The New Frontier of Smart Grids

An Industrial Electronics Perspective

XINGHUO YU, CARLO CECATI,THARAM DILLON, andM. GODOY SIMOES

The power grid is amassive intercon-nected network usedto deliver electricityfrom suppliers toconsumers and hasbeen a vital energy

supply. To minimize the impact of cli-mate change while at the same timemaintaining social prosperity, smart

energy must be embraced to ensurea balanced economical growth andenvironmental sustainability. There-fore, in the last few years, the newconcept of a smart grid (SG) becamea critical enabler in the contempo-rary world and has attracted increas-ing attention of policy makers andengineers. This article introduces themain concepts and technologicalchallenges of SGs and presents theauthors views on some required

challenges and opportunities pre-sented to the IEEE Industrial Elec-tronics Society (IES) in this new andexciting frontier.

Electricity and the Electric Grid

Electricity became the subject ofscientific interest in the late 17thcentury with the work of William Gil-bert. Since then, a number of greatdiscoveries and technological devel-opments have been achieved. The

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Date of publication: 23 September 2011

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greatest discovery of them all wasfrom Michael Faraday, who discov-ered the principle of electromagneticinduction in 1831. At the turn of the20th century, the inventions and dis-coveries by Thomas Edison andNikola Tesla laid the foundations forbuilding modern electric grids. Thegrid serves as the major means ofvital energy supply. As shown inFigure 1, distinct operations of electricgrids include generation, transmis-sion, and distribution. The electricityis first generated and then transmit-ted over long distances to the substa-tions where it is further distributedto the consumers. The generationsystem is driven from several forms

of energy, such as high-density energysources of coal, gas, and oil, as well asdiffusible renewable sources such ashydro, dispatchable biomass, solarenergy, and wind. Presently, the domi-nating generation mechanism is byelectromechanical generators drivenby heat engines fueled by chemicalcombustion or nuclear fission. Tradi-tional fossil fuel power plants have avery low efficiency, i.e., from source(coal) to the end user, approaching anoverall 30% (thermodynamical cycleshave a limited efficiency and there areseveral other losses, including thetransmission and distribution losses),whereas local generation from renew-able energy (RE) sources will have a

much higher efficiency (estimated tobe about 70%). Data from the Environ-mental Investigation Agency (EIA)International Energy Statistics 2010supports that 63% electricity in theUnited States comes from fossil fuelcombustion, while in China, it ismore than 70%, with most developedcountries within the same range.The transmission system is usuallycomposed of higher voltage trans-mission lines that transport electric-ity for long distances and deliver todistribution substations where thevoltage is lowered for further distri-bution to consumers through distri-bution networks.

Need for Smart Energy

Smart energy refers to making energyuse more efficient by utilizing theintegration of advanced technologiessuch as information and communica-tion technologies (ICTs) and elec-tronics and material engineering aimedat maintaining an environmentallysustainable system. Smart energy isneeded for a number of reasons. Theprimary reason is the limited avail-ability of non-RE sources such as coal,gas, and oil on Earth. It is estimatedthat Earth has only a few decades ofsupply left from these non-RE sour-ces. On the other hand, RE from sour-ces such as hydro, biomass, solar,and geothermal energy and wind isplaying a more important role forfuture energy supply. Advanced tech-nologies are needed to make theseenergy supplies more reliable andsecure [1]. While it is predicted thatRE will be the major future energysupply in the long run, non-RE willcontinue to be the dominant energysource for the middle and short termbecause they are still more economi-cally feasible with higher energydensity and easy access for its use.However, government incentives andlarger-scale deployment are makingRE more affordable. The secondaryreason to move toward RE is relatedto pollution concerns; almost allenergy production and usage involvespollution to the environment andsocial costs that are usually hiddenfrom the average user (e.g., large

Generation

Transmission

Distribution

Industry

Commercial

Residential

FIGURE 1 The traditional electric grid.

The electric grid is a massive interconnected

network used to deliver electricity from

suppliers to consumers.

50 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011

hydropower projects). For instance,electricity generation from coal andoil yields carbon dioxide (which causesglobal warming), nitrous oxide (whichcauses smog that is harmful to theelderly), and particulate or dust air(which increases the risk of lungcancer).

All of these reasons require us tothink seriously about how to ensureenvironmental sustainability whilemaintaining needed economic growth.Smart energy is about taking a holisticapproach in dealing with efficientenergy supply and demand from eco-nomical, environmental, and socialperspectives. For example, there aremany strategies being developed onhow to improve efficiency with lesswaste and better quality of service. Italso requires a paradigm change indealing with energy supply anddemand, e.g., new technologies toharvest and use RE, improved en-ergy distribution to optimize theassets utilization and reduction ofcapital expenditure, and improvedmanagement of energy use to re-duce losses with embedded genera-tion technologies.

More broadly, smart energy encom-passes a wide range of research anddevelopment issues such as industrysector-wide standardization, policyframework and reform, operationaltechnologies and systems (e.g., con-trol systems, grid security and stabil-ity, fault detection and prediction,data and communication, demandmanagement, self-healing grids, andlong distance energy supply), informa-tion and social technologies andsystems for carbon mitigation, grid-to-customer integration, customerbehaviors, cross-sector large-scalemodeling, and optimization [2].

