• No results found

DESIGN GUIDELINES FOR SMART GRID IMPLEMENTATIONS This section presents several basic guidelines that should be followed when

Communication Networks

6.5 DESIGN GUIDELINES FOR SMART GRID IMPLEMENTATIONS This section presents several basic guidelines that should be followed when

design-ing SG networks. The design steps that should be followed are:

• Proper definition of the SG requirements: The first step when implementing SGs is to define the relevant requirements according to the DSO specifica-tions. Many DSOs have deployed backbone fiber communication networks to monitor their crucial operations, especially in the high-voltage grid [53];

hence, in many cases, SNs are already deployed. On the other hand, DSOs have not yet deployed last-mile communication networks to exchange infor-mation with the various nodes of the transmission and distribution grids as well as with the end users. In these cases, the deployment of NANs and FANs is imperative. Therefore, the current stage of SG penetration in each case will determine the priorities in SGN deployment.

Despite the specific needs of DSOs, all SGNs should enable end users’ con-nectivity with the utilities’ premises through a reliable, secure, highly avail-able, cost-effective, and resilient infrastructure. The need for reliability and security may lead to the selection of wired options for SGN implementation, whereas the necessity for optimal management of the cost–resilience trade-off

146 Smart Grids may lead to the installation of redundant infrastructures or sophisticated PLC transmission schemes and optimal network planning, as indicated in [54].

• SG services: After the requirements imposed by the DSOs have been speci-fied and the SGN type has been selected, the SG services must be defined.

There are many reviews on QoS requirements of various SG services deployed over NANs, FANs, or SNs. As previously discussed in this chap-ter, SNs incorporate delay-sensitive applications. However, most of these networks have already been deployed and are properly managed. As far as DA is considered, the service requirements might vary significantly, since protection services impose strict delay constraints, whereas, on the other hand, metering services might be more tolerant to delays. In the case of NANs, the need for security, resilience, and anonymity calls for a robust and scalable SGN rather than a high-capacity one.

• Communications network support: Directly related to the SG services is the selection of the SGN transmission medium. To support mission-critical applications, the implementation of one-hop communication technologies is imperative; hence, wireless cognitive approaches are preferable. On the other hand, PLC seems preferable for delay-tolerant applications. The ser-vices QoS specified in the previous step usually leads to the deployment of hybrid networks. Techniques that offer interoperability or handle the per-formance trade-offs of the various transmission options are necessary. The SG interoperability standard [18] defines the interfaces between various SG entities, but does not handle issues related to data aggregation techniques or to the interoperability of various communications schemes.

• Design of IT applications: Last but not least is the design of IT applications that constitute the interface of the SG nodes with the SG. As far as utilities are concerned, these applications should address the issue of big data; that is, they should effectively handle the data generated by the SG devices. In this context, many application-based platforms employing semantic reposi-tories [55] or cloud-based approaches [56] are considered in the literature.

Regardless of the application platform employed, the introduction of novel aggregation schemes leads to a significant reduction of unnecessary net-work traffic, enhancing the SGN performance.

6.6 CONCLUSIONS

The transition toward smart power grids that support two-way flows of energy and information is imperative. In this framework, new services are introduced related to energy systems, communications platforms, and IT applications. The PLC transmis-sion option is a strong candidate to support the various types of SGNs that should interoperate to offer reliable, end-to-end SG services:

1. BB-PLC gives rise to a high-capacity infrastructure capable of efficiently handling the traffic generated by CPNs.

2. Both NB-PLC and BB-PLC options may collect the traffic generated by CPNs and route this to the utilities’ back offices, thus supporting NANs’

Role of PLC Technology in Smart Grid Communication Networks 147

communication. When combined with data aggregation techniques, PLC may satisfy an essential prerequisite of the SG, namely, preserving infor-mation anonymity. Also, when combined with injected power control, slot reuse in TDMA-based PLC transmission enhances network performance to meet the QoS constraints of time-critical applications.

3. NB-PLC seems appropriate to handle the low-rate traffic generated in het-erogeneous FANs, especially when transmission of SG information from remote SGC nodes on the MV grid is required. When combined with the use of efficient network layer protocols, FANs may support the seamless integration of wireless and wired segments, increasing network availability.

In conclusion, there is no specific approach to designing SGNs. SG applications of variable QoS responding to different DSOs’ requirements may lead to different SG implementations. The need for resilience, security, reliability, and scalability, combined with the need for a cost-effective design, will determine how the various communications networks supporting the SG will be implemented.

