In CfSOtago/gridCarbon: Code to analyse the carbon footprint of NZ electricity generation over time

knitr::opts_chunk$set(echo = TRUE) # but folded by yaml setting

library(gridCarbon) # useful functions

myLibs <- c("data.table", # fast data munching
            "lubridate", # time stuff
            "flextable", # pretty tables
            "ggplot2", # plots obvs
            "here", # where are we?
            "hms" # more time stuff
            )
gridCarbon::loadLibraries(myLibs) # load the libs (installs if not present)

# local functions ----
makeFlexTable <- function(df, cap = "caption", digits = 1){
  # makes a pretty flextable - see https://cran.r-project.org/web/packages/flextable/index.html
  ft <- flextable::flextable(df)
  ft <- colformat_double(ft, digits = digits)
  ft <- fontsize(ft, size = 9)
  ft <- fontsize(ft, size = 10, part = "header")
  ft <- set_caption(ft, caption = cap)
  return(ft)
}

# parameters ----
dataPath <- path.expand("~/Dropbox/data/UK_NGESO/genMix/") # change to your data loc or for extra points download from https://data.nationalgrideso.com/carbon-intensity1/historic-generation-mix/r/historic_gb_generation_mix#

#dt <- data.table::fread("https://data.nationalgrideso.com/backend/dataset/88313ae5-94e4-4ddc-a790-593554d8c6b9/resource/f93d1835-75bc-43e5-84ad-12472b180a98/download/df_fuel_ckan.csv")

plotCap <- "Data: NG ESO Generation Mix\nPlot: @dataknut ([e-mail])\nCode: https://github.com/dataknut/gridCarbon"

License

Code copyright the author (2022)

License:

• CC-BY

To cite:

• Anderson, B, (2022) GB generation share trends: NG ESO data. https://dataknut.github.io/gridCarbon/rmd/gbGenMixTrends.html

Code:

• https://github.com/dataknut/gridCarbon/blob/master/rmd/gbGenMixTrends.Rmd

Introduction

Inspired by https://twitter.com/DrSimEvans/status/1508409309775994892

Data

This analysis uses the UK NG ESO generation mix data. The data also happens to include a half-hourly carbon intensity value.

As far as we can work out this data does not include distributed (i.e. non-grid connected) generation such as small scale wind, solar, hydro, biomass etc which is connected to the LV network. This means the ESO data is likely to underestimate total generation and potentially underestimate the proportion of total generation that is renewable. It is possible that this could be fixed using embedded wind & solar generation data from the demand update file.

dt_orig <- data.table::fread(paste0(dataPath, "df_fuel_ckan.csv"))

dt_orig[, dv_dateTime := lubridate::as_datetime(DATETIME)] # proper date time

message("Data range from: ", min(dt_orig$dv_dateTime))
message("...to: ", max(dt_orig$dv_dateTime))

The data covers more years than we need - we'll start at 2012 too.

Recreating DrSimEvans' plot

This looks like daily data.

• Solar + wind (% of gen) vs coal + gas (& of gen))

# add together the % and totals we want (half-hourly)
dt_orig[, dv_coal_gas_pc := COAL_perc + GAS_perc]
dt_orig[, dv_solar_wind_pc := SOLAR_perc + WIND_perc]
dt_orig[, dv_coal_gas := COAL + GAS]
dt_orig[, dv_solar_wind := SOLAR + WIND]

# keep the vars we want for clarity
temp <- dt_orig[, .(dv_dateTime, dv_coal_gas, dv_solar_wind,
                    dv_coal_gas_pc, dv_solar_wind_pc, GENERATION)]

temp[, dv_date := lubridate::date(dv_dateTime)]

# aggregate to daily data for plotting
plotDT <- temp[,
               .(mean_dv_solar_wind_pc = mean(dv_solar_wind_pc),
                 mean_dv_coal_gas_pc = mean(dv_coal_gas_pc),
                 total_dv_coal_gas = sum(dv_coal_gas),
                 total_dv_solar_wind = sum(dv_solar_wind),
                 total_GENERATION = sum(GENERATION),
                 nObs = .N), # to check for days with < 48 half hours
               keyby = .(dv_date)
               ]
plotDT[, dv_year := lubridate::year(dv_date)] # for plots
plotDT[, total_dv_coal_gas_pc := total_dv_coal_gas/total_GENERATION] # daily %
plotDT[, total_dv_solar_wind_pc := total_dv_solar_wind/total_GENERATION]

message("Check for days with less than 48 hours - this will be truncated data")
table(plotDT$nObs)

Figure \@ref(fig:plotMeanHalfHourPC) shows the mean half-hourly % generation by each type per day. This is slightly convoluted - it is the mean of the sum of the 48 daily half-hourly XXX_perc values in the original data where XXX is the generation type. Unfold the code above for clarity.

