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  • 1.
    Campana, Pietro Elia
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Lastanao, Pablo
    Mälardalen University.
    Zainali, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zhang, J.
    Uppsala University, Department of Earth Sciences, SE, Uppsala, Sweden.
    Landelius, T.
    Swedish Meteorological and Hydrological Institute, SE, Norrköping, Sweden.
    Melton, F.
    NASA Ames Research Center Cooperative for Research in Earth Science and Technology (NASA ARC-CREST), Moffett Field, United States.
    Towards an operational irrigation management system for Sweden with a water–food–energy nexus perspective2022In: Agricultural Water Management, ISSN 0378-3774, E-ISSN 1873-2283, Vol. 271, article id 107734Article in journal (Refereed)
    Abstract [en]

    The 2018 drought in Sweden prompted questions about climate-adaptation and -mitigation measures – especially in the agricultural sector, which suffered the most. This study applies a water–food–energy nexus modelling framework to evaluate drought impacts on irrigation and agriculture in Sweden using 2018 and 2019 as case studies. A previous water–food–energy nexus model was updated to facilitate an investigation of the benefits of data-driven irrigation scheduling as compared to existing irrigation guidelines. Moreover, the benefits of assimilating earth observation data in the crop model have been explored. The assimilation of leaf area index data from the Copernicus Global Land Service improves the crop yield estimation as compared to default crop model parameters. The results show that the irrigation water productivities of the proposed model are measurably improved compared to conventional and static irrigation guidelines for both 2018 and 2019. This is mostly due to the advantage of the proposed model in providing evapotranspiration in cultural condition (ETc)-driven guidelines by using spatially explicit data generated by mesoscale models from the Swedish Meteorological and Hydrological Institute. During the drought year 2018, the developed model showed no irrigation water savings as compared to irrigation scenarios based on conventional irrigation guidelines. Nevertheless, the crop yield increase from the proposed irrigation management system varied between 10% and 60% as compared to conventional irrigation scenarios. During a normal year, the proposed irrigation management system leads to significant water savings as compared to conventional irrigation guidelines. The modelling results show that temperature stress during the 2018 drought also played a key role in reducing crop yields, with yield reductions of up to 30%. From a water–food–energy nexus, this motivates the implementation of new technologies to reduce water and temperature stress to mitigate likely negative effects of climate change and extremes. By using an open-source package for Google Earth®, a demonstrator of cost-effective visualization platform is developed for helping farmers, and water- and energy-management agencies to better understand the connections between water and energy use, and food production. This can be significant, especially during the occurrence of extreme events, but also to adapt to the negative effects on agricultural production of climate changes.

  • 2.
    Campana, Pietro Elia
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Stridh, Bengt
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Hörndahl, T.
    Swedish University of Agricultural Sciences, Department of Biosystems and Technology, Alnarp, Sweden.
    Svensson, S. -E
    Swedish University of Agricultural Sciences, Department of Biosystems and Technology, Alnarp, Sweden.
    Zainali, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ma Lu, Silvia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zidane, Tekai Eddine Khalil
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    De Luca, P.
    Barcelona Supercomputing Center, Barcelona, Spain.
    Amaducci, S.
    Università Cattolica del Sacro Cuore, Department of Sustainable Crop Production, Piacenza, Italy.
    Colauzzi, M.
    Università Cattolica del Sacro Cuore, Department of Sustainable Crop Production, Piacenza, Italy.
    Experimental results, integrated model validation, and economic aspects of agrivoltaic systems at northern latitudes2024In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 437, article id 140235Article in journal (Refereed)
    Abstract [en]

