Best Readings in Smart Grid Communications

Petar Popovski has been leading the team that created the Best Readings in Smart Grid Communications. The list of best readings can be found here:

The list has been divided into six topics:

  • General Survey-Type and Big-Picture Books and Papers
  • Communications and Networks to Enable the Smart Grid
  • Cyber Security and Privacy
  • Architectures, Control and Operation for the Smart Grid, Microgrids and Distributed Resources
  • Demand Response and Dynamic Pricing
  • Data Management and Grid Analytics

Reliable Reporting for Massive M2M Communications

In many scenarios, M2M communications involve a massive number of low-rate connections. A showcase application is smart metering, consisting of a massive number of devices, up to 30000 [1], where meters periodically report energy consumption to a remote server for control and billing purposes. Massive communications present a new operating mode, not originally considered in the cellular radio access and that us why M2M-reengineering of the cellular systems has been so much into the research focus. In such a setting, conventional assessment of the system based on the average throughput is not sufficient.

We have instead asked a different question. Let us assume that each message that comes to the sensor needs to be reported within the deadline TRI with guaranteed reliability of, say 99.99%. There is a large number N of potentially reporting devices. How many cellular resources need to be used for such reporting and how to arrange those resources? There are two things that prevent us from assigning deterministic resources for reporting. One is that the arrival of a message at the device is a random process. Another is that the transmission from the device has a random outcome – the packet may end up in error and it will need to be retransmitted. In summary, the amount of resources required by each device is random.

We present a solution to this problem in our paper recently accepted at IEEE Wireless Communications Letters:

[1] Corrales Madueno, G.; Stefanovic, C.; Popovski, P., “Reliable Reporting for Massive M2M Communications With Periodic Resource Pooling,” Wireless Communications Letters, IEEE , vol.3, no.4, pp.429,432, Aug. 2014
doi: 10.1109/LWC.2014.2326674

The key idea to combat the individual randomness associated with each device is to take advantage of the statistical regularity that arises due to the fact that the number of devices N is massive. We show how to allocate resources using this statistical regularity and still guarantee 99.99% to each individual sensor.

A condensed version of the letter is given below.

How to achieve  reliability without sacrificing system efficiency?


Figure 1: Representation of the LTE uplink resource structure, where a set of RBs has been reserved for M2M purposes.

In [1], we consider a system with a periodically occurring pool of resources that are reserved M2M communications and shared for uplink transmission by all M2M devices (see previous figure). The re-occurring period is selected such that if a report is transmitted successfully within the upcoming resource pool, then the reporting deadline is met.


Figure 2: a) Periodically occurring M2M resource pool. b) Division of M2M resource pool in the pre-allocated and common pool.

The M2M resource pool is divided into two parts, denoted as the preallocated and  common pool, which reoccur with period TRI, as depicted in previous figure. We assume that there are N reporting devices, and each device is preallocated an amount of RBs from the preallocated pool dimensioned to accommodate a single report and an indication if there are more reports, termed excess reports, from the same device to be transmittedwithin the same interval. The common pool is used to allocate resources for the excess reports, as well as all the retransmissions of the reports/packets that were erroneously received.

The question that arises is:  How many periodically reporting devices can be supported with a desired reliability of report delivery (i.e., 99.99%), for a given number of resources reserved for M2M communications?

We note that, if each device has a deterministic number of packets to transmit in each resource pool and if there are no packet errors, then the problem is trivial, because a fixed number of resources can be preallocated periodically to each device. However, if the number of packets, accumulated between two reporting instances, is random and the probability of packet error is not zero, then the number of transmission resources required per device in each transmission period is random.

The derivation of the individual reporting reliability can be found in [1]. Promising results have been shown in the context of LTE, where even with the lowest-order modulation only 9% of the system resources are required to serve 30K M2M devices with a reliability of 99.99% for a report size of 100 bytes. The proposed method can be applied to other systems, such as 802.11ah.


Figure 3: Fraction of system capacity used for M2M services, when P[Φ] ≤ 10−3, RI of 1 minute, RS of 100 bytes, bandwidth of 5 MHz and pe = 10−1.


