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IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  > In Press, Accepted Manuscript
J. Chen, K. Li, Z. Qin, D. Zhao, and C. Zhang, “A privacy-preserving distributed optimization algorithm with prescribed-time convergence using a deep learning adversarial network for an integrated energy system,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126008
Citation: J. Chen, K. Li, Z. Qin, D. Zhao, and C. Zhang, “A privacy-preserving distributed optimization algorithm with prescribed-time convergence using a deep learning adversarial network for an integrated energy system,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126008
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A Privacy-Preserving Distributed Optimization Algorithm With Prescribed-Time Convergence Using a Deep Learning Adversarial Network for an Integrated Energy System

doi: 10.1109/JAS.2026.126008
Funds:  This work was supported in part by the Key Program of the National Natural Science Foundation of China (62133008), the Joint Funds of the National Natural Science Foundation of China (U23A20333), and the Young Scientists Fund of the National Natural Science Foundation of China (62403282)
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    Abstract
  • This paper investigates the economic dispatch problem in multi-agent integrated energy systems (IES). To satisfy the stringent requirements for privacy preservation and convergence speed, a prescribed-time distributed optimization algorithm featuring an encryption mechanism based on a deep adversarial network is proposed. First, an economic dispatch model for an IES with coupled electrical and heating systems is established and formulated as a constrained distributed optimization problem. Second, a projection-based prescribed-time distributed algorithm is developed, and its convergence within a prescribed time is rigorously proven using Lyapunov theory. Subsequently, a deep learning-based encryption method, inspired by multi-agent game theory, is designed to enable rapid encryption and decryption of exchanged information while significantly enhancing data fidelity and security against attacks. Finally, case studies conducted on a modified IEEE 30-bus and 14-node test system, representing a coupled electrical and heating IES, demonstrate that the proposed method exhibits excellent convergence performance and robust privacy-preserving capabilities.

     

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