the role of communication systems in smart grids ...[25], and other aspects of smart grids, such as...

Computer Communications 36 (2013) 1665–1697

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Computer Communications

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Review

The role of communication systems in smart grids: Architectures,technical solutions and research challenges

0140-3664/$ - see front matter � 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.comcom.2013.09.004

⇑ Corresponding author.E-mail addresses: [email protected] (E. Ancillotti), [email protected] (R.

Bruno), [email protected] (M. Conti).

Emilio Ancillotti, Raffaele Bruno ⇑, Marco ContiItalian National Research Council (CNR), Institute for Informatics and Telematics (IIT), Via G. Moruzzi 1, 56124 Pisa, Italy

a r t i c l e i n f o a b s t r a c t

Article history:Received 3 February 2013Received in revised form 3 September 2013Accepted 6 September 2013Available online 19 September 2013

Keywords:Smart gridCommunication architecture and protocolsControl and managementMiddlewareSecurity

The purpose of this survey is to present a critical overview of smart grid concepts, with a special focus onthe role that communication, networking and middleware technologies will have in the transformation ofexisting electric power systems into smart grids. First of all we elaborate on the key technological, eco-nomical and societal drivers for the development of smart grids. By adopting a data-centric perspectivewe present a conceptual model of communication systems for smart grids, and we identify functionalcomponents, technologies, network topologies and communication services that are needed to supportsmart grid communications. Then, we introduce the fundamental research challenges in this field includ-ing communication reliability and timeliness, QoS support, data management services, and autonomicbehaviors. Finally, we discuss the main solutions proposed in the literature for each of them, and we iden-tify possible future research directions.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

The term smart grid is commonly used to refer to a modernizedelectrical system, in which new and more sustainable models ofenergy production, distribution and usage will be made possibleby incorporating in the power system: (a) pervasive communica-tion and monitoring capabilities, and (b) more distributed andautonomous control and management functionalities [1,2]. As amatter of fact existing electric grids are a large-scale, unidirec-tional and centralized systems in which the electricity is deliveredfrom remote power plants through a tree-based distributionsystem to local customers with pre-established load profiles [3].However, a number of technological innovations, as well asenvironmental and economical concerns, have emerged in the lastdecade that have made traditional electric power systems outdatedand not well suited to meet the reliability, efficiency and sustain-ability requirements posed by those changes [4].

Although there might be different views on what will be thedefinitive model of a smart grid, the following key capabilitiesare widely recognized as essential for the successful implementa-tion of smart grids [5]:

� To enable the massive deployment and efficient use of dis-tributed energy resources, including renewable energysources and energy storage systems;

� To enhance the efficiency, resilience and sustainability ofan electric grid by incorporating real-time distributed intel-ligence enabling automated protection, optimization andcontrol functions;

� To allow the interaction of consumers with energy manage-ment systems to enable demand-response and load shap-ing functionalities;

� To enable real-time, scalable situational awareness of gridstatus and operations through the deployment of advancedmetering and monitoring systems;

� To support the electrification of transportation systems byfacilitating the deployment of plug-in electric vehiclesand their use as mobile energy resources.

From a practical point of view the above vision requires the per-vasive deployment of ‘‘intelligent’’ devices [6] (e.g., sensors, actua-tors, smart appliances, smart meters, embedded computers, etc.)that are capable of collecting real-time and fine-grained informa-tion about electricity usage patterns, as well as about the statusof distributed energy resources and other components of the elec-tric grid. This huge amount of heterogeneous information collectedby the metering and monitoring infrastructures that will be incor-porated in a smart grid must be shared in a reliable and secure

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Table 1Abbreviations.

AMI Advanced metering infrastructureAMR Automatic meter readingBAN body area networkDER Distribute Energy ResourcesDG Distributed generationDR Demand responseFAN Field area networksEMS Energy management systemEV Electric vehicleHAN Home area networkHMI Human machine interfaceIED Intelligent electronic deviceP2P Peer-to-peerPLC Programmable logic controller/ Power line communicationPMU Phasor measurement unitRTU Remote telemetry unitSCADA Supervisory control and data acquisitionSDO Standards developing organizationSMG Smart micro-gridV2G Vehicle-to-gridVPP Virtual power plantWAN Wide area networkWASA Wide-area situational awarenessWLAN Wireless local area network

1666 E. Ancillotti et al. / Computer Communications 36 (2013) 1665–1697

manner to a multitude of distributed energy management systems.These systems are responsible for analyzing the received data, pre-dicting and detecting possible issues (e.g., failures, energy short-ages, disturbances), and making decisions to control andoptimize the operations of the power system. Note that most ofthese management and control functions will be executed in re-sponse to local events but they may have an impact on the resil-iency and efficiency of large portions of the electric grid.

To support information collection, distribution and analysis, aswell as automated control and optimization of the power system,we argue that the smart grid communication system will rely ontwo major subsystems: a communication infrastructure and a mid-dleware platform. The communication infrastructure consists of aset of communication technologies, networks and protocols that:(i) support communication connectivity amongst devices or gridsub-systems, and (ii) enable the distribution of information andcommands within the power system. As better explained in the fol-lowing, basic requirements for such communication infrastructureare scalability, reliability, timeliness and security. The middlewareplatform consists of a software layer, which is situated betweenthe applications and the underlying communication infrastructure,providing the services needed to build efficient distributed func-tions and systems. Specifically, a middleware runs on the devicesthat are part of the smart grid communication infrastructure tosupport: (i) data management services (e.g., data sharing, storage,and processing), (ii) standard communication and programminginterfaces for distributed applications, and (iii) computationalintelligence and autonomic management capabilities. Further-more, it is of paramount importance to ensure secure and reliableoperations of the smart grid communication system to protect theentire smart grid. However, security solutions can not be confinedto a single component of the smart grid communication system butsecurity is a cross-layer issue because it is equally important to: (i)secure devices, information, and services, (ii) preserve data integ-rity, confidentiality and authenticity, and (iii) ensure very highavailability of electricity provision. Then, the purpose of this surveyis to provide a general reference architecture of the smart gridcommunication system and its major components. We utilize ourreference model to identify basic system requirements and keytechnologies, with special focus on communication technologiesand protocols, data management services, autonomic control func-tions and security mechanisms. Furthermore, this survey providesa comprehensive and up-to-date survey of state-of-the-art solu-tions for each of the above research challenges, and we discussadvantages and shortcomings of each proposed technique. Finally,we also identify main open issues and future research directions.

It is important to point out that other surveys exist targetingvarious aspects of smart grids. There are a number of positional pa-pers that focus on explaining what makes an electric grid a smartgrid. Those papers have the purpose of setting a common back-ground and vision about smart grids, and to specify roadmapsand guidelines for the development of smart grids [1,2,7–10].Other surveys have investigated the communication networks forsmart grids in terms of requirements, enabling technologies andpossible network architectures [11–18]. In [19] the research chal-lenges for smart grid communications are analyzed from an indus-trial perspective in terms of interoperability, scalability andsecurity. There are also surveys that focus exclusively on cyber-security issues for smart grids [20–24], smart grid standardization[25], and other aspects of smart grids, such as future utility controlcenters [26] or future technologies for the transmission grids [27].The work in [28] provides a detailed overview of routing solutionsfor smart grid communications. The survey in [29] focuses on threemajor systems of smart grids, namely the smart infrastructure sys-tem, the smart management system, and the smart protection sys-tem. According to the view in [29] the smart infrastructure system

is responsible for maintaining communication connectivity amongsystems, devices, and applications, which is similar to the role ofthe communication infrastructure described in this survey. In gen-eral, some of the cited surveys can be regarded as complementaryto our work. However, our survey is distinct from those papers inthe sense that it adopts a data-centric perspective and it tries to ex-plain how communication networks will allow smart grid applica-tions to collect, share and use information data in a timely, reliableand secure manner.

The rest of this paper, is organized as follows. In Section 2 wefirst outline key drivers for smart grids and we provide an outlookon the transition from existing power systems to future smartgrids. We also describe the characteristics of the most innovativeservices and applications that smart grids can enable in Section 3.In Section 4 we introduce a conceptual framework of the maincomponents of a smart grid communication system by followinga bottom-up approach. Specifically, we will start by outlining themost popular communication technologies for smart grids. Then,we continue with describing in details the various proposals avail-able in literature to implement communication infrastructures(Section 5), middleware platforms (Section 6) and and securitymechanisms (Section 7) for smart grids, along with the mostimportant open issues. Finally, in Section 8 we conclude our surveyby summarizing lessons learned. In addition, refer to Table 1 forthe abbreviations used in this survey.

2. The evolution path towards the smart grid

In this section we overview the infrastructure that underliesexisting power systems to clarify its major shortcomings and thekey drivers for smart grids. Furthermore, we discuss the mostimportant new paradigms for electricity generation, delivery andconsumption that are envisioned for smart grids.

2.1. Design principles and structure of traditional electricity grids

The essential purpose of an electric grid is to deliver electricityto customers, which are the termination points of the power distri-bution system. In the early days of electricity distribution, electricgrids were isolated systems in which electric power was producedby small generators using direct current (DC). However, DC-basedelectricity could only be transmitted over short distances due to

E. Ancillotti et al. / Computer Communications 36 (2013) 1665–1697 1667

power line losses. With the advent of alternating current (AC) atthe end of the 19th century it was possible to distribute electricityover long distances using high voltages more efficiently than withDC. Then, rapid industrialization, urbanization and economicdevelopment required the deployment of large-scale infrastruc-tures for electricity delivery, which were operated through na-tional monopolies. The structure of those national electric gridshas remained (almost) unchanged till nowadays, and it consistsof three main components: (1) generation, (2) transmission and(3) distribution [2].

In a traditional power grid, the generation sub-system relies ona small number of large power plants using conventional (coal, oil,natural gas, and nuclear) resources to produce electricity. Then,high-voltage transmission lines, which form the transmission net-work, are used to transfer electricity across long distances frompower plants to electric substations. A substation includes trans-formers to change voltage levels from high transmission voltagesto lower distribution voltages. Furthermore, substations performseveral other important functions, such as grid protection andpower control. Substations, medium- and low-voltage power lines,and electric meters form the distribution network. It is importantto point out that a power system must be engineered such thatelectricity production always matches electricity demands. Thisimplies that any change in power demands should be accommo-dated through an equal change in power supply. To deal withunexpected peak demands special power generators are deployedin existing power grids, which have very short start-up delays.Note that battery technologies are still expensive and inefficient,thus energy storage systems cannot be employed on a large scaleto mitigate the impact of sudden load changes and fluctuations.Even if electric utilities keep generation capacity in reserves thatcan be accessed quickly, electricity imbalances are still possiblefor a number of reasons, and existing power systems may sufferfrom grid instabilities and power outages. Furthermore, as powergrids are complex highly interconnected systems, a failure at onelocation can easily trigger a cascade effect that could result into re-gional blackouts, such as in the case of the Northeast US and Can-ada blackouts of 1965 and 2003 [30].

To improve the reliability of electricity provision and to supportdistribution automation, in the 1960s electric utilities started inte-grating Supervisory Control And Data Acquisition (SCADA) systemsin their grid infrastructures. SCADA is not a specific technology but

Fig. 1. An example of a SCADA-based control system.

generally refers to computer-based centralized systems imple-menting control applications for industrial processes. As shownin Fig. 1, a SCADA system usually consists of the followingcomponents:

� Remote telemetry units (RTUs): microprocessor-controlleddevices responsible for: (i) interacting with sensors, (ii)converting sensor readings to standard data formats, and(iii) delivering sensed data to monitoring stations;

� Programmable logic controller (PLCs): minicomputers thatare used as field devices for process control and machineautomation (e.g., to open or close circuit breakers atsubstations);

� A supervisory computer-based system that is composed ofseveral remote units and a master station, and it is used tocollect the data from RTUs, perform data analysis and sendcommands to PLCs;

� Databases for storing historical data, measuring trends andderiving forecasts;

� Human–Machine Interfaces (HMIs) to present a simplifiedrepresentation of the system and its status to a humanoperator, who can make supervisory decisions;

� A communication infrastructure connecting the supervi-sory system to the other SCADA components.

Early SCADA systems were installed in substations and trans-mission networks, and they were using dedicated point-to-pointcommunication links (e.g. telephone lines or optical fibers) to con-nect the remote stations with the utility control center. Most ven-dors of SCADA products had developed simple proprietarycommunication protocols to connect the master stations with RTUsand PLCs [31]. However, as SCADA solutions were widely deployedby electrical utilities to control and protect the power grid, some ofthose protocols, such as Modbus [32], emerged as standard de factorecognized by all major SCADA vendors, while power engineeringstandardization bodies started to release public standard protocols,such as IEC 61850 [33]. Furthermore, the SCADA centralized archi-tecture has also evolved over time, allowing more distributed pro-cessing and control, and the interconnection of different SCADAsystems through wide-area networks [3,31]. However, as ex-plained in following sections, SCADA systems do not appear suit-able for implementing the fully decentralized and autonomouscontrol functions demanded by future smart-grid applications.

2.2. Key challenges for existing electric grids

From the beginning of 1990s we are witnessing a rapid transfor-mation of existing electric grids, which is driven not only by tech-nological innovations but also by economical, regulatory andsocietal factors. Those changes impose new operational scenariosand technical challenges to power systems, which we outline inthe following.

First of all, the soaring demand for new power supplies, and theincreasing public awareness of the need of more sustainablesources of energy, are promoting the use of renewable energy re-sources (e.g., wind and solar technologies) for power generation[34]. According to various reports [35,36], half of the estimated194 gigawatts (GW) of new electric capacity added globally during2010 was derived by renewable resources. Although most of theelectricity produced by renewable energy sources still comes fromlarge-scale facilities (e.g. wind farms, solar parks, and biomasspower plants), situation is rapidly changing with the proliferationof small-scale distributed generators using renewable resources.Many argue that there might be many advantages in the distrib-uted generation (DG) model [37]. For instance, DG ensures thatelectricity generation is located near the place where energy is

1 IED is a term used to indicate microprocessor-based controllers of power systemequipment that are provided with advanced monitoring and communication capa-bilities. IEDs are expected to replace RTUs and PLCs of SCADA systems [3].


used, reducing power losses and congestion on transmission lines[38–40]. However, as explained in Section 2.1, the existing distri-bution networks are not designed to handle power flowing fromend users to substations, and this may negatively impact on distri-bution stability [41]. Most importantly, renewable energy re-sources are intermittent and highly variable, and the uncertaintyin energy supply can cause reliability problems or power qualitydegradations (e.g., undesired voltage fluctuations) [42,43]. Thisnecessarily requires more sophisticated coordination and controltechniques than those supported in current electric grids [44]. Re-cently, utility companies have started introducing automated me-ter reading (AMR) systems in their distribution networks toremotely collect data from the meters (e.g., consumption records,alarms) at customers’ premises [45,46]. However, AMR systemsare typically designed using simple one-way communication infra-structures that do not allow pervasive control of an electric grid[15].

Another factor that is contributing to the uptake of DG technol-ogies is the deregulation of energy markets [47]. More specifically,power systems are no longer national monopolies, but there areindependent power producers selling electricity to utility compa-nies, and independent operators maintaining and controlling re-gional transmission and distribution networks. Then, electricityprices are determined in an electronic auction market accordingto demand and supply principles [48]. At least two types of elec-tricity markets exist, which are regulated by different rules: (i)the wholesale electricity market for trading large amounts of en-ergy between generators, system operators and retailers, and (ii)the retail electricity market, in which electricity retailers sell en-ergy to end users [49]. Following the dynamics of those markets,energy costs fluctuate, and prices raise as demands increase. Thefirst consequence of this competitive market structure is that cur-rent power systems have increasingly ‘‘meshed’’ topologies,emerging from the interconnection of several smaller grids. Fur-thermore, energy trading across regional power grids is causinguncertainties in energy delivery that current electric grids are notwell suited to handle. Finally, in liberalized market environments,small electricity consumers can exploit DG technologies to becomepotential producers. However, this requires improved system flex-ibility to preserve the reliability and stability of the power system[42].

The desire of reducing the environmental impact of our lifestyleis also fostering the market penetration of electric vehicles (EVs).However, the mass adoption of EVs is not without challenges forelectric grids [7,50]. On the one hand, EVs represent new mobileloads for the power system, which can significantly alter typicalpatterns of energy usage in households, while causing a dramaticsoaring of electricity demands. For instance, the simultaneouscharging of several EVs located in the same area can easily deter-mine unexpected peak loads and rapid fluctuations of power de-mands in different parts of the electric grid [51]. Therefore, newmanagement capabilities are needed to regulate the rechargingprocess of EVs with the objective of flattening aggregated power de-mands and mitigating load imbalances in power systems [52,53].

2.3. The next-generation electric grid

As introduced in Section 1 next-generation electric grids arecommonly denoted as smart grids. Although there are slightly dif-ferent views about the ultimate model of a smart grid, a consensusis forming about the new technologies and paradigms that areessential for the successful deployment of a smart grid, namely:ð1Þ advanced metering infrastructure, ð2Þ distributed energy re-sources, ð3Þ smart micro-grids, and ð4Þ vehicle-to-grid technologies[1,2,7,9,10,54]. Fig. 2 provides a schematic view of the smart gridreference architecture used for the following discussion.

