Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2025 , Vol. 59 >Issue 11: 1592 - 1602

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.616

New Type Power System and the Integrated Energy

Neural Branching Power-Computation Network Fast Optimization Method Considering Computing Demand Response

  • ZHANG Lei ,
  • LI Ran ,
  • TANG Lun ,
  • CHEN Sijie ,
  • ZHAO Shizhen ,
  • SU Fu
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  • 1 College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, China
    2 Shanghai Non-Carbon Energy Conversion and Utilization Institute, Shanghai 200240, China
    3 Key Laboratory of Control of Power Transmission and Conversion of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
    4 School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
    5 State Grid Sichuan Electric Power Company, Chengdu 610041, China

Received date: 2023-12-06

  Revised date: 2023-12-23

  Accepted date: 2024-01-17

  Online published: 2024-02-28

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Abstract

The rapid development of data centers makes it possible for them to participate in power system dispatching as demand response. By dispatching the computing resources in data centers between regions, it is possible to achieve energy saving, emission reduction, and cost saving. However, considering the demand response of data center computing resources in power system dispatching faces the problem of insufficient computing speed. To address this issue, a neural branching power-computation network fast optimization method considering computing demand response is proposed. First, a unit commitment double-layer model considering computing power resource demand response is established. Then, the graph convolutional neural network and the branch and bound method are combined and applied to the double-layer model. Through historical data training, the neural branching power-computation network fast optimization method considering computing demand response has the ability to quickly determine the order of branch and bound variables and minimize the number of iterations, which significantly improves the solution speed and realizes the fast solution of unit combination demand response considering data center computing resources. The performance of the proposed method is verified in the simulation scenario of “east data, west computing” project. The proposed method achieves an average solution time reduction of 39.1% compared to the pseudo-cost branching algorithm, 38.1% compared to the commercial solver CPLEX, and 13.5% compared to the machine learning-based optimization acceleration algorithm Extratrees, the average solution time is reduced by 13.5%. In addition, the system coordinated dispatching frequency is increased from 1 time/h to 4 times/h, and the maximum potential reduction in wind curtailment of the total wind power generation during 24 h accounts for 17.42%.

Key words： mixed integer linear programming (MILP); unit commitment; demand response; Internet data center (IDC); neural branching

Cite this article

ZHANG Lei , LI Ran , TANG Lun , CHEN Sijie , ZHAO Shizhen , SU Fu . Neural Branching Power-Computation Network Fast Optimization Method Considering Computing Demand Response[J]. Journal of Shanghai Jiaotong University, 2025 , 59(11) : 1592 -1602 . DOI: 10.16183/j.cnki.jsjtu.2023.616

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