Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Optimal placement of data concentrators for expansion of the smart grid communications network

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 28/01/2019, Accepted 01/07/2019, Revised 25/05/2019, Published 17/07/2019

Evolving power systems with increasing renewables penetration, along with the development of the smart grid, calls for improved communication networks to support these distributed generation sources. Automatic and optimal placement of communication resources within the advanced metering infrastructure is critical to provide a high-performing, reliable, and resilient power system. Three network design formulations based on mixed-integer linear and non-linear programming approaches are proposed to minimise network congestion by optimising residual buffer capacity through the placement of data concentrators and network routeing. Results on a case study show that the proposed models improve network connectivity and robustness, and increase average residual buffer capacity. Maximising average residual capacity alone, however, results in both oversaturated and underutilised nodes, while maximising either minimum residual capacity or total reciprocal residual capacity can yield much-improved network load allocation. Consideration of connection redundancy improves network reliability further by ensuring quality-of-service in the event of an outage. Analysis of multi-period network expansion shows that the models do not deviate significantly from optimal when used progressively (within 5% deviation), and are effective for utility planners to use for smart grid expansion.

Inspec keywords: optimisation; smart power grids; integer programming; telecommunication network reliability; linear programming; distributed power generation

Other keywords: robustness; improved communication networks; average residual buffer capacity; much-improved network load allocation; network connectivity; communication resources; nonlinear programming approaches; optimal placement; minimum residual capacity; network congestion; maximising average residual capacity; advanced metering infrastructure; network routeing; resilient power system; total reciprocal residual capacity; distributed generation sources; data concentrators; multiperiod network expansion shows; renewables penetration; evolving power systems; network reliability; network design formulations; smart grid expansion; mixed-integer linear; smart grid communications network

Subjects: Reliability; Optimisation techniques; Distributed power generation; Power system management, operation and economics; Optimisation techniques

