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Towards Quality of Service and Fairness in Smart Grid Applications
No full textDue to the increasing amount of distributed renewable energy generation and the emerging high demand at consumer connection points, e. g., electric vehicles, the power distribution grid will reach its capacity limit at peak load times if it is not expensively enhanced. Alternatively, smart flexibility management that controls user assets can help to better utilize the existing power grid infrastructure for example by sharing available grid capacity among connected electric vehicles or by disaggregating flexibility requests to hybrid photovoltaic battery energy storage systems in households. Besides maintaining an acceptable state of the power distribution grid, these smart grid applications also need to ensure a certain quality of service and provide fairness between the individual participants, both of which are not extensively discussed in the literature. This thesis investigates two smart grid applications, namely electric vehicle charging-as-a-service and flexibility-provision-as-a-service from distributed energy storage systems in private households.
The electric vehicle charging service allocation is modeled with distributed queuing-based allocation mechanisms which are compared to new probabilistic algorithms. Both integrate user constraints (arrival time, departure time, and energy required) to manage the quality of service and fairness. In the queuing-based allocation mechanisms, electric vehicle charging requests are packetized into logical charging current packets, representing the smallest controllable size of the charging process. These packets are queued at hierarchically distributed schedulers, which allocate the available charging capacity using the time and frequency division multiplexing technique known from the networking domain. This allows multiple electric vehicles to be charged simultaneously with variable charging currents. To achieve high quality of service and fairness among electric vehicle charging processes, dynamic weights are introduced into a weighted fair queuing scheduler that considers electric vehicle departure time and required energy for prioritization. The distributed probabilistic algorithms are inspired by medium access protocols from computer networking, such as binary exponential backoff, and control the quality of service and fairness by adjusting sampling windows and waiting periods based on user requirements.
The second smart grid application under investigation aims to provide flexibility provision-as-a-service that disaggregates power flexibility requests to distributed battery energy storage systems in private households. Commonly, the main purpose of stationary energy storage is to store energy from a local photovoltaic system for later use, e. g., for overnight charging of an electric vehicle. This is optimized locally by a home energy management system, which also allows the scheduling of external flexibility requests defined by the deviation from the optimal power profile at the grid connection point, for example, to perform peak shaving at the transformer. This thesis discusses a linear heuristic and a meta heuristic to disaggregate a flexibility request to the single participating energy management systems that are grouped into a flexibility pool. Thereby, the linear heuristic iteratively assigns portions of the power flexibility to the most appropriate energy management system for one time slot after another, minimizing the total flexibility cost or maximizing the probability of flexibility delivery. In addition, a multi-objective genetic algorithm is proposed that also takes into account power grid aspects, quality of service, and fairness among par-ticipating households. The genetic operators are tailored to the flexibility disaggregation search space, taking into account flexibility and energy management system constraints, and enable power-optimized buffering of fitness values.
