Now showing 1 - 3 of 3
  • Publication
    Metadata only
    AI-based charging management for the integration of electric vehicles using a reference low voltage grid in Hamburg
    In recent years, electric vehicles (EVs) are considered to be a promising way to reduce greenhouse gas emissions from the transportation sector. However, the increasing penetration of EVs into the distribution network (DN) raises serious concerns about the network’s safe and reliable operation. The uncontrolled EV charging with random behavior will lead to volatile load peaks on the distribution transformer. In order to obtain more transformer loading capacity available for integration of further EVs, distributed energy resources (DERs) and related devices, such as heat pumps, the transformer loading must be limited to a certain range. For this reason, an intelligent charging management based on model-free Reinforcement Learning (RL) is proposed in this work. The RL management is able to control the charging power of all EVs connected to the network without previous knowledge about the arriving- and leaving time. The needed information for the RL-agent to perceive the current state of the system is formed with cumulated values such as the total energy requirement and the total charging power demand of all EVs. In this paper, the RL algorithm is trained on real-world energy consumption data for a month and on a reference network, created with selected characteristics of a substation network area in the northeast of Hamburg. Comparing with uncontrolled charging, the simulation results show that the RL-based charging management avoids 99 % of threshold violations regarding transformer loading and results in 1% of EV energy requirement is not satisfied. Through sensitivity analysis regarding the state space representation in the employed RL process, the necessity of providing the state of charge (SOC) or the energy requirements of EV users are proven to improve the charging control performance.
  • Publication
    Metadata only
    A Systematic Review of Ground-Based Infrastructure for the Innovative Urban Air Mobility
    (Sciendo, 2022-12-16) ;
    Eltgen, Jil
    ;
    Fraske, Tim
    ;
    Swaid, Majed
    ;
    Berling, Jan
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    Röntgen, Ole
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    ;
    The increasing level of urbanisation and traffic congestion promotes the concept of urban air mobility (UAM), which has become a thriving topic in engineering and neighbouring disciplines. the development of a suitable ground-based infrastructure is necessary to supply these innovative vehicles, which mainly includes networks of take-off and landing sites, facilities for maintenance, energy supply, and navigation and communication capabilities. Further requirements comprise robust business and operating models for emerging service providers and regulatory frameworks, particularly regarding safety, liability and noise emissions. the objective of this study is to provide an overview of the current results and developments in the field of UAM ground-based infrastructure by conducting a systematic literature review (SLr) and to identify the most relevant research gaps in the field. For the systematic literature analysis, our search string contains vertiports and the equivalents, UAM and equivalents, and search phrases for the individual domains. In the final analysis 64 articles were included, finding a strong focus on simulations and vertiport networks, while specific case studies and related aspects like automated MrO and urban planning appear less frequently. Therefore, this article provides insights for a more holistic perspective on challenges and necessities of future UAM.
  • Publication
    Open Access
    Analyse von Energieverbrauchsmodellen für elektrisch betriebene Transportdrohnen
    (Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg, Fakultät für Elektrotechnik, Professur für Elektrische Energiesysteme, 2022) ; ; ;