    Agrivoltaic systems, which allow the coexistence of crop and electricity production on the same land, are an integrated water–energy–food nexus solution that allows the simultaneous attainment of conflicting Sustainable Development Goals. This study aims to analyse experimental results on the responses of ley grass yield and quality to shadings in the first agrivoltaic system in Sweden. It also aims to validate an integrated modelling platform for assessing agrivoltaic systems' performances before installation. An economic analysis is carried out to compare the profitability of agrivoltaic versus conventional ground-mounted photovoltaic systems and, using a Monte Carlo Analysis, to identify the parameters that most affect the profitability. Despite the agrivoltaic systems’ supporting structures and photovoltaic modules producing an average ∼25% reduction in photosynthetically active radiation at ground level, no statistically significant difference was observed between the yield of the samples under the agrivoltaic system compared to the yield of the samples in the reference area. The agrivoltaic system attained land equivalent ratios of 1.27 and 1.39 in 2021 and 2022, respectively. The validation results of the integrated modelling platform show that the sub-model concerning the crop yield response to shading conditions tends to underestimate ∼7% the actual average crop yield under the agrivoltaic system. The results of the economic analysis show that, from a net present value perspective, agrivoltaic systems have a profitability that is ∼30 times higher than a conventional crop rotation in Sweden.

  • 3.
    Campana, Pietro Elia
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Stridh, Bengt
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zainali, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ma Lu, Silvia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Andersson, Ulf
    Kärrbo Prästgård AB, Sweden.
    Nordström, Josefin
    Solkompaniet Sverige AB, Sweden.
    Bergdahl, Pontus
    Solkompaniet Sverige AB, Sweden.
    Hörndahl, Torsten
    Swedish University of Agricultural Sciences, SLU, Sweden.
    Svensson, Sven-Erik
    Swedish University of Agricultural Sciences, SLU, Sweden.
    Evaluation of the first agrivoltaic system in Sweden2023Report (Other academic)
    Abstract [en]

    Photovoltaic (PV) systems in Sweden have primarily been seen as an energy efficiency measure to reduce the amount of purchased electricity for buildings, both residential and commercial. Only recently utility-scale solar systems have begun to increase their share of the solar market to support national energy and emissions targets. Due to the economies of scale, conventional ground-mounted PV (CGMPV) installations represent the best solution for producing electricity at the lowest specific initial investment costs. This relatively new solar market segment, with large-scale ground-mounted solar farms on agricultural land, has faced several challenges with the permitting process. Agricultural land that is suitable for cultivation is of "national importance" according to the Swedish Environmental Code. Cultivable agricultural land may be exploited for other purposes on a permanent basis only if it is necessary to satisfy essential societal interests and there is no other possible land to use within the area in question. Traditionally, ground-mounted solar farms have increased competition for land resources for food production and drawn criticism in the so-called "food-versus-fuel (electricity)" debate over whether agricultural land should be used for electricity generation or food production. Agrivoltaic (APV) systems represent an intelligent solution to avoid land use competition by combining arable farming and electricity production on the same agricultural land. The main objective of this project was to study how APV systems perform from an energy, agricultural and economic perspective compared to CGMPV systems and agriculture production. The project aimed to highlight advantages and disadvantages of APV systems at northern latitudes with an energy-food-water perspective. The aim was pursued by establishing an APV test site, the first APV system in Sweden, monitoring its performance both from an energy and agricultural point of view, and developing new techno-economic models. Data from the APV test site were used to better understand how APV systems at northern latitudes affect: 1) the efficiency of the solar modules; 2) crop productivity, and 3) the financial return for ground-based solar PV systems. The first agrivoltaic system in Sweden has been built on a permanent ley grass field, at Kärrbo Prästgård, Västerås, and research activities have been carried out on the ley grass during 2021 and 2022. As in previous research studies in other countries, we defined three sub-fields: 1) a sub-field is covered only by the ley grass (reference area), 2) a sub-field is a CGMPV system 11.8 kWp solar PV system with two rows of solar modules with a 30° tilt and 3) the last subfield is a  22.8 kWp APV system with three rows of vertically mounted solar modules, with ley grass between the modules. This field set-up allowed for comparisons between practices (agriculture and electricity generation) and technologies (CGMPV systems versus APV systems). The calculated specific electricity production during a typical meteorological year for the APV system and the CGMPV system was 1,067 kWh/kWp/year and 1,116 kWh/kWp/year, respectively. Nevertheless, the APV system tends to have higher efficiency than the CGMPV systems due to the solar irradiation patterns on the solar cell surfaces and wind cooling of the PV modules. The main results of the project in terms of shadow effects on the ley grass showed that the APV system did not significantly affect the productivity of the forage grass in 2021-2022. There was no statistically significant difference between the yield of the samples taken in the APV system and the reference area. Even so, the yield per hectare is reduced by approximatively 10%, when the distance between the vertically mounted solar modules is 10 meters, due to the area under the solar modules that cannot be mechanically harvested. The measurements performed at the test site allowed us to validate the earlier developed model for both electricity production and the effects of shading on crop production. Having a model to assess crop yields under APV systems is of utmost importance to be able to pre-assess the system's effects on food production, which is one of the main goals of APV system regulations worldwide. From an economic perspective, APV systems cannot compete with CGMPV systems due to lower electricity production per hectare, lower density of the solar modules per hectare, and higher investment costs per hectare. Nevertheless, APV systems can be the solution to overcome the legal obstacles that prohibit or hinder the use of agricultural land for electricity generation with PV systems. 

