Researchers from Cornell University have developed a novel control framework for agrivoltaic systems designed to address the challenge of balancing a system’s energy generation with crop light requirements.
“While scientists have proposed several optimization algorithms to try and solve this issue in recent years, the industry lacks a generalized, adaptable control framework to implement them,” corresponding author Max Zhang told pv magazine.
The new framework combines a proactive decision-making approach with a reactive strategies mechanism which, according to the research paper, allows for past and future conditions to be accounted for simultaneously when determining the solar panel tilt angle at a given time over the course of the growing season.
“Consequently, it is able to provide a systematic and effective response to environmental and operational conditions and their uncertainties while considering system constraints such as inverter capacity, capabilities that are largely absent from existing methods,” the research paper continues.
The proactive control strategy uses weather forecasts and crop growth models to generate a panel tilt schedule, maximizing energy production while satisfying crop light requirements and other system constraints. “It calculates the optimal panel tilt angles throughout the day to maximize solar power generation, while ensuring the crops are expected to receive their required daily sunlight,” Zhang said.
The framework’s reactive mechanism monitors real-time conditions to cover the real-time lift the crops actually receive. Zhang explained that if the plants experience a sunlight deficit caused by prolonged cloud cover, the system is capable of updating its target settings and will direct the panels to let more light through in the following days to make up for the shortfall.
Zhang told pv magazine that this combination of anticipating the weather and reacting to real-time plant data outperforms existing methods, with test results showing that the new strategies improved performance.
“For a representative crop light requirement of 30 mol·m⁻²·d⁻¹, previous approaches could leave crops with light deficits of up to 43%. The new control framework reduced the maximum deficit to 8%,” Zhang said. “Across many simulations, the simpler rule-based method performed similarly to the optimization-based one when the solar system was sized near a standard DC/AC ratio of 1. At higher DC/AC ratios, the optimization-based strategy produced up to 14% more energy without compromising crop light requirements.”
In the research paper’s conclusion, the researchers write that a key strength of the control framework is its generalizability across different crops, climates, and system configurations and its compatibility with both heuristic and optimization-based proactive control algorithms.
Zhang also pointed out the architecture of the framework is designed to be plug and play, allowing for software developers and solar operators to incorporate their optimization algorithms directly into the framework. “By providing a flexible, generalizable architecture that integrates predictive planning with reactive compensations, this control framework makes agrivoltaics highly viable and scalable, even in regions with challenging, cloudy climates,” he concluded.
The proposed framework is presented in the research paper “An integrated control framework for optimal sunlight sharing in agrivoltaic systems,” available in the journal Solar Energy.
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