Importance subsampling for power system planning under multi-year demand and weather uncertainty

This paper introduces a generalised version of importance subsampling for time series reduction/aggregation in optimisation-based power system planning models. Recent studies indicate that reliably determining optimal electricity (investment) strategy under climate variability requires the consideration of multiple years of demand and weather data. However, solving planning models over long simulation lengths is typically computationally unfeasible, and established time series reduction approaches induce significant errors. The importance subsampling method reliably estimates long-term planning model outputs at greatly reduced computational cost, allowing the consideration of multi-decadal samples. The key innovation is a systematic identification and preservation of relevant extreme events in modeling subsamples. Simulation studies on generation and transmission expansion planning models illustrate the method’s enhanced performance over established "representative days" clustering approaches. The models, data and sample code are made available as open-source software

Directional interactions in semiflexible single-chain polymer folding

Precise control over folded conformations of synthetic polymers is highly desirable in the development of functional nanomaterials for diverse applications. Introducing monomers capable of strong intramolecular hydrogen bonding is a promising route to achieve this control. In the present work we report the use of Wang–Landau Monte Carlo simulations of coarse-grained copolymers to explore the design parameters of these systems on their pathway to collapse. The highly directional nature of hydrogen-bonded supramolecular interactions is modelled by a directional non-bonded potential while a harmonic bending potential is used to take into account the flexibility of the polymer chain, thus making it possible to look at the interplay of both factors. The introduction of directional interactions in the copolymer chain leads to a sharper coil-globule collapse when compared to homopolymers composed of isotropic interacting beads only. Simultaneously, some of the stiffness-dependent structural properties become exacerbated when directional beads are present. Finally, from the heat capacity profiles for the different chain stiffness values we are able to distinguish the prevalence of the collapse of the backbone for highly flexible chains, while as chain stiffness increases folding of the co-polymer due to the directional interactions becomes the dominant feature

Dual-mode humidity detection using a lanthanide-based metal-organic framework: towards multifunctional humidity sensors

Combined photoluminescence and impedance spectroscopy studies show that a europium-based metal–organic framework behaves as a highly effective and reliable humidity sensor, enabling dual-mode humidity detection

Reducing climate risk in energy system planning: a posteriori time series aggregation for models with storage

The growth in variable renewables such as solar and wind is increasing the impact of climate uncertainty in energy system planning. Addressing this ideally requires high-resolution time series spanning at least a few decades. However, solving capacity expansion planning models across such datasets often requires too much computing time or memory. To reduce computational cost, users often employ time series aggregation to compress demand and weather time series into a smaller number of time steps. Methods are usually a priori, employing information about the input time series only. Recent studies highlight the limitations of this approach, since reducing statistical error metrics on input time series does not in general lead to more accurate model outputs. Furthermore, many aggregation schemes are unsuitable for models with storage since they distort chronology. In this paper, we introduce a posteriori time series aggregation schemes that preserve chronology and hence allow modelling of storage technologies. Our methods adapt to the underlying energy system model; aggregation may differ in systems with different technologies or topologies even with the same time series inputs. They do this by using operational variables (generation, transmission and storage patterns) in addition to time series inputs when aggregating. We investigate a number of approaches. We find that a posteriori methods can perform better than a priori ones, primarily through a systematic identification and preservation of relevant extreme events. We hope that these tools render long demand and weather time series more manageable in capacity expansion planning studies. We make our models, data, and code publicly available

In Silico Analysis Identifies Intestinal Transit as a Key Determinant of Systemic Bile Acid Metabolism

Bile acids fulfill a variety of metabolic functions including regulation of glucose and lipid metabolism. Since changes of bile acid metabolism accompany obesity, Type 2 Diabetes Mellitus and bariatric surgery, there is great interest in their role in metabolic health. Here, we developed a mathematical model of systemic bile acid metabolism, and subsequently performed in silico analyses to gain quantitative insight into the factors determining plasma bile acid measurements. Intestinal transit was found to have a surprisingly central role in plasma bile acid appearance, as was evidenced by both the necessity of detailed intestinal transit functions for a physiological description of bile acid metabolism as well as the importance of the intestinal transit parameters in determining plasma measurements. The central role of intestinal transit is further highlighted by the dependency of the early phase of the dynamic response of plasma bile acids after a meal to intestinal propulsion