2.3.1. Advanced metering infrastructure (AMI)As previously observed SCADA and AMR systems were the first

attempts to introduce simple digital communication capabilities inpower systems. However, smart grid applications will need perva-sive and real-time control of each grid component and not only ofsmart meters and substations. For this reason, a smart grid shouldincorporate a pervasive and scalable two-way communicationinfrastructure to enable more distributed command-and-controlfunctionalities. One of the most important components of thiscommunication infrastructure is the advanced metering infrastruc-ture (AMI) [55], which will be used to interconnect the smart me-ters (i.e., electricity meters that incorporate networking and datamanagement functionalities) installed at end customers’ premiseswith other control systems and data aggregators (see Figs. 2 and 5for illustrative examples). Then, AMI systems can contribute in sev-eral ways to the realization of the smart grid vision. First, electricutilities can use AMI as data acquisition networks to monitor: (i)power quality, (ii) how much electricity is produced/stored byDER (distributed energy resources) units, and (iii) power consump-tion of household appliances. This large amount of metering datacan be exploited to proactively identify failure conditions andanomalies and to take appropriate countermeasures, or to imple-ment sophisticated techniques to regulate electricity usage pat-terns (e.g., dynamic pricing or scheduling of residential loads). Ina more general view, AMI networks can be foreseen to interconnectnot only smart meters but also a variety of intelligent electronic de-vices (IEDs),1 which will be massively dispersed within smart grids.Communication architectures and technologies suitable for AMI willbe analyzed in details in Section 5. It is also important to point outthat AMI will allow smart grids to collect a huge volume of hetero-geneous data from a large number of sources. How to efficientlyaggregate, store and analyze this data is the subject of intensive re-search (see Section 6 for an overview on such body of work).

2.3.2. Distributed energy resources (DER)As discussed in Section 2.2, in existing power systems it is

becoming increasingly common a more distributed generation ofelectricity. This trend is rapidly gaining momentum as DG technol-ogies improve, and utilities envision that a salient feature of smartgrids could be the massive deployment of decentralized power stor-age and generation systems, also called distributed energy re-sources or DERs. We have already mentioned that smart gridswill facilitate the integration of small-scale renewable energysources (e.g., solar panels in residential applications), which willhelp to reduce demand for fossil-fuel power plants and to increasesupply redundancy [34]. Distributed energy storage is widely rec-ognized as a key enabler of smart grids for its role in complement-ing renewable generation by smoothing out power fluctuations[56,57]. For instance, surplus energy can be stored during condi-tions of low demand and supplied back during periods of heavyload. Moreover, energy storage systems generally have a quickerresponse than conventional power generators, and they can beused to increase system reliability [58,59]. However, how to coor-dinate diverse DER technologies, which have different capabilitiesand characteristics (e.g., energy capacity and time response func-tions), is not well understood yet.

2.3.3. Smart micro-grid (SMG)As shown in Fig. 2, a smart micro-grid (or micro-grid for brev-

ity) is a single, autonomous, self-sustainable power system formedby an interconnection of distributed energy resources, whichserves various electricity customers (e.g., residential buildings,

Fig. 2. Reference model of a smart grid.


commercial premises and small industries) located near one an-other [60,61]. A micro-grid should be able to either operate inte-grated with the utility grid or disconnected (i.e., islanded) fromthe distribution system. This also implies that a micro-grid shouldhave its own management system to support the control functionsneeded to autonomously regulate electricity flows [62], as well asto participate into the energy market for electricity trading [63]. Itis important to notice that the micro-grid concept is not totallynew and there are already many practical examples of micro-gridapplications, such as industrial micro-grids, which provide high-quality and reliable power to large industrial loads, or utility mi-cro-grids, which serve loads in either densely populated urbanareas or rural regions [64]. However, we can expect a proliferationof SMGs in future power systems, and in particular of customer-driven micro-grids, which will be established though contractualagreements among residential customers [57]. On the one hand,customer-driven (or community) micro-grids appear as the mostsuitable technology to optimize the deployment of DERs. On theother hand, micro-grids can also represent the best approach totackle the complexity of pervasive deployment of intelligent anddistributed control functions in electric grids. Indeed, each micro-grid can be seen as a smart grid at a much smaller scale. Thus,many experts believe that a smart grid can progressively emergethrough the peer-to-peer interconnection of an increasing numberof micro-grids [2,65,66].

2.3.4. Vehicle-to-grid (V2G)As pointed out in Section 2.2, the increasing use of EVs pose

new challenges to electric grids because electricity demands willrise significantly. Thus, a considerable body of work is concentrat-ing on defining control strategies to distribute spatially and tempo-rally EV charging in order to avoid peak loads and to optimallyutilize grid capacity [52,67,53]. On the other hand, EVs can also

offer many advantages to smart grids. Specifically, many studieshave shown that during the day cars are parked most of the time,thus a large number of EVs can be assumed to be connected to apower socket at any given point in time [68]. Consequently, whenEVs are plugged into the electric grid their batteries can be used asbackup energy storage systems. For instance, EVs can supply backpart of their stored electric power to stabilize the electricity pro-duced by intermittent renewable energy sources. This newpower-generation paradigm is generally known as vehicle-to-grid(V2G) technology [54,69]. To some extent V2G technologies canbe seen as a special case of DERs with the additional complexitythat power sources are mobile [9].

3. Smart grid applications

The smart grid vision entails innovative services and applica-tions in addition to technological transformations. In the followingwe summarize the salient features of four major smart grid appli-cations, which are also illustrated in Fig. 2. This is useful to identifythe key requirements for smart grid communication systems.

3.1. Wide-area situational awareness (WASA)

One of the most important applications that smart grids shouldsupport is real-time wide-area situational awareness (WASA), whichis defined as the ability to build a high-resolution description of thecurrent state of the power grid over a wide area. Then, this infor-mation can be analyzed, for instance to predict the evolution ofthe power grid state under different operational conditions and en-ergy control strategies [26,70–72].

Intuitively, WASA applications rely on a pervasive monitoringinfrastructure to collect real-time data from widely dispersedsensors. The WASA monitoring system is expected to incorporate


diverse types of sensors, including sources of non-electrical data,such as weather information that is used to predict changes inwind and solar power generation. Specifically, synchronized mea-surement technologies are emerging as an essential enabler forWASA applications, especially for power control and protection[70]. The most advanced type of sensors based on this technologyare the Phasor Measurement Units (PMUs), which provide syn-chronized, real-time and high-resolution (up to 60 samples persecond) measures of voltage and frequency parameters for thetransmission lines they are connected to [73]. Note that many sys-tem operators have already started deploying PMUs in their trans-mission networks [74,75]. It is also important to point out thatWASA applications impose stringent latency requirements, andcommunication delays are the dominant component in the totalsystem delays. Thus, special attention should be dedicated to theproper design of a communication infrastructure that is capableof ensuring delay guarantees.

Another key component of a WASA application is the set of toolsthat are used to analyze the huge amount of raw and heteroge-neous data received from multiple sources that exist in the powergrid. Specifically, simulation tools suitable for large-scale powersystems are needed to identify trends, diagnose undesired behav-iors and to detect in advance potential vulnerabilities [1]. How tocombine simulation-based analysis of the power system withmodel-based analysis, with the objective of improving accuracyand convergence times of state estimation, is an important re-search topic [26]. Finally, visualization tools should be developedto present the output of data analysis in an effective and flexiblemanner, e.g. by combining geospatial information with electricinformation [76].

3.2. Energy management systems (EMS)

As observed in Section 2.1, in current electric grids, manage-ment functionalities are supported by utility control centers, whichare typically implemented through distributed SCADA systems [3].However, it is widely recognized that in smart grids it will be nec-essary to adopt a more decentralized control model in which mul-tiple autonomous and independent energy management systems(EMSs) cooperate to achieve desired control objectives by exploit-ing the information collected by WASA applications. Furthermore,EMSs should be capable of operating on different parts of the gridand at different scales [9]. For instance, there will be home EMSs(HEMSs) to monitor and control the electricity usage in house-holds, and building EMSs (BEMSs) coordinating multiple HEMSsto maximize the energy efficiency of entire buildings [77]. At a lar-ger scale, there will be EMSs providing advanced management andcontrol services for substations, micro-grids [63] or other subsys-tems of the power grid [44,78]. In general, several technologies be-yond classical SCADA solutions can be considered to implementthose EMSs, including service-oriented architectures (SOA), grid/cloud computing, multi-agent systems, etc. In particular, in Sec-tion 6.3 we will focus our attention on the large body of researchwork dedicated to the development of multi-agent control systemsfor smart grids.

3.3. Demand response (DR)

The term demand response (DR) is used to indicate a variety ofmechanisms that smart grids will utilize to dynamically shapethe electricity consumption of end users in response to energy sup-ply conditions with the objective of improving the efficiency andreliability of electricity provision [49,79]. As an example, signifi-cant savings in power generation costs can be achieved by flatten-ing out peak demands. Furthermore, approximately 20% of thetotal power generation capacity is deployed as backup reserve for

coping with peak demands but it is in use only 5% of the time [66].DR applications could help to obtain a more efficient utilization ofthe existing energy resources. The approach most commonly pro-posed for implementing DR applications is dynamic pricing. Basi-cally, by adopting variable electricity tariffs the utility companiescan incentivize customers to lower peak demands and smooth de-mand profiles by: (i) shifting some power-demanding householdactivities (e.g., dishwashers) to off-peak periods, (ii) temporarilychanging the settings of energy-demanding appliances, such asheaters and air conditioners, or (iii) activating residential powergenerators to supplement the electricity provided by the distribu-tion grid [49,80]. In the long term the expected potential benefitsof DR would be to reduce overall electricity prices, and to signifi-cantly improve grid reliability and stability through a betterspreading of electricity demands [79,81]. It is also useful to pointout that different control approaches can be used to achieve DRobjectives [77]. On the one end of the spectrum of proposed strat-egies there are pure customer-side techniques in which individualhouses use real-time prices to make independent decisions abouttheir energy usage profiles [82]. On the other hand of the spectrumthere are utility-driven techniques, commonly known as direct loadcontrol (DLC) mechanisms, which allow electric utilities to sche-dule power consumption of residential appliances [83,84]. In gen-eral, the former approach requires more intelligent HEMSs, whilethe latter approach is easier to implement but it can suffer fromscalability issues.

3.4. Virtual power plant (VPP)

It is expected that the proliferation of DG technologies in smartgrids could lead to a new paradigm for power generation, calledvirtual power plant (VPP). More specifically, a VPP consists of a largecluster of distributed power generators co-located with each otherin a single site, which are jointly managed and controlled. A VPPproduces a total capacity similar to the one of a conventionalpower plant [85]. On the other hand, a VPP appears to the electric-ity markets and the grid system operators as a variable-size powerplant ensuring more flexibility than conventional power plants[86–88]. For instance, a VPP is expected to be able to react tochanges in customers’ load conditions much faster than traditionalpower plants [61]. In addition, the VPP approach could allow aneasier integration of intermittent renewable resources into gridoperations, because it implements the control functions neededto deal with power fluctuations. Finally, many studies argue thatthe VPP model provides an effective aggregation technique to con-trol fleets of EVs as a single entity, thus facilitating the integrationof V2G services into smart grids [69,89,90]. However, a VPP is alsoa complex system and the development of suitable energy man-agement systems that can be used to implement the VPP modelis still an open issue.

3.5. Smart grid standardization efforts

There is a general consensus that standardization is one of thekey issues in the design of smart grids [25,5,91,92]. Indeed, theadoption of inter-operability standards for the overall system is acritical prerequisite for making the smart grid system a reality.However, a smart grid is a complex system that requires differentlayers of interoperability. For instance, standards for smart meters,smart devices, charging interfaces with electric vehicles are essen-tial to facilitate market penetration of new smart grid products andservices, as well as seamless interoperability between them. Simi-larly, all smart grid applications illustrated in previous sections re-quire exchanges of information, for which interoperabilitystandards are needed. Finally, a smart grid consists of many differ-ent domains and actors (i.e., generation, transmission, distribution,


markets, operations, service provider, and customer), and standardinterfaces are needed among them.

There are many internationally recognized professional associ-ations, e.g, Institute of Electrical and Electronics Engineers (IEEE),Internet Engineering Task Force (IETF) or International Electrotech-nical Commission (IEC), international standardization bodies, e.g.,International Telecommunication Union (ITU), regional standardi-zation organizations, e.g., National Institute for Standards andTechnology (NIST), and industrial alliances, e.g., ZigBee, IPSO andHomePlug, working towards the development of standards forsmart electricity systems. However, each entity typically targetsa different market sector or application scenario. For example, IEEEfocuses mainly on standards that cover media-related interopera-bility problems (i.e. message delivery over different communica-tion technologies), while the activity of the IETF mainly coversinteroperability issues related to transport and application areas.It is also evident that electrical industries can leverage many Inter-net-related standards to build a highly interoperable informationand communication infrastructure for smart grids [91,92]. In Sec-tion 5 we will explain the details of some of the most importantcommunication standards that can be applied to the smart grid do-main, while in the following we focus on the most representativestandards for smart grid applications.

Within the IEC there are many Technical Committees (TCs) thatare working on standards for smart grid applications, such as TC57, which prepares standards for equipment and systems used inthe control, protection and automation of power systems, includ-ing EMS and SCADA [93], or TC 13, which prepares standards forsmart metering systems, for electrical energy measurement, andcustomer information and payment [94]. Up to now, more than ahundred IEC standards can be identified relevant to the smart grid[95]. In these core standards, IEC/TR 62357 describes a SOA-basedreference architecture for power system automation and providesa framework to illustrate the interdependencies between all theexisting object models, services and protocols in TC 57 [96]. Atthe bottom level of this architecture there is the IEC 61850 familyof standards, which specify communication networks and systemsfor power utility automation, with a special focus on substationautomation [33]. Note that the abstract data models defined inIEC 61850 have been mapped to a number of protocols that canrun over generic TCP/IP networks and/or switched Ethernet. Otherseries of core standards define a Common Information Model (CIM)to standardize the exchange of information between different clas-ses of applications, such as Energy Management related applica-tions (IEC 61970 family [97]) and electrical distribution systems(IEC 61968 family [98]). Data exchange for meter reading, tariffand load control is specifically addressed in the IEC 62056 seriesof standards [99]. Finally, security aspects for the TC57 series ofprotocols are handled in the standard IEC 62351.

IEEE is also very active in the smart grid sector and it has re-cently launched a new project called P2030 to establish a frame-work for achieving smart grid interoperability [100]. Up to now,nearly a hundred standards have been released or are under devel-opment, which cover different issues relevant to the smart grid,ranging from monitoring and control applications to communica-tions over power lines [101]. Of particular importance is the IEEE1815 standard, which has ratified the Distributed Network Proto-col (DNP3) standard for communications in electric power systems[102]. DNP3 plays a crucial role in modern SCADA systems, becauseit is primarily used for reliable and secure communications be-tween control centers and RTUs or IEDs. In addition, interoperabil-ity standards are under development to support a transparentmapping between IEEE 1815 and IEC 61850. With regard to mon-itoring applications, it is also important to mention the IEEE C37family of standards, which defines communication protocols forreal-time PMU data exchange [103]. The series of standards IEEE

1547 provide guidelines criteria and requirements for intercon-necting distributed energy resources to electric power systems,including standards for monitoring, information exchange andcontrol [104]. Finally, IEEE is actively working towards standardsfor power line communications considering both broadband andnarrowband technologies through the IEEE 1901 [105] and IEEE1901.2 working groups, respectively.

4. Reference model of communication systems for smart grids

In the vision presented so far, and widely accepted by utilitycompanies, regulators and academia, the smart grid architecturerequires the integration of the energy infrastructure, which isresponsible for electricity generation, delivery and consumption,with a communication system, which provides support for auto-mated and distributed monitoring, management and optimizationfunctions [5]. The communication system entails different technol-ogies, components and services, and in this survey we analyze twoof its major parts:

Communication infrastructure. The communication infrastruc-ture (or network) is responsible for providing the connectivity ser-vice among individual electric devices or entire grid sub-systems. Inthe context of smart grids, the key priorities of this communicationnetwork are: (a) to ensure reliable and real-time data collectionfrom an enormous number of widely dispersed data sources, and(b) to support the various communication services (e.g., multicastand group communications) that are needed by power controlapplications to distribute commands and configuration instructionsin the power system. As explained in following sections, this com-munication infrastructure is envisioned as a collection of intercon-nected networks that will be structured into a hierarchy of at leastthree main tiers or domains: (1) local area networks for the accessgrid segment and the end customers, (2) field area networks for thedistribution segment, and (3) wide area networks for the utilitybackbone. A variety of technologies, network topologies and com-munication protocols are considered for each of these categories[14,16].

Middleware platform. The middleware is a software layer run-ning above the communication network, which provides commu-nication and data management services for distributedapplications, as well as standard interfaces between applicationsand smart grid devices. Different types of middleware solutions ex-ist that differentiate from each other for the set of abstractions andprogramming interfaces they provide to applications, such as dis-tributed objects, event notifications, distributed content manage-ment, synchronous/asynchronous communication functions, etc.[106]. Furthermore, middleware is increasingly used to createpeer-to-peer (P2P) overlays, i.e., distributed systems in which de-vices (peers) self-organize into a network and cooperate with eachother by contributing part of their (storage, computing, band-width) resources to offer useful services, such as data search, dis-tributed storage, or computational intelligence [107,108]. Giventhe ability of P2P technologies to scale with increasing numbersof devices and services, several studies have proposed to useP2P-based middleware technologies to deal with the complexityof managing and controlling smart grids [109].

In the remaining of this survey we take a bottom-up approachto describe the main approaches proposed in the literature to buildcommunication and middleware solutions suitable for smart grids.Fig. 3 provides a scheme to guide the following discussion. Westart in Section 5.1 from the communication infrastructure byanalyzing the requirements that this component must meet in or-der to support smart grid applications. For the sake of clarity wecategorize these requirements into two classes: (i) quantitativerequirements, which define target performance metrics for data

Fig. 4. Protocol stack specified in ZigBee SEP 2.0.


communications, and (ii) qualitative requirements, which definethe functional characteristics that this communication infrastruc-ture should exhibit [110,14,111]. Then, we continue in Section 5.2by overviewing the wired and wireless communication technolo-gies that are applicable in the smart grid context, and we identifymajor advantages and limitations of each solution. Furthermore,the full specification of a communication architecture requires todefine: (a) which are the communicating entities and how theyare organized in network topologies, and (b) which are thecommunication protocols used to exchange messages in standard-ized formats and to support various communication services.Section 5.3 deals with the first topic, while Section 5.4 deals thisthe second one.