[1] Corrales Madueno, G.; Stefanovic, C.; Popovski, P., “Reliable Reporting for Massive M2M Communications With Periodic Resource Pooling,” Wireless Communications Letters, IEEE , vol.3, no.4, pp.429,432, Aug. 2014
doi: 10.1109/LWC.2014.2326674

(Also available in


SUNSEED project starts at massM2M group

We announce the beginning of the research project “Sustainable and robust networking for smart electricity distribution – SUNSEED”, funded under call FP7-ICT-2013-11 (STREP). The project started in February 2014, and its duration is three years.

SUNSEED proposes an evolutionary approach to exploitation of already existing communication networks both of energy and telecom operators. The objective of the project is to converge these networks and form a communication infrastructure for future smart energy grids offering open services. The networks’ convergence will be carried out in six steps: overlap, interconnect, interoperate, manage, plan and open; each step involves identification of the related smart grid service requirements and implementation of the appropriate solutions. SUNSEED approach promises much lower investments and total cost of ownership for future smart energy grids with dense distributed energy generation and prosumer involvement.

The researchers from massM2M group, Department of Electronic Systems, AAU, will be primarily involved in M2M aspects of the smart grid services. The focus of the work will be on (1) enhancing and reengineering of the cellular access network, in order to increase its capacity and reliability in order to provide support for massive number of smart grid devices, like PMUs, smart meters, e-cars etc., (2) upgrading the core network in order to provide advanced reliability features, such as path diversity, advanced optimization and healing.

Researchers affiliated to the project: Dr. Cedomir Stefanovic, Dr. Nuno Pratas, Prof. Petar Popovski.

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, stories/EDMI.aspx.
[5] Quebec Press Release Smart Metering., Accessed in December 2012,
[6] Endesa Press Release Smart Metering., Accessed in January 2012,
[7] 3GPP, “Service Requirements for Machine-Type Communications (Stage 1),” 3rd Generation Partnership Project (3GPP), TS 22.368, June 2010. [Online]. Available:
[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.


First impressions on the IEEE 802.11ah standard amendment

As highlighted in the previous blog post, there is a new emerging standard in the M2M arena based on the IEEE 802.11 standards family. This standard is being developed under the IEEE 802.11ah group, and aims to define the physical (PHY) and medium access control (MAC) layers that operate at radio frequencies below 1 GHz. One of the goals of this standard is to ensure that the transmission ranges up to 1 km and that the data rates per user are above 100 kbit/s.

The standard is currently being drafted, but some essential details about this new standard are already available, which we will highlight in this blog post. It is important to emphasize that although the IEEE 802.11ah standard will define operations below 1 GHz, it will not use the TV white space bands (54-698 MHz in the US), which are targeted instead by IEEE 802.11af.

The PHY transmission in IEEE 802.11ah is an OFDM based waveform consisting of a total of 64 tones/sub-carriers (including tones allocated as pilot, guard and DC), which are spaced by 31.25 kHz. The modulations supported include BPSK, QPSK and 16 to 256 QAM. It will support multi user MIMO and single user beam forming.

In [1] is stated that stations will support the reception of 1 MHz and 2 MHz PHY transmissions. The channelization (i.e. operating frequency) depends on the region. In Europe it will be within 863-868 MHz, allowing either five 1 MHz channels or two 2 MHz channels. While in the US the available band will be within 902-928 MHz, allowing either twenty-six 1MHz channels or thirteen 2MHz channels. In Japan, the available band is within 916.5-927.5 MHz, with eleven 1MHz channels. In China the available band will be within 755-787 MHz, with thirty-two 1 MHz channels. South Korea and Singapore also have specific channelizations that can be found in [1].

The MAC layer will include a power saving mechanism and an alternative approach to perform channel access, which will allow an access point to support thousands of stations, as required for M2M applications. The channel access also supports a mode of operation where only a restricted number of stations can transmit.