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  • 4.
    Elkadeem, M. R.
    et al.
    Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
    Zainali, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ma Lu, Silvia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Younes, A.
    Geography and GIS Department, Faculty of Arts, Kafrelsheikh University, Kafrelsheikh, Egypt.
    Abido, M. A.
    SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.
    Amaducci, S.
    Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy.
    Croci, M.
    Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy.
    Zhang, J.
    Department of Earth Sciences, Uppsala University, Uppsala, Sweden.
    Landelius, T.
    Swedish Meteorological and Hydrological Institute, Norrköping, Sweden.
    Stridh, Bengt
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Agrivoltaic systems potentials in Sweden: A geospatial-assisted multi-criteria analysis2024In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 356, article id 122108Article in journal (Refereed)
    Abstract [en]

    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. 

  • 5.
    Lu, Silvia Ma
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Yang, D.
    School of Electrical Engineering and Automation, Harbin Institute of Technology 2 , Harbin, Heilongjiang, China.
    Anderson, M. C.
    USDA ARS, Hydrology and Remote Sensing Laboratory 3 , Beltsville, Maryland 20705, USA.
    Zainali, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Stridh, Bengt
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Photosynthetically active radiation separation model for high-latitude regions in agrivoltaic systems modeling2024In: Journal of Renewable and Sustainable Energy, E-ISSN 1941-7012, Vol. 16, no 1, article id 013503Article in journal (Refereed)
    Abstract [en]

    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.

  • 6.
    Ma Lu, Silvia
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zainali, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Stridh, Bengt
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Amaducci, S.
    Univ Cattolica Sacro Cuore, Dept Sustainable Crop Prod, Via Emilia Parmense 84, I-29122 Piacenza, Italy..
    Colauzzi, M.
    Univ Cattolica Sacro Cuore, Dept Sustainable Crop Prod, Via Emilia Parmense 84, I-29122 Piacenza, Italy..
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Photosynthetically active radiation decomposition models for agrivoltaic systems applications2022In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 244, p. 536-549Article in journal (Refereed)
    Abstract [en]

    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 predicting the performance of solar photovoltaic systems. In open-field agrivoltaic systems (i.e., the dual use of land for both agricultural activities and solar power conversion), cultivated crops receive unequal amounts of direct, diffuse, and reflected photosynthetically active radiation (PAR). These uneven amounts depend on where the crops are growing due to the non-homogenous shadings caused by the presence of the installed solar panels (above the crops or vertically mounted). It is known that, per unit of total PAR, diffuse PAR is more efficient for canopy photosynthesis than is direct PAR. For this reason, it is essential to estimate the diffuse PAR component when agrivoltaic systems are being assessed, in order to properly predict the crop yield. Since PAR is the electro-magnetic radiation in the 400-700 nm waveband that can be used for photosynthesis by the crops, several stand-alone decomposition models typically used to split global horizontal irradiance are selected in this study to decompose 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 models' original publications and locally fitted coefficients. The results showed weaker performances in all stand-alone models for non-locally fitted coefficients (nRMSE ranging from 27% to 43%). However, performances improve with re-parameterization, with a highest nRMSE of 35.24% in Lanna. The Y(ANG)2 decomposition model is the best-performing one, with the lowest nRMSE of 23.75% in Norunda when applying re-estimated coefficients. Country level sets of coefficients for the best-performing models (Y(ANG)2 and STARKE) are given after parameterization using combined data for all three locations in Sweden. These Sweden-fitted models are tested and show an nRMSE of 25.08% (Y(ANG)2) and 28.60% (STARKE). These results can be used to perform estimations of the PAR diffuse component in Sweden wherever ground measurements are not available. The overall methodology can be similarly applied to other countries.

