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2023Author
Hassouna, MohamedHetzel, ManuelSmirnov, NikitaPilchau, Wenzel Pilar vonCui, HenningFeyrer, GeorgHussaini, MortesaBoysen, JonasKisselbach, TimoAl-Falouji, GhassanWazed Ali, MohammadMeitz, LukasHenrichs, EliaShmelkin, IljaSubject
004 Data processing and computer science 600 Technology Proaktives ComputingAdaptives SystemSelbstorganisationMetadata
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Organic Computing
Organic Computing
Doctoral Dissertation Colloquium 2022
Abstract
Electromobility plays an increasingly important role in the energy transition. Electric vehicle charging poses challenges to the electricity grid due to high peak demand situations. Therefore, it is crucial to leverage the vehicle’s flexibility when charging can be delayed. Conventional charging management systems cannot adapt to frequently changing conditions such as fluctuating renewable energy output, electricity prices and user behavior. In this article, we investigate the application of reinforcement learning approaches for self-adaptive charging management. In particular, we identify challenges regarding realistic environments and adaption to varying topologies and connections among charging stations. We describe related approaches and propose ideas and planned experiments to overcome these problems by utilizing generative models and graph neural networks.
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Link zu kassel university pressCitation
@book{doi:10.17170/kobra-202302107484,
author={Hassouna, Mohamed and Hetzel, Manuel and Smirnov, Nikita and Pilchau, Wenzel Pilar von and Cui, Henning and Feyrer, Georg and Hussaini, Mortesa and Boysen, Jonas and Kisselbach, Timo and Al-Falouji, Ghassan and Wazed Ali, Mohammad and Meitz, Lukas and Henrichs, Elia and Shmelkin, Ilja},
editor={Krupitzer, Christian and Tomforde, Sven},
title={Organic Computing},
publisher={kassel university press},
year={2023}
}
0500 Oax 0501 Text $btxt$2rdacontent 0502 Computermedien $bc$2rdacarrier 1100 2023$n2023 1500 1/eng 2050 ##0##http://hdl.handle.net/123456789/14854 3000 Hassouna, Mohamed 3010 Hetzel, Manuel 3010 Smirnov, Nikita 3010 Pilchau, Wenzel Pilar von 3010 Cui, Henning 3010 Feyrer, Georg 3010 Hussaini, Mortesa 3010 Boysen, Jonas 3010 Kisselbach, Timo 3010 Al-Falouji, Ghassan 3010 Wazed Ali, Mohammad 3010 Meitz, Lukas 3010 Henrichs, Elia 3010 Shmelkin, Ilja 4000 Organic Computing / Hassouna, Mohamed 4030 4060 Online-Ressource 4085 ##0##=u http://nbn-resolving.de/http://hdl.handle.net/123456789/14854=x R 4204 \$dBuch 4170 5550 {{Proaktives Computing}} 5550 {{Adaptives System}} 5550 {{Selbstorganisation}} 7136 ##0##http://hdl.handle.net/123456789/14854
Krupitzer, Christian Tomforde, Sven 2023-06-28T13:24:14Z 2023-06-28T13:24:14Z 2023 doi:10.17170/kobra-202302107484 http://hdl.handle.net/123456789/14854 eng kassel university press Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International http://creativecommons.org/licenses/by-sa/4.0/ Automatic Computing ProActive Computing Complex Adaptive Systems Self-Organisation 004 600 Organic Computing Buch Electromobility plays an increasingly important role in the energy transition. Electric vehicle charging poses challenges to the electricity grid due to high peak demand situations. Therefore, it is crucial to leverage the vehicle’s flexibility when charging can be delayed. Conventional charging management systems cannot adapt to frequently changing conditions such as fluctuating renewable energy output, electricity prices and user behavior. In this article, we investigate the application of reinforcement learning approaches for self-adaptive charging management. In particular, we identify challenges regarding realistic environments and adaption to varying topologies and connections among charging stations. We describe related approaches and propose ideas and planned experiments to overcome these problems by utilizing generative models and graph neural networks. open access Hassouna, Mohamed Hetzel, Manuel Smirnov, Nikita Pilchau, Wenzel Pilar von Cui, Henning Feyrer, Georg Hussaini, Mortesa Boysen, Jonas Kisselbach, Timo Al-Falouji, Ghassan Wazed Ali, Mohammad Meitz, Lukas Henrichs, Elia Shmelkin, Ilja XVI, 199 Seiten Kassel 978-3-7376-1100-8 Proaktives Computing Adaptives System Selbstorganisation Doctoral Dissertation Colloquium 2022 publishedVersion Intelligent Embedded Systems Band 24 true 39,00 Intelligent Embedded Systems Naturwissenschaft, Technik, Informatik, Medizin Sammelband FB 16 Elektrotechnik / Informatik Softcover DIN A5 true
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