Digital Twin for Secure Peer-to-Peer Trading in Cyber-Physical Energy Systems

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-28 DOI:10.1109/TNSE.2024.3507956

Yushuai Li;Peiyuan Guan;Tianyi Li;Kim Guldstrand Larsen;Marco Aiello;Torben Bach Pedersen;Tingwen Huang;Yan Zhang

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引用次数: 0

Abstract

The secure sharing of data is crucial for peer-to-peer energy trading. However, the vulnerability of Information and Communication Technology (ICT) infrastructures to cyberattacks, e.g., Denial of Service (DoS) attacks, poses a significant challenge. A possible solution is to use Digital Twin (DT) modeling of the physical system, which provides robust digital mapping and Big Data processing capabilities that facilitate data recovery. To this end, this paper proposes a DT-enabled energy trading framework for cyber-physical energy systems that offers data analytic and recovery capabilities to defend from DoS attacks. With this framework, a new distributed approximate-newton trading algorithm with a switched triggering control strategy is proposed. Therein, the DT model is employed to achieve data recovery and adjust the system evolution of trading trajectory during attack periods. This enables the proposed method to find optimal trading solutions even in the presence of DoS attacks. Theoretical analysis results demonstrate the correctness of the proposed method. Furthermore, numerical simulations are conducted to assess the effectiveness of the proposed method.

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.