A Model Predictive Control Approach to Dual-Axis Agrivoltaic Panel Tracking

TL;DR

This paper introduces a model predictive control method for dual-axis agrivoltaic panels that optimizes power and crop yield by dynamically adjusting panel positions based on real-time data, improving land use efficiency.

Contribution

It presents a convex optimization-based MPC approach for dual-axis agrivoltaic tracking, balancing power output and crop yield, with case studies demonstrating its effectiveness.

Findings

Active panel adjustment improves land equivalent ratio up to 1.897

The approach effectively manages trade-offs between power and crop yield

Forecast errors impact MPC performance but still enable significant gains

Abstract

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.