)
# 
# re_fe <- read.csv("./results/re_fe.csv")
# new1 <- re_fe %>% filter(YEAR==2019)
# 
# bau_fe <- rbind(bau_fe, new1)
# 
# bau_fe["Scenario"] = sc_bau 
# re_fe["Scenario"] = sc_nm
# 
# fe_sec = rbind(bau_fe, re_fe)

fe_sec_f <-  fe_sec %>%
    filter(YEAR %in% year_vector) %>%
    group_by(YEAR, Tech_name, Scenario) %>% 
    summarise(VALUE=sum(VALUE)) %>% 
    #mutate(Tech_name = factor(Tech_name, levels=P_tech_names)) %>% 
    #filter(Tech_name %in% c("Wind Tech", "Solar Tech")) %>% 
    ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name)) +
    geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 0.4)+
   scale_fill_viridis_d() +
   labs(fill = "", y = "Final Energy Consuption \n by sector [PJ]") +
    labs(x = "") +
   facet_grid(.~YEAR) +
   theme_bw() + #+
   rh_theme +
   theme(axis.text.x = element_text(angle = 90))

## `summarise()` has grouped output by 'YEAR', 'Tech_name'. You can override using
## the `.groups` argument.

fe_sec_f

fe_sec %>%filter(YEAR %in% c(2019,2050)) %>% filter(Tech_name=='Transport') %>% 
    group_by(YEAR, Scenario) %>% summarise(sum(VALUE))

## `summarise()` has grouped output by 'YEAR'. You can override using the
## `.groups` argument.

by Fuel

bau_fe_fl <- read.csv("./results/bau_fe_f.csv")
bau_fe_fl["Scenario"] = sc_bau 

re_fe_fl <- read.csv("./results/re_fe_f.csv")
re_fe_fl["Scenario"] = sc_nm

fe_fuel = rbind(bau_fe_fl, re_fe_fl)

#
#2019
#
# bau_fe_fl <- read.csv("./results/bau_fe_f.csv")
# bau_fe_fl <- bau_fe_fl %>% filter(!YEAR==2019)
# 
# re_fe_fl <- read.csv("./results/re_fe_f.csv")
# new2 <- re_fe_fl %>% filter(YEAR==2019)
# 
# bau_fe_fl <- rbind(bau_fe_fl, new2)
# 
# bau_fe_fl["Scenario"] = sc_bau 
# re_fe_fl["Scenario"] = sc_nm
# 
# 
# fe_fuel = rbind(bau_fe_fl, re_fe_fl)

fe_fuel_f <-  fe_fuel %>%
    filter(YEAR %in% year_vector) %>%
    ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name_finalmix)) +
    geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 0.4)+
   scale_fill_viridis_d() +
   labs(fill = "", y = "Final Energy Consuption \n by fuel [PJ]") +
   facet_grid(.~YEAR) +
    labs(x = "")+
   theme_bw() + #+
   rh_theme +
   theme(axis.text.x = element_text(angle = 90))
fe_fuel_f

fe_fuel %>%filter(YEAR %in% c(2019,2050)) %>% filter(Tech_name_finalmix=='Liquid fossil fuels') %>% 
    group_by(YEAR, Scenario) %>% summarise(sum(VALUE))

## `summarise()` has grouped output by 'YEAR'. You can override using the
## `.groups` argument.

FE-fuel-secto

FE <- ggarrange(fe_sec_f, fe_fuel_f,
                   ncol = 1,
                   labels = c('A','B'), font.label=list(size=6), 
                   common.legend = FALSE, legend='right', align = "v")
FE

ggsave(FE, filename = './figures/FE.pdf', width = 12, height = 8, units = 'cm' )
ggsave(FE, filename = './figures/FE.jpg', width = 12, height = 8, units = 'cm' )

by Tech-sector mix

bau_fe_sem <- read.csv("./results/bau_sem.csv")
bau_fe_sem["Scenario"] = sc_bau 

re_fe_sem <- read.csv("./results/re_sem.csv")
re_fe_sem["Scenario"] = sc_nm

fe_sem = rbind(bau_fe_sem, re_fe_sem)

