(657d) Developing Optimization Framework for Sustainable Co-Production of Food and Energy

The world population approaches nearly 11 billion by the end of the current century- a "Full Earth" scenario. Meeting the global food and energy demands of "Full Earth" using existing technology methods will place competition for the global surface area. A sustainable solution to this problem is to co-produce food and energy by mounting elevated Photovoltaic (PV) arrays on agricultural lands, a concept we refer to as "Aglectric Farming".

In an aglectric farm, the solar radiation is distributed between plants and the solar panels. The distribution of radiation should be such that there is minimal impact on the crop yield while the PV power output density is maximized. This radiation distribution in effect is a consequence of panel tracking algorithm for the given panel geometry.

In this work, we introduce a framework to optimize the tracking profile as a function of time. As a part of this optimization framework, a detailed analysis of experimental data and sensitivity analysis is conducted to generate additional constraints to help the optimizer converge faster. This method is developed for infinitely long panels so that the variation in radiation happens in only dimension i.e. perpendicular to the central axis of the panels. A preliminary model for radiation prediction as a function of panel tracking profile and geometrical parameters is embedded in the optimization framework alongwith an approximation for the plant biomass production.