In the second part of this survey we concentrate on middlewareplatforms for smart grids. In particular, in Section 6.1 we focus onmiddleware techniques specifically designed for supporting datamanagement services that are able to meet the scalability, reliabil-ity and real-time requirements of applications that must process,store and share data from a large number of heterogeneous datasources. For instance, as explained in the following sections, mostapplications for grid protection require that monitored data isdelivered immediately and automatically to appropriate controlentities. To ensure data availability and timeliness in such large-scale and heterogeneous systems it is necessary to adopt more ad-vanced data management models than classical client–server orpolling techniques. Furthermore, in Section 6.3 we also explorethe key role of middleware technologies in supporting intelligentsystems, and in particular multi-agent systems, which are usedto provide flexible, decentralized and autonomous managementfunctions in smart grids.

To conclude this survey in Section 7 we analyze a cross-layer is-sue affecting both communication infrastructures and middlewareplatforms, that is security and privacy. Some challenges will not bedissimilar from those of other large-scale telecommunications net-works, but new vulnerabilities are due to the fact that smart gridsare complex systems that integrate physical infrastructures withinformation and communication technologies. It is also importantto point out that electricity must always be available in power sys-tems. Thus, continuous availability must be considered the mostimportant security objective in smart grids. On the contrary, inmost telecommunications networks confidentiality and integrityare often considered more critical than availability. As describedin following sections, this implies that security mechanisms for

Fig. 3. Reference model of the communication system for smart grids.

smart grids must operate in a timely manner and without inter-rupting the grid services.

5. Communication infrastructures for smart grids

There are several important issues in communication networkdesign, such as:

� Which communication technologies should be used toestablish links between devices?

� Which network topologies are applicable in the context ofelectric grid infrastructures, and how communication tech-nologies and grid geography affect the topology of thenetwork?

� Which networking and transport protocols are the mostappropriate for meeting the requirements of smart gridcommunications?

There is a general agreement that it is not possible to give aunique answer to the above questions because smart grids willoperate in different environments and use cases. In the rest of thissection we focus on discussing the main advantages and disadvan-tages of the different options that have been proposed in the liter-ature, along with open issues and future research directions.

5.1. System requirements

To identify which communication technologies are suitable forsmart grids first of all it is necessary to specify the basic require-ments that smart grid communication infrastructures should sat-isfy. Therefore, utility companies, research organizations andgovernments have elaborated several reports on the communica-tion needs of smart grid services [110,111,14,17]. In the followingwe list the most important requirements focusing on two catego-ries: (i) quantitative requirements, which specify in a measurablemanner the target communication performance demanded by theapplications, and (ii) qualitatively requirements, which identifythe capabilities that must be supported by the communicationsystem.

5.1.1. Quantitative requirementsThe key communication requirements for smart grid applica-

tions can be summarized as follows:


� Latency: In general most control and protection functions inpower systems have tight delay constraints and requireprompt transmission of information. For instance, in thecase of distribution automation the IEDs that are deployedin substations should send their measurements to dataaggregators within 4 ms, while communications betweendata aggregators and utility control centers require a net-work latency 68–12 ms [112]. Other applications are lesstime-critical and can tolerate higher network delays (e.g.,most smart meters today send their readings periodicallyevery 15 min). It is also important to point out that net-work latencies depend on several factors. Therefore, com-munications with low and stable latencies require acommunication network specifically designed to optimizedelay performance [14].

� Reliability: Critical functionalities in smart grid require veryhigh levels of reliability (up to 99.9999% reliability whichcorresponds to a total outage period shorter than one sec-ond per year) [110], which cannot be obtained if the under-lying communication infrastructure does not support veryreliable communications [113]. Note that there is a numberof possible causes for network failures, including link/nodefailures, routing inconsistencies, overloading, etc., whichmake necessary the use of different techniques to copewith them. However, redundancy (e.g., multiple copies ofthe same messages, multiple paths used for the same infor-mation flow, multiple servers to execute a task, etc.) will beof paramount importance to ensure reliability in large-scale networks. In addition, data may have different levelsof criticality, and there are messages that can occasionallytolerate losses [114]. Thus, the communication networkshould provide applications with the ability to selectbetween different priority levels for data transmissions.

� Data rate: The bandwidth requirements of smart metersand other sensors that are used in electric grids are typi-cally modest (each meter reading requires about 300 kbps).However, bandwidth demands are growing and the band-width used by smart meters, PMUs and other IEDs willprobably be in the range between 10kbps and 100kbps[110]. Then, the data rate of the communication channelcan become a serious concern, especially in the utility corebackbone, due to the high number of IEDs that are expectedto be connected to smart grids. It is also interesting to notethat many studies argue that it will be necessary to over-provision the communication infrastructure in terms ofchannel bandwidth to ensure low transmission delaysand reduce packet losses on transmission buffers [16].

5.1.2. Qualitative requirementsThe key capabilities for smart grid communications can be sum-

marized as follows:

� Scalability: A smart grid can involve millions of users andeven more devices. Thus, scalability is probably one of themost intuitive requirements for the smart grid communica-tion system. However, there are different types ofscalability, like load scalability (i.e., the ability of the com-munication system to easily handle an increasing amountof data traffic or service requests) or geographic scalability(i.e., a network that is deployable in a wide range of sizesand configurations). Similarly, different measurements ofscalability can be considered, such as the size of routingtables as the number of nodes increases, or the amount ofcommunication resources used by each node [115,16]. Dis-tributed communication architectures have emerged in thepast to support Internet services in a scalable manner, such

as peer-to-peer networks (P2P) [107], which could also beapplied in the smart grid context. However, the scalabilityissue in smart grids is further exacerbated by the fact thatmost of the grid devices will be limited in terms of storage,computing and communication capabilities.

� Interoperability: As pointed out above, and better describedin the following sections, many different devices, commu-nication technologies and networking protocols will beused in smart grids. Therefore, it is essential to ensure theinteroperability between those different communicationnetworks. Standards and open network architectures (i.e.,using non proprietary protocols like IP-based networks)will play a key role for interoperability at all the layers ofthe network architecture [5]. On the other hand, network-ing elements could be introduced to provide translationservices between different standards (e.g., network gate-ways that route packets between different networks thatuse separate protocols). It is important to note that net-work interoperability is one face t of the interoperabilityproblem, but application interoperability is also essential.For instance, application interoperability requires stan-dards to ensure that applications assign the same meaningto exchanged messages.

� Flexibility: Flexibility of the smart grid communication sys-tem is a multi-faceted concept. On the one hand, flexibilityentails the ability to support heterogeneous smart grid ser-vices, which have different reliability and timelinessrequirements. On the other hand, flexibility also impliesthe ability to provide different communication models.For instance, multipoint-to-point (MP2P) communicationsare important in monitoring applications, which requireto periodically and simultaneously collect status informa-tion from a large number of sensors. Point-to-multipoint(P2MP) communications, or more in general group-basedcommunications, are equally important because are usedto distribute commands and configuration instructions toelectric devices [116,14]. In summary, there is a need fornetworking technologies and protocols with a high degreeof flexibility and (self-) adaptability because the same com-munication infrastructure must satisfy the requirements ofdifferent applications running on top of it.

� Security: A smart grid is a critical infrastructure that needsto be robust against failures and attacks. Thus, stringentsecurity requirements are imposed on its communicationinfrastructure in terms of resilience to cyber attacks andprotection of customers’ privacy [117]. For instance, thecommunication system must ensure that devices are wellprotected by physical attacks, unauthorized entities cannothave access to the metering information, or that sensitivedata cannot be modified while in transit in the network.Similarly, the communication system must be robustagainst network attacks that aim at disrupting the commu-nication services to damage electricity provision. Authenti-cation, encryption, trust management, and intrusiondetection are examples of important security mechanismsthat must be supported in smart grids to prevent, detectand mitigate such network attacks [20].

5.2. Communication technologies and standards

Communication technologies can be classified into two broadcategories: wired technologies and wireless technologies. Gener-ally speaking wired technologies are considered superior to wire-less technologies in terms of reliability, security and bandwidthbecause cables are easier to protect from interference andeavesdroppers. Furthermore, the equipment is generally cheaper


compared to wireless solutions, as well as the cost of maintenance.On the other hand, wireless networks ensures low installationcosts and flexible deployments with minimal cabling, which areessential characteristics to rapidly provide network connectivityover wide areas or in areas where there is not a pre-existing com-munication infrastructure. Moreover, new approaches have beenproposed to improve the energy efficiency of mobile-connected de-vices [118]. In addition, recent advances in broadband wirelesstechnologies are providing data rates and network capacities com-parable to those of popular wired networks. For these reasons,electric utilities are increasingly relying on wireless technologiesto build their communication infrastructures [12].

The rest of this section is dedicated to analyze the most impor-tant wired and wireless technologies and standards that can beutilized in a smart grid. Furthermore, a summary of the mainfeatures of wired and wireless communication technologies are re-ported in Table 2 and Table 3, which compare those two classes ofcommunication technologies in terms of: standardization activi-ties, maximum data rates, transmission ranges, frequency bands,and applicability scope in the smart grid communication system.The tables also summarize the advantages and disadvantages ofeach technology.

5.2.1. Wired technologiesTraditionally, wired communication technologies were

preferred by utility operators because were considered the mostreliable option for a communication network. The most importantwired technologies that are used in smart grids are:

Power line communications (PLC). PLC technologies utilize exist-ing power cables for information exchange [55]. This allows utilitycompanies to use a single infrastructure for both power and datatransmission. For this reason, PLC systems have been proposed asa cost-effective and straightforward solution to grid communica-tion needs. As an example, the majority of AMR deploymentsaround the world are using PLC technologies for transmittingmetering data [46]. However, the use of power lines to providereliable data transmissions has to face a number of technical chal-lenges due to the signal propagation characteristics of typicalpower cables, such as high signal attenuation, disruptive interfer-ence from other power signals, including nearby electric appli-ances or external electromagnetic sources [119,120]. It is alsoimportant to note that there are two major families of PLC technol-ogies that operate in different bands and have different capabili-ties. More precisely, there are narrowband PLC (NB-PLC)technologies that operate in transmission frequencies of up to500 kHz. Within this frequency range, the resulting data rates aremodest, from 1 bps to �10 Kbps up to �500 Kbps. NB-PLC can beused on both high and low voltage lines, and trial deploymentshave demonstrated that it is possible to cover very large distances(150 km or more) [75]. The other category of PLC solutions includesbroadband PLC (BB-PLC) technologies, which target significantlyhigher bandwidth performance than NB-PLC (up to 200 Mbps) byoperating over much higher frequency bands (2–30 MHz). On thedownside, the higher frequency bands that are used in BB-PLCreduce the maximum coverage and reliability of data communica-tions. Thus, BB-PLC is mainly considered for in-home applications.Nevertheless, device manufacturers have recently announced newBB-PLC modems that can support data rates of about 10 Mbps upto distances of 8 km over transmission lines [55].

Many standards have been developed or are under specificationfor the PLC systems described above. The most important indus-trial association that provides widely-adopted technology specifi-cations for in-home PLC systems is the HomePlug PowerlineAlliance[121]. Over the past years this standardization group hasreleased several standards, which have progressively enabledincreasing channel rates, starting from 4 Mbps (HomePlug 1.0) to

85 Mbps (HomePlug Turbo), and, more recently, 200 Mbps (Home-Plug AV and AV2). It is useful to note that HomePlug AV is a quiteadvanced technology that not only provides high-quality, multi-stream networking over power lines, but it also offers severalco-existence operational modes. For instance, it is backward com-patible with HomePlug 1.0, it enables inter-networking withdevices using the IEEE 1901 standard (also known as Broadbandover Powerline (BPL) technology) [119,105], and it is the firststandard to allow hybrid home networks, combining wired andwireless devices [122]. Finally, the HomePlug Alliance has alsodesigned the HomePlug Green PHY (GP) Specification, which is alower data rate, lower power version of HomePlug AV, fullyinteroperable with HomePlug AV and IEEE 1901 products,which is expected to significantly reduce power consumptionsand costs.

Optical communications. In the last decades optical communica-tion technologies have been widely used by electric utilities tobuild the communication backbone interconnecting substationswith control centers [3]. The major advantages of this communica-tion technology are: (a) its ability to transmit data packets overdistances in the order of several kilometers providing a total band-width of tens of Gbps (by aggregating multiple individual fibers),and (b) its robustness against electromagnetic and radio interfer-ence, making it suitable for high-voltage environments. For in-stance, several restoration and protection schemes have beendevised for optical grids, which can overcome simple network fail-ures by providing backup paths [123]. In addition, a special type ofoptical cables, called Optical Power Ground Wire (OPGW), com-bines the functions of grounding and optical communications,allowing long-distance transmissions at high data rates. Thus,OPGWs have been used in the construction of transmission anddistribution lines [124]. It is reasonable to believe that fiber-opticcommunications will play a key role also in smart grids. Recentstudies are also expanding the scope of optical communicationsby proposing the use of optical fibers to provide smart grid servicesdirectly to end customers [125,126], although the cost of fiberinstallation is recognized as an obstacle for the adoption of thistechnology. It is important to point out that the use of optical com-munications in access networks, also known as fiber-to-the-home(FTTH), is made possible by the advent of passive optical network(PON) technologies. Specifically, PONs do not require electricallypowered switching equipment but they use only optical splittersto separate and collect optical signals as they move through thenetwork. Furthermore, PONs enable a single optical fiber to servemultiple premises in a point-to-multipoint fashion, which permitsto support network topologies suitable for access networks (e.g.,tree-based topologies) [127]. Among the many variants of PONtechnologies, Ethernet PON (EPON) has been attracting much inter-est from grid operators because it enables the use of the standardEthernet communication protocol over an optical network. This of-fers significant benefits over other PON solutions because it facili-tates the interoperability with existing IP-based networks.

Digital Subscriber Lines (DSL). DSL generally refers to a suite ofcommunication technologies that enable digital data transmissionsover telephone lines. The main advantage of DSL technologies isthat electric utilities can interconnect residential users to controlcenters avoiding the additional cost of deploying their own com-munication infrastructure. On the downside, a communicationfee must be paid to the telecommunications operators maintainthe network infrastructure. Note that there are a number of DSLvariants, ranging from basic Asymmetric DSL (ADSL), which sup-ports up to 8 Mbps in the downstream and up to 640 kbps in theupstream, to ADSL2+ with a maximum theoretical download andupload speed of 24 Mbps and 1 Mbps respectively. Very-high-bit-rate DSL (VDSL or VHDSL) provides faster data transmission overcopper wires (up to 52 Mbps downstream and 16 Mbps/s

Table 2Summary of wired communication technologies for smart grids.

Family Standards Data rate Coverage Scope Advantages Disadvantages

PLC � NB-PLC: ISO/IEC 14908–3 (Lon-Works), ISO/IEC 14543–3-5(KNX), CEA-600.31 (CEBus), IEC61334–3-1, IEC 61334–5 (FSK andSpread-FSK)� BB-PLC: TIA-1113 (HomePlug 1.0),

IEEE 1901, ITU-T G.hn (G.9960/G.9961)� non-SDO NB-PLC: Insteon, X10,

G3-PLC, PRIME� non-SDO BB-PLC: HomePlug AV/

Extended, HomePlug Green PHY,HD-PLC

� NB-PLC: 1–10 Kbps for low data-rate PHYs, 10–500 Kbps for highdata-rate PHYs� BB-PLC: 1–10 Mbps (up to

200 Mbps on very short distance)

� NB-PLC: 150 km ormore� BB-PLC: �1.5 km

� NB-PLC: large-scaleAMI, FAN, WAN� BB-PLC: HAN/

small-scale AMI

� Large-scale communication infra-structure is already established� Physical separation from other tele-

communications networks� Low operational costs

� Multiple non-interoperabletechnologies� High signal attenuation and

channel distortion� Disruptive interference from

electric appliances and otherelectromagnetic sources� Difficult to support high bit

rates� Routing is complex� Standards evolve relatively

slowly

Optical Fibers � AON: IEEE 802.3ah� PON: ITU-T G.983 (BPON), ITU-T

G.984 (GPON) IEEE 1901, IEEE802.3ah (EPON)

� IEEE 802.3ah (AON): 100 Mbps up/down� BPON: 155–622 Mbps up/down� GPON: 155–2448 Mbps up, 1.244–

2.448 Gbps down� EPON: 1 Gpbs up/down

� IEEE 802.3ah(AON): up to 10 Km� BPON, GPON: up to

20–60 Km� EPON: up to 10–

20 Km

� WAN� AMI (with

FTTH systems)

� Long-distance communications(much longer than DSL)� Ultra-high bandwidth (suitable for

supporting multimedia services toresidential customers)� Robustness against electromag-

netic and radio interference

� High network deployment costs(lower with PONs than AONs)� High cost of terminal

equipment� Difficult to upgrade� Not suitable for metering

applications

DSL � ITU G.991.1 (HDSL)� ITU G.992.1 (ADSL), ITU G.992.3

(ADSL2), ITU G.992.5 (ADSL2+)� ITU G.993.1 (VDSL), ITU G.993.1

(VDSL2)

� ADSL: 8 Mbps down and 1.3 Mbpsup� ADSL2: 12 Mbps down and up to

3.5 Mbps up� ADSL2+: 24 Mbps down and up to

3.3 Mbps up� VDSL: 52–85 Mbps down and 16–

85 Mbps up� VDSL2: up to 200 Mbps down/up

� ADSL: up to 4 km� ADSL2: up to 7 km� ADSL2+: up to 7 km� VDSL: up to 1.2 km� VDSL2: 300 m

(maximum rate) –1 Km (50 Mbps)

� AMI� FAN

� Large-scale communication infra-structure is already established� Most commonly deployed broad-

band technology for residentialcustomers

� Telco operators can charge util-ities high prices to use theirnetworks� Not suitable for network back-

haul (long distances result intodata rate degradation)

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Table 3Summary of wireless communication technologies for smart grids.