Residential, Commercial, Industry

fe_sem1 <- fe_sem %>% 
  filter(grepl("Resid|Comm|Indu", Tech_name))

fe_sem1_f <-  fe_sem1 %>%
    filter(YEAR %in% year_vector) %>%
    ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name)) +
    geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 0.4)+
   scale_fill_viridis_d() +
   labs(fill = "", x="", y = "Final Energy Consuption \n by Sector [PJ]") +
   facet_grid(.~YEAR) +
   theme_bw() + #+
   rh_theme +
   theme(axis.text.x = element_text(angle = 90))+
   #theme(legend.margin = margin(t=-5, b=0, unit='mm'))+
   guides(fill = guide_legend(ncol = 3))  
fe_sem1_f

# ggsave(fe_sem1_f, filename = './figures/fe_sem_res_com_ind.pdf', width = 12, height = 8, units = 'cm' )
# ggsave(fe_sem1_f, filename = './figures/fe_sem_res_com_ind.jpg', width = 12, height = 8, units = 'cm' )

Agriculture, Fisheries

fe_sem2 <- fe_sem %>% 
  filter(grepl("Fish|Agri", Tech_name))

fe_sem2 %>% filter(grepl("Agr", Tech_name)) %>% filter(YEAR %in% c(2019))

fe_sem2 %>% filter(grepl("Fish",Tech_name)) %>% filter(YEAR %in% c(2019, 2050))

fe_sem2_f <-  fe_sem2 %>%
    filter(YEAR %in% year_vector) %>%
    ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name)) +
    geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 0.4)+
   scale_fill_viridis_d() +
   labs(fill = "", x="", y = "Final Energy Consuption \n by Sector [PJ]") +
   facet_grid(.~YEAR) +
   theme_bw() + #+
   rh_theme +
   theme(axis.text.x = element_text(angle = 90))+
   theme(legend.margin = margin(t=-5, b=0, unit='mm'))+
   guides(fill = guide_legend(ncol = 3)) 
fe_sem2_f

FE2 <-  ggarrange(fe_sem1_f, fe_sem2_f, nrow = 2,
             widths = c(1,1), legend = "bottom", labels = c('A', 'B'),
             font.label=list(size=6), align = "h")
FE2

ggsave(FE2, filename = './figures/fe_sem_res_com_ind.pdf', width = 12, height = 8, units = 'cm' )
ggsave(FE2, filename = './figures/fe_sem_res_com_ind.jpg', width = 12, height = 8, units = 'cm' )

Cooking

fe_sem %>%
  filter(grepl("Cooking urban|Cooking rural", Tech_name)) %>% 
  filter(YEAR %in% c(2019,2050)) %>% 
  filter(Scenario=="Bau")

fe_sem3_u_f <- fe_sem %>%
  filter(grepl("Cooking urban", Tech_name)) %>% 
  filter(YEAR %in% year_vector) %>%
  mutate(Tech_name = str_replace(Tech_name, "Cooking urban", "Urban")) %>% 
  ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name)) +
  geom_bar(stat = "identity", colour= "black",
           linewidth = 0.05, width = 0.4)+
  scale_fill_viridis_d() +
  labs(fill = "", x="",y = "Final Energy Consuption [PJ]") +
  facet_grid(.~YEAR) +
  theme_bw() + #+
  rh_theme +
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.margin = margin(t=-5, b=0, unit='mm'))
fe_sem3_u_f

fe_sem3_r_f <- fe_sem %>%
  filter(grepl("Cooking rural", Tech_name)) %>% 
  filter(YEAR %in% year_vector) %>%
  mutate(Tech_name = str_replace(Tech_name, "Cooking rural", "Rural")) %>% 
  #mutate(Sector = ifelse(grepl("rural", Tech_name), "Rural", "Urban")) %>% 
  ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name)) +
  geom_bar(stat = "identity", colour= "black",
           linewidth = 0.05, width = 0.4)+
  scale_fill_viridis_d() +
  labs(fill = "",  x="", y = "Final Energy Consuption [PJ]") +
  facet_grid(.~YEAR) +
  theme_bw() + #+
  rh_theme +
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.margin = margin(t=-5, b=0, unit='mm'))
fe_sem3_r_f

COOK <- ggarrange(fe_sem3_u_f, fe_sem3_r_f,
                   ncol = 1,
                   labels = c('A','B'), font.label=list(size=6), 
                   common.legend = FALSE, legend='bottom', align = "v")
COOK

ggsave(COOK, filename = './figures/Cook.pdf', width = 12, height = 8, units = 'cm' )
ggsave(COOK, filename = './figures/Cook.jpg', width = 12, height = 8, units = 'cm' )

Transport

fe_sem4 <- read.csv("fe_sem4.csv")

fe_sem4_FTR_f <-  fe_sem4 %>%
    filter(grepl("FTR", Tech_name)) %>% 
    filter(YEAR %in% year_vector) %>%
    mutate(Tech_name = str_replace(Tech_name, "FTR-", "")) %>% 
    ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name)) +
    geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 0.4)+
   scale_fill_viridis_d() +
   labs(fill = "", x="", y = "Freight Final \n Energy Consuption [PJ]") +
   facet_grid(.~YEAR) +
   theme_bw() + #+
   rh_theme +
   theme(axis.text.x = element_text(angle = 90)) +
   theme(legend.margin = margin(t=-5, b=0, unit='mm')) +
   theme(legend.text = element_text(size = 4))+
   guides(fill = guide_legend(ncol = 6))
fe_sem4_FTR_f

fe_sem4 %>% filter(grepl("FTR", Tech_name)) %>% filter(YEAR %in% year_vector) %>% filter(YEAR %in% c(2019,2050)) %>% group_by(Scenario, YEAR) %>% summarise(sum(VALUE))

## `summarise()` has grouped output by 'Scenario'. You can override using the
## `.groups` argument.

fe_sem4_PTR_f <-  fe_sem4 %>%
    filter(grepl("PTR", Tech_name)) %>% 
    filter(YEAR %in% year_vector) %>%
    mutate(Tech_name = str_replace(Tech_name, "PTR-", "")) %>% 
    ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name)) +
    geom_bar(stat = "identity", colour= "black",
                    linewidth = 0.05, width = 0.4)+
   scale_fill_viridis_d() +
   labs(fill = "", x="", y = "Passenger Final \n Energy Consuption [PJ]") +
   facet_grid(.~YEAR) +
   theme_bw() + #+
   rh_theme +
   theme(axis.text.x = element_text(angle = 90)) +
   theme(legend.margin = margin(t=-5, b=0, unit='mm')) +
   theme(legend.text = element_text(size = 4))+
   guides(fill = guide_legend(ncol = 6))
fe_sem4_PTR_f

fe_sem4 %>% filter(grepl("PTR", Tech_name)) %>% filter(YEAR %in% year_vector) %>% filter(YEAR %in% c(2019,2050)) %>% group_by(Scenario, YEAR) %>% summarise(sum(VALUE))

## `summarise()` has grouped output by 'Scenario'. You can override using the
## `.groups` argument.

Transport <- ggarrange(fe_sem4_FTR_f, fe_sem4_PTR_f,
                   ncol = 1, heights = c(1,1.25),
                   labels = c('A','B'), font.label=list(size=6), 
                   common.legend = FALSE, legend='bottom', align = "v")
Transport

ggsave(Transport, filename = './figures/Transport.pdf', width = 12, height = 8, units = 'cm' )
ggsave(Transport, filename = './figures/Transport.jpg', width = 12, height = 8, units = 'cm' )

Emissions

bau_aet <- read.csv("./results/bau_aet.csv")
bau_aet["Scenario"] = sc_bau 

re_aet <- read.csv("./results/re_aet.csv")
re_aet["Scenario"] = sc_nm

aet = rbind(bau_aet, re_aet)

aet_names <- c("Agriculture-mecanization", "Agriculture-pump",
               "Cooking urban", "Cooking rural",
               "Fisheries", "Industry", "Transport",
               "CCGT", "Biomass PP", "Coal PP")

aet_f <- aet %>%
  filter(YEAR %in% year_vector) %>%
  mutate(Tech_name = ifelse(grepl("Import", Tech_name),
                            "Coal PP", Tech_name)) %>%
  mutate(Tech_name = factor(Tech_name, levels=aet_names)) %>%
  mutate(VALUE = VALUE/1000) %>% 
  ggplot(aes(x=Scenario, y=VALUE, fill=Tech_name)) +
  geom_bar(stat = "identity", colour= "black",
           linewidth = 0.05, width = 0.4) +
  scale_fill_viridis_d(direction = -1) +
  labs(fill = "", x="", y = "CO2 Emissions [Mton]") +
  facet_grid(.~YEAR) +
  rh_theme +
  scale_fill_grey(start = 0.9, end = 0.1, na.value = "red", aesthetics = "fill") +
  guides(fill = guide_legend(ncol = 6))  +
  theme(axis.text.x = element_text(angle = 90, size = 5))+
  theme(legend.margin = margin(t=-5, b=0, unit='mm'))

## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.

aet_f

ggsave(aet_f, filename = './figures/aet.pdf', width = 12, height = 6, units = 'cm' )
ggsave(aet_f, filename = './figures/aet.jpg', width = 12, height = 6, units = 'cm' )

aet_f2 <- aet %>%
  filter(YEAR %in% year_vector) %>%
  mutate(Tech_name = ifelse(grepl("Import", Tech_name),
                            "Coal PP", Tech_name)) %>%
  mutate(Tech_name = factor(Tech_name, levels=aet_names)) %>%
  ggplot( aes(x=Tech_name, y=VALUE) ) +
  #geom_segment( aes(x=Tech_name ,xend=Tech_name, y=0, yend=VALUE), color="grey") +
  #geom_point(size=3, color="#69b3a2") +
  geom_bar(stat = "identity", colour= "black",
           linewidth = 0.05, width = 0.4) +
  
  
  coord_flip() +
  rh_theme +
  facet_grid(Scenario~YEAR) +
  theme(
      panel.grid.minor.y = element_blank(),
      panel.grid.major.y = element_blank(),
      legend.position="none"
    ) +
  xlab("")
aet_f2

aet %>% filter(YEAR %in% c(2019, 2050)) %>%
  mutate(Tech_name = ifelse(grepl("Import", Tech_name), "Coal PP", Tech_name)) #%>%

  #group_by(YEAR, Scenario) %>% summarise(sum(VALUE))

Emission reduction

emi_red <- read_xlsx("../Emissions_reduction.xlsx", sheet = 'E_total')
emi_red

emi_red_f <- emi_red %>% 
  pivot_longer(!y,
     names_to = 'Emission',
     values_to = 'VALUE'
   ) %>%
  filter(y %in% year_vector) %>% 
  ggplot() +
  geom_line(data=. %>% 
              filter(Emission %in% c('BAU', 'RE')), 
                     aes(x=y, y=VALUE, color=Emission),
                     linewidth=0.30) +
  geom_point(data=. %>% 
              filter(Emission %in% c('BAU', 'RE')),
             aes(x=y, y=VALUE, shape=Emission, color=Emission),
             size=0.750) +
  geom_bar(data=. %>% 
              filter(!Emission %in% c('BAU', 'RE', 'sec_bau', 'trans_bau')),
           aes(x=y, y=VALUE, fill=Emission),
           stat = "identity", colour= "black",
           linewidth = 0.05, width = 1, alpha=0.50) +
  rh_theme +
  labs(color = "", x="", y = "CO2 Emissions [Mton]",
       shape='', fill='')+
  scale_x_continuous(breaks = seq(2020, 2050, by=5))+
  #scale_y_continuous(limits= c(0,15),breaks = seq(0, 15, by=2.5))+
  scale_color_brewer(type='div', palette = 'Dark2', direction = -1) +
  scale_fill_grey()

emi_red_f

ggsave(emi_red_f, filename = './figures/emi_red_f.pdf', width = 10, height = 6, units = 'cm' )
ggsave(emi_red_f, filename = './figures/emi_red_f.jpg', width = 10, height = 6, units = 'cm' )

Specific Demand Profile

sdp <- read.csv('./spec_demand_pro/spec_dem_prof.csv')
head(sdp)

sdp_f <- sdp %>% 
  filter(!FUEL == 'AGRELC') %>% 
  mutate(TR = factor(TR, levels=hr_list)) %>% 
  ggplot() +
  geom_line(aes(x=TIMESLICE, y=VALUE), color='darkblue', size=0.20) +
  geom_point(data = . %>% filter(VALUE>0.75),
             aes(x=TIMESLICE, y=VALUE), color='darkred', size=0.10, alpha=0.75) +
  facet_grid(FUEL ~ TR, scales = 'free_x') +
  rh_theme +  # Assuming rh_theme is a function that returns a theme
  labs(fill = "", x="", y = "")

