Can resource classes substitute spatial resolution in energy system models? A spatial scaling analysis.

While regional aggregation of areas can increase the computation speed of energy system models (ESM), this can also lead to an underestimation of the localised high quality of variable renewable energy (VRE) sources, which vary strongly depending on the site. Partly, the power transmission grid is able to level out the locality of production and consumption with energy systems spanning across regions as far as the European continent with large amount of power traded, which leads to a spatial averaging of the feed in profiles. Resource classes can subdivide VRE potentials into different quality classes of distinct feed-in profiles and potentials to compensate for the coarser resolution of the aggregated regions. This will lead to higher full load hours for the better located VRE power plants and thus lower levelised costs of electricity for those plants. This talk will examine the trade-offs between a high spatial resolution on the one hand and the accuracy of the feed in of VRE sources on the other hand. Therein the focus lies on the dispatch and expansion of the different technologies. The investigation focusses on wind resource classes, since wind speeds vary much more spatially than solar irradiance. Germany has been chosen for this investigation as its wind resource is diverse: the flatt northern parts offer high wind potentials whereas the resource quality in the south is much more dependent on the local topology

Speeding up energy system optimization models - lessons learned from heuristic approaches, parallel solvers and large scale models

Most state-of-the art optimizing energy system models are characterised by a high temporal and spatial resolution to include detailed information of local weather conditions. This became necessary through the integration of renewable energy sources such as photovoltaics and wind energy. Similarly the integration of cross-sectoral technologies for the decarbonisation of the energy, heating and transportation sector makes energy system models more complex and as a consequence the time required for solving the problem. To address this increasing computational demand the BEAM-ME project brought together experts from the fields of energy systems analysis, mathematics, operations research, and informatics to establish interdisciplinary solutions. The talk provides an overview of the final project results and more in-depth highlights from two stage heuristic approaches and the parallel interior point solver PIPS-IPM++. Depending on the problem at hand and available computation resources a speed-up factor of up to 26 was achieved. Taking up the results from the BEAM-ME project an outlook on the follow-up project UNSEEN shows how the significant reduction in time required for solving the problems can be used to generate a more holistic view on the near-optimal solution space. This allows providing decision makers with a wide range of alternatives showing the trade-offs between several decision criteria

Glucose Binding Drives Reconfiguration of a Dynamic Library of Urea-Containing Metal-Organic Assemblies.

A bis-urea-functionalized ditopic subcomponent assembled with 2-formylpyridine and FeII , resulting in a dynamic library of metal-organic assemblies: an irregular FeII4 L6 structure and three FeII2 L3 stereoisomers: left- and right-handed helicates and a meso-structure. This library reconfigured in response to the addition of monosaccharide derivatives, which served as guests for specific library members, and the rate of saccharide mutarotation was also enhanced by the library. The (P) enantiomer of the FeII2 L3 helical structure bound β-D-glucose selectively over α-D-glucose. As a consequence, the library collapsed into the (P)-FeII2 L3 helicate following glucose addition. The α-D-glucose was likewise transformed into the β-D-anomer during equilibration and binding. Thus, β-D-glucose and (P)-3 amplified each other in the product mixture, as metal-organic and saccharide libraries geared together into a single equilibrating system.European Research Council (695009), UK Engineering and Physical Sciences Research Council (EPSRC EP/P027067/1), National Natural Science Foundation of China (No. 21971210), Natural Science Foundation of Shaanxi Province (2019KJXX-062) and the Chinese Scholarship Council (CSC), the Alexander von Humboldt Foundation for a Feodor Lynen Research Fellowship, the Rashkind Family Endowment, the Chenery Endowment, and the Donors of the American Chemical Society Petroleum Research Fun

A multi-perspective approach for exploring the scenario space of future power systems

There are many possible future energy systems – many of them unforeseen. We explore the range of parameter uncertainty and quantify parameter interrelations to generate multiple scenarios. Only sensible parameter combinations remain as in-puts to an energy system optimization and coupled models. In the past, computa-tional limitations have been a major obstacle to calculate such an enormous space of scenarios. Opposed to that, we use high-performance computing. To utilize the HPC-system efficiently the parallel solver for linear programs PIPS-IPM++ is applied. We integrate it into a tool chain of different components including sce-nario generation, energy system optimization and results evaluation and tackle the challenge of coupling a large diversity of software packages in a fully automated HPC workflow. This enables the calculation of all scenarios in a matter of days. Furthermore, we use a set of 37 indicators to provide a comprehensive assess-ment of the simulated energy systems. In this way, we cover multiple perspec-tives, such as system adequacy, security of supply or behavior of market actors. Whereas scenarios with low spatial resolution do not lead to clear results, higher resolutions do. Yet, we identified three clusters of scenarios, among which a group with high natural gas dependency is found. This allows to study disruptive events like price shocks in a vast parameter space and to identify countermeasures for the long-term