Family Standards Data rate Coverage Scope Advantages Disadvantages

WPAN � IEEE 802.15.4� Non-SDO: ZigBee, WirelessHART,

ISA 100.11a (all based on IEEE802.15.4)

� IEEE 802.15.4: 256 Kbps � IEEE 802.15.4: Between 10 and 75 m � V2G� HAN� AMI

� Very low power consumption� Cheap equipment� Suitable for devices with low

memory and computing power� New standards provide full

interoperability with IPv6-based networks

� Low bandwidth� Do not scale to large networks

WiFi � IEEE 802.11e (QoS enhancements)� IEEE 802.11n (ultra-high network

throughput)� IEEE 802.11s (mesh networking)� IEEE 802.11p (WAVE - wireless

access in vehicular environments)

� IEEE 802.11e/s: up to54 Mbps� IEEE 802.11n: up to

600 Mbps

� IEEE 802.11e/s/n: up to 300 m(outdoors)� IEEE 802.11p: up to 1 Km

� V2G� HAN� AMI

� Low-cost network deployments(unlicensed spectrum)� Cheap equipment� High flexibility, suitable for dif-

ferent use cases

� High interference since it operatesin a very crowded unlicensedspectrum� power consumption might be too

high for many smart grid devices� Simple QoS support (basically traf-

fic prioritization)

WiMAX � IEEE 802.16 (fixed and mobilebroadband wireless access)� IEEE 802.16j (multihop relay)� IEEE 802.16 m (advanced air

interface)

� 802.16: 128 Mbps downand 28 Mbps up� 802.16 m: 100 Mbps for

mobile users, 1 Gbps forfixed users

� IEEE 802.16: 0–10 km� IEEE 802.16 m: 0–5 (optimum), 5–30

(acceptable), 30–100 (reduced perfor-mance) km

� AMI� FAN� WAN

� Suitable for thousands of simul-taneous users� Longer distances that WiFi� A connection-oriented control

of the channel bandwidth� More sophisticated QoS mecha-

nisms than 802.11e.

� Network management is complex� High cost of terminal equipment� Use of licensed spectrum

3G/4G � 3G: UMTS (HSPA, HSPA+)� 4G: LTE, LTE-Advanced

� HSPA: 14.4 Mbps down and5.75 Mbps up� HSPA+: 84 Mbps down and

22 Mbps up� LTE: 326 Mbps down and

86 Mbps up� LTE-Advanced: 1 Gbps

down and 500 Mbps up

� HSPA+: 0–5 km� LTE-Advanced: 0–5 (optimum), 5–30

(acceptable),30–100 (reduced perfor-mance) km

� V2G� HAN� AMI

� Able to support tens of millionsof devices� Low power consumption of ter-

minal equipment� Cellular operators are launching

smart gridspecific servicesolutions� High flexibility, suitable for dif-

ferent use cases� Use of licensed spectrum

reduces interference� Open industry standards

� Cellular operators can charge util-ities high prices to use theirnetworks� Use of licensed spectrum increases

cost� Difficult tot ensure delay

Satellite � LEO: Iridium, Globalstar,� MEO: New ICO� GEO: Inmarsat, BGAN, Swift,

MPDS

� Iridium: 2.4 to 28 Kbps� Inmarsat-B: 9.6 up to

128 Kbps� BGAN: 384 up to 450 Kbps

� Depend on number of satellites andtheir beams

� AMI� WAN

� Long distance� Highly reliable

� High cost of terminal equipment� High latency

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upstream), and on coaxial cable (up to 85 Mbps down- and up-stream), but it can only operate over short distances (about1.2 km). Second generation systems (VDSL2) promise to obtaindata rates exceeding 100 Mbps in both upstream and downstreamdirections at a range of 300 m.

5.2.2. Wireless technologiesNowadays, there are different technologies and standards for

wireless communications, which can be easily classified based ontheir transmission ranges. In the following we overview the mostimportant wireless technologies that are applicable to smart gridsby going from the technology with the smallest coverage area tothe technology with the largest one:

802.15.4-based networks. The IEEE 802.15.4 technology [128] isthe reference standard that specifies the physical and MAC layersfor low-rate, low-power and low-cost wireless personal area net-works (LR-WPANs). Indeed, the basic IEEE 802.15.4 physical layeroffers data rates of 250 Kbps over distances of about 10 m,although alternate physical layer standards have been proposedthat allow higher communication throughput [129]. The supportednetwork topologies are star (single-hop), cluster-tree, and mesh(multi-hop). In each type of topology there is a special node, calledPAN coordinator, which is responsible for managing the entire net-work. Networks with tree or mesh topologies have also specialnodes, called routers, that relay messages by establishing multi-hop connections between end devices and PAN coordinator. Thestandard also defines two different channel access methods thatprovide support for different power management mechanismsand channel sharing algorithms. Furthermore, within the 802.11working group, a new task group, called 802.15.4 g, has been estab-lished to design PHY layer enhancements to legacy 802.15.4 suit-able for smart utility networks (SUNs) [130]. However, it is alsoknown that IEEE 802.15.4 power management mechanism can leadto very low packet delivery ratios if the MAC parameters setting isnot appropriate [131]. A distributed algorithm is proposed in [132]to autonomously configure the 802.15.4 MAC layer to minimizethe power consumption while meeting the reliability requirementsof applications.

It is important to point out that the IEEE 802.15.4 standard isthe basis for many other industrial standards for monitoring andcontrol applications. Among these industrial efforts the mostimportant ones are the ISA 100.11a standard [133], the Wireless-HART standard [134], and the ZigBee standards [135]. More specif-ically, the first two standards are primarily designed for industrialautomation and control systems. They both use 802.15.4-basedradios but they replace the 802.15.4 MAC protocol with a colli-sion-free TDMA-based scheme. Furthermore, they both includeadditional adaptation layers to support distributed command-and-control applications. Note that WirelessHART is not acompletely new technology but it is a backward-compatibleenhancement of the HART Communication Protocol, which is anopen standard commonly used in the automation industry forthe last 20 years. In addition, in 2010 WirelessHART has been ap-proved by the International Electrotechnical Commission (IEC) asan international standard (IEC 62591 [134]). Note that one of themain advantages of IEEE 802.15.4 over other short-range radiotechnologies is the very low power consumption [136]. However,ZigBee is certainly the most widely adopted technology for LR-WPANs in both industrial and commercial environments becauseit is considered simpler and less expensive than other solutions.Specifically, ZigBee is a specification for a suite of protocols that ex-tend the IEEE 802.15.4 standard with additional network manage-ment capabilities, security functions and application supportsublayers. One of the most important features of the ZigBeestandard is the definition of application profiles that allow multiplevendors to create interoperable products. These profiles provide a

description of: (a) the devices supported for a specific application,and (b) the data formats, message types and communication mod-els to be used by those devices. Relevant to smart grid is the ZigBeeSmart Energy Profile (SEP), which provides an interface for manag-ing appliances that monitor, control, and automate the deliveryand use of energy [137]. At the time of the writing two SEP versionsexist, SEP 1.x and SEP 2.0. SEP 2.0 was developed in cooperationwith other standardization groups, such as IPSO (IP for Smart Ob-jects) [138] and HomePlug [121] industrial alliances. It offersnew capabilities, such as control of plug-in hybrid electric vehiclesand apartment buildings. In addition, it integrates some IETF stan-dards, such as 6LowPAN and ROLL [139], which will ensure fullinteroperability between ZigBee networks and IPv6-based net-works (see Fig. 4). More details on those standards are reportedin Section 5.4.1.

IEEE 802.11-based networks (WiFi). The family of IEEE 802.11standards, also known as WiFi, is certainly the suite of wirelesscommunication technologies mostly used for home and local areanetworking (i.e., WLANs). The main reasons of this success are: (i)WiFi operates in unlicensed 2.4 GHz and 5 GHz frequency bands,(ii) WiFi uses simple and flexible access schemes based on CSMA/CA principles, and (iii) low-cost radio interfaces exist. The originalversion of the IEEE 802.11 standard was released in 1997 and clar-ified in 1999, but since then several amendments have been ap-proved adding new features and extended capabilities. Today thevast majority of WiFi radio interfaces are dual band and they havethe capability to transmit on the 5 GHz band using 802.11a physi-cal mode, and also in the 2.4 GHz band using 802.11b/g/n physicalmodes [140,141]. The highest data rates are supported by 802.11n,which integrates the OFDM-based transmission schemes used in802.11a/g with multiple-input multiple-output (MIMO) antennasto boost the maximum data rate of 54 Mbps supported in802.11a/g up to 150 Mbps. Given the large number of data ratesthat are available, several algorithms have been proposed for dy-namic rate adaptation [142]. Note that the transmission ranges de-pend on a number of factors including transmission powers,antenna types, indoor or outdoor environments, and modulationschemes. Experimental studies indicate that reasonable outdoorranges can be up to 300 m for 802.11n-based radio interfaces. Fi-nally, the flexibility of 802.11 standard allow their use in variousparts of the smart grid communication system, as better explainedin Section 5.3. On the downside, CSMA/CA access schemes are lessenergy-efficient than TDMA-based schemes (e.g., 802.15.4 MAC).Thus, various techniques have been devised to minimize the en-ergy consumption of the WLAN interface, including power savingmodes, packet compression and aggregation, or duty cycling dur-ing contention periods [143].

Besides 802.11n, there are three other standards in the IEEE802.11 family that are expected to be important for smart gridcommunications. The first one is the 802.11e standard [144] be-cause it offers QoS features (e.g, traffic prioritization, schedulingand admission control) that are suitable for delay-sensitive appli-cations. The second one is the 802.11s standard [145], which de-fines mechanisms to support multi-hop transmissions and tobuild wireless mesh networks on top of the 802.11 physical layer.Finally, the third one is the 802.11p standard [146], which definesenhancements to basic 802.11 standard to support wireless accessin vehicular environments. Thus, the 802.11p standard will be oneof the key enabling technologies for V2G systems.

IEEE 802.16-based networks (WiMAX). The IEEE 802.16 standard,commercialized under the name of WiMAX, was firstly released in2001 to support long-distance (up to 7–10 km) broadband (up to100 Mbps) wireless communications, especially in rural and sub-urban areas [147]. Conceptually, IEEE 802.16 is conceived as acomplementary technology to IEEE 802.11 because it is designedto support: (i) thousands of simultaneous users over larger areas,


(ii) a connection-oriented control of the channel bandwidth, and(iii) more sophisticated QoS mechanisms than the traffic categoriesdefined in 802.11e. On the downside, 802.16-based networks re-quire a more complex network management and they operate onlicensed frequency bands, which make 802.16 technology moresuitable for network operators. As for 802.11, different versionsof WiMAX technologies exist. The most recent version is the2009 release [148], which includes many advanced features suchas: (i) OFDMA, MIMO and various types of adaptive modulationand coding schemes, (ii) support for multicast and broadcast ser-vices, and (iii) seamless handover for nomadic users. In addition,different types of multi-hop relaying techniques are specified inthe 802.16j standard [149] to enable larger coverage areas andmore flexible deployments. Finally, an important evolution of the802.16 standard family currently under development is the802.16 m amendment [150], whose goal is to provide at least100 Mbps data throughput at high mobility (350 km/h) and 1 Gbpsat low mobility. Furthermore, 802.16 m will support handoverwith other radio access technologies, including 802.11 and cellular.Note that data rates and coverage ranges of 802.16 technologiesmake them suitable for connecting large facilities (e.g., powerplants) to utility control centers, as well as to deploy AMI networksin scarcely populated areas.

3G/4G cellular networks. One of the main advantages of publiccellular networks over other wireless communication technologiesis the larger coverage area. For these reasons, in the past, utilitieshave extensively used cellular technologies, such as GSM, GPRSand EDGE, for data communications in SCADA and AMR systems[151,152,45]. A shortcoming of cellular data services is that theyare relatively expensive. Furthermore, cellular networks generallyprovide variable throughput and latency performance, dependingon the number of other users served by the same base station.On the other hand, cellular networks are witnessing a rapid evolu-tion and new generations of cellular technologies supporting high-er data rates and more sophisticated data communication servicesare being developed. Today, the most widely commercialized mo-bile cellular systems are based on the third generation (3G forshort) of cellular technologies. 3G standards are developed andmaintained by the 3GPP industrial organization. UMTS systems,first offered in 2001, are still the most popular 3G standards.Among the various radio interfaces used in UMTS systems, thehighest speeds are provided by the Evolved High-Speed Packet Ac-cess standard (HSPA+) [15], which can support data rates up to168 Mbps in the downlink and 22 Mbps in the uplink. The succes-sors of 3G standards will be 4G systems, which are designed to en-able mobile ultra-broadband Internet access. A candidate 4Gsystem under development by 3GPP is the Long Term Evolution(LTE) - Advanced standard, a major enhancement of currently de-ployed LTE systems [153]. The main new capabilities introducedin LTE-Advanced with respect to previous 3G technologies include:(i) bandwidth and spectrum flexibility, (ii) an easier handoff be-tween different networks, (iii) better support for heterogeneousnetwork architectures ranging from macro-cells to femtocells,and (iv) more advanced mobile networking capabilities. Note thatanother candidate for 4G system is the 802.16 m technology,which was described in the previous section, although it is not tar-geting ubiquitous connectivity as LTE does.

Satellite. Satellite systems support communications with vari-able bandwidth and latency performance using satellites stationedon orbits at different altitudes, including Low Earth Orbits (LEO),Medium Earth Orbit (MEO), and Geostationary Earth Orbits (GEO)[154]. Satellite systems using high altitude orbits have the advan-tage of not requiring tracking antennas, which are expensive. Thedownside is that they are affected by higher transmission delays(up to one second if link-layer acknowledgments are used). Onthe other hand, LEO satellite systems are less expensive to deploy.

Traditionally, electric utilities have considered satellite communi-cations for SCADA systems and other services provided in ruralor geographically remote locations, which are either beyond thecoverage of terrestrial communication networks, or it is difficult(and costly) to reach with dedicated fibers. Recent advances in sa-tellite systems, with the development of smaller and low-cost sta-tions, can open up new opportunities for the use of satellitecommunications in smart grids. For instance, satellite systemscan be used to provide backup communication services at criticalsubstations or backhaul transport services for AMI networks.

5.3. Communication network architecture

In Section 4 we have anticipated that the smart grid communi-cation infrastructure is commonly envisioned as a hierarchical net-work with a three-tier architecture, consisting of: (1) an access tier,(2) a distribution tier, and (3) a core tier. The purpose of this sec-tion is to further elaborate on this view and to present a detailedreference model for such communication infrastructure. This refer-ence architecture describes the communication capabilities thatshould be provided to electric devices at different tiers. Further-more, it explains how devices should be organized into networktopologies. In addition, we discuss how small-scale networks couldbe interconnected to build a large-scale communication systemproviding end-to-end connectivity over a regional or national area.Finally, this reference network architecture allows us to easily mapthe communication technologies described in Section 5.2 onto thedifferent components of the communication infrastructure (thismapping is also reported in Table 2 and Table 3). To guide the fol-lowing discussion in Fig. 5 we illustrate our reference architecturalmodel by giving an example of a possible smart grid communica-tion network. This example has been obtained by considering thesmart grid illustrated in Fig. 2 and substituting the energy flowswith information flows.

Access tier. The communication networks deployed in the accesstier of the smart grid communication infrastructure are responsiblefor: (i) enabling real-time information flows between end custom-ers and energy management systems, and (ii) allowing a more ac-tive role of end customers in the electricity market and the powergrid management. Therefore, home area networks (HANs) are veryimportant for the access tier because they can provide low-costsolutions to support monitoring and control of electric devices thatare deployed at customers’ premises. Both low-range wired andwireless technologies can be used to build such networks.However, WiFi is generally considered too expensive for mostHAN devices and it consumes too much power, while ZigBee isthe de facto standard used by many manufactures to providelow-power wireless communication capabilities to a variety of tinydevices (e.g., sensors) [155]. Nevertheless, HAN gateways shouldbe equipped with multiple radio interfaces to facilitate theintegration of different classes of devices. It is also important topoint out that similar concepts can be applied to networks withlarger scales such as Building Area Networks (BANs) and IndustrialArea Networks (IANs), which will be used in the access tier tomonitor and control the electricity consumption in buildings andindustrial facilities.

The access tier must also provide connectivity services suitablefor electric vehicles, which will have the twofold roles of mobileconsumers and mobile storage of electricity. In this case wirelesscommunication technologies are the most natural choice for sup-porting V2G scenarios. However, different use cases can be envis-aged that require different networking solutions. For instance,electric vehicles need to interact with HANs when parked at homesor offices, and WiFi-based LAN solutions appear the best option. Onthe other hand, electric vehicles can stay connected to the smartgrid communication system when on the move by using public

Fig. 5. An example of an end-to-end communication infrastructure for smart grids.


3G/4G cellular networks or a WiFi-enabled roadside communica-tion infrastructure [156]. More generally, vertical handover tech-niques can be used by moving vehicles to switch from onewireless network to another while maintaining seamless connec-tivity. A survey of the main vertical and horizontal handover ap-proaches in the literature can be found in [157,158].