References

    1. 1)
      • 23. Park, S., Lee, J., Kim, J., et al: ‘ExLL: an extremely low-latency congestion control for mobile cellular networks’. Proc. 14th Int. Conf. Emerging Networking Experiments and Technologies, Heraklion, Crete, 2019, pp. 307–319.
    2. 2)
      • 14. Gourdin, E., Klopfenstein, O.: ‘Comparison of different QoS-oriented objectives for multicommodity flow routing optimization’. Proc. Int. Conf. Telecommunications, Funchal, Portugal, 2006, pp. 1–4.
    3. 3)
      • 10. Klinkert, A.: ‘Designing smart-grid telecommunications systems via interval flow network optimization’, Southern Methodist University, 2018.
    4. 4)
      • 29. Schneider, K.P., Chen, Y., Chassin, D.P., et al: ‘Modern grid initiative: distribution taxonomy final report’, Pacific Northwest National Laboratory, 2008.
    5. 5)
      • 3. Fernández, E., Landete, M.: ‘Fixed-charge facility location problems’, in: Laporte, G., Nickel, S., Saldanha da Gama, F. (Eds.): ‘Location science’ (Springer International Publishing, Cham, 2015, 1st edn.), pp. 47–77.
    6. 6)
      • 16. Andreadou, N., Kotsakis, E., Masera, M.: ‘Smart meter traffic in a real LV distribution network’, Energies, 2018, 11, (5), pp. 1–27.
    7. 7)
      • 19. Xu, S., Qian, Y., Qingyang Hu, R.: ‘Reliable and resilient access network design for advanced metering infrastructures in smart grid’, IET Smart Grid, 2018, 1, (1), pp. 24–30.
    8. 8)
      • 31. Remy, J.G., Letamendia, C.: ‘LTE standards and architecture’, LTE standards, 2014, pp. 1–112.
    9. 9)
      • 35. ‘Gurobi optimizer reference manual’. Available at http://www.gurobi.com, accessed 18 November 2018.
    10. 10)
      • 13. Kong, P.Y.: ‘Cost efficient data aggregation point placement with interdependent communication and power networks in smart grid’, IEEE Trans. Smart Grid, 2019, 10, (1), pp. 74–83.
    11. 11)
      • 24. Lopez Aguilera, E., Casademont, J., Cotrina, J.: ‘Propagation delay influence in IEEE 802.11 outdoor networks’, Wirel. Netw., 2010, 16, (4), pp. 1123–1142.
    12. 12)
      • 34. Hart, W.E., Laird, C.D., Watson, J.P., et al: ‘Pyomo–optimization modeling in python’, vol. 67, (Springer, Cham, 2017, 2nd edn.).
    13. 13)
      • 8. Rastgoo, R., Sattari Naeini, V.: ‘Tuning parameters of the QoS-aware routing protocol for smart grids using genetic algorithm’, Appl. Artif. Intell., 2016, 30, (1), pp. 52–76.
    14. 14)
      • 22. Nagai, Y., Zhang, L., Okamawari, T., et al: ‘Delay performance analysis of LTE in various traffic patterns and radio propagation environments’. 2013 IEEE 77th Vehicular Technology Conf. (VTC Spring), Dresden, Germany, 2013, pp. 1–5.
    15. 15)
      • 33. Hart, W.E., Watson, J.P., Woodruff, D.L.: ‘Pyomo: modeling and solving mathematical programs in python’, Math. Program. Comput., 2011, 3, (3), pp. 219–260.
    16. 16)
      • 20. Patel, A., Aparicio, J., Tas, N., et al: ‘Assessing communications technology options for smart grid applications’. 2011 IEEE Int. Conf. Smart Grid Communications, Brussels, Belgium, October 2011, pp. 126–131.
    17. 17)
      • 7. Jahromi, A.E., Rad, Z.B.: ‘Optimal topological design of power communication networks using genetic algorithm’, Sci. Iranica, 2013, 20, (3), pp. 945–956.
    18. 18)
      • 21. Budka, K.C., Deshpande, J.G., Thottan, M.: ‘Communication networks for smart grids making smart grid real’ (Springer, London, 2014, 1st edn.).
    19. 19)
      • 15. Niyato, D., Wang, P.: ‘Cooperative transmission for meter data collection in smart grid’, IEEE Commun. Mag., 2012, 50, (4), pp. 90–97.
    20. 20)
      • 4. Kojima, H., Tsuchiya, T., Fujisaki, Y.: ‘The aggregation point placement problem for power distribution systems’, IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2018, E101A, (7), pp. 1074–1082.
    21. 21)
      • 5. Qiu, L., Chandra, R., Jain, K., et al: ‘Optimizing the placement of integration points in multi-hop wireless networks’. Proc. ICNP, Berlin Germany, 2004, vol. 4, pp. 1–12.
    22. 22)
      • 27. ‘Smart meters: compliance with radio frequency exposure standards’. Available at https://www.gsma.com/publicpolicy/wp-content/uploads/2015/06/gsma_smart-meters_2015.pdf, accessed 25 October 2018.
    23. 23)
      • 2. Daskin, M., Snyder, L., Berger, R.T.: ‘Facility location in supply chain design’, in: Langevin, A., Riopel, D. (Eds.): Logistics Systems: Design and Optimization (Springer, Boston, MA, 2005), pp. 39–65.
    24. 24)
      • 28. Rupasinghe, N., Guvenc, I.: ‘Licensed-assisted access for WiFi-LTE coexistence in the unlicensed spectrum’. 2014 IEEE Globecom Workshops (GC Wkshps), Austin, USA, 2014, pp. 894–899.
    25. 25)
      • 17. Kuzlu, M., Pipattanasomporn, M., Rahman, S.: ‘Communication network requirements for major smart grid applications in HAN, NAN and WAN’, Comput. Netw., 2014, 67, pp. 74–88.
    26. 26)
      • 30. Bellekens, B., Tian, L., Boer, P., et al: ‘Outdoor IEEE 802.11ah range characterization using validated propagation models’. GLOBECOM 2017 IEEE Global Communications Conf., Singapore, 2018, pp. 1–6.
    27. 27)
      • 18. Parikh, P.P., Kanabar, M.G., Sidhu, T.S.: ‘Opportunities and challenges of wireless communication technologies for smart grid applications’. IEEE PES General Meeting, Minneapolis, USA, July 2010, pp. 1–7.
    28. 28)
      • 12. Huang, X., Wang, S.: ‘Aggregation points planning in smart grid communication system’, IEEE Commun. Lett., 2015, 19, (8), pp. 1315–1318.
    29. 29)
      • 1. Saputro, N., Akkaya, K., Uludag, S.: ‘A survey of routing protocols for smart grid communications’, Comput. Netw., 2012, 56, (11), pp. 2741–2771.
    30. 30)
      • 26. Hari, K.V.S., Smith, M.S.: ‘Channel models for fixed wireless applications’, IEEE 802.16 broadband wireless access working group, 2003.
    31. 31)
      • 6. Amaldi, E., Capone, A., Cesana, M., et al: ‘Optimization models and methods for planning wireless mesh networks’, Comput. Netw., 2008, 52, (11), pp. 2159–2171.
    32. 32)
      • 25. Abhayawardhana, V.S., Wassell, I.J., Crosby, D., et al: ‘Comparison of empirical propagation path loss models for fixed wireless access systems’. 2005 IEEE 61st Vehicular Technology Conf., Stockholm, Sweden, 2005, vol. 1, pp. 73–77.
    33. 33)
      • 9. Tavasoli, M., Yaghmaee, M.H., Mohajerzadeh, A.H.: ‘Optimal placement of data aggregators in smart grid on hybrid wireless and wired communication’. 2016 Fourth IEEE Int. Conf. Smart Energy Grid Engineering, Sege, 2016, pp. 332–336.
    34. 34)
      • 11. Hassan, A., Zhao, Y., Pu, L., et al: ‘Evaluation of clustering algorithms for DAP placement in wireless smart meter network’. 2017 Ninth Int. Conf. Modelling, Identification and Control (ICMIC), Kunming, China, 2017, pp. 1085–1090.
    35. 35)
      • 32. Hamid, M.: ‘Path loss models for LTE and LTE-A relay stations’, Univ. J. Commun. Netw., 2013, 1, (4), pp. 119–126.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-stg.2019.0006
Loading

Related content

content/journals/10.1049/iet-stg.2019.0006
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address