• Energy Research
• 2025-2025close
• CA close
• DE close
• FR close

The severity of drought is predicted to increase across Europe due to climate change. Droughts can substantially impact terrestrial nitrogen (N) cycling and the corresponding microbial communities. Here, we investigated how ammonia-oxidizing bacteria (AOB), archaea (AOA), and complete ammonia oxidizers (comammox) as well as inorganic N pools and N2O fluxes respond to simulated drought under different cropping systems. A rain-out shelter experiment was conducted as part of a long-term field experiment comparing cropping systems that differed mainly in fertilization strategy (organic, mineral, or mixed mineral and organic) and plant protection management (biodynamic versus conventional pesticide use). We found that the effect of drought varied depending on the specific ammonia-oxidizing (AO) groups and the type of cropping system. Drought had the greatest impact on the structure of the AOA community compared to the other AO groups. The abundance of ammonia oxidizers was also affected by drought, with comammox clade B exhibiting the highest sensitivity. Additionally, drought had, overall, a stronger impact on the AO community structure in the biodynamic cropping system than in the mixed and mineral-fertilized conventional systems. The responses of ammonia-oxidizing communities to drought were comparable between bulk soil and rhizosphere. We observed a significant increase in NH4+ and NO3− pools during the drought period, which then decreased after rewetting, indicating a strong resilience. We further found that drought altered the complex relationships between AO communities and mineral N pools, as well as N2O fluxes. These results highlight the importance of agricultural management practices in influencing the response of nitrogen cycling guilds and their processes to drought. Soil Biology and Biochemistry, 201 ISSN:0038-0717 ISSN:1879-3428

Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

In the last decade, the manufacturing capacity of silicon, the dominant PV technology, has increasingly been concentrated in China. This has led to PV cost reduction of approximately 80%, while, at the same time, posing risks to PV supply chain security. Recent advancements of novel perovskite tandem PV technologies as an alternative to traditional silicon-based PV provide opportunities for diversification of the PV manufacturing capacity and for increasing the GHG emission benefit of solar PV. Against this background, we estimate the current and future cost-competitiveness and GHG emissions of a set of already commercialized as well as emerging PV technologies for different production locations (China, USA, EU), both at residential and utility-scale. We find EU and USA-manufactured thin-film tandems to have 2 to 4% and 0.5 to 2% higher costs per kWh and 37 to 40%and 32 to 35% less GHG emissions per kWh at residential and utility-scale, respectively. Our projections indicate that they will also retain competitive costs (up to 2% higher)and a 20% GHG emissions advantage per kWh in 2050.