Fink, Andreas

Verschiedene Maßnahmen zum angestrebten Klimaschutz gehen mit einer verstärkten Verwendung elektrischer Energie einher. Dabei stellt sich bei der Nutzung volatiler regenerativer Energiequellen wie Sonne und Wind die Herausforderung einer räumlichen und zeitlichen Abstimmung von Erzeugung und Verbrauch elektrischer Energie. Batteriespeicher können hier einen effektiven Beitrag leisten (z.B. in Haushalten, im Verkehrssektor und in der Industrie). Im Hinblick auf die Wirtschaftlichkeit erfordert dies eine zielgerichtete Dimensionierung und Betriebsführung entsprechender Batterie-Energie-Speichersysteme (BESS) unter Berücksichtigung der für den Verschleiß und die Alterung von (Lithiumionen-)Batterien relevanten Faktoren. Hierbei wird zwischen der nutzungsabhängigen (zyklischen) und der altersabhängigen (kalendarischen) Degradation unterschieden, welche die noch verfügbare Kapazität (gemäß State of Health, SoH) und damit die Lebensdauer und entsprechende Kosten von Batterien nicht-linear beeinflussen. In der Literatur finden sich hierzu unterschiedliche Annahmen und Ansätze, wie sich die Verwendung von Batterien auf den SoH auswirkt und wie ein wirtschaftlicher Einsatz von Batterien unter Berücksichtigung der Degradationseffekte gestaltet werden kann. Dieser Beitrag gibt einen Überblick über diese Problematik unter Berücksichtigung neuerer Arbeiten aus der Literatur und dort noch nicht ausreichend adressierter Aspekte. Es wird ein integratives Modell für die Auslegung und den Betrieb von BESS entwickelt, welches die Auswirkungen beider Degradationsarten auf die Dimensionierung und Nutzung eines BESS einbezieht. Dabei wird berücksichtigt, dass ein abnehmender SoH bei gleichbleibendem Energiebedarf zu einer höheren Degradation führt, da dann die Zyklenanzahl zunimmt und das BESS schneller degeneriert. Darüber hinaus ist zu beachten, dass die Betriebsführung von BESS mit verschiedenerlei Unsicherheiten behaftet ist (sowohl bei der Erzeugung als auch beim Verbrauch, was sich auch in dynamischen Preisen im Stromnetz zeigt). Das formulierte Basiskonzept zur wirtschaftlichen Auslegung des Batteriebetriebs soll die Grundlage für weiterführende stochastische sequenzielle Entscheidungsverfahren und zugehörige Analysen bilden, die zu einer kosteneffizienten Dimensionierung und Betriebsführung von BESS beitragen.

Increasing amounts of data and simulations in scientific areas enforce the need of improved software performance. The maintaining scientific staff is often not primarily trained for this purpose or lacks personnel and time to address software performance issues. A particular aim of the dtec.bw-funded project hpc.bw is to tackle some of these shortcomings. A pillar of the hpc.bw agenda is the offer of a low-threshold consultancy and development support focused on performance engineering. This paper provides an insight on our related activities. We illustrate the structure of our annual calls for short-term performance engineering projects, we outline our results at the example of the performance engineering project “benEFIT - Numerical simulation of non-destructive testing in concrete”, and we draw a first conclusion on the current procedure.

The dtec.bw project CoupleIT! – IT-based sector coupling: Digitally controlled fuel cell and electrolyzer technologies for stationary and mobile applications is an interdisciplinary approach to combine a wide range of competencies from disciplines as varied as electrical power systems, economic and social sciences, computer sciences and networks as well as sustainable development and social acceptance research. As such, this article is composed of individual contributions, constituting the main chapters that showcase general approaches and motivations but also concrete results. This compendium article starts in with a delineation of the motivation behind research in so-called microgrids composed of fuel cell and electrolyzer components and a presentation of the microgrid architecture opted for in this project. Chapter two goes into more detail on the side of electrical engineering and the feasibility of a parallel operation of inverters in microgrids to achieve the ability for an upscaling. Chapter three highlights economic and technological factors for an economically viable and grid-maintaining deployment of a hydrogen-based energy system. In addition, degradation of Li-ion batteries is discussed against the background of their flexible operation in a microgrid and other scenarios. Chapter four grants a glimpse into the field of computer science and the possibility to use artificial intelligence and neural networks for a new way to simulate the behaviour of matter on atomic and molecular scales. This approach holds potential to increase the efficiency of fuel cells by improving the molecular design of fuel cell membranes used within this project. Chapter five elucidates the intricacies of secure communication within one but also between multiple microgrids, an important aspect for achieving a resilient system. Chapter six concludes this compendium by highlighting the human perspective seen from the field of psychological acceptance research nested in the broader context of sustainable development. Among other things, areas of potential barriers to a public acceptance of hydrogen technology are identified and ways to overcome those barriers proposed. This interdisciplinary round trip starts with electrical engineering (chapters one and two), economic and social sciences (chapter three), followed by computer sciences (chapter four) and computer networks (chapter five) whence the baton is passed for one last time to the field of sustainable development and psychological acceptance research (chapter six).

