Computer Science ›› 2022, Vol. 49 ›› Issue (6): 44-54.doi: 10.11896/jsjkx.220400002

• Smart IoT Technologies and Applications Empowered by 6G • Previous Articles     Next Articles

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 (
  • 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.(
  • Supported by:
    National Natural Science Foundation of China(62171218).

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)

CLC Number: 

  • TP393
[1] ESTEBSARI A,MAZZARINO P R,BOTTACCIOLI L,et al.IoT-Enabled Real-Time Management of Smart Grids with Demand Response Aggregators[J].IEEE Transactions on Industry Applications,2021,58(1):102-112.
[2] HERMANN D T,DONATIEN N,ARMEL T K F,et al.Techno-economic and environmental feasibility study with demand-side management of photovoltaic/wind/hydroelectricity/battery/diesel:A case study in Sub-Saharan Africa[J].Energy Conversion and Management,2022,258:115494.
[3] PFEIFER A,HERC L,BJELIĆ I B,et al.Flexibility index and decreasing the costs in energy systems with high share of renewable energy[J].Energy Conversion and Management,2021,240:114258.
[4] TANG R,WANG S,YAN C.A direct load control strategy of centralized air-conditioning systems for building fast demand response to urgent requests of smart grids[J].Automation in Construction,2018,87:74-83.
[5] BAHRAMI S,AMINI M H,SHAFIE-KHAH M,et al.A decentralized electricity market scheme enabling demand response deployment[J].IEEE Transactions on Power Systems,2017,33(4):4218-4227.
[6] LIANG H,MA J,LIN J.Robust Distribution System Expansion Planning Incorporating Thermostatically-Controlled-Load Demand Response Resource[J].IEEE Transactions on Smart Grid,2021,13(1):302-313.
[7] NIKZAD M,MOZAFARI B.Reliability assessment of incentive-and priced-based demand response programs in restructured power systems[J].International Journal of Electrical Power & Energy Systems,2014,56:83-96.
[8] LIU N,YU X,WANG C,et al.Energy-sharing model withprice-based demand response for microgrids of peer-to-peer prosumers[J].IEEE Transactions on Power Systems,2017,32(5):3569-3583.
[9] VARDAKAS J S,ZORBA N,VERIKOUKIS C V.A survey on demand response programs in smart grids:Pricing methods and optimization algorithms[J].IEEE Communications Surveys & Tutorials,2014,17(1):152-178.
[10] ESTHER B P,KUMAR K S.A survey on residential demand side management architecture,approaches,optimization models and methods[J].Renewable and Sustainable Energy Reviews,2016,59:342-351.
[11] GHORBANIAN M,DOLATABADI S H,SIANO P.Game theo-ry-based energy-management method considering autonomous demand response and distributed generation interactions in smart distribution systems[J].IEEE Systems Journal,2020,15(1):905-914.
[12] LI Y,WANG C,LI G,et al.Optimal scheduling of integrated demand response-enabled integrated energy systems with uncertain renewable generations:A Stackelberg game approach[J].Energy Conversion and Management,2021,235:113996.
[13] KWAC J,FLORA J,RAJAGOPAL R.Household energy con-sumption segmentation using hourly data[J].IEEE Transactions on Smart Grid,2014,5(1):420-430.
[14] KANG J,LEE J H.Electricity customer clustering following experts’ principle for demand response applications[J].Energies,2015,8(10):12242-12265.
[15] YANG J,ZHAO J,WEN F,et al.A model of customizing electricity retail prices based on load profile clustering analysis[J].IEEE Transactions on Smart Grid,2018,10(3):3374-3386.
[16] YANG J,ZHAO J,WEN F,et al.A framework of customizing electricity retail prices[J].IEEE Transactions on Power Systems,2017,33(3):2415-2428.
[17] HABEN S,SINGLETON C,GRINDROD P.Analysis and clustering of residential customers energy behavioral demand using smart meter data[J].IEEE Transactions on Smart Grid,2015,7(1):136-144.
[18] ZHOU K,YANG S,SHAO Z.Household monthly electricity consumption pattern mining:A fuzzy clustering-based model and a case study[J].Journal of Cleaner Production,2017,141:900-908.
[19] MELICIO R.Electricity demand profile prediction based onhousehold characteristics[C]//12th International Conference on the European Energy Market-EEM 2015.2015:1-5.
[20] SHI H,XU M,LI R.Deep learning for household load forecasting-A novel pooling deep RNN[J].IEEE Transactions on Smart Grid,2017,9(5):5271-5280.
[21] SUN M,WANG Y,TENG F,et al.Clustering-based residential baseline estimation:A probabilistic perspective[J].IEEE Transa-ctions on Smart Grid,2019,10(6):6014-6028.
[22] FAN C,XIAO F,ZHAO Y.A short-term building cooling load prediction method using deep learning algorithms[J].Applied Energy,2017,195:222-233.
[23] CAI M,PIPATTANASOMPORN M,RAHMAN S.Day-aheadbuilding-level load forecasts using deep learning vs.traditional time-series techniques[J].Applied Energy,2019,236:1078-1088.
[24] KONG W,DONG Z Y,JIA Y,et al.Short-term residential load forecasting based on LSTM recurrent neural network[J].IEEE Transactions on Smart Grid,2017,10(1):841-851.
[25] KIM T Y,CHO S B.Predicting residential energy consumption using CNN-LSTM neural networks[J].Energy,2019,182:72-81.
[26] YU M,HONG S H.Incentive-based demand response conside-ring hierarchical electricity market:A Stackelberg game approach[J].Applied Energy,2017,203:267-279.
[27] MOHSENIAN-RAD A H,WONG V W S,JATSKEVICH J,et al.Autonomous demand-side management based on game-theo-retic energy consumption scheduling for the future smart grid[J].IEEE Transactions on Smart Grid,2010,1(3):320-331.
[28] ZHAO C,WANG J,WATSON J P,et al.Multi-stage robustunit commitment considering wind and demand response uncertainties[J].IEEE Transactions on Power Systems,2013,28(3):2708-2717.
[29] JAIN A K.Data clustering:50 years beyond K-means[J].Pattern Recognition Letters,2010,31(8):651-666.
[30] SCHUBERT E,ZIMEK A.ELKI:A large open-source library for data analysis-ELKI Release 0.7.5” Heidelberg“[J].arXiv:1902.03616,2019.
[31] APS M.Mosek optimization toolbox for matlab[M].User’sGuide and Reference Manual,Version,2019.
[32] Pecan street inc.dataport[EB/OL].https://datapo-
[33] WANG Z F,SCAGLIONE A,THOMAS R J.Power Grid Network Analysis for Smart Grid Applications[M].Smart Grids.2017:151-177.
[34] ROUSSEEUW P J.Silhouettes:a graphical aid to the interpretation and validation of cluster analysis[J].Journal of Computational and Applied Mathematics,1987,20:53-65.
[35] AL-JARRAH O Y,AL-HAMMADI Y,YOO P D,et al.Multi-layered clustering for power consumption profiling in smart grids[J].IEEE Access,2017,5:18459-18468.
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