REFERENCES

1. European Commission, The EU climate and energy package 2020. Available at: http://

ec.europa.eu/clima/policies/package/index_en.htm.

2. Hochgraf, C., Tripathi, R., Herzberg, S., Smart grid charger for electric vehicles using existing cellular networks and SMS text messages, in Proceedings of the First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 167–172, 4–6 October 2010, Gaithersburg, MD.

3. Bumiller, G., Lampe, L., Hrasnica, H., Power line communication networks for large-scale control and automation systems, IEEE Communications Magazine, 48(4), 106–113, 2010.

4. Rafiei, M., Eftekhari, S.M., A practical smart metering using combination of power line communication (PLC) and WiFi protocols, in Proceedings of 17th Conference on Electrical Power Distribution Networks (EPDC), pp. 1–5, 2–3 May 2012, Tehran.

5. Aggarwal, A., Kunta, S., Verma, P.K., A proposed communications infrastructure for the smart grid, in Proceedings of Innovative Smart Grid Technologies (ISGT), pp. 1–5, 19–21 January 2010, Gaithersburg, MD.

6. IEEE P1901, IEEE standard for broadband over power line networks: Medium access control and physical layer specifications, IEEE Std 1901-2010, pp. 1–1586, December 2010.

7. ITU-T Recommendation G.9960, Unified high-speed wire-line based home networking transceivers—Foundation, January 2010.

8. Patel, A., Aparicio, J., Tas, N., Loiacono, M., Rosca, J., Assessing communications tech-nology options for smart grid applications, in Proceedings of IEEE Conference on Smart Grid Communications, pp. 126–131, 17–20 October 2011, Brussels.

9. Sarafi, A., Tsiropoulos, G., Cottis, P., Hybrid wireless-broadband over power lines: A promising broadband solution in rural areas, IEEE Communications Magazine, 47(11), 140–147, 2009.

10. Fang, X., Misra, S., Xue, G., Yang, D., Smart grid—The new and improved power grid:

A survey, IEEE Communications Surveys and Tutorials, 14(4), 944–980, 2012.

11. Lopes, A.J., Lezama, R., Pineda, R., Model-based systems engineering for smart grids as systems of systems, Procedia Computer Science, 6, 441–450, 2011.

148 Smart Grids

12. Metke, A.R., Ekl, R.L., Security technology for smart grid networks, IEEE Transactions on Smart Grid, 1(1), 99–107, 2010.

13. Sadeghi, S., Yaghmaee Moghddam, M.H., Bahekmat, M., Heydari Yazdi, A.S., Modeling of smart grid traffics using non-preemptive priority queues, in Proceedings of 2012 2nd Iranian Conference on Smart Grids (ICSG), pp. 1–4, 24–25 May 2012, Tehran, Iran.

14. National Institute of Standards and Technology, NIST framework and roadmap for smart grid interoperability standards, release 1.0, January 2010. Available at: http://www.

nist.gov/public_affairs/releases/upload/smartgrid_interoperability_final.pdf (accessed March 2013).

15. Hwang, T., Choi, M., Kang, S., Lee, I., Design of application-level reference models for micro energy grid in IT perspective, in Proceedings of 8th International Conference on Computing and Networking Technology (ICCNT), pp. 180–183, 27–29 August 2012, Gyeongju.

16. Lo, C., Ansari, N., The progressive smart grid system from both power and communica-tions aspects, IEEE Communicacommunica-tions Surveys and Tutorials, 14(3), 799–821, 2012.

17. Leeds, D.J., The smart grid in 2010: Market segments, applications and industry players, GTM Research, 2009. Available at: http://www.greentechmedia.com/research/report/

smart-grid-in-2010 (accessed March 2013).

18. IEEE Std 2030-2011. IEEE guide for smart grid interoperability of energy technology and information technology operation with the electric power system (EPS), end-use applications, and loads, IEEE Std 2030-2011, pp. 1–126, 10 September 2011.

19. Molderink, A., Bakker, V., Bosman, M.G.C., Hurink, J.L., Smit, G.J.M., Management and control of domestic smart grid technology, IEEE Transactions on Smart Grid, 1(2), 109–119, 2010.

20. IEC, Road vehicles—Vehicle to grid communication interface—Part 1: General infor-mation and use-case definition, ISO/IEC 15118-1, 2010.

