National Electricity Market Optimiser

NEMO dispatch
plotThe National Electricity Market Optimiser (NEMO) is a chronological production cost model for testing and optimising different portfolios of renewable and fossil electricity generation technologies. It was first developed by Dr Ben Elliston in 2011 at the Centre for Energy and Environmental Markets, University of New South Wales. NEMO has been developed and improved over the past decade and has a growing number of users. It is the original energy model to be named NEMO.

NEMO has models for the following generator types: wind (including offshore), photovoltaics, concentrating solar power (CSP), hydropower, pumped storage hydro, biomass, black coal, open cycle gas turbines (OCGTs), combined cycle gas turbines (CCGTs), diesel generators, coal with carbon capture and storage (CCS), CCGT with CCS, geothermal, demand response, batteries, electrolysers, hydrogen fuelled gas turbines, and more.

NEMO is free software and is licensed under the GPL version 3 license. It requires no proprietary software to run, making it particularly accessible to the governments of developing countries, academic researchers and students. The model is available for others to inspect and, importantly, to validate the results.


The easiest way to install NEMO is with the Python pip utility which will install other packages to satisfy dependencies:

$ pip install nemopt

System requirements

NEMO should run on any operating system where Python 3 is available (eg, Windows, Mac OS X, Linux). NEMO utilises some add-on packages: DEAP, Gooey, Matplotlib, Numpy, Pandas and Pint.

For simple simulations or scripted sensitivity analyses, a laptop or desktop PC will be adequate. However, for optimising larger systems, a cluster of compute nodes is desirable. The model is scalable and you can devote as many locally available CPU cores to the model as you wish. Due to a lack of active development, support for SCOOP has been removed. It will be soon replaced with something like Ray.


Documentation will be progressively added to a user's guide in the form of a Jupyter notebook.

Auto-generated library documentation exists for the nemo module. This is useful when building new tools that use the simulation framework.

The model is described in an Energy Policy paper titled Least cost 100% renewable electricity scenarios in the Australian National Electricity Market by Elliston, MacGill and Diesendorf (2013). NEMO no longer uses genetic algorithms, but has adopted the better performing CMA-ES method. However, the approach of searching for least cost solutions is the same.

Source code

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The NEMO source code (written in Python) is easy to extend and modify. The source code is distributed under the GNU General Public License. Code snapshots are available as a ZIP archive or from Github. NEMO has a very comprehensive testsuite that aims for complete code coverage.

Enhancements and bug fixes are very welcome. Please report bugs in the issue tracker. Authors retain copyright over their work.

Mailing list

The nemo-devel mailing list is where users and developers can correspond.

Useful references

Australian cost data are taken from the Australian Energy Technology Assessments (2012, 2013), the Australian Power Generation Technology Report (2015) and the CSIRO GenCost reports (2021, 2022, 2023). The GenCost reports provide the basis of the input cost assumptions for the AEMO Integrated System Plan. Costs for other countries may be added in time.

Renewable energy trace data covering the Australian National Electricity Market territory are taken from the AEMO 100% Renewables Study. An accompanying report describes the method of generating the traces.


Early development of NEMO was financially supported by the Australian Renewable Energy Agency (ARENA). Thanks to undergraduate and postgraduate student users at UNSW who have provided valuable feedback on how to improve (and document!) the model.

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