M2M solutions for smart grid applications

In the emerging area of M2M communications, Automatic Meter Reading (AMR) is a showcase application: a large number of meters use sophisticated wireless networking for two-way communication with a central controller/data collector. The same holds for other smart grid applications, such as Automated Demand Response (ADR), substation and distributed energy resources automation/monitoring/control, and Wide Area Measurement System (WAMS), which all could be categorized within M2M communications.

The usage of wireless techniques for M2M communication has been made possible due to the level of maturity attained by the wireless technologies: small, inexpensive embedded devices have significant computational power and operate at very low power levels. M2M communication has significantly different requirements from, e. g. human -centric services (download, web browsing, video streaming), where large data volumes are sent and high data rate is required. In majority of the scenarios, M2M communication is based on intermittent transmission/reception of small data portions and pose requirements that are different from the ones according to which the common wireless protocols are designed. Some of the most important requirements are the following:
• Transmission from a massive number of devices and maintenance of a large number of active connections;
• Ability to send a small amount of data while decreasing the overhead percentage;
• Real-time communication with low latency;
• Certain connections that carry critical control data require a high degree of reliability, such that a connection should be kept alive more than 99.XX % of the time.
These requirements become more challenging when one considers the forecasts that state that by 2020 there will be 50 billion M2M connected wireless devices [1], spanning a wide application range: smart grid, smart metering, control/ monitoring of homes and industry, e-health, etc. While there are many ongoing standardization activities [2], M2M communication solutions have started to be deployed through the existing cellular interfaces, such as GSM and LTE; in fact, cellular networks are and will continue to be short to medium term enablers for M2M applications, due to their ubiquitous coverage and well understood and developed business/engineering platforms [3].

Indeed, in the past few years it has been observed an increase in the number of networked machines connected to cellular networks, like deployment of cellular-based wireless smart meters [4]. Some of those deployments are very large, such as Hydro- Quebec in Canada [5], with about 3.8 million devices that periodically send only a few bytes (KW/h consumption for instance). Another example is happening in Spain and Portugal, where Endesa, the largest Iberian operator, will replace a total of 13 million electric meters with smart meters by 2018 [6]. Since neither GSM nor LTE are originally designed to support massive M2M communication, there are ongoing research and standardization activities to modify those interfaces, notably LTE, in order to support the M2M traffic characteristics [7].

The adequate provision of M2M applications brings many challenges to cellular networks; the foremost being the support of the massive simultaneous transmission of low data rate messages. This led 3GPP to initiate a study item that concluded with the proposal of several key adaptations to the 3GPP cellular networks architecture, which will allow to both handle M2M traffic, denoted as Machine-Type Communications (MTC) within 3GPP [8], [9], and reduce the impact on human centric communications. The foreseen changes in order to support M2M traffic should happen both in the access and core network, and alleviate the radio and signaling network congestions that could lead to large delays, packet loss and, in the extreme case, service unavailability. Of particular interest are enhanced load control mechanisms in the radio access network, which include: access class barring [10], [11]; orthogonal resources [12]; dynamic resources allocation [13]; back-off; slotted access; pull-based.

Another recent standardization activity, spurred foremost by M2M applications, is within the scope of IEEE, where 802.11ah task group is developing a WLAN standard tailored for Wi-Fi-enabled devices to get guaranteed access for short and massive data transmissions [14]. The standard is still in its preliminary stages and it’s future operation is centering on the following principles: operating frequencies below 1GHz, BPSK, QPSK modulations and 16/256 QAM, while channel access should be group based, supporting up to 6000 devices simultaneously.

Finally, a potential, light weight solution for gathering of smart metering data is usage of Wireless M Bus technology [15]. However, this standard essentially foresees only uplink transmissions of metered data and lacks feedback control link, as well as capabilities of autonomous and adaptable operation in changing networking scenarios.

[1] Q. D. Vo, J. P. Choi, H. M. Chang, and W. C. Lee, “Green perspective cognitive radio-based m2m communications for smart meters,” in Information and Communication Technology Convergence (ICTC), 2010 International Conference on. IEEE, 2010, pp. 382–383.
[2] L. X. D. Niyato 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.
[3] David Boswarthick, Omar Elloumi, Olivier Hersent, Eds,“M2M Communications: A Systems Approach”, Wiley, 2012.
[4] Sierra Wireless Product Webpage., Accessed in October 2012, http://www.sierrawireless.com/en/Solutions/customer stories/EDMI.aspx.
[5] Quebec Press Release Smart Metering., Accessed in December 2012, http://www.rogers.com/web/link/showNewsDetail?fromWhere=linkInRSSXml&rssBusiUnit=W&NewsID=1906177072.
[6] Endesa Press Release Smart Metering., Accessed in January 2012, http://www.endesasmartgrid.com/index.php/en/smart-homes/smartmetering.
[7] 3GPP, “Service Requirements for Machine-Type Communications (Stage 1),” 3rd Generation Partnership Project (3GPP), TS 22.368, June 2010. [Online]. Available: http://www.3gpp.org/ftp/Specs/html-info/23368.htm
[8] 3GPP TR 37.868 V11.0, Study on RAN Improvements for Machine-type Communications, October 2011.
[9] M.-Y. Cheng, G.-Y. Lin, H.-Y. Wei, and A.-C. Hsu, “Overload control for machine-type-communications in lte-advanced system,” IEEE Communications Magazine, vol. 50, pp. 38 –45, June 2012.
[10] S.-Y. Lien, T.-H. Liau, C.-Y. Kao, and K.-C. Chen, “Cooperative access class barring for machine-to-machine communications,” IEEE Transactions on Wireless Communications, vol. 11, January 2012.
[11] J.-P. Cheng, C. han Lee, and T.-M. Lin, “Prioritized random access with dynamic access barring for ran overload in 3gpp lte-a networks,”in GLOBECOM Workshops, 2011 IEEE, pp. 368 –372, December 2011.
[12] K.-D. Lee, S. Kim, and B. Yi, “Throughput comparison of random access methods for m2m service over lte networks,” in GLOBECOM Workshops, 2011 IEEE, pp. 373 –377, December 2011.
[13] M. J. Anthony Lo, Yee Wei Law and M. Kucharzak, “Enhanced lte advanced random-access mechanism for massive machine-to-machine (m2m) communications,” in 27th World Wireless Research Forum (WWRF) Meeting, 2011.
[14] S. Aust, R. V. Prasad, and I. G. M. M. Niemegeers, “IEEE 802.11ah: Advantages in standards and further challenges for sub 1 GHz Wi-Fi”, In 2012 IEEE International Conference on Communications ICC, December 2012
[15] EN 13757, “Communication systems for meters and remote reading of meters.” Part 4: Wireless meter readout (Radio meter reading for operation in the 868 MHz to 870 MHz SRD band), 2005.

CS, NP, GCM