According to Cotton Australia, energy costs in the cotton industry have increased by 350% from 2000 to 2014. Energy (electricity and diesel) costs for Australian cotton growers are expected to continue to increase by 2.9–7.2% annually until 2040. Diesel fuel provides at least 90% of the direct energy harnessed in farms. On average, irrigation accounts for 50–75% of the total direct energy consumption on-farm. An increasing number of alternative irrigation systems, for example, centre pivots and lateral move systems, are expected to lead to highly significant energy costs associated with water pumping and machine operation. On the other hand, the costs of renewable energy continue to decrease, providing cotton growers with another option for energy supply. Renewable energy can be used to design the corresponding microgrids to irrigate cotton farms. The designed renewable microgrids can reduce these cotton farms' energy consumption costs and greenhouse gas emissions. This study aims to develop tailor-made renewable power planning and energy management plans for cotton-farm microgrids to secure power supply and reduce energy costs. In addition, we seek to optimize the microgrid's operation considering the uncertainty of environmental and demand factors on cotton farms to achieve cost savings for cotton stakeholders.

In this thesis, the first part presents an optimization model for cotton farm microgrid design, which explores available renewable energy sources (RESs) and energy storage options to ensure a reliable power supply for cotton farms. By using the RES power supply, renewable energy is optimally utilized to satisfy the seasonal load demand, and the grid power is used as a backup power source. The objectives of optimization include investment cost, operating cost, and a simple payback period. A case study is undertaken using historical energy consumption data from a cotton farm in Gunnedah, New South Wales, to verify the applicability of the proposed approach.

The second part of this study presents a model predictive control (MPC) approach to the above designed cotton farm microgrid to minimize the water pumping operational cost while taking full advantage of renewable energy sources. The reason for using MPC is its ability to handle noise, disturbance, and real-time parameter changes. Microgrids at two different cotton farms are used for case studies to validate the proposed MPC methodology. 

The third part of this study addresses the problem of optimizing cotton farm operating costs under uncertainties. An MPC approach is adopted to maximize the usage of renewable energy and minimize the overall water pumping cost during the cotton growth and irrigation period. To deal with the uncertainties in renewable generation, water demand, precipitation and evaporation, the operation problem of the cotton farm pumping system is formulated as a stochastic MPC problem to cater to real-time changes in uncertain weather conditions and irrigation demand. Static and dynamic scenario generation-reduction techniques are applied to obtain typical scenarios and the corresponding probabilities, which are further applied to formulate the stochastic optimization problem to deal with uncertainties.