Both smart grid applications are validated on a realistic power distribution grid with real driving patterns and energy profiles for photovoltaic generation and household consumption. The results of all proposed algorithms are analyzed with respect to a set of newly defined metrics on quality of service, fairness, efficiency, and utilization of the power distribution grid. One of the main findings is that none of the tested algorithms outperforms the others in all quality of service metrics, however, integration of user expectations improves the service quality compared to simpler approaches. Furthermore, smart grid control that incorporates users and their flexibility allows the integration of high-load applications such as electric vehicle charging and flexibility aggregation from distributed energy storage systems into the existing electricity distribution infrastructure. However, there is a trade-off between power grid aspects, e. g., grid losses and voltage values, and the quality of service provided. Whenever active user interaction is required, means of controlling the quality of service of users’ smart grid applications are necessary to ensure user satisfaction with the services provided
A large‑scale comparison of human‑written versus ChatGPT‑generated essays
No full textChatGPT and similar generative AI models have attracted hundreds of millions of users and have become part of the public discourse. Many believe that such models will disrupt society and lead to significant changes in the education system and information generation. So far, this belief is based on either colloquial evidence or benchmarks from the owners of the models—both lack scientific rigor. We systematically assess the quality of AI-generated content through a large-scale study comparing human-written versus ChatGPT-generated argumentative student essays. We use essays that were rated by a large number of human experts (teachers). We augment the analysis by considering a set of linguistic characteristics of the generated essays. Our results demonstrate that ChatGPT generates essays that are rated higher regarding quality than human-written essays. The writing style of the AI models exhibits linguistic characteristics that are different from those of the human-written essays. Since the technology is readily available, we believe that educators must act immediately. We must re-invent homework and develop teaching concepts that utilize these AI models in the same way as math utilizes the calculator: teach the general concepts first and then use AI tools to free up time for other learning objectives
Optimization of a Redox-Flow Battery Simulation Model Based on a Deep Reinforcement Learning Approach
No full textVanadium redox-flow batteries (VRFBs) have played a significant role in hybrid energy storage systems (HESSs) over the last few decades owing to their unique characteristics and advantages. Hence, the accurate estimation of the VRFB model holds significant importance in large-scale storage applications, as they are indispensable for incorporating the distinctive features of energy storage systems and control algorithms within embedded energy architectures. In this work, we propose a novel approach that combines model-based and data-driven techniques to predict battery state variables, i.e., the state of charge (SoC), voltage, and current. Our proposal leverages enhanced deep reinforcement learning techniques, specifically deep q-learning (DQN), by combining q-learning with neural networks to optimize the VRFB-specific parameters, ensuring a robust fit between the real and simulated data. Our proposed method outperforms the existing approach in voltage prediction. Subsequently, we enhance the proposed approach by incorporating a second deep RL algorithm—dueling DQN—which is an improvement of DQN, resulting in a 10% improvement in the results, especially in terms of voltage prediction. The proposed approach results in an accurate VFRB model that can be generalized to several types of redox-flow batteries
Differential testing for machine learning: an analysis for classification algorithms beyond deep learning
No full textDifferential testing is a useful approach that uses different implementations of the same algorithms and compares the results for software testing. In recent years, this approach was successfully used for test campaigns of deep learning frameworks. There is little knowledge about the application of differential testing beyond deep learning. Within this article, we want to close this gap for classification algorithms. We conduct a case study using Scikit-learn, Weka, Spark MLlib, and Caret in which we identify the potential of differential testing by considering which algorithms are available in multiple frameworks, the feasibility by identifying pairs of algorithms that should exhibit the same behavior, and the effectiveness by executing tests for the identified pairs and analyzing the deviations. While we found a large potential for popular algorithms, the feasibility seems limited because, often, it is not possible to determine configurations that are the same in other frameworks.
The execution of the feasible tests revealed that there is a large number of deviations for the scores and classes. Only a lenient approach based on statistical significance of classes does not lead to a huge amount of test failures. The potential of differential testing beyond deep learning seems limited for research into the quality of machine learning libraries. Practitioners may still use the approach if they have deep knowledge about implementations, especially if a coarse oracle that only considers significant differences of classes is sufficient
Illusions of sovereignty : understanding populist crowds with Hannah Arendt
No full textThis article reconstructs Hannah Arendt's theoretical arguments in relation to current authoritarian-populist crowds, which can be understood as organized mobs of the twenty-first century. Drawn from all classes and originating in societal and political disenfranchisement, in Arendt's understanding they are rebellious nihilists who falsely believe they represent the people as a whole while they exclude any citizens who do not share their tribal nationalism and leader worshiping. Illuminating conditions of their emergence, Arendt also helps to elucidate what drives the populist crowds’ illusions about an uncompromising “sovereign will” they and their leaders claim to embody. Such illusions benefit from broader modern trends eroding differences between facts, opinion, truth, and lies. In public environments suffering from destabilized factual truths, organized lies can easily fill a political vacuum generated by crises of political modernity. Unpacking interrelated theoretical trajectories, it is argued that an Arendtian framework can significantly contribute to the study of present-day authoritarian populism
Three Essays on Firm Value and Firm Risk and their Relation to IT-Exposure, Corporate Social Responsibility, and Religiosity
No full text1. IT-Exposure and Firm Value: We analyze the joint influence of a firm’s information technology (IT)-Exposure and investment behavior on firm value. Estimating a firm’s (partial) IT-Exposure allows for distinguishing between firms with a business model that is challenged by IT above and below market average. Hence, we estimate the annual IT-Exposure of a firm using a 3-factor Fama-French model extended by an IT-proxy. Subsequently, we analyze the relationship with Tobin’s Q in a panel data context, accounting for the relationship between IT-Exposure and investments proxied by R&D as well as CapEx. We use more than 48,000 firm-year observations for firms in the Russell 3000 Index covering the period 1990 to 2018. Although IT-Exposure has a negative impact on firm value, this discount can be overcompensated by up to 2.1 times by sufficient investments through R&D and CapEx, giving a firm with an average Tobin’s Q a premium of 14.8% to 19.2%, while controlling for endogeneity.
