Marginal Emissions
Contents
Marginal Emissions#
Exploring the functionality of NEMED to extract regional marginal emitters from price setting and generator emissions data
Data Preparation#
Import Packages#
Show code cell content
import nemed
import pandas as pd
# To generate plots shown
import plotly.graph_objects as go
import plotly.express as px
import string
# Open plot in browser (optional)
import plotly.io as pio
pio.renderers.default = "browser"
Processing Marginal Emissions Data#
Regional Marginal Emissions can be extracted in NEMED using get_marginal_emissions. The following inputs must be specified:
start_timedefine the start of the historical period to collect data for. Must be in the format: “yyyy/mm/dd HH:MM”end_timedefine the end of the historical period to collect data for. Must be in the format: “yyyy/mm/dd HH:MM”cachespecify the local file directory to temporarily store downloaded files
The returned dataframe will contain timeseries data with columns:
Column |
Type |
Description |
|---|---|---|
Time |
datetime |
Timestamp reported as end of dispatch interval. |
Region |
string |
The NEM region corresponding to the marginal emitter data. |
Intensity_Index |
float |
The intensity index [tCO2e/MWh] (as by weighted contributions) of the price-setting generators. |
DUID |
string |
Unit identifier of the generator with the largest contribution on the margin for that Time-Region. |
CO2E_ENERGY_SOURCE |
string |
Unit energy source with the largest contribution on the margin for that Time-Region. |
The simpliest way to collect emissions data is:
Note
Extracting 1 month of marginal emissions data was found to take approx. 2 minutes in testing.
Extracting 1 year of marginal emissions data was found to take approx. 30 minutes. Cached files amount to 3 GB.
result = nemed.get_marginal_emissions(start_time="2021/01/01 00:00",
end_time="2022/01/01 00:00",
cache="E:/TEMPCACHE2/")
Show code cell output
WARNING: Warning: Gen_info table only has most recent NEM registration and exemption list. Does not account for retired generators
INFO: Processing Price Setter Files...
0it [00:00, ?it/s]
Reading selected 366 JSON files to pandas, of cached files
INFO: Loading Cached Price Setter Files...
100%|██████████| 366/366 [01:42<00:00, 3.56it/s]
result.head()
| Time | Region | Intensity_Index | DUID | CO2E_ENERGY_SOURCE | |
|---|---|---|---|---|---|
| 0 | 2021-01-01 00:05:00 | NSW1 | 0.908305 | VP6 | Black coal |
| 1 | 2021-01-01 00:05:00 | QLD1 | 0.855969 | VP6 | Black coal |
| 2 | 2021-01-01 00:05:00 | SA1 | 0.867177 | VP5 | Black coal |
| 3 | 2021-01-01 00:05:00 | TAS1 | 0.000000 | GORDON | Hydro |
| 4 | 2021-01-01 00:05:00 | VIC1 | 0.895716 | VP6 | Black coal |
Aggregate/Filter data#
Data retrieve from NEMED can be manually filtered. For example sorting by region we can produce…
result['Date'] = result['Time'].dt.date
result['Hour'] = result['Time'].dt.hour
result['Season'] = result['Time'].dt.month%12 // 3 +1
result['Season'].replace({1:'Summer', 2:'Autumn', 3:'Winter', 4:'Spring'}, inplace=True)
Example Chart 1#
Which fuel (generator) type is most often the marginal emitter?#
The below chart can be reproduced using the code below (toggle the cell to view)
result['Date'] = result['Time'].dt.date
result['Hour'] = result['Time'].dt.hour
result['Season'] = result['Time'].dt.month%12 // 3 +1
result['Season'].replace({1:'Summer', 2:'Autumn', 3:'Winter', 4:'Spring'}, inplace=True)
Show code cell content
def NORD_theme():
plotly_NORD_theme = pio.templates["plotly_white"]
plotly_NORD_theme.layout.plot_bgcolor = "#f4f4f5"
plotly_NORD_theme.layout.paper_bgcolor = "#f4f4f5"
plotly_NORD_theme.layout.xaxis.gridcolor = '#d8dee9'
plotly_NORD_theme.layout.yaxis.gridcolor = '#d8dee9'
return plotly_NORD_theme
def set_font_size(layout, font_size=16):
layout['titlefont']['size'] = font_size + 4
layout.legend['font']['size'] = font_size
for ax in [item for item in layout if item.__contains__('xaxis')]:
layout[ax].titlefont.size = font_size
layout[ax].tickfont.size = font_size
for ax in [item for item in layout if item.__contains__('yaxis')]:
layout[ax].titlefont.size = font_size
layout[ax].tickfont.size = font_size
def plot_marginal_fuelsrc(plt_df):
df = plt_df[plt_df['Region']!="TAS1"].copy(deep=True)
df['Region'] = df['Region'].str.rstrip(string.digits)
# Chart
fig = px.histogram(
df,
x="Hour",
color="CO2E_ENERGY_SOURCE",
facet_col="Season",
facet_row="Region",
barnorm="percent",
category_orders={"Season":["Spring", "Summer", "Autumn", "Winter"]},
height=1000,
width=1200,
color_discrete_sequence=['#0C0A0C','#5E3F1C','#00527A','#5A9367','#8F6593','#FFB41F','#D2F1E4','#E9724C',
'#6D250D','#F4B8A4'])
# Layout
fig.update_layout(title=dict(text=f"Historical CY2021 Marginal Emitter (by Fuel Source)<br>"+\
"<sub>NEMED | Marginal Emissions Methodology | Categorisation by Fuel Source (Mainland Regions)</sub>",
y=0.95),
template=NORD_theme(),
legend={'title':'', 'orientation':'h', 'xanchor': 'center', 'x': 0.5, 'y':-0.1},
margin=dict(l=120, r=60, t=140, b=60))
# Axis Formatting
fig.update_yaxes(title_text=None, mirror=True, showgrid=False)
fig.update_yaxes(title="% of dispatch intervals", tickvals=[0,25,50,75,100], col=1,)
fig.update_xaxes(mirror=True, showgrid=False)
ax_time = dict(title_text="Time of Day [h]")
fig.update_layout(xaxis=ax_time, xaxis2=ax_time, xaxis3=ax_time, xaxis4=ax_time)
# Font Formatting
FONT_SIZE = 16
FONT_STYLE = "Raleway"
fonts = dict(tickfont=dict(size=FONT_SIZE, family=FONT_STYLE),
titlefont=dict(size=FONT_SIZE, family=FONT_STYLE))
fig.update_layout(xaxis=fonts, xaxis2=fonts, xaxis3=fonts, xaxis4=fonts,
yaxis=fonts, yaxis5=fonts, yaxis9=fonts, yaxis13=fonts,
legend=dict(font=dict(size=FONT_SIZE-2, family=FONT_STYLE)),
title_font_family=FONT_STYLE,
title_font_size=22)
fig.update_annotations(font=dict(size=FONT_SIZE, family=FONT_STYLE))
return fig
fig = plot_marginal_fuelsrc(result)
fig.show()
Interactive Plot
Click the image to open the plot as an interactive plotly
