Geospatial Modelling of Australia's National Electricity Market

Overview

This repository complements the following publication: Xenophon, A.K., & Hill, D.J. (2018). Open grid model of Australia's National Electricity Market allowing backtesting against historic data. Scientific data, 5(1), 1-21. http://doi.org/10.1038/sdata.2018.203

Code within this repository compiles information relating to the operation of Australia's National Electricity Market (NEM). The NEM's topology is based on geospatial datasets obtained from Geosience Australia [1, 2, 3]. Data describing the technical and economic parameters related to NEM participants are obtained from the Australian Energy Market Operator (AEMO). Public tables within AEMO's Market Management System Data Model (MMSDM) [4] contain basic generator parameters, such as registered capacities and fuel types. Economic parameters for generators are obtained from AEMO's National Transmission Network Development Plan (NTNDP) database [5].

Load signals are obtained from [4], however these data are only given for each of the NEM's five regions (SA, NSW, QLD, TAS, VIC). Disaggregation of these regional load signals to individual nodes used methods presented in [6, 7], where the geospatial distribution of electricity demand is assumed to follow that of population. Data from the Australian Bureau of Statistics (ABS) describing the geospatial distribution of population within Australia are obtained from [8], while state and territory boundaries are obtained from [9]. Dispatch signals are also obtained from [4], and are used to construct nodal power injection signals for intermittent renewable generators. These dispatch data can also be used to benchmark market models against realised outcomes - a unique feature of this dataset.

The code used to construct the network and generator datasets, and also implement power-flow and unit commitment models, is contained within several Jupyter Notebooks, with a summary of each presented below.

Notebook Name	Description
assemble_network	Compile network information from GA and ABS
collate_generator_data	Compile generator information from AEMO's MMSDM and NTNDP database
extract_load_and_dispatch_signals	Extract regional load signals and generator dispatch profiles from AEMO's MMSDM database
DCOPF	Run direct-current optimal power-flow (DCOPF) model
UC	Run unit commitment (UC) model
plotting	Plot results
create_tables	Create LaTeX tables summarising datasets

Zenodo link

Network and generator datasets constructed using code in this repository are also listed at the following repository:

Caveats

• This dataset does not contain information related to power system components that provide reactive power support to the grid e.g. capacitor banks. Information related to these network elements may be included in the future if additional data become available.

Setup notes

Creating a conda environment is strongly recommended. An environment.yml file is included within the src folder which may assist in setting up a working environment. Please also note that several packages have been installed using conda-forge.

Usage notes

If running for the first time, execute notebooks in the order in which they appear (e.g. first execute the notebook within 1_network, then execute the notebook within 2_generators and so on).

If running assemble_network.ipynb, be aware that this notebook requires nem_zones.py to be run first. The pickled file generated by nem_zones.py is used as an input by assemble_network.ipynb.

Gurobi has been used to solve the models presented in DCOPF.ipynb and UC.ipynb. As mentioned in #2, open-source alternatives are GLPK and IPOPT, which can be used to run the DCOPF and UC models respectively.

References

[1] - Commonwealth of Australia (Geoscience Australia), Electricity Transmission Lines (2017), at http://pid.geoscience.gov.au/dataset/ga/83105

[2] - Commonwealth of Australia (Geoscience Australia), Electricity Transmission Substations (2017), at http://pid.geoscience.gov.au/dataset/ga/83173

[3] - Commonwealth of Australia (Geoscience Australia), Power Stations (2017), at http://pid.geoscience.gov.au/dataset/ga/82326

[4] - Australian Energy Markets Operator. Data Archive (2018). at http://www.nemweb.com.au/#mms-data-model

[5] - Australian Energy Markets Operator. NTNDP Database. (2018). at https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Planning-and-forecasting/National-Transmission-Network-Development-Plan/NTNDP-database

[6] - Zhou, Q. & Bialek, J. W. Approximate model of european interconnected system as a benchmark system to study effects of cross-border trades. IEEE Trans. Power Syst. 20, 782–788 (2005).

[7] - Jensen, T. V. & Pinson, P. RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system. Sci. Data 4, 170175 (2017).

[8] - Australian Bureau of Statistics. Regional Population Growth, Australia, 2014-15. (2016). at http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3218.02014-15?OpenDocument

[9] - Australian Bureau of Statistics. Local Government Areas ASGS Ed 2016 Digital Boundaries in ESRI Shapefile Format. (2016). at http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/1270.0.55.003July%202016?OpenDocument

Creative Commons Attributions

Geospatial datasets obtained from Geoscience Australia are made available under the following license:

© Commonwealth of Australia (Geoscience Australia) 2017. With the exception of the Commonwealth Coat of Arms, and where otherwise noted, this product is provided under a Creative Commons Attribution 4.0 International Licence. http://creativecommons.org/licenses/by/4.0/legalcode

Population datasets obtained from the Australian Bureau of Statistics are made available under the following license:

http://creativecommons.org/licenses/by/4.0/legalcode