Hub Price Pull Example
Imports
[1]:
import time
import os
import pandas as pd
from tyba_client.client import Client, Market
PAT = os.environ["TYBA_PAT"]
client = Client(PAT)
# Relevant API services
services = client.services
lmp = services.lmp
ancillary = services.ancillary
User inputs
[2]:
iso = 'CAISO'
start_year = 2024
end_year = 2024
Energy Pull - define ISO/RTO
[3]:
services.get_all_isos().json()
[3]:
['PJMISO', 'CAISO', 'NEISO', 'NYISO', 'ERCOT', 'SPPISO', 'MISO']
Pull All Nodes for the ISO/RTO
[4]:
# Get all nodes for RTO
price_nodes = lmp.get_all_nodes(iso=iso).json()
node_directory = pd.DataFrame(price_nodes)
print(node_directory.head()) # Display the first few rows to check the content
da_end_year rt_end_year rt_start_year name \
0 2025.0 2025 2023 CLAP_WWRSR1-APND
1 2025.0 2025 2015 ELCENTRO_2_N001:IVLY2
2 2025.0 2025 2022 KERMAC_LNODEITED
3 2025.0 2025 2021 VIEWLND_LNODE787
4 2025.0 2025 2010 BUTTE_1_N101
id da_start_year zone type substation
0 10017280350 2023.0 SDGE GENERATOR NaN
1 10003899356 2015.0 SPTIE
2 10016667720 2022.0 PNM LOAD
3 10016243530 2021.0 SCL LOAD VIEWLAND-HOFFMAN
4 20000000353 2010.0 PGE LOAD BUTTE
[5]:
node_directory
[5]:
| da_end_year | rt_end_year | rt_start_year | name | id | da_start_year | zone | type | substation | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 2025.0 | 2025 | 2023 | CLAP_WWRSR1-APND | 10017280350 | 2023.0 | SDGE | GENERATOR | NaN |
| 1 | 2025.0 | 2025 | 2015 | ELCENTRO_2_N001:IVLY2 | 10003899356 | 2015.0 | SPTIE | ||
| 2 | 2025.0 | 2025 | 2022 | KERMAC_LNODEITED | 10016667720 | 2022.0 | PNM | LOAD | |
| 3 | 2025.0 | 2025 | 2021 | VIEWLND_LNODE787 | 10016243530 | 2021.0 | SCL | LOAD | VIEWLAND-HOFFMAN |
| 4 | 2025.0 | 2025 | 2010 | BUTTE_1_N101 | 20000000353 | 2010.0 | PGE | LOAD | BUTTE |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 22178 | 2025.0 | 2025 | 2022 | HUGHSON_LNODEDR8 | 10016664459 | 2022.0 | TIDC | LOAD | HUGHSON |
| 22179 | 2025.0 | 2025 | 2022 | COTTONWD_1_COTTONWODLNODE | 10016930051 | 2022.0 | AVA | LOAD | NaN |
| 22180 | 2025.0 | 2025 | 2010 | SPICAMIN_1_N001 | 20000003437 | 2010.0 | PGE | LOAD | SPICAMIN |
| 22181 | 2025.0 | 2025 | 2023 | FREDONIA_LNODELD | 10017110123 | 2023.0 | WALC | LOAD | NaN |
| 22182 | 2025.0 | 2025 | 2016 | VENWIND_1_N104 | 10002898249 | 2016.0 | SCE | GENERATOR |
22183 rows × 9 columns
[6]:
node_directory.type.unique()
[6]:
array(['GENERATOR', 'SPTIE', 'LOAD', 'N/A', 'INTERTIE', 'AGGREGATE',
'HUB'], dtype=object)
Filter down to zonal LMPs
[12]:
multiple_nodes = node_directory[node_directory['type'] == 'HUB']
[13]:
multiple_nodes
[13]:
| da_end_year | rt_end_year | rt_start_year | name | id | da_start_year | zone | type | substation | |
