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Novel scheme for reducing communication data traffic in advanced metering infrastructure networks

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Abstract

A smart grid uses automation and information communication technologies to guarantee its integrity. The first step of smart grid construction is the deployment of an advanced metering infrastructure system, which can read meter data automatically and maximize energy efficiency. Messages are collected at hubs distributed throughout the smart grid and sent through limited network resources. The advanced metering infrastructure system comprises a meter data management system, concentrators, relay stations between a set of smart meters and the control center, and smart meters that can collect the information of thousands of users. Concerns regarding this system are the loading on the control center, the volume of data traffic, and the quantity of the data. This study analyzes various situations of data traffic, uses Poisson process, and proposed two schemes to reduce the transmission frequency and the total data volume. A concentrator is determined to reduce the transmission frequency and compress the data from smart meters to the control center, thereby reducing the total data volume. Furthermore, the proposed scheme can reduce the loading on the control center.

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References

  1. Fang X, Misra S, Xue G, Yangand D (2012) Smart grid-the new and improved power grid: a survey. IEEE Commun Surv Tutor 14(4):944–980

    Article  Google Scholar 

  2. Yu N, Shah S, Johnson R, Sherick R, Hong M, Loparo K (2015) Big data analytics in power distribution systems. In: 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)

  3. Ma R, Chen H-H, Huang Y-R, Meng W (2013) Smart grid communication: its challenges and opportunities. IEEE Trans Smart Grid 4(1):36–46

    Article  Google Scholar 

  4. Erol-Kantarci M, Mouftah HT (2015) Energy-efficient information and communication infrastructures in the smart grid: a survey on interactions and open issues. IEEE Commun Surv Tutor 17(1):179–197

    Article  Google Scholar 

  5. Gungor VC, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke GP (2013) A survey on smart grid potential applications and communication requirements. IEEE Trans Ind Inf 9(1):28–42

    Article  Google Scholar 

  6. Castillo-Cagigal M, Matallanas E, Mass-Bote D, Caamano-Martin E, Gutierrez A, Monasterio F (2010) Self-consumption enhancement with storage system and demand-side management: GeDELOS-PV system. In: Proceedings of 5th International Renewable Energy Storage Conference (IRES). pp. 3–7

  7. Chen L, Li N, Jiang L (2012) Control and optimization methods for electric smart grid

  8. Roesener C, Ichimura J, Nishi H (2006) Implement of internet based demand controlling system. Proc Int Conf Ind Inf 381–386

  9. Fan Z, Kulkarni P, Gormus S, Efthymiou C, Kalogridis G, Sooriyabandara M, Zhu Z, Lambotharan S, Chin WH (2013) Smart grid communications: overview of research challenges, solutions, and standardization activities. IEEE Commun Surv Tutor 15(1):21–38

    Article  Google Scholar 

  10. Ghofrani M, Hassanzadeh M, Etezadi-Amoli M, Fadali M (2011) Smart meter based short-term load forecasting for residential customers. In: Proceedings of North American Power Symposium (NAPS). 1–5

  11. Wang S, Cui L, Que J, Choi D, Jiang X, Cheng S, Xie L (2012) A randomized response model for privacy preserving smart metering. IEEE Trans Smart Grid 3(3):1317–1324

    Article  Google Scholar 

  12. Gungor VC, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke GP (2011) Smart grid technologies: communication technologies and standards. IEEE Trans Ind Inf 7(4):529–542

    Article  Google Scholar 

  13. Yan Y, Qian Y, Sharif H, Tipper D (2013) A survey on smart grid communication infrastructures: motivations, requirements, and challenges. IEEE Commun Surv Tutor 15(1):5–20

    Article  Google Scholar 

  14. Yan Y, Hu RQ, Das SK, Sharif H, Qian Y (2013) An efficient security protocol for advanced metering infrastructure in smart grid. IEEE Netw 27(4):64–71

    Article  Google Scholar 

  15. Yang Y, Roy S (2014) Grouping-based MAC protocols for EV charging data transmission in smart metering network. IEEE J Sel Areas Commun 32(7):1328–1343

    Article  Google Scholar 

  16. Yu K, Arifuzzaman M, Wen Z, Zhang D, Sato T (2015) A key management scheme for secure communications of information centric advanced metering infrastructure in smart grid. IEEE Trans Instrum Meas 64(8):2072–2085

    Article  Google Scholar 

  17. Fadlullah ZM, Fouda MM, Kato N, Takeuchi A, Iwasaki N, Nozaki Y (2011) Toward intelligent machine-to-machine communications in smart grid. IEEE Commun Mag 49(4):60–65

    Article  Google Scholar 

  18. Bernaudo D, Barto L, Spizzirri S, Penn T (2010) SmartGrid/AEIC AMI interoperability standard guidelines for ANSI C12.19/IEEE 1377/MC12.19 end device communications and supporting enterprise devices, networks and related accessories. In: The Association of Edison Illuminating Companies, Meter and Service Technical Committee report