The Concept of SGs

The term SG refers to electricity net-works that can intelligently integratethe behavior and actions of all usersconnected to it, e.g., generators, cus-tomers, and those that do bothtoefficiently deliver sustainable, eco-nomical, and secure electricity sup-plies. In the United States, the meaningof SG is much broader, referring to ameans to transform the electric in-dustry from a centralized, producer-controlled network to one that is less

centralized and more consumer-interactive, by bringing the philoso-phies, concepts, and technologiesthat enabled the Internet to the utilityand the electric grid [42]. In China, SGrefers to a more physical network-based approach to ensure energysupply is secure, reliable, more re-sponsive, and economic in an envi-ronmentally sustainable manner [43].In Europe, SG refers to a broader soci-ety participation and integration of allEuropean countries in an RE-basedsystem [44]. A vision of an SG is illus-trated in Figure 2. The National Insti-tute of Standards and Technology(NIST) provides a conceptual modelas shown in Figure 3, which definesseven important domains: bulk gener-ation, transmission, distribution, cus-tomers, service provider, operations,and markets. In the United States, theimportance of SG is currently consid-ered as equivalent to what was takenfor the Eisenhower Highway System(envisioned in the 1950s to transformthe transportation infrastructure inthe United States). In SG, the tradi-tional role of central generation,transmission, and distribution is

Smart Grid

Solar Panel

RE

ConventionalPower Plants

Nuclear Plant Thermal Plant

Consumers

Commercial

Industry

ResidentialMicrogrid

SolarPanels

Wind Farm

Electric Vehicle

EnergySecurity

DemandManagement

SmartAppliances

GreenhouseGas Reduction

Information andCommunication

Technology

EnergyStorage

Storage Communication

FIGURE 2 The future electric grid.

SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 51

transformed by aggregation of dis-tributed resources, which results ina microgrid architecture as shown inFigure 4 [3]. In the microgrid, somefeeders can have sensitive loads thatrequire local generation. Intentionalislanding from the grid is providedby static switches that can separatethem in less than a cycle. When themicrogrid is connected, power fromlocal generation can be directed tothe feeder with noncritical loads orbe sold to the utility if agreed orallowed by net metering. In addition,a microgrid can be designed for therequirements of end users, a starkdifference from the central genera-tion paradigm.

Key Issues in SGs

There are several technical chal-lenges facing SGs: intermittency of RE

Computer

Markets

Operations

Service Provider

Customer

DistributionTransmission

Generation

FIGURE 3 NIST conceptual model of SGs.

Solar WaterHeatingPhotovoltaic

Array

Heat Pump

MicroturbineFuel Cell

Small Hydro DR Wind Turbine DR

Transmission DistributionCentral

Generation

Residential or Commercial Small DR

InterconnectingHardware

InterconnectingHardware

Traditional Loads

TraditionalLoads

Local Generator

StaticSwitch

Industrial DR

FIGURE 4 Amicrogrid architecture.


generation that affects electricityquality; large-scale networks of smalldistributed generation mechanisms,e.g., photovoltaic (PV) panels, bat-teries, wind and solar, and plug-inhybrid electric vehicles (PHEVs),which result in high complexity.

Another important characteristicof power usage is that the peak ofelectricity usage is normally around30% above the average electricityusage, which means reducing peakswould result in an increased capacityof energy supply, allowing the avail-ability of future growing energyneeds while delaying building morenew power generation plants. Oneimportant concept can be defined aswasteless, i.e., finding the bottleneckof unnecessary waste. For example,energy use for electricity transmis-sion and distribution may take up to14% of the input energy generated.Therefore, embedded generation andsiting generators close to the point ofconsumption are key considerationsin reducing wasted energy (such aconcept is usually defined as distrib-uted generation).

A more significant issue is how touse ICT, electronics, and other ad-vanced technologies to enhance theefficiency of energy use. This includesnew technologies (e.g., smart metersand telecommunication technologies)for sensing, transmission, and pro-cessing information relating to gridconditions, which are vital for timelymonitoring and controlling the net-work to ensure efficient energysupply, security, and safety of thenetwork and demand management tomeet the customer needs.

To address the above issues, thefollowing technological advances arerequired:n Distributed control: Control needs

to be distributed, enabling lowercommunication needs if grid com-ponents such as source, loads,and storage units can be con-trolled locally or can make somedecisions by themselves [4], [5].

n Demand prediction: This technologyalready exists at the transmissionlevel but is very rare at the distri-bution level. It estimates demand

on a given portion of the grid a fewhours or days in advance.

n Generation prediction: Generationcan be estimated, mostly for REresources such as solar panelsand wind turbines. These estima-tions heavily rely on weather pre-dictions and are indispensable tobe able to schedule the use ofnon-RE sources by utilities andto integrate intermittent energysources.

n Demand response: Reducing peakdemand is an essential function-ality to achieve a more efficientgrid. Mechanisms such as loadshedding and dynamic pricing canhelp reduce total demand. An-other approach to limiting demandpeaks is automatic demand dis-patch, which consists of delayingthe use of some loads in time.SG as a multidisciplinary field

presents many challenges and oppor-tunities for industrial electronics re-search and development, which areconcerned with the application ofelectronics and electrical sciences.These applications enhance the in-dustrial and manufacturing processes,addressing the latest developmentsin intelligent and computer controlsystems, robotics, factory communi-cations and automation, flexible man-ufacturing, data acquisition and signalprocessing, vision systems, and powerelectronics. Therefore, the authors arenext presenting some of their views onthe future developments in three keyresearch themes in IES that aredirectly related to SG: power electron-ics, intelligent systems and control,and IT infrastructure.