  • 7.
    Ma Lu, Silvia
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zainali, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Sundström, Elin
    Mälardalen University.
    Nygren, Anton
    Mälardalen University.
    Stridh, Bengt
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Validation of Vertical Bifacial Agrivoltaic and Other Systems Modelling: Effect of Dynamic Albedo on Irradiance and Power Output Estimations2023Conference paper (Refereed)
    Abstract [en]

    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. 

  • 8.
    Zainali, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Microclimate modelling for agrivoltaic systems2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    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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  • 9.
    Zainali, Sebastian
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Lindahl, Johan
    Chalmers University of Technology, Department of Technology Management and Economics, SE-412 96, Göteborg, Sweden.
    Lindén, Johan
    Mälardalen University, School of Business, Society and Engineering, Industrial Economics and Organisation.
    Stridh, Bengt
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    LCOE distribution of PV for single-family dwellings in Sweden2023In: Energy Reports, E-ISSN 2352-4847, Vol. 10, p. 1951-1967Article in journal (Refereed)
    Abstract [en]

    In Sweden, the installations of solar photovoltaic systems are growing rapidly, and especially the market segment of small-scale distributed systems is experiencing positive growth. The current installation volumes exceed the expectations of the Swedish authorities. This study presents an up-to-date assessment of the levelized cost of electricity to be used for both agencies in their long-term scenario work of PV development and for private investors for estimating the upfront and future costs and risks associated with photovoltaic systems. The analysis is based on the turnkey system cost of 6,098 single-family dwelling photovoltaic systems commissioned in Sweden between the 1st of January 2019 and 1st of July 2020. The statistics of system investments costs are complemented by literature studies and by interviews of relevant stakeholders for the other input parameters needed to calculate the Levelized Cost of Electricity (LCOE). A Monte Carlo analysis was applied on all the input parameters provides relevant insight into the range of LCOE values. The unsubsidized levelized cost of electricity for most systems ranged from 0.85 SEK/kWh (25th percentile) to 1.15 SEK/kWh (75th percentile), with a mean at 1.02 SEK/kWh at reasonable real discount rate of 2%, but that extreme values can reach 0.30 SEK/kWh at a 0% discount rate and 5.70 SEK/kWh at a 5% discount rate. Taking into account the current (2023) Swedish tax reduction for investment in green technologies that amounts to an effective deduction of 19.4% of the total system investment costs lowers the LCOE to mean at 0.82 SEK/kWh at real discount rate of 2%. The LCOE for single-family dwelling photovoltaic systems are generally lower than the assumed LCOE in long-term scenario studies of the Swedish electricity system. This finding helps to explain to the authorities the unexpected fast deployment of distributed photovoltaic systems in Sweden.

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  • 10.
    Zainali, Sebastian
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ma Lu, Silvia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Stridh, Bengt
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Amaducci, S.
    Università Cattolica del Sacro Cuore, Dept. of Sustainable Crop Production, Piacenza, Italy.
    Colauzzi, M.
    Università Cattolica del Sacro Cuore, Dept. of Sustainable Crop Production, Piacenza, Italy.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Direct and diffuse shading factors modelling for the most representative agrivoltaic system layouts2023In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 339, article id 120981Article in journal (Refereed)
    Abstract [en]

    Agrivoltaic systems are becoming increasingly popular as a crucial technology for attaining multiple sustainable development goals, such as affordable and clean energy, zero hunger, clean water and sanitation, and climate action. However, a comprehensive understanding of the shading effects on crops is essential for choosing an optimal agrivoltaic system, as an incorrect choice can result in significant crop yield reductions. In this study, fixed vertical, one-axis tracking, and two-axis tracking photovoltaic arrays were developed for agrivoltaic applications to analyse the shading conditions on the ground used for crop production. The models demonstrated remarkable accuracy in comparison to commercial software such as PVsyst® and SketchUp®. These models will help to reduce crop yield uncertainty under agrivoltaic systems by providing accurate photosynthetically active radiation distribution at the crop level. The photosynthetically active radiation distribution was further analysed using a light homogeneity index, and the results showed that homogeneity and photosynthetically active radiation reduction varied significantly depending on the agrivoltaic system design, ranging from 86% to 95%, and 11% to 22%, respectively. Studying the effect of shading with distribution analysis is crucial for identifying the most suitable agrivoltaic system layout for specific crops and geographical locations.

