A Model Predictive Control Approach to Dual-Axis Agrivoltaic Panel Tracking
This addresses the competing land demands for agriculture and solar energy by enabling more efficient dual-use systems, though it is incremental as it applies existing control methods to a specific domain.
The paper tackles the problem of optimizing dual-axis agrivoltaic panel tracking to balance power production and crop yield, using a model predictive control approach with convex relaxations, achieving a land equivalent ratio of up to 1.897.
Agrivoltaic systems--photovoltaic (PV) panels installed above agricultural land--have emerged as a promising dual-use solution to address competing land demands for food and energy production. In this paper, we propose a model predictive control (MPC) approach to dual-axis agrivoltaic panel tracking control that dynamically adjusts panel positions in real time to maximize power production and crop yield given solar irradiance and ambient temperature measurements. We apply convex relaxations and shading factor approximations to reformulate the MPC optimization problem as a convex second-order cone program that determines the PV panel position adjustments away from the sun-tracking trajectory. Through case studies, we demonstrate our approach, exploring the Pareto front between i) an approach that maximizes power production without considering crop needs and ii) crop yield with no agrivoltaics. We also conduct a case study exploring the impact of forecast error on MPC performance. We find that dynamically adjusting agrivoltaic panel position helps us actively manage the trade-offs between power production and crop yield, and that active panel control enables the agrivoltaic system to achieve land equivalent ratio values of up to 1.897.