Agrivoltaic systems integrate photovoltaic (PV) energy production with agricultural activities, addressing the critical challenges of land use optimization and sustainable energy generation in the context of climate c...
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Agrivoltaic systems integrate photovoltaic (PV) energy production with agricultural activities, addressing the critical challenges of land use optimization and sustainable energy generation in the context of climate changes and food security. These systems are pivotal in offering a promising solution in mitigating the environmental and social impacts of utility-scale PV installations, such as habitat disruption and competition with agricultural land. This study evaluates a patented V-shaped bifacial photovoltaic system with a single-axis solartracking, designed to optimize energy capture but also to minimize shading effects on crops like vineyards. A custom Python-based algorithm using PVlib was developed to simulate the performance of the system, accounting for mutual shading, multiple solar radiation reflections, and dynamic tilt adjustments. Simulations conducted for Palermo, Italy, revealed that the system collects 5.2 % less solar irradiation than traditional side-by-side configurations but achieves an annual energy output of 2089.3 kWh per pair of panels, along with 24 % reduction in land use. These results highlight the system capability to optimize spatial efficiency while maintaining high energy production. The novelty of this work lies in its tailored simulation approach, addressing the unique geometry and operational dynamics of the V-shaped configuration, and its potential adaptability to diverse agrivoltaics scenarios. Unlike existing tools and methodologies in the literature, this work introduces a customized Python-based model specifically designed to analyse the performance of this innovative structure, which is of recent conception and lacks precedent in both academic studies and commercial software solutions. By advancing the methodological framework for integrating renewable energy with agriculture, this study contribute to the broader goals of sustainable development and climate resilience.
As the importance of sustainable energy has been rapidly growing, a concentrative photovoltaic (CPV) solar system is drawing much attention. In order for a system to operate efficiently, a deliberate solartracking sy...
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As the importance of sustainable energy has been rapidly growing, a concentrative photovoltaic (CPV) solar system is drawing much attention. In order for a system to operate efficiently, a deliberate solartracking system must be equipped because an optimal tilt of solar panel is changed as the Sun orbits its trajectory. However, many existing tracking methods did not clearly consider the effect of various weather conditions, even though the performance of tracking method is subject to them. In this paper, we propose a CPV solar system that chooses the most proper solartracking method among the group of heterogeneous trackingalgorithms, based on an inference on the current weather conditions with Bayesian network (BN). We use 13 features derived from image processing and implement four trackingalgorithms which have relative performance depending on nine different weather conditions. We constructed the working CPV system and collected the 1630 image data every three minutes for five hours over a period of 16 days. The proposed BN shows 93.9% accuracy for inferencing weather conditions, and the proposed system shows 16.58% higher power productivity, compared to a pinhole system and other existing methods. (C) 2019 Elsevier B.V. All rights reserved.
This study evaluates and compares two types of interspersed bifacial agrivoltaic systems in Belgium: a fixed vertical system and a dynamic single-axis tracker, focusing on sugarbeet cultivation. Additionally, the impa...
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This study evaluates and compares two types of interspersed bifacial agrivoltaic systems in Belgium: a fixed vertical system and a dynamic single-axis tracker, focusing on sugarbeet cultivation. Additionally, the impact of different trackingalgorithms on crop yield and quality is investigated. The main approach involves developing an empirical crop model based on radiation use efficiency (RUE) and utilizing a radiation tool to simulate crop yield and quality. Field measurements of electricity and crop output are conducted over a span of two years, 2021 and 2022. The findings reveal that the dynamic solar tracker outperforms the fixed vertical bifacial setup in both years. The smart-trackingalgorithm, applied under optimal watering conditions in the 2021 season, leads to a significant increase in energy yield (+30%) and land use efficiency (+20%) at a lower cost, while maintaining comparable crop yields to the vertical setup. However, limitations are observed in the empirical crop growth model's ability to account for climatic variability in the dry 2022 year, reducing its usefulness during the design phase. Notably, the practical implementation of the systems highlights challenges during construction and exploitation, emphasizing the significance of considering these practical factors together with climate, structure and crop choice assessing the effectiveness and to de-risk investments.
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