Hua, Z., Djemame, K. orcid.org/0000-0001-5811-5263, Tziritas, N. et al. (1 more author) (Accepted: 2024) A Framework for Digital Twin Collaboration. In: Proceedings of the 2024 Winter Simulation Conference. 2024 Winter Simulation Conference, 15-18 Dec 2024, Orlando, Florida. ACM ISBN 9798331534202
Abstract
Digital Twins (DTs) have emerged as a powerful tool for modeling Large Complex Systems (LCSs). Their strength lies in the detailed virtual models that enable accurate predictions, presenting challenges in traditionally centralized approaches due to the immense scale and decentralized ownership of LCSs. This paper proposes a framework that leverages the prevalence of individual DTs within LCSs. By facilitating the exchange of decisions and predictions, this framework fosters collaboration among autonomous DTs, enhancing performance. Additionally, a trust-based mechanism is introduced to improve system robustness against poor decision-making within the collaborative network. The framework's effectiveness is demonstrated in a virtual power plant (VPP) scenario. The evaluation results confirm the system's objectives across various test cases and show scalability for large deployments.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://dl.acm.org/doi/abs/10.5555/3712729.3712982?download=true |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Distributed Systems & Services |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Jul 2024 09:53 |
Last Modified: | 27 Feb 2025 10:55 |
Status: | Published |
Publisher: | ACM |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214767 |