Power ElectronicsThe technology of power electron-ics is fundamental in SG develop-ment because they will have a deeperpenetration of renewable and alterna-tive energy sources, which require

power converter systems. Typically,a power converter is an interfacebetween SG and local power sources[6]. Moreover, they are required byseveral subsystems involving energystorage or harmonic compensationinterconnecting areas or separatedgrids [7].

Primarily, RE such as solar (PV)and wind play a significant role as themain sources for SG, while minihydro,geothermal, dispatchable biomass,tidal, and even hydrogen-based fuelcells can also be incorporated. REsources are increasingly being in-stalled in residential and commercialapplications (typically with powerrange of a kilowatt), and many coun-tries are already incorporating asignificant portfolio in distributedenergy, with expected growth duringthe next few years [8]. However, theintermittent nature of RE affects theoutput characteristics of generatorand converter sets (i.e., their voltage,frequency, and power); hence, theycannot be used in stand-alone config-urations and must be compensatedby integration with energy storage. Apower electronic converter is alwaysneeded to allow energy storage dur-ing surplus of input power and com-pensation in case of lack of inputpower. Figure 5 shows the effect thata power converter must considerabsorbed power by the load versuspower injected into the grid. The acload is absorbing active power PL,and the reactive power QL is not sup-plied by the inverter, the power fac-tor may fall out of the prescribedlimits allowed by the utility, and pos-sibly the inverter must supply reac-tive power in addition to the activepower. Through converters, severalsources of energy can be integratedto the grid as shown in Figure 6. Fossilfuel usually depends on thermody-namical cycles and large rotating mac-hines; therefore, an ac/ac conversion

The term SG refers to electricity networks that

can intelligently integrate the behavior and

actions of all users connected to it.


is necessary. Wind, hydro, and naturalgas usually require rotating machinesas well, but a large storage unit mustcompensate their intermittency [9].Sunlight, hydrogen, and sometimesnatural gas require dc/dc conversion,with integration to the ac grid throughinverters, while most of the time usingbatteries to compensate for theirintermittency. Figure 6 also shows theneeds of islanded operation and the

required needs for disconnecting andconnecting to the grid in accordanceto the real-time needs. In Figure 7, adistributed generation system archi-tecture is shown, where Figure 7(a)shows a typical dc link integrationvery commonly used when dc sources(PVs, fuel cells, and batteries) are inte-grated. Figure 7(b) shows a typical aclink integration, where turbines androtating machines are integrated

through the utility line frequency,and Figure 7(c) shows a high-frequency ac link integration, wherefast response and decreased systemsize can be achieved. When intercon-nected with distribution systems,these small, modular generationmechanisms can form a new type ofpower system called the microgrid,and when associated with control andintelligence, can be called an SG [3].

Depending on the available sour-ces, inverters, rectifiers, and dc/dcconverters are required. A rectifiermight be a front end for an electricgrid connected to a load or an in-verter can be the interface with localgeneration. There are other convert-ers for intermediate stages, necessaryfor adapting the energy produced bythe source in such a way that both theenergy source and the inverter oper-ate at their highest efficiency.

Power converters for SG integra-tion and particularly inverters pre-sent a higher complexity whencompared with those used in indus-trial or stand-alone RE systemsbecause they have to efficiently man-age bidirectional power flow as wellas critical situations. They must becapable of either absorbing (in acontrolled manner) energy from thegrid for supplying the local load orinjecting the surplus of the locallyproduced energy into the grid [10].Moreover, they must be capable ofmitigating fluctuations and distor-tions, thus reducing the size of low-pass filters. These functions requirenew functions not commonly avail-able in standard converters.

Renewable and alternative en-ergy systems require the followingspecifications:n High efficiency: Obviously, only a

negligible part of the power shouldbe dissipated during conversion.This requirement is severely af-fected by input and output energyfluctuations and by conversionefficiency, changing with thequantity of energy at input/out-put terminals. The converter hasto operate in continuous track-ing of the input/output quanti-ties and a subsequent real-time

ac/acConversion

Synchronous orAsynchronous

ac/acConversion

Synchronous

Storage

Sunlight

Hydrogen

Natural Gas

Fossil Fuel

Hydro

Wind

Local Heat Recovery IslandedOperation

UtilityGrid

Inte

rcon

nect

ion

dc/dcConversion

FIGURE 6 Integration of several sources of energy into the grid.

Embedded generation and siting generators

close to the point of consumption are key

considerations in reducing wasted energy.

dc/dc + dc/acConverter

AlternativeEnergy Source

ac Source

ac Load

PC PCPSQS

QLQC PL = PS + PC

FIGURE 5 Active and reactive power balance for alternative energy conversion.


adjustment of the converter pa-rameter ensuring the highest en-ergy transfer. This requires twoor more conversion stages (typi-cally ac/dc and/or dc/dc and/ordc/ac in wind, hydro, and geo-thermal generators).

n Optimal energy transfer: All REsources are energy constrainedand as such they need algorithmsto achieve the maximum powerpoint. Usually, PV arrays and windgenerators must be intercon-nected with maximum power

point tracking (MPPT) to opti-mize the energy transfer.

n Bidirectional power flow: In almostall cases, the power converterhas to be able to indifferentlysupply either the local load and/or the grid.

