• Energy Research
• 2016-2025close
• natural sciences close
• Research Repository of Catalonia close

ABSTRACTWind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly‐to‐seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub‐daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub‐optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub‐daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day‐to‐day variability.

The use of functional fillers can be advantageous in terms of cost reduction and improved properties in plastics. There are many types of fillers used in industry, organic and inorganic, with a wide application area. As a response to the growing concerns about environmental damage that plastics cause, recently fillers have started to be considered as a way to reduce it by decreasing the need for petrochemical resources. Life cycle assessment (LCA) is identified as a proper tool to evaluate potential environmental impacts of products or systems. Therefore, in this study, the literature regarding LCA of plastics with functional fillers was reviewed in order to see if the use of fillers in plastics could be environmentally helpful. It was interesting to find out that environmental impacts of functional fillers in plastics had not been studied too often, especially in the case of inorganic fillers. Therefore, a gap in the literature was identified for the future works. Results of the study showed that, although there were not many and some differences exist among the LCA studies, the use of fillers in plastics industry may help to reduce environmental emissions. In addition, how LCA methodology was applied to these materials was also investigated.

AbstractSki tourism is a major sector of mountain regions economy, which is under the threat of long-term climate change. Snow management, and in particular grooming and artificial snowmaking, has become a routine component of ski resort operations, holding potential for counteracting the detrimental effect of natural snow decline. However, conventional snowmaking can only operate under specific meteorological conditions. Whether snowmaking is a relevant adaptation measure under future climate change is a widely debated issue in mountainous regions, with major implications on the supply side of this tourism industry. This often lacks comprehensive scientific studies for informing public and private decisions in this sector. Here we show how climate change influences the operating conditions of one of the main ski tourism markets worldwide, the French Alps. Our study addresses snow reliability in 129 ski resorts in the French Alps in the 21st century, using a dedicated snowpack model explicitly accounting for grooming and snowmaking driven by a large ensemble of adjusted and downscaled regional climate projections, and using a geospatial model of ski resorts organization. A 45% snowmaking fractional coverage, representative of the infrastructures in the early 2020s, is projected to improve snow reliability over grooming-only snow conditions, both during the reference period 1986–2005 and below 2 °C global warming since pre-industrial. Beyond 3 °C of global warming, with 45% snowmaking coverage, snow conditions would become frequently unreliable and induce higher water requirements.

The oxidation of methane (CH4) using biofilters has been proposed as an alternative to mitigate anthropogenic greenhouse gas emissions with a low concentration of CH4 that cannot be used as a source of energy. However, conventional biofilters utilize organic packing materials that have a short lifespan, clogging problems, and are commonly inoculated with non-specific microorganisms leading to unpredictable CH4 elimination capacities (EC) and removal efficiencies (RE). The main objective of this work was to characterize the oxidation of CH4 in two biotrickling filters (BTFs) packed with polyethylene rings and inoculated with two methanotrophic bacteria, Methylomicrobium album and Methylocystis sp., in order to determine EC and CO2 production (pCO2) when using a specific inoculum. The repeatability of the results in both BTFs was determined when they operated at the same inlet load of CH4. A dynamic mathematical model that describes the CH4 abatement in the BTFs was developed and validated using mass transfer and kinetic parameters estimated independently. The results showed that EC and pCO2 of the BTFs are not identical but very similar for all the conditions tested. The use of specific inoculum has shown a faster startup and higher EC per unit area (0.019 gCH4 m-2 h-1) in comparison to most of the previous studies at the same CH4 load rate (23.2 gCH4 m-3 h-1). Global mass balance showed that the maximum reduction of CO2 equivalents was 98.5 gCO2eq m-3 h-1. The developed model satisfactorily described CH4 abatement in BTFs for a wide range of conditions.

Density Functional Theory (DFT) has become the quasi-standard for ab-initio simulations for a wide range of applications. While the intrinsic cubic scaling of DFT was for a long time limiting the accessible system size to some hundred atoms, the recent progress with respect to linear scaling DFT methods has allowed to tackle problems that are larger by many orders of magnitudes. However, as these linear scaling methods were developed for insulators, they cannot, in general, be straightforwardly applied to metals, as a finite temperature is needed to ensure locality of the density matrix. In this paper we show that, once finite electronic temperature is employed, the linear scaling version of the BigDFT code is able to exploit this locality to provide a computational treatment that scales linearly with respect to the number of atoms of a metallic system. We provide prototype examples based on bulk Tungsten, which plays a key role in finding safe and long-lasting materials for Fusion Reactors. We believe that such an approach might help in opening the path towards novel approaches for investigating the electronic structure of such materials, in particular when large supercells are required.