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

sdp_f

ggsave(sdp_f, filename = './figures/sdp_f.pdf', width = 12, height = 6, units = 'cm' )
ggsave(sdp_f, filename = './figures/sdp_f.jpg', width = 12, height = 6, units = 'cm' )

Coal max generation vs actual

cgen_bau <- read.csv('./results/coal_dif_gen_bau.csv')
cgen_re <-  read.csv('./results/coal_dif_gen_re.csv')

cgen <- cgen_bau %>% 
  left_join(cgen_re %>% select(all_of(c('YEAR','ac_gen_re')))
            , by=c('YEAR')) %>%
  rename(max = max_gen_bau,
         ac_BAU = ac_gen_bau,
         '12 TS' = ac_gen_re ) %>% 
  select(!c(Tech_name, X)) %>% 
  pivot_longer(!YEAR,
     names_to = 'TR',
     values_to = 'VALUE'
   )
cgen

coal_72 <- filter_coal(re_PbT_72, '72 TS') 
coal_144 <- filter_coal(re_PbT_144, '144 TS')
coal_288 <- filter_coal(re_PbT_288, '288 TS')

#bind all data frames
cgen <- rbind(cgen, coal_72, coal_144, coal_288)
cgen

cg_list <-  c('max', 'ac_BAU', '12 TS', '72 TS', '144 TS', '288 TS')

cgen_f <- cgen %>%
  mutate(TR = factor(TR, levels=cg_list)) %>%
  ggplot(aes(x=YEAR, y=VALUE, group=TR)) +
  geom_line(aes(color=TR), size=0.25) +
  geom_point(aes(shape= TR, color=TR), size=0.15, alpha=0.75) +
  rh_theme +  # Assuming rh_theme is a function that returns a theme
  labs(color = "", x="", y = 'Generation [PJ]', shape='')+
  scale_x_continuous(breaks = seq(2020, 2050, by=5))+
  scale_y_continuous(limits= c(0,15),breaks = seq(0, 15, by=2.5))+
  scale_color_brewer(type='div' ,palette = 'Dark2') 
cgen_f

ggsave(cgen_f, filename = './figures/cgen_f.pdf', width = 12, height = 6, units = 'cm' )
ggsave(cgen_f, filename = './figures/cgen_f.jpg', width = 12, height = 6, units = 'cm' )

Goa capacity

goa_cap <- data.frame(
  Power_Plant = c('Coal', 'Coal', 'Nuclear', 'NGS', 'NGS', 'Wind', 'Solar'),
  Sector = c('Central', 'Private', 'Central', 'Central', 'Private', 'RPO', 'RPO'),
  Capacity = c(572.4, 12, 31.5, 25.1, 60, 31, 50)
)
goa_cap$Type_sector <- paste(goa_cap$Power_Plant, goa_cap$Sector) 
#   pivot_longer(cols = starts_with('Sec'),
#                names)
goa_cap

Figure

#https://r-charts.com/part-whole/pie-chart-ggplot2/

# The palette with black:
cbPalette <- c("#999999", "#cfcfcf", "#D55E00","#acace6", "#663399","#F0E442", "#56B4E9")

goa_cap_f <- goa_cap %>% 
  ggplot(aes(x='', y=Capacity, fill=Type_sector))+
  geom_bar(stat = 'identity', width=1, colour= "white", linewidth = 0.05)+
  coord_polar('y', start=0)+
  geom_text(aes(label = Capacity), position = position_stack(vjust=0.5), size=2,
            color="black")+
  rh_theme+
  labs(x = NULL, y = NULL, fill = NULL)+
  theme(axis.line = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_blank())+
  scale_fill_manual(values=cbPalette)

goa_cap_f

ggsave(goa_cap_f, filename = './figures/Figure_02.pdf', width = 8, height = 6, units = 'cm' )
ggsave(goa_cap_f, filename = './figures/Figure_02.jpg', width = 8, height = 6, units = 'cm' )