VARCompensators

StationaryGeneration

HF or60-Hz

RotatoryGeneration

HF or60-Hz

RotatoryStorage

HFAC Link

HFAC Loads

60-Hz Grid

dc Loads

StationaryStorage


RotatoryStorage

dc Link

ac LoadsRotatory

Generation

StationaryStorage

60-Hz Grid

ac Loads

ac Loadsand VAR

Compensators

60-Hz Grid

StationaryStorage


RotatoryStorage

ac Link

ac LoadsRotatory

Generation

(a)

(b)

(c)

FIGURE 7 Energy integration with (a) dc link, (b) ac link, and (c) HFAC link.


n High reliability: The continuity ofservice is a major issue when de-livering energy.

n Synchronization capabilities: Allpower sources connected withthe grid have to be fully synchron-ized, thus ensuring high efficiencyand eliminating failures, and there-fore, standards such as IEEE 1547[45] should be incorporated inthe power electronic interfaces.

n Electromagnetic interferences (EMIs)filtering: The quality of the en-ergy injected on the grid mustrespect electromagnetic compat-ibility (EMC) standards.

n Smart metering: The converterbetween the local source/load andthe grid must be capable of track-ing the energy consumed by loador injected on the grid transmit-ting. Real-time information mustbe passed to an automatic billingsystem capable of taking into ac-count parameters as the buy/sellenergy in real time at the besteconomic conditions and inform-ing the owner of the installationof all required pricing parameterdecisions.

n Communication: Intelligent func-tioning of SGs depends on theircapability to support communi-cations at the same time thatpower flows in the systems. Suchfunctions are fundamental for over-all system optimization and forimplementing sophisticated dis-patching strategies [11].

n Fault tolerance: A key issue for theSG is a built-in ability of avoidingpropagation of failures among thenodes and to recover from localfailures. This capability should bemanaged by the power converter,which should incorporate moni-toring, communication systems,and reconfiguration systems.

n Extra intelligent functions capa-ble of making the user interface

friendly and accessible anywherethrough Internet-based communi-cations.SG systems require power con-

verters with functional controls forsmart power generation with possi-bility of supplying power to localloads as well as to the utility. A utilitycould also request an SG user toprovide voltage support at the pointof common coupling (PCC). There-fore, the primary intent of a smartinverter is to enable efficient inter-connection and economical opera-tion of dispersed installations to theutility grid interacting with smartmetering, incorporation of smartappliances, provision of pricing infor-mation and/or some control optionsto the consumers, and informationexchange for a fully networked sys-tem enabled by massively deployedsensors. Traditionally, voltage sagsin distribution systems are correctedusing utility operated capacitorbanks. However, with the advent ofsmart inverters, these services mayalso be managed by the customer.This represents one of the tenets ofthe SG initiative, i.e., enabling activeparticipation of consumers in thedemand response using timely infor-mation and control options.

Converters: Generation

from Solar Energy

PV cells are dc sources where the cur-rent depends on the sunlight intensityand voltage depends on temperature.Those cells are arranged in seriesand/or in parallel, achieving the de-sired level of voltage and current. Adc/dc converter provides the neces-sary voltage boost and regulation(under control of an MPPT algo-rithm) necessary for extracting thehighest power from the sun. Thesealgorithms vary the duty cycleattempting to maintain fixed outputand at the same time highest PV

energy extraction. The dc/dc con-verter can be either with or without atransformer; the latter is inserted forproviding galvanic insulation and out-put voltage-level amplification. Thepresence of a transformer reduces theoverall efficiency due to copper andcore losses and increases the cost ofthe residential applications.

For medium-high power, there arelimitations on the availability of suita-ble high-frequency transformers forhigh power (typically limited to 20kVA applications). However, high-frequency transformers are commonin low to medium power applications(such as residential inverters and dcpower supplies). Line frequency trans-formers may be used in grid interface,but there are power electronic topolo-gies specifically designed to avoidtransformers or magnetic components.Recently, there has been a significantinterest in the use of resonant andquasi-resonant dc/dc converters in PVgeneration systems, because of theirhigh efficiency and reduced switchinglosses [12], [13]. However, these con-verters are complex to control, partic-ularly when a wide input voltagevariation may occur as in PV applica-tions because the resonance phenom-ena are strictly connected to thevalues of the so-called resonant tankwhile the input voltage variations canbe contrasted by varying the operat-ing frequency.

The output of a dc/dc converteris applied to a PWM inverter with gridsynchronization capabilities, neces-sary for correct synchronous opera-tions followed by a tight low-passfilter, necessary for respecting EMCstandards. Phase shifting among thedistinct generators is usually ad-dressed by a phase-locked loop (PLL)used for a correct generation of theac voltage by the inverter, thus avoid-ing current circulations due to aphase shift among the inverter andthe grid.

Recently, an increasing interesthas been found in new topologies,which may allow improvements in theconversion process, such as cascadedH-bridge multilevel converter fordc generators (PV, fuel cells). Such

An agent is a software entity that can represent

and control an actuator component, such as a

source, a storage unit, or a load.


topology consists of a number of H-bridges connected in series, each onewith its own generator, obtained by agroup of cells [14], [15]. The advan-tages are better utilization of solarcells and output voltage waveform,achieving a significant reduction ofthe output filter and an increase in theefficiency of PV energy conversionbecause of their improved utilization.Another interesting approach con-sists of the use of low-power separateconverters, one for each panel or for asmall group of panels, directly pro-ducing the desired output voltagelevel. In this case, advantages may bederived from an improved sun energyconversion with reduced losses (out-put currents depend of the outputvoltage level) and lower wiring costs.An energy storage system may be con-nected in parallel at the inverter inputterminal for reducing the impact of PVenergy fluctuations [16].

