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
7	Independent Electricity System Operator	ieso	IESO

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	2024-05-08 00:00:00-07:00	2024-05-08 00:00:00-07:00	2024-05-08 00:05:00-07:00	118	5385	768	242	153	295	0	1144	3532	4530	94	6221	0
1	2024-05-08 00:05:00-07:00	2024-05-08 00:05:00-07:00	2024-05-08 00:10:00-07:00	123	5367	768	243	151	294	0	1144	3209	4513	647	6229	0
2	2024-05-08 00:10:00-07:00	2024-05-08 00:10:00-07:00	2024-05-08 00:15:00-07:00	134	5344	769	242	152	294	0	1144	3077	4461	1099	6085	0
3	2024-05-08 00:15:00-07:00	2024-05-08 00:15:00-07:00	2024-05-08 00:20:00-07:00	134	5388	769	241	153	293	0	1144	3119	4453	1190	5878	0
4	2024-05-08 00:20:00-07:00	2024-05-08 00:20:00-07:00	2024-05-08 00:25:00-07:00	131	5398	769	242	154	293	0	1144	3090	4433	1100	5960	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
76	2024-05-08 06:20:00-07:00	2024-05-08 06:20:00-07:00	2024-05-08 06:25:00-07:00	1173	2431	773	241	152	296	0	1141	5419	3952	1954	4841	0
77	2024-05-08 06:25:00-07:00	2024-05-08 06:25:00-07:00	2024-05-08 06:30:00-07:00	1586	2352	773	239	152	297	0	1142	5415	3939	1589	5012	0
78	2024-05-08 06:30:00-07:00	2024-05-08 06:30:00-07:00	2024-05-08 06:35:00-07:00	2071	2276	773	240	153	298	0	1142	5349	3929	1361	5029	0
79	2024-05-08 06:35:00-07:00	2024-05-08 06:35:00-07:00	2024-05-08 06:40:00-07:00	2633	2232	773	240	152	297	0	1142	5179	3889	1070	5109	0
80	2024-05-08 06:40:00-07:00	2024-05-08 06:40:00-07:00	2024-05-08 06:45:00-07:00	3253	2225	773	241	152	297	0	1142	5111	3875	681	5085	0

81 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	2024-05-08 00:00:00-07:00	2024-05-08 00:00:00-07:00	2024-05-08 00:05:00-07:00	21583.0
1	2024-05-08 00:05:00-07:00	2024-05-08 00:05:00-07:00	2024-05-08 00:10:00-07:00	21739.0
2	2024-05-08 00:10:00-07:00	2024-05-08 00:10:00-07:00	2024-05-08 00:15:00-07:00	21866.0
3	2024-05-08 00:15:00-07:00	2024-05-08 00:15:00-07:00	2024-05-08 00:20:00-07:00	21854.0
4	2024-05-08 00:20:00-07:00	2024-05-08 00:20:00-07:00	2024-05-08 00:25:00-07:00	21803.0
...	...	...	...	...
76	2024-05-08 06:20:00-07:00	2024-05-08 06:20:00-07:00	2024-05-08 06:25:00-07:00	22214.0
77	2024-05-08 06:25:00-07:00	2024-05-08 06:25:00-07:00	2024-05-08 06:30:00-07:00	22258.0
78	2024-05-08 06:30:00-07:00	2024-05-08 06:30:00-07:00	2024-05-08 06:35:00-07:00	22320.0
79	2024-05-08 06:35:00-07:00	2024-05-08 06:35:00-07:00	2024-05-08 06:40:00-07:00	22386.0
80	2024-05-08 06:40:00-07:00	2024-05-08 06:40:00-07:00	2024-05-08 06:45:00-07:00	22493.0

81 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	2024-05-08 00:00:00-04:00	2024-05-08 00:00:00-04:00	2024-05-08 01:00:00-04:00	2024-05-08 00:00:00-04:00	13256
1	2024-05-08 01:00:00-04:00	2024-05-08 01:00:00-04:00	2024-05-08 02:00:00-04:00	2024-05-08 00:00:00-04:00	12774
2	2024-05-08 02:00:00-04:00	2024-05-08 02:00:00-04:00	2024-05-08 03:00:00-04:00	2024-05-08 00:00:00-04:00	12486
3	2024-05-08 03:00:00-04:00	2024-05-08 03:00:00-04:00	2024-05-08 04:00:00-04:00	2024-05-08 00:00:00-04:00	12360
4	2024-05-08 04:00:00-04:00	2024-05-08 04:00:00-04:00	2024-05-08 05:00:00-04:00	2024-05-08 00:00:00-04:00	12500
...	...	...	...	...	...
139	2024-05-13 19:00:00-04:00	2024-05-13 19:00:00-04:00	2024-05-13 20:00:00-04:00	2024-05-08 00:00:00-04:00	16295
140	2024-05-13 20:00:00-04:00	2024-05-13 20:00:00-04:00	2024-05-13 21:00:00-04:00	2024-05-08 00:00:00-04:00	16339
141	2024-05-13 21:00:00-04:00	2024-05-13 21:00:00-04:00	2024-05-13 22:00:00-04:00	2024-05-08 00:00:00-04:00	15749
142	2024-05-13 22:00:00-04:00	2024-05-13 22:00:00-04:00	2024-05-13 23:00:00-04:00	2024-05-08 00:00:00-04:00	14652
143	2024-05-13 23:00:00-04:00	2024-05-13 23:00:00-04:00	2024-05-14 00:00:00-04:00	2024-05-08 00:00:00-04:00	13578

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

• Installation
• Available Methods
  • CAISO
  • Ercot
  • ISONE
  • MISO
  • NYISO
  • PJM
  • SPP
• LMP Pricing Data
  • Support
  • How to use
• 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