• Energy Research
• 2021-2025close
• GB close
• DE close
• Applied Energy close

Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on the analysis of sequences of ground-taken sky images or satellite observations. Despite encouraging results, a recurrent limitation of existing deep learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a frequent temporal lag and limited ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to not only predict future irradiance levels and associated uncertainties, but also segmented images, which provide richer information on the local irradiance map. We show that ECLIPSE anticipates critical events and reduces temporal delay while generating visually realistic futures. The model characteristics and properties are investigated with an ablation study and a comparative study on the benefits and different ways to integrate auxiliary data into the modelling. The model predictions are also interpreted through an analysis of the principal spatio-temporal components learned during network training. Manuscript accepted for publication in Applied Energy

Thin-film photovoltaics (PV) cells offer several benefits over conventional first-generation PV technologies, including lighter weight, flexibility, and lower power generation cost. Among the competing thin-film technologies, chalcogenide solar cells offer promising performance on efficiency and technological maturity level. However, in order to appraise the performance of the technology thoroughly, issues such as raw materials scarcity, toxicity, and environmental impacts need to be investigated in detail. This paper therefore, for the first time, presents a cradle to gate life cycle assessment for four different emerging chalcogenide PV cells, and compares their results with copper zinc tin sulfide (CZTS) and the commercially available CIGS to examine their effectiveness in reducing the environmental impacts associated with PV technologies. To allow for a full range of indicators, life cycle assessment methods CML 2001, IMPACT 2002+, and ILCD 2011 were used to analyse the results. The results identify environmental hotspots associated with different materials and components and demonstrate that using current efficiencies, the environmental impact of copper indium gallium selenide (CIGS) for generating 1kWh electricity was lower than that of the other studied cells. However, at comparable efficiencies the antimony-based cells offered the lowest environmental impacts in all impact categories. The effect of materials used was also found to be lower than the impact of electricity consumed throughout the manufacturing process, with the absorber layer contributing the most to the majority of the impact categories examined. In terms of chemicals consumed, cadmium acetate contributed significantly to the majority of the environmental impacts. Stainless steel in the substrate/insulating layer and molybdenum in the back contact both contributed considerably to the toxicity and ozone depletion impact categories. This paper demonstrates considerable environmental benefits associated with non-toxic chalcogenide PV cells suggesting that the current environmental concerns can be addressed effectively using alternative materials and manufacturing techniques if current efficiencies are improved. Peer Reviewed

Access to decentralized energy solutions is critical for the development of rural communities in Sub-Saharan Africa (SSA). One form of decentralized energy technology, agrivoltaics (AV), has gained attention in the last decade as a means to tackle energy poverty in off-grid communities. The success of renewable energy (RE) projects of any kind, however, depends on existing challenges and risks that may be faced at different stages. Therefore, the aim of this paper is to identify and analyze the main risk factors when implementing agrivoltaics on a community level in SSA. Based on the PESTLE framework, the paper develops a first risk classification system for international agrivoltaic projects in rural farming communities in SSA. By making use of a mixed-methods approach, the research enables the prioritization of potential risks in terms of their occurrence and significance; furthermore, it discusses mitigation strategies. Results indicate that there are various multifaceted risks associated with agrivoltaics, primarily including financial challenges and regulatory barriers. In addition, the study emphasizes the importance of stakeholder involvement as well as a need for long-term community engagement and capacity building to ensure a successful implementation and sustainability of agrivoltaic projects.