Converters: Generation

from Wind Energy

Wind energy conversion systems(WECS) consists of an ac generator(synchronous or asynchronous ma-chine) and a power converter, usuallyconsisting of a cascade ac/dc rectifier,dc/dc converter (useful for dc linkvoltage regulation and control), anddc/ac converter. Modern WECS in-clude an active rectifier, rather thana simple diode bridge, resulting inimproved efficiency of the conver-sion process and for the generatoritself, which can operate closer to itsoptimum conditions than using thesimple diodes. In this case, dc/dcconversion may be avoided by im-plementing a back-to-back converter.Dc/dc (if present) and the dc/ac con-versions are not dissimilar from thoseused in PV converters except thatusually WECS produce higher powerlevels (up to 10 MVA) and the MPPT isdesigned to optimize the turbine aero-dynamics [17]. Multilevel convertersappear very interesting and promis-ing, but, different from the previouscase, the source is unique; therefore,other topologies such as neutral pointclamped or the flying capacitor maybe employed in both the ac/dc and

dc/ac stages [18], [19]. Matrix con-verters can also be considered for ac/ac applications [20].

Flexible Alternating Current

Transmission Systems

Flexible alternating current transmis-sion systems (FACTS) have beendeveloped over the past two deca-des, to increase the efficiency oftransmission lines through the use ofpower converters, which providecontinuous injection of lead or lagcurrents to maintain the right dis-placement of either current or volt-age and to reduce the apparent lineimpedance. FACTS also make the sys-tem more reliable by reducing tran-sient line disturbances such as glitchesand voltage sags and more intelligentbecause power flow can be com-pletely controlled with power con-verters such as static synchronouscompensators (STATCOMs), unifiedpower flow controller (UPFC), andvarious pulsewidth modulated cas-caded topologies employing insulatedgate bipolar transistors (IGBTs) athigh-voltage levels [21]. FACTS havebeen typically applied to transmissionlines, but they have also become im-portant for large distributed genera-tion applications, such as wind farmsor large central solar systems, and itis expected that FACTS technology isto be further applied to distributionsystems that will be redesigned in thenear future for the SG. It is expectedthat those functions in charge ofSTATCOMs, UPFC, and other convert-ers specifically designed for FACTSwould be incorporated within thealready existing power convertersfor the SG.

Intelligent Systemsand ControlSGs are highly complex, nonlineardynamical networks by nature that

present many theoretical and practi-cal challenges. Monitoring and con-trol are the key issues that need to beaddressed to make SG more intelli-gent and equipped with self-healing,self-organizing, and self-configuringcapabilities. This requires much moreefficient information (signal) sensing,transmission, and synthesis. The ex-isting technologies for monitoring,assessment, and control were pre-dominantly developed in the 1960s,and the grid operations are ratherreactive, with a number of criticaltasks performed by human opera-tors based on the presented raw dataand past experiences [23]. There aretwo questions: 1) how to automatethe acquisition of useful operationinformation to make informed opera-tion decision in a timely fashion and2) how to present the information tousers in a most compelling and in-formed way to help users make high-level operation decision without bog-ging down into unnecessary waste oftime in understanding rather rawdata. This all becomes more critical asthe information available will growexponentially with more sensors/meters installed.

Dealing with Network Complexity

With increasing complexity com-pounded by the distributed nature ofRE, real-time performance is a bottle-neck in deriving just-enough andjust-in-time information for SG tooperate efficiently. The intermittentavailability of RE requires considera-tion of the entire operation regime todeal with the associated problemssuch as storages and variable powerquality [23]. The bidirectional elec-tricity flow in the SG due to penetra-tion of a large number of smallgeneration systems and versatileusages also pose challenges. Tradi-tional state-space modeling and

WoT is a flexible andmobile framework that

creates a network among the different devices

by deploying sensors.


control methodologies may not besuitable for such tasks. A paradigmshift may be needed in the way thenetwork is dealt with. One promisingmethodology is the complex net-work (CN) theory [26], which origi-nated from the graph theory and canbe used in combination with existingmethods and tools to simplify theanalysis and design so that timelyresponse is possible. The essence ofthis theory is to study the subjectsystem from the aspects of structureand dynamical function of a collec-tion of nodes and links without rely-ing heavily on the dimensionality ofthe system. Typical complex net-works include regular networks, ran-dom networks, small-world networks,and scale-free networks as shown inFigure 8. Such a theory has found itsapplication in power network vulner-ability analysis [27], [28]. How toembed the CN theory into the sensing,modeling, analysis and control designto bring out fast and reliable control-lers is challenging.

Information Sensing

and Processing

The deployment of a large quantity ofsmart meters requires fast real-timedata sensing, transmission, andsynthesis to make it usable for deci-sion-making for SG operations andcontrol. New methods are needed toautomate monitoring, assessment,and control of grid operations tomeet economical, social, and envi-ronmental requirements. The keytasks involved in SG include fault andstability diagnosis, reactive powercontrol, distributed generation foremergency use, network reconfigura-tion, system restoration, and demandside management analysis [22]. Thisrequires advanced technologies toenable intelligent real-time monitor-ing, assessment, and control of SGthrough ICT.

