en.Wedoany.com Reported - A research team from Cornell University in the United States has developed a new control framework for agrivoltaic systems to address the challenge of balancing power generation with crop light requirements. The framework combines proactive decision-making methods with reactive strategy mechanisms to determine the optimal tilt angle of solar panels at each stage of the growing season.
According to research lead Max Zhang, various optimization algorithms have been proposed in academia in recent years, but practical implementation has been difficult due to the lack of a universal and flexibly adaptable control framework. The new framework uses weather forecasts and crop growth models to generate panel tilt schedules, maximizing power generation while meeting the crop's daily light requirements. Simultaneously, by monitoring environmental conditions in real-time, the system automatically adjusts target settings for subsequent days to allow more light through when plants experience a light deficit due to persistent cloudy weather.
Test results show that the framework outperforms existing methods. When the typical crop light requirement is 30 mol·m⁻²·d⁻¹, traditional methods can lead to a crop light deficit of up to 43%, whereas the new framework reduces the maximum deficit to 8%. When the solar system is configured with a standard DC/AC ratio (around 1), simple rule-based strategies perform similarly to optimization-based strategies. If a higher DC/AC ratio is adopted, the optimization-based strategy can produce 14% more energy without sacrificing crop light requirements.
The researchers point out that the key advantage of this framework is its universality—it can be adapted to different crops, climates, and system configurations, while being compatible with both heuristic and optimization-based proactive control algorithms. Its architecture is designed to be plug-and-play, allowing software developers and photovoltaic operators to directly integrate optimization algorithms into the framework. Max Zhang stated that this flexible architecture, which integrates predictive planning with reactive compensation, makes agrivoltaics highly feasible and scalable even in cloudy regions with complex climates.
The related research findings were published as a paper in the journal Solar Energy, titled "An integrated control framework for optimal sunlight sharing in agrivoltaic systems".
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