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Adaptive Dispatching of Mobile Charging Stations using Multi-Agent Graph Convolutional Cooperative-Competitive Reinforcement Learning

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autorschaft

  • Shimon Wonsak
  • Nils Henke
  • Mohammad Al-Rifai
  • Michael Nolting
  • Wolfgang Nejdl

Organisationseinheiten

Externe Organisationen

  • Volkswagen AG

Details

OriginalspracheEnglisch
Titel des Sammelwerks32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
UntertitelACM SIGSPATIAL 2024
Herausgeber/-innenMario A. Nascimento, Li Xiong, Andreas Zufle, Yao-Yi Chiang, Ahmed Eldawy, Peer Kroger
Seiten410-420
Seitenumfang11
ISBN (elektronisch)9798400711077
PublikationsstatusVeröffentlicht - 22 Nov. 2024
Veranstaltung32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024 - Atlanta, USA / Vereinigte Staaten
Dauer: 29 Okt. 20241 Nov. 2024

Abstract

Battery electric vehicles (BEV) offer an opportunity to decrease transportation and mobility emissions significantly. The availability of charging station networks and infrastructure is crucial for the proliferation of BEVs. While the expansion of the charging networks is still slow, optimal utilization of the existing infrastructure and dispatching of mobile charging stations can serve as a bypass while more charging stations are built. In this work, we propose a novel multi-agent reinforcement learning - AdapMCS - approach for optimizing the adaptive dispatching of mobile charging stations to maximize the number of served charging requests by a charging station operator while improving the customer experience. By combining graph neural networks with reinforcement learning our approach is able to adapt to dynamic spatio-temporal changes in the demand distribution, for example, during big events such as concerts or fairs. Furthermore, we conduct a thorough evaluation using a publicly available real-world dataset and simulation of dynamic demand distribution changes. The results show that our adaptive dispatching approach is able to deal with the demand shifts and achieve significant gains for both customers, in terms of reducing waiting and charging times, and operators, in terms of increasing their profit.

ASJC Scopus Sachgebiete

Zitieren

Adaptive Dispatching of Mobile Charging Stations using Multi-Agent Graph Convolutional Cooperative-Competitive Reinforcement Learning. / Wonsak, Shimon; Henke, Nils; Al-Rifai, Mohammad et al.
32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: ACM SIGSPATIAL 2024. Hrsg. / Mario A. Nascimento; Li Xiong; Andreas Zufle; Yao-Yi Chiang; Ahmed Eldawy; Peer Kroger. 2024. S. 410-420.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Wonsak, S, Henke, N, Al-Rifai, M, Nolting, M & Nejdl, W 2024, Adaptive Dispatching of Mobile Charging Stations using Multi-Agent Graph Convolutional Cooperative-Competitive Reinforcement Learning. in MA Nascimento, L Xiong, A Zufle, Y-Y Chiang, A Eldawy & P Kroger (Hrsg.), 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: ACM SIGSPATIAL 2024. S. 410-420, 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024, Atlanta, USA / Vereinigte Staaten, 29 Okt. 2024. https://doi.org/10.1145/3678717.3691262
Wonsak, S., Henke, N., Al-Rifai, M., Nolting, M., & Nejdl, W. (2024). Adaptive Dispatching of Mobile Charging Stations using Multi-Agent Graph Convolutional Cooperative-Competitive Reinforcement Learning. In M. A. Nascimento, L. Xiong, A. Zufle, Y.-Y. Chiang, A. Eldawy, & P. Kroger (Hrsg.), 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: ACM SIGSPATIAL 2024 (S. 410-420) https://doi.org/10.1145/3678717.3691262
Wonsak S, Henke N, Al-Rifai M, Nolting M, Nejdl W. Adaptive Dispatching of Mobile Charging Stations using Multi-Agent Graph Convolutional Cooperative-Competitive Reinforcement Learning. in Nascimento MA, Xiong L, Zufle A, Chiang YY, Eldawy A, Kroger P, Hrsg., 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: ACM SIGSPATIAL 2024. 2024. S. 410-420 doi: 10.1145/3678717.3691262
Wonsak, Shimon ; Henke, Nils ; Al-Rifai, Mohammad et al. / Adaptive Dispatching of Mobile Charging Stations using Multi-Agent Graph Convolutional Cooperative-Competitive Reinforcement Learning. 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: ACM SIGSPATIAL 2024. Hrsg. / Mario A. Nascimento ; Li Xiong ; Andreas Zufle ; Yao-Yi Chiang ; Ahmed Eldawy ; Peer Kroger. 2024. S. 410-420
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title = "Adaptive Dispatching of Mobile Charging Stations using Multi-Agent Graph Convolutional Cooperative-Competitive Reinforcement Learning",
abstract = "Battery electric vehicles (BEV) offer an opportunity to decrease transportation and mobility emissions significantly. The availability of charging station networks and infrastructure is crucial for the proliferation of BEVs. While the expansion of the charging networks is still slow, optimal utilization of the existing infrastructure and dispatching of mobile charging stations can serve as a bypass while more charging stations are built. In this work, we propose a novel multi-agent reinforcement learning - AdapMCS - approach for optimizing the adaptive dispatching of mobile charging stations to maximize the number of served charging requests by a charging station operator while improving the customer experience. By combining graph neural networks with reinforcement learning our approach is able to adapt to dynamic spatio-temporal changes in the demand distribution, for example, during big events such as concerts or fairs. Furthermore, we conduct a thorough evaluation using a publicly available real-world dataset and simulation of dynamic demand distribution changes. The results show that our adaptive dispatching approach is able to deal with the demand shifts and achieve significant gains for both customers, in terms of reducing waiting and charging times, and operators, in terms of increasing their profit.",
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