intro-to-carbon-intensity.RmdElectricity is not made equal and it will have a smaller or greater carbon footprint (or carbon intensity) depending on its source:
library(intensegRid) # carbon intensity per electricity source get_factors() #> key value #> 1 Biomass 120 #> 2 Coal 937 #> 3 Dutch Imports 474 #> 4 French Imports 53 #> 5 Gas (Combined Cycle) 394 #> 6 Gas (Open Cycle) 651 #> 7 Hydro 0 #> 8 Irish Imports 458 #> 9 Nuclear 0 #> 10 Oil 935 #> 11 Other 300 #> 12 Pumped Storage 0 #> 13 Solar 0 #> 14 Wind 0
Current carbon intensity
## carbon intensity for the whole Britain for the current 1/2 hr period get_british_ci() #> # A tibble: 1 x 5 #> from to forecast actual index #> <dttm> <dttm> <int> <int> <chr> #> 1 2020-11-01 21:00:00 2020-11-01 21:30:00 92 88 low
Current carbon intensity for specified dates
## function arguments start <- "2019-04-01" end <- "2019-04-07" get_british_ci(start, end) #> # A tibble: 336 x 5 #> from to forecast actual index #> <dttm> <dttm> <int> <int> <chr> #> 1 2019-03-31 23:30:00 2019-04-01 00:00:00 203 158 low #> 2 2019-04-01 00:00:00 2019-04-01 00:30:00 196 161 moderate #> 3 2019-04-01 00:30:00 2019-04-01 01:00:00 188 166 moderate #> 4 2019-04-01 01:00:00 2019-04-01 01:30:00 183 171 moderate #> 5 2019-04-01 01:30:00 2019-04-01 02:00:00 181 170 moderate #> 6 2019-04-01 02:00:00 2019-04-01 02:30:00 182 168 moderate #> 7 2019-04-01 02:30:00 2019-04-01 03:00:00 184 163 moderate #> 8 2019-04-01 03:00:00 2019-04-01 03:30:00 184 162 moderate #> 9 2019-04-01 03:30:00 2019-04-01 04:00:00 184 169 moderate #> 10 2019-04-01 04:00:00 2019-04-01 04:30:00 184 183 moderate #> # ... with 326 more rows
If you want to understand the exact composition of the UK-wide electricity over time, you can use get_mix() function:
# electricity composition in the current 30 mins get_mix() #> # A tibble: 9 x 4 #> fuel perc from to #> <chr> <dbl> <dttm> <dttm> #> 1 biomass 6.4 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 2 coal 0 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 3 imports 9.7 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 4 gas 20.4 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 5 nuclear 20.9 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 6 other 0 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 7 hydro 3.4 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 8 solar 0 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 9 wind 39.1 2020-11-01 21:00:00 2020-11-01 21:30:00
# electricity composition for the specified dates get_mix(start, end) #> # A tibble: 3,024 x 4 #> from to fuel perc #> <dttm> <dttm> <chr> <dbl> #> 1 2019-03-31 23:30:00 2019-04-01 00:00:00 biomass 9.9 #> 2 2019-03-31 23:30:00 2019-04-01 00:00:00 coal 0 #> 3 2019-03-31 23:30:00 2019-04-01 00:00:00 imports 10 #> 4 2019-03-31 23:30:00 2019-04-01 00:00:00 gas 28.9 #> 5 2019-03-31 23:30:00 2019-04-01 00:00:00 nuclear 26.1 #> 6 2019-03-31 23:30:00 2019-04-01 00:00:00 other 0.3 #> 7 2019-03-31 23:30:00 2019-04-01 00:00:00 hydro 1.3 #> 8 2019-03-31 23:30:00 2019-04-01 00:00:00 solar 0 #> 9 2019-03-31 23:30:00 2019-04-01 00:00:00 wind 23.5 #> 10 2019-04-01 00:00:00 2019-04-01 00:30:00 biomass 9.7 #> # ... with 3,014 more rows
Finally, you can access summarised carbon intensity statistics for the specified dates:
get_stats(start, end) #> # A tibble: 1 x 6 #> from to max average min index #> <dttm> <dttm> <int> <int> <int> <chr> #> 1 2019-04-01 00:00:00 2019-04-07 23:59:00 294 224 112 moderate
Additionally, you can add a block argument that will group the statistics by specified-length blocks, for example a block length of 2 (hrs over a 24 hr period) will return 12 items with the average, max, min for each 2 hr block.
