Ctrl+K

Load, Fuel Mix, and LMP Data

Load, Fuel Mix, and LMP Data#

This notebook walk through how to use gridstatus to access to the data availabe on OASIS.

While we will be using CAISO in this example, most of this API will also work with all other ISOs.

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

caiso = gridstatus.CAISO()

Historical Fuel Mix#

start = pd.Timestamp("April 10, 2018").normalize()
end = pd.Timestamp.now().normalize()
mix_df = caiso.get_fuel_mix(start, end=end, verbose=False)

100%|██████████| 1696/1696 [15:48<00:00,  1.79it/s]

hourly_mix = mix_df.set_index("Time").resample("H").mean()
monthly_mix = hourly_mix.resample("MS").sum().reset_index()[1:-1]
top_sources = (
    monthly_mix[monthly_mix.columns[1:]]
    .sum()
    .sort_values(ascending=False)
    .index.tolist()
)
fig = px.bar(monthly_mix, x="Time", y=top_sources, title="Fuel Mix by Month - CAISO")
fig.show("svg", width=1200, height=600)
../../_images/3ff84db83153fe5301b9d0a02ce9c3e10a0e4f9c06bfe2f1404a8cbf728627fe.svg

Historical Load#

start = pd.Timestamp("April 10, 2018").normalize()
end = pd.Timestamp.now().normalize()
load_df = caiso.get_load(start, end=end)

100%|██████████| 1696/1696 [13:27<00:00,  2.10it/s]

hourly_load = load_df.set_index("Time").resample("H").mean()
monthly_load = hourly_load.resample("MS").sum().reset_index()[1:-1]
fig = px.line(monthly_load, x="Time", y="Load", title="Monthly Load - CAISO")
fig.show("svg", width=1200, height=600)
../../_images/3a18f6ac36615950c2c08b8ffcbd6d015fbcf63ba5bfd234725bb7dad95ed439.svg

Historical Locational Marginal Pricing (LMP)#

You can supply whatever nodes or market you’d like, but for now let’s download data for 3 trading hubs in the Day Head Hourly Market.

Note: CAISO only provides last 39 months of data.

start = pd.Timestamp("Jan 1, 2020").normalize()
end = pd.Timestamp.now().normalize()

locations = ["TH_NP15_GEN-APND", "TH_SP15_GEN-APND", "TH_ZP26_GEN-APND"]

lmp_df = caiso.get_lmp(
    start=start, end=end, market="DAY_AHEAD_HOURLY", locations=locations, sleep=5
)

100%|██████████| 35/35 [22:04<00:00, 37.85s/it]

negative_lmps = lmp_df[lmp_df["LMP"] < 0].set_index("Time")
negative_per_month = (
    negative_lmps.groupby("Location").resample("MS")["LMP"].count().reset_index()
)
fig = px.bar(
    negative_per_month,
    x="Time",
    y="LMP",
    title="Negative LMPs per Month - CAISO",
    color="Location",
)
fig.update_yaxes(title="Number of Negative LMPs")
fig.show("svg", width=1200, height=600)
../../_images/9b9be3d7c2b8fc3c4adc1233244c40ca38535aaf81f5fe2a3a30fabfd83efbd9.svg

Gas Prices#

CAISO also publish information about gas prices and greenhouse gas allownces that we will download

start = start = pd.Timestamp("Jan 01, 2022").normalize()
end = pd.Timestamp.now().normalize()

gas_price_df = caiso.get_gas_prices(start=start, end=end, fuel_region_id="FRPGE2GHG")

100%|██████████| 11/11 [01:03<00:00,  5.78s/it]

start = start = pd.Timestamp("Jan 01, 2022").normalize()
end = pd.Timestamp.now().normalize()

ghg_df = caiso.get_ghg_allowance(start=start, end=end)

100%|██████████| 11/11 [01:03<00:00,  5.75s/it]
Contents