Get ERCOT Ancillary Services Prices

import gridstatus
import pandas as pd
import plotly.express as px

iso = gridstatus.Ercot()

Get Ancillary Services Prices

Note: the time specified is the delivery time of the ancillary service

df = iso.get_as_prices("today")
df.head(5)

	Time	Market	Non-Spinning Reserves	Regulation Down	Regulation Up	Responsive Reserves
0	2022-11-29 00:00:00-06:00	DAM	0.75	4.00	3.19	2.39
1	2022-11-29 01:00:00-06:00	DAM	0.55	3.69	4.69	2.69
2	2022-11-29 02:00:00-06:00	DAM	0.48	2.41	3.19	2.39
3	2022-11-29 03:00:00-06:00	DAM	0.69	3.50	4.69	2.69
4	2022-11-29 04:00:00-06:00	DAM	0.50	4.00	5.98	2.69

Get Historical AS Prices

ERCOT publishes the last 30 days of prices.

thirty_days_ago = pd.Timestamp.now().date() - pd.Timedelta(days=30)
df = iso.get_as_prices(start=thirty_days_ago, end="today")
df

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	Time	Market	Non-Spinning Reserves	Regulation Down	Regulation Up	Responsive Reserves
0	2022-10-30 00:00:00-05:00	DAM	2.85	2.34	3.89	1.89
1	2022-10-30 01:00:00-05:00	DAM	2.72	1.61	3.59	1.59
2	2022-10-30 02:00:00-05:00	DAM	2.01	0.68	2.81	1.39
3	2022-10-30 03:00:00-05:00	DAM	2.00	1.41	3.39	1.39
4	2022-10-30 04:00:00-05:00	DAM	2.00	1.69	3.67	1.49
...	...	...	...	...	...	...
740	2022-11-29 19:00:00-06:00	DAM	1.74	2.69	2.31	2.00
741	2022-11-29 20:00:00-06:00	DAM	1.09	2.49	2.31	2.00
742	2022-11-29 21:00:00-06:00	DAM	1.04	3.49	3.49	2.39
743	2022-11-29 22:00:00-06:00	DAM	1.00	4.00	5.98	3.00
744	2022-11-29 23:00:00-06:00	DAM	1.04	5.00	5.00	3.00

745 rows × 6 columns

data = df.set_index("Time").drop(columns=["Market"])
data.columns.name = "AS Type"
fig = px.line(
    data,
    x=data.index,
    y=data.columns,
    title="Ancillary Services Prices for ERCOT, last 30 days",
)
fig.update_yaxes(title="Market Clearing Price")
fig.show("svg", width=1200, height=600)