An Industrial Electronics Perspective

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

The power grid is a

massive intercon-

nected network used

to deliver electricity

from suppliers to

consumers and has

been a vital energy

supply. To minimize the impact of cli-

mate change while at the same time

maintaining social prosperity, smart

energy must be embraced to ensure

a balanced economical growth and

environmental sustainability. There-

fore, in the last few years, the new

concept of a smart grid (SG) became

a critical enabler in the contempo-

rary world and has attracted increas-

ing attention of policy makers and

engineers. This article introduces the

main concepts and technological

challenges of SGs and presents the

authors’ views on some required

challenges and opportunities pre-

sented to the IEEE Industrial Elec-

tronics Society (IES) in this new and

exciting frontier.

Electricity and the Electric Grid

Electricity became the subject of

scientific interest in the late 17th

century with the work of William Gil-

bert. Since then, a number of great

discoveries and technological devel-

opments have been achieved. TheDigital Object Identifier 10.1109/MIE.2011.942176

Date of publication: 23 September 2011

© INGRAM PUBLISHING

1932-4529/11/$26.00&2011IEEE SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 49

greatest discovery of them all was

from Michael Faraday, who discov-

ered the principle of electromagnetic

induction in 1831. At the turn of the

20th century, the inventions and dis-

coveries by Thomas Edison and

Nikola Tesla laid the foundations for

building modern electric grids. The

grid serves as the major means of

vital energy supply. As shown in

Figure 1, distinct operations of electric

grids include generation, transmis-

sion, and distribution. The electricity

is first generated and then transmit-

ted over long distances to the substa-

tions where it is further distributