What is the gridstatus library?

The gridstatus open-source library is a Python library maintained by Grid Status that provides a consistent API for accessing raw electricity supply, demand, and pricing data for the major Independent System Operators (ISOs) in the United States and Canada. It currently supports data from CAISO, SPP, ISONE, MISO, ERCOT, NYISO, PJM, IESO, AESO, and the EIA.

GridStatus.io and Hosted API

This library provides minimally-processed data. If you need production-ready data, consider using our hosted API or visit GridStatus.io to see the data in a web interface.

If you are trying to use our hosted API, you might want to check out the gridstatusio library.

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
7	Independent Electricity System Operator	ieso	IESO

Next, we can select an ISO we want to use

caiso = gridstatus.CAISO()

Fuel Mix

ISOs share a common API with methods like get_fuel_mix and get_load. Here is how we can get the fuel mix

caiso.get_fuel_mix("today")

2026-04-21 02:22:50 - INFO - Fetching URL: https://www.caiso.com/outlook/current/fuelsource.csv?_=1776738170

	Time	Interval Start	Interval End	Solar	Wind	Geothermal	Biomass	Biogas	Small Hydro	Coal	Nuclear	Natural Gas	Large Hydro	Batteries	Imports	Other
0	2026-04-20 00:00:00-07:00	2026-04-20 00:00:00-07:00	2026-04-20 00:05:00-07:00	-56	2704	697	128	160	261	0	2275	3623	3295	1783	6403	0
1	2026-04-20 00:05:00-07:00	2026-04-20 00:05:00-07:00	2026-04-20 00:10:00-07:00	-55	2707	686	129	155	259	0	2275	3787	3360	1819	6606	0
2	2026-04-20 00:10:00-07:00	2026-04-20 00:10:00-07:00	2026-04-20 00:15:00-07:00	-55	2687	696	129	147	258	0	2276	3890	3512	1397	6780	0
3	2026-04-20 00:15:00-07:00	2026-04-20 00:15:00-07:00	2026-04-20 00:20:00-07:00	-55	2685	699	129	138	256	0	2275	4010	3528	1069	6878	0
4	2026-04-20 00:20:00-07:00	2026-04-20 00:20:00-07:00	2026-04-20 00:25:00-07:00	-56	2630	700	128	135	255	0	2275	4158	3534	964	6826	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
227	2026-04-20 18:55:00-07:00	2026-04-20 18:55:00-07:00	2026-04-20 19:00:00-07:00	2062	3528	519	122	144	263	0	2270	4611	3301	6909	2886	0
228	2026-04-20 19:00:00-07:00	2026-04-20 19:00:00-07:00	2026-04-20 19:05:00-07:00	1496	3582	521	121	144	263	0	2271	4555	3298	7459	3098	0
229	2026-04-20 19:05:00-07:00	2026-04-20 19:05:00-07:00	2026-04-20 19:10:00-07:00	1106	3694	534	122	144	262	0	2272	4383	3256	7801	3033	0
230	2026-04-20 19:10:00-07:00	2026-04-20 19:10:00-07:00	2026-04-20 19:15:00-07:00	778	3817	536	125	144	261	0	2269	4388	3279	7982	2984	0
231	2026-04-20 19:15:00-07:00	2026-04-20 19:15:00-07:00	2026-04-20 19:20:00-07:00	483	3927	536	124	145	262	0	2269	4413	3282	8044	3189	0

232 rows × 16 columns

Load

or the energy demand throughout the current day as a Pandas DataFrame

caiso.get_load("today")

2026-04-21 02:22:50 - INFO - Fetching URL: https://www.caiso.com/outlook/current/demand.csv?_=1776738170

	Time	Interval Start	Interval End	Load
0	2026-04-20 00:00:00-07:00	2026-04-20 00:00:00-07:00	2026-04-20 00:05:00-07:00	21242.0
1	2026-04-20 00:05:00-07:00	2026-04-20 00:05:00-07:00	2026-04-20 00:10:00-07:00	21565.0
2	2026-04-20 00:10:00-07:00	2026-04-20 00:10:00-07:00	2026-04-20 00:15:00-07:00	21752.0
3	2026-04-20 00:15:00-07:00	2026-04-20 00:15:00-07:00	2026-04-20 00:20:00-07:00	21693.0
4	2026-04-20 00:20:00-07:00	2026-04-20 00:20:00-07:00	2026-04-20 00:25:00-07:00	21619.0
...	...	...	...	...
227	2026-04-20 18:55:00-07:00	2026-04-20 18:55:00-07:00	2026-04-20 19:00:00-07:00	26130.0
228	2026-04-20 19:00:00-07:00	2026-04-20 19:00:00-07:00	2026-04-20 19:05:00-07:00	26243.0
229	2026-04-20 19:05:00-07:00	2026-04-20 19:05:00-07:00	2026-04-20 19:10:00-07:00	26225.0
230	2026-04-20 19:10:00-07:00	2026-04-20 19:10:00-07:00	2026-04-20 19:15:00-07:00	26304.0
231	2026-04-20 19:15:00-07:00	2026-04-20 19:15:00-07:00	2026-04-20 19:20:00-07:00	26306.0

232 rows × 4 columns

Load Forecast

Another dataset we can query is the load forecast

nyiso = gridstatus.NYISO()
nyiso.get_load_forecast("today")

2026-04-21 02:22:50 - INFO - Requesting http://mis.nyiso.com/public/csv/isolf/20260420isolf.csv

/home/docs/checkouts/readthedocs.org/user_builds/isodata/checkouts/v0.36.0/gridstatus/nyiso.py:1226: FutureWarning: Parsed string "04/19/26 07:30 EDT" included an un-recognized timezone "EDT". Dropping unrecognized timezones is deprecated; in a future version this will raise. Instead pass the string without the timezone, then use .tz_localize to convert to a recognized timezone.
  return pd.Timestamp(last_updated_date, tz=self.default_timezone)

	Time	Interval Start	Interval End	Forecast Time	Load Forecast
0	2026-04-20 00:00:00-04:00	2026-04-20 00:00:00-04:00	2026-04-20 01:00:00-04:00	2026-04-19 07:30:00-04:00	13639
1	2026-04-20 01:00:00-04:00	2026-04-20 01:00:00-04:00	2026-04-20 02:00:00-04:00	2026-04-19 07:30:00-04:00	13279
2	2026-04-20 02:00:00-04:00	2026-04-20 02:00:00-04:00	2026-04-20 03:00:00-04:00	2026-04-19 07:30:00-04:00	13061
3	2026-04-20 03:00:00-04:00	2026-04-20 03:00:00-04:00	2026-04-20 04:00:00-04:00	2026-04-19 07:30:00-04:00	13018
4	2026-04-20 04:00:00-04:00	2026-04-20 04:00:00-04:00	2026-04-20 05:00:00-04:00	2026-04-19 07:30:00-04:00	13270
...	...	...	...	...	...
139	2026-04-25 19:00:00-04:00	2026-04-25 19:00:00-04:00	2026-04-25 20:00:00-04:00	2026-04-19 07:30:00-04:00	14937
140	2026-04-25 20:00:00-04:00	2026-04-25 20:00:00-04:00	2026-04-25 21:00:00-04:00	2026-04-19 07:30:00-04:00	15088
141	2026-04-25 21:00:00-04:00	2026-04-25 21:00:00-04:00	2026-04-25 22:00:00-04:00	2026-04-19 07:30:00-04:00	14767
142	2026-04-25 22:00:00-04:00	2026-04-25 22:00:00-04:00	2026-04-25 23:00:00-04:00	2026-04-19 07:30:00-04:00	14190
143	2026-04-25 23:00:00-04:00	2026-04-25 23:00:00-04:00	2026-04-26 00:00:00-04:00	2026-04-19 07:30:00-04:00	13528

144 rows × 5 columns

Historical Data

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")

2026-04-21 02:22:51 - INFO - Fetching URL: https://www.caiso.com/outlook/history/20200101/demand.csv?_=1776738171

	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

• Installation
• LMP Pricing Data
• Interconnection Queues
  • NYISO
  • CAISO
  • SPP
  • Ercot
  • MISO
  • ISONE
  • PJM

Examples

• CAISO Examples
  • Get CAISO Ancillary Services
    • Ancillary Services Prices
    • Additional Ancillary Services Data
      • Available columns
  • Load, Fuel Mix, and LMP Data
    • Historical Fuel Mix
    • Historical Load
    • Historical Locational Marginal Pricing (LMP)
    • Gas Prices
  • Solar and Wind Curtailment
    • Get Curtailment Data
    • Visualizing Curtailment
      • Cumulative Curtailment by Year
      • Monthly Curtailment
      • Average Hourly Curtailment
  • Heatmap of Day Ahead LMPs
    • Get LMPs
    • Make Heatmap
  • Downloading CAISO Oasis Datasets
• ERCOT Examples
  • Get ERCOT Ancillary Services Prices
    • Get Ancillary Services Prices
    • Get Historical AS Prices
  • Settlement Point Prices
    • The get_spp Method
      • Date
      • Market
      • Location Type
    • Example: Fetch latest real-time market (RTM) by zone
    • Example: Fetch day-ahead market (DAM) for today by hub
    • Example: Fetch DAM for 2 days ago by node
• ERCOT API Examples
  • ERCOT API Examples
    • Step 0: List All Endpoints
    • Step 1: Describe an Endpoint
    • Step 2: Call an Endpoint
• NYISO Examples
  • Capacity Price Data
  • Fuel Mix Over Time
    • Get Fuel MIx Data
• PJM Examples
  • Load Data Examples
    • Walkthrough on how to query Load data from PJM using gridstatus
    • Get Load Data
    • Query for Date Range
  • LMP Data Examples
    • Walkthrough on how to query LMP data from PJM using gridstatus
    • Available Markets
    • Query LMPs for Hubs
    • Query by Date Range
    • Query LMP All Nodes
    • Query by Location Type
    • Query Real Time 5 Minute Market
    • Get List of Node Ids
• 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
• IESO Examples
  • IESO Load Examples
    • Get Five Minute Load
    • Load Forecast
    • Zonal Load Forecast
    • Fuel Mix
    • Generator Output and Capability/Available Capacity
• Other Examples
  • Interconnection Queues
    • NYISO Interconnection Queue
    • Days in Queue
    • Top Active Project Types
    • Proposed Completion Date
    • Get Interconnection Queue Data from All ISOs
  • Saving Data Locally

Reference

• API Reference
• Changelog
• Contributing