Energy in Agriculture

Ideas and solutions

The on-farm agriculture sector offers a huge opportunity to capture energy. Agriculture is large user of external non-renewable energy for fuel and fertiliser production (as well as electricity). So, there is a need for the production of energy on farm both for achieving a net-zero agriculture and for carbon balance.

Solar and wind are the major sources of energy for farms. Here we concentrate on solar because of Australia’s large solar insolation almost everywhere.

There are two key sets of research issues in relation to solar energy production on farm.

What is the optimal location and extent of solar infrastructure on farm? Disregarding roofs of buildings there are three areas on farm which are potential locations.

Prime productivity land for crop and/or pastures

The key question is whether it is economic to remove such land from production in order to produce energy. The quality of light interception generally does not depend on the quality of the soil/land. A useful principle is to impact food security as little as possible to produce energy.

Marginal land for cropping and/or pasture production

The trade-off between energy production and food production is clearer for such land. There are other considerations such as the trade-off between energy production and biodiversity credits (or a mix of both).

Farm water bodies such as dams

Generally, not used for food production but large surface areas are prone to evaporative loss of valuable water resources. Floating solar, depending on infrastructural costs would seem a useful possibility.

We need scenario modelling to find optimal solutions to the amount and scale of energy-producing and storage infrastructure both economically and environmentally.

Having produced (and stored) energy – the second set of research issues is around the use of such energy. Is it possible to convert the energy to fuel (electric or hydrogen) or fertiliser or to return it to the grid? Which combination of these options is environmentally and economically optimal?

We will formulate two robust optimization models for the specific sets of questions presented. These models will aim to provide solutions that remain effective under various uncertainties and unexpected changes in parameters.

Set A: Optimization of Solar Infrastructure Location on Farm

Model: Robust Multi-Objective Optimization for Solar Panel Placement
Objective Functions:
Maximize energy production.
Minimize the impact on food production.
Minimize installation and maintenance costs.
Maximize biodiversity credits (if applicable).

Decision Variables:
Placement on prime productivity land, marginal land, and water bodies.
Type of solar infrastructure (e.g., floating solar).

Constraints:
Land and water availability.
Budget constraints.
Regulatory compliance.
Environmental considerations (e.g., soil quality, water evaporation).

Robustness Approach:
Scenario-Based Robust Optimization: Identify different scenarios representing uncertainties in weather conditions, land quality, cost variations, etc. Solve the optimization problem for the worst-case scenario to ensure robust solutions.

Suggested Methods:
Robust Linear Programming, if the objectives and constraints are linear.
Robust Metaheuristic Algorithms like Robust Particle Swarm Optimization or Robust Genetic Algorithms for more complex models.

Set B: Optimization of Energy Conversion and Usage

Model: Robust Optimization for Energy Storage, Conversion, and Grid Return
Objective Functions:
Maximize the efficiency of energy conversion to fuel or fertilizer.
Maximize the profit from energy return to the grid.
Minimize environmental impact.

Decision Variables:
Amount of energy to be stored, converted to fuel or fertilizer, or returned to the grid.
Choice of energy conversion technology (e.g., electric or hydrogen fuel creation).

Constraints:
Energy production rates.
Storage capacity.
Market demand for fuel or fertilizer.
Grid regulations and capacity.

Robustness Approach:
Parameter-Based Robust Optimization: Define uncertainty sets for uncertain parameters like market prices, conversion efficiency, grid capacity, etc. Optimize the model to perform well across the entire uncertainty set.

Suggested Methods:
Robust Non-linear Programming, if the model involves non-linear objectives and constraints.
Advanced Robust Optimization techniques like Min-Max or Min-Max Regret formulations, tailored to the specific uncertainties involved.

These two robust optimization models would provide decision-makers with solutions that remain feasible and effective under various uncertainties related to solar energy implementation in the agricultural sector. By doing so, they ensure sustainability, efficiency, and resilience in the planning and operation of energy systems on farms.

Funding opportunities

There are funding opportunities from the rural research and development corporations, principally for this project we would target Cotton RDC, Grains RDC, Horticulture Innovation Australia and MLA.