• Energy Research
• Open Access close
• Restricted close
• Open Source close
• DE close
• CA close
• NL close

• Recent studies have demonstrated sibling vs stranger differences in group root allocation in plants, suggesting that plants have the potential for kin discrimination in competition. However, morphology differences could potentially be generated by competition-based mechanisms. Here, we tested these hypotheses for the sibling vs stranger differences in root allocation in Cakile edentula. • Seeds were planted in pairs of either kin (siblings) or strangers, from all combinations of eight families, to give eight kin (sibling) and 28 stranger pair identities. Because the species has a seed dimorphism, the 10 replicates of each pair identity included both seed types. Root allocation, size inequality between seedlings in a pair, and competitive ability were derived from measures of biomass and height. • Cakile edentula seedlings demonstrated the same kin recognition response previously observed in juvenile plants, with lower root allocation in kin pairs than stranger pairs. The seed dimorphism was not associated with root allocation. • The two competitive mechanisms, genetic differences in competitive ability and increased size inequality in stranger groups, did not explain the root allocation differences in these seedlings. Kin recognition offered the most probable explanation for the differences in root allocation between sibling and stranger pairs.

Exterior insulation finishing systems (EIFSs) can efficiently promote energy efficiency of buildings. In this study, an EIFS with high thermal efficiency is presented to improve the insulation behavior of building enclosure. Based on heat transfer analysis results, energy simulations of buildings with fire spread prevention structures were performed. Results revealed that heat flow through the wall increased by 10.3 % when using a metal rail to fix the insulation; in contrast, using non-combustible phenolic foam reduces heat flow by 37.4 %, satisfying the requirement for fire spread prevention structures. Additionally, the energy consumption decreased by 8.8 % when both mineral wool and phenolic foam were applied. Fire spread prevention structures are essential to improve the fire safety performance of buildings. This external insulation system efficiently promote energy saving in building; additionally, leveraging a phase change material to improve the thermal storage performance of the building can reduce energy consumption by up to 11.9 %.

In the current situation with the unprecedented deployment of clean technologies for electricity generation, it is natural to expect that storage will play an important role in electricity networks. This paper provides a qualitative methodology to select the appropriate technology or mix of technologies for different applications. The multiple comparisons according to different characteristics distinguish this paper from others about energy storage systems. Firstly, the different technologies available for energy storage, as discussed in the literature, are described and compared. The characteristics of the technologies are explained, including their current availability. In order to gain a better perspective, availability is cross-compared with maturity level. Moreover, information such as ratings, energy density, durability and costs is provided in table and graphic format for a straightforward comparison. Additionally, the different electric grid applications of energy storage technologies are described and categorised. For each of the categories, we describe the available technologies, both mature and potential. Finally, methods for connecting storage technologies are discussed.

-

A comprehensive model was developed to simulate gasification of pine sawdust in the presence of both air and steam. The proposed model was built upon the premise of an existing Aspen Plus-based biomass gasification model with select improvements. These include the addition of a temperature-dependent pyrolysis model, an updated hydrodynamic model, more extensive gasification kinetics and the inclusion of tar formation and reaction kinetics. The Aspen Plus process simulator was used in combination with a more extensive Fortran subroutine to appropriately model a Bubbling Fluidized Bed (“BFB”) gasifier. The validity of the model was compared on two fronts: Firstly, the accuracy of the model’s predictions in comparison with the actual experimental results; and secondly, the relative accuracy of the model prediction in comparison to the original base-model.

AbstractCompanies are forced by rising energy costs to seize control of their energy consumption to maintain contestability. There-fore, transparency over the companies’ energy flux along the production process is required. Energy data management software is helpful, but cost-intensive. Hence, especially small and medium sized enterprises (SME) spare this investment. In this paper, the requirements for an energy controlling infrastructure in SME are elaborated, followed by a deduced software-architecture which supports the respective controlling structure. Further, the prototypical realization of the corresponding tool “Green-Cockpit” will be presented. The free of cost, open source and web-based tool is designed to help companies monitor, interpret, analyze, plan and report their energy consumption. The Green Cockpit tool outperforms other energy management software at management disciplines with its ability to not only analyze energy consumption, but plan and control it additionally.

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.