Skip to main content

Advertisement

Springer Nature Link
Log in

Putting Sensor Data to the Service of the Smart Grid: From the Substation to the AMI

  • Published:
Journal of Network and Systems Management Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

One of the requirements of a smart grid (SG) is making the electrical network and its subsystems aware of their condition. The deployment of various sensing devices plays an essential part in achieving this goal. Nevertheless, data generated by this deployment needs to be well managed so that it can be leveraged for operational improvement. Data aggregation is perceived as an important technique for managing data in the SG in general, and in its Advanced Metering Infrastructure (AMI) in particular. Indeed, data aggregation techniques have been used in order to reduce communication overhead in SG networks. However, in order to fully take advantage of the aggregation process, some level of intelligence should be introduced at concentrator nodes to make the network more responsive to local conditions. Moreover, by using a more meaningful aggregation technique, entities can be accurately informed of any disturbance. This paper contributes an agent-based approach for data and energy management in an SG. It also proposes CoDA, a correlation-based data aggregation technique designed for the AMI. CoDA employs fuzzy logic to evaluate the correlation between several messages received from Smart Meters (SMs). Analysis and simulation results show the benefits of the proposed approach w.r.t. both packet concatenation and no aggregation approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Phasor Measurement Unit

  2. The numbers refer to those shown in Fig. 4

References

  1. McCaugherty,D.: The IEEE computer society smart grid vision project, architectural concepts (2014)

  2. U.S. Department of Energy. The NETL modern grid strategy powering our 21st-century economy: Advanced metering infrastructure. Technical Report (2008)

  3. Barbato, A., Capone, A., Chen, L., Martignon, F., Paris, S.: A distributed demand-side management framework for the smart grid. Elsevier Comput. Commun. 57, 13–24 (2015)

    Article  Google Scholar 

  4. U.S. Department of Energy. Smart grid investments improve grid reliability, resilience, and storm responses. Technical Report (2014)

  5. Helmer, T.: Volt/var control options and how to leverage ami data. PowerGrid Int. 17(08), 45–49 (2012)

    Google Scholar 

  6. Germano, C., Cole, C.: The ami communications network: where it’s headed and how to get there (2013)

  7. Song, Z., Zhou, Q., Yang, Z.: How to use ami data to improve security of electric network operations (2015)

  8. Rogers, A. , Jennings, N.: Intelligent agents for the smart grid. PerAda Magazine, pp. 1–2 (2010)

  9. Colson, C.M., Nehrir, M.H.: Integrating microgrids and multi-agent management (2014)

  10. Jiang, B., Fei, Y.: Smart home in smart microgrid: a cost-effective energy ecosystem with intelligent hierarchical agents. IEEE Trans. Smart Grid 6(1), 3–13 (2015)

    Article  Google Scholar 

  11. Vytelingum, P., Voice, T.D., Ramchurn, S. D., Rogers,A., Jennings, N.R. : Agent-based micro-storage management for the smart grid. In The Ninth International Conference on Autonomous Agents and Multiagent Systems, pp. 39–46 (2010)

  12. Hinrichs, C., Vogel, U., Sonnenschein, M.: Approaching decentralized demand side management via self-organizing agents. In 2011 AAMAS Workshop On Agent Technologies for Energy Systems, pp. 1–8 (2011)

  13. Fang, Xi, Misra, S., Xue, Guoliang, Yang, Dejun: Managing smart grid information in the cloud: opportunities, model, and applications. Net. IEEE 26(4), 32–38 (2012)

    Article  Google Scholar 

  14. Bera, S., Misra, S., Rodrigues, J.J.P.C.: Cloud computing applications for smart grid: a survey. IEEE Trans. Parallel Distrib. Syst. 26(5), 1477–1494 (2015)

    Article  Google Scholar 

  15. Naveen, P., Kiing Ing, W., Danquah, M.K., Sidhu, A.S., Abu-Siada, A. : Cloud computing for energy management in smart grid—an application survey. IOP Conference Series: Materials Science and Engineering, vol. 121(1), (2016)

  16. Stojmenovic, I.: Fog computing: a cloud to the ground support for smart things and machine-to-machine networks. In Telecommunication Networks and Applications Conference (ATNAC), 2014 Australasian, pp. 117–122 (2014)

  17. Raghavendra Nagesh, D. Y., Vamshi Krishna, J. V. , Tulasiram, S. S.: A real-time architecture for smart energy management. In Innovative Smart Grid Technologies (ISGT), 2010, pp. 1–4 (2010)

  18. Li Ji Chenlin Liao Yan Li, Lifang Wang.: A data warehouse architecture supporting energy management of intelligent electricity system. In Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE), 2013, (2013)

  19. Ramesh, V., Khan, U. , Ilic, M.D.: Data aggregation strategies for aligning pmu and ami measurements in electric power distribution networks. In North American Power Symposium (NAPS), 2011, pp. 1–7 (2011)

