What is Grid Status?
Contents
What is Grid Status?#
gridstatus is a standardized Python API to electricity supply, demand, and pricing data for the major Independent System Operators (ISOs) in the United States.
Currently gridstatus supports CAISO, SPP, ISONE, MISO, Ercot, NYISO, and PJM.
We’d love to answer any usage or data access questions! Please let us know by posting a GitHub issue.
5 Minute Overview#
First, we can see all of the ISOs that are supported
import gridstatus
gridstatus.list_isos()
| Name | Id | Class | |
|---|---|---|---|
| 0 | Midcontinent ISO | miso | MISO |
| 1 | California ISO | caiso | CAISO |
| 2 | PJM | pjm | PJM |
| 3 | Electric Reliability Council of Texas | ercot | Ercot |
| 4 | Southwest Power Pool | spp | SPP |
| 5 | New York ISO | nyiso | NYISO |
| 6 | ISO New England | isone | ISONE |
Next, we can select an ISO we want to use
caiso = gridstatus.CAISO()
Fuel Mix#
All ISOs have the same API to methods like get_fuel_mix, get_load, and get_status, etc. Here is how we can get the fuel mix
caiso.get_fuel_mix("today")
| Time | Interval Start | Interval End | Solar | Wind | Geothermal | Biomass | Biogas | Small Hydro | Coal | Nuclear | Natural Gas | Large Hydro | Batteries | Imports | Other | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-07-03 00:00:00-07:00 | 2023-07-03 00:00:00-07:00 | 2023-07-03 00:05:00-07:00 | -13 | 3644 | 902 | 268 | 221 | 384 | 4 | 2259 | 13069 | 3874 | -67 | 3277 | 0 |
| 1 | 2023-07-03 00:05:00-07:00 | 2023-07-03 00:05:00-07:00 | 2023-07-03 00:10:00-07:00 | -13 | 3612 | 903 | 270 | 221 | 383 | 5 | 2258 | 13233 | 3705 | 137 | 2905 | 0 |
| 2 | 2023-07-03 00:10:00-07:00 | 2023-07-03 00:10:00-07:00 | 2023-07-03 00:15:00-07:00 | -13 | 3607 | 902 | 271 | 221 | 380 | 4 | 2258 | 13334 | 3660 | 192 | 2727 | 0 |
| 3 | 2023-07-03 00:15:00-07:00 | 2023-07-03 00:15:00-07:00 | 2023-07-03 00:20:00-07:00 | -13 | 3603 | 903 | 271 | 221 | 379 | 4 | 2258 | 13442 | 3653 | 69 | 2699 | 0 |
| 4 | 2023-07-03 00:20:00-07:00 | 2023-07-03 00:20:00-07:00 | 2023-07-03 00:25:00-07:00 | -13 | 3622 | 903 | 270 | 221 | 379 | 4 | 2257 | 13656 | 3625 | -63 | 2528 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 94 | 2023-07-03 07:50:00-07:00 | 2023-07-03 07:50:00-07:00 | 2023-07-03 07:55:00-07:00 | 10531 | 2895 | 906 | 288 | 219 | 358 | 3 | 2261 | 11077 | 2703 | -2208 | -1298 | 0 |
| 95 | 2023-07-03 07:55:00-07:00 | 2023-07-03 07:55:00-07:00 | 2023-07-03 08:00:00-07:00 | 10784 | 2895 | 905 | 288 | 220 | 358 | 3 | 2261 | 11051 | 2500 | -2117 | -1428 | 0 |
| 96 | 2023-07-03 08:00:00-07:00 | 2023-07-03 08:00:00-07:00 | 2023-07-03 08:05:00-07:00 | 11130 | 2862 | 903 | 289 | 221 | 359 | 3 | 2261 | 11130 | 2096 | -1864 | -1574 | 0 |
| 97 | 2023-07-03 08:05:00-07:00 | 2023-07-03 08:05:00-07:00 | 2023-07-03 08:10:00-07:00 | 11368 | 2851 | 906 | 289 | 221 | 359 | 3 | 2261 | 11420 | 1853 | -2088 | -1057 | 0 |
| 98 | 2023-07-03 08:10:00-07:00 | 2023-07-03 08:10:00-07:00 | 2023-07-03 08:15:00-07:00 | 11776 | 2847 | 905 | 290 | 221 | 359 | 3 | 2261 | 11436 | 1658 | -2210 | -885 | 0 |
99 rows × 16 columns
Load#