There are several use cases for this standard [2], which include:

  • Sensor Networks – where the IEEE 802.11ah is used as the communication medium for the transmission of short-burst data messages from sensors, which include smart metering;
  • Backhaul networks for sensors – where the IEEE 802.11ah can be used to create the backhaul of mesh networks created by IEEE 802.15.4 networks;
  • Extended Wi-Fi range for cellular traffic off-loading – where the IEEE 802.11ah can be used to off-load traffic from a cellular network. The caveat is that the performance should be at least comparable with the one from the cellular network;
  • M2M communications – Whereas current systems are optimized more for human-to-human (H2H) communications, IEEE 802.11ah standard will mainly consider sensing applications.
  • Rural communication – Wireless communication in rural areas has led to some effort that is also titled as bridging the digital divide. Large potential is given by sub 1 GHz due to the wider supported range.

In future blog posts, we will follow up with the standardization activities in IEEE 802.11ah.

Continue reading

Distributed control of DGs within microgrid

In this blog we continue our overview on the use of M2M communications in microgrids (see

As outlined in the previous blog, the future smartgrid is envisioned as a network of microgrids. Microgrid is a small-scale electrical network consisting of localized distributed generators, storage capacities and loads, interconnected by low-voltage (LV) cables.

Distributed generators (DGs) within microgrid typically exploit renewable energy sources (such are photo-voltaic and wind generators) whose behavior cannot be controlled, posing new challenges with respect to balancing the generated power and user loads. Basically, when controlling the DGs within microgrid, the imperative is that their output voltages and frequencies should match.

In traditional grids, the voltage and frequency outputs of generators are regulated using the so-called droop control. Droop-control is essentially a proportional control algorithm that is executed locally at each generator, driving output voltage and frequency based on the locally measured active and reactive powers. Loads and generators in traditional grids are interconnected using high-voltage cables, which have much greater reactance then resistance. Taking into account this fact, it can be shown that, when the active and reactive power that are flowing into the cable from the generator are expressed through the generator’s voltage (and assuming that the power angle is small), the active power depends on the generator’s frequency and the reactive power depends on the generator’s voltage. The overall result is that, in traditional droop control, the frequency is regulated by the local measurements of the active power, and voltage is regulated by the measurements of the reactive power.

Traditional droop control cannot be used directly in AC microgrids[*], as resistance of LV cables dominates over reactance. One way to address this issue is to modify the droop control such that control algorithm includes the information exchanged among DGs[**] using communication network.

In a recent paper [1], the authors apply the paradigm of distributed consensus algorithms, exploited in their earlier works (see previous blog,, to conduct series of local exchanges among DGs, in order for all DGs to learn what is the global state of active and reactive power. Traditional droop control is then augmented to include this information, such that frequency controller includes a term proportional to the differences between the desired and the actual active and reactive power, and the voltage controller uses a corresponding integral term. Using small signal analysis, the authors show that improved stability of the microgrid is achieved with respect to the traditional drop control.

The authors of [2] consider a similar setting, but the focus of their work is the impact of communication impairments, such are packet loss and delay, on the (modified) droop control, rather than on the control algorithm itself. The key observation is that outdated information about remote measurements, when combined with locally obtained measurements, can adversely affect the performance of the droop control. In order to neutralize this effect, the authors suggest a scheme in which locally obtained signal is delayed using the same delay distribution of the remote signal. The local delay is realized using Smith predictor and it is statistically the same as the delay of the remote signal. Finally, the authors present the simulation results obtained in an example microgrid with two DGs, showing that in the proposed scheme a more stable active power output is achieved, compared to the case when there is no local delay.

Recently, our group started collaboration on the same topic with a group at Department of Energy Technology at our university, with an aim of designing of robust and simple communication algorithms in the framework of DG control, both in AC and DC microgrids. The initial results are encouraging, however, we leave their presentation for our future blogs.

