• Energy Research

The use of cover crops (CCs) during winter can improve the structure and water retention capacity of the soil. Additionally, the harvested CCs could be used as substrate in an anaerobic digestion (AD) plant. This paper aims at assessing the environmental and economic consequences of planting rye as a winter CC (after maize) and its use as co-substrate in an AD plant (Rye scenario) instead of leaving the land fallow during winter and use solely maize for co-digestion with manure (NoRye scenario). The life cycle assessment (LCA) of 1 MJ of produced bioenergy (36% electricity and 64% heat) shows significant benefits for marine eutrophication for the Rye scenario due to reductions in nitrate leaching. However, the lower specific yield of rye and the biogas potential for the Rye scenario resulted in higher total impacts on climate change and resource depletion (higher use of machinery and infrastructures for 1 MJ of produced bioenergy), as compared to the use of maize in the NoRye scenario. Based on the analysis, possible methodological improvements are highlighted, in particular for the simulation of field emissions and regionalization of impacts. From an economic point-of-view, planting rye during winter could generate additional revenues for the farmer. However, the calculation incorporates large uncertainties, linked mainly to price volatility, seasonal weather conditions (and related yield variations), and to the possible influence of CCs on the summer crop yield. In conclusion, this paper presents a first overview of the sustainability performances of using rye as a CC for energy purposes.

The production of biogas from energy crops, organic waste and manure has augmented considerably the amounts of digestate available in Flanders. This has pushed authorities to steadily introduce legislative changes to promote its use as a fertilising agent. There is limited arable land in Flanders, which entails that digestate has to compete with animal manure to be spread. This forces many anaerobic digestion plants to further treat digestate in such a way that it can either be exported or the nitrogen be removed. Nevertheless, the environmental impact of these treatment options is still widely unknown, as well as the influence of these impacts on the sustainability of Flemish anaerobic digestion plants in comparison to other regions where spreading of raw digestate is allowed. Despite important economic aspects that must be considered, the use of Life Cycle Assessment (LCA) is suggested in this study to identify the environmental impacts of spreading digestate directly as compared to four different treatment technologies. Results suggest relevant environmental gains when the digestate mix is treated using the examined conversion technologies prior to spreading, although important trade-offs between impact categories were observed and discussed. The promising results of digestate conversion technologies suggest that further LCA analyses should be performed to delve into, for instance, the appropriateness to shift to nutrient recovery technologies rather than digestate conversion treatments.

Abstract The authors developed a forecasting model for Luxembourg, able to predict the expected regional PV power up to 72 h ahead. The model works with solar irradiance forecasts, based on numerical weather predictions in hourly resolution. Using a set of physical equations, the algorithm is able to predict the expected hourly power production for PV systems in Luxembourg, as well as for a set of 23 chosen PV-systems which are used as reference systems. Comparing the calculated forecasts for the 23 reference systems to their measured power over a period of 2 years, revealed a comparably high accuracy of the forecast. The mean deviation (bias) of the forecast was 1.1% of the nominal power – a relatively low bias indicating low systemic error. The root mean square error (RMSE), lies around 7.4% - a low value for single site forecasts. Two approaches were tested in order to adapt the short-term forecast, based on the present forecast deviations for the reference systems. Thereby, it was possible to improve the very short term forecast on the time horizon of 1–3 h ahead, specifically for the remaining bias, but also systemic deviations can be identified and partially corrected (e.g. snow cover).

Abstract The expansion of Short Rotation Coppice (SRC) practices is mainly driven by the viability of SRC wood as an alternative to other renewable and non-renewable fuels in energy production, but also to the capacity of increasing biodiversity and the supply of ecosystem services locally. To delve into these environmental synergies and possible trade-offs, the Life Cycle Assessment method was applied to seven SRC experimental sites recently implemented in Flanders (Belgium). These have differing land use objectives and, thus, present different species proportions and plantation density. For instance, most sites are either planted with willow and poplar clones, or with a mix of the two with local tree species in order to activate temporary unused industrial lands or enhance the local ecosystem functionality. A regular 3 to 7-year rotation was simulated up to year 2033 using CO2FIX given that trees were yet to be harvested at the time of the assessment. Yields were first estimated over time: SRC systems composed by mixed species presented the highest productivity and also the best environmental performance profiles. Overall, the highest environmental impacts were due to consumption of diesel during the cyclic harvests, but also to fertilization activities. Uncertainty distribution ranges were determined for the most critical parameters and a Monte Carlo analysis was performed to obtain average impact scores with variability ranges. While replacing hardwood with wood from SRC chips was not found to be advantageous because of e.g. larger metal, fossil and ozone depletion potentials, benefits were observed for land use reduction and climate change mitigation. Due to frequent rotations, the beneficial trends for the latter seem sufficient to compensate the negative effects of the other impacts on human health and ecosystems quality.

Abstract The use of renewable energy is a possible solution to reduce the contribution to climate change of human activities. Nevertheless, there is much controversy about the non-climate related environmental impacts of renewable energy as compared to fossil energy. The aim of this study is to assess a new technology of biomethane production by monofermentation of cultivated crops. Based on the results of an attributional Life Cycle Assessment (LCA), the contribution to climate change of biomethane production and injection into the grid is 30–40% (500a time horizon) or 10–20% (100a) lower than the contribution of natural gas importation. The reduction depends mainly on the biogas yield, the amount of readily available nitrogen in the digestate and the type of agricultural practices. Nevertheless, the natural gas definitively generates far lower ecosystem quality and human health damages than the biomethane production. Farming activities have the most important contribution to the damages mainly because of land occupation and the use of fertilizer. The main improvement opportunities highlighted are: the increase of biogas yield, the choice of good agricultural practices and the cultivation of winter or summer crops exclusively. Future research should include the emission and sequestration of CO2 from soil. The ripple effects related to the total increase of farming area and the consequences of farming activities on the food production chain should be addressed as well. To this aim, the switch to consequential LCA is a critical challenge, from both the methodological and application point of view, to support decision-making.

This paper develops a data-driven model for assessing the availability of flexibility from individual household devices, at house level. The model predicts the potential shift, increase or decrease of the energy consumption of a particular type of device at a given time, in response to a price signal, and for each house separately. Therefore, the location of the flexibility source is known with accuracy. The model has an Auto-Encoder architecture based on Convolution Neural Network. The augmented model demonstrates good performance in terms of predicting the time-shift in load.