https://www.mdu.se/

Proper resilience metrics (RMs) for energy systems are necessary to be identified, evaluated, and implemented to improve energy system resilience. The proposed framework aims to be implemented as a tool, in the form of an energy resilience matrix, for energy system evaluation. The application of the engineering and infrastructure part of the framework was demonstrated in evaluation of a sport activity area located in Sweden. The results show potential to improve the resilience by installing solar cells and increase battery storage capacity in the area, connect with neighbouring area and utilize vehicle to grid.

Agrivoltaic systems emerge as a promising solution to the ongoing conflict between allocating agricultural land for food production and establishing solar parks. This field experiment, conducted during the spring and summer seasons of 2023, aims to showcase barley production in a vertical agrivoltaic system compared to open-field reference conditions at Kärrbo Prästgård, near Västerås, Sweden. The dataset presented in this article encompasses both barley kernel and straw yields, kernel crude protein levels, starch content in kernels and thousand kernel weight. All collected data underwent analysis of variance (ANOVA) with Tukey pairwise comparison when possible, using dedicated software RStudio 4.3.2. This dataset article illustrates the effects of the vertical agrivoltaic design system on barley productivity. Interested researchers can benefit from this data to better comprehend barley yield under this specific agrivoltaic design and conduct further analyses and comparisons with yields from different locations or design configurations. The experimental data holds the potential to foster collaborations and advance research in agrivoltaic systems, providing a valuable resource for anyone interested in the subject. It was observed that the mean barley yield in all the different areas of the vertical agrivoltaic system were higher than the one in the control area. Additionally, weather and solar irradiance data collected during the growing season are provided in the repository for further usage.

The performance of lithium-ion batteries (LIBs) is sensitive to the operating temperature, and the design and operation of battery thermal management systems reply on accurate information of LIBs' temperature. This study proposes a data-driven model based on neural network (NN) for estimating the temperature profile of a LIB module. Only one temperature measurement is needed for the battery module, which can assure a low cost. The method has been tested for battery modules consisting of prismatic and cylindrical batteries. In general, a good accuracy can be observed that the root mean square error (RMSE) of esitmated temperatures is less than 0.8 °C regardless of the different operating conditions, ambient temperatures, and heat dissipation conditions.

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.

Global warming and global food security make it critical to develop energy -efficient, cost effective and sustainable food production systems with the least environmental footprint. Today, urban farming is supplying 15 -20 % of the world ‘s food and will play a more pivotal role in ensuring food security for burgeoning populations in the future. As one of the most critical growth -promoting agents for plants, carbon dioxide (CO 2 ) is commonly added in urban farming to enhance the yield and productivity, which can be about 9 -45 %. In order to improve energy efficiency and reduce cost, judicious selection and integration of CO 2 enrichment technologies are crucial. This reviews the features and limitations of various technologies that have been used or can be used for urban farms. Key performance indicators, including CO 2 purity, enrichment energy consumption and enrichment cost, have been employed for technology comparison. Combustion and decomposing technologies are characterized as low cost and simple equipment; however, the pollutants and odor produced by these technologies limit their applications in urban farms. Direct air CO 2 capture technologies are emerging technologies; however, their high investment cost and energy consumption need to be reduced before they can be widely adopted. Suggestions on future research and development to advance the capabilities of urban farms are also provided.

This study investigates the performance of agrivoltaic systems employing bifacial photovoltaic modules. A comparison between yield in Sweden and Italy was carried out. Three agrivoltaic system designs were evaluated: vertical fixed, single-axis tracker, and dual-axis tracker. The results showed that the specific production varied between 1090 to 1440 kWh/kWp/yr in Sweden and 1584 to 2112 kWh/kWp/yr in Italy, where the lowest production was obtained with the vertical fixed agrivoltaic system while the highest production was obtained with the dual-axis tracking agrivoltaic system. The vertical fixed design had a higher electricity production during low solar elevation angles, while the single-axis and dual-axis tracking designs had significantly higher power production during mid-day. The electricity production gain using a dual-axis tracker design was mostly during mid-day, but the increase compared to the single-axis tracker was only 1-2%. The study concludes that low-height, fixed agrivoltaic systems without tracking are well-suited for high-latitude countries like Sweden, while elevated systems with tracker solutions are more suitable for locations like Italy. The findings suggest that the performance of agrivoltaic systems with bifacial photovoltaic modules is highly dependent on geographical location and the specific characteristics of the crops grown beneath them.

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.

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.

Offshore wind energy can be considered one of the renewable energy sources with high force potential installed in marine areas. Consequently, the best wind farm layouts identified for constructing combined offshore renewable energy farms are crucial. To this aim, offshore wind potential analysis is essential to highlight the best offshore wind layouts for farm installation and development. Furthermore, the offshore wind farm layouts must be designed and developed based on the offshore wind accurate assessment to identify previously untapped marine regions. In this case, the wind speed distribution and correlation, wind direction, gust speed and gust direction for three sites have been analyzed, and then two offshore wind farm layout scenarios have been designed and analyzed based on two offshore wind turbine types in the Northwest Persian Gulf. In this case, offshore wind farm layouts software and tools have been reviewed as ubiquitous software tools. The results show Beacon M28 and Sea Island buoys location that the highest correlation between wind and gust speeds is between 87% and 98% in Beacon M28 and Sea Island Buoy, respectively. Considerably, the correlation between wind direction and wind speed is negligible. The Maximum likelihood algorithm, the WAsP algorithm, and the Least Squares algorithm have been used to analyze the wind energy potential in offshore buoy locations of the Northwest Persian Gulf. In addition, the wind energy generation potential has been evaluated in different case studies. For example, the Umm Al-Maradim buoy area has excellent potential for offshore wind energy generation based on the Maximum likelihood algorithm, WAsP algorithm, and Least Squares algorithm.

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.