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This paper aims to carry out an integrated Life Cycle Assessment to evaluate the environmental performance of retrofitting an existing first-generation ethanol plant for sustainable aviation fuel production. Two scenarios are defined, considering: (i) the incorporation of an alcohol-to-jet conversion pathway into a current bioethanol plant in Spain to produce synthetic paraffinic kerosene with aromatics biofuel, and (ii) the operation of two different plants, namely, an existing bioethanol production facility and a new-build ATJ plant. Results indicate that the global warming impact associated with the retrofitting of the bioethanol facility is 44.15 gCO 2eq per MJ of final fuels produced. The main GHG impact is associated with the operation of both the cogeneration system (∼42 %) and the natural gas boilers (∼25 %) to cover the heat and power requirements of the bioethanol facility. Findings concerning the climate change impact of the new-build ATJ plant exhibit analogous behavior; the GHG impact is estimated at 44.53 gCO 2eq /MJ fuels. Future electricity supply mixes with high shares of renewables could lead to substantially lower GHG emissions (up to 61 %) in aviation fuel production, as compared to conventional jet fuels. The replacement of natural gas with sustainable alternatives could further decrease the global warming impact up to 97 %. [ABSTRACT FROM AUTHOR]

Heating up of photovoltaic modules during operation results in a significant power output reduction. This effect is especially relevant in regions with the highest photovoltaic potential, where irradiance and ambient temperature are particularly high. In this paper, a novel low enthalpy geothermal cooling system that improves the efficiency of a commercial solar module is described and experimentally validated. Two heat exchangers, one attached to the backside of the module, and another introduced inside a 15 m deep borehole, are connected in a closed circuit. A water-based coolant is pumped through the system, evacuating the excess heat from the module, and dissipating it underground in a clean and efficient manner, without additional water consumption after filling the system. The performance of the cooling system was experimentally tested by implementing it, for the first time, in a single-axis solar tracking isolated photovoltaic facility. Extensive tests were carried out in Spain, between September 2021 and August 2022 under a wide variety of environmental conditions. The efficiency improvement system has demonstrated its technical feasibility over a one-year period, showing a peak net efficiency improvement up to 13.4 %. Moreover, a positive net efficiency improvement has been demonstrated all year long, even during the winter months. [ABSTRACT FROM AUTHOR]

Solar PV technologies have been expanding rapidly, and the installed power has increased, especially in the last decade. Their inclusion in university buildings arises as a key factor for sustainable transition in cities, since they have an important potential as case studies for urban experimentation positive effects on students and other stakeholders. This paper proposes a method to determine the availability for installing solar PV in universities' rooftops, considering simulation and cartography data, together with a final experimental validation stage. Unavailable areas for each rooftop are determined, including obstacles, the inner use of the rooftop and shadowed areas. Finally, the method verifies the technical suitability for all the proposed scenarios and applies an economic-environmental multicriteria evaluation to select the most suitable one. The methodology is validated in the Universitat Politècnica de València Vera-Campus. In total, 88913 m2 of the rooftops are suitable for solar PV incorporation, representing 48 % of the total surface. 52 % of the solar PV installable capacity takes place in surfaces which can incorporate more than 100 kW, 20.6 % between 50 and 100 kW and 27.4 % less than 50 kW. Finally, the multicriteria evaluation proved that the scenario with all the possible installations is the most suitable one. [ABSTRACT FROM AUTHOR]

Extreme Wind Speed events (EWS) are responsible for the worst damages caused by wind in wind farms. An accurate estimation of the frequency and intensity of EWS is essential to avoid wind turbine damage and to minimize cut-out events in these facilities. In this paper we discuss how generative Data Augmentation (DA) techniques improve the performance of Machine Learning (ML) and Deep Learning (DL) algorithms in EWS prediction problems. These problems are usually tackled as classification tasks, which are highly unbalanced due to the small number of EWS events in wind farms. Different versions of Variational AutoEncoders (VAE) are proposed and analysed in this work (VAEs, Conditional VAEs (CVAEs) and Class-Informed VAEs (CI-VAE)) as generative DA techniques to balance the data in EWS problems, leading to better performance of the prediction systems. The proposed generative DA techniques have been compared against traditional DA algorithms in a real problem of EWS prediction in Spain, considering ERA5 reanalysis data as predictive variables. The results showed that the CI-VAE with a Convolutional Neural Network approach obtained the best results, with values of Precision 0.62, Recall 0.74 and F1 score 0.67, improving up to 4% the results of the method without data augmentation techniques. [Display omitted] • Generative data augmentation techniques can improve extreme wind prediction. • Variational Autoencoders modifications are used to generate synthetic wind extreme samples. • Experiments on real wind speed data from a Spanish wind farm are presented. • Comparisons with traditional data augmentation techniques show prediction improvements. [ABSTRACT FROM AUTHOR]

This paper presents a model for optimizing the life cycle economic and environmental impacts of a hybrid renewable energy and battery storage system – as energy supply technologies (EST) for off-grid farms. Micro-hydropower and photovoltaics represent the primary renewable energy (RE) sources, with batteries and a diesel generator considered as possible back-up systems. A modified Non-dominated Sorting Genetic Algorithm II (NSGA-II) was developed and applied to a case study farm in Southern Spain and provided different EST solutions to optimize a design based on economic (life cycle costing or LCC) and environmental (life cycle assessment or LCA) constraints. The results highlighted that cases with a strong dependence on batteries could achieve minimum costs and lower impacts for climate change and energy resources (non-renewable fuels). In contrast, the optimal system for minimizing the consumption of material resources (metals/minerals) resulted in higher fuel use, which was up to 25% more expensive. The optimal EST solutions used up to 37% of the total energy generated; however, the results were sensitive to energy production-demand distributions. This methodology can help identify the most cost-effective solutions with the lower environmental impacts for decarbonizing the agricultural sector. [Display omitted] [ABSTRACT FROM AUTHOR]