Crystalline and Spectroscopic Experimental Study of the Dinitromesithylen (DNM) Compared with the Theoretical Results

International audienceThe aim by our group is to understand the behavior of the grouping Me starting from the study of mols. having a great symmetry. In this part of work, it is had the cryst. structure of the dinitromesitylen (DNM) who is solved starting from the diffraction of x-​rays starting from a monocrystal at the ambient temp. Parallel to the exptl. study, we undertook theor. calcns. conformation of the insulated mol. of DNM by using the methods of the DFT (D. Functional Theory)​. Calcns. of optimization of the mol. conformation of the DNM by using the chain of program GAUSSIAN03 and functional MPW1PW91, B3LYP level with the 6-​311G and LANL2DZ bases gave a conformation Cs with results very close to the expt. for the lengths and the angles of bond. The computation results obtained starting from the base set (6-​311G) and functional MPW1PW91 give for the conformation of Dinitromesitylen (DNM) a good agreement of about a 1.9​% for the lengths of bond and 1.2​% for the angles of bond compared with the results of the diffraction of x-​rays. Calcns. of Raman and infra-​red spectroscopy undertaken starting from the results of optimization by using same functional MPW1PW91 and B3LYP and the sets of bases 6-​311G LanL2DZ led to the values of frequencies very close to the exptl. results

Two new triterpenoid saponins from the leaves of Bupleurum lancifolium (Apiaceae)

Chemical investigation of the leaves of Bupleurum lancifolium led to the isolation and identification of two triterpenoid saponins previously undescribed named 3-O-[α-L-rhamnopyranosyl (1 → 4)-β-D-glucopyranosyl] echinocystic acid 28-O-β-D-glucopyranosyl ester (1) and 3-O-[α-L-rhamnopyranosyl (1 → 4)-β-D-glucopyranosyl] oleanolic acid 28-O-β-D-glucopyranosyl ester (2) along with the two known compounds isorhamnetin 3-rutinoside (3) and rutin (4). Their structures were elucidated by different spectroscopic methods, including HRESIMS analysis as well as 1D and 2D NMR experiments

The Sensitivity of Power System Expansion Models

Power system expansion models are a widely used tool for planning powersystems, especially considering the integration of large shares of renewableresources. The backbone of these models is an optimization problem, whichdepends on a number of economic and technical parameters. Although theseparameters contain significant uncertainties, the sensitivity of power systemmodels to these uncertainties is barely investigated. In this work, we introduce a novel method to quantify the sensitivity ofpower system models to different model parameters based on measuring theadditional cost arising from misallocating generation capacities. The value ofthis method is proven by three prominent test cases: the definition of capitalcost, different weather periods and different spatial and temporal resolutions.We find that the model is most sensitive to the temporal resolution. Fur-thermore, we explain why the spatial resolution is of minor importance andwhy the underlying weather data should be chosen carefully

SciGRID - Offenes Referenzmodell Europäischer Übertragungsnetze für wissenschaftliche Untersuchungen

Die Modellierung von Energiesystemen spielt eine wichtige Rolle bei der Planung und Bewertung zukünftiger Energiesysteme. Die Modellierung erfordert den Zugriff auf verschiedene Modelle und Datensätze auf unterschiedlichen Skalen und Abstraktionsebenen. Solche Modelle und Datensätze betreffen Stromnetze, die in Energiesystemmodellen abgebildet werden müssen. Doch trotz ihrer großen Bedeutung sind Stromnetzmodelle und Datensätze für Europa nicht öffentlich zugänglich. Dieser Mangel an Informationen führt zu vielen negativen Aspekten, von denen einige im Folgenden aufgeführt werden. Die Validierung von Ergebnissen aus Stromnetzsimulationen (oder Energiesystemsimulationen) sind sehr begrenzt bis unmöglich. Innovationen auf diesem Forschungsgebiet werden gehemmt, da die Wissenschaftler begrenzten Zugang zu Netzdaten und -modellen haben. Zudem haben andere Stakeholder wenig Vertrauen in die Modellierungsergebnisse aufgrund der mangelnden Transparenz, was eine Einschätzung der Qualität des Modells sowie der Annahmen und Vereinfachungen bei der Beschaffung bzw. Nutzung von Stromnetzmodellen und –daten erschwert. Diese Aspekte sind insbesondere vor dem Hintergrund der nationalen und internationalen Diskussionen um den Ausbau der Stromnetze und der damit verbundenen Investitionen von großer Bedeutung. In diesem Zusammenhang wurde im Rahmen des SciGRID Projekts ein Open Source und Open Data Modell des europäischen Stromübertragungsnetzes entwickelt

