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Evaluating new technological developments for energy systems is becoming more and more complex. The overall application environment is a continuously growing and interconnected cyber-physical system so that analytical assessment is practically impossible to realize. Consequently, new solutions must be evaluated in simulation studies. Due to the interdisciplinarity of the simulation scenarios, various heterogeneous tools must be connected. This approach is known as co-simulation. During the last years, different approaches have been developed or adapted for applications in energy systems. In this paper, two co-simulation approaches are compared that follow generic, versatile concepts. The tool mosaik, which has been explicitly developed for the purpose of co-simulation in complex energy systems, is compared to the High Level Architecture (HLA), which possesses a domain-independent scope but is often employed in the energy domain. The comparison is twofold, considering the tools' conceptual architectures as well as results from the simulation of representative test cases. It suggests that mosaik may be the better choice for entry-level, prototypical co-simulation while HLA is more suited for complex and extensive studies.

Private households constitute a considerable share of Europe's electricity consumption. The current electricity distribution system treats them as effectively passive individual units. In the future, however, users of the electricity grid will be involved more actively in the grid operation and can become part of intelligent networked collaborations. They can then contribute the demand and supply flexibility that they dispose of and, as a result, help to better integrate renewable energy in-feed into the distribution grids. © 2014 IEEE.

A direct synthesis of lactams (5-, 6-, and 7-membered) starting from amino-alcohols in a bienzymatic cascade is reported. Horse liver alcohol dehydrogenase together with the NADH oxidase from Streptococcus mutans were applied for the oxidative lactamization of various amino alcohols. Crucial parameters for the efficiency of this cascade reaction were elucidated. This report represents a direct approach for biocatalytic oxidative lactamization reaction.

A down-converting (DC) layer placed on the front side of a silicon solar cell has the potential to generate more than one low-energy photon for every incident high-energy photon. Recent theory has predicted that DC in conjunction with a silicon solar cell can achieve a conversion efficiency of up to 38.6%. This paper examines the application of rare-earth-doped inorganic materials for achieving external quantum efficiencies greater than unity and enhancing the conversion efficiency of silicon solar cells. Such DC mechanisms have been applied in similar phosphor materials used by the lighting and display industries. The opportunities for the DC of high-energy solar photons to multiple photons with energy greater than the silicon bandgap are discussed.

In efforts to mitigate climate change, energy systems are undergoing a profound transition towards low-carbon systems. This transition does not only involve changes in energy technologies but importantly it is shaped and incentivized by changes in the rules and regulations that govern energy markets. It is a normative transition, focused at achieving secure, affordable, and sustainable energy provision. In a multidisciplinary approach, this paper proposes a framework that highlights the role of normative principles - i.e. values - in socio-technical systems. Building on the Institutional Analysis and Development (IAD) framework, the analysis explicates how values relate to institutional change in the case of the energy transition.

AbstractPrecipitation (P) and potential evaporation (Ep) are commonly studied drivers of changing freshwater availability, as aridity (Ep/P) explains ∼90% of the spatial differences in mean runoff across the globe. However, it is unclear if changes in aridity over time are also the most important cause for temporal changes in mean runoff and how this degree of importance varies regionally. We show that previous global assessments that address these questions do not properly account for changes due to precipitation, and thereby strongly underestimate the effects of precipitation on runoff. To resolve this shortcoming, we provide an improved Budyko‐based global assessment of the relative and absolute sensitivity of precipitation, potential evaporation, and other factors to changes in mean‐annual runoff. The absolute elasticity of runoff to potential evaporation changes is always lower than the elasticity to precipitation changes. The global pattern indicates that for 83% of the land grid cells runoff is most sensitive to precipitation changes, while other factors dominate for the remaining 17%. This dominant role of precipitation contradicts previous global assessments, which considered the impacts of aridity changes as a ratio. We highlight that dryland regions generally display high absolute sensitivities of runoff to changes in precipitation, however within dryland regions the relative sensitivity of runoff to changes in other factors (e.g., changing climatic variability, CO2‐vegetation feedbacks, and anthropogenic modifications to the landscape) is often far higher. Nonetheless, at the global scale, surface water resources are most sensitive to temporal changes in precipitation.

Coastal climate impact studies make increasing use of multi-source and multi-dimensional atmospheric and environmental datasets to investigate relationships between climate signals and the ecological response. The large quantity of numerically simulated data may, however, include redundancy, multi-colinearity and excess information not relevant to the studied processes. In such cases techniques for feature extraction and identification of latent processes prove useful. Using dimensionality reduction techniques this research provides a statistical underpinning of variable selection to study the impacts of atmospheric processes on coastal chlorophyll-a concentrations, taking the Dutch Wadden Sea as case study. Dimension reduction techniques are applied to environmental data simulated by the Delft3D coastal water quality model, the HIRLAM numerical weather prediction model and the Euro-CORDEX climate modelling experiment. The dimension reduction techniques were selected for their ability to incorporate (1) spatial correlation via multi-way methods (2), temporal correlation through Dynamic Factor Analysis, and (3) functional variability using Functional Data Analysis. The data reduction potential and explanatory value of these methods are showcased and important atmospheric variables affecting the chlorophyll-a concentration are identified. Our results indicate room for dimensionality reduction in the atmospheric variables (2 principle components can explain the majority of variance instead of 7 variables), in the chlorophyll-a time series at different locations (two characteristic patterns can describe the 10 locations), and in the climate projection scenarios of solar radiation and air temperature variables (a single principle component function explains 77% of the variation for solar radiation and 57% of the variation for air temperature). It was also found that solar radiation followed by air temperature are the most important atmospheric variables related to coastal chlorophyll-a concentration, noting that regional differences exist, for instance the importance of air temperature is greater in the Eastern Dutch Wadden Sea at Dantziggat than in the Western Dutch Wadden Sea at Marsdiep Noord. Common trends and different regional system characteristics have also been identified through dynamic factor analysis between the deeper channels and the shallower intertidal zones, where the onset of spring blooms occurs earlier. The functional analysis of climate data showed clusters of atmospheric variables with similar functional features. Moreover, functional components of Euro-CORDEX climate scenarios have been identified for radiation and temperature variables, which provide information on the dominant mode (pattern) of variation and its uncertainties. The findings suggest that radiation and temperature projections of different Euro-CORDEX scenarios share similar characteristics and mainly differ in their amplitudes and seasonal patterns, offering opportunities to construct statistical models that do not assume independence between climate scenarios but instead borrow information (“borrow strength”) from the larger pool of climate scenarios. The presented results were used in follow up studies to construct a Bayesian stochastic generator to complement existing Euro-CORDEX climate change scenarios and to quantify climate change induced trends and uncertainties in phytoplankton spring bloom dynamics in the Dutch Wadden Sea.