These challenges require signifi-cant research in assessing whetherexisting theories and tools are ad-equate and what the limitations are.Furthermore, a new generation of

tools may be needed, such as thosebased on the CN theory to deal withproblems associated specificallywith SG. For example, the rolling outof advanced metering infrastructure(AMI) makes it possible to acquirereal-time information of energy use,connect RE to grids, manage poweroutages and faster restoration, faultdetection, and early warning. Howto fast process an extremely largevolume of signals and sensors,retrieving required information,identifying operation patterns, andcontrol of power systems is an openquestion. Data-mining technologiesmay be suitable for dealing with thehuge dimensions of data sets, butthey are unable to deal with the time-series nature of the metering data in atimely fashion. Time series analysismethods may be suitable for dealingwith temporal nature of the meteringdata. However, they are unable to dealwith the huge dimensionality of thedata sets. Bringing these two schoolsof thoughts together will give rise toefficient and effective data sensing,processing, and synthesis methodsfor SGs. For example, data stream anal-ysis can be an effective technology[25] and may become a significant toolin combination with the CN theory.

Intelligent Systems

Future SG requires not only automa-tion of operations at the lower opera-tional levels, but also high-leveldecisions to take consideration ofmacro economical and social require-ments. Decision support is also a keyin making SG more responsive to userdemands. A typical decision supportframework shown in Figure 9 is aknowledge-based meta-fuzzy system,incorporating expert systems andextended fuzzy systems including anew meta-fuzzy logic mechanism anda discourse semantics as an explana-tory mechanism [30]. One challengeis to overcome the lack of decisiontransparency to the end users in thecurrent decision-support systemsand avoid a black box system,which inhibits users to apply thembecause they are not allowed toaccess the sophisticated reasoning

(a) (b)

(c) (d)

FIGURE 8 Typical types of complex networks. (a) Regular network. (b) Random network.(c) Small-world network. (d) Scale-free network.


process of the tool. There is a needfor an effective explanation to signifi-cantly improve the usability of suchtools. It is obvious that neither tradi-tional knowledge-based systems norquantitative-based machine learningalgorithms are directly applicable,because they focus on providing gen-eral recommendations and lack a mec-hanism to deal with problem-specifictuning. Operational staff need to con-tinuously access new information, aswell as assess and reflect on their ownpractice for decision-making. Theyalso require knowledge of decisionheuristics and practice-based reflec-tion-in-action support [31].

Since distribution systems werenot designed for bidirectional powerflow, the current state-of-the-art dis-tribution systems have very limitedsmart behavior capabilities, and it isexpected that in the near future thedistribution systems will have amajor redesign in their infrastruc-ture. Making a grid smarter requiresthe ability for it to take into accountall the available information as partof the decision-making process.

Recently, the approach of multiagentsystems (MASs) is shown as an inter-esting solution for this challenge. Anagent is a software entity that canrepresent and control an actuatorcomponent, such as a source, a stor-age unit, or a load. Agents can com-municate and interact with eachother and their environment. Thisallows them to cooperate or competetoward local and/or global goals. AMAS is thus a group of agents, eachof them with a given intelligencecapacity, forming a kind of distributedintelligent system. An application ofMAS technology to enable active con-trol functions in the distribution net-work is introduced in [32], whichfocuses on three main aspects of dis-tributed state estimation, voltagecoordinated control, and power flowmanagement. By providing a highlevel of efficiency, flexibility, and intel-ligence, this concept creates an im-portant element of the SG. In additionto the new control methods such asMAS, new functionalities will need toemerge and be supported by futurecontrol systems [33].

Control Systems

SG systems are extremely complexwith large numbers of diverse com-ponents connected through a vastand geographically extended net-work. SG systems exhibit the follow-ing features: 1) a large-scale networkstructure; 2) many of the controlsare embedded in the system, withsome having scope for variable struc-ture tuning; future control designs,which must allow for and enlistwhere possible these existing con-trols; 3) the overall control schemehas a hierarchical structure; 4) theavailable control actions are alreadylargely physically determined andhave diverse timing, cost and priorityfor action; 5) the control goals aremultiobjective with local and globalrequirements, which vary with sys-tem operating state, e.g., normal andinsecure states in power systems;and 6) there is a need for a high levelof distributed global control mecha-nism, which can provide a metaviewto coordinate local controllers [34].

The nature of such a complex net-work poses new challenges for the

Knowledge Base

ActualResults/Cases

VariableMembership

Editor

IfThenRuleEditor

Input Fuzzifier InferenceEngineMeta

Consequent

DiscourseSemantics

Output 1

Output 2

Output 3

Output n

Discourse LayerEditor Layer

Data Layer

Sensor/UserLayer

System Layer

Real-World Layer

Output Layer

Data Set,Anecdote

Reference,Cases, Etc.

ManualOperations,

Sensors, Etc.