Distribution tier. The communication networks deployed in thedistribution tier of the smart grid communication infrastructureare responsible for: (i) enabling the state estimation and real-timecontrol of the distribution grid, (ii) supporting the interconnectionof local area networks (i.e., HANs, BANs and IANs) with the smartgrid communication backbone, and (iii) providing the communica-tion support for implementing the data management services thatare needed to efficiently handle the large amount of data collectedin the distribution grid. As observed in Section 2.3, AMI networkswill be a key component of the distribution tier because they inter-connect smart meters with data aggregators and control systemsdeployed in the distribution grid. It is important to point out thatthere is not a single communication technology that can meetthe requirements of all AMI deployment scenarios, and utilitycompanies are considering both wired and wireless communica-tion technologies for building their AMI systems. Options includeconventional PLC technologies, point-to-point communicationsusing cellular or medium-range wireless (e.g., WiMax or WiFi)technologies; multi-hop wireless technologies, such as mesh net-working solutions, which can provide a flexible and easy to deployextension of the existing wired networks [159]. Given theheterogeneity of communication technologies, specific technolo-gies are needed to support internetworking and to provideseamless service provisioning. The IP Multimedia Subsystem(IMS) is a promising solution to address this issue, since it offersthe needed interworking environment and service flexibility forthe integration of wireless access technologies [160]. The 802.21standard is also supporting various mechanisms that can be usedto enable the seamless interoperation between heterogeneoustechnologies [161].

In addition to AMI networks the distribution tier will includespecialized networks to provide reliable communications to a largenumber of heterogeneous sensors and actuators that will be de-ployed in smart grids to monitor and control power system equip-ment (e.g., circuit breakers, feeders and substation transformers,DER units, etc.). These networks are commonly named Field AreaNetworks (FANs), or Neighborhood Area Networks (NANs) [14,16].In some cases the communication technologies used in FANs willnot be dissimilar from the ones that are considered for AMI net-works. However, FANs can also be considered an evolution of exist-ing SCADA-based networks that are used for power grid protection,and they will have more stringent real-time requirements thanAMI networks. Furthermore, elements in a FAN can be physicallydistant from each other. Thus, 4G technologies (e.g., LTE) will havea key role in most FAN deployments, while they are less importantin AMI networks [15].

Core tier. The core tier of the smart grid communication systemconsists of a Wide Area Network (WAN). Such high-capacity com-munication backbone is used to deliver the large amounts of datacollected by the highly dispersed AMI systems and FANs to remotecontrol centers over long distances. Various options have been con-sidered for the deployment of a WAN meeting the requirements ofsmart grid communications, such as all-IP core networks or MPLS-based networks. However, the fundamental choice that electricutilities are facing is between the deployment of private WANsand the use of public data networks. Several factors are influencingthe decision of grid operators and the need of high reliability, secu-rity and low latency are the most important ones, along with eco-nomical affordability. As a matter of fact, a growing number ofutilities are choosing to deploy a private hybrid fiber/wireless net-work as the backbone for their electric grid [162].

So far we have presented a general reference model of the net-work topology for the smart grid communication system. To con-clude this section we further elaborate on some specific areasthat are worth exploring: (i) the applicability of Internet conceptsand models to smart grid communications, (ii) AMI deployments,


and (iii) the applicability of multi-hop wireless technologies forsmart grid communications.

5.3.1. Similarities between the Internet and the smart gridcommunication infrastructure

In this section we discuss the fundamental similarities that mayexist between the architectural model of the Internet and the ref-erence model of the smart grid communication network that wehave described above [163,164]. To recognize those similarities isimportant because they motivate the adoption of Internet designprinciples when designing scalable, reliable and secure networkingsolutions for the smart grid [14,165,16]. For instance, both theInternet and the power grid have emerged from the incrementalinterconnection of an enormous number of (computing, sensingand electric) devices. Both the Internet and the power grid arehighly heterogeneous and wide-area complex systems, which mustsupport various degrees of autonomous control at different timescales. Finally, both the Internet and the power grid are witnessinga transition from a structure with a clear distinction between thecore network and the access network (with almost all the systemintelligence residing in the core) to a more federated system wherethe intelligence of the network (i.e., its ability to distribute, store,or modify information and energy, respectively) can be migratedto the periphery [166–168]. To cope with scalability, heterogeneityand decentralization requirements, the Internet architecture hasrelied on the interconnection of small-scale subnetworks, whichwere then organized into a hierarchy of networks covering largergeographic areas. As we have described earlier, such structure isalso conceived as the most suitable for smart grids in which indi-vidual electric subsystems have the responsibility of controllingseparate geographical regions (e.g. micro-grids). Then, such electricsubsystems can be easily mapped into separated communicationsubsystems interconnected with each other so as to form a hierar-chy of communication networks [169]. Furthermore, the IP stackhas proven its interoperability and extensibility as the basis ofthe global Internet over the last 30 years. Thus, Internet technolo-gies may be seen as a promising solution for the interoperabilityproblem in the smart grid communication infrastructure [91]. Onthe downside, there are well-known Internet problems (like secu-rity) that may hinder a tighter integration between the smart gridcommunication infrastructure and the Internet.

5.3.2. Examples of communication networks for AMI systemsThe network architecture model we have outlined above pro-

vides a unified conceptual framework to support end-to-end com-munication services in smart grids. However, in the literature thereare also several examples of specialized communication networksthat have been designed for specific components of the smart gridcommunication infrastructure. In particular, the design of commu-nication networks for AMI systems has attracted much researchinterest. A full-fledged network architecture for AMI systems is de-scribed in [170]. In that architecture the smallest communicationsubsystem is the HAN, which corresponds to an individual apart-ment. Then, a number of HANs are aggregated into a BAN, whileseveral BANs form a NAN. Distinguishing features of this networkarchitecture are the use of: (i) low-power wireless communicationtechnologies in the HANs, (ii) different types of gateways to controlthe communications of increasingly larger areas and to facilitatethe interconnections of different subsystems, and (iii) cellular(3G) technologies to enable communications between multipleBAN gateways and a NAN gateway. Note that in [170] it is alsoenvisaged that the 3G network interconnecting the gateways is adedicated network separated from public cellular systems to en-sure improved safety and reliability.

AMI networks similar to the one proposed in [170] have beenalso described in [171,83,45,172]. In particular, in [171] data

management services are deployed within the NAN gateways,which are called concentrators, to support aggregation and storingof the information flows coming from multiple BANs. Furthermore,a base station is deployed to serve an area containing multipleNANs, which receives the data sent by NAN concentrators overdedicated WiMAX channels, and forwards this information to theregional control center over a wired network (e.g., using the Inter-net core backbone). In general, the trend is to prefer licensed wire-less technologies, such as WiMAX [11,83] and GSM/GPRS [45,172],for the communications in the neighborhood area networks, be-cause grid operators can establish their own private networkinfrastructure, which makes easier to guarantee the required com-munication reliability and QoS. On the other hand, unlicensedwireless technologies, such as ZigBee [173,174,172,175,176] andWiFi [177], are the main standards that are being considered forcommunications within houses or buildings, although they havebeen also proposed as less expensive options for supporting com-munications in NANs [178]. As observed in Section 5.2, some ofthose standards have been also recently extended to provide spe-cific support for the interoperability of devices that will be usedin smart grids, such as the ZigBee SEP [179,137].

5.3.3. Multi-hop wireless networking in smart gridsIn this section we discuss the potential role that the multi-hop

communication paradigm can have in a smart grid. Specifically, wefocus our attention on two of the most mature and consolidatedexamples of networking technologies using multi-hop wirelesscommunications: wireless mesh networks and wireless sensor net-works. Interested readers can find an up-to-date overview of re-search and development in the broader area of networks basedon the multi-hop ad hoc networking paradigm in [180].

Wireless mesh networks. A wireless mesh network (WMN) is adynamically self-organized network in which nodes automaticallyestablish and maintain network connectivity through multi-hopwireless paths. There is a general agreement that wireless meshnetworking is a key enabling technology for most next-generationwireless networks [159]. WMNs have the potential to bring manyadvantages also to the smart grid communication system andmany studies have advocated the use of WMNs in smart grids[11,16]. For instance, a multi-hop wireless communication systeminherently provides multiple redundant paths between any pair ofcommunicating nodes. This eliminates single point of failures andmitigates the occurrence of bottleneck links, increasing the com-munication reliability with respect to conventional infrastruc-ture-based wireless networks [181,182]. Furthermore, multiplepaths can be used to achieve a more balanced distribution of trafficover the network [183] or to minimize the energy used by themesh backbone to route traffic [184]. Note that the use of self-organizing networking technologies allows to provide self-healingcapabilities to the communication network, which is necessary tomeet the reliability requirements of smart grid communications.At the same time, wireless multi-hop communications are a low-cost solution to increase the coverage of existing wired networksand to deal with the difficulties of deploying new cables in existingfacilities. Finally, as observed in Section 5.2 most popular wirelesstechnologies, including WiFi, ZigBee and WiMAX, are now includ-ing amendments to their standards to provide support formulti-hop communications (e.g, 802.11s and 802.16j). Thus, meshnetworking capabilities are expected to be supported by mostcommodity products. Recently, millimeter-wave links that operatein the 71–86 GHz frequency band have also been proposed as acost-effective high-speed alternative for fixed wireless mesh net-works, with data rates up to 1 Gbps [185].

Wireless sensor networks. A wireless sensor network (WSN)consists of autonomous sensor devices monitoring physical orenvironmental phenomena, which employ multi-hop wireless


communication technologies to cooperatively transfer their read-ings to a common sink or gateway. Most advanced sensors can alsoperform simple actions (e.g., close a circuit). In this case the termwireless sensor and actuator network (WSAN) is generally pre-ferred to denote a wireless network that is able to perform distrib-uted sensing and acting tasks. As observed in Section 3.1, WSNs areexpected to play a key role in enabling the pervasive monitoring ofelectric grids. It is important to point out that WSN is not a novelconcept. There are more than two decades of intensive researchin this field and several surveys exist in the literature that outlineexisting approaches for routing and data collection in WSNs [186–188]. In particular, energy awareness is a very important designconsideration for protocols and algorithms in sensor networks,and the design of a WSN should consider the amount of energyeach protocol can spend to perform its tasks [189]. In addition toenergy efficiency, there are many other technical challenges to ad-dress before successfully applying WSN technologies in the smartgrid domain. Specifically, the harsh environmental conditions oftypical power systems, such as medium-voltage substations orpower control rooms, pose significant issues to the reliability andlatency of wireless communications, which also impact on the de-sign of communication protocols for WSNs. Most importantly, inmany classical applications for wireless sensor networks the goalis to detect the occurrence of an event (e.g., when the ambient tem-perature is above a given threshold), and it is sufficient that a smallsubset of the sensors that have observed the same event are able toreport it to the application. This characteristic enables the use ofmechanisms such as duty-cycling and data fusion to reduce spa-tially-correlated contention between nodes in the same neighbor-hood, as well as to improve energy efficiency and network lifetime[190]. On the contrary, in many smart grid applications the senseddata of each individual sensor might be needed. For instance, this isthe case of smart metering applications, which must collect infor-mation from each smart meter. This means that existing data col-lection techniques for wireless sensor networks should bemodified to meet this additional constraint. Furthermore, in wire-less sensor and actor networks there is a need for suitable coordi-nation mechanisms between sensing and control functions toensure that correct actions can be taken within the strict time con-straints of most power grid processes.

We conclude this section by pointing out that most WSNs forsmart grids will be deployed in conditions that can make very dif-ficult to replace batteries. Furthermore, maintenance costs due tothe replacement of a large number of batteries may be excessive.To cope with these difficulties, many studies advocate the use ofenergy harvesting techniques to allow sensor nodes to take powerfrom the surrounding environment. Energy harvesting covers awide range of different technologies. Solar energy is the most obvi-ous power source and photovoltaic cells can be used to obtainpower from it. However, this solution is not applicable indoors.In smart grid environments electromagnetic induction seems themost feasible technology, but there are other approaches that canwork as well. For instance, using the difference in temperature

Fig. 6. Classification of research trends in the area of rou

with thermal energy harvesting can be effective in sub-stations,where there is a significant temperature gradient. For sensors de-ployed in buildings, vibrational energy can also be used. The down-side of energy harvesting technologies is that they are still unableto provide a sustained energy supply to support continuous oper-ation. Therefore, new power management schemes, transmissiontechniques, data aggregation and link scheduling algorithms, androuting protocols are needed to exploit the sporadic availabilityof energy [191–193].

5.4. Communication protocols

To allow communications in a distributed network formed bydevices from different vendors and using heterogeneous communi-cation technologies, it is necessary to specify a suite of protocolsto: (i) associate node identities to network devices (naming andaddressing functions), (ii) establish network paths between nodes(routing function); (iii) define message formats and rules to ex-change those messages (transport function); and (iv) support ad-vanced networking services such as broadcast, multicast, QoSand security. A comprehensive overview of all the network proto-cols and services that have been proposed in the literature forsmart grid communications might require a separate survey perse. Thus, we focus on three important areas that we believe still en-tail major research challenges: (1) routing protocols, (2) QoS sup-port, and (3) transport protocols.

5.4.1. Routing protocolsAs observed above, routing protocols are responsible for estab-

lishing communication paths among nodes in a network. As dis-cussed in Section 5.3, in a smart grid different classes of devices,communication technologies and network topologies will be useddepending on applications and use cases. This implies that multi-ple routing protocols should be employed to enable smart gridcommunications in the different networks forming the smart gridcommunication infrastructure. It is important to point out that inthe past electric utilities preferred to avoid using routing protocolsto communicate with field devices due to potential ðaÞ real-timeviolations and ðbÞ security attacks. However, as motivated in Sec-tion 5.3 electrical grids are progressively migrating towards IP-based network architectures. Therefore, it is expected that routingprotocols will be increasingly more important, especially in the ac-cess tier of the smart grid communication system.

It is out of the scope of this survey to outline all the differentrouting protocols that have been proposed so far for the variousnetwork categories described in Section 5.3. Interested readerscan find a comprehensive survey of routing protocols for HANand NANs in [28]. On the contrary, in the following we focus ourattention on routing and data forwarding schemes for networksusing wireless multi-hop communications (i.e., WMNs and WSNs)because we believe this is the area in which most researchchallenges are yet to be addressed, especially as far as QoS supportis concerned. For the sake of clarity, in Fig. 6 we provide a

ting for WMNs and WSNs in the smart grid context.


classification of the research trends in the area of routing for smartgrids, which we have analyzed in the following.

In the literature there exists a large amount of work on routingfor ad hoc networks, as testified by the considerable number of dif-ferent protocols that have been proposed, and the standardizationof few of them by the IETF MANET working group. Furthermore, anumber of surveys can be found on this area, which provide com-prehensive classifications and comparisons of existing routing ap-proaches [187,194–196]. Relevant to this survey are the studiesthat are dedicated to the development of a more solid understand-ing on the applicability of those routing approaches in the smartgrid domain, and AMI systems in particular [197,177,198]. One ofthe main findings of those studies is that geographic routing strat-egies, which use geographical coordinates of networks nodes tooptimize the path selection (e.g., selecting as intermediate relaythe node that is geographically the closest one to the final destina-tion), seem the best schemes for smart grid communicationsbecause they minimize routing overheads and they work particu-larly well in static networks. For instance, the authors in [197]consider an exemplifying PLC network used in low- andmedium-voltage distribution grids, and show how different loca-tion-based routing strategies cope with variable link qualitiesand node failures.

Another research area related to routing in smart grid networksinclude the adaptation of routing schemes designed for general-purpose WMNs to the context of smart grid communications. In[199], the reliability of the AODV routing protocol is investigatedin a distribution grid topology that spans many kilometers. Anew multi-path routing protocol for multi-channel WMNs, calledDMMR, is proposed in [200], which tends to choose paths that donot share network nodes to reduce the probability that multiplepaths are broken at the same time when a single node fails. In thisway, DMMR ensures stable end-to-end connections. Other workshave explored the applicability of the Hybrid Wireless Mesh Proto-col, or HWMP, specified in the IEEE 802.11s standard [145], forrouting in WMNs that are used for the monitoring and manage-ment of electric grids. HWMP operates directly at the link layerand it uses MAC addresses for routing instead of IP addresses. Fur-thermore, it is an hybrid protocol that adopts a tree-based hierar-chical routing scheme for data transfers from network nodes tomesh gateways, and an AODV-like routing scheme for data trans-fers between network nodes. A known issue of HWMP is routeinstability in case of link failures and the work in [201] proposesnew routing metrics to reduce route fluctuations. A multi-pathextension to the tree-based routing strategy used in HWMP is alsodescribed in [202]. Alternative tree-based routing schemes are pro-posed in [203,204] by employing data forwarding mechanisms in-spired by distributed depth-first search algorithms. Furthermore,to reduce network control overheads those schemes use data pack-ets to detect loops and to propagate the information on failed links.In this way, routing tables can be updated quickly and with littleoverhead.

Another considerable amount of work has been conducted onthe design of routing protocols for WSNs that are able to cope withthe limitation of computing power and memory size of embeddeddevices. Indeed, many of the devices installed in the last mile of anAMI network, such as home appliances, IEDs, and smart meterswill be based on tiny micro controllers. Most of the above-de-scribed routing protocols for WMNs are not applicable to those de-vices because they perform complex computations and store alarge amount of routing information. For these reasons, popularwireless standards for sensor and control systems, such as ZigBeeand WirelessHART, have implemented much simpler routingschemes. Specifically, ZigBee specification include three routingprotocols: (1), a tree-based routing scheme for data collection atthe network sink, (2) a variant of the AODV algorithm that is used

to establish on-demand multi-hop network paths, and (3) a sourcerouting for communications between the sink node and the enddevices. It is also important to point out that in the last few yearsthere has been a growing interest from the research community indesigning optimized networking protocols for WSNs, which couldpotentially match the requirements of the smart grid (interestedreaders are referred to [186,187,205] for detailed surveys). IETFhad recently released a new standard specifying an IPv6-basedrouting protocol, called Routing Protocol for Low Power and LossyNetworks (RPL) [139], which is rapidly emerging as the most ma-ture and commercially viable routing solution for large-scale AMIsystems [206]. Specifically, RPL organizes the network topologyinto a tree-based structure (called Directed Acyclic Graph, DAG)and it adopts a gradient-based data forwarding scheme to mini-mize memory usage and protocol complexity. Given the impor-tance of RPL applications, a few papers have addressed theperformance evaluation of RPL in different use cases [207–210].In particular, results shown in [209] for a medium-scale outdoornetwork deployment indicate that RPL ensures smaller routing ta-bles and lower control overheads than conventional shortest-pathrouting algorithms. On the other hand, the study in [210] pointsout that RPL nodes may suffer from severe unreliability problems,mainly because RPL often selects sub-optimal paths with low qual-ity links. Other studies have focused on proposing enhancementsto the basic RPL standard. The work in [211] proposes to constructthe DAG assigning to each link in the network a cost depending onthe ETX metric, a popular routing metric initially proposed forwireless multi-hop routing protocols [212]. In [178] extensionsto RPL are proposed to enable smart meters to automatically dis-cover connectivity and recover from loss of connectivity. The effec-tiveness of the local repair mechanisms used in RPL is alsoinvestigated in [213]. However, RPL applicability in large-scale(thousands of nodes) networks with stringent reliability require-ments is still an open issue. In addition, the stability of DAG struc-ture with accurate PLC channel models and real-world trafficpatterns needs to be examined.