|---|---|---|---|---|---|---|---|---|---|
| 1651 | 2024.0 | 2024 | 2022 | TH_NP15_GEN_OFFPEAK-APND | 10000802793 | 2022.0 | N/A | HUB | |
| 2373 | 2025.0 | 2025 | 2010 | TH_NP15_GEN-APND | 20000004677 | 2010.0 | N/A | HUB | |
| 3543 | 2024.0 | 2024 | 2022 | TH_ZP26_GEN_ONPEAK-APND | 10000802780 | 2022.0 | N/A | HUB | |
| 7511 | 2024.0 | 2024 | 2022 | TH_SP15_GEN_OFFPEAK-APND | 10000802794 | 2022.0 | N/A | HUB | |
| 9244 | 2025.0 | 2025 | 2010 | TH_ZP26_GEN-APND | 20000004670 | 2010.0 | N/A | HUB | |
| 15438 | 2025.0 | 2025 | 2010 | TH_SP15_GEN-APND | 20000004682 | 2010.0 | N/A | HUB | |
| 17254 | 2024.0 | 2024 | 2022 | TH_NP15_GEN_ONPEAK-APND | 10000802781 | 2022.0 | N/A | HUB | |
| 17780 | 2024.0 | 2024 | 2022 | TH_SP15_GEN_ONPEAK-APND | 10000802779 | 2022.0 | N/A | HUB | |
| 18085 | 2024.0 | 2024 | 2022 | TH_ZP26_GEN_OFFPEAK-APND | 10000802792 | 2022.0 | N/A | HUB |
Filter out nodes with insufficient data range
[14]:
# Filter out nodes with insufficient data range
valid_nodes = multiple_nodes[
(multiple_nodes['rt_start_year'] <= start_year) &
(multiple_nodes['rt_end_year'] >= end_year) &
(multiple_nodes['da_start_year'] <= start_year) &
(multiple_nodes['da_end_year'] >= end_year)
]
print(valid_nodes)
da_end_year rt_end_year rt_start_year name \
1651 2024.0 2024 2022 TH_NP15_GEN_OFFPEAK-APND
2373 2025.0 2025 2010 TH_NP15_GEN-APND
3543 2024.0 2024 2022 TH_ZP26_GEN_ONPEAK-APND
7511 2024.0 2024 2022 TH_SP15_GEN_OFFPEAK-APND
9244 2025.0 2025 2010 TH_ZP26_GEN-APND
15438 2025.0 2025 2010 TH_SP15_GEN-APND
17254 2024.0 2024 2022 TH_NP15_GEN_ONPEAK-APND
17780 2024.0 2024 2022 TH_SP15_GEN_ONPEAK-APND
18085 2024.0 2024 2022 TH_ZP26_GEN_OFFPEAK-APND
id da_start_year zone type substation
1651 10000802793 2022.0 N/A HUB
2373 20000004677 2010.0 N/A HUB
3543 10000802780 2022.0 N/A HUB
7511 10000802794 2022.0 N/A HUB
9244 20000004670 2010.0 N/A HUB
15438 20000004682 2010.0 N/A HUB
17254 10000802781 2022.0 N/A HUB
17780 10000802779 2022.0 N/A HUB
18085 10000802792 2022.0 N/A HUB
Helper Functions
[15]:
def reformat_df(result):
df = pd.concat(
{node: pd.DataFrame(results['prices'], index=pd.to_datetime(results['datetimes'])) for node, results in result.items()},
axis=1
)
return df.droplevel(axis=1, level=1).tz_localize(None)
def lmp_pull(directory, start_year, end_year, markets):
import pandas as pd
nodemap = dict(zip(directory.id, directory.name)) # Converts the nodes DataFrame into a dictionary
market_options = {'RT': Market.RT, 'DA': Market.DA} # Maps market keys to their corresponding Market options
results = {}
# Split node IDs into batches of 8
node_ids = list(nodemap.keys())
batches = [node_ids[i:i + 8] for i in range(0, len(node_ids), 8)]
for market in markets:
