Technical University of Berlin

About: Technical University of Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Laser & Catalysis. The organization has 27292 authors who have published 59342 publications receiving 1414623 citations. The organization is also known as: Technische Universität Berlin & TU Berlin.

System LCOE: What are the Costs of Variable Renewables?

Abstract: Levelized costs of electricity (LCOE) are a common metric for comparing power generating technologies. However, there is qualified criticism particularly towards evaluating variable renewables like wind and solar power based on LCOE because it ignores integration costs that occur at the system level. In this paper we propose a new measure System LCOE as the sum of generation and integration costs per unit of VRE. For this purpose we develop a conclusive definition of integration costs. Furthermore we decompose integration costs into different cost components and draw conclusions for integration options like transmission grids and energy storage. System LCOE are quantified from a power system model and a literature review. We find that at moderate wind shares (~20%) integration costs can be in the same range as generation costs of wind power and conventional plants. Integration costs further increase with growing wind shares. We conclude that integration costs can become an economic barrier to deploying VRE at high shares. This implies that an economic evaluation of VRE must not neglect integration costs. A pure LCOE comparison would significantly underestimate the costs of VRE at high shares. System LCOE give a framework of how to consistently account for integration costs and thus guide policy makers and system planers in designing a cost-efficient power system.

Towards exact molecular dynamics simulations with machine-learned force fields.

Abstract: Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.

A Systematic Look at Prototyping

Abstract: This paper originates from a series of discussions between programme committee members during the preparation of the Working Conference on Prototyping. While trying to define the topic of the conference, it became clear to us that we each held our own viewpoint on the subject. Views differed as to the specific use of terminology as well as the application-oriented emphasis on particular strategies, and so did our judgements about the potential usefulness of prototyping. The views did not, however, seem contradictory but rather complementary.

Towards demand-side solutions for mitigating climate change

Abstract: Research on climate change mitigation tends to focus on supply-side technology solutions A better understanding of demand-side solutions is missing We propose a transdisciplinary approach to identify demand-side climate solutions, investigate their mitigation potential, detail policy measures and assess their implications for well-being

A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

Abstract: Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices in critical infrastructures like health, transportation, environmental control, and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multi-tenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users’ right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analyzed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.