or the energy demand throughout the current day as a Pandas DataFrame
caiso.get_load("today")
| Time | Interval Start | Interval End | Load | |
|---|---|---|---|---|
| 0 | 2023-07-03 00:00:00-07:00 | 2023-07-03 00:00:00-07:00 | 2023-07-03 00:05:00-07:00 | 27332.0 |
| 1 | 2023-07-03 00:05:00-07:00 | 2023-07-03 00:05:00-07:00 | 2023-07-03 00:10:00-07:00 | 27293.0 |
| 2 | 2023-07-03 00:10:00-07:00 | 2023-07-03 00:10:00-07:00 | 2023-07-03 00:15:00-07:00 | 27237.0 |
| 3 | 2023-07-03 00:15:00-07:00 | 2023-07-03 00:15:00-07:00 | 2023-07-03 00:20:00-07:00 | 27139.0 |
| 4 | 2023-07-03 00:20:00-07:00 | 2023-07-03 00:20:00-07:00 | 2023-07-03 00:25:00-07:00 | 27007.0 |
| ... | ... | ... | ... | ... |
| 94 | 2023-07-03 07:50:00-07:00 | 2023-07-03 07:50:00-07:00 | 2023-07-03 07:55:00-07:00 | 24861.0 |
| 95 | 2023-07-03 07:55:00-07:00 | 2023-07-03 07:55:00-07:00 | 2023-07-03 08:00:00-07:00 | 24900.0 |
| 96 | 2023-07-03 08:00:00-07:00 | 2023-07-03 08:00:00-07:00 | 2023-07-03 08:05:00-07:00 | 24953.0 |
| 97 | 2023-07-03 08:05:00-07:00 | 2023-07-03 08:05:00-07:00 | 2023-07-03 08:10:00-07:00 | 25041.0 |
| 98 | 2023-07-03 08:10:00-07:00 | 2023-07-03 08:10:00-07:00 | 2023-07-03 08:15:00-07:00 | 25179.0 |
99 rows × 4 columns
Load Forecast#
Another dataset we can query is the load forecast
nyiso = gridstatus.NYISO()
nyiso.get_load_forecast("today")
| Time | Interval Start | Interval End | Forecast Time | Load Forecast | |
|---|---|---|---|---|---|
| 0 | 2023-07-03 00:00:00-04:00 | 2023-07-03 00:00:00-04:00 | 2023-07-03 01:00:00-04:00 | 2023-07-03 00:00:00-04:00 | 17515 |
| 1 | 2023-07-03 01:00:00-04:00 | 2023-07-03 01:00:00-04:00 | 2023-07-03 02:00:00-04:00 | 2023-07-03 00:00:00-04:00 | 16755 |
| 2 | 2023-07-03 02:00:00-04:00 | 2023-07-03 02:00:00-04:00 | 2023-07-03 03:00:00-04:00 | 2023-07-03 00:00:00-04:00 | 16219 |
| 3 | 2023-07-03 03:00:00-04:00 | 2023-07-03 03:00:00-04:00 | 2023-07-03 04:00:00-04:00 | 2023-07-03 00:00:00-04:00 | 15916 |
| 4 | 2023-07-03 04:00:00-04:00 | 2023-07-03 04:00:00-04:00 | 2023-07-03 05:00:00-04:00 | 2023-07-03 00:00:00-04:00 | 15921 |
| ... | ... | ... | ... | ... | ... |
| 139 | 2023-07-08 19:00:00-04:00 | 2023-07-08 19:00:00-04:00 | 2023-07-08 20:00:00-04:00 | 2023-07-03 00:00:00-04:00 | 21669 |
| 140 | 2023-07-08 20:00:00-04:00 | 2023-07-08 20:00:00-04:00 | 2023-07-08 21:00:00-04:00 | 2023-07-03 00:00:00-04:00 | 21186 |
| 141 | 2023-07-08 21:00:00-04:00 | 2023-07-08 21:00:00-04:00 | 2023-07-08 22:00:00-04:00 | 2023-07-03 00:00:00-04:00 | 20798 |
| 142 | 2023-07-08 22:00:00-04:00 | 2023-07-08 22:00:00-04:00 | 2023-07-08 23:00:00-04:00 | 2023-07-03 00:00:00-04:00 | 19905 |
| 143 | 2023-07-08 23:00:00-04:00 | 2023-07-08 23:00:00-04:00 | 2023-07-09 00:00:00-04:00 | 2023-07-03 00:00:00-04:00 | 18832 |
144 rows × 5 columns
Historical Data#
When supported, you can use the historical method calls to get data for a specific day in the past. For example,
caiso.get_load("Jan 1, 2020")
| Time | Interval Start | Interval End | Load | |
|---|---|---|---|---|
| 0 | 2020-01-01 00:00:00-08:00 | 2020-01-01 00:00:00-08:00 | 2020-01-01 00:05:00-08:00 | 21533 |