get_stats(start, end, block = 2) #> # A tibble: 84 x 6 #> from to max average min index #> <dttm> <dttm> <int> <int> <int> <chr> #> 1 2019-04-01 00:00:00 2019-04-01 02:00:00 171 167 161 moderate #> 2 2019-04-01 02:00:00 2019-04-01 04:00:00 169 166 162 moderate #> 3 2019-04-01 04:00:00 2019-04-01 06:00:00 230 207 183 moderate #> 4 2019-04-01 06:00:00 2019-04-01 08:00:00 240 237 232 moderate #> 5 2019-04-01 08:00:00 2019-04-01 10:00:00 226 213 201 moderate #> 6 2019-04-01 10:00:00 2019-04-01 12:00:00 196 192 187 moderate #> 7 2019-04-01 12:00:00 2019-04-01 14:00:00 205 196 188 moderate #> 8 2019-04-01 14:00:00 2019-04-01 16:00:00 241 225 209 moderate #> 9 2019-04-01 16:00:00 2019-04-01 18:00:00 269 259 250 moderate #> 10 2019-04-01 18:00:00 2019-04-01 20:00:00 279 275 271 high #> # ... with 74 more rows
This package allows you to access carbon intensity data per UK country, i.e. England, Scotland and Wales (Northern Ireland is not included) with get_national_ci():
# Current carbon intensity per UK country get_national_ci() #> # A tibble: 162 x 9 #> from to regionid dnoregion shortname fuel #> <dttm> <dttm> <int> <chr> <chr> <chr> #> 1 2020-11-01 21:00:00 2020-11-01 21:30:00 1 Scottish~ North Sc~ biom~ #> 2 2020-11-01 21:00:00 2020-11-01 21:30:00 1 Scottish~ North Sc~ coal #> 3 2020-11-01 21:00:00 2020-11-01 21:30:00 1 Scottish~ North Sc~ impo~ #> 4 2020-11-01 21:00:00 2020-11-01 21:30:00 1 Scottish~ North Sc~ gas #> 5 2020-11-01 21:00:00 2020-11-01 21:30:00 1 Scottish~ North Sc~ nucl~ #> 6 2020-11-01 21:00:00 2020-11-01 21:30:00 1 Scottish~ North Sc~ other #> 7 2020-11-01 21:00:00 2020-11-01 21:30:00 1 Scottish~ North Sc~ hydro #> 8 2020-11-01 21:00:00 2020-11-01 21:30:00 1 Scottish~ North Sc~ solar #> 9 2020-11-01 21:00:00 2020-11-01 21:30:00 1 Scottish~ North Sc~ wind #> 10 2020-11-01 21:00:00 2020-11-01 21:30:00 2 SP Distr~ South Sc~ biom~ #> # ... with 152 more rows, and 3 more variables: perc <dbl>, forecast <int>, #> # index <chr>
# Current carbon intensity for England # Function also accepts region values: "Scotland" and "Wales" get_national_ci(region = "England") #> # A tibble: 9 x 9 #> regionid dnoregion shortname from to fuel #> <int> <chr> <chr> <dttm> <dttm> <chr> #> 1 15 England England 2020-11-01 21:00:00 2020-11-01 21:30:00 biom~ #> 2 15 England England 2020-11-01 21:00:00 2020-11-01 21:30:00 coal #> 3 15 England England 2020-11-01 21:00:00 2020-11-01 21:30:00 impo~ #> 4 15 England England 2020-11-01 21:00:00 2020-11-01 21:30:00 gas #> 5 15 England England 2020-11-01 21:00:00 2020-11-01 21:30:00 nucl~ #> 6 15 England England 2020-11-01 21:00:00 2020-11-01 21:30:00 other #> 7 15 England England 2020-11-01 21:00:00 2020-11-01 21:30:00 hydro #> 8 15 England England 2020-11-01 21:00:00 2020-11-01 21:30:00 solar #> 9 15 England England 2020-11-01 21:00:00 2020-11-01 21:30:00 wind #> # ... with 3 more variables: perc <dbl>, forecast <int>, index <chr>
# Carbon intensity for all