Szenarien mit Energieinfrastrukturausfällen unter Einbezug multipler Parameterunsicherheiten

Der Einsatz von Modellen zur Erstellung und Untersuchung von Szenarien ist ein wesentliches Instrument der Energiesystemanalyse. Für die Politikberatung ist die Frage nach der Verlässlichkeit von solchen Szenarien von großer Wichtigkeit, da diese mit großen Unsicherheiten behaftet sein können. Diesem Problem wird in im Forschungsprojekt UNSEEN begegnet: durch das Abfahren eines sehr großen Parameterraums konnten bereits mehr als 1000 Energieszenarien automatisch generiert, berechnet und ausgewertet werden, darunter auch 100 räumlich hoch-aufgelöste Stromsystemmodelle Deutschlands. Letztere Modelle eignen sich auch zur Untersuchung der Auswirkungen von Ausfällen der darin explizit modellierten Energieinfrastrukturen, also von Kraftwerksstandorten, Übertragungsnetzleitungen und Umspannwerken. Der wesentlichen Herausforderung, dafür eine Vielzahl aufwendiger Modellrechnungen performant durchzuführen, begegnen wir mittles eines auf High-Performance-Computing angepassten Modellierungs-Workflows, welcher den entstehenden Szenarioraum auf Basis multi-kriterieller Indikatoren (u. a. zu Angemessenheit, Betriebssicherheit und Wirtschaftlichkeit) bewertbar macht. Die ersten Analysen dieses Szenarioraums zeigen, dass >Best-Perfoming< Szenarien verhältnismäßige geringe Zubauraten für Windkraft aufweisen, bei einer Reduktion der CO2-Emissionen im Stromsektor um 85%-89% gegenüber 1990

How Large Can We Build a Cyclic Assembly? Impact of Ring Size on Chelate Cooperativity in Noncovalent Macrocyclizations

This is the peer-reviewed version of the following article: Angewandte Chemie International 56, 49 (2017): 15649-15653, which has been published in final form at https://doi.org/10.1002/anie.201709563. This article may be used for non-commercial purposes in accordance with Wiley-VCH Terms and Conditions for Self-Archiving.Self-assembled systems rely on intramolecular cooperative effects to control their growth and regulate their shape, thus yielding discrete, well-defined structures. However, as the size of the system increases, cooperative effects tend to dissipate. We analyze here this situation by studying a set of oligomers of different lengths capped with guanosine and cytidine nucleosides, which associate in cyclic tetramers by complementary Watson–Crick H-bonding interactions. As the monomer length increases, and thus the number of C(sp)–C(sp 2 ) σ-bonds in the π-conjugated skeleton, the macrocycle stability decreases due to a notable reduction in effective molarity (EM), which has a clear entropic origin. We determined the relationship between EM or ΔS and the number of σ-bonds, which allowed us to predict the maximum monomer lengths at which cyclic species would still assemble quantitatively, or whether the cyclic species would not able to compete at all with linear oligomers over the whole concentration rangeFunding from the European Union (ERC-Starting Grant 279548) and MINECO (CTQ2014-57729-P) is gratefully acknowledge

Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System

Improvements in modeling energy systems of populous emerging economies are highly decisive for a successful global energy transition. The models used – increasingly published open source – still suffer from the lack of appropriate open data. As an illustrative example, we take the Brazilian energy system, which has great potential for renewable energy resources but still relies heavily on fossil fuels. We provide a comprehensive open dataset for scenario analyses, which can be directly used with the popular open energy system model PyPSA and other modeling frameworks. It includes three categories: 1) time series data of variable renewable potentials, electricity load profiles, inflows for the hydropower plants, and cross-border electricity exchanges, 2) geo-referenced data for 27 defined regions, and 3) tabular data, which contains power plant data with installed and planned generation capacities, aggregated grid network topology, biomass thermal plant potential, as well as scenarios of energy demand. This data fosters global or country-specific energy system studies based on open data relevant to decarbonizing Brazil’s energy system

Light-powered reversible guest release and uptake from Zn4L4 capsules

A strategy for light-powered guest release from a tetrahedral capsule has been developed by incorporating azobenzene units at its vertices. A new Zn4L4 tetrahedral capsule bearing 12 diazo moieties at its metal-ion vertices was prepared from a phenyldiazenyl-functionalized subcomponent and a central trialdehyde panel. Ultraviolet irradiation caused isomerization of the peripheral diazo groups from the thermodynamically preferred trans configuration to the cis form, thereby generating steric clash and resulting in cage disassembly and concomitant guest release. Visible-light irradiation drove cage re-assembly following re-isomerization of the diazo groups to the trans form, resulting in guest re-uptake. A detailed 19F NMR study elucidated how switching led to guest release: each metal vertex tolerated only one cis-azobenzene moiety, with further isomerization leading to cage disassembly