| 1 | 2020-01-01 00:05:00-08:00 | 2020-01-01 00:05:00-08:00 | 2020-01-01 00:10:00-08:00 | 21429 |
| 2 | 2020-01-01 00:10:00-08:00 | 2020-01-01 00:10:00-08:00 | 2020-01-01 00:15:00-08:00 | 21320 |
| 3 | 2020-01-01 00:15:00-08:00 | 2020-01-01 00:15:00-08:00 | 2020-01-01 00:20:00-08:00 | 21272 |
| 4 | 2020-01-01 00:20:00-08:00 | 2020-01-01 00:20:00-08:00 | 2020-01-01 00:25:00-08:00 | 21193 |
| ... | ... | ... | ... | ... |
| 283 | 2020-01-01 23:35:00-08:00 | 2020-01-01 23:35:00-08:00 | 2020-01-01 23:40:00-08:00 | 20494 |
| 284 | 2020-01-01 23:40:00-08:00 | 2020-01-01 23:40:00-08:00 | 2020-01-01 23:45:00-08:00 | 20383 |
| 285 | 2020-01-01 23:45:00-08:00 | 2020-01-01 23:45:00-08:00 | 2020-01-01 23:50:00-08:00 | 20297 |
| 286 | 2020-01-01 23:50:00-08:00 | 2020-01-01 23:50:00-08:00 | 2020-01-01 23:55:00-08:00 | 20242 |
| 287 | 2020-01-01 23:55:00-08:00 | 2020-01-01 23:55:00-08:00 | 2020-01-02 00:00:00-08:00 | 20128 |
288 rows × 4 columns
Frequently, we want to get data across multiple days. We can do that by providing a start and end parameter to any iso.get_* method
caiso_load = caiso.get_load(start="Jan 1, 2021", end="Feb 1, 2021")
caiso_load
| Time | Interval Start | Interval End | Load | |
|---|---|---|---|---|
| 0 | 2021-01-01 00:00:00-08:00 | 2021-01-01 00:00:00-08:00 | 2021-01-01 00:05:00-08:00 | 21937.0 |
| 1 | 2021-01-01 00:05:00-08:00 | 2021-01-01 00:05:00-08:00 | 2021-01-01 00:10:00-08:00 | 21858.0 |
| 2 | 2021-01-01 00:10:00-08:00 | 2021-01-01 00:10:00-08:00 | 2021-01-01 00:15:00-08:00 | 21827.0 |
| 3 | 2021-01-01 00:15:00-08:00 | 2021-01-01 00:15:00-08:00 | 2021-01-01 00:20:00-08:00 | 21757.0 |
| 4 | 2021-01-01 00:20:00-08:00 | 2021-01-01 00:20:00-08:00 | 2021-01-01 00:25:00-08:00 | 21664.0 |
| ... | ... | ... | ... | ... |
| 8923 | 2021-01-31 23:35:00-08:00 | 2021-01-31 23:35:00-08:00 | 2021-01-31 23:40:00-08:00 | 20054.0 |
| 8924 | 2021-01-31 23:40:00-08:00 | 2021-01-31 23:40:00-08:00 | 2021-01-31 23:45:00-08:00 | 19952.0 |
| 8925 | 2021-01-31 23:45:00-08:00 | 2021-01-31 23:45:00-08:00 | 2021-01-31 23:50:00-08:00 | 19859.0 |
| 8926 | 2021-01-31 23:50:00-08:00 | 2021-01-31 23:50:00-08:00 | 2021-01-31 23:55:00-08:00 | 19763.0 |
| 8927 | 2021-01-31 23:55:00-08:00 | 2021-01-31 23:55:00-08:00 | 2021-02-01 00:00:00-08:00 | 19650.0 |
8928 rows × 4 columns
We can now see there is data for all of January 2021
import plotly.express as px
fig = px.line(caiso_load, x="Time", y="Load", title="CAISO Load - Jan '21")
fig
Next Steps#
The best part is these APIs work in the same way across all the supported ISOs!
Getting Started
Examples
- CAISO Examples
- ERCOT Examples
- NYISO Examples
- PJM Examples
- SPP Examples
- LMP Data Examples
- Walkthrough on how to query LMP data from SPP using gridstatus
- Available Markets
- Available Location Types
- Query Latest Real-Time LMPs by Hub
- Query Latest Real-Time LMPs by Settlement Location
- Query Latest Real-Time LMPs by Interface
- Query Today’s Real-Time LMPs by Interface
- Query Day-Ahead LMPs by Hub
- Query Day-Ahead LMPs by Interface
- LMP Data Examples
- Other Examples
Reference