CEEM's researchers believe in the value of open source modelling in the Energy and Environmental research space. In this regard, we have developed a series of open source tools which are listed below. For a list of some of our under development tools you can refer CEEM's Github page



NEMOSIS - NEM Open Source Information Service:

Open-source access to Australian National Electricity Market data.

Links: Github , Paper


NEMO - National Electricity Market Optimiser Tool:

NEMO, the National Electricity Market Optimiser, is a chronological dispatch model for testing and optimising different portfolios of conventional and renewable electricity generation technologies. It has been developed since 2011 and is maintained by Ben Elliston through his PhD at CEEM. NEMO is available under a free software license (GPL version 3) and 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 to validate results.

Links: Github, OzLabs


TDA - Tariff Design and Analysis Tool:

We have developed a modelling tool to assist stakeholders wishing to contribute to network tariff design in the Australian National Electricity Market. It is an open source modelling tool to assist stakeholders in assessing the implications of different possible network tariff designs, and hence facilitate broader engagement in the relevant rule making and regulatory processes in the NEM. Our tool takes public energy consumption data from over 5000 households in NSW, and allows users test a wide range of existing, proposed and possible tariffs structures to see their impacts on network revenue and household bills. Demographic survey data of the households allows you to explore the impacts of these tariffs on particular household types – for example, families with young children.  The tool can also show how well different tariffs align these household bills with a households’ contribution to network peak demand.  The tool and data are open source – you can check, validate and add your own data sets; test existing or even design your own tariffs, and validate and even modify the underlying algorithms.

Links: Project pageGithubResearchgate


Microgrid Model

The Community Microgrid model can be used to model the electrical and financial flows for a microgrid with behind the meter PV and a centralised battery. 


Nempy - Open Source model of NEM dispatch procedure:

A flexible tool kit for modelling Australia's National Electricity Market dispatch procedure.

Links: Github


Renewable PPA Tool:

Open source tools to assist large energy users, energy consumers, buyers’ groups and local government to contract with off-site renewables projects through a PPA and therefore meet their renewables and emissions goals; and assist in PPA monitoring, to ensure value for energy consumers. More information and the tools themselves can be found here.


 OpenCEM - Open-source Capacity Expansion Modelling platform:

A free electricity sector modelling tool that aims to support transparent and well informed analysis of technology and policy options for future planning of Australia's electricity system. With openCEM, you can run unlimited scenarios to explore the implications of your assumptions about future energy technologies and policies on our National Electricity Market. More info can be found at 

Link: Github, Website 


morePvs - Solar Apartments model

The Multi-Occupancy Residential Electricity with PV and Storage (morePVs) model  used to model the electrical and financial flows in apartment buildings with shared or individual PV and batteries installed behind the meter or distributed through embedded networks.

This is a command-line implementation written in Python. For more details, see the Project page or Github repository


Australia-China Research Program on Market Mechanisms for Climate Change Policy

With the CEEM MC-ELECT modelling tool we evaluate the potential impacts of an energy, climate policy or policy mix on future generation investment in China’s electricity sector in 2030. This modelling tool extends the conventional load duration curve (LDC) optimal generation mix methods by incorporating Monte Carlo Simulation (MCS) to formally accounts for key uncertainties in generation investment and planning decision-making, including technology costs, carbon pricing and air emissions control. The tool uses the Mean Variance Portfolio (MVP) Theory to compare trade-offs between different future generation portfolios in terms of expected electricity generation costs and their associated uncertainties as well as their environmental impacts based on the calculated expected emissions of carbon emissions and local air pollutants (NOx, SO2, PM2.5).

Link: Github