21. Deshpande, J.G., Kim, E., Thottan, M., Differentiated services QoS in smart grid com-munication networks, Bell Labs Technical Journal Wiley Subscription Services, 16(3), 61–81, 2011.

22. Khan, R.H., Khan, J.Y., A comprehensive review of the application characteristics and traffic requirements of a smart grid communications network, Computer Networks, 57(3), 825–845, 2013.

23. Kansal, P., Bose, A., Bandwidth and latency requirements for smart transmission grid applications, IEEE Transactions on Smart Grid, 3(3), 1344–1352, 2012.

24. IEEE Std 1646-2004. IEEE standard communication delivery time performance require-ments for electric power substation automation, IEEE Std 1646-2004, pp. 0_1–24, 2005.

25. Usman, A., Shami, S.D., Evolution of communication technologies for smart grid appli-cations, Renewable and Sustainable Energy Reviews, 19, 191–199, 2013.

26. Galli, S., Logvinov, O., Recent developments in the standardization of power line com-munications within the IEEE, IEEE Comcom-munications Magazine, 46(7), 64–71, 2008.

27. Rahman, M.M., Hong, C.S., Lee, S., Lee, J., Razzaque, M.A., Kim, J.H., Medium access control for power line communications: An overview of the IEEE 1901 and ITU-T G.hn standards, IEEE Communications Magazine, 49(6), 183–191, 2011.

28. Galli, S., Scaglione, A., Wang, Z., For the grid and through the grid: The role of power line communications in the smart grid, Proceedings of the IEEE, 99(6), 998–1027, 2011.

29. Galli, S., Kurobe, A., Ohura, M., The inter-PHY protocol (IPP): A simple coexistence protocol for shared media, in Proceedings of IEEE International Symposium on Power Line Communications and Its Applications, ISPLC, pp. 194–200, 29 March–1 April 2009, Dresden, Germany.

30. Oksman, V., Galli, S., G.hn: The new ITU-T home networking standard, IEEE Communications Magazine, 47(10), 138–145, 2009.

Role of PLC Technology in Smart Grid Communication Networks 149

31. Galli, S., Scaglione, A., Wang, Z., Power line communications and the smart grid, in Proceedings of First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 303–308, 4–6 October 2010, Gaithersburg, MD.

32. IEEE, Low frequency narrow-band power line communications. Available at: http://

grouper.ieee.org/groups/1901/2/ (accessed January 2013).

33. Wang, Z., Wang, Y., Wang, J., Overlapping clustering routing algorithm based on L-PLC meter reading system, in Proceedings of IEEE International Conference on Automation and Logistics, ICAL ‘09, pp. 1350–1355, 5–7 August 2009, Shenyang, China.

34. Liu, X., Wang, W., Zheng, J., Hai, T., Zhang, L., Liu, B., Tangential connection clustering routing algorithm for L-PLC based AMR systems, in Proceedings of 7th International Power Electronics and Motion Control Conference (IPEMC), vol. 4, pp. 2932–2936, 2–5 June 2012, Harbin, China.

35. Yu, R., Zhang, Y., Gjessing, S., Yuen, C., Xie, S., Guizani, M., Cognitive radio based hierarchical communications infrastructure for smart grid, IEEE Network, 25(5), 6–14, 2011.

36. Vineeta, Thathagar, J.K., Cognitive radio communication architecture in smart grid reconfigurability, in Proceedings of 1st International Conference on Emerging Technology Trends in Electronics, Communication and Networking (ET2ECN), pp. 1–6, 19–21 December 2012, Surat, Gujarat, India.

37. Yu, R., Zhang, Y., Chen, Y., Hybrid spectrum access in cognitive neighborhood area net-works in the smart grid, in IEEE Wireless Communications and Networking Conference (WCNC), pp. 1478–1483, 1–4 April 2012, Shanghai.

38. Lopez, G., Moura, P.S., Custodio, V., Moreno, J.I., Modeling the neighborhood area networks of the smart grid, in Proceedings of IEEE International Conference on Communications (ICC), pp. 3357–3361, 10–15 June 2012, Ottawa, ON.

39. Chauvenet, C., Tourancheau, B., Genon-Catalot, D., Goudet, P.-E., Pouillot, M., A com-munication stack over PLC for multi physical layer IPv6 networking, in Proceedings of First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 250–255, 4–6 October 2010, Gaithersburg, MD.