Explanation

FIGURE 9 An industrial decision support framework.


existing control theory. Control oflarge-scale systems has been re-searched for many years. A commonphilosophy is to use a decentralizedapproach that considers the large-scale systems as a collection of inter-connected subsystems, with a decom-position that is derived directly fromthe physical description of the prob-lem and leads to a natural grouping ofstate variables. For ill-coupled subsys-tems, this allows the control to be for-mulated based on local states andfeedback while considering globalinfluence [35]. There has been exten-sive research on the control of large-scale systems in a decentralized wayand its applications in large-scalepower systems [36]. However, mostdecentralized control methods relyon modeling the systems with fullstates, which is not feasible in verylarge-scale network systems such asSG because of their huge dimensional-ity and complexity. A new way ofthinking is to consider the connectiv-ity and topological structures as fac-tors based on the CN theory toovercome the dimensionality andcomplexity problem [26], whichwould simplify the modeling and con-trol tasks. Some exploitation of thisidea has been seen in related areassuch as pinning control of complexnetworks (taking advantage of the

topological structure of the networkto simplify the analysis and controldesign) [37]. Many control compo-nents in SG have switching elements,e.g., converter controls and powersystems stabilizers. How to make useof CN theory in a large-scale distrib-uted, switching-based control system,and available intelligent discontinu-ous controllers [29] is another areaworth exploring.

IT InfrastructureIT infrastructure is the backbone ena-bler for SG to be aware of what isgoing on, deciding best strategies formonitoring and control and respond-ing to demand side responses whilekeeping the grids to operate effi-ciently, cost less, and neutralize thenegative impact on environments.This can be achieved by smart two-way communication (smart link) anddevices (e.g., smart meters). A plat-form for information exchange isneeded that enables smart applian-ces and smart meters to exchangethe information between them asshown in Figure 10. The cyber-physi-cal systems (CPSs) can offer such aplatform that allows for both thedigital information as well as tradi-tional energy (for example, electric-ity) to flow through a two-way smartinfrastructure.

CyberPhysical Systems

CPS was defined by the National Sci-ence Foundation (NSF) as physicaland engineered systems whose oper-ations are monitored, coordinated,controlled, and integrated by a com-puting and communication core.Since its inception, CPS has beenapplied in multiple disciplines suchas embedded systems and sensornetworks. More specifically, CPS canbe considered as a networked infor-mation system that is tightly cou-pled with the physical process andenvironment through a massivenumber of geographically distrib-uted devices [38]. As networked in-formation systems, CPS involvescomputation, human activities, andautomated decision-making enabledby ICT. More importantly, thesecomputations, human activities, andintelligent decisions are aimed atmonitoring, controlling, and inte-grating physical processes and envi-ronments to support operations andmanagement in the physical world.The scale of such information systemsranges from microlevel, embeddedsystems to ultralarge systems of sys-tems. This thus breaks the boundarybetween the cyber and the physicalby providing a unified infrastructurethat permits integrated modelsaddressing issues from both worldssimultaneously.

To realize the CPS architecture inthe SG, we need a special-purposededicated infrastructure, which shouldhave wireless sensors connected tothe Internetreal-time and secureseveral protocol-exchange mecha-nisms for exchanging the informa-tion. This can be achieved by usingthe Internet of things or Web of things(WoT) computing paradigm as a dy-namic global network infrastructurewith self-configuring capabilities basedon standard and interoperable com-munication protocols. Here, physicaland virtual things have identities,physical attributes, virtual personal-ities, and use intelligent interfaces andare seamlessly integrated into theinformation network [39]. WoT is aflexible and mobile framework thatcreates a network among the different

Utility Grid

Utility Provider

Smart Devices

Smart Storage

Smart Meters

Smart Gateway

Smart Link(Price)

Smart Link(Consumption)

On-DemandProvision

Deliver

FIGURE 10 Smart link between the utility grid and smart gateway.


devices by deploying sensors, thusturning them into smart devices. Suchwireless sensing technologies can as-sist in using the energy efficiently in anumber of ways. The building block forhaving a WoT-based communicationplatform is representational statetransfer (REST), which is a specificarchitectural style [40] based on thearchitecture of the Web and the HTTP1.1 protocol, which has become themost successful large-scale distrib-uted application. REST specificallyintroduces numerous architecturalconstraints to the existing Web serv-ices architecture elements to: 1)simplify interactions and composi-tions between service requestersand providers and 2) leverage theexisting World Wide Web (WWW)architecture wherever possible.

The WoT framework for CPS hasfive layers: device, kernel, overlay,context, and application program-ming interface (API). Underneath theWoT framework is the cyberphysicalinterface (e.g., sensors, actuators) thatinteracts with the surrounding physi-cal environment. The cyberphysicalinterface is an integral part of the CPSthat produces a large amount of data.The WoT framework allows the cyberworld to observe, analyze, under-stand, and control the physical worldusing these data to perform missiontime-critical tasks. The WoT-basedCPS architecture is shown in Figure 11.

Realization of WoT-Based

CPS Architecture

To realize the SG framework by us-ing the WoT-based CPS architecture,

some of the challenges that need tobe addressed are as follows [41]:n IP addressable things and smart

gateways: When a bidirectionalcommunication link exists betweenthe providers and consumers, theinformation exchanged betweenthe various smart devices andsmart meters has to be regulatedthrough a smart gateway.

n Flexibility in wireless communica-tion: A key element to facilitateWoT-based architecture is theability to deploy sensors at dif-ferent devices with flexibilityand mobility using WSN technol-ogy, resulting in 1) reduced in-stallation, integration, operation,and maintenance costs, 2) speedyinstallation and removal, 3) mobileand temporary installations,

CPS Developers

CPS Users

WoT APIWoT ContextWoT Overlay

WoT Overlay

WoT Kernel

WoT Device

CPS Node

WoT Overlay

WoT Kernel

WoT Device

CPS Node WoT Overlay

WoT Kernel

WoT Device

CPS Node

WoT Overlay

WoT Kernel

WoT Device

CPS Node

CPSDesktops

CPS Mashups

Actuators

Sensors

PhysicalEnvironment

CPS Event

CPS Event

xy

xy

FIGURE 11 Reference architecture of CPS.


