Renewable energy microgrids for the Australian cotton industry

This project will create a decision support system (DSS) to evaluate the use of renewable energy on Australian cotton farms and investigate the optimal combination of distributed generation and storage systems.

Data science

Infrastructure, utilities & transport



3 years

Academic team

Associate Professor Li Li

Associate Professor Jiangfeng Zhang

Mr Forrest Lin

Engagement model


Cotton Research & Development Corporation (CRDC)

Future applications

Microgrid planning

Microgrid operation

Microgrid optimisation

Decision support system


Diesel fuel accounts for at least 90% of the direct energy used on Australian cotton farms but with prices increasing by 2.9-7.2% every year, the cost to both growers and the environment is unsustainable. In contrast, the cost of renewable energy continues to decrease. It is estimated that by 2030, 56% of power capacity will be supplied by renewables, offering cotton growers a viable alternative for their energy provision.  

The first step in the potential shift to renewables is to prove the use of alternative energy technologies on irrigated cotton farms and to explore policy options, such as microgrids.



A microgrid is a self-sufficient energy system that uses one or more types of distributed energy (wind turbine, generator, solar panel) to produce its power.The ability to isolate from the main grid, and the use of localised, distributed power supplies, means microgrids are a reliable and resilient source of energy in remote locations.

The UTS Tech Lab team will model the load demand and power systems of cotton farms, integrating energy appliances such as diesel and wind generators, solar photovoltaics, biomass gasifiers, battery banks and pumped water storage into a microgrid.

Based on the input of farm and historical weather data, the cost of the microgrid, including setup and maintenance, will be optimised to reduce reliance on traditional power sources. The best microgrid option will be delivered to farmers, with the aim of providing a 10% improvement in operational efficiency and 30% reduction in energy consumption.