미세먼지 주범 석탄화력발전소, 대선 후보 입장은? 한겨례 신문 2017년 기사를 보면 석탄화력발전소에 대한 구체적인 정보가 담겨있다. World Resources Institute Global Power Plant Database 에서 전세계 화력발전소 중 한국만 추려 본다.
library(tidyverse)
plant_df <- read_csv("data/globalpowerplantdatabasev120/global_power_plant_database.csv")
plant_tbl <- plant_df %>%
filter(country == "KOR") %>%
select("name", "capacity_mw", "latitude", "longitude", fuel = "primary_fuel", year = "commissioning_year", "owner", "url",
generation_gwh = "estimated_generation_gwh")
plant_tbl %>%
reactable::reactable()회사별 용량과 발전양 순위는 다음과 같다.
library(reactable)
plant_tbl %>%
group_by(owner) %>%
summarise(capacity_mw = sum(capacity_mw, na.rm = TRUE),
generation_gwh = sum(generation_gwh, na.rm = TRUE)) %>%
arrange(desc(generation_gwh)) %>%
reactable::reactable( columns = list(
owner = colDef(name = "소유 회사", minWidth = 100),
capacity_mw = colDef(name = "용량(MW)", format = colFormat(digits = 0, separators = TRUE)),
generation_gwh = colDef(name = "발전(GWh)", format = colFormat(digits = 0, separators = TRUE))
))발전에는 다양한 연료가 투입되는데 국내에 소재한 발전소 용량과 발전에 대해 살펴보자.
plant_tbl %>%
group_by(fuel) %>%
summarise(capacity_mw = sum(capacity_mw, na.rm = TRUE),
generation_gwh = sum(generation_gwh, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(generation_gwh)) %>%
mutate(용량비율 = capacity_mw / sum(capacity_mw),
발전비율 = generation_gwh / sum(generation_gwh)) %>%
bind_rows(summarise(.,
across(where(is.numeric), sum),
across(where(is.character), ~"합계"))) %>%
reactable::reactable( defaultPageSize = 11,
columns = list(
fuel = colDef(name = "연료", minWidth = 70),
capacity_mw = colDef(name = "용량(MW)", format = colFormat(digits = 0, separators = TRUE)),
용량비율 = colDef(name = "용량비율", format = colFormat(percent = TRUE, digits = 1)),
generation_gwh = colDef(name = "발전(GWh)", format = colFormat(digits = 0, separators = TRUE)),
발전비율 = colDef(name = "발전비율", format = colFormat(percent = TRUE, digits = 1))
))소유 회사와 발전에 사용되는 연료를 교차 분석하여 보자.
먼저 용량에 대해 석탄을 기준으로 의존도가 높은 회사를 살펴보자.
plant_tbl %>%
group_by(fuel, owner) %>%
summarise(capacity_mw = sum(capacity_mw, na.rm = TRUE)) %>%
pivot_wider(names_from = fuel, values_from = capacity_mw, values_fill = 0) %>%
arrange(desc(Coal)) %>%
reactable::reactable( defaultPageSize = 10,
columns = list(
owner = colDef(name = "회사", minWidth = 150),
Biomass = colDef(name = "바이오", format = colFormat(digits = 0, separators = TRUE)),
Coal = colDef(name = "석탄", format = colFormat(digits = 0, separators = TRUE)),
Gas = colDef(name = "가스", format = colFormat(digits = 0, separators = TRUE)),
Hydro = colDef(name = "수력", format = colFormat(digits = 0, separators = TRUE)),
Nuclear = colDef(name = "원자력", format = colFormat(digits = 0, separators = TRUE)),
Oil = colDef(name = "원유", format = colFormat(digits = 0, separators = TRUE)),
Solar = colDef(name = "태양열", format = colFormat(digits = 0, separators = TRUE)),
Waste = colDef(name = "폐열", format = colFormat(digits = 0, separators = TRUE)),
`Wave and Tidal` = colDef(name = "해수", format = colFormat(digits = 0, separators = TRUE)),
Wind = colDef(name = "풍력", format = colFormat(digits = 0, separators = TRUE))
)) 앞서 용량(Capacity)에 대해 살펴봤다면 발전(Generation)에 대해 살펴보자.