5.4.2. QoS supportIn Section 5.1 we have observed that reliability and timeliness

are key requirements for smart grid communications. However,different smart grid applications may have different constraintsfor latencies and communication reliability. For instance, in basicmetering applications a delay of few seconds when collecting me-tered data is tolerable, while applications monitoring transmissionlines should operate on a time scale of few milliseconds. Similarly,most grid protection applications, which involve the remote con-trol of critical grid components such as breakers and switches, re-quire very high levels of communication reliability to avoid powergrid instability. Consequently, the smart grid communication infra-structure should adopt suitable mechanisms to enforce differentQoS guarantees to network flows depending on the applicationconstraints. In telecommunications networks QoS differentiationis typically achieved through resource reservation and traffic prior-itization. Specifically, various approaches can be employed to pri-oritize important and time-critical network flows over lesscritical data traffic. For instance, many MAC layers (e.g., 802.11eand 802.16) support the specification of different traffic categoriesand they use scheduling algorithms to provide bandwidth differen-tiation [214,215]. However, MAC-based solutions are generallylimited to provide QoS guarantees on single communication links.For this reason, there is an increasing awareness that a full-fledgedQoS-based architecture is needed to satisfy the differentrequirements of smart grid applications. Several QoS-basedframeworks have been proposed for the Internet, such as IntegratedServices (IntServ) [216], Differentiated Services (DiffServ) [217], andMulti-Protocol Label Switching (MPLS) [218], which could be applied


also in smart grid communication networks. It is out of the scope ofthis work to present a description of those solutions. However, wewant to remark that it is still an open issue to decide which ofthose QoS-based architectures is the most appropriate for thesmart grid domain. The study in [219] proposes to use MPLS tech-nologies to handle fine-grained bandwidth management in smartgrids communication systems, while the study in [220] discusseshow to integrate MPLS with DiffServ. A multi-service routing archi-tecture is described in [221], which uses the DiffServ model in thedata plane, the RTCP protocol for performance monitoring andmulti-service path calculation in the control plane [221]. A detailedand fair comparison of different QoS approaches, and their testingin real-world deployment are still missing. Note that recently mostof the research efforts in this area have been focused on the devel-opment of optimization frameworks to compute network pathsthat satisfy multiple QoS constraints simultaneously (also knownas constrained-based routing or QoS routing), because this is anessential feature for any communication infrastructure that aimsat guaranteeing QoS. A comprehensive survey of existing algo-rithms for constrained-based routing can be found in [222]. Giventhe heterogeneity of the smart grid, traditional methodologies can-not be directly applied due to the requirements of high computingand storage capabilities. Thus, new schemes are needed that can beimplemented by both powerful and resource-limited devices. Pre-liminary results on QoS routing solutions specific to smart gridshave been developed in [223,224]. However, it is still an openproblem to understand the impact of power system dynamics onthe stability of QoS routing, or how to define QoS requirementsin the context of smart grid.

5.4.3. Transport protocolTransport protocols are responsible for delivering data among

application processes running on separate hosts in a network[225]. Besides simple inter-process communications there aremany other services that can be optionally provided by a transportprotocol, such as data integrity, congestion avoidance and flowcontrol. The simplest transport protocol in the Internet protocolsuite is UDP, which is connection-less (i.e., it does not set up a ded-icated end-to-end connection) and it does not provide any guaran-tee on packet delivery. However, UDP can be used tighter with theReal-time Transport Protocol (RTP), which provides specific mech-anisms to improve the delivery of audio and video over IP networks[226]. For instance, RTP packets carry information (e.g., timestamp) that allows to implement jitter compensation at the receiv-ers. Furthermore, RTP supports data transfer to multiple destina-tions through IP multicast. On the other hand, the most commonalternative to UDP is the Transport Control Protocol (TCP). TCP ismore sophisticated than UDP because: (i) it provides connection-oriented communications, (ii) it uses message numbering andretransmissions to recover packet losses and suppressed duplicatedata, (iii) it supports reordering of out-of-order data, and (iv) itimplements flow and congestion control techniques. Since themajority of smart grid applications require reliable communica-tions, TCP seems the natural choice also for the smart grid commu-nication system. However, TCP does not provide guarantees onnetwork delays experienced by transmitted packets. In addition,the timeouts used to detect some packet losses can cause notice-ably delay spikes. Thus, TCP cannot adequately meet the require-ments of smart grid communications in terms of timely datadelivery, especially for data traffic that it is inherently periodic(e.g., metering readings, measurements from PMUs, etc.). Further-more, data sources in most smart grid applications generate small-sized packets at a low rate. In this case, TCP congestion control canbe ineffective and it can cause useless retransmissions of packetsand throughput degradations. For these reasons, a few studies havebeen recently conducted to design either suitable modifications to

TCP or totally new transport protocols in order to achieve lower la-tency while preserving reliability in data-collection applications.One example is the scalable and secure transport protocol that isproposed in [227], called SSTP, which is suitable for monitoringapplications in which a large number of clients infrequently com-municate with servers. More specifically, one of the main goals ofSSTP is scalability, and this is achieved by assuming that the serverdoes not continuously maintain state information for each session(i.e., sensor device) but it encrypts the session state and it trans-mits the encrypted information to the associated client, whichtemporarily stores it. Then, the client returns to the server the en-crypted state with his next message. Furthermore, SSTP assumesthat in-order delivery is not required as data is always time-stamped at the sender side. Finally, a SSTP client can immediatelysend a message without any delay unless its sending window,whose size is determined by the number of clients associated tothe same server, is full. Results shown in [227] indicate that SSTPcan provide much lower end-to-end delays than TCP. An alterna-tive approach, called Split and Aggregated TCP (SA-TCP), is pro-posed in [228], which employs split and aggregation techniques.More specifically, in SA-TCP each client creates a TCP connectionwith a single intermediate node, called transport aggregator, whichis deployed before a bottleneck link. Then, this node aggregates allreceived data and it creates another TCP connection with the finaldata collector. In this way, a bottleneck can be shared betweenmany meters more fairly and the number of packet retransmis-sions is reduced. On the downside, TCP splitting approaches aremore vulnerable to security attacks, and packets may suffer longerdelays. An alternative congestion control technique is proposed in[229] by adapting the monitoring rate of smart meters to the avail-able bandwidth. The basic idea is to formulate an optimizationproblem to determine which is the amount of traffic that can be re-duced at different locations in the network without affecting thegrid operations. It is also useful to point out that there is a largebody of work focusing on reliable data collection protocols forWSNs (see [230,188] for survey papers on this area). However,those transport protocols can not be applied to smart grid monitor-ing applications without substantial modifications for a number ofreasons: (i) in smart grid each packet conveys unique informationassociated to a specific meter at a given time instant, thus dataaggregation techniques cannot significantly reduce the total vol-ume of data traffic to be delivered, and (ii) redundant deploymentis not a feasible solution for achieving reliability because smartmeters identify specific end users.

6. Middleware platforms for smart grids

Many smart grid applications will require a precise estimationof the state of large portions of the power system in order to con-trol and optimize the electricity delivery and usage. However, notall smart grid applications require the same sets of data, nor thesame reporting frequency. For instance, off-line trend analysis doesnot impose tight delay constraints, while control and protectionfunctions need real-time transmission of data (and commands).Therefore, scalable data management systems specialized forsmart grids are needed to share this large amount of information,and to timely deliver relevant data to applications that really needit [231].

In Section 2.1 we have pointed out that SCADA systems alreadyinclude solutions for storing and analyzing data collected from alarge number of RTUs. However, those solutions rely primarily oncentralized databases, which do not provide the level of scalabilityand flexibility needed to handle the huge increase in data storageand processing that will occur with smart grids. On the other hand,there is an increasing consensus among electric utilities that

Fig. 7. Classification of research trends in the area of middleware for smart grids.


middleware technologies are needed to build large-scale datasharing systems because they provide efficient data managementservices suitable for distributed environments. In addition, middle-ware can help to successfully handle the inherent complexity ofbuilding robust and autonomous control applications in heteroge-neous distributed systems [232]. For instance, existing middlewareplatforms provide various optimized tools and mechanisms forcooperatively controlling complex systems consisting of a largenumber of highly interconnected and interdependent componentsoperating in dynamic environments. In particular, multi-agentmiddleware platforms are drawing much attention from research-ers in the smart grid area because they appear the most suitabletechnology to provide self-adaptation and self-healing capabilitiesto smart grids [233].

In the following sections we will outline the most importantmiddleware platforms proposed so far for smart grid control appli-cations. Note that most middleware platforms are designed follow-ing a given model, or paradigm, which describes the approach usedto manage communications and distribution of resources. Thus, toguide the following discussion in Fig. 7 we also provide a classifi-cation of the related work based on three main categories: (i) mid-dleware services for data management; (ii) object-orientedmiddleware, and (iii) multi-agent systems.

6.1. Middleware services for data management

One of the earliest examples of a full-fledged middleware plat-form supporting data management for smart grid applications isknown as GridStat [234–236]. More specifically, GridStat providesa publish/subscribe communication model in which a grid objectcan be either a producer or a consumer of data. The consumerscan declare to the middleware system their interests in one ormore types of data (e.g., a data aggregator may be interested inreceiving at a higher frequency measurements from a set of smartmeters), while the producers simply announce the availability oftheir data. Then, the middleware platform is responsible for dis-tributing the relevant data to the subscribed consumers [237]. Itis important to observe that the communication primitives pro-vided by the middleware simplify the interactions between pub-lishers and subscribers because subscribers do not need to knowthe identity nor the location of publishers, and vice versa. Thisdecoupling is one of the main features that facilitate large-scaledeployments. In the case of GridStat, the publishers are eithersources of measurements and control settings (status variables),which are periodically updated by the middleware, or sources ofalarm conditions (status alerts) that require immediate attention.On the other hand, subscribers are application programs that usethese variables and alerts. Then, the GridStat middleware providesthe services needed to control the way information is disseminatedand accessed. For instance, a directory service is implemented toallow subscribers to find particular status variables of interest.Moreover, the publishers have the ability to inform the middle-

ware infrastructure about the semantic of the status variables theymonitor, such as type, availability frequency, and priority. Simi-larly, the applications can specify their QoS requirements for dataaccess, such as the maximum delay for receiving status updates,the minimum update frequency, or the maximum number of up-dates that can be lost per unit time. In order to meet these QoSrequirements directly at the middleware layer, the GridStat archi-tecture requires the deployment of two classes of special deviceswithin the smart grid:

� status routers, which establish redundant paths betweenthe middleware peers to support QoS-aware multicast ofperiodic status updates;

� brokers, which are organized into a hierarchy of manage-ment entities that negotiate with the subscribers their ini-tial QoS requirements in order to make them less stringentif necessary. In addition, brokers also handle the resourceallocation in the communication network to ensure thatthe selected paths meet the QoS requirements of each sub-scription request.

A Java-based implementation of key mechanisms in the Grid-Stat framework is described and experimentally evaluated in termsof delivery latency and throughput in [238]. Although GridStat of-fers a flexible and robust communication framework, it also suffersfrom a series of limitations. First of all, it only supports the publish/subscribe communication model, which is appropriate for deliver-ing status updates or alerts, while alternative communication ser-vices (e.g., remote procedure calls for invoking commands) areneeded in other use cases. In addition, GridStat relies on a rigidhierarchy of brokers that allocate resources at status routers andconfigure the network paths to meet the QoS application require-ments. This scheme can generate significant signaling overheads.Therefore, other studies have considered alternative approachesbased on self-organizing P2P technologies. For instance, a data-centric information infrastructure that uses a publish/subscribecommunication model to deliver time-sensitive data betweenEMSs and distribution substations is designed in [109]. However,in this case the publish/subscribe system is not implemented usinga centralized directory service as in [234], but through a distrib-uted content overlay created over a networked pool of storagedisks. In [109] it is assumed that these storage units are installedat substations but also sensors can be allowed to share part of theirmemory resources. Then, a distributed hash table (DHT) is employedfor efficient and scalable data retrieval. More specifically, a hashfunction is used to generate a unique key per each data item, whichis then stored and/or replicated among the peers in the networkbased on their identifiers. In this way, the overhead of both datastorage and data search is evenly distributed among peers. This ap-proach not only addresses the scalability issues caused by central-ized storage repositories, but it also ensures improved reliability byavoiding single points of failures. In general, DHT-based overlay


networks can ensure a very efficient retrieval of single data itemsdue to the use of key-based routing approaches [107]. Therefore,other papers have proposed to use DHTs for supporting large-scaledata sharing and decentralized data repositories [231,239]. Analternative approach is proposed in [240], in which a middleware,called SeDAX, is developed to enable secure, large-scale data shar-ing to support both transaction and query-based communications.Specifically, SeDAX leverages on the properties of Delaunay Trian-gulation (DT) graphs to design efficient message forwardingschemes based on geometric hash functions. However, the creationand maintenance of a structured overlay network that maps eachdata key to a peer in the overlay is generally incurring high signal-ing overheads. Thus, other studies argue that unstructured P2Pnetworks, which do not maintain a rigid overlay network, are moresuitable for smart grid applications in which data must be distrib-uted to a large number of interested parties at the same time [241].

6.2. Object-oriented middleware

The middleware solutions described in the previous sectionwere primarily targeting the scalability of data dissemination anddata sharing functions. However, as discussed in Section 5.1, inter-operability between different devices, networks and communica-tion technologies is another key requirement that smart gridcommunication systems must satisfy. The middleware paradigmthat is commonly considered the most suitable for supportinginteroperability between heterogeneous distributed systems isthe object-oriented paradigm [106]. Specifically, in object-orientedmiddleware platforms, like CORBA [242] or Ice [243], resources,processes and components are abstract objects that implementstandard interfaces, which hide all internal implementation detailsof the object. Furthermore, the object abstraction allows to invokeservices or make calls to procedures on remote systems by alsohiding the differences of underlying networking technologies. Thisdistributed object-oriented paradigm is used in CoSGrid (Control-ling the Smart Grid) [244], a middleware that provides supportto specify special remote objects called embedding metering de-vices (EMDs). An EMD implements sensing and controlling capabil-ities, share information via remote method invocations, and it canbe used to manage arbitrary smart grid components, from individ-ual appliances to entire substations. EMD prototypes for differenttypes of devices that work with CORBA and Ice are described in[232]. In addition, CoSGrid defines a set of interfaces for basicsmart grid services, such as metering, notification, node and dataaggregation, which can be used to ease the development of newdistributed smart grid applications. A similar approach is followedby OHNet (Objected-based Middleware for Home Network) [245],an object-oriented middleware for supporting the interoperabilitybetween home devices and smart grid devices. In this system homedevices can be associated to state, function, control or streamingobjects on the basis of their features, while discovery, connectionand management objects are used to activate related functionson home devices. Then, OHNet ensures interoperability among de-vices, which may adopt different communication protocols andtechnologies, through the definition of the Virtual Network Adap-ter (VNA). Specifically, VNA is an abstraction layer that instantiatesthe invocations of abstract methods into protocol-dependentimplementations. An alternative approach to build a Cyber-Physi-cal Home Control System, i.e., a system that allows users to controlappliances in the physical environment by intuitive operationthrough a virtual home on the network, is proposed in [246]. Thekey idea is to use an OSGi-based service architecture to supportservice-oriented remote control methods for home appliances.Note that the design of user-friendly and service-oriented HEMSsis a very active research area. For instance, the authors of [247] de-scribe a system to control and manage home appliances in the

framework of Universal Plug-and-Play (UPnP). Finally, the OSGiplatform is also used in [248] to construct a general middlewarefor IoT applications involving RFID tagged objects, sensor networksand pervasive computing technologies.

Finally, it is worth noting that in the power engineering com-munity there is an increasing consensus on the importance of theobject-oriented paradigm, especially for the specification of inter-operability standards. One example is the Common InformationModel (CIM) [97], which is a series of open standards released bythe International Electrotechnical Commission (IEC) to allow dif-ferent applications to exchange information about the status ofelectric grid components. For instance, CIM-based models havebeen proposed to exchange power system data between: (i) energymanagement systems (IEC 61970 standards [97]), (ii) electrical dis-tribution systems (IEC 61968 standards [98]), and (iii) intelligentelectric devices within substations (EC 61850 standards [249]).Then, CIM-based standards provide a common vocabulary toformally describe all the major components of a power system.Support is also provided to define multiple object classes andattributes, as well as the relationships between them [250,251].Furthermore, the CIM architecture supports composition ofdifferent classes and attributes, which facilitates model flexibilityand extensibility. However, it is important to note that CIM onlyfacilitates the exchange of power system data in object-orientedcommunication systems, but how to extract useful informationfrom an abstract representation of the power data is still an openissue.

