The goal of rgridstatus is to provide a R API wrapper for the GridStatusIO API.
See also my blog post describing some features of the package.
You can install the development version of rgridstatus from GitHub with:
# install.packages("devtools")
devtools::install_github("andypicke/rgridstatus")You will need to register for an API key. By default, all functions assume you have stored your API key in your .Renviron file with the name GRIDSTATUS_API_KEY.
library(rgridstatus)
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr 1.1.4 ✔ readr 2.1.5
#> ✔ forcats 1.0.0 ✔ stringr 1.5.1
#> ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
#> ✔ purrr 1.0.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
info <- get_available_datasets()
head(info)
#> id
#> 1 all_records
#> 2 all_records_timeseries
#> 3 caiso_as_prices
#> 4 caiso_as_procurement_dam
#> 5 caiso_curtailed_non_operational_generator_report
#> 6 caiso_curtailment
#> name
#> 1 All Records
#> 2 All Records Timeseries
#> 3 CAISO AS Prices
#> 4 CAISO AS Procurement DAM
#> 5 CAISO Curtailed Non Operational Generator Report
#> 6 CAISO Curtailment
#> description
#> 1 Top daily records for various metrics across all ISOs
#> 2 Timeline of new records for various metrics in all ISOs
#> 3 Ancillary Services Prices as reported by CAISO.\n\n**AS Mapping**\n\nThe *_EXP show only the intertie resources.\n \n- AS_SP26_P: Resources in AS_SP26\n- AS_NP26_P: Resources in AS_NP26\n- AS_SP15_P: Resources in AS_SP15\n- AS_NP15_P: Resources in AS_NP15\n- AS_SP26_EXP_P: Resources in AS_SP26_EXP which are not in AS_SP26\n- AS_NP26_EXP_P: Resources in AS_NP26_EXP which are not in AS_NP26\n- AS_SP15_EXP_P: Resources in AS_SP15_EXP which are not in AS_SP15\n- AS_NP15_EXP_P: Resources in AS_NP15_EXP which are not in AS_NP15\n- AS_CAISO_NP26_P: Resources in AS_CAISO which are not in AS_SP26\n- AS_CAISO_SP26_P: Resources in AS_CAISO which are not in AS_NP26\n- AS_CAISO_NP15_P: Resources in AS_CAISO which are not in AS_SP15\n- AS_CAISO_SP15_P: Resources in AS_CAISO which are not in AS_NP15
#> 4 Ancillary services procurement data from CAISO.\nIncludes total MW procured and costs. Corresponds\nto CAISO AS_RESULTS dataset on OASIS. See caiso_as_prices dataset\nfor per MW prices that result from market run.
#> 5 Prior Day Curtailed Non-Operational Generator Report as reported by CAISO.\n\nGlossary: https://www.caiso.com/glossary
#> 6 Daily Curtailment data as reported by CAISO.\n\nNote: CAISO did not publish curtailment reports for 2024-07-26 and 2024-07-27.
#> earliest_available_time latest_available_time source
#> 1 2017-12-13T22:05:00+00:00 2024-09-17T00:15:00+00:00 gridstatus
#> 2 2011-01-01T06:30:00+00:00 2024-09-11T23:10:00+00:00 gridstatus
#> 3 2010-01-01T08:00:00+00:00 2024-09-25T06:00:00+00:00 caiso
#> 4 2010-01-01T08:00:00+00:00 2024-09-25T06:00:00+00:00 caiso
#> 5 2021-06-18T07:00:00+00:00 2024-09-24T07:00:00+00:00 caiso
#> 6 2017-01-01T12:00:00+00:00 2024-09-23T00:00:00+00:00 caiso
#> last_checked_time
#> 1 2024-09-24T16:51:59+00:00
#> 2 2024-09-24T16:51:59+00:00
#> 3 2024-09-24T16:54:44+00:00
#> 4 2024-09-24T16:54:50+00:00
#> 5 2024-09-24T15:36:34+00:00
#> 6 2024-09-24T16:54:53+00:00
#> primary_key_columns
#> 1 iso, rank, record_type, metric_name
#> 2 iso, interval_start_utc, record_type, metric_name
#> 3 interval_start_utc, region, market
#> 4 interval_start_utc, region
#> 5 publish_time_utc, outage_mrid, curtailment_start_time
#> 6 interval_start_utc, curtailment_type, curtailment_reason, fuel_type