2. Corporate Social Responsibility, Risk, and Firm Value: An Unconditional Quantile Regression Approach: This paper examines the impact of corporate social responsibility (CSR) on firm risk, comprising total risk, idiosyncratic risk, and systematic risk, as well as firm value. We focus on analyzing the interrelationships along the entire distribution of the dependent variables, thus estimating an unconditional quantile regression (UQR). The analysis is based on CSR scores from Refinitiv and MSCI, using up to 12,013 firm-year observations over the period 2002 to 2019 for all U.S. companies listed on NYSE, NASDAQ, and AMEX. UQR reveals strongly heterogeneous effects along the unconditional quantiles of the dependent variables, which are reflected in sign changes, magnitude and significance variations. For CSR we find a risk-reducing as well as value-enhancing effect. When applying fixed effects OLS, we can just partly confirm the risk-reducing and value-enhancing effect of CSR shown in the literature.
3. Heterogenous Effects of Religiosity on Firm Risk and Firm Value: An Unconditional Quantile Regression Approach: This paper examines the impact of religiosity on firm risk, comprising total risk, idiosyncratic risk, and systematic risk, as well as firm value. We focus on analyzing the interrelationships along the entire distribution of the dependent variables, thus estimating an unconditional quantile regression (UQR). The analysis is based on all U.S. companies listed on NYSE, NASDAQ, and AMEX for the period from 1980 through 2020. UQR reveals strongly heterogeneous effects along the unconditional quantiles of the dependent variables, which are reflected in sign changes, magnitude and significance variations. Overall, the risk-reducing effect of religiosity is more pronounced in the higher quantiles of the distribution. We further observe a value-reducing as well as value-enhancing religiosity effect. When applying fixed effects OLS, we can confirm the risk-reducing and non-existing value effect of religiosity shown in the literature. The robustness of our results is underpinned by a battery of additional tests
Digitalität – Dispositiv / Methoden / Analyse. Bestandsaufnahme aus mediensemiotischer Perspektive
No full textDer Beitrag „Digitalität – Dispositiv / Methoden / Analyse. Bestandsaufnahme aus mediensemiotischer Perspektive“ von Martin Hennig, Jan-Oliver Decker und Hans Krah skizziert grundlegend den semiotischen Zugang zu und eine analytische Methodik von Digitalität, unter Fokussierung multimodaler Konstellationen
Parzival, multimodal. Digitale Zugänge zu illustrierten Parzival-Handschriften
No full textDer Beitrag „Parzival, multimodal. Digitale Zugänge zu illustrierten Parzival-Handschriften“ von Andrea Sieber und Julia Siwek widmet sich an der Schnittstelle von fachwissenschaftlicher Expertise und kompetenzorientierter Anwendung digitalen Zugängen zu illustrierten Parzival-Handschriften. Er analysiert die multimodalen Besonderheiten der Digitalisate und zeigt auf, wie diese in einem digitalen Lehr-Lern-Medium im H5P-Format für die Förderung multimodaler Kompetenz eingesetzt werden können
Bildung trotz Bologna. Analyse zum Bildungsbegriff an deutschen Universitäten im Kontext der europäischen Hochschulreform
No full textAusgehend von der Kritik am Bologna-Prozess und dem in diesem Zusammenhang immer wieder referenzierten Humboldt’schen Ideal der deutschen Universität untersucht die Arbeit die Frage, ob sich Widersprüche zwischen diesem neuhumanistisch geprägten Ideal einer Bildung durch Wissenschaft und dem Bildungsverständnis des Bologna-Prozesses auf europäischer Ebene feststellen lassen. Die Arbeit ergänzt dabei den hermeneutischen bildungsphilosophischen und universitätshistoriographischen Diskurs um die Idee von Universität durch eine empirisch-qualitative Textanalyse zentraler Texte des Bologna-Prozesses.