6.3. Multi-agent intelligent systems

As observed at the beginning of this section, smart grids arecomplex systems consisting of a large number of heterogeneousand interdependent components operating in dynamic environ-ments. Therefore, decentralized management is considered theonly feasible approach to control the operations of electric gridsover multiple time-scales [252,235,2]. However, with decentral-ized control it is difficult to achieve global optimization objectives,such as efficiency and stability of the entire grid. On the otherhand, recently the emerging field of autonomic distributed comput-ing has produced innovative middleware technologies to buildintelligent distributed systems that are capable of managing,repairing, optimizing and protecting themselves without humanintervention [253]. Among the various approaches that have beenproposed so far to implement autonomic management capabilitiesin distributed environments, multi-agent systems (MAS in short) arethe most popular in the smart grid research community[254,4,255,233]. This is also demonstrated by the large numberof multi-agent systems that have been designed for a variety ofsmart grid applications, including power system restoration[256,257], fault diagnosis [258–261], management of distributedenergy resources [262–264], demand-side management [265],management of energy storage systems [266,78], optimization ofEV operations[267–269,53], substation automation [270], distribu-tion control [271], network monitoring [272,261] and visualization[273], electricity market simulation [274,275], profiling of powergeneration and energy usage patterns [276], management of mi-cro-grids and VPPs [277,262,62,278,279].

A MAS is a software system that is composed of multiple auton-omous components – the intelligent agents – that interact witheach other and react to environmental changes in order to accom-plish a given task (e.g., to control a physical resource, or to solve acomplex problem in a distributed manner) [280]. In the simplestcase agents are reactive objects that can only respond to signalsfrom the environment. In more advanced systems, agents can alsobe proactive in the sense that they are programmed to execute dif-ferent actions in order to achieve a global goal. In addition, agents


can cooperate and coordinate with each other in order to find thebest sequence of actions that could achieve that goal. To this endagents can employ various artificial intelligence techniques,including machine learning, fuzzy logic, neural networks or geneticalgorithms for local decision making. Although an agent can showa high degree of flexibility and autonomy because it can dynami-cally change its behavior to achieve a given goal, it is also subjectto an important limitation. Specifically, given the scale of the sys-tem to control the agent can only observe and measure small por-tions of the grid (i.e., the environment is only partially observable).This implies that an agent can not use global knowledge to makeits decisions. Finally, it is useful to observe that the environmentthat is observable by a MAS can be either physical (e.g., transmis-sion lines, generators of renewable energy, electrical appliances,etc.), and in this case it is observed through sensors; or virtual(e.g., databases, computing facilities, other agents), and in this caseit is observable through programming interfaces [255].

From the above discussion we can observe that the main advan-tage of agent-based technologies in smart grids is to provide adecentralized management solution based on autonomous localdecisions that can ensure a high level of flexibility and robustness.Although there are already many smart grid applications whereMAS technologies have been investigated, there are also unsolvedtechnical issues, which must be addressed in order to use this tech-nology in real-world deployments. In the following we outlinethree of the most important technical challenges.

6.3.1. Functional architectures for MASA number of different functional architectures have been pro-

posed in literature to build multi-agent systems [254], but thereis not yet a consensus on which one of those approaches is themost suitable for smart grid applications. It is important to observethat flexible, extensible, and open architectures, where agents can beeasily added or removed, should be preferred to closed architec-tures, where agent interactions are fixed at design time. Thearchitectural model most commonly adopted in power systems isthe multi-layered architecture. For instance, in [264] a three-layerarchitecture is proposed for managing distributed energy re-sources. In this model, the bottom layer consists of agents manag-ing physical resources (e.g., energy generators and power storagesystems). The middle layer includes agents that provide high-levelmanagement services (e.g., fault diagnosis, protection and restora-tion, optimization of power parameters, etc.) to the agents con-nected to the physical resources. Finally, the top layer containsthe agents handling the user interfaces. To improve the scalabilityof MAS solutions many studies have proposed to group agents,especially those operating on physical resources, into clusters(e.g., a micro-grid cluster or a VPP cluster). Indeed, clustering andcoalition formation are common techniques in multi-agent sys-tems for reducing architecture complexity. For instance in [269]coalitions are used to integrate in a more efficient way electricvehicles into the electricity grid. In this case an aggregator agent,called coalition server, is used to hide the details about the individ-ual vehicles, and to present a group of vehicles as a single resourceto grid operators.

Alternative two-layer architecture models are considered in[257] for power system restoration, and in [272,281] for the man-agement of the distribution grid. More precisely, in [257,272] twotypes of agents are considered: equipment agents and facilitatoragents. The former are controlling physical resources, such astransmission lines, transformers and phase controllers, while thelatter are used to promote the cooperation of the equipment agentsthat are associated to them. For instance, in [272] a facilitator agentis responsible for controlling an entire electric substation. A moreelaborate architecture is described in [270], which defines multipletypes of facilitators that perform different tasks such as: device

control, data acquisition and transfer, data analysis and data que-rying. The use of facilitator agents is also proposed in [259]. How-ever, in this case the facilitator agent is responsible for maintaininga list of services (or resources) that other agents in the system canoffer (or control). With the support of a nameserver agent, whichmaintains the names and locations of each agent (e.g., IP ad-dresses), a facilitator allows other agents to dynamically enter/leave the system and register/deregister their locations andcapabilities.

6.3.2. InteroperabilityWhen developing a multi-agent system it is essential to use

standardized agent models to allow agents to easily cooperate,irrespective of their different capabilities and functions, or the plat-forms used to develop them. For these reasons in the MAS commu-nity there is a significant body of work focusing on theformalization of:

(a) agent specification languages, which are standards for defin-ing messages types and agent interaction models. TodayFIPA-ACL is used by MAS developers as the de facto standardfor message exchange and interaction protocols [282];

(b) ontologies, which define a common ‘‘vocabulary’’ of termsand concepts that agents are able to exchange and interpret.

Currently, the trend in the community of MAS developers is toimplement application-specific ontologies. However, interopera-bility between multi-agent systems using different ontologies isdifficult to obtain, even if they run on the same platform or theyare based on similar concepts [283]. A solution to this problem isto employ a two-layer model for ontology specification. Specifi-cally, the ontology in the top level, called ‘‘upper’’ ontology in[283], is responsible for defining the basic concepts that are incommon to most smart grid applications (e.g., substation, switch,voltage, etc.), and it is used as a template for defining more specificontologies for different applications. It is also useful to note thatexisting object-oriented standards for information exchange inpower engineering applications, such as CIM [97] and IEC 61850[249], can be easily used as reference models when developingan upper ontology for smart grid applications.

6.3.3. Implementation platformsIn recent years several tools and programming frameworks

have been provided to develop multi-agent systems in the contextof smart grid applications, and interested readers can refer to [284]for a complete survey. However, the open-source Java Agent Devel-opment Framework (JADE) [285] is probably the most commonplatform for developing MAS solutions for smart grid applications.Most of existing MAS implementations are prototypes used for lab-based experiments [286,277], which aim at demonstrating the fea-sibility of the MAS approach. Recently some multi-agent systemshave been tested into operational electric grids. For instance, amulti-agent system, called Protection Engineering DiagnosticAgents (PEDA) [259], has been used by a transmission systemoperator in the U.K. to interpret SCADA-related data and to provideonline diagnostic information and alarms [287]. In [269] a multi-agent system is implemented using JADE to integrate a group ofelectric vehicles into the electric grid and to support both demandresponse and V2G services. However, to demonstrate that MAStechnologies meet the robustness and reliability requirements oftypical power grid applications, large scale testing is still needed.A valuable option to avoid the costs and risks associated to testnew technologies in real power grids is to employ realisticsimulation tools that simultaneously model electric power scenar-ios and the behaviors of computer communication protocols. Anexample is provided by EPOCHS [288], which is a combined


simulation environment that federates commercial electric simula-tors with ns-2, a popular open-source communication-networksimulator [289].

Before concluding this section it is necessary to observe thatalternative technologies have also been considered for buildinglarge-scale and interoperable power system applications, includingservice-oriented architectures (SOA) [290], grid and cloud comput-ing [291–293] and web services [294]. However, agent-based tech-nologies appear the most suitable solution for distributed controlapplications that must support autonomous behaviors, as alsodemonstrated by the wide range of applications in which multi-agent systems are being developed. Interested readers are also re-ferred to [290–292,294] for a more detailed discussion about thetechnical challenges that should be addressed to apply in the smartgrid domain the other approaches mentioned above to build large-scale interoperable dynamic systems.

7. Security mechanisms for smart grids

Electric utilities consider security, protection and reliability ofthe electric infrastructures one of their key priorities because fail-ures or malfunctions in power systems not only would have aneconomical impact, but could also cause serious damages to peo-ple. Historically security in power systems was obtained by ensur-ing physical protection of power generators and distribution grids,as well as physical isolation of utility control centers. Furthermore,closed and dedicated communication networks running proprie-tary protocols were typically used in SCADA-based power controlsystems [3]. On the other hand, it is reasonable to expect that insmart grids the deployment of distributed and autonomous controlfunctions, as well as the adoption of open network architectures,will bring new security vulnerabilities to smart grids and they willallow a variety of unforeseen security attacks. Different categoriesof security attacks specific to the smart grid domain have beendiscussed and identified in previous survey papers[117,21,295,296,24,297]. Based on those studies, the most impor-tant vulnerabilities of the smart grid communication system canbe broadly classified as follows:

� Device vulnerabilities: IEDs will be widely deployed in smartgrids to monitor and remotely control electricity productionand distribution processes. However, malicious users or attack-ers can compromise these devices, e.g., to manipulate senseddata or to disrupt normal grid operations. Furthermore, manyIEDs will support wireless communications to facilitate deploy-ment and simplify the access to information. An intuitive down-side of wireless communication technologies is that they rely onan inherently unprotected physical medium. This makes easierto capture private information (eavesdropping), disrupt commu-nications with noise signals (jamming attack), or generate fakemessages (injecting attack). Solutions of these attacks includeencryption of messages for data integrity and confidentiality,authentication of network access, and various randomizedtransmission methods, such as spread spectrum techniques [23].� Network vulnerabilities: The adoption of open network architec-

tures, off-the-shelf network devices and publicly available com-munication standards is needed to meet the flexibility,scalability and interoperability requirements discussed in Sec-tion 5.1. However, this also causes various security problemsobserved in other telecommunications networks adopting openarchitectures (e.g., Internet), such as malicious modification ofrouting information, DNS hacking, and various types of denialof service (DoS) attacks. More specifically, in a typical DoS attackan attacker can control a set of nodes to overwhelm other nodeswith data traffic, resulting in excessive network delays or evencommunication failures. For instance, compromised smart

meters can be forced to flood an EMS with meaningless mes-sages. In this way an EMS would consume all its computationaland communication resources and it would not be able to timelyreact to legitimate requests. To prevent these network attacks,access control and intrusion detection are essential securitymechanisms. Similarly, authentication and authorizationschemes are also necessary to support secure remote configura-tion and control of geographically dispersed devices [298].� Data vulnerabilities: Data manipulation is an important security

issue because an attacker can modify data or control commandsto compromise electric grid reliability. However, smart gridswill also be increasingly vulnerable to data attacks designedto compromise the privacy of customers. For instance, theman-in-the-middle attack is a popular method used by mali-cious users to gain access to information without physicallycompromising the target. If an attacker is able to snoop themetering data transmitted from the consumer’s home it couldinfer consumer’s habits and activities. For instance, it wouldbe possible to learn whether a home is occupied or not bydetecting power consumption signatures of specific activities(e.g., watching television or using the microwave) [299]. As bet-ter explained in following sections, encryption, messageauthentication and intrusion detection schemes are needed topreserve data integrity and confidentiality in smart grids [21].

Finally, it is important to point out that security techniquesdeveloped for other telecommunications networks can fail to meetthe security requirements of smart grid communication systemsbecause [300]:

(i) Many security attacks envisioned for smart grid communica-tion systems do not have a counterpart in other computernetworks because they can target either the physical powersystem or the communication infrastructure or both [301].For instance, in the power system there are several controlloops used to control physical aspects of generation, trans-mission, and distribution processes, and an attack directedat the communication networks underlying these controlloops can have a system-wide impact on power systemstability.

(ii) Typical security objectives and priorities for smart grids aredifferent from those of most telecommunications networks.Indeed, in most networks data confidentiality and integrityare more important than communication availability. Onthe contrary, in a power system electricity must always beavailable. Thus, availability is the most important securityobjective along with the protection of consumers’ privacy.

(iii) Most devices that are used in smart grids are expected to beheavily constrained in terms of computation capabilities anddata storage. Thus, security techniques developed for high-capability Internet-enabled devices (e.g., servers, laptops,or smartphones) are not suitable for smart grid devices.

In the rest of this section we will present some fundamentalsecurity techniques that should be integrated in smart grids forimproving the robustness of smart grids against various securityattacks and privacy risks. Furthermore, we will also discuss a fewrepresentative solutions in each category to clarify interestingopen problems. For the sake of clarity, in Fig. 8 we provide a clas-sification of major research trends in the area of security for smartgrids, which we have also outlined in following sections.

7.1. PKI management

A public key infrastructure (PKI) is a fundamental security toolin most telecommunications networks and distributed systems in

Fig. 8. Classification of research trends in the area of security for smart grids.


general. Recently the design of PKIs suitable for smart grids hasbeen extensively studied [302,303]. Basically, a PKI is a system ofsoftware and hardware components providing digital signatures,called certificates, which are used to identify a certain entity(e.g., a device, a program, an organization). These certificates aresecurely stored by a trusted certificate authority (CA), which alsodecides when and how to renew or revoke them. For instance,smart meters can register their serial numbers with a CA. Typically,a PKI employs public key cryptography techniques, which arebased on the simultaneous creation of a pair of public and privatekeys, to securely store and exchange certificates. The public keyis publicly available (as part of the digital certificates) in centralrepositories and it is used to encrypt messages, while the privatekey is known only to the recipient of the message and it is usedfor decryption. This is substantially different from symmetric keycrypto-systems, where there is an initial exchange of a shared se-cret key, which would require to pre-establish a secure communi-cation channel. Finally, PKI also supports message authenticationbecause the sender can use its private key to encrypt a digital cer-tificate, which is then utilized to sign transmitted messages.

Although PKI is the most popular key management scheme inthe Internet there are some issues to apply existing PKI solutionsto the smart grid domain [302,304]. First of all, there is a scalabilityproblem because a PKI for smart grids should maintain certificatesfor millions of devices. Furthermore, differently from classical PKIsystems, we cannot assume that identities are the only relevantproperties that should be certified in smart grid devices. For in-stance, context information such as the installation location ofthe devices could also be included in the certificate. However, thisinformation is known only after the installation and it may changedynamically. Hence, digital certificates associated to smart grid de-vices should also be dynamic. Finally, smart grids must supportreal-time and reliable operations. Therefore, PKI technologiesshould meet strict delay constraints and provide fault toleranceto ensure high availability. However, a PKI involves centralizedauthorities and complex certification policies and it might be quitechallenging to satisfy latency requirements.

To address the above issues a number of research directions arecurrently being investigated. For instance, it is generally agreedthat more automated configuration tools are needed for PKI sys-tems in smart grids. These tools will be used to allow each organi-zation to set its own security policies (how private keys areprotected, which is the validity time of a certificate, how certifi-cates are revoked, etc.) and to easily adapt these policies to the dif-ferent requirements of smart grid devices and applications [302]. Alarge body of work is also dedicated to the design of PKI architec-tures that are able to meet the reliability, scalability and delayrequirements of smart grids. For instance, a novel key managementscheme is proposed in [305], which combines public-key and

symmetric key approaches to achieve simplicity and to improvescalability. Basically, trust anchors are deployed in the PKI system,which employ robust public key methods to establish symmetrickeys between data aggregators and data collectors. Then, trust del-egation mechanisms are used to allow simple sensors nodes to ac-cess the grid and to communicate with data agents. However, thekey distribution scheme proposed in [305] can suffer from theman-in-the-middle attack during the initial authentication phase.To solve this issue, in [306] it is proposed to use trusted third par-ties to distribute shared keys among the components of the smartgrid. The focus of the work in [307] is the scalability and interop-erability of the key management system. In particular, the keymanagement scheme proposed in [307] uses existing standard pro-tocols for network access authentication (i.e., EAP) to avoid thatmultiple authentication and key establishment processes are per-formed across different protocols and link-layer technologies. Asimple PKI system is designed in [308], which assumes that a un-ique machine number is available at each device, which can beused by a central server holding a master key to generate uniqueprivate keys. The main advantage of this scheme is that it doesnot require to separately configure each device. On the other hand,unique machine identifiers are not always available. Finally, a pub-lic key crypto-system is proposed in [309], which exploits homo-morphic encryption techniques to avoid that a pair of public andprivate keys is generated for each communication link betweencustomers and utility control centers. In general, an aspect thatneeds much further investigation is the design of enhancementsto existing PKI systems to support new use cases, in particularthose due to the increasing adoption of mobile electric vehicles.

7.2. Authentication and access control

An essential security mechanism is the authentication method,which is used to verify device identities and data validity. As ob-served above PKI systems inherently provide device authenticationthrough digital signatures. The authenticated device identity canbe exploited to establish shared secret keys that are used forencrypting and authenticating data packets. It is important to notethat symmetric key cryptography is generally preferred to publickey cryptography for data authentication because it uses the samekey for both decryption and encryption, and this results in fastercryptographic algorithms that requires less processing power.However, authentication schemes suitable for smart grids shouldtake into account the limited resources (i.e., low memory and com-putational capacity) of most smart grid devices, as well as thestringent delay requirements of smart grid applications. A light-weight message authentication scheme is proposed in [310], whichaims at minimizing the number of messages exchanged duringauthentication. This is achieved by combining the Diffie-Hellman


protocol for key establishment and an hash-based messageauthentication code (MAC) technique. An authentication protocolis proposed in [311] for multicast communications, which reducesstorage and bandwidth overhead with respect to other schemesthat use public-key signature over multiple messages. An alterna-tive authentication approach is proposed in [312], which ensuresboth authentication and data integrity by jointly using digital sig-natures and timestamps. Finally, three authentication mechanismsare specified in [313] for devices typically used in HANs, such assmart meters, smart household appliances, and electric vehiclesmoving. Note that EVs pose the most complex authentication prob-lems because they can move between different home areanetworks.