[1] H. Liang et al, “Decentralized Inverter Control in Microgrids Based on Power Sharing Information through Wireless Communications”, to be presented at IEEE GLOBECOM 2012

[2] S. Ci et al, “Impact of Wireless Communication Delay on Load Sharing Among Distributed Generation Systems Through Smart Microgrids”, IEEE Wireless Communications, June 2012


[*] Regulation of DGs in DC microgrids will be addressed in our future blogs.

[**] Droop control is actually applied at inverters that connect DGs to the interconnecting AC bus.

M2M communications in microgrids

As traditional energy resources are becoming increasingly scarce and thus more expensive (not to mention their adverse effects on the environment), the efforts towards energy generation from alternative, environmentally friendly resources and smart energy consumption are rapidly becoming concern of governments, industry and academia all over the world. The recent buzz-term smart grid symbolizes this important concept of the efficient generation, distribution and exploitation in case of electrical energy. In brief, smart grid comprises all elements of the electrical grid, but it is much more than just a collection of its parts – it is a dynamic system that interconnects them in a meaningful manner for the benefit of the end users. As such, smart grid heavily relies on communications between consumers, suppliers, smart devices and applications.

Smart grids of the future are envisioned as networks of integrated microgrids – (geographically) localized collections of generators, storage capacities and users (loads) that operate as singly entity with the goal of smart energy exploitation. With respect to this, the centralized approach for the regulation (i.e., control) of microgrid operation imposes itself naturally. However, the decentralized, distributed approach for microgrid control (known as the paradigm of multiagent systems) is a more favorable one, as it is inherently more robust and scalable. Naturally, distributed control should leverage on adequate distributed communication techniques.

A recent issue of IEEE Wireless Communications (Jun 2012), under the topic “Recent advances in wireless technologies for smart grid”, presents a couple of research articles devoted to the wireless communications within microgrid. Particularly, a generalized approach for the multiagent coordination is assessed in [1], investigating the usage of consensus algorithms for distributed dissemination of information among the agents. The idea behind consensus algorithms is a simple one – series of local exchanges among the agents should result with each agent having the same insight into the global network parameters (in other words, communicating only locally spreads information globally – a well-known phenomenon in the social networks). In turn, this allows agents to separately apply the control algorithms using the same data, harmonizing their operation to achieve the optimal performance of microgrid. As the actual communication technology, the authors suggest use of WiFi (IEEE 802.11) or ZigBee (IEEE 802.15.4). The above generalized approach can be applied to a number of issues in smart grid operation, such are:

  • Economic dispatch problem,
  • Decentralized invertor problem,
  • Fault detection and recovery,
  • Agent clock synchronization (when GPS is not used),…

Economic dispatch

A related article [2] gives a nice analysis of the economic dispatch problem when the impact of communications is included. Economic dispatch [3] is a short-term determination of the optimal output of a number of electricity generation facilities to meet the system load at the lowest possible cost, while serving power to the public in a robust and reliable manner. The authors of [2] develop a plausible cost model which, besides generation costs, cost of purchasing power and cost of generating extra power, includes both the communication costs and the impact of communication errors on the possible deviation of system operation from the optimum. It is shown that the use of distributed consensus algorithms can in general steer the system operation towards the optimum. Further, it is shown that use of communication links that interconnect distant agents (which are not directly connected by WiFi/Zigbee links) can speed up the convergence to the desired state of optimal operation, lowering the total cost. On the other hand, these “global” links are provided using cellular communications, which increases communications costs and thus increases the total costs; the overall result being that the amount of cellular link usage should be carefully selected.

Although these initial research efforts are promising, a lot remains to be done. The impact of wireless communications impairments on microgrid operation is still not adequately addressed nor experimentally verified. With respect to the latter, it remains to be seen how effectively will WiFi/ZigBee networks (which are almost exclusively operating in a star/tree topology) support the execution of the distributed consensus algorithms.

[1] H. Liang et al, “Multiagent Coordination in Microgrids via Wireless Networks”, IEEE Wireless Communications, Jun 2012

[2] H. Liang et al, “Decentralized Economic Dispatch in Microgrids via Heterogeneous Wireless Networks”, IEEE Journal on Selected Areas In Communications, July 2012