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Edge–Cloud Collaborative Optimization Scheduling of an Industrial Park Integrated Energy System

Author

Listed:
  • Gengshun Liu

    (Guoneng Xinjiang Ganquanbao Comprehensive Energy Co., Ltd., Urumqi 830019, China)

  • Xinfu Song

    (Economic and Technological Research Institute, State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830002, China)

  • Chaoshan Xin

    (Economic and Technological Research Institute, State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830002, China)

  • Tianbao Liang

    (School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yang Li

    (School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Kun Liu

    (School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Due to the large proportion of China’s energy consumption used by industry, in response to the national strategic goal of “carbon peak and carbon neutrality” put forward by the Chinese government, it is urgent to improve energy efficiency in the industrial field. This paper focuses on the optimization of an integrated energy system with supply–demand coordination in an industrial park. This optimization is formulated as a “node-flow” model. Within the model, each node is designed according to the objective function of its own operation and the energy coupling relationship. The flow model is designed based on the energy flow interaction relationship between each node. Based on the “node-flow” model, an edge–cloud information interaction mechanism based on energy transfer balance between nodes is proposed to describe the way the system interacts with information, and a distributed iterative optimization algorithm based on edge–cloud collaboration is designed to realize the optimization decision of each node. The performance of the method proposed in this paper is demonstrated using a practical case study of an industrial park integrated energy system in Xinjiang. The results show that the proposed model can effectively improve the utilization efficiency of multi-energy synergy and complementation in the industrial park, and the proposed algorithm can shorten the solution time by more than 50% without significantly affecting the accuracy of the solution.

Suggested Citation

  • Gengshun Liu & Xinfu Song & Chaoshan Xin & Tianbao Liang & Yang Li & Kun Liu, 2024. "Edge–Cloud Collaborative Optimization Scheduling of an Industrial Park Integrated Energy System," Sustainability, MDPI, vol. 16(5), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1908-:d:1346120
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    References listed on IDEAS

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