A data protection mechanism that it is closely related to deviceauthentication is access control, which aims at ensuring that accessto objects (i.e., any entity containing information or providing ser-vices) is permitted only to authorized users. The most common ac-cess control schemes in telecommunications networks are basedon access control lists (ACLs), which specify the identities of usersallowed to access shared objects and what privileges they have.However, ACL-based approaches may not scale with the hugenumber of customers and resources involved in smart grid ser-vices. Therefore, most of the research in this area is focusing onthe formulation of role-based access control methods, in which per-missions to perform certain operations are assigned to specificroles rather than user identities. A network access control modelfor power system with micro-grids is described in [314], in whicheach micro-grid is a separate network domain with an indepen-dent control center that maintains XML-based security policiesand roles. In this scheme a role is defined as a collection of privi-leges that can be executed by authorized users in local and remotedomains. Note that each user can be assigned with multiple roles.Furthermore, to improve scalability roles are structured into a hier-archy in which the ‘‘parent’’ role directly inherits all the privilegesof its ‘‘children’’ roles. In [315] it is proposed to store data in an en-crypted form that depends on the data attributes. Then, only usershaving the same attributes as the data can decrypt those messages.Finally, an alternative scheme is proposed in [316] to use smartmeters as firewalls between home area networks and the utilitycore backbone.

7.3. Privacy protection and data integrity

The most intuitive method to ensure data privacy is to use acryptographic protocol, which preserves data confidentiality byencrypting data messages between data originators (e.g., smartmeters) and utility providers. The device authentication schemesdescribed in the previous section can be used to create the sharedsecret keys that are needed for message encryption. However, themassive deployment of smart meters and smart appliances alsoraises new privacy concerns. Specifically, it becomes an importantissue to avoid that malicious users can learn information aboutcustomers’ behaviors and habits. For these reasons several privacyprotection methods specific for smart metering have been recentlyproposed [22]. These solutions can be categorized into two sepa-rated classes: anonymization techniques and randomizationtechniques,

Anonymization techniques achieve privacy protection formetering data by anonymizing energy usage measurements in or-der to make more difficult to associate metering information to aparticular smart meter or customer. In [317] this is achieved byassuming that a trusted third party provides pseudonymous iden-tities, which are then used to anonymize high-frequency meteringdata. Note that this metering data is the most critical to infer usagepatterns of specific electrical appliances, while low-frequencymetering readings used for billing purposes are rarely enough to

offer adequate privacy. The use of a trusted third party is also pro-posed in [318], although this approach introduces additional com-plexities and requirements to the security framework. Moreefficient schemes can be designed by exploiting secure data aggre-gation techniques. For instance a privacy-preserving aggregationscheme, called EPPA, is designed in [319], which permits to per-form data aggregation at intermediate proxies without decryptingthe data received by individual smart meters. A new encryptionmechanism is also proposed in [299], which allows a group ofsmart meters to encrypt their data in such a way that electric util-ities can reconstruct aggregated energy usage information withoutdecrypting them. Note that the main open issue in this class of pri-vacy protection schemes is how to preserve the ability of electricutilities to know the instantaneous electricity consumption, whichis needed to support demand response and direct load control.

Randomization techniques are used in particular to protect theconfidentiality of usage patterns of household electric appliances.More specifically, this class of schemes achieve privacy protectionby manipulating the metering data with some stochastic perturba-tion function so that it is difficult to infer patterns of energy con-sumption. For instance, a simple privacy scheme is developed in[318], which adds random Gaussian noises to each smart meter.This approach is simple but not very robust because it applies ran-domization on true readings and statistical methods could be usedto extract load profiles. More sophisticated schemes are developedin [320], which mix actual smart meter readings with faked read-ings sampled from arbitrary distributions. However, further stud-ies are needed to understand which is the level of uncertaintythat is added by such randomization methods, and whether usefulinformation about energy consumption can be maintained in themodified data. Finally, an alternative approach is proposed in[321,322] by assuming that smart homes will contain a variety ofenergy storage and generation systems, which can be used toobfuscate appliance load signatures by randomly mixing utilityelectricity with electricity from local generators.

7.4. Intrusion detection

Although several security mechanisms can be used to deal withsecurity attacks, more sophisticated attackers can exploit unknownsystem vulnerabilities and succeed in compromising network com-ponents. In this case an intrusion detection system (IDS) is essen-tial to identify the attack and trigger appropriate countermeasures.There are different approaches that can be applied to design intru-sion detection techniques, but at least three major methodologiescan be identified [323]:

� Anomaly detection: anomaly detection recognizes intrusiveactions by looking for deviations from normal behaviorsof protocols, programs or processes. This approach requiresthe specification of accurate statistical models of normalprofiles, which can be quite difficult and costly to obtain.The main advantage of anomaly detection is its ability todetect unknown attacks. However, anomaly detection tech-niques also produce a high number of false alarms if themodel is not sufficiently accurate. Furthermore, thisapproach is suitable for relatively stable systems, otherwisenew models of normal operations should be developedafter any configuration change.

� Signature detection: signature detection is complementaryto anomaly detection because it uses deterministic patternsof known attacks to characterize malicious behaviors. Themain advantage of signature-based methods is that theyprovide high accuracy and a low number of false alarms.However, there are also some drawbacks such as theinability of these schemes to detect novel attacks, whose


signatures are unknown. Furthermore, pattern matchingtechniques and classification algorithms typically used insignature detection methods can be quite costly in termsof computing and data storage.

� Specification-based approach: specification-based methodsdetect attacks by defining ordered sequence of events,called policies, which corresponds to correct protocolbehaviors. A violation to this set of constraints and rulesis considered a security violation. A specification-basedIDS is able to detect unknown attacks as for anomaly detec-tion. However, an obvious downside of this class of tech-niques is that the development of specifications is timeconsuming and protocol specific. Another issue is thatspecifications are difficult to verify on devices that havelimited memory and computational power.

In the following we outline most representative solutions forintrusion detection in the smart grid domain based on the abovecategories.

A key design problem for both anomaly and signature detectionmethods is to collect empirical data from smart grids and to extractaccurate models for normal and malicious behaviors, respectively.Note that many intrusion detection schemes use neural networksto learn normal profiles and detect deviations from these profiles[323,324]. However, those techniques do not seem suitable forsmart grids because they are quite demanding in terms of compu-tational and memory overheads. Furthermore, they do not guaran-tee deterministic response times. A variety of techniques havebeen proposed to address those issues. For instance, in [325] super-vised learning models known as support vector machines (SVM)are used to extract features (e.g., connection duration and protocoltype) and recognize patterns representing normal communicationsbecause of their high accuracy and scalability. In [326] it is shownthat Decision Tree techniques can outperform other supervisedmachine learning techniques to implement real-time intrusiondetection. A novel unsupervised network intrusion detection sys-tem based on clustering techniques is decribed in [327]. Alterna-tively, if service level agreements (SLAs) are established betweenthe service provider and its customers then SLA monitoring canbe used to detect service violations and to verify intrusion at-tempts [328]. In addition, [325] defines a hierarchical IDS solution,in which an IDS module is installed at each protocol layer. Then,individual IDS modules are trained using data that are relevantto their level, but they also cooperate to detect possible attacks.In [329] malicious events are modeled as random variables follow-ing a Gaussian process. For instance, a random variable may beused to represent the number of malfunctioning smart meters ina building, or the number of failed authentication attempts in a gi-ven time interval. Then, conventional stochastic tools are used topredict the occurrence of an attack. Distributed and low-complex-ity pattern recognition algorithms are also developed in [330,331].Finally, many anomaly detection algorithms utilize a sensitivitythreshold to detect violations of normal behaviors, and the properselection of this threshold is essential to reduce the occurrence offalse alarms. In [332] fuzzy-logic rules are developed for tuningthis sensitivity threshold based on an estimate of the systemknowledge obtained by the IDS module. However, an intelligentadversary could compromise the IDS and adapt its actions to re-duce the probability to be detected. How to dynamically adjustthe sensitivity threshold in response to more intelligent attacksis still an open issue.

An important challenge for both anomaly-based and signature-based IDSs is to identify which are the relevant events to monitorin the power system, and many studies exist that try to classifydifferent types of attacks. Since a specification-based IDS do notrequire empirical data to detect intrusions, this class of intrusion

detection schemes has been considered as a valid alternative tomore sophisticated IDS solutions, at least in the early stage ofsmart grid development. For instance, in [333] a specification-based intrusion detection method is designed for the C12.22standard protocol, which specifies an application-level messagingprotocol to exchange power data over a network. In [334] a speci-fication-based IDS is developed of the IEEE 802.15.4 standard ap-plied in home area networks. However, there is still a highoverhead associated to these schemes, and it might be impracticalto run them on smart grid devices.

7.5. Security analysis

Utility companies need formal security analysis or simulationtools to quantify the robustness provided by adopted securitymechanisms, as well as to predict the impact of attackers on smartgrid operations and reliability. Obviously there are many tools toanalyze vulnerabilities in traditional telecommunications net-works [335] (e.g., to identify misconfiguration problems), and torate and score known vulnerabilities [336]. However, new specificsolutions are needed for smart grids due to the heterogeneity ofdevices, protocols and configurations. An automated tool for AMIconfiguration verification, called SmartAnalyzer, is developed in[337] using formal analysis based on Satisfiability Modulo Theories(SMT). Basically, this tool encodes AMI configuration templates,which describe device configurations, network topologies, moni-toring schedules, into SMT logic. Then, an SMT solver is used toverify this configurations against a set of high-level constraints(e.g., reachability between devices, schedule and resource con-straints) and user-driven constraints (e.g., data protection, dataintegrity, transmission reliability). The output of this verificationprocess is a report that indicates reasons of constraint violationsand possible remedies. An alternative approach to quantify the de-gree of damages that cyber attacks can cause to a power systems isdesigned in [338]. The key idea is that both physical components(i.e., electric grid elements) and cyber components (i.e., networkdevices and functionalities) can be modeled as objects to whichstate information is associated. Then, directed graphs are used torepresent state dependencies amongst the various objects. Finally,a system of differential equations is utilized to specify the rulesgoverning state evolution, and to model cause-effect relationships.However, identifying cause-effect relationships in large-scalepower systems is a challenging task. Another class of methodsfor risk analysis in smart grids rely on computational intelligenceapproaches. A useful technique in this class is fuzzy logic becauseit supports probabilistic risk assessment by measuring uncertain-ties in the decision making processes and allowing for incompleteor ambiguous data (fuzzy data). An overview of the mainalgorithms in computation intelligence used in smart grid riskassessment is reported in [339]. A key open issue in the area of riskassessment is the design of toolkits that automatically generatemodels of the power system from specifications, operatingprocedures and network topologies to describe the vulnerabilitiesof devices, services and network connections. Note that power gridmodels are essential to identify system vulnerabilities, predict newattacks and evaluate potential service damages [340].

Related to risk assessment (as well as intrusion detection) is thedesign of mechanisms to evaluate the trustworthiness of smartgrid objects (e.g., devices, service providers, entire subsystems).Specifically, a trust system allows an entity to assess the reliabilityof another entity before deciding to interact with it (e.g. to use aservice provided by that entity) [341]. In this way, a trust modelcan be used to determine risks and to give a weight to those risks.Typically, trust models are associated to reputation systems, whichenable peers to rate each other after the completion of a certaintask. Then, a reputation system uses the aggregated ratings about


a given peer to derive its reputation score (or trustworthiness). Asan example, power protection applications generally rely on thecooperation among grid components to cope with grid failures orgrid instability. Thus, it will become very important to decidewhich is the best entity to collaborate with, and the reputationplays a key role in the decision process. For these reasons, variousstudies exist that have explored how to integrate reputation-basedtrust management systems in smart grids. For instance, in [342] atrust management methodology is developed to verify if individualsmart meters generate selfish and malicious reports on energyusage. Furthermore, one of the advantages of a trust system is toprovide an additional layer of protection in power systems imple-menting agent-based control functions as discussed in [343,344].Specifically, those studies consider a backup protection schemeand propose trust metrics to improve the reliability of networkcommunications. Similarly, in [345] an early warning system is de-signed for predicting and anticipating cascading failures in powersystems, which uses a reputation mechanism for controlling net-work behaviors. Furthermore, nodes in a network can be assigneddifferent roles and perform different functions depending on theirtrust levels [346]. Note that the design of suitable techniques forquantifying trust levels in dynamic environments while preservingdata privacy is an open issue.

8. Summary and outlook on key research areas

In this survey we have advocated the view of a smart grid as theoutcome of an evolutionary transformation of the existing electric-ity network towards an optimized and sustainable energy system.We have analyzed the several factors that are contributing to thisevolution, ranging from technological developments (e.g., theintroduction of electric vehicles, the deployment of renewable en-ergy resources, the recent advancements in energy storage sys-tems, time-synchronized measurement technologies of electricityquality) to economical and societal drivers (e.g., liberalization ofenergy markets with the advent of prosumers, new environmentalconcerns about climate changes and pollution, soaring growth ofenergy demands, need for higher operating efficiency, active par-ticipation of consumers in demand response, better resiliencyagainst both physical and cyber attacks). The incorporation in thesmart grid of a pervasive monitoring and communication infra-structure is essential to: (a) measure the system state, (b) collectthe sensor information and propagate control signals, and (c) allowdistributed smart applications to control power flows in the net-work. Thus, the goal of this survey was to present a complete mod-el of this monitoring and communication infrastructure, coveringall layers of its architecture, ranging from communication technol-ogies at the physical layer, to communication topologies and pro-tocols at the network layer, and concluding with middlewareservices needed to support the new applications in a distributedfashion.

We believe that this survey could provide the reader with auseful overview of the state-of-the-art solutions proposed at alllayers of the smart grid communication infrastructure, as well asa guide of the open research issues. Hereafter, we aim at summa-rizing some of the novel research directions that are still worthexploring.

� Due to the massive number of data sets that will be col-lected in smart grids, classical database-management toolscan be unable to process them within acceptable delays. Inaddition to data analysis, other critical issues are data stor-age, search and visualization. Nowadays there are manyapplication domains that are already witnessing an explo-sion of collected data, such as healthcare, retail markets,online social sites, but smart grids have the potential to

become one of the most demanding one [347]. However,since temporal and spatial variations of most electric pro-cesses are small, it is reasonable to expect that most mea-surements in this massive amount of data will beredundant. This creates many opportunities for the designof data mining techniques able to provide data compres-sion. Furthermore, the design of novel knowledge-discov-ery methodologies that are specific for electric data is avery compelling research topic. Finally, cloud computingis considered an important data and computing model fordata intensive smart grid applications.

� Energy routers indicate devices that allow units of energy(locally generated, stored, and forwarded) to be dispatchedwhen and where it is needed [164]. The deployment ofenergy routers within the smart grid can enable innovativeparadigms for energy distribution and control, in whichenergy is logically packetized, buffered and forwarded overthe physical energy network. While energy routers guaran-tee more flexible end efficient power distribution and thisfacilitates an increased use of renewable energy sources,they also pose new challenges. As an example, an energyrouter should be able to dynamically redirect incomingenergy flows towards outgoing energy flows. Thus, newpower electronics are required in energy routers to imple-ment automatic energy control. Similarly, energy routersshould implement distributed intelligence to control theenergy routing process. Furthermore, innovative ways ofdispatching energy in a smart grid can be devised takingadvantage of EVs. For instance, we can use the batteriesof EVs as a mean of physically moving electrical energy.In this way EVs can support a delay-tolerant transfer ofenergy between homes. However, to quantify which arethe potential gains of this approach a detailed cost-benefitanalysis is necessary.

� The design of optimization strategies for the smart grid is avery active research area, and there is a rich set of method-ologies and algorithms from different domains that havebeen considered for this purpose. For instance, there areseveral algorithms in the literature to determine the opti-mal load schedules of groups of domestic appliances How-ever, how to design management and control schemes ableto provide real-time and wide-area control with limitedcomputational costs is still an open problem. We believethat flow-based congestion control algorithms, which havebeen commonly applied in large-scale information net-works, such as the Internet, may be a promising and attrac-tive approach to mitigate power congestion by reducingpeak loads. For instance, there are electric devices thatcan elastically adapt the amount of instantaneous powerthey need, such as many common household appliances.Then, those devices could intelligently increase/decreasetheir power demands depending on congestion feedbacksignals from the utilities.

� Due to the intrinsic differences between conventionalpower grids and smart grids, existing electric power simu-lation/analysis tools will not be able to accurately modeland predict the behavior of new power grids. For instance,more precise models of renewable energy resources atincreasingly lower time and spatial scales are needed toincrease the reliability of protection and control systems.Similarly, the behaviors of power systems will be increas-ingly dependent on external factors, such as the reliabilityof the smart grid communication network. Thus, powersimulators should integrate tools to model each componentof a smart grid, as well as the possible interactions betweencomponents.


� Security mechanisms are an essential part of a smart grid.Although a considerable amount of research has been con-ducted in this field many open issues still exist because theincreased interconnection and integration, e.g., betweenelectric grid, monitoring and communication network, datamanagement systems and applications, also introduce newcyber-vulnerabilities into the smart grid. The most compel-ling research challenges are the design of suitable mecha-nisms to protect the confidentiality and integrity ofmetering data, as well as new mechanisms to control theaccess to smart-grid components and resources given thatphysical isolation of the power grid might not be feasibleanymore.

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the role of communication systems in smart grids ...[25], and other aspects of smart grids, such as...

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