plant_tbl %>%
group_by(fuel, owner) %>%
summarise(generation_gwh = sum(generation_gwh, na.rm = TRUE)) %>%
pivot_wider(names_from = fuel, values_from = generation_gwh, values_fill = 0) %>%
arrange(desc(Coal)) %>%
reactable::reactable( defaultPageSize = 10,
columns = list(
owner = colDef(name = "회사", minWidth = 150),
Biomass = colDef(name = "바이오", format = colFormat(digits = 0, separators = TRUE)),
Coal = colDef(name = "석탄", format = colFormat(digits = 0, separators = TRUE)),
Gas = colDef(name = "가스", format = colFormat(digits = 0, separators = TRUE)),
Hydro = colDef(name = "수력", format = colFormat(digits = 0, separators = TRUE)),
Nuclear = colDef(name = "원자력", format = colFormat(digits = 0, separators = TRUE)),
Oil = colDef(name = "원유", format = colFormat(digits = 0, separators = TRUE)),
Solar = colDef(name = "태양열", format = colFormat(digits = 0, separators = TRUE)),
Waste = colDef(name = "폐열", format = colFormat(digits = 0, separators = TRUE)),
`Wave and Tidal` = colDef(name = "해수", format = colFormat(digits = 0, separators = TRUE)),
Wind = colDef(name = "풍력", format = colFormat(digits = 0, separators = TRUE))
)) 국내 전체 발전소 위경도 정보가 있어 이를 반영하여 발전소 위치를 지도에 시각화 시켜보자.
library(glue)
library(leaflet)
plant_tbl %>%
leaflet() %>%
addProviderTiles(provider = providers$OpenStreetMap) %>%
addMarkers(lng=~longitude, lat = ~latitude, clusterOptions = markerClusterOptions(),
popup = ~ as.character(glue("<strong> {name} </strong> <br>
· 회사: {owner}<br>
· 용량: {scales::comma(capacity_mw, accuracy = 1) } MW<br>
· 발전: {scales::comma(generation_gwh, accuracy = 1)} GWh<br>
· 연료: {fuel}")))석탄 발전소만 추려 지도에 시각화 시켜보자.
plant_tbl %>%
filter(fuel == "Coal") %>%
leaflet() %>%
addProviderTiles(provider = providers$OpenStreetMap) %>%
addMarkers(lng=~longitude, lat = ~latitude, clusterOptions = markerClusterOptions(),
popup = ~ as.character(glue("<strong> {name} </strong> <br>
· 회사: {owner}<br>
· 용량: {scales::comma(capacity_mw, accuracy = 1) } MW<br>
· 발전: {scales::comma(generation_gwh, accuracy = 1)} GWh<br>
· 연료: {fuel}")))석탄 발전소 용량을 기준으로 시도 경계를 추가하여 가시성을 높여보자.
library(sf)
korea_sf <- st_read("data/map/HangJeongDong_ver20201001.geojson")Reading layer `1' from data source `/Users/kwangchunlee/swc/esg/data/map/HangJeongDong_ver20201001.geojson' using driver `GeoJSON'
Simple feature collection with 3492 features and 8 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 124.6097 ymin: 33.11187 xmax: 131.8713 ymax: 38.61695
geographic CRS: WGS 84sido_sf <- korea_sf %>%
group_by(sido, sidonm) %>%
summarise(cnt = n()) %>%
ungroup() %>%
select(-cnt)
plant_tbl %>%
filter(fuel == "Coal") %>%
leaflet() %>%
## 석탄 화력 발전소 위치 --------------------------------------
addProviderTiles(provider = providers$OpenStreetMap) %>%
addMarkers(lng=~longitude, lat = ~latitude, clusterOptions = markerClusterOptions(),
popup = ~ as.character(glue("<strong> {name} </strong> <br>
· 회사: {owner}<br>
· 용량: {scales::comma(capacity_mw, accuracy = 1) } MW<br>
· 발전: {scales::comma(generation_gwh, accuracy = 1)} GWh<br>
· 연료: {fuel}"))) %>%
addCircleMarkers( radius = ~ (capacity_mw)^(1/2),
stroke = FALSE, fillOpacity = 0.5) %>%
## 시도 경계 shapefile --------------------------------------
addPolygons(data = sido_sf, opacity = 1.0, fillOpacity = 0.1,
weight = 1,
highlightOptions = highlightOptions(color = "black", weight = 3, bringToFront = TRUE),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto"))추정 발전량을 기준으로 시도 경계를 포함시켜 지도 위에 시각화를 해 보자.
