A model predictive control approach to dual-axis agrivoltaic panel tracking

Document Type

Article

Publication Date

10-15-2026

Abstract

Agrivoltaic systems – photovoltaic 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 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 optimization problem as a convex second-order cone program that determines the photovoltaic 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 model predictive control 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 for lettuce and 1.689 for tomatoes.

Publication Title

Renewable Energy

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