NEMED
A python package to retrieve and process historical emissions data of the National Electricity Market, reproduced by datasets published by the Australian Energy Market Operator.
https://github.com/unsw-ceem/nemed
Category: Emissions
Sub Category: Carbon Intensity and Accounting
Keywords
aemo australia cdeii emissions energy national-electricity-market nem python
Last synced: about 21 hours ago
JSON representation
Repository metadata
National Electricity Market Emissions Data Tool
- Host: GitHub
- URL: https://github.com/unsw-ceem/nemed
- Owner: UNSW-CEEM
- License: bsd-3-clause
- Created: 2022-08-12T23:05:23.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-05-20T10:11:06.000Z (11 months ago)
- Last Synced: 2025-04-18T21:19:25.053Z (9 days ago)
- Topics: aemo, australia, cdeii, emissions, energy, national-electricity-market, nem, python
- Language: Python
- Homepage: http://nemed.readthedocs.io/
- Size: 26.2 MB
- Stars: 15
- Watchers: 1
- Forks: 4
- Open Issues: 5
- Releases: 5
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
README.md
NEMED
NEMED[^1], or NEM Emissions Data, is a python package to retrieve and process historical emissions data of the National Electricity Market (NEM), reproduced by datasets published by the Australian Energy Market Operator (AEMO).
[^1]: Not to be confused with "Nemed", "Nimeth" of the Irish legend, who was the leader of the third group of people to settle in Ireland.
Installation
pip install nemed
Introduction
This tool is designed to allow users to retrieve historical NEM regional emissions data, either total or marginal emissions, for any 5-minute dispatch interval or aggregations thereof. Total emissions data produced by NEMED is given as both absolute total emissions (tCO2-e) and as an emissions intensity index (tCO2-e/MWh). Marginal emissions data reflects the price setter of a particular region, yielding an emissions intensity index (tCO2-e/MWh) corresponding to a particular plant.
Although data is published by AEMO via the Carbon Dioxide Equivalent Intensity Index (CDEII) Procedure this only reflects a daily summary for each region by total and (average) emissions intensity.
How does NEMED calculate emissions?
Total Emissions are computed by considering 5-minute generation dispatch data for each generator in the NEM for each respective region, along with their CO2-equivalent emissions factors per unit (generator) level. A detailed method of the process to produce results for total emissions(tCO2-e) and the corresponding emisssions intensities can be found here. The tool is able to provide these metrics on a dispatch interval basis, or aggregated to hourly, daily, monthly or yearly measures. For more advanced users, the emissions associated with each generator and hence that generator's contribution to total regional emissions can be extracted.
Marginal Emissions are computed by identifying the marginally dispatched generators from AEMO's Price Setter files, mapping emissions intensity metrics mentioned above and computing marginal emissions intensity (tCO2-e/MWh).
How accurate is NEMED?
A series of benchmark results for total emissions shows a comparison between AEMO's daily CDEII reported emissions figures and NEMED's emissions figures which have been aggregated from a 5-minute dispatch-interval resolution to a daily basis.
The example includes a region by region comparison for each metric, while an overview of the historical NEM Emissions Intensity produced using NEMED is shown here.
Usage
Examples
Examples can be found in NEMED's documentation.
Possible Use Cases
Some example use cases of data produced from this tool include:
- Analysis of historical emissions between NEM regions, generation technologies contributions to them and assessing the difference between total and marginal emissions.
- Using emissions intensities traces (total and marginal) from NEMED in counter-factual optimisation models; studying the influence of shadow-pricing carbon or imposing carbon constraints.
- Considering the emissions assosciated with grid-energy consumption for residential/C&I consumers, or in counterfactual studies of hypothetical EV usage or H2 electrolyser operation.
Contributing
Interested in contributing? Check out the contributing guidelines, which also includes steps to install NEMED for development.
Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
License
NEMED was created by Declan Heim and Shayan Naderi. It is licensed under the terms of the BSD 3-Clause license.
Credits
This package was created using the UNSW CEEM template. It also adopts functionality from sister tools including NEMOSIS and NEMPY.
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: NEMED
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Declan
family-names: Heim
email: [email protected]
affiliation: >-
Collaboration on Energy and Environmental
Markets, UNSW Sydney
- given-names: Shayan
family-names: Naderi
affiliation: >-
Collaboration on Energy and Environmental
Markets, UNSW Sydney
Owner metadata
- Name: Collaboration on Energy and Environmental Markets (CEEM)
- Login: UNSW-CEEM
- Email:
- Kind: organization
- Description:
- Website: http://ceem.unsw.edu.au/
- Location: Sydney Australia
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/33536784?v=4
- Repositories: 27
- Last ynced at: 2024-05-11T05:45:05.583Z
- Profile URL: https://github.com/UNSW-CEEM
GitHub Events
Total
- Issues event: 1
- Watch event: 4
Last Year
- Issues event: 1
- Watch event: 4
Committers metadata
Last synced: 6 days ago
Total Commits: 65
Total Committers: 3
Avg Commits per committer: 21.667
Development Distribution Score (DDS): 0.169
Commits in past year: 5
Committers in past year: 2
Avg Commits per committer in past year: 2.5
Development Distribution Score (DDS) in past year: 0.4
| Name | Commits | |
|---|---|---|
| dec-heim | 9****m | 54 |
| dec-heim | d****m@g****m | 9 |
| nick-gorman | n****5@g****m | 2 |
Committer domains:
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 9
Total pull requests: 11
Average time to close issues: about 1 month
Average time to close pull requests: 5 days
Total issue authors: 3
Total pull request authors: 3
Average comments per issue: 0.11
Average comments per pull request: 0.18
Merged pull request: 10
Bot issues: 0
Bot pull requests: 0
Past year issues: 1
Past year pull requests: 1
Past year average time to close issues: N/A
Past year average time to close pull requests: 7 days
Past year issue authors: 1
Past year pull request authors: 1
Past year average comments per issue: 0.0
Past year average comments per pull request: 1.0
Past year merged pull request: 1
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- dec-heim (6)
- prakaa (2)
- Hossein-Saberi (1)
Top Pull Request Authors
- dec-heim (9)
- nick-gorman (1)
- ShayanNaderi (1)
Top Issue Labels
- enhancement (4)
- bug (2)
- invalid (2)
- help wanted (1)
- documentation (1)
Top Pull Request Labels
- bug (2)
- duplicate (2)
Dependencies
- actions/cache v2 composite
- actions/checkout v3 composite
- actions/setup-python v4 composite
- snok/install-poetry v1 composite
- 168 dependencies
- datetime *
- joblib ^1.2.0
- nemosis *
- nempy *
- pandas ^1.2
- pathlib *
- plotly *
- python >= 3.8, <4.0
- requests *
- tqdm *
- xmltodict *
Score: 4.0943445622221