  • 11.
    Zainali, Sebastian
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ma Lu, Silvia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Stridh, Bengt
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Amaducci, Stefano
    Università Cattolica del Sacro Cuore, Dept. of Sustainable Crop Production, Emilia Parmense 84, Piacenza, Italy.
    Colauzzi, Michele
    Università Cattolica del Sacro Cuore, Dept. of Sustainable Crop Production, Emilia Parmense 84, Piacenza, Italy.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Direct and diffuse shading factors modelling for the most representative agrivoltaic system layoutsManuscript (preprint) (Other academic)
    Abstract [en]

    Agrivoltaic systems are becoming more popular as a critical technology for attaining several sustainable development goals such as affordable and clean energy, zero hunger, clean water and sanitation, and climate action. However, understanding the shading effects on crops is fundamental to choosing an optimal agrivoltaic system as a wrong choice could lead to severe crop reductions. In this study, fixed vertical, one-axis tracking, and two-axis tracking photovoltaic arrays for agrivoltaic applications are developed to analyse the shading conditions on the ground used for crop production. The models have shown remarkably similar accuracy compared to commercial software such as PVsyst® and SketchUp®. The developed models will help reduce the crop yield uncertainty under agrivoltaic systems by providing accurate photosynthetically active radiation distribution at the crop level. The distribution was further analysed using a light homogeneity index and calculating the yearly photosynthetically active radiation reduction. The homogeneity and photosynthetically active radiation reduction varied significantly depending on the agrivoltaic system design, from 91% to 95% and 11% to 34%, respectively. To identify the most suitable agrivoltaic system layout dependent on crop and geographical location, it is of fundamental importance to study the effect of shadings with distribution analysis.

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  • 12.
    Zainali, Sebastian
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Qadir, Omar
    Mälardalen University.
    Parlak, Sertac Cem
    Mälardalen University.
    Lu, Silvia Ma
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Stridh, Bengt
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Computational fluid dynamics modelling of microclimate for a vertical agrivoltaic system2023In: Energy Nexus, ISSN 2772-4271, Vol. 9, article id 100173Article in journal (Refereed)
    Abstract [en]

    The increasing worldwide population is leading to a continuous increase in energy and food demand. These increasing demands have led to fierce land-use conflicts as we need agricultural land for food production while striving towards renewable energy systems such as large-scale solar photovoltaic (PV) systems, which also require in most of the cases agricultural flat land for implementation. It is therefore essential to identify the interrelationships between the food, and energy sectors and develop sustainable solutions to achieve global goals such as food and energy security. A technology that has shown promising potential in supporting food and energy security, as well as supporting water security, is agrivoltaic (AV) systems. This technology combines conventional farm activities with PV systems on the same land. Understanding the microclimatic conditions in an AV system is essential for an accurate assessment of crop yield potential as well as for the energy performance of the PV systems. Nevertheless, the complex mechanisms governing the microclimatic conditions under agrivoltaic systems represent an underdeveloped research area. In this study, a computational fluid dynamics (CFD) model for a vertical AV system is developed and validated. The CFD model showed PV module temperature estimation errors in the order of 0–2 °C and ground temperature errors in the order of 0–1 °C. The shading caused by the vertical PV system resulted in a reduction of solar irradiance by 38%. CFD modelling can be seen as a robust approach to analysing microclimatic parameters and assessing AV system performance.

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  • 13.
    Zainali, Sebastian
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Yang, Dazhi
    Harbin Institute of Technology, China.
    Landelius, Tomas
    Swedish Meteorological and Hydrological Institute, Sweden.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Site adaptation with machine learning for a Northern Europe gridded global solar irradiance product2023In: Energy and AI, ISSN 2666-5468, Vol. 15, article id 100331Article in journal (Refereed)
    Abstract [en]

    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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