A research team from Cornell University has developed a control framework that simultaneously takes into account past and future conditions when determining the tilt angle of solar panels. That’s what the researchers said pv magazine it is designed to be plug-and-play, allowing software developers and solar operators to integrate their optimization algorithms directly into the framework.
Researchers from Cornell University in the United States have developed a new control framework for agrivoltaic systems, designed to address the challenge of balancing a system’s energy generation with crop light needs.
“Although scientists have proposed several optimization algorithms in recent years to solve this problem, the industry lacks a general, adaptable control framework to implement them,” said corresponding author Max Zhang. pv magazine.
The new framework combines a proactive decision-making approach with a reactive strategy mechanism that, according to the research paper, allows past and future conditions to be simultaneously taken into account when determining the tilt angle of the solar panels at any given time during the course of the growing season.
“As a result, it is able to provide a systematic and effective response to environmental and operational conditions and their uncertainties, while taking into account system limitations such as inverter capacity, capabilities that are largely missing in existing methods,” the research paper continues.
The proactive control strategy uses weather forecasts and crop growth models to generate a tilt schedule for the panels, maximizing energy production while meeting crop light requirements and other system constraints. “It calculates the optimal tilt angles of the panels during the day to maximize solar energy generation while ensuring that the crops receive the expected daily sunlight,” said Zhang.
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 shortage of sunlight due to prolonged cloud cover, the system is able to update the target settings and will direct the panels to let in more light in the following days to compensate for the shortage.
Zhang told pv magazine that this combination of anticipating the weather and responding to real-time factory 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 a light deficit of up to 43%. The new control framework reduced the maximum deficit to 8%,” said Zhang. “In many simulations, the simpler rule-based method performed similarly to the optimization-based method when the solar system had a default DC/AC ratio of 1. At higher DC/AC ratios, the optimization-based strategy produced up to 14% more energy without compromising the crop’s light needs.”
In the conclusion of the research article, 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 that the framework’s architecture is plug-and-play, allowing software developers and solar operators to integrate their optimization algorithms directly into the framework. “By providing a flexible, generalizable architecture that integrates predictive planning with reactive offsets, this control framework makes agricultural voltaics 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 distribution of sunlight in agrivoltaic systems”, available in the magazine Solar energy.
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