  20. Bartoli, A., Hernandez-Serrano, J., Soriano, M., Dohler, M., Kountouris, A. , Barthel, D.: Secure lossless aggregation for smart grid m2m networks. In 2010 First IEEE International Conference on Smart Grid Communications, pp. 333–338 (2010)

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

    Article  Google Scholar 

  22. Dietzel, S.,Bako,B., Schoch,E., Kargl,E.: A fuzzy logic based approach for structure-free aggregation in vehicular ad-hoc networks. In Proceedings of the sixth ACM international workshop on VANET, pp. 79–88 (2009)

  23. Tal, I. , Muntean, G.: Using fuzzy logic for data aggregation in vehicular networks. In 2012 IEEE/ACM 16th DS-RT, pp. 151–154 (2012)

  24. Chang, C.S., Wang, Z., Yang, F., Tan, W.W.: Hierarchical fuzzy logic system for implementing maintenance schedules of offshore power systems. Smart Grid IEEE Trans. 3(1), 3–11 (2012)

    Article  Google Scholar 

  25. Lee, Sang-Hyun, Lee, Sang-Joon, Moon, Kyung-Il: Smart grid knowledge representation and reasoning based on adaptive neuro-fuzzy inference system. Int. J. Softw. Eng. Appl. 8(2), 207–212 (2014)

    Google Scholar 

  26. Ahmad, Saif, Baig, Zubair A.: Fuzzy-based optimization for effective detection of smart grid cyber-attacks. Int. J. Smart Grid Clean Energy 1(1), 15–21 (2012)

    Article  Google Scholar 

  27. HevinRajesh, D., Paramasivan, B.: Fuzzy based secure data aggregation technique in wireless sensor networks. J. Comput. Sci. 8, 899–907 (2012)

    Article  Google Scholar 

  28. Klaimi, Joelle, Rahim-Amoud, Rana, Merghem-Boulahia, Leila, Jrad, Akil: An agent-based approach for energy management in smart-grids. Highlights Pract. Appl. Agents Multi Agent Syst. Sustain. PAAMS 524, 225–236 (2015)

    Google Scholar 

  29. Prakash,P.: Data concentrators: the core of energy and data management (2013)

  30. Matta,N., Rahim-Amoud, R., Merghem-Boulahia, L., Jrad, A., Nolot, F.: Pribacc: In-network sensor data processing for efficient smart grid monitoring applications. In Global Information Infrastructure Symposium (GIIS), 2013, pp. 1–6 (2013)

  31. Pappis, C.P., Siettos, C.: Fuzzy reasoning. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies, pp. 437–474. Springer, Berlin (2005)

    Chapter  Google Scholar 

  32. Zadeh, L.A.: Fuzzy logic=computing with words. Fuzzy Syst. IEEE Trans. 4(2), 103–111 (1996)

    Article  Google Scholar 

  33. Mendel, J.M.: Fuzzy logic systems for engineering: a tutorial. Proc. IEEE 83(3), 345–377 (1995)

    Article  Google Scholar 

  34. Dernoncourt, F.: Introduction à la logique floue April 2011. (In French)

  35. NICTA. Castalia wireless sensor network simulator (2013)

  36. Lopez, G., Moura, P.S., Custodio, V., Moreno, J.I.: Modeling the neighborhood area networks of the smart grid. In Communications (ICC), 2012 IEEE International Conference on, pp. 3357–3361 (2012)

  37. Hoch, M.: Comparison of plc g3 and prime. In Power Line Communications and Its Applications (ISPLC), 2011 IEEE International Symposium on, pp. 165–169 (2011)

Download references

Acknowledgements

This work was supported in part by grants from the Troyes University of Technology, the Lebanese University, and the AUF-BMO.

Author information

Author notes

  1. Natalie Matta

    Present address: EDST, Centre-Azm, Laboratory of the Electronic Systems, Telecommunications and Networks (LaSTRe), Lebanese University, Tripoli, Lebanon

Authors and Affiliations

  1. EDST, Centre-Azm, Laboratory of the Electronic Systems, Telecommunications and Networks (LaSTRe), Lebanese University, Tripoli, Lebanon

    Rana Rahim-Amoud & Akil Jrad

  2. ICD, HETIC, ERA, UMR 6281, CNRS, Troyes University of Technology, Troyes, France

    Natalie Matta & Leila Merghem-Boulahia

Authors
  1. Natalie Matta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rana Rahim-Amoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leila Merghem-Boulahia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Akil Jrad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Natalie Matta.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Matta, N., Rahim-Amoud, R., Merghem-Boulahia, L. et al. Putting Sensor Data to the Service of the Smart Grid: From the Substation to the AMI. J Netw Syst Manage 26, 108–126 (2018). https://doi.org/10.1007/s10922-017-9409-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10922-017-9409-0

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy