计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 44-54.doi: 10.11896/jsjkx.220400002

• 6G 赋能智慧物联网技术与应用* 上一篇    下一篇

Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids

Ran WANG1,2, Jiang-tian NIE3, Yang ZHANG1,2, Kun ZHU1,2   

  1. 1 College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
    2 Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 211106,China
    3 School of Computer Science and Engineering,Nanyang Technological University,639798,Singapore
  • 收稿日期:2022-04-01 修回日期:2022-05-01 出版日期:2022-06-15 发布日期:2022-06-08
  • 通讯作者: Jiang-tian NIE (jnie001@e.ntu.edu.sg)
  • 作者简介:Ran WANG,born in 1989,Ph.D,asso-ciate professor.His main research interests include network performance ana-lysis,smart grid and Internet of electric vehicles,etc.
    Jiang-tian NIE,born in 1994,Ph.D.Her main research interests include network economics,game theory,crowd sensing and learning.(wangran@nuaa.edu.cn)
  • 基金资助:
    National Natural Science Foundation of China(62171218).

Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids

Ran WANG1,2, Jiang-tian NIE3, Yang ZHANG1,2, Kun ZHU1,2   

  1. 1 College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
    2 Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 211106,China
    3 School of Computer Science and Engineering,Nanyang Technological University,639798,Singapore
  • Received:2022-04-01 Revised:2022-05-01 Online:2022-06-15 Published:2022-06-08
  • Contact: Jiang-tian NIE (jnie001@e.ntu.edu.sg)
  • About author:Ran WANG,born in 1989,Ph.D,asso-ciate professor.His main research interests include network performance ana-lysis,smart grid and Internet of electric vehicles,etc.
    Jiang-tian NIE,born in 1994,Ph.D.Her main research interests include network economics,game theory,crowd sensing and learning.(wangran@nuaa.edu.cn)
  • Supported by:
    National Natural Science Foundation of China(62171218).

摘要: As a typical industrial Internet of things (IIOT) service,demand response(DR) is becoming a promising enabler for intelligent energy management in 6G-enabled smart grid systems,to achieve quick response for supply-demand mismatches.How-ever,existing literatures try to adjust customers’ load profiles optimally,instead of electricity overhead,energy consumption patterns of residential appliances,customer satisfaction levels,and energy consumption habits.In this paper,a novel DR method is investigated by mixing the aforementioned factors,where the residential customer cluster is proposed to enhance the performance.Clustering approaches are leveraged to study the electricity consumption habits of various customers by extracting their features and characteristics from historical data.Based on the extracted information,the residential appliances can be scheduled effectively and flexibly.Moreover,we propose and study an efficient optimization framework to obtain the optimal scheduling solution by using clustering and deep learning methods.Extensive simulation experiments are conducted with real-world traces.Numerical results show that the proposed DR method and optimization framework outperform other baseline schemes in terms of the system overhead and peak-to-average ratio (PAR).The impact of various factors on the system utility is further analyzed,which provides useful insights on improving the efficiency of the DR strategy.With the achievement of efficient and intelligent energy management,the proposed method also promotes the realization of China’s carbon peaking and carbon neutrality goals.

关键词: 6G-enabled industrial Internet of things(IIOT), Customer clustering, Deep learning, Demand response(DR), Smart srid(SG)

Abstract: As a typical industrial Internet of things (IIOT) service,demand response(DR) is becoming a promising enabler for intelligent energy management in 6G-enabled smart grid systems,to achieve quick response for supply-demand mismatches.How-ever,existing literatures try to adjust customers’ load profiles optimally,instead of electricity overhead,energy consumption patterns of residential appliances,customer satisfaction levels,and energy consumption habits.In this paper,a novel DR method is investigated by mixing the aforementioned factors,where the residential customer cluster is proposed to enhance the performance.Clustering approaches are leveraged to study the electricity consumption habits of various customers by extracting their features and characteristics from historical data.Based on the extracted information,the residential appliances can be scheduled effectively and flexibly.Moreover,we propose and study an efficient optimization framework to obtain the optimal scheduling solution by using clustering and deep learning methods.Extensive simulation experiments are conducted with real-world traces.Numerical results show that the proposed DR method and optimization framework outperform other baseline schemes in terms of the system overhead and peak-to-average ratio (PAR).The impact of various factors on the system utility is further analyzed,which provides useful insights on improving the efficiency of the DR strategy.With the achievement of efficient and intelligent energy management,the proposed method also promotes the realization of China’s carbon peaking and carbon neutrality goals.

Key words: 6G-enabled industrial Internet of things(IIOT), Customer clustering, Deep learning, Demand response(DR), Smart srid(SG)

中图分类号: 

  • TP393
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