In einem ersten Schritt wird das Leitbild Bildung durch Wissenschaft in seiner historischen Entwicklung und Manifestation analysiert und mit aktuellen Diskussionen zur Rolle von Universitäten in der Wissensgesellschaft zusammengeführt. In einem zweiten Schritt wird das Bildungsverständnis analysiert, das sich sowohl in den Kommuniqués der Ministerialtreffen des Bologna-Prozesses als auch in exemplarisch ausgewählten Stakeholder-Dokumenten aus der Bologna Follow-Up Group findet. Das Bildungsverständnis wird dabei als latentes Konzept verstanden, das durch die Aufgabenzuschreibungen an Hochschulen in den drei Aufgabenbereichen Lehre, Forschung und Transfer mittels einer qualitativen Inhaltsanalyse kodiert und anschließend analysiert wird.
Im Kontrast der beiden Ergebnisse zeigt sich, dass die Kommuniqués der Ministerialkonferenzen in den ersten Jahren stark von einer auf Qualifizierungsaspekte beschränkten instrumentell-ökonomischen Sicht von Bildung geprägt waren, sich seit 2010 aber stärker dem Ideal einer Bildung durch Wissenschaft annähern. Auch die Analyse der Stakeholder-Dokumente, die diesen politischen Prozess begleiten und informieren, zeigen ebenfalls – mit Ausnahme der Arbeitgebervertretung – entweder eine konstante Argumentation zumindest teilweise im Sinne dieses Ideals, oder aber eine Annäherung hin zur Bildung durch Wissenschaft.
Die Arbeit kommt zu dem Ergebnis, dass sich diese Widersprüche, wie sie von Kritikerinnen und Kritikern am Bologna-Prozess geäußert wurden, also tatsächlich wahrnehmen lassen, dieser Fakt jedoch hauptsächlich für die erste Hälfte des Bologna-Prozesses gelten kann. Durch den argumentativen Wandel in den Kommuniqués hin zu Positionen, die auch Teil des Bildungsideals deutscher Universitäten darstellen, entstehen Möglichkeitsräume für Universitäten, ihr Ideal unter den Bedingungen der Studienreform umzusetzen
Health challenges of the 21st century - Empirical essays on the health and economic burden of non-communicable diseases and climate change in Southeast Asia
No full textIn the ongoing 21st century, low- and middle-income countries will face two health challenges that are thoroughly different from what these countries have been dealing with in preceding centuries. First, they are confronted with surging rates of non-communicable diseases (NCDs), and second, climate change will take its toll and is predicted to cause catastrophic health impairments and exacerbate chronic health conditions further. Both will pose a disproportionate health and economic burden on low- and middle-income countries, which are also the countries least able to cope with them. By threatening individual health and socioeconomic improvements, and by putting an immense burden on already constrained health care systems, they impede the progress in poverty reduction and widen health inequities between the rich and the poor.
Against this background, this thesis investigates the potential of NCD prevention and treatment measures in the context of Southeast Asia, with case studies in Indonesia. Specifically, it seeks to understand what kind of health interventions have the potential to be (cost-)effective considering the cultural background, lifestyle, health literacy and health system capacities in the region. Further, this thesis analyzes the interplay between NCDs and climate change and assesses the financial burden that both might pose in the decades to come. Hence, this thesis contributes to a better understanding of how the two health challenges of the 21st century, NCDs and climate change, can be addressed in the context of Southeast Asia and offers insights into what type of health policies and interventions can play a supportive role