| 1. |
- Lu, Silvia Ma, et al.
(författare)
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Photosynthetically active radiation separation model for high-latitude regions in agrivoltaic systems modeling
- 2024
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Ingår i: Journal of Renewable and Sustainable Energy. - 1941-7012. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Photosynthetically active radiation is a key parameter for determining crop yield. Separating photosynthetically active radiation into direct and diffuse components is significant to agrivoltaic systems. The varying shading conditions caused by the solar panels produce a higher contribution of diffuse irradiance reaching the crops. This study introduces a new separation model capable of accurately estimating the diffuse component from the global photosynthetically active radiation and conveniently retrievable meteorological parameters. The model modifies one of the highest-performing separation models for broadband irradiance, namely, the Yang2 model. Four new predictors are added: atmospheric optical thickness, vapor pressure deficit, aerosol optical depth, and surface albedo. The proposed model has been calibrated, tested, and validated at three sites in Sweden with latitudes above 58 °N, outperforming four other models in all examined locations, with R2 values greater than 0.90. The applicability of the developed model is demonstrated using data retrieved from Sweden's first agrivoltaic system. A variety of data availability cases representative of current and future agrivoltaic systems is tested. If on-site measurements of diffuse photosynthetically active radiation are not available, the model calibrated based on nearby stations can be a suitable first approximation, obtaining an R2 of 0.89. Utilizing predictor values derived from satellite data is an alternative method, but the spatial resolution must be considered cautiously as the R2 dropped to 0.73.
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| 2. |
- Ma Lu, Silvia, et al.
(författare)
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Validation of Vertical Bifacial Agrivoltaic and Other Systems Modelling : Effect of Dynamic Albedo on Irradiance and Power Output Estimations
- 2023
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Konferensbidrag (refereegranskat)abstract
- In agrivoltaic systems combining solar photovoltaic and agricultural activities, ground albedo is mainly characterized by the crop and its seasonal variations. This study examines the effects of using fixed, satellite-derived, and hourly measured albedo on the performance of a vertical bifacial system and a 1-axis tracking system using a bifacial photovoltaic model (AgriOptiCE®). The model is developed with Matlab® and partially based on the open-source package pvlib. AgriOptiCE® is firstly validated by comparing estimated front and rear irradiances with on-site measurements for specific periods from a 1-axis tracker site in Golden, USA and a vertical agrivoltaic system in Västerås, Sweden. Furthermore, photovoltaic system power output estimations using AgriOptiCE® are also validated for the vertical agrivoltaic system and the conventional ground-mounted fixed-tilt system at the same location. The validations demonstrate the high accuracy of the proposed model in estimating front and rear irradiances and power output, obtaining R2 > 0.85 for all the studied cases. The study results indicate that measured albedo provides the highest accuracy, while satellite- derived albedo has poorer results due to the broader spatial, temporal, and spectral resolution. Fixed albedo is not recommended for yearly assessment of bifacial PV systems because it cannot account for snow events and daily variations, resulting in lower overall accuracy.
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| 3. |
- Zainali, Sebastian, 1995-
(författare)
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Microclimate modelling for agrivoltaic systems
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Increasing global electricity consumption and population growth have resulted in conflicts between renewable energy sources, such as bioenergy and ground-mounted photovoltaic systems, owing to the limited availability of suitable land caused by competing land uses. This challenge is further compounded by the intertwined relationship between energy and agri-food systems, where approximately 30% of global energy is consumed. In addition, considering that agricultural irrigation accounts for 70% of water use worldwide, its impact on both land and water resources becomes a critical concern. Agrivoltaics offers a potential solution to this land use conflict. However, a knowledge gap remains regarding the impact of integrating these techniques on microclimatic conditions. Addressing this gap is crucial because these conditions directly affect the growth and development of crops, as well as the efficiency of energy yields in photovoltaic panels. Experimental facilities offer valuable insights tailored to specific locations and system designs. Although they provide an in-depth understanding of a particular location, the extrapolation of this information to different locations or alternative systems may be limited. Therefore, the broader applicability of these insights to diverse settings or alternative systems remains unclear. In this thesis, a modelling procedure was developed to evaluate the photosynthetically active radiation reaching crops in typical agrivoltaic configurations across three diverse geographical locations in Europe. This is essential for understanding how solar panel shading affects the incoming photosynthetically active radiation required for crop photosynthesis. Furthermore, computational fluid dynamics were employed to model and assess the microclimate of an experimental agrivoltaic system. The developed model revealed significant variations in photosynthetically active radiation distribution across different agrivoltaic systems and locations, emphasising the need for tailored designs for optimal energy yield and crop productivity. Computational fluid dynamics analysis demonstrated its effectiveness in evaluating microclimatic parameters such as air and soil temperature, wind speed, and solar irradiance within agrivoltaic systems, providing valuable insights for system optimisation. By bridging a knowledge gap, this thesis contributes to the understanding of the modelling and simulation of agrivoltaic system microclimates, thereby facilitating the sustainable coexistence of renewable electricity conversion and agriculture.
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| 4. |
- Zainali, Sebastian, 1995-, et al.
(författare)
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Site adaptation with machine learning for a Northern Europe gridded global solar irradiance product
- 2023
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Ingår i: Energy and AI. - 2666-5468. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Gridded global horizontal irradiance (GHI) databases are fundamental for analysing solar energy applications' technical and economic aspects, particularly photovoltaic applications. Today, there exist numerous gridded GHI databases whose quality has been thoroughly validated against ground-based irradiance measurements. Nonetheless, databases that generate data at latitudes above 65˚ are few, and those available gridded irradiance products, which are either reanalysis or based on polar orbiters, such as ERA5, COSMO-REA6, or CM SAF CLARA-A2, generally have lower quality or a coarser time resolution than those gridded irradiance products based on geostationary satellites. Amongst the high-latitude gridded GHI databases, the STRÅNG model developed by the Swedish Meteorological and Hydrological Institute (SMHI) is likely the most accurate one, providing data across Sweden. To further enhance the product quality, the calibration technique called "site adaptation" is herein used to improve the STRÅNG dataset, which seeks to adjust a long period of low-quality gridded irradiance estimates based on a short period of high-quality irradiance measurements. This study introduces a novel approach for site adaptation of solar irradiance based on machine learning techniques, which differs from the conventional statistical methods used in previous studies. Seven machine-learning algorithms have been analysed and compared with conventional statistical approaches to identify Sweden's most accurate algorithms for site adaptation. Solar irradiance data gathered from three weather stations of SMHI is used for training and validation. The results show that machine learning can substantially improve the STRÅNG model's accuracy. However, due to the spatiotemporal heterogeneity in model performance, no universal machine learning model can be identified, which suggests that site adaptation is a location-dependant procedure.
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