Ancillary Pull Example

Imports

[1]:

import time
import os
import pandas as pd
from tyba_client.client import Client, Market, AncillaryService

PAT = os.environ["TYBA_PAT"]
client = Client(PAT)
# client = Client('Your PAT') #Insert your PAT here

# Relevant Data Classes
services = client.services
lmp = services.lmp
ancillary = services.ancillary

Pricing Regions

[2]:

as_regions = ancillary.get_pricing_regions(
    iso="CAISO",
    service=AncillaryService.REGULATION_UP,
    market=Market.DA).json()
as_regions = pd.DataFrame(as_regions)
as_regions

[2]:

	region	start_year	end_year
0	Pacific Northwest - SP15	2010	2025
1	WAPA	2010	2025
2	WAPA	2010	2025
3	WAPA	2010	2025
4	ZP26	2010	2025
...	...	...	...
219	ZP26	2010	2025
220	Mexico	2010	2025
221	SMUD	2010	2025
222	Sutter	2010	2025
223	NP15	2010	2025

224 rows × 3 columns

Helper Functions

[3]:

def as_pull(services, regions, start_year, end_year):
    all_prices = {}
    for region in regions:
        for name, service in services.items():
            print(region,name)
            as_prices = ancillary.get_prices(
                iso="CAISO",
                service=service,
                market=Market.DA,
                region=region,
                start_year=start_year,
                end_year=end_year,
            ).json()
            tdom = pd.to_datetime(as_prices['datetimes']).tz_localize(None)
            all_prices[(region,name)] = pd.DataFrame(as_prices['prices'],index=tdom,columns=[name])
    return pd.concat(all_prices,axis=1).droplevel(1,axis=1)

Pull All Services & Single Region

[4]:

services = {
    'reg_up':AncillaryService.REGULATION_UP,
    'reg_down':AncillaryService.REGULATION_DOWN,
    'reserves':AncillaryService.RESERVES,
}
regions = [
    'SP15',
]

[5]:

as_prices = as_pull(services,regions,2019,2023)
as_prices = as_prices.droplevel(0,axis=1)

SP15 reg_up
SP15 reg_down
SP15 reserves

[6]:

as_prices.head()

[6]:

	reg_up	reg_down	reserves
2019-01-01 00:00:00	8.17	15.29	1.00
2019-01-01 01:00:00	1.66	1.01	1.00
2019-01-01 02:00:00	1.59	1.01	0.25
2019-01-01 03:00:00	1.29	1.01	0.25
2019-01-01 04:00:00	7.42	1.39	0.25

[24]:

as_prices.to_csv('example_sp15_ancillary_prices.csv')

Pull All Services & Multiple Regions

[7]:

services = {
    'reg_up':AncillaryService.REGULATION_UP,
    'reg_down':AncillaryService.REGULATION_DOWN,
    'reserves':AncillaryService.RESERVES,
}
regions = ['SP15','NP15']

[8]:

as_prices2 = as_pull(services,regions,2021,2021)
as_prices2

SP15 reg_up
SP15 reg_down
SP15 reserves
NP15 reg_up
NP15 reg_down
NP15 reserves

[8]:

	SP15			NP15
	reg_up	reg_down	reserves	reg_up	reg_down	reserves
2021-01-01 00:00:00	1.63	11.88	1.63	4.23	3.00	1.25
2021-01-01 01:00:00	1.63	13.93	1.63	4.23	2.70	1.25
2021-01-01 02:00:00	1.63	10.58	1.63	1.65	3.50	1.25
2021-01-01 03:00:00	1.63	10.66	1.63	1.65	3.90	1.25
2021-01-01 04:00:00	1.63	10.15	1.63	1.66	3.30	1.25
...	...	...	...	...	...	...
2021-12-31 19:00:00	6.57	5.00	6.57	4.97	0.99	6.57
2021-12-31 20:00:00	6.46	5.13	6.46	6.46	4.87	6.46
2021-12-31 21:00:00	4.75	0.21	3.00	4.75	2.26	3.00
2021-12-31 22:00:00	5.15	4.00	5.00	0.40	1.99	5.00
2021-12-31 23:00:00	7.85	5.00	4.42	7.85	5.00	4.42

8760 rows × 6 columns

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