the UK countries for specified dates get_national_ci(start = start, end = end) #> # A tibble: 54,432 x 9 #> from to regionid dnoregion shortname fuel #> <dttm> <dttm> <int> <chr> <chr> <chr> #> 1 2019-03-31 23:30:00 2019-04-01 00:00:00 1 Scottish~ North Sc~ biom~ #> 2 2019-03-31 23:30:00 2019-04-01 00:00:00 1 Scottish~ North Sc~ coal #> 3 2019-03-31 23:30:00 2019-04-01 00:00:00 1 Scottish~ North Sc~ impo~ #> 4 2019-03-31 23:30:00 2019-04-01 00:00:00 1 Scottish~ North Sc~ gas #> 5 2019-03-31 23:30:00 2019-04-01 00:00:00 1 Scottish~ North Sc~ nucl~ #> 6 2019-03-31 23:30:00 2019-04-01 00:00:00 1 Scottish~ North Sc~ other #> 7 2019-03-31 23:30:00 2019-04-01 00:00:00 1 Scottish~ North Sc~ hydro #> 8 2019-03-31 23:30:00 2019-04-01 00:00:00 1 Scottish~ North Sc~ solar #> 9 2019-03-31 23:30:00 2019-04-01 00:00:00 1 Scottish~ North Sc~ wind #> 10 2019-03-31 23:30:00 2019-04-01 00:00:00 2 SP Distr~ South Sc~ biom~ #> # ... with 54,422 more rows, and 3 more variables: perc <dbl>, forecast <int>, #> # index <chr>
The API allows you extract information for UK regions using get_regional_ci() function, that accepts region_id as a required argument. You can access a handy region_id lookup table as a package dataset:
regions_lookup #> Region ID Shortname #> 1 1 North Scotland #> 2 2 South Scotland #> 3 3 North West England #> 4 4 North East England #> 5 5 Yorkshire #> 6 6 North Wales #> 7 7 South Wales #> 8 8 West Midlands #> 9 9 East Midlands #> 10 10 East England #> 11 11 South West England #> 12 12 South England #> 13 13 London #> 14 14 South East England #> 15 15 England #> 16 16 Scotland #> 17 17 Wales
For example, let’s access the current carbon intensity for London:
get_regional_ci(region_id = 13) #> # A tibble: 9 x 9 #> regionid dnoregion shortname from to fuel #> <int> <chr> <chr> <dttm> <dttm> <chr> #> 1 13 UKPN Lon~ London 2020-11-01 21:00:00 2020-11-01 21:30:00 biom~ #> 2 13 UKPN Lon~ London 2020-11-01 21:00:00 2020-11-01 21:30:00 coal #> 3 13 UKPN Lon~ London 2020-11-01 21:00:00 2020-11-01 21:30:00 impo~ #> 4 13 UKPN Lon~ London 2020-11-01 21:00:00 2020-11-01 21:30:00 gas #> 5 13 UKPN Lon~ London 2020-11-01 21:00:00 2020-11-01 21:30:00 nucl~ #> 6 13 UKPN Lon~ London 2020-11-01 21:00:00 2020-11-01 21:30:00 other #> 7 13 UKPN Lon~ London 2020-11-01 21:00:00 2020-11-01 21:30:00 hydro #> 8 13 UKPN Lon~ London 2020-11-01 21:00:00 2020-11-01 21:30:00 solar #> 9 13 UKPN Lon~ London 2020-11-01 21:00:00 2020-11-01 21:30:00 wind #> # ... with 3 more variables: perc <dbl>, forecast <int>, index <chr>
Similarly to other functions in the package, get_regional_ci() also accepts start and end arguments:
