Mapping and Analysis of the Reactive Power Balance in the Danish Transmission Network

This paper investigates the reactive power balance of the Zealand side of the Danish transmission system (DK2) by using QV-curves. The study is performed in cooperation with Energinet, who is the Danish transmission system operator (TSO). Firstly, this paper aims to map the reactive power balance with the current challenges in the system, which appears due to a decision of changing overhead lines in the scenic area to cables. Secondly, a method is derived for obtaining a comprehensive overview of the impacts that future projects might have on the system. By dividing the transmission system into smaller areas, it is possible to analyze how the reactive power will affect the voltage; moreover, it is favorable to analyze and handle the challenges in the reactive power balance locally. This helps the TSO to quickly determine the lack of reactive power devices and issues that might occur in future expansions of the system. For this paper, a full-scale model of DK2 and SCADA-data has been utilized. It covers the period from 01-01-2016 to 20-08-2017 between the TSO and the Distribution System Operator (DSO). The studies have shown how the location of the wind production will create issues in the reactive power balance

Using Polarized Spectroscopy to Investigate Order in Thin-Films of Ionic Self-Assembled Materials Based on Azo-Dyes

Three series of ionic self-assembled materials based on anionic azo-dyes and cationic benzalkonium surfactants were synthesized and thin films were prepared by spin-casting. These thin films appear isotropic when investigated with polarized optical microscopy, although they are highly anisotropic. Here, three series of homologous materials were studied to rationalize this observation. Investigating thin films of ordered molecular materials relies to a large extent on advanced experimental methods and large research infrastructure. A statement that in particular is true for thin films with nanoscopic order, where X-ray reflectometry, X-ray and neutron scattering, electron microscopy and atom force microscopy (AFM) has to be used to elucidate film morphology and the underlying molecular structure. Here, the thin films were investigated using AFM, optical microscopy and polarized absorption spectroscopy. It was shown that by using numerical method for treating the polarized absorption spectroscopy data, the molecular structure can be elucidated. Further, it was shown that polarized optical spectroscopy is a general tool that allows determination of the molecular order in thin films. Finally, it was found that full control of thermal history and rigorous control of the ionic self-assembly conditions are required to reproducibly make these materials of high nanoscopic order. Similarly, the conditions for spin-casting are shown to be determining for the overall thin film morphology, while molecular order is maintained

POMFinder: Identifying polyoxometalate cluster structures from pair distribution function data using explainable machine learning

Characterisation of material structure with Pair Distribution Function (PDF) analysis typically involves refining a structure model against an experimental dataset. However, finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. We present POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometalate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is demonstrated to identify suitable POMs on experimental data, including in situ data collected with fast acquisition time. This automated approach shows significant potential for identifying suitable structure models for structure refinements to extract quantitative, structural parameters in materials chemistry research. The code is open source and user-friendly, making it accessible to those without prior ML knowledge. We also demonstrate that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques compared to conventional refinement methods