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	2023-03-24 00:00:00-07:00	2023-03-24 00:00:00-07:00	2023-03-24 00:05:00-07:00	-41	5529	931	255	218	270	3	2271	5573	3498	-184	6005	0
1	2023-03-24 00:05:00-07:00	2023-03-24 00:05:00-07:00	2023-03-24 00:10:00-07:00	-41	5490	931	252	215	271	4	2271	5567	3584	-66	5928	0
2	2023-03-24 00:10:00-07:00	2023-03-24 00:10:00-07:00	2023-03-24 00:15:00-07:00	-41	5491	931	250	213	271	3	2271	5602	3587	-62	5851	0
3	2023-03-24 00:15:00-07:00	2023-03-24 00:15:00-07:00	2023-03-24 00:20:00-07:00	-41	5478	932	252	213	271	3	2271	5733	3575	-132	5736	0
4	2023-03-24 00:20:00-07:00	2023-03-24 00:20:00-07:00	2023-03-24 00:25:00-07:00	-41	5446	931	251	212	271	4	2270	5694	3512	-72	5753	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
164	2023-03-24 13:40:00-07:00	2023-03-24 13:40:00-07:00	2023-03-24 13:45:00-07:00	6865	3188	927	242	212	313	2	2272	4393	2079	-1305	1107	0
165	2023-03-24 13:45:00-07:00	2023-03-24 13:45:00-07:00	2023-03-24 13:50:00-07:00	6823	3134	927	242	212	313	2	2273	4192	2076	-1193	1149	0
166	2023-03-24 13:50:00-07:00	2023-03-24 13:50:00-07:00	2023-03-24 13:55:00-07:00	6850	3128	928	243	212	313	2	2273	4006	2076	-875	912	0
167	2023-03-24 13:55:00-07:00	2023-03-24 13:55:00-07:00	2023-03-24 14:00:00-07:00	6838	3168	928	247	212	298	2	2272	4041	2093	-966	948	0
168	2023-03-24 14:00:00-07:00	2023-03-24 14:00:00-07:00	2023-03-24 14:05:00-07:00	6739	3220	926	250	212	282	2	2272	4028	2127	-956	971	0

169 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	2023-03-24 00:00:00-07:00	2023-03-24 00:00:00-07:00	2023-03-24 00:05:00-07:00	22987.0
1	2023-03-24 00:05:00-07:00	2023-03-24 00:05:00-07:00	2023-03-24 00:10:00-07:00	23058.0
2	2023-03-24 00:10:00-07:00	2023-03-24 00:10:00-07:00	2023-03-24 00:15:00-07:00	23070.0
3	2023-03-24 00:15:00-07:00	2023-03-24 00:15:00-07:00	2023-03-24 00:20:00-07:00	22985.0
4	2023-03-24 00:20:00-07:00	2023-03-24 00:20:00-07:00	2023-03-24 00:25:00-07:00	22945.0
...	...	...	...	...
164	2023-03-24 13:40:00-07:00	2023-03-24 13:40:00-07:00	2023-03-24 13:45:00-07:00	16373.0
165	2023-03-24 13:45:00-07:00	2023-03-24 13:45:00-07:00	2023-03-24 13:50:00-07:00	16379.0
166	2023-03-24 13:50:00-07:00	2023-03-24 13:50:00-07:00	2023-03-24 13:55:00-07:00	16390.0
167	2023-03-24 13:55:00-07:00	2023-03-24 13:55:00-07:00	2023-03-24 14:00:00-07:00	16360.0
168	2023-03-24 14:00:00-07:00	2023-03-24 14:00:00-07:00	2023-03-24 14:05:00-07:00	16356.0

169 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	2023-03-24 00:00:00-04:00	2023-03-24 00:00:00-04:00	2023-03-24 01:00:00-04:00	2023-03-24 00:00:00-04:00	13521
1	2023-03-24 01:00:00-04:00	2023-03-24 01:00:00-04:00	2023-03-24 02:00:00-04:00	2023-03-24 00:00:00-04:00	13101
2	2023-03-24 02:00:00-04:00	2023-03-24 02:00:00-04:00	2023-03-24 03:00:00-04:00	2023-03-24 00:00:00-04:00	12872
3	2023-03-24 03:00:00-04:00	2023-03-24 03:00:00-04:00	2023-03-24 04:00:00-04:00	2023-03-24 00:00:00-04:00	12802
4	2023-03-24 04:00:00-04:00	2023-03-24 04:00:00-04:00	2023-03-24 05:00:00-04:00	2023-03-24 00:00:00-04:00	13029
...	...	...	...	...	...
139	2023-03-29 19:00:00-04:00	2023-03-29 19:00:00-04:00	2023-03-29 20:00:00-04:00	2023-03-24 00:00:00-04:00	17318
140	2023-03-29 20:00:00-04:00	2023-03-29 20:00:00-04:00	2023-03-29 21:00:00-04:00	2023-03-24 00:00:00-04:00	17202
141	2023-03-29 21:00:00-04:00	2023-03-29 21:00:00-04:00	2023-03-29 22:00:00-04:00	2023-03-24 00:00:00-04:00	16602
142	2023-03-29 22:00:00-04:00	2023-03-29 22:00:00-04:00	2023-03-29 23:00:00-04:00	2023-03-24 00:00:00-04:00	15629
143	2023-03-29 23:00:00-04:00	2023-03-29 23:00:00-04:00	2023-03-30 00:00:00-04:00	2023-03-24 00:00:00-04:00	14731

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
      • Monthly Curtailment
      • Average Hourly Curtailment
  • Heatmap of Day Ahead LMPs
    • Get LMPs
    • Make Heatmap
• 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
• 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
• 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