We present some of the ongoing standardisation work in M2M communications followed by the application of machine-to-machine (M2M) communications to smart grid. We analyse and discuss the enabling technologies in M2M and present an overview of the communications challenges and research opportunities with a focus on wireless sensor networks and their applications in a smart grid environment. 1. Introduction Smart grid (SG) networks will be characterised by the tight integration of a flexible and secure communications network with novel energy management techniques requiring a very large number of sensor and actuator nodes. The communications network will not only facilitate advanced control and monitoring, but also support extension of participation of generation, transmission, marketing, and service provision to new interested parties. In order to realise the intelligent electricity network, machine-to-machine (M2M) communication is considered as a building block for SG as a means to deploy a wide-scale monitoring and control infrastructure, thus bringing big opportunities for the information and communication technology (ICT) industry. For example, smart metering in M2M can facilitate flexible demand management where a smart meter (SM) is a two-way communicating device that measures energy (electricity, gas, water, or heat) consumption and communicates that information via some communications means back to the local utility. With near realtime information available for example based on the flow of energy in the grid, different levels of tariff can be calculated and made available for the consumer, the consumer can make a smarter and more responsible choice. The information generated by SM therefore acts like “glue” allowing various components of SG to work together efficiently. There are also various large-scale wireless sensor and actuator networks (WSAN) deployed in SG (such as the electric power system generation, or home applications) in order to carry out the monitoring task, for example [1]. These WSANs with the collaborative and self-healing nature have an important role to play in realising some of the functionalities needed in SG. On the other hand, there is also cellular M2M where cellular technology plays an important role in M2M communications due to its good coverage, promising data rates for many applications, and so forth. However, in this paper, we mainly focus on WSAN where various short-range wireless technologies are used to support various M2M applications. There are currently various standardisation activities in M2M communications
References
[1]
R. A. León, V. Vittal, and G. Manimaran, “Application of sensor network for secure electric energy infrastructure,” IEEE Transactions on Power Delivery, vol. 22, no. 2, pp. 1021–1028, 2007.
[2]
O. Elloumi, J. M. Ballot, and D. Boswarthick, “Smart Grid- an introduction,” presentation slides, TCM2M #9, 2010.
[3]
U.S. Department of Energy, “The SG: An Introduction,” September 2008.
[4]
M. di Bisceglie, C. Galdi, A. Vaccaro, and D. Villacci, “Cooperative sensor networks for voltage quality monitoring in SGs,” in Proceedings of the IEEE Bucharest PowerTech Conference, 2009.
[5]
M. Starsinic, “System architecture challenges in the home M2M network,” in Proceedings of the Applications and Technology Conference, (LISAT '10), pp. 1–7, 2010.
[6]
R. Musaloiu-E, C. J. M. Liang, and A. Terzis, “Koala: ultra-low power data retrieval in wireless sensor networks,” Information Processing in Sensor Networks, pp. 421–432, 2008.
[7]
RFC 3561, http://www.ietf.org/rfc/rfc3561.txt.
[8]
D. Wang, Z. Tao, J. Zhang, and A. Abouzeid, “RPL based routing for advanced metering infrastructure in smart grid,” in Proceedings of the ICC, pp. 1–6, 2010.
[9]
V. C. Gungor, B. Lu, and G. P. Hancke, “Opportunities and challenges of wireless sensor networks in smart grid—a case study of link quality assessments in power distribution systems,” IEEE Transactions on Industrial Electronics, vol. 57, no. 99, pp. 3557–3564, 2010.
[10]
M. A. H. El-Sayed and A. S. Alfuhaid, “ANN-based approach for fast fault diagnosis and alarm handling of power systems,” in Proceedings of the 5th International Conference on Advances in Power System Control, Operation and Management (APSCOM '00), pp. 54–58, October, 2000.
[11]
M. Ma, D. Zhao, X. Zhang, and D. Liu, “China's research status quo and development trend of power grid fault diagnosis,” in Proceedings of the Asia-Pacific Power and Energy Engineering Conference (APPEEC '10), pp. 1–4, 2010.
[12]
H. T. Zhang, F. Y. Xu, and L. Zhou, “Artificial Neural Network for load forecasting in smart grid,” in Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC '10), vol. 6, pp. 3200–3205, 2010.
[13]
F. Y. Xu, M. C. Leung, and L. Zhou, “A RBF network for short-term load forecast on microgrid,” in Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC '10), vol. 6, pp. 3195–3199, 2010.
[14]
E. Hooper, “Strategic and intelligent smart grid systems engineering,” in Proceedings of the International Conference for Internet Technology and Secured Transactions (ICITST '10), 2010.
D. Niyato, L. Xiao, and P. Wang, “Machine-to-machine communications for home energy management system in smart grid,” IEEE Communications Magazine, vol. 49, no. 4, pp. 53–59, 2011.
