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")

2026-04-10 20:51:10 - INFO - Fetching URL: https://www.caiso.com/outlook/current/fuelsource.csv?_=1775854270

	Time	Interval Start	Interval End	Solar	Wind	Geothermal	Biomass	Biogas	Small Hydro	Coal	Nuclear	Natural Gas	Large Hydro	Batteries	Imports	Other
0	2026-04-10 00:00:00-07:00	2026-04-10 00:00:00-07:00	2026-04-10 00:05:00-07:00	-57	2988	563	211	161	227	0	2278	5295	3299	2145	6453	0
1	2026-04-10 00:05:00-07:00	2026-04-10 00:05:00-07:00	2026-04-10 00:10:00-07:00	-57	2836	563	210	163	229	0	2279	5368	3487	2621	6077	0
2	2026-04-10 00:10:00-07:00	2026-04-10 00:10:00-07:00	2026-04-10 00:15:00-07:00	-57	2714	562	210	161	230	0	2278	5518	3520	2727	5910	0
3	2026-04-10 00:15:00-07:00	2026-04-10 00:15:00-07:00	2026-04-10 00:20:00-07:00	-57	2647	561	209	161	231	0	2279	5565	3494	2679	5908	0
4	2026-04-10 00:20:00-07:00	2026-04-10 00:20:00-07:00	2026-04-10 00:25:00-07:00	-57	2563	560	208	162	231	0	2279	5649	3524	2583	5919	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
161	2026-04-10 13:25:00-07:00	2026-04-10 13:25:00-07:00	2026-04-10 13:30:00-07:00	18302	3922	578	179	161	210	0	2274	2568	994	-7454	-43	0
162	2026-04-10 13:30:00-07:00	2026-04-10 13:30:00-07:00	2026-04-10 13:35:00-07:00	18631	4030	579	178	160	210	0	2275	2592	982	-7999	217	0
163	2026-04-10 13:35:00-07:00	2026-04-10 13:35:00-07:00	2026-04-10 13:40:00-07:00	18830	4055	578	175	160	211	0	2272	2641	950	-8384	389	0
164	2026-04-10 13:40:00-07:00	2026-04-10 13:40:00-07:00	2026-04-10 13:45:00-07:00	18865	4159	580	176	161	211	0	2274	2665	942	-8817	706	0
165	2026-04-10 13:45:00-07:00	2026-04-10 13:45:00-07:00	2026-04-10 13:50:00-07:00	18578	4213	580	177	160	212	0	2274	2671	942	-8724	788	0

166 rows × 16 columns

Load

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

caiso.get_load("today")

2026-04-10 20:51:11 - INFO - Fetching URL: https://www.caiso.com/outlook/current/demand.csv?_=1775854271

	Time	Interval Start	Interval End	Load
0	2026-04-10 00:00:00-07:00	2026-04-10 00:00:00-07:00	2026-04-10 00:05:00-07:00	23177.0
1	2026-04-10 00:05:00-07:00	2026-04-10 00:05:00-07:00	2026-04-10 00:10:00-07:00	23491.0
2	2026-04-10 00:10:00-07:00	2026-04-10 00:10:00-07:00	2026-04-10 00:15:00-07:00	23569.0
3	2026-04-10 00:15:00-07:00	2026-04-10 00:15:00-07:00	2026-04-10 00:20:00-07:00	23479.0
4	2026-04-10 00:20:00-07:00	2026-04-10 00:20:00-07:00	2026-04-10 00:25:00-07:00	23435.0
...	...	...	...	...
161	2026-04-10 13:25:00-07:00	2026-04-10 13:25:00-07:00	2026-04-10 13:30:00-07:00	20809.0
162	2026-04-10 13:30:00-07:00	2026-04-10 13:30:00-07:00	2026-04-10 13:35:00-07:00	21053.0
163	2026-04-10 13:35:00-07:00	2026-04-10 13:35:00-07:00	2026-04-10 13:40:00-07:00	21020.0
164	2026-04-10 13:40:00-07:00	2026-04-10 13:40:00-07:00	2026-04-10 13:45:00-07:00	21178.0
165	2026-04-10 13:45:00-07:00	2026-04-10 13:45:00-07:00	2026-04-10 13:50:00-07:00	20892.0

166 rows × 4 columns

Load Forecast

Another dataset we can query is the load forecast

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

2026-04-10 20:51:11 - INFO - Requesting http://mis.nyiso.com/public/csv/isolf/20260410isolf.csv

/home/docs/checkouts/readthedocs.org/user_builds/isodata/checkouts/latest/gridstatus/nyiso.py:1226: FutureWarning: Parsed string "04/09/26 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	2026-04-10 00:00:00-04:00	2026-04-10 00:00:00-04:00	2026-04-10 01:00:00-04:00	2026-04-09 07:30:00-04:00	13822
1	2026-04-10 01:00:00-04:00	2026-04-10 01:00:00-04:00	2026-04-10 02:00:00-04:00	2026-04-09 07:30:00-04:00	13379
2	2026-04-10 02:00:00-04:00	2026-04-10 02:00:00-04:00	2026-04-10 03:00:00-04:00	2026-04-09 07:30:00-04:00	13083
3	2026-04-10 03:00:00-04:00	2026-04-10 03:00:00-04:00	2026-04-10 04:00:00-04:00	2026-04-09 07:30:00-04:00	12970
4	2026-04-10 04:00:00-04:00	2026-04-10 04:00:00-04:00	2026-04-10 05:00:00-04:00	2026-04-09 07:30:00-04:00	13158
...	...	...	...	...	...
139	2026-04-15 19:00:00-04:00	2026-04-15 19:00:00-04:00	2026-04-15 20:00:00-04:00	2026-04-09 07:30:00-04:00	16483
140	2026-04-15 20:00:00-04:00	2026-04-15 20:00:00-04:00	2026-04-15 21:00:00-04:00	2026-04-09 07:30:00-04:00	16419
141	2026-04-15 21:00:00-04:00	2026-04-15 21:00:00-04:00	2026-04-15 22:00:00-04:00	2026-04-09 07:30:00-04:00	15798
142	2026-04-15 22:00:00-04:00	2026-04-15 22:00:00-04:00	2026-04-15 23:00:00-04:00	2026-04-09 07:30:00-04:00	14904
143	2026-04-15 23:00:00-04:00	2026-04-15 23:00:00-04:00	2026-04-16 00:00:00-04:00	2026-04-09 07:30:00-04:00	13994

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")

2026-04-10 20:51:11 - INFO - Fetching URL: https://www.caiso.com/outlook/history/20200101/demand.csv?_=1775854271

	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