  19. Queen EE (2011) A discussion of smart meters and RF exposure issues. In: Edison Electric Institute, Washington, D.C, A Joint Project of the EEI and AEIC Meter Committee

  20. Das U, Dev VC, Namboodiri V (2018) On the effectiveness of data aggregation to manage network congestion in smart grid AMI. In: IEEE Power and Energy Society Innovative Smard Grid Technologies Conference

  21. Das U, Namboodiri V (2019) A quality-aware multi-level data aggregation approach to manage smart grid AMI traffic. IEEE Trans Parallel Distrib Syst 30(2):245–256

    Article  Google Scholar 

  22. Okafor KC, Ononiwu GC, Sam GVC, Chihindu CCU (2018) Towards complex dynamic fog network orchestration using embedded neural switch. Int J Comput Appl 43:91

    Google Scholar 

  23. Okafor KC, Achumba IE, Chukwudebe GA, Ononiwu GC (2017) Leveraging fog computing for scalable IoT datacenter using spine-leaf network topology. J Electr Comput Eng. https://doi.org/10.1155/2017/2363240

    Article  Google Scholar 

  24. Okafor KC (2019) Dynamic reliability modelling of cyber-physical edge computing network. Int J Comput Appl. https://doi.org/10.1080/1206212X.2019.1600830

    Article  Google Scholar 

  25. Aroussi S, Mellouk A (2014) Survey on machine learning-based QoE-QoS correlation models. In: 2014 International Conference on Computing, Management and Telecommunications (ComManTel)

  26. Kaur M, Gianey HK, Singh D, Sabharwal M (2019) Multi-objective differential evolution based random forest for e-health applications. Mod Phys Lett B 33(05):1950022

    Article  Google Scholar 

  27. Lo YL, Huang SC, Lu CN (2014) Transformational benefits of AMI data in transformer load modeling and management. IEEE Trans Power Deliv 29(2):742–750

    Article  Google Scholar 

  28. Karimi B, Namboodiri V, Jadliwala M (2013) On the scalable collection of metering data in smart grid through message concatenation. In: Proceedings of IEEE International Conference on Smart Grid Communications. pp. 318–323

  29. Karimi B, Namboodiri V, Jadliwala M (2015) Scalable meter data collection in smart grids through message concatenation. IEEE Trans Smart Grid Commun 6(4):1697–1706

    Article  Google Scholar 

  30. Allalouf M, Gershinsky G, Lewin-Eytan L, Naor J (2014) Smart grid network optimization: data-quality-aware volume reduction. IEEE Syst J 8(2):450–460

    Article  Google Scholar 

  31. Zhou J, Qingyang HuR, Qian Y (2012) Scalable distributed communication architectures to support advanced metering infrastructure in smart grid. IEEE Trans Parallel Distrib Syst 23(9):1632–1642

    Article  Google Scholar 

  32. PRIME Alliance, PRIME: powerline intelligent metering evolution, Available at http://www.prime-alliance.org, last accessed, May 2016

  33. Luan W, Sharp D, Landcashire S (2010) Smart grid communication network capacity planning for power utilities. In: Proceedings of IEEE/PES Transmission & Distribution Conference & Exposition (T&D). pp. 1–4

  34. Johnson D, Menezes A, Vanstone S (2001) The elliptic curve digital signature algorithm (ECDSA). Int J Inf Secur 1:36–63

    Article  Google Scholar 

  35. Li H, Han Z, Lai L, Qiu R, Yang D (2011) Efficient and reliable multiple access for advanced metering in future smart grid. In: Proceedings of IEEE International Conference on Smart Grid Communications. pp. 440–444

  36. Choi K, Chae K (2014) Data aggregating using temporal and spatial correlations in advanced metering infrastructure. In: Proceedings of The International Conference on Information Networking. pp. 541–544

  37. Gross D, Shortle JF, Thompson JM, Carl M (2008) Fundamentals of queueing theory

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Authors and Affiliations

  1. National Cheng Kung University, No. 1, Dasyue Rd, East District, Tainan, Taiwan, ROC

    Sun-Yuan Hsieh, Chih-Wei Hsu, Cheng-Han Yeh & Geng-Hua Zhang

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  1. Sun-Yuan Hsieh
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  2. Chih-Wei Hsu
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  3. Cheng-Han Yeh
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  4. Geng-Hua Zhang
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Correspondence to Sun-Yuan Hsieh.

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Hsieh, SY., Hsu, CW., Yeh, CH. et al. Novel scheme for reducing communication data traffic in advanced metering infrastructure networks. J Supercomput 78, 8219–8246 (2022). https://doi.org/10.1007/s11227-021-04143-2

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