4) real-time and up-to-date en-ergy consumption and informa-tion services available at anytime,anywhere, and 5) enhanced visu-alization, foresight, and forecast-ing capabilities.

n Common embedded platform forinformation exchange: The follow-ing features must be investigatedwhen developing the WoT archi-tecture: context independence,service node, or a resource model;accessibility; data exchange; loca-tion transparency; contracts; plugand play; and automation.

n Representation of events: Variousevents such as meter reading,meter control, meter events, cus-tomer data synchronization, andcustomer switching need to bedefined. These complex eventsshould be decomposed into anaggregation of simpler events.

n Abstraction of suitable events:Abstraction of the smart deviceinformation for event and infor-mation representation, composi-tion of data from multiple sensorsbased upon the requirements laidby a particular application sce-nario, decomposition of complexfunctionality into aggregations ofsimpler sensors, semantics enrich-ment during the sensor compositionphase to support automatic sen-sor discovery, selection, and com-position should be defined.Thus, the provision of IT infra-

structure for SG poses importantarchitectural, device structure, andsoftware and system abstraction chal-lenges, which are expected to beaddressed over the next few years.

Discussion and ConclusionsIn this article, we have introducedsome background and basic conceptsof SGs. We have presented somefuture research and development chal-lenges and opportunities in the SG inthree related but distinct focal areasas pertinent to IES. It should beemphasized that future developmentsin these three focal areas are not sup-posed to stand alone and need to beintegrated. For example, an SG can beframed as a series of loosely coupled

microgrid clusters, with each clusterpossibly including one or more ro-tating machines (wind turbines,microhydro generators, cogenerationsystems, etc.), a number of direct PVpower injection systems, consumerloads, and power-electronic compen-sators such as localized STATCOMs.A holistic design approach can betaken to subdivide a global optimiza-tion task into subtasks for local clus-ters so that a global control strategycan be formed and converters can bedesigned to respond to coordinatedlocal subtasks to enable a global con-trol that is distributed and hierarchi-cal. We hope this article serves thepurpose of inspiring researchers andpractitioners to become further in-volved in this exciting frontier of SG.

AcknowledgmentWe would like to acknowledge assis-tance from Prof. Elizabeth Chang andDr. Omar Hussain for discussions aboutthis article and Dr. Ajendra Dwivedifor assistance in drawing the figures.

BiographiesXinghuo Yu ([email protected]) isthe director of Platform Technolo-gies Research Institute at Royal Mel-bourne Institute of Technology (RMIT)University, Australia. He has pub-lished more than 380 refereed papersin technical journals, books, and con-ference proceedings. He is the vicepresident of planning and develop-ment of the IES, an IEEE IES Distin-guished Lecturer, and chair of theIES Technical Committee on SGs. Hestarted his SG research from a pro-ject on detection of leakage currentson distribution networks with Austra-lian utilities in 2005, funded by theAustralian Research Council. He is aFellow of the IEEE and also a fellowof the Australian Computer Society(ACS) and the Institution of Engi-neers Australia (IEAust). His researchinterests include variable structureand nonlinear control, complex andintelligent systems, and industrialapplications.

Carlo Cecati ([email protected])is a professor of industrial electronics

and drives at the University ofLAquila, Italy. For the last 15 years,he has been a member of the organ-izing committees of numerous IECONand ISIE and an active member ofthe IES. He is a cochair of the IESCommittee on SGs and a member ofthe Committee on RE Systems andthe Committee on Power Electronics.Since 2009, he has been coeditor-in-chief of IEEE Transactions on Indus-trial Electronics. He is a Fellow of theIEEE. His research interests coverseveral aspects of power electronics,electrical drives, RE, and SGs.

Tharam Dillon ([email protected]) is a research pro-fessor at the Digital Ecosystems andBusiness Intelligence Institute, CurtinUniversity of Technology, Australia.He has published more than 800papers in international conferencesand journals, eight authored books,and six edited books. He developedthe most widely used methods forload forecasting, system price fore-casting in deregulated systems, andmedium-term economic productionplanning for hydrothermal systems.This work led to his work in SG. Avariant of this is already being im-plemented for remote sites underthe Smart Camp ARC project. He isa Life Fellow of the IEEE and a fel-low of ACS and IEAust. His researchinterests include Web semantics,ontologies, Internet computing, CPS,neural nets, software engineering,and data mining and power systemscomputation.

M. Godoy Simoes ([email protected]) received the Ph.D. degreefrom the University of Tennessee,Knoxville, in 1995. He is currentlywith the Colorado School of Mines,where he has been establishing re-search and education activities in thedevelopment of intelligent controlfor high-power-electronics applica-tions in renewable- and distribute-d-energy systems. He was a pastchair for the IAS IACC and cochairfor the IES Committee on SGs. Hehas been involved in activitiesrelated to the control and manage-ment of smartgrid applicationssince 2002 with his NSF Career


Award for Intelligent-Based Per-formance Enhancement Control ofMicropower Energy Systems. He isa Senior Member of the IEEE.

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