#> publish_time_column time_index_column subseries_index_column
#> 1 <NA> interval_start_utc <NA>
#> 2 <NA> interval_start_utc <NA>
#> 3 <NA> interval_start_utc region
#> 4 <NA> interval_start_utc region
#> 5 publish_time_utc curtailment_start_time <NA>
#> 6 <NA> interval_start_utc <NA>
#> all_columns
#> 1 rank, iso, interval_start_utc, date, record_type, metric_name, metric_value, BIGINT, TEXT, TIMESTAMP, DATE, TEXT, TEXT, DOUBLE PRECISION, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE
#> 2 iso, interval_start_utc, date, record_type, metric_name, metric_value, TEXT, TIMESTAMP, DATE, TEXT, TEXT, DOUBLE PRECISION, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE
#> 3 interval_start_utc, interval_end_utc, region, market, non_spinning_reserves, regulation_down, regulation_mileage_down, regulation_mileage_up, regulation_up, spinning_reserves, TIMESTAMP, TIMESTAMP, VARCHAR, VARCHAR, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE
#> 4 interval_start_utc, interval_end_utc, region, market, non_spinning_reserves_procured_mw, non_spinning_reserves_self_provided_mw, non_spinning_reserves_total_mw, non_spinning_reserves_total_cost, regulation_down_procured_mw, regulation_down_self_provided_mw, regulation_down_total_mw, regulation_down_total_cost, regulation_mileage_down_procured_mw, regulation_mileage_down_self_provided_mw, regulation_mileage_down_total_mw, regulation_mileage_down_total_cost, regulation_mileage_up_procured_mw, regulation_mileage_up_self_provided_mw, regulation_mileage_up_total_mw, regulation_mileage_up_total_cost, regulation_up_procured_mw, regulation_up_self_provided_mw, regulation_up_total_mw, regulation_up_total_cost, spinning_reserves_procured_mw, spinning_reserves_self_provided_mw, spinning_reserves_total_mw, spinning_reserves_total_cost, TIMESTAMP, TIMESTAMP, VARCHAR, VARCHAR, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE
#> 5 publish_time_utc, outage_mrid, resource_name, resource_id, outage_type, nature_of_work, curtailment_start_time, curtailment_end_time, curtailment_mw, resource_pmax_mw, net_qualifying_capacity_mw, TIMESTAMP, INTEGER, VARCHAR, VARCHAR, VARCHAR, VARCHAR, TIMESTAMP, TIMESTAMP, DOUBLE PRECISION, DOUBLE PRECISION, DOUBLE PRECISION, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE
#> 6 interval_start_utc, interval_end_utc, curtailment_type, curtailment_reason, fuel_type, curtailment_mwh, curtailment_mw, TIMESTAMP, TIMESTAMP, VARCHAR, VARCHAR, VARCHAR, INTEGER, INTEGER, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE
#> number_of_rows_approximate table_type is_in_snowflake data_frequency
#> 1 560 materialized_view FALSE IRREGULAR
#> 2 1871 materialized_view FALSE IRREGULAR
#> 3 621445 table TRUE 1_HOUR
#> 4 623449 table TRUE 1_HOUR
#> 5 2200875 table TRUE IRREGULAR
#> 6 67748 table TRUE 5_MINUTES
#> is_published created_at updated_at
#> 1 TRUE <NA> <NA>
#> 2 TRUE <NA> <NA>
#> 3 TRUE 2023-04-20T07:00:37+00:00 2024-08-21T18:50:24+00:00
#> 4 TRUE 2023-11-20T15:34:56+00:00 2024-08-21T18:50:57+00:00
#> 5 TRUE 2023-10-04T00:04:17+00:00 2024-09-17T01:15:18+00:00
#> 6 TRUE 2023-04-20T16:35:38+00:00 2024-09-17T01:15:27+00:00
#> source_url
#> 1 <NA>
#> 2 <NA>
#> 3 http://oasis.caiso.com/mrioasis/logon.do
#> 4 http://oasis.caiso.com/mrioasis/logon.do
#> 5 https://www.caiso.com/market-operations/outages/curtailed-and-non-operational-generators
#> 6 https://www.caiso.com/about/our-business/managing-the-evolving-grid
#> publication_frequency
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 <NA>Most of the “sources” are ISOs.
