Fuel Mix Over Time

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

iso = gridstatus.NYISO()

Get Fuel MIx Data

df = iso.get_fuel_mix(start="Nov 1, 2017", end="Dec 1, 2022")
df

100%|██████████| 61/61 [01:23<00:00,  1.37s/it]

	Time	Dual Fuel	Hydro	Natural Gas	Nuclear	Other Fossil Fuels	Other Renewables	Wind
0	2017-11-01 00:05:00-04:00	1815.0	3291.0	1594.0	5402.0	0.0	290.0	261.0
1	2017-11-01 00:10:00-04:00	1705.0	3185.0	1541.0	5401.0	0.0	285.0	259.0
2	2017-11-01 00:15:00-04:00	1683.0	3049.0	1498.0	5402.0	0.0	286.0	286.0
3	2017-11-01 00:20:00-04:00	1612.0	3022.0	1488.0	5402.0	0.0	285.0	313.0
4	2017-11-01 00:25:00-04:00	1561.0	2935.0	1488.0	5402.0	0.0	287.0	296.0
...	...	...	...	...	...	...	...	...
543073	2022-11-30 23:40:00-05:00	2454.0	2434.0	3673.0	3342.0	0.0	253.0	1651.0
543074	2022-11-30 23:45:00-05:00	2447.0	2352.0	3628.0	3342.0	0.0	252.0	1684.0
543075	2022-11-30 23:50:00-05:00	2420.0	2309.0	3566.0	3342.0	0.0	253.0	1715.0
543076	2022-11-30 23:55:00-05:00	2342.0	2256.0	3538.0	3342.0	0.0	252.0	1728.0
543077	2022-12-01 00:00:00-05:00	2253.0	2283.0	3557.0	3342.0	0.0	251.0	1673.0

543078 rows × 8 columns

The call above retried fuel mix data in 5 minute increments. Let’s resample the data to monthly

# resample to month start (MS) frequency
fuel_mix_hourly = df.set_index("Time").resample("H").mean()
data = fuel_mix_hourly.resample("MS").sum()[:-1].reset_index()
data.head(10)

	Time	Dual Fuel	Hydro	Natural Gas	Nuclear	Other Fossil Fuels	Other Renewables	Wind
0	2017-11-01 00:00:00-04:00	1.573968e+06	2.531928e+06	1.820341e+06	3.691667e+06	34647.991342	204631.604057	446180.586391
1	2017-12-01 00:00:00-05:00	2.180086e+06	2.510918e+06	2.165724e+06	3.663322e+06	115217.033671	200007.652359	398377.363705
2	2018-01-01 00:00:00-05:00	2.622223e+06	2.282970e+06	2.188042e+06	4.028542e+06	296391.763349	176729.788301	507351.954197
3	2018-02-01 00:00:00-05:00	1.279852e+06	2.235497e+06	1.910894e+06	3.315114e+06	23610.114286	166662.434649	443330.772107
4	2018-03-01 00:00:00-05:00	1.624682e+06	2.709626e+06	1.902294e+06	3.573660e+06	123399.206231	193060.572636	344040.517233
5	2018-04-01 00:00:00-04:00	1.628499e+06	2.512376e+06	2.029388e+06	2.870185e+06	24508.221605	194875.623428	374113.947364
6	2018-05-01 00:00:00-04:00	2.033018e+06	2.567769e+06	1.852400e+06	3.419482e+06	28013.870976	187603.310749	295070.172210
7	2018-06-01 00:00:00-04:00	2.463700e+06	2.232738e+06	2.109071e+06	3.800927e+06	16820.590832	192735.332003	214476.829655
8	2018-07-01 00:00:00-04:00	3.815281e+06	2.273509e+06	2.957611e+06	3.884576e+06	159995.689560	200652.408333	205469.735165
9	2018-08-01 00:00:00-04:00	4.103557e+06	2.227516e+06	3.057461e+06	3.731969e+06	98027.052796	201173.598958	204928.367176

top_sources = data.set_index("Time").sum().sort_values(ascending=False).index.tolist()
top_sources

['Nuclear',
 'Dual Fuel',
 'Hydro',
 'Natural Gas',
 'Wind',
 'Other Renewables',
 'Other Fossil Fuels']

fig = px.bar(data, x="Time", y=top_sources, title="Fuel Mix over Time in NYISO")
fig.update_layout(xaxis_title="Date", yaxis_title="Megawatts (MWh)")
fig.show("svg", width=1200, height=600)

we can also look at percentages over time

percentages = data.set_index("Time")

percentages = percentages.div(percentages.sum(axis=1), axis=0).reset_index()

fig = px.bar(percentages, x="Time", y=top_sources, title="Fuel Mix over Time in NYISO")
fig.update_layout(xaxis_title="Date", yaxis_title="Percent")
fig.show("svg", width=1200, height=600)

carbon_free = ["Hydro", "Nuclear", "Wind", "Other Renewables"]
data["Total Carbon Free"] = data[carbon_free].sum(axis=1)
fig = px.line(
    data,
    x="Time",
    y=["Total Carbon Free"] + carbon_free,
    title="Carbon Free Supply over Time in NYISO",
)
fig.update_layout(xaxis_title="Date", yaxis_title="Megawatts (MWh)")
fig.show("svg", width=1200, height=600)