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

• Published in: Ji suan ji ke xue Vol. 49; no. 6; pp. 44 - 54
• Main Authors: Ran, Wang, Jiang-Tian, Nie, Yang, Zhang, Kun, Zhu
• Format: Journal Article
• Language: Chinese; English
• Published: Chongqing Guojia Kexue Jishu Bu 01.06.2022; Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 211106,China%School of Computer Science and Engineering,Nanyang Technological University,639798,Singapore; College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China; Editorial office of Computer Science
• Subjects: Carbon; Clustering; Customer satisfaction; demand response(dr)|customer clustering|deep learning|6g-enabled industrial internet of things(iiot)|smart srid(sg); Demand side management; Electric power demand; Electricity consumption; Energy consumption; Energy management; Feature extraction; Household appliances; Industrial applications; Internet of Things; Optimization; Residential energy; Smart grid; Deep learning; Demand response(DR); Customer clustering; Smart srid(SG); 6G-enabled industrial Internet of things(IIOT)
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.220400002

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.