In the scope of the dtec.bw project hpc.bw, innovative HPC hardware resources were procured to investigate their performance for HSU-relevant compute-intensive software. Benchmarks for different software packages were conducted, and respective results are reported and documented in the following, considering the Intel Xeon architecture used in the HPC cluster HSUper, AMD EPYC 7763 and ARM FX700.

Benchmarking the performance of one’s application in high performance computing (HPC) systems is critically important for reducing runtime and energy costs. Yet, accessing the plethora of relevant metrics that impact performance is often challenging, particularly for users without hardware experience. In this paper, we introduce the novel benchmarking tool xbat developed by MEGWARE GmbH. xbat requires no setup from the user side, and it allows the user to run, monitor and evaluate their application from the tool’s web interface, consolidating the entire benchmarking process in an approachable, intuitive workflow. We demonstrate the capabilities of the tool using benchmark applications of varying complexity and show that it can manage all aspects of the benchmarking workflow in a seamless manner. In particular, we focus on the open-source molecular dynamics research software ls1 mardyn, and the closed-source optimisation package Gurobi. Both packages present unique challenges. Mixed-integer programming solvers, such as those integrated in the Gurobi software, exhibit significant performance variability, so that seemingly innocuous parameter changes and machine characteristics can affect the runtime drastically, and ls1 mardyn comes with an auto-tuning library AutoPas, that enables the selection of various node-level algorithms to compute molecular trajectories. Focusing on these two packages, we showcase the practicality, versatility and utility of xbat, and share its current and future developments.

Battery electric freight vehicles have the potential to mitigate the local urban road freight transport emissions, but their numbers are still insignificant. Logistics companies often consider electric vehicles as too costly compared to vehicles powered by combustion engines. The current literature suggests that increasing the mileage can maximize the competitiveness of electric freight vehicles. In this manuscript we develop a generic model to determine the cost-optimal balance between a high utilization of electric freight vehicles – which often have low operational costs – and their required expensive battery replacements. Our work relies on empirical findings of the real-world energy consumption from a large German field test with vehicles of 7.5 and 12 tons, respectively. Our results suggest that increasing the range to the technical maximum by intermediate (quick) charging and multi-shift usage is not the most cost-efficient strategy in every case. A low daily mileage is more cost-efficient at high energy prices or consumptions, relative to the diesel prices or consumptions, or if the battery is not safeguarded by a long battery warranty. In practical applications our model may help companies to choose the most suitable electric vehicle for the application purpose, or the optimal trip length from a given set of options. For policymakers, our analysis provides insights on the relevant parameters that may either reduce the cost-gap at lower daily mileages, or increase the utilization of electric urban freight vehicles, in order to abate the negative impact of urban road freight transport on the environment.

Decision making in operational planning is increasingly affected by conflicting interests of different stakeholders such as subcontractors, customers, or strategic partners. Addressing this, automated negotiation is a well-suited mechanism to mediate between stakeholders and search for jointly beneficial agreements. However, the outcome of a negotiation is strongly dependent on the applied negotiation protocol defining the rules of encounter. Although protocol design is well discussed in literature, the question on which protocol should be selected for a given scenario is little regarded so far. Since negotiation problems and protocols are very diverse, the protocol choice itself is a challenging task. In this study, we propose a decision support system for negotiation protocol selection (DSS-NPS) that is based on a machine learning approach – an artificial neural network (ANN). Besides presenting and discussing the system, we, furthermore, evaluate the design artifact in elaborate computational experiments that take place in an intercompany machine scheduling environment. Our findings indicate that the proposed decision support system is able to improve the outcome of negotiations by finding adequate protocols dynamically on the basis of the underlying negotiation problem characteristics.

The increasing coupling of planning and scheduling between different companies leads to novel challenges in devising and implementing effective decision support systems. In this paper, we describe a hard decentralized scheduling problem with heterogeneous machines and competing job sets that belong to different self-interested stakeholders (agents). These agents want to minimize their costs that consist of individual tardiness cost as well as their share of the machine operating cost. The determination of a beneficial solution, i.e., a respective contract in terms of a common schedule, is particularly difficult due to information asymmetry and self-interested behavior of the involved agents. To solve this coordination problem, we present two automated negotiation protocols with a set of optional building blocks. In the first protocol, new solutions are iteratively generated as mutations of a single provisional contract and proposed to the agents, while feasible rules with quotas restrict the acceptance decisions of the agents and, thus, the successive adaptation of the provisional contract. The second protocol is based on a population of contracts and mimics evolutionary processes. For evaluation purposes, we built a simulation testbed and conducted computational experiments. The computational study shows that the protocols can achieve high quality solutions very close to results from centralized multi-criteria procedures. Particular building block configurations yield improved outcomes, e.g., in case that the agents are also allowed to make contract proposals. Thus, the presented approach contributes to the methodology and practice of collaborative decision making.