SCiGRID_gas

Vortrag ueber den SciGRID_gas Datensat

SciGRID_gas: The combined IGG gas transmission network data set

Here, this document describes the resulting data set “IGG”, where all missing values have been estimated using heuristic processes, and was generated by combining the following data sources: • InternetDaten data set (INET) • Gas Infrastructure Europe data set (GIE) • Gas Storages Europe data set (GSE) The goal of SciGRID_gas is to develop methods to create an automated process that can generate a gas transmission network data set for Europe. Gas transmission networks are fundamental for simulations by the gas transmission modelling community, to derive major dynamic characteristics. Such simulations have a large scope of application, for example, they can be used to perform case scenarios, to model the gas consumption, to minimize leakages and to optimize overall gas distribution strategies. The focus of SciGRID_gas will be on the European transmission gas network, but the principal methods will also be applicable to other geographic regions. Data required for such models are the gas facilities, such as compressor stations, LNG terminals, pipelines, etc. One needs to know their locations, in addition to a large range of attributes, such as pipeline diameter and capacity, compressor capacity, configuration, etc. Most of this data is not freely available. However throughout the SciGRID_gas project it was determined, that data can be found and grouped into two fundamental different groups: a) OSM data, and b) non-OSM data. The OSM data consists of geo-referenced facility data that is stored in the OpenStreetMap (OSM) data base, and is freely available. However, the OSM data set currently contains hardly any other information than the location of the facilities. The Non-OSM data set can fill some of those gaps, by supplying information such as pipeline diameter, compressor capacity and more. Part of the SciGRID_gas project is to mine and collate such data, and combine it with the OSM data set. In addition heuristic tools are required to fill data gaps, so that a complete gas network data set can be generated. In this document, the chapter “Introduction” will supply some background information on the SciGRID_gas project, followed by the chapter “Data structure“ that gives a detailed description of the data structure that is being used in the SciGRID_gas project. Chapter “Data sources” describes the INET, GIE and GSE data sets. To remove any missing data, the chapter “Heuristic methods” describes in detail, how missing attribute values (e.g. pipeline diameter) were generated. This is followed by the chapter “Final data set”, which gives a brief overview on each set of components and in addition summarizes the changes to a previously published SciGIRD_gas data set. The appendix contains a glossary, references, location name alterations convention and finishes with the table of country abbreviation

SciGRID_gas: The raw INET data set

The goal of SciGRID_gas is to develop methods to create an automated process that can generate a gas transmission network data set for Europe. Gas transmission networks are fundamental for simulations by the gas transmission modelling community, to derive major dynamic characteristics. Such simulations have a large scope of application, for example, they can be used to perform case scenarios, to model the gas consumption, to minimize leakages and to optimize overall gas distribution strategies. The focus of SciGRID_gas will be on the European transmission gas network, but the principal methods will also be applicable to other geographic regions. Data required for such models are the gas facilities, such as compressor stations, LNG terminals, pipelines, etc. One needs to know their locations, in addition to a large range of attributes, such as pipeline diameter and capacity, compressor capacity, configuration, etc. Most of this data is not freely available. However throughout the SciGRID_gas project it was determined, that data can be found and grouped into two fundamental different groups: a) OSM data, and b) non-OSM data. The OSM data consists of geo-referenced facility data that is stored in the OpenStreetMap (OSM) data base, and is freely available. However, the OSM data set currently contains hardly any other information than the location of the facilities. The Non-OSM data set can fill some of those gaps, by supplying information such as pipeline diameter, compressor capacity and more. Part of the SciGRID_gas project is to mine and collate such data, and combine it with the OSM data set. In addition heuristic tools are required to fill data gaps, so that a complete gas network data set can be generated. This document here describes the so called “InternetData” data set (INET), which is one of the fundamental building blocks of the SciGRID_gas project. The INET data set is the first data set to be published as part of the SciGRID_gas project. This document explains the origin and structure of the data set. In this document, the chapter “Introduction” will supply some background information on the SciGRID_gas project, followed by the chapter “Data structure”, which gives a detailed description of the data structure that is being used in the SciGRID_gas project. Chapter “Data sources” describes the INET data sets

SciGRID_gas: The raw EMAP data set

The goal of SciGRID_gas is to develop methods to create an automated process that can generate a gas transmission network data set for Europe. Gas transmission networks are fundamental for simulations by the gas transmission modelling community, to derive major dynamic characteristics. Such simulations have a large scope of application, for example, they can be used to perform case scenarios, to model the gas consumption, to minimize leakages and to optimize overall gas distribution strategies. The focus of SciGRID_gas will be on the European transmission gas network, but the principal methods will also be applicable to other geographic regions. Data required for such models are the gas facilities, such as compressor stations, LNG terminals, pipelines, etc. One needs to know their locations, in addition to a large range of attributes, such as pipeline diameter and capacity, compressor capacity, configuration, etc. Most of this data is not freely available. However throughout the SciGRID_gas project it was determined, that data can be found and grouped into two fundamental different groups: a) OSM data, and b) non-OSM data. The OSM data consists of geo-referenced facility data that is stored in the OpenStreetMap (OSM) data base, and is freely available. However, the OSM data set currently contains hardly any other information than the location of the facilities. The Non-OSM data set can fill some of those gaps, by supplying information such as pipeline diameter, compressor capacity and more. Part of the SciGRID_gas project is to mine and collate such data, and combine it with the OSM data set. In addition heuristic tools are required to fill data gaps, so that a complete gas network data set can be generated. Here, this document describes one of the non-OSM data set, called the “EMAP” data set, which was generated from an EntsoG PDF map [EntsoG20]. This document here explains the origin and structure of this single data sets, and how the data set was generated. In this document, the chapter “Introduction” will supply some background information on the SciGRID_gas project, followed by the chapter “Data structure”, that gives a detailed description of the data structure that is being used in the SciGRID_gas project. Chapter “Data sources” describes the EMap data set. The appendix contains a glossary, references, location name alterations convention and finishes with the table of country abbreviatio