Electricity Power Plants in the United States

#Read in data sets.

raw_df <- read_excel("egrid2018_data.xlsx", skip = 1, sheet = "PLNT18")
raw_df_st <- read_excel("egrid2018_data.xlsx", skip = 1, sheet = "ST18")

clean_df <- raw_df %>% 
  clean_names() %>% 
  select(pstatabb, cntyname, fipsst, fipscnty, plprmfl, plfuelct, coalflag, namepcap, plnoxrta, plso2rta, plco2rta, plch4rta, pln2orta, plc2erta, plso2an, plnoxan, plco2eqa, plngenan) #Selecting the cateogries of interest

clean_st_df <- raw_df_st %>% 
  clean_names() %>% 
  select(pstatabb, stnoxrta, stso2rta, namepcap, stgenatn, stc2erta, sttrpr, stgspr, sttnpr, stngenan) %>% 
  mutate(gas_ratio = stgspr/sttnpr) #Calculating the percentage of gas-generated electricity among all fossil fuel-generated electricity.

Part One. The average electricity GHG emission factor on a state level.

state_bubble <- ggplot(clean_st_df, aes(x = sttrpr*100, y = stc2erta, size = stngenan)) +
  geom_point(alpha = 0.8, color = "#5BBCD6")+
  scale_size(range = c(.1, 10), name="Total Generation (MWh)")+
  xlab("Renewable Electricity Percentage")+
  ylab("Average Electricity GHG Emission Factor (lb/MWh)")+
  geom_text_repel(aes(label = pstatabb), size = 2)+
  theme_classic()

  
state_bubble

Figure 2. The Relationship between Average Electricity GHG Emission Factor and Renewable Generation Percentage on a State Level. Each bubble stands for a state. The size of the bubble indicates the total electricity generation in the state. The data for generation and GHG emission are from the year of 2018. (Source: eGrid, 2018)

clean_st_fossil <- clean_st_df %>% 
  filter(sttnpr > 0.80)

state_fs_bubble <- ggplot(clean_st_fossil, aes(x = gas_ratio*100, y = stc2erta, size = stngenan)) +
  geom_point(alpha = 0.8, color = "#F98400")+
  scale_size(range = c(.1, 10), name="Total Generation (MWh)")+
  xlab("Pencentage of Natural-gas-electricity among Fossil-fuel-electricity")+
  ylab("Average Electricity GHG Emission Factor (lb/MWh)")+
  geom_text_repel(aes(label = pstatabb), size = 2)+
  theme_classic()

state_fs_bubble

Figure 3. The Relationship between Average Electricity GHG Emission Factor and the Pencentage of Natural-gas-electricity among Fossil-fuel-electricity for States with More than 80% Non-renewable Electricity. Each bubble stands for a state. The size of the bubble indicates the total electricity generation in the state. The data for generation and GHG emission are from the year of 2018. (Source: eGrid, 2018)

Part Two. The generation plants in ten most populated states.

gas_df <- clean_df %>% 
  filter (plfuelct %in% c("GAS", "OIL", "COAL"))%>% # Selecting all fossil fuels
  group_by(pstatabb) %>% 
  select(-coalflag) %>% 
  drop_na() %>% 
  filter(pstatabb %in% c("CA", "TX", "FL", "NY", "PA", "IL", "OH", "GA", "NC", "MI"))# Selecting the top ten populated states

gasvis2 <- ggplot(gas_df, aes(x = pstatabb, y = plso2an))+
  geom_jitter(width = 0.20, aes(color = plfuelct))+
  theme_classic()+
  scale_y_continuous(expand = c(0, 0))+
  scale_color_manual(values=wes_palette(n=3, name="Darjeeling1"))+
  xlab("State")+
  ylab("2018 Annual Sulfur Dioxide Emssion (tons)")+
  labs(color = "Plant Type")

gasvis2

Figure 4. Total Annual \(SO_2\) Emission on a Power Plant Level for Ten Most Populated States. Each point stands for a power plant. The color of the point indicates the type of the power plant. The data for generation and GHG emission are from the year of 2018. (Source: eGrid, 2018)

gasvis4 <- ggplot(gas_df, aes(x = pstatabb))+
  geom_jitter(width = 0.20, aes(y = plnoxan,color = plfuelct))+
  theme_classic()+
  scale_y_continuous(expand = c(0, 0))+
  scale_color_manual(values=wes_palette(n=3, name="Darjeeling1"))+
  xlab("State")+
  ylab("2018 Annual NOx Emssion (tons)")+
  labs(color = "Plant Type")

gasvis4

Figure 5. Total Annual \(NO_x\) Emission on a Power Plant Level for Ten Most Populated States. Each point stands for a power plant. The color of the point indicates the type of the power plant. The data for generation and GHG emission are from the year of 2018. (Source: eGrid, 2018)

gasvis5 <- ggplot(gas_df, aes(x = pstatabb))+
  geom_jitter(width = 0.20, aes(y = plco2eqa,color = plfuelct))+
  theme_classic()+
  scale_y_continuous(expand = c(0, 0))+
  scale_color_manual(values=wes_palette(n=3, name="Darjeeling1"))+
  xlab("State")+
  ylab("2018 Annual GHG Emssion (tons carbon dioxide equivalent)")+
  labs(color = "Plant Type")

gasvis5

Figure 6. Total Annual GHG Emission on a Power Plant Level for Ten Most Populated States. Each point stands for a power plant. The color of the point indicates the type of the power plant. The data for generation and GHG emission are from the year of 2018. (Source: eGrid, 2018)

Part Three. Electricity generation and emissions for California counties

ca_df <- clean_df %>% 
  filter(pstatabb == "CA") %>% 
  select(-coalflag) %>% 
  mutate(cntyname = str_to_title(cntyname)) %>% 
  group_by(cntyname) %>% 
  mutate(plso2an = replace_na(plso2an, 0)) %>% 
  mutate(plnoxan = replace_na(plnoxan, 0)) %>% 
  filter (is.na (plngenan) == FALSE) %>% 
  summarise(nox_total = sum(plnoxan),
            so2_total = sum(plso2an),
            gen_total = sum(plngenan)) %>% 
  mutate(nox_rt = scales::number(nox_total*2000/gen_total, accuracy = 0.001),
         so2_rt = scales::number(so2_total*2000/gen_total, accuracy = 0.001)) %>% 
  mutate(nox_rt = cell_spec(nox_rt, "html", bold = T, color = ifelse(nox_rt > 0.377, "#F2AD00", "#00A08A"), background = "#d7eff5"),
         so2_rt = cell_spec(so2_rt, "html", bold = T, color = ifelse(so2_rt > 0.036, "#F2AD00", "#00A08A"), background = "#d7eff5")) 


title <- c("County", "Total NOx Emission (tons)", "Total SO2 Emission (tons)", "Total Net Generation (MWh)", "NOx Emission Rate (lb/MWh)", "SO2 Emission Rate(lb/MWh)")

colnames(ca_df) <- title

my_tbl <- ca_df %>% 
  kable(escape=F, align = "r") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>% 
  column_spec(1, bold = T, color = "white", background = "#046C9A") %>% 
  row_spec(0, bold = T, color = "white", background = "#F98400")%>%
  scroll_box(width = "100%", height = "300px")

Table 1. 2018 Electricity Generation and Emission for California Counties. Total \(NO_x\) And \(SO_2\) emission, total net generation amount, and corresponding emission factor are shown in the table. If the emission factor for a pollutant type is higher than California average, it is marked as yellow. Otherwise, it is marked as green. (Source: eGrid, 2018)