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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.

With growing gas and oil prices, electricity generation based on these fossil fuels is becoming increasingly expensive. Furthermore, the vision of natural gas as a transition fuel is subject to many constraints and uncertainties of economic, environmental, and geopolitical nature. Consequently, renewable energies such as solar and wind power are expected to reach new records of installed capacity over the upcoming years. Considering the above, North Africa is one of the regions with the largest renewable resource potential globally. While extensively studied in the literature, these resources remain underutilized. Thus, to contribute to their future successful deployment and integration with the power system, this study presents a spatial and temporal analysis of the nature of solar and wind resources over North Africa from the perspective of energy droughts. Both the frequency and maximal duration of energy droughts are addressed. Both aspects of renewables’ variable nature have been evaluated in the North Atlantic Oscillation (NAO) context. The analysis considers the period between 1960 and 2020 based on hourly reanalysis data (i.e., near-surface shortwave irradiation, wind speed, and air temperature) and the Hurrel NAO index. The findings show an in-phase relationship between solar power and winter NAO index, particularly over the coastal regions in western North Africa and opposite patterns in its eastern part. For wind energy, the connection with NAO has a more zonal pattern, with negative correlations in the north and positive correlations in the south. Solar energy droughts dominate northern Tunisia, Algeria, and Morocco, while wind energy droughts mainly occur in the Atlas Mountains range. On average, solar energy droughts tend not to exceed 2–3 consecutive days, with the longest extending for five days. Wind energy droughts can be as prolonged as 80 days (Atlas Mountains). Hybridizing solar and wind energy reduces the potential for energy droughts significa

Agrivoltaic systems represent an intelligent solution combining electricity production from solar photovoltaic technology with agricultural production to avoid 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 about 8.6% of the Swedish territory, approximately 38,485 km2, is suitable for installing agrivoltaic systems. Among this area, about 0.2% is classified as “excellent”, about 15% as “very good”, about 72% as “good”, about 13% as “moderate”, and about 0.1% as “poor”. Most “excellent”-classified areas are in Kalmar, Skåne, and Gotland. In contrast, most “very good” sites are in Skåne, Kalmar, and Östergötland. By deploying vertically mounted agrivoltaic systems with bifacial photovoltaic modules, the total potential installed capacity for “excellent” areas is about 2.5 GWp, while for areas classified “excellent” and “very good” is about 221 GWp. The total “excellent” areas can potentially supply about 2.4 TWh of electricity 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. The County of Västra Götaland shows the greatest potentials in terms of total potential electricity supply from agrivoltaic systems with about 227 TWh, followed by Skåne with a total potential of 206 TWh.