# input is info from get_available_datasets()
sources <- get_source_names(info)
sources
#> [1] "all" "caiso" "dataset" "eia" "ercot" "ieso" "isone"
#> [8] "isos" "miso" "nyiso" "pjm" "spp"The dataset names all start with the ISO/source, so it is easy to filter by those:
info_caiso <- get_available_datasets() |>
dplyr::filter(stringr::str_detect(name,"^CAISO_")) # name starts with "CAISO_"updates <- get_dataset_updates()
head(updates)
#> id dataset time_utc num_rows_updated
#> 1 1 pjm_lmp_real_time_5_min 2023-04-19T22:10:19+00:00 34
#> 2 2 miso_lmp_real_time_5_min 2023-04-19T22:10:41+00:00 345
#> 3 3 miso_load 2023-04-19T22:11:46+00:00 1
#> 4 4 miso_fuel_mix 2023-04-19T22:11:54+00:00 1
#> 5 5 caiso_fuel_mix 2023-04-19T22:11:55+00:00 4
#> 6 6 caiso_load 2023-04-19T22:11:56+00:00 1Again, you can filter the results to get updates for a specific ISO/source:
updates_caiso <- updates |> dplyr::filter(stringr::str_detect(dataset, "^caiso_"))
updates_caiso
#> id dataset time_utc num_rows_updated
#> 1 5 caiso_fuel_mix 2023-04-19T22:11:55+00:00 4
#> 2 6 caiso_load 2023-04-19T22:11:56+00:00 1
#> 3 7 caiso_lmp_real_time_5_min 2023-04-19T22:12:21+00:00 12
#> 4 8 caiso_storage 2023-04-19T22:12:28+00:00 1
#> 5 30 caiso_fuel_mix 2023-04-19T22:17:27+00:00 3
#> 6 31 caiso_load 2023-04-19T22:17:27+00:00 1
#> 7 32 caiso_lmp_real_time_5_min 2023-04-19T22:17:51+00:00 12
#> 8 33 caiso_storage 2023-04-19T22:17:57+00:00 1
#> 9 41 caiso_lmp_real_time_15_min 2023-04-19T22:20:18+00:00 12
#> 10 57 caiso_fuel_mix 2023-04-19T22:22:36+00:00 7
#> 11 58 caiso_load 2023-04-19T22:22:37+00:00 1
#> 12 59 caiso_lmp_real_time_5_min 2023-04-19T22:23:01+00:00 12
#> 13 60 caiso_storage 2023-04-19T22:23:07+00:00 1
#> 14 76 caiso_fuel_mix 2023-04-19T22:27:34+00:00 6
#> 15 77 caiso_load 2023-04-19T22:27:34+00:00 1
#> 16 81 caiso_lmp_real_time_5_min 2023-04-19T22:27:58+00:00 12
#> 17 82 caiso_storage 2023-04-19T22:28:04+00:00 1df <- get_gridstatus_dataset(wh_dataset = "caiso_fuel_mix",
start_time = "2024-09-03",
end_time = "2024-09-05")
str(df)
#> 'data.frame': 575 obs. of 15 variables:
#> $ interval_start_utc: POSIXct, format: "2024-09-03 00:00:00" "2024-09-03 00:05:00" ...
#> $ solar : int 14210 13948 13542 13020 12638 12029 11346 10671 10154 9555 ...
#> $ wind : int 1837 1828 1789 1799 1795 1792 1825 1821 1872 1874 ...
#> $ geothermal : int 727 727 729 728 729 729 729 727 728 729 ...
#> $ biomass : int 346 347 347 347 348 348 349 347 347 348 ...
#> $ biogas : int 157 157 157 156 155 155 156 156 157 156 ...
#> $ small_hydro : int 261 259 259 261 259 259 260 260 258 258 ...
#> $ coal : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ nuclear : int 2241 2242 2243 2243 2241 2243 2242 2243 2243 2244 ...
#> $ natural_gas : int 10173 10440 10408 10707 11173 11582 11906 12065 12162 12262 ...
#> $ large_hydro : int 3030 3464 3644 3681 3701 3740 3774 3780 3811 3822 ...
#> $ batteries : int 239 -92 -8 228 295 444 861 1626 2016 2437 ...
#> $ imports : int 2040 1913 2130 2199 2180 2315 2448 2312 2342 2550 ...
#> $ other : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ datetime_local : POSIXct, format: "2024-09-02 17:00:00" "2024-09-02 17:05:00" ...{rgridstatus} contains a convenience function pivot_gen_long() to pivot the fuel mix dataframe to a long format. This can make it easier to plot multiple timeseries.
df_long <- pivot_gen_long(df)
head(df_long)
#> # A tibble: 6 × 4
#> interval_start_utc datetime_local fuel_type generation
#> <dttm> <dttm> <chr> <int>
#> 1 2024-09-03 00:00:00 2024-09-02 17:00:00 solar 14210
#> 2 2024-09-03 00:00:00 2024-09-02 17:00:00 wind 1837
#> 3 2024-09-03 00:00:00 2024-09-02 17:00:00 geothermal 727
#> 4 2024-09-03 00:00:00 2024-09-02 17:00:00 biomass 346
#> 5 2024-09-03 00:00:00 2024-09-02 17:00:00 biogas 157
#> 6 2024-09-03 00:00:00 2024-09-02 17:00:00 small_hydro 261With the data in a long format, we can just specify that the color or linetype should correspond to the fuel_type.
df_long |>
ggplot(aes(datetime_local, generation)) +
geom_line(aes(color = fuel_type)) 