Photosynthetically active radiation (PAR) is a key parameter for modelling the photosynthetic behaviour of plants in response to sunlight and, subsequently, for determining crop yield. Separating PAR into direct and diffuse components is of significance to agrivoltaic systems, which combine solar energy conversion and agricultural farming on the same portion of land. Placing photovoltaic on agricultural land results in varying shading conditions throughout the day and seasons, producing a higher contribution of incident diffuse PAR to the crops beneath the system in these shaded regions. Additionally, photosynthesis is more efficient under conditions of diffuse PAR than direct PAR per unit of total PAR. This work introduces a new separation model for PAR, which is able to accurately estimate diffuse PAR from the global one. The model modifies the YANG2 model, by adding four new predictors: the optical thickness of PAR, vapour pressure deficit, aerosol optical depth, and albedo of PAR. The proposed model has been calibrated, tested, and validated at three sites in Sweden with latitudes above 58° N, obtaining R2 exceeding 0.91 and nRMSE less than 17%. Compared to YANG2, which was previously found to be a high-performance model, the new model is superior by up to 1% both in R2 and nRMSE. Additionally, an analysis of the seasonal trends and variation of the different PAR components is provided to alleviate the dearth of PAR studies in high-latitude regions.

ccurate assessment of the Photosynthetically Active Radiation (PAR) and Global Horizontal Irradiance (GHI) at the crop level is paramount for accurately assessing the energy balances and the crop yield under agrivoltaic systems. The shadings produced by the photovoltaic modules and structures of the agrivoltaic systems cause a non-homogeneous distribution of PAR and GHI at the crop level. It is thus essential to calculate their distribution at a high spatial resolution within the agrivoltaic field. Using field data and commercial software, we have validated a high spatial resolution model (i.e., 25 cm × 25 cm) for PAR and GHI distribution within a vertical agrivoltaic system. The results show good model accuracy with coefficients of determination higher than 0.98 when comparing GHI from the model developed in this study and commercial software based on ray tracing. Model applications are presented with consideration to the computation of surface temperature, evapotranspiration, crop yield, and soil moisture. The model developed in this study shows good agreement in terms of crop yield computations as compared to a previously published model for agrivoltaic systems simulations and optimization (Campana et al., 2021). The added value of the model presented in this study consists in performing high spatial resolution computations of microclimatic parameters and crop yield within the agrivoltaic field.

Decomposition models of solar irradiance estimate the magnitude of diffuse horizontal irradiance from global horizontal irradiance. These two radiation components are well-known to be essential for the prediction of solar photovoltaic systems performance. In open-field agrivoltaic systems, that is the dual use of land for both agricultural activities and solar power conversion, cultivated crops receive an unequal amount of direct, diffuse and reflected photosynthetically active radiation (PAR) depending on the area they are growing due to the non-homogenously shadings caused by the solar panels installed (above the crops or vertically mounted). It is known that PAR is more efficient for canopy photosynthesis under conditions of diffuse PAR than direct PAR per unit of total PAR. For this reason, it is fundamental to estimate the diffuse PAR component in agrivoltaic systems studies to properly predict the crop yield. Since PAR is the part of electromagnetic radiation in the waveband from 400 to 700 nm that can be used for photosynthesis by the crops, several stand-alone decomposition models of solar irradiance are selected in this study to partition PAR into direct and diffuse. These models are applied and validated in three locations in Sweden: Lanna, Hyltemossa and Norunda, using the coefficients stated on the original publications of the models and locally fitted coefficients. Results showed weaker performances in all stand-alone models for non-locally fitted coefficients (nRMSE ranging from 29% to 95%). However, performances improve with re-parameterization, reaching highest nRMSE of 37.94% in Lanna. YANG2 decomposition model is the best-performing one, reaching lowest nRMSE of 24.31% in Norunda applying re-estimated coefficients. Country level sets of coefficients for the best-performing models, YANG2 and STARKE, are given after parameterization using joined data of the three locations in Sweden. These Sweden-fitted models are tested and showing nRMSE of 25.56% (YANG2) and 28.36% (STARKE). These results can be used to perform estimations of PAR diffuse component in Sweden where measurements are not available, and the overall methodology can be similarly applied to other countries.

Agrivoltaic systems represent an intelligent solution combining electricity production from solar photovoltaic technology with agricultural production and avoiding land use conflicts. Geographic Information System technologies can support the implementation and spread of agrivoltaic systems by identifying the most suitable areas using useful spatially explicit information concerning techno-agro-socio-economic criteria. In this study, we have developed a procedure to identify and classify suitable areas for agrivoltaic systems in Sweden. An Ordinal Priority Approach based multi-criteria decision making algorithm is established to calculate the weights of the selected evaluation criteria through expert interviews. The land use data refers to the Corine Land Cover 2018 product.The results show that 8.55% of the Swedish territory, approximately 38,485 km2, is suitable for installing APV systems. Among this area, 0.17% is classified as "excellent", about 15% as "very good", about 72% as "good", about 13.1% as "moderate", and less than 0.1% as "poor". Through the deployment of vertically mounted agrivoltaic systems with bifacial photovoltaic modules, the total "excellent" areas can potentially supply 2.44 TWh against the electricity consumption in 2021 of about 143 TWh. On the other hand, the land classified as "excellent" and "very good" could potentially provide about 207 TWh, which is a much higher production capacity than the 2021 electricity consumption. The total potential installed capacity for "excellent" areas is 2.3 GWp, while for areas classified "excellent" and "very good" is 201 GWp.