print(f"Pulling data for market: {market_options[market]}")
# Temporary list to store batch results
batch_results = []
for batch in batches:
print(f"Processing batch: {batch}")
prices = lmp.get_prices(
node_ids=batch, # Pass batch of 8 node IDs
market=market_options[market], # Specify the market
start_year=int(start_year),
end_year=int(end_year),
).json()
# Reformat and rename columns with the node names
batch_df = reformat_df(prices).rename(nodemap, axis=1)
batch_results.append(batch_df)
# Concatenate all batch results for this market
results[market] = pd.concat(batch_results, axis=0)
# Combine all markets into a single DataFrame with a MultiIndex
return pd.concat(results, axis=1)
Pull prices for valid HUBs
[16]:
lmps = lmp_pull(valid_nodes, start_year, end_year, ['RT', 'DA'])
Pulling data for market: realtime
Processing batch: ['10000802793', '20000004677', '10000802780', '10000802794', '20000004670', '20000004682', '10000802781', '10000802779']
Processing batch: ['10000802792']
Pulling data for market: dayahead
Processing batch: ['10000802793', '20000004677', '10000802780', '10000802794', '20000004670', '20000004682', '10000802781', '10000802779']
Processing batch: ['10000802792']
[17]:
lmps.head()
[17]:
| RT | DA | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TH_NP15_GEN_OFFPEAK-APND | TH_NP15_GEN-APND | TH_ZP26_GEN_ONPEAK-APND | TH_SP15_GEN_OFFPEAK-APND | TH_ZP26_GEN-APND | TH_SP15_GEN-APND | TH_NP15_GEN_ONPEAK-APND | TH_SP15_GEN_ONPEAK-APND | TH_ZP26_GEN_OFFPEAK-APND | TH_NP15_GEN_OFFPEAK-APND | TH_NP15_GEN-APND | TH_ZP26_GEN_ONPEAK-APND | TH_SP15_GEN_OFFPEAK-APND | TH_ZP26_GEN-APND | TH_SP15_GEN-APND | TH_NP15_GEN_ONPEAK-APND | TH_SP15_GEN_ONPEAK-APND | TH_ZP26_GEN_OFFPEAK-APND | |
| 2024-01-01 00:00:00 | 46.0780 | 46.0780 | 0.0 | 44.5333 | 44.9106 | 44.5333 | 0.0 | 0.0 | NaN | 46.75774 | 46.75774 | 0.0 | 46.08000 | 45.84287 | 46.08000 | 0.0 | 0.0 | NaN |
| 2024-01-01 01:00:00 | 46.4224 | 46.4224 | 0.0 | 44.1787 | 44.9486 | 44.1787 | 0.0 | 0.0 | NaN | 46.04052 | 46.04052 | 0.0 | 45.32571 | 45.16708 | 45.32571 | 0.0 | 0.0 | NaN |
| 2024-01-01 02:00:00 | 47.3373 | 47.3373 | 0.0 | 45.3447 | 45.9565 | 45.3447 | 0.0 | 0.0 | NaN | 45.13062 | 45.13062 | 0.0 | 44.91129 | 44.51086 | 44.91129 | 0.0 | 0.0 | NaN |
| 2024-01-01 03:00:00 | 46.9617 | 46.9617 | 0.0 | 44.7927 | 45.5005 | 44.7927 | 0.0 | 0.0 | NaN | 44.93121 | 44.93121 | 0.0 | 44.56314 | 44.22495 | 44.56314 | 0.0 | 0.0 | NaN |
| 2024-01-01 04:00:00 | 47.1285 | 47.1285 | 0.0 | 44.9711 | 45.6992 | 44.9711 | 0.0 | 0.0 | NaN | 44.55260 | 44.55260 | 0.0 | 44.17126 | 43.81786 | 44.17126 | 0.0 | 0.0 | NaN |
[16]:
lmps.to_csv(f"Output/{iso}_hub_energyprices_{start_year}_{end_year}.csv")