get_regional_ci(region_id = 13, start, end) #> # A tibble: 3,024 x 9 #> dnoregion shortname region_id from to fuel #> <chr> <chr> <int> <dttm> <dttm> <chr> #> 1 UKPN Lon~ London 13 2019-03-31 23:30:00 2019-04-01 00:00:00 biom~ #> 2 UKPN Lon~ London 13 2019-03-31 23:30:00 2019-04-01 00:00:00 coal #> 3 UKPN Lon~ London 13 2019-03-31 23:30:00 2019-04-01 00:00:00 impo~ #> 4 UKPN Lon~ London 13 2019-03-31 23:30:00 2019-04-01 00:00:00 gas #> 5 UKPN Lon~ London 13 2019-03-31 23:30:00 2019-04-01 00:00:00 nucl~ #> 6 UKPN Lon~ London 13 2019-03-31 23:30:00 2019-04-01 00:00:00 other #> 7 UKPN Lon~ London 13 2019-03-31 23:30:00 2019-04-01 00:00:00 hydro #> 8 UKPN Lon~ London 13 2019-03-31 23:30:00 2019-04-01 00:00:00 solar #> 9 UKPN Lon~ London 13 2019-03-31 23:30:00 2019-04-01 00:00:00 wind #> 10 UKPN Lon~ London 13 2019-04-01 00:00:00 2019-04-01 00:30:00 biom~ #> # ... with 3,014 more rows, and 3 more variables: perc <dbl>, forecast <int>, #> # index <chr>
Finally, the API allows you to access carbon intensity data per postcode. However, it only accepts outward postcode, i.e. one or two letters, followed by one or two digits. For example, the following code will access the carbon intensity information for EN2 area for the current 1/2 hr:
get_postcode_ci(postcode = "EN2") #> # A tibble: 9 x 10 #> regionid dnoregion shortname postcode from to #> <int> <chr> <chr> <chr> <dttm> <dttm> #> 1 9 UKPN East East Eng~ EN2 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 2 9 UKPN East East Eng~ EN2 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 3 9 UKPN East East Eng~ EN2 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 4 9 UKPN East East Eng~ EN2 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 5 9 UKPN East East Eng~ EN2 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 6 9 UKPN East East Eng~ EN2 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 7 9 UKPN East East Eng~ EN2 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 8 9 UKPN East East Eng~ EN2 2020-11-01 21:00:00 2020-11-01 21:30:00 #> 9 9 UKPN East East Eng~ EN2 2020-11-01 21:00:00 2020-11-01 21:30:00 #> # ... with 4 more variables: fuel <chr>, perc <dbl>, forecast <int>, #> # index <chr>
As always, we can pass start and end arguments to the function to extend the time window:
get_postcode_ci(postcode = 'EN2', start, end) #> # A tibble: 3,024 x 9 #> region shortname postcode from to fuel perc #> <int> <chr> <chr> <dttm> <dttm> <chr> <dbl> #> 1 10 East Eng~ EN2 2019-03-31 23:30:00 2019-04-01 00:00:00 biom~ 0 #> 2 10 East Eng~ EN2 2019-03-31 23:30:00 2019-04-01 00:00:00 coal 0 #> 3 10 East Eng~ EN2 2019-03-31 23:30:00 2019-04-01 00:00:00 impo~ 3.3 #> 4 10 East Eng~ EN2 2019-03-31 23:30:00 2019-04-01 00:00:00 gas 8.7 #> 5 10 East Eng~ EN2 2019-03-31 23:30:00 2019-04-01 00:00:00 nucl~ 42.1 #> 6 10 East Eng~ EN2 2019-03-31 23:30:00 2019-04-01 00:00:00 other 0 #> 7 10 East Eng~ EN2 2019-03-31 23:30:00 2019-04-01 00:00:00 hydro 0 #> 8 10 East Eng~ EN2 2019-03-31 23:30:00 2019-04-01 00:00:00 solar 0 #> 9 10 East Eng~ EN2 2019-03-31 23:30:00 2019-04-01 00:00:00 wind 45.9 #> 10 10 East Eng~ EN2 2019-04-01 00:00:00 2019-04-01 00:30:00 biom~ 0.2 #> # ... with 3,014 more rows, and 2 more variables: forecast <int>, index <chr>