Skip to main content
SpringerLink
Log in

LTE Delay Assessment for Real-Time Management of Future Smart Grids

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

This study investigates the feasibility of using Long Term Evolution (LTE) cellular networks for the real-time smart grid state estimation. The smart grid state estimation requires measurement reports from different nodes installed in the smart grid. Therefore, the uplink LTE radio delay performance is selected as key performance indicator for the collection of the desired measurements. The analysis is conducted for two types of measurement nodes, namely Smart Meters (SMs) and Wide Area Monitoring and Supervision (WAMS) nodes, installed in the (future) smart grids. The SM and WAMS measurements are fundamental input for the real-time state estimation of the smart grid. The LTE delay evaluation approach is performed via ‘snap-shot’ system level simulations of an LTE system where the physical resource allocation, modulation and coding scheme selection and retransmissions are modelled. The impact on the LTE delay is analyzed for two different LTE resource allocation approaches, namely, simple random scheduler with fixed LTE physical resource allocation per user, and time-based LTE scheduler with flexible LTE physical resource allocation per user. The results show that time-delay prioritized scheduling in combination with flexible PRB assignment greatly reduces the maximum delay when compared to simple random scheduling and fixed PRB assignment. This type of scheduling approach and flexible PRB allocation is recommended for supporting time critical smart grid applications within LTE.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Notes

  1. For example, if an operator would allocate 10% of a 10 MHz LTE carrier for supporting smart grid traffic then NTOTAL, PRB = 0.1 × 50 = 5 PRBs.

  2. In LTE the minimum time needed for a transmitter to realize its previous transmission is erroneously received and needs to be re-transmitted is 8 TTIs.

  3. As the MCS is selected so that the BLER is kept below 10% it is assumed that in average 5% of the transmissions will be erroneously received.

References

  1. 3GPP TR 36.912 version 12.0.0, feasibility study for further advancements for E-UTRA (LTE-Advanced), September 2014

  2. 3GPP TS 36.213, Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures (Table 8.6.1–1 and Table 7.1.7.2–1)

  3. 5G Infrastructure Association, 5G and energy, white paper, Version 1.0, 30 September 2015

  4. Adamiak M, Premerlani W, Kasztenny B. Synchrophasors: Definition, Measurements, and Application, available for download at https://www.gedigitalenergy.com/

  5. Dimitrova DC, van den Berg JL, Heijenk G, Litjens R. (2011) LTE uplink schedulink-flow level analysis, Multiple Access Communications Lecture Notes in Computer Science 6886

  6. FP7 SUNSEED, Deliverable D2.1.1, preliminary requirements and architectures for DSO-telecom converged communication networks in dense DEG smart energy grid networks, July, 2014

  7. FP7 SUNSEED project Deliverable D3.1, Traffic modelling, communication requirements and candidate network solutions for real-time smart grid control, April 2015, available for download from http://sunseed-fp7.eu/deliverables/

  8. Horsmanheimo S, Maskey N, Tuomimaki L, Feasibility study of utilizing mobile communications for smart grid applications in urban area, IEEE International Conference on Smart Grid Communications (SmartGridComm), Venice, Italy, November 3–6, 2014

  9. IEC 61850–5. Communication networks and systems for power utility automation – part 5: Communication requirements for functions and device models, January 2013

  10. Jorguseski L et al. LTE delay assessment for real-time Management of Future Smart-Grids, 1st EAI SmartGIFT conference, Liverpool, UK, May 19–20 2016

  11. Karupongsiri C, Munasinghe KS, Jamalipour A, Random access issues for smart grid communications in LTE networks, 8th International Conference on Signal Processing and Communication Systems (ICSPCS), Gold Coast, Australia, December 15–17, 2014

  12. Litjens R, Toh Y, Zhang H, Blume O (2014) Assessment of the energy efficiency enhancement of future mobile networks, proceedings of WCNC 14, Istanbul, Turkey

  13. Lixia M. IEEE 1588 synchronization in distributed measurement Systems for Electric Power Networks, PhD Thesis, University of Cagliari, March 2012

  14. Louvros S, Paraskevas M, Triantafyllou V, Baltagiannis A LTE uplink delay constraints for smart grid applications, 19th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Athens, Greece, December 1–3, 2014

  15. Maskey N, Horsmanheimo S, Tuomimaki L, Analysis of latency for cellular networks for smart grid in suburban area, 5th IEEE PES innovative smart grid technologies Europe (ISGT Europe), Istanbul, Turkey, October 12-15, 2014

  16. Mavenir, Latency considerations in LTE, White paper, September 2014

  17. Motorola. (2008) 3GPP contribution R1–081638, TBS and MCS Signaling and Tables

  18. Nagai Y, Zhang L, Okamawari T, Fujii T. Delay performance analysis of LTE in various traffic patterns and radio propagation environments, 77th IEEE Vehicular Technology Conference (VTC Spring), Dresden, Germany, June 2–5, 2013

  19. Nokia Siemens Networks LTE-capable transport: a quality user experience demands an end-to-end approach, Whitepaper, 2011

  20. Yuzhe Xu, Fischione C. Real-time scheduling in LTE for smart grids, Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on, May 2012

  21. Zhang L, Okamawari T, Fujii T Performance evaluation of end-to-end communication quality of LTE, 75th IEEE Vehicular Technology Conference (VTC Spring), Yokohama, Japan, May 6–9, 2012

Download references

Acknowledgments

We thank all the colleagues from the FP7 SUNSEED project consortium for the numerous discussions that were useful input for the study presented in this paper.

Funding

This work is partly funded by the FP7 SUNSEED project, with EC grant agreement no: 619437.

Author information

Authors and Affiliations

  1. TNO, Networks Department, Anna van Buerenplein 1, 2595 DA, Den Haag, The Netherlands

    Ljupco Jorguseski, Haibin Zhang, Sylvie Dijkstra-Soudarissanane & Michal Golinski

Authors
  1. Ljupco Jorguseski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haibin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sylvie Dijkstra-Soudarissanane
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michal Golinski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ljupco Jorguseski.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jorguseski, L., Zhang, H., Dijkstra-Soudarissanane, S. et al. LTE Delay Assessment for Real-Time Management of Future Smart Grids. Mobile Netw Appl 24, 1742–1749 (2019). https://doi.org/10.1007/s11036-018-1050-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-018-1050-y

Keywords

Search

Navigation