Learn more about the series of open-source tools.
CEEM's researchers believe in the value of open source modelling in the Energy and Environmental research space. 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.
A Python package for downloading historical data published by the Australian Energy Market Operator (AEMO). Data available includes energy and FCAS market prices, regional demand and generation summaries, generator dispatch targets and SCADA, interconnector flows and losses, generator bids, and generic constraint formulations and marginal values.
The National Electricity Market Optimiser (NEMO) is a chronological dispatch model for testing and optimising different portfolios of conventional and renewable electricity generation technologies. It was first developed in 2011 by Ben Elliston through his PhD at CEEM, University of New South Wales. NEMO continues to be actively developed and improved with a growing number of users.
The modelling tool aims to assist stakeholders, including consumer advocates and researchers, to investigate how different tariff structures impact on the expected bills of different types of residential consumers. It also estimates how well the tariffs align these customer bills with their impact on longer-term network costs. The tool builds on research and analysis currently being undertaken by CEEM and aims to support submissions to network pricing and tariff structure proposals.
The software models the electrical and financial flows of a community microgrid, an emerging business model where businesses or residences with solar and a centralised battery system can share energy.
Nempy is a Python package for modelling the dispatch procedure of the Australian National Electricity Market (NEM). It can be used to formulate very simple dispatch models, or more complex ones by adding features such as ramping constraints, interconnectors, FCAS markets and more.
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.
MorePVs is a techno-economic modelling tool of apartment building electricity networks. It simulates 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 model will help strata bodies, embedded network operators and other stakeholders to identify optimal organisational arrangements and financial settings to incentivise all owners and residents and thereby increase deployment.
The MC-ELECT modelling tool was developed as part of the Australia-China Research Program on Market Mechanisms for Climate Change Policy. It evaluates 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 account 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).
The Solar-Curtailment tool was built for the RACE for 2030, Curtailment and Network Voltage Analysis Study (CANVAS) project.
This Python package measures the amount of curtailed energy from a residential or commercial distributed energy resource such as distributed PV (D-PV) and/or battery energy storage system (BESS) via one of the following inverter power quality response modes (PQRM):
This tool can benefit researchers and future projects which would like to understand and quantify Distributed Energy Resources (DER) curtailment due to different PQRMs.
RSPCT is a web application that allows users to simulate pre-cooling and solar pre-cooling for a range of existing buildings in the Australian building stock. It contains a database of buildings' thermal behavior and can simulate the AC demand, indoor temperature, electricity cost, and thermal discomfort index.
NEMED, 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).
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.