plant_tbl %>%
filter(fuel == "Coal") %>%
leaflet() %>%
## 석탄 화력 발전소 위치 --------------------------------------
addProviderTiles(provider = providers$OpenStreetMap) %>%
addMarkers(lng=~longitude, lat = ~latitude, clusterOptions = markerClusterOptions(),
popup = ~ as.character(glue("<strong> {name} </strong> <br>
· 회사: {owner}<br>
· 용량: {scales::comma(capacity_mw, accuracy = 1) } MW<br>
· 발전: {scales::comma(generation_gwh, accuracy = 1)} GWh<br>
· 연료: {fuel}"))) %>%
addCircleMarkers( radius = ~ (generation_gwh)^(1/3),
stroke = FALSE, fillOpacity = 0.5) %>%
## 시도 경계 shapefile --------------------------------------
addPolygons(data = sido_sf, opacity = 1.0, fillOpacity = 0.1,
weight = 1,
highlightOptions = highlightOptions(color = "black", weight = 3, bringToFront = TRUE),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto"))발전소 용량(capacity)과 발전량(generation)과의 관계를 발전소가 나름 어느 정도 있는 석탄, 가스, 수력을 추려 회귀모형을 만들어보자.
library(broom)
plant_nest_tbl <- plant_tbl %>%
filter(fuel %in% c("Coal", "Gas", "Hydro")) %>%
na.omit() %>%
select(name, fuel, capacity_mw, generation_gwh) %>%
group_by(fuel) %>%
nest() %>%
ungroup()
generation_model <- function(df) {
lm(generation_gwh ~ capacity_mw, data = df)
}
plant_nest_tbl <- plant_nest_tbl %>%
mutate(model = map(data, generation_model),
model_glance = map(model, glance),
rsquare = map_dbl(model_glance, ~.$r.squared))
plant_nest_tbl# A tibble: 3 x 5
fuel data model model_glance rsquare
<chr> <list> <list> <list> <dbl>
1 Gas <tibble[,3] [18 × 3]> <lm> <tibble[,12] [1 × 12]> 1
2 Coal <tibble[,3] [10 × 3]> <lm> <tibble[,12] [1 × 12]> 1
3 Hydro <tibble[,3] [7 × 3]> <lm> <tibble[,12] [1 × 12]> 1회귀식과 발전 원료를 나눠 용량과 발전량과의 상관 관계를 살펴보자.
library(rbokeh)
library(trelliscopejs)
# 시각화 -----
coal_plot <- function(data, model) {
figure(xlim = c(0, 3000), ylim=c(0, 15000), tools = NULL) %>%
ly_points(capacity_mw, generation_gwh, data=data, hover=data) %>%
ly_abline(model)
}
# trelliscope 그래프
plant_nest_tbl <- plant_nest_tbl %>%
mutate(plot = map2_plot(data, model, coal_plot)) %>%
arrange(rsquare)
plant_nest_tbl %>%
trelliscope(name = "power_plant", nrow=1, ncol=3, path="coal_power_plant")데이터 과학자 이광춘 저작
kwangchun.lee.7@gmail.com