In this post, Peter Bermel, the Elmore Professor of Electrical and Computer Engineering in the College of Engineering and a member of the Birck Nanotechnology Center, discusses his recently published research “Optimized agrivoltaic tracking for nearly-full commodity crop and energy production” which appears in Renewable and Sustainable Energy Reviews with the support of the National Science Foundation and the Office of Naval Research.
What did you want to know?
We wanted to know whether we can successfully grow corn with mechanized planting and harvesting under an array of photovoltaic panels, commonly known as solar panels.
What did you achieve?
In our experiment, we used normal single-axis tracking, and recorded typical single-axis tracker photovoltaic power production and somewhat reduced corn yield. Based on our simulation and experimental data, we predict that it is possible to maintain nearly full productivity of corn through critical time anti-tracking, which would allow corn yield to increase close to the yield of the control field, while still producing 87% of the power associated with a conventional single-axis tracking solar farm.
Adjusting the tracking algorithm allows for leaning toward maximizing crop production or power production. This could be determined on individual sites based on the desire of key stakeholders. For example, the tracking algorithm could be adjusted in response to changes in prices of electricity and corn, or policy requirements.
What is the impact of this research?
Increasing our ability to continue practicing row-crop agriculture in the Midwest, while also increasing our renewable energy production capabilities by bringing large areas of farmland into practical contention.