文献类型：期刊文献

英文题名：Asynchronous double consensus-based distributed optimization for sustainable industrial utility systems

作者：Kong, Minxue[3]; Peng, Xin[2,3,4]; Li, Zhi[3]; Shen, Feifei[3]; Liu, Yurong[3,4]; Zhong, Weimin[1,2]

机构：[1] State Key Laboratory of Chemical Engineering and Low-Carbon Technology, East China University of Science and Technology, Shanghai, 200237, China; [2] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [3] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [4] State Key Laboratory of Industrial Control Technology, East China University of Science and Technology, Shanghai, 200237, China

年份：2025

卷号：331

外文期刊名：Energy

收录：EI(收录号：20252418582983)

语种：英文

外文关键词：Electric manufacturing plants - Gas plants - Location - NP-hard - Solar fuels - Solar heating - Topology

摘要：Industrial utility systems are essential to large-scale chemical plants, providing both electrical and thermal energy to support industrial processes. However, traditional centralized optimization methods, which rely on a central node for decision-making and synchronization, suffer from inherent limitations such as computational load concentration, poor robustness, and inadequate adaptability to dynamic environments. These issues significantly constrain system efficiency and scalability. Integrating renewable energy sources presents an additional challenge, primarily due to the conflict between the intermittent nature of renewable energy and the need for stability in the utility system's energy supply. This study introduces a distributed optimization framework based on an asynchronous double-consensus algorithm to address these issues. The framework is designed to optimize utility system operations while enhancing computational efficiency and scalability. To further improve system resilience, a dynamic asynchronous communication strategy is proposed to handle device failures and communication constraints. The effectiveness of the proposed method is demonstrated through a case study of a real industrial energy system. The results show a significant reduction in computational complexity, with an overall system cost reduction of 6.6%. The proposed algorithm reduces execution time by approximately 91%–92% compared to the conventional centralized approach. Moreover, it guarantees convergence to the optimal solution within a finite number of iterations, attaining a convergence accuracy of 2.651×10?6. ? 2025