40. Dawson-Haggerty, S., Tavakoli, A., Culler, D., Hydro: A hybrid routing protocol for low-power and lossy networks, in Proceedings of First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 268–273, 4–6 October 2010, Gaithersburg, MD.

41. Ko, J., Terzis, A., Dawson-Haggerty, S., Culler, D.E., Hui, J.W., Levis, P., Connecting low-power and lossy networks to the internet, IEEE Communications Magazine, 49(4), 96–101, 2011.

42. Kapareliotis, E.S., Drakakis, K.E., Dimitriadis, H.P.K., Capsalis, C.N., Fault recogni-tion on power networks via SNR analysis, IEEE Transacrecogni-tions on Power Delivery, 24(4), 2428–2433, 2009.

43. Qureshi, M., Raza, A., Kumar, D., Kim, S.S., Song, U.S., Park, M.W., Jang, H.S., Yang, H.S., Park, B.S., A survey of communication network paradigms for substation automa-tion, in Proceedings of IEEE International Symposium on Power Line Communications and Its Applications, ISPLC, pp. 310–315, 2–4 April 2008, Jeju, Jeju Island.

44. Krishnamachari, B., Estrin, D., Wicker, S., The impact of data aggregation in wireless sensor networks, in Proceedings of the 22nd International Conference on Distributed Computing Systems Workshops, pp. 575–578, July 2002, Vienna, Austria.

45. Solis, I., Obraczka, K., In-network aggregation trade-offs for data collection in wireless sensor networks, International Journal of Sensor Networks, 1(3–4), 200–212, 2006.

46. Lu, C., Liang, X., Li, X., Lin, X., Shen, X., EPPA: An efficient and privacy-preserving aggregation scheme for secure smart grid communications, IEEE Transactions on Parallel and Distributed Systems, 23(9), 1621–1631, 2012.

150 Smart Grids

47. Gungor, V.C., Lu, B., Hancke, G.P., Opportunities and challenges of wireless sensor net-works in smart grid, IEEE Transactions on Industrial Electronics, 57(10), 3557–3564, 2010.

48. Arefin, A.S., Kashem Mia, M.A., NP-completeness of the minimum edge-ranking spanning tree problem on series-parallel graphs, in Proceedings of 10th International Conference on Computer and Information Technology, pp. 1–4, 27–29 December 2007, Dhaka, Bangladesh.

49. Incel, O.D., Ghosh, A., Krishnamachari, B., Chintalapudi, K., Fast data collection in tree-based wireless sensor networks, IEEE Transactions on Mobile Computing, 11(1), 86–99, 2012.

50. Kim, M.S., Kim, J., Kim, J., Yoo, Y., Design and implementation of MAC protocol for SmartGrid HAN environment, in Proceedings of IEEE 11th International Conference on Computer and Information Technology (CIT), pp. 212–217, 31 August–2 September 2011, Pafos.

51. Bu, S., Yu, F.R., Liu, P.X., Zhang, P., Distributed scheduling in smart grid communi-cations with dynamic power demands and intermittent renewable energy resources, in Proceedings of IEEE International Conference on Communications Workshops (ICC), pp. 1–5, June 2011, Kyoto, Japan.

52. Moscibroda, T., The worst-case capacity of wireless sensor networks, in Proceedings of 6th International Symposium on Information Processing in Sensor Networks, IPSN, pp. 1–10, 25–27 April 2007, Cambridge, MA.

53. Livieratos, S., Vogiatzaki, E., Cottis, P., A generic framework for the evaluation of the benefits expected from the smart grid, Energies, 6, 988–1008, 2013.

54. Canale, S., Di Giorgio, A., Lanna, A., Mercurio, A., Panfili, M., Pietrabissa, A., Optimal planning and routing in medium voltage power line communications networks, IEEE Transactions on Smart Grid, (99), 1–9, 2012.

55. Pena, A., Penya, Y.K., Distributed semantic repositories in smart grids, in Proceedings of 9th IEEE International Conference on Industrial Informatics (INDIN), pp. 721–726, 26–29 July 2011, Lisbon, Portugal.

56. Simmhan, Y., Prasanna, V., Aman, S., Kumbhare, A., Liu, R., Stevens, S., Zhao, Q., Cloud-based software platform for big data analytics in smart grids, Computing in Science and Engineering, 15(4), 38–47, 2013.

151

7 Power Grid Network