The smoothed curves are estimated for each year. The lines terminate at the maximum value for the year. I'm still trying to decide if they tell us anything useful.

ggplot2::ggplot(plotDT[dv_year > 2011], aes(x = mean_dv_solar_wind_pc, 
                            y = mean_dv_coal_gas_pc,
                            colour = as.factor(dv_year))) +
  geom_point() +
  geom_smooth() +
  scale_colour_viridis_d(name = "Year") +
  labs(x = "Solar & wind (mean % of half-hourly generation per day)",
       y = "Coal & gas (mean % of half-hourly generation per day)",
       caption = plotCap)

# save it
ggplot2::ggsave(filename = "GB_plotMeanHalfHourPC.png", 
                path = here::here("rmd","plots"),
                height = 5)

Download plot.

Figure \@ref(fig:plotDailyPC) shows the percentage of daily generation by type. This is less convoluted as it is the sum of generation per day for the two categories (solar + wind vs gas + coal) as a % of total daily generation.

Again the smoothed curve is estimated for each year.

ggplot2::ggplot(plotDT[dv_year > 2011], aes(x = 100 * total_dv_solar_wind_pc, 
                            y = 100 * total_dv_coal_gas_pc,
                            colour = as.factor(dv_year))) +
  geom_point() +
  geom_smooth() +
  scale_colour_viridis_d(name = "Year") +
  labs(x = "Solar & wind (% of total daily generation)",
       y = "Coal & gas (% of total daily generation)",
       caption = plotCap)

ggplot2::ggsave(filename = "GB_plotDailyPC.png", 
                path = here::here("rmd","plots"),
                height = 5)

Download plot.

Half-hourly plots

Just cos we can... helpfully split into 'peak' and 'off peak' periods.

Peak period definitions:

• Morning 07:00 - 09:00
• Daytime 09:00 - 16:00
• Evening 16:00 - 21:00
• Night - all other times

Again the smoothed curve is estimated for each year (and demand period).

dt_orig[, dv_year := lubridate::year(dv_dateTime)]
dt_orig[, dv_hms := hms::as_hms(dv_dateTime)]
# half-hours are the start of the half hours (we think)
dt_orig[, dv_peak := "Night"] # default
dt_orig[, dv_peak := ifelse(dv_hms >= hms::as_hms("07:00:00") & dv_hms < hms::as_hms("09:00:00") , "Morning peak", dv_peak)]
dt_orig[, dv_peak := ifelse(dv_hms > hms::as_hms("09:00:00") & dv_hms < hms::as_hms("16:00:00") , "Daytime", dv_peak)]
dt_orig[, dv_peak := ifelse(dv_hms >= hms::as_hms("16:00:00") & dv_hms < hms::as_hms("21:00:00") , "Evening peak", dv_peak)]

# re-order for plots & tables

dt_orig[, dv_peak := factor(dv_peak, levels = c("Morning peak", "Daytime", "Evening peak", "Night"))]

#table(dt_orig$dv_peak)

# t <- dt_orig[, .(nObs = .N,
#                  startHour = min(lubridate::hour(dv_hms)),
#                  endHour = max(lubridate::hour(dv_hms))
#                  ), keyby = .(dv_peak)]
# 
# makeFlexTable(t, cap = "Definition of peak")

dt_orig[, dv_hour := lubridate::hour(dv_dateTime)]
# check coding
# table(dt_orig$dv_hour, dt_orig$dv_peak, useNA = "always")

ggplot2::ggplot(dt_orig[dv_year > 2011], aes(x = dv_solar_wind_pc, 
                            y = dv_coal_gas_pc,
                            colour = as.factor(dv_year))) +
  geom_point() +
  facet_wrap(. ~ dv_peak) +
  geom_smooth() +
  scale_colour_viridis_d(name = "Year") +
  labs(x = "Solar & wind (% of half-hourly generation)",
       y = "Coal & gas (% of half-hourly generation)",
       caption = plotCap)

ggplot2::ggsave(filename = "GB_plotHalfHourlyPC.png", 
                path = here::here("rmd","plots"),
                height = 5)

Download plot.

Summary

That's it.

You might want to look at recent academic research on this topic:

• [@staffell_measuring_2017]
• [@staffell_increasing_2018]

Annex

Data descriptors

NGESO gen mix data

skimr::skim(dt_orig)

R environment

Packages etc:

• base R [@baseR]
• bookdown [@bookdown]
• data.table [@data.table]
• ggplot2 [@ggplot2]
• here [@here]
• hms [@hms]
• knitr [@knitr]
• lubridate [@lubridate]
• rmarkdown [@rmarkdown]

References

CfSOtago/gridCarbon documentation built on April 5, 2022, 6:46 a.m.