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")

2026-03-14 02:04:31 - INFO - Fetching URL: https://www.caiso.com/outlook/current/fuelsource.csv?_=1773453871

	Time	Interval Start	Interval End	Solar	Wind	Geothermal	Biomass	Biogas	Small Hydro	Coal	Nuclear	Natural Gas	Large Hydro	Batteries	Imports	Other
0	2026-03-13 00:00:00-07:00	2026-03-13 00:00:00-07:00	2026-03-13 00:05:00-07:00	-56	2524	674	220	163	261	0	2282	5064	3242	663	8571	0
1	2026-03-13 00:05:00-07:00	2026-03-13 00:05:00-07:00	2026-03-13 00:10:00-07:00	-55	2531	674	220	165	259	0	2282	5153	3035	1292	8309	0
2	2026-03-13 00:10:00-07:00	2026-03-13 00:10:00-07:00	2026-03-13 00:15:00-07:00	-55	2530	674	220	164	251	0	2281	5189	2875	1452	8423	0
3	2026-03-13 00:15:00-07:00	2026-03-13 00:15:00-07:00	2026-03-13 00:20:00-07:00	-55	2504	674	219	163	245	0	2281	5199	2822	1348	8488	0
4	2026-03-13 00:20:00-07:00	2026-03-13 00:20:00-07:00	2026-03-13 00:25:00-07:00	-55	2481	674	219	163	245	0	2281	5205	2834	1288	8459	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
224	2026-03-13 18:40:00-07:00	2026-03-13 18:40:00-07:00	2026-03-13 18:45:00-07:00	725	2086	664	201	164	247	0	2284	4017	3988	9277	5338	0
225	2026-03-13 18:45:00-07:00	2026-03-13 18:45:00-07:00	2026-03-13 18:50:00-07:00	468	2121	664	200	165	248	0	2283	4106	3962	9475	5343	0
226	2026-03-13 18:50:00-07:00	2026-03-13 18:50:00-07:00	2026-03-13 18:55:00-07:00	314	2197	664	201	164	247	0	2282	4150	3985	9560	5310	0
227	2026-03-13 18:55:00-07:00	2026-03-13 18:55:00-07:00	2026-03-13 19:00:00-07:00	71	2302	664	201	164	248	0	2283	4161	3978	9630	5317	0
228	2026-03-13 19:00:00-07:00	2026-03-13 19:00:00-07:00	2026-03-13 19:05:00-07:00	-18	2383	664	202	165	249	0	2283	4154	3982	9724	5306	0

229 rows × 16 columns

Load

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

caiso.get_load("today")

2026-03-14 02:04:32 - INFO - Fetching URL: https://www.caiso.com/outlook/current/demand.csv?_=1773453872

	Time	Interval Start	Interval End	Load
0	2026-03-13 00:00:00-07:00	2026-03-13 00:00:00-07:00	2026-03-13 00:05:00-07:00	22604.0
1	2026-03-13 00:05:00-07:00	2026-03-13 00:05:00-07:00	2026-03-13 00:10:00-07:00	22908.0
2	2026-03-13 00:10:00-07:00	2026-03-13 00:10:00-07:00	2026-03-13 00:15:00-07:00	23082.0
3	2026-03-13 00:15:00-07:00	2026-03-13 00:15:00-07:00	2026-03-13 00:20:00-07:00	22978.0
4	2026-03-13 00:20:00-07:00	2026-03-13 00:20:00-07:00	2026-03-13 00:25:00-07:00	22961.0
...	...	...	...	...
224	2026-03-13 18:40:00-07:00	2026-03-13 18:40:00-07:00	2026-03-13 18:45:00-07:00	28025.0
225	2026-03-13 18:45:00-07:00	2026-03-13 18:45:00-07:00	2026-03-13 18:50:00-07:00	28096.0
226	2026-03-13 18:50:00-07:00	2026-03-13 18:50:00-07:00	2026-03-13 18:55:00-07:00	28206.0
227	2026-03-13 18:55:00-07:00	2026-03-13 18:55:00-07:00	2026-03-13 19:00:00-07:00	28099.0
228	2026-03-13 19:00:00-07:00	2026-03-13 19:00:00-07:00	2026-03-13 19:05:00-07:00	28150.0

229 rows × 4 columns

Load Forecast

Another dataset we can query is the load forecast

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

2026-03-14 02:04:32 - INFO - Requesting http://mis.nyiso.com/public/csv/isolf/20260313isolf.csv

/home/docs/checkouts/readthedocs.org/user_builds/isodata/checkouts/v0.35.0/gridstatus/nyiso.py:1226: FutureWarning: Parsed string "03/12/26 07:20 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	2026-03-13 00:00:00-04:00	2026-03-13 00:00:00-04:00	2026-03-13 01:00:00-04:00	2026-03-12 07:20:00-04:00	15328
1	2026-03-13 01:00:00-04:00	2026-03-13 01:00:00-04:00	2026-03-13 02:00:00-04:00	2026-03-12 07:20:00-04:00	14875
2	2026-03-13 02:00:00-04:00	2026-03-13 02:00:00-04:00	2026-03-13 03:00:00-04:00	2026-03-12 07:20:00-04:00	14606
3	2026-03-13 03:00:00-04:00	2026-03-13 03:00:00-04:00	2026-03-13 04:00:00-04:00	2026-03-12 07:20:00-04:00	14506
4	2026-03-13 04:00:00-04:00	2026-03-13 04:00:00-04:00	2026-03-13 05:00:00-04:00	2026-03-12 07:20:00-04:00	14700
...	...	...	...	...	...
139	2026-03-18 19:00:00-04:00	2026-03-18 19:00:00-04:00	2026-03-18 20:00:00-04:00	2026-03-12 07:20:00-04:00	19373
140	2026-03-18 20:00:00-04:00	2026-03-18 20:00:00-04:00	2026-03-18 21:00:00-04:00	2026-03-12 07:20:00-04:00	19211
141	2026-03-18 21:00:00-04:00	2026-03-18 21:00:00-04:00	2026-03-18 22:00:00-04:00	2026-03-12 07:20:00-04:00	18591
142	2026-03-18 22:00:00-04:00	2026-03-18 22:00:00-04:00	2026-03-18 23:00:00-04:00	2026-03-12 07:20:00-04:00	17670
143	2026-03-18 23:00:00-04:00	2026-03-18 23:00:00-04:00	2026-03-19 00:00:00-04:00	2026-03-12 07:20:00-04:00	16747

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")

2026-03-14 02:04:32 - INFO - Fetching URL: https://www.caiso.com/outlook/history/20200101/demand.csv?_=1773453872

	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