• Energy Research
• DE close
• BE close

The leafy seadragon certainly is among evolution's most "beautiful and wonderful" species aptly named for its extraordinary camouflage mimicking its coastal seaweed habitat. However, limited information is known about the genetic basis of its phenotypes and conspicuous camouflage. Here, we revealed genomic signatures of rapid evolution and positive selection in core genes related to its camouflage, which allowed us to predict population dynamics for this species. Comparative genomic analysis revealed that seadragons have the smallest olfactory repertoires among all ray-finned fishes, suggesting adaptations to the highly specialized habitat. Other positively selected and rapidly evolving genes that serve in bone development and coloration are highly expressed in the leaf-like appendages, supporting a recent adaptive shift in camouflage appendage formation. Knock-out of bmp6 results in dysplastic intermuscular bones with a significantly reduced number in zebrafish, implying its important function in bone formation. Global climate change-induced loss of seagrass beds now severely threatens the continued existence of this enigmatic species. The leafy seadragon has a historically small population size likely due to its specific habitat requirements that further exacerbate its vulnerability to climate change. Therefore, taking climate change-induced range shifts into account while developing future protection strategies.

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.

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.

Little is known about the validity of repeated 24-h dietary recalls (24-HDR) as a measure of total energy intake (EI) in young children. This study aimed to evaluate the validity of proxy-reported EI by comparison with total energy expenditure (TEE) measured by the doubly labeled water (DLW) technique.The agreement between EI and TEE was investigated in 36 (47.2% boys) children aged 4-10 years from Belgium and Spain using subgroup analyses and Bland-Altman plots. Low-energy-reporters (LER), adequate-energy-reporters (AER) and high-energy-reporters (HER) were defined from the ratio of EI over TEE by application of age- and sex-specific cut-off values.There was good agreement between means of EI (1500 kcal/day) and TEE (1523 kcal/day) at group level though in single children, i.e. at the individual level, large differences were observed. Almost perfect agreement between EI and TEE was observed in thin/normal weight children (EI: 1511 kcal/day; TEE: 1513 kcal/day). Even in overweight/obese children the mean difference between EI and TEE was only -86 kcal/day. Among the participants, 28 (78%) were classified as AER, five (14%) as HER and three (8%) as LER.Two proxy-reported 24-HDRs were found to be a valid instrument to assess EI on group level but not on the individual level.

A reduced-order, “grey-box” model of an active distribution network, intended for dynamic simulations of the transmission system, is derived. The network hosts inverter-based generators as well as static and motor loads, whose dynamic parameters are affected by uncertainty. This issue is addressed using Monte-Carlo simulations. The parameters of the equivalent are adjusted to match as closely as possible the average of the randomized responses, while their dispersion is accounted for through the weights of the weighted least-square minimization. A procedure is used to remove from the identification the parameters with negligible impact. To avoid over-fitting, the equivalent is tuned for multiple large-disturbance simulations. A recursive procedure is used to select the smallest possible subset of disturbances involved in the least-square minimization. Simulation results with a 75-bus MV test-system are reported. They show that the equivalent is able to reproduce with good accuracy the discontinuous controls of inverter-based generators, such as reactive current injection and tripping.

Cogeneration of heat and electricity is an important pillar of energy and climate policy. To plan the production and distribution system of combined heat and power (CHP) systems for residential heating, suitable methods for decision support are needed. For a comprehensive feasibility analysis, the integration of the location and capacity planning of the power plants, the choice of customers, and the network planning of the heating network into one optimization model are necessary. Thus, we develop an optimization model for electricity generation and heat supply. This mixed integer linear program (MILP) is based on graph theory for network flow problems. We apply the network location model for the optimization of district heating systems in the City of Osorno in Chile, which exhibits the “checkerboard layout” typically found in many South American cities. The network location model can support the strategic planning of investments in renewable energy projects because it permits the analysis of changing energy prices, calculation of break-even prices for heat and electricity, and estimation of greenhouse gas emission savings.

Pervasive computing and business process modeling are increasingly joining forces, as mobile human users shall be seamlessly integrated into business processes. In respective scenarios, humans use mobile devices and wireless technology to interact with workflows running in a powerful back-end infrastructure. However, the frequent interaction between humans and workflows causes a high communication overhead and, thus, high energy consumption on mobile devices. This impacts the usability and efficiency of the business process due to rapidly drained batteries and the resulting short life-times of the devices and applications. We present an approach based on a minimum-cut algorithm for reducing costly data transmissions during workflow execution by distributing parts of a workflow to the users' devices. Our motivation is to reduce the energy consumption on the mobile devices and, thus, avoid draining batteries in the field. We prove that our algorithm finds the optimal solution for a given network and workflow, decreasing the energy consumed on mobile devices by 32-37% compared to an approach where the entire workflow is executed in the infrastructure. Thus, in typical domains like logistics and health care, one third of the energy can be saved. This either means that devices have to be charged less frequently, leading to less distraction in the business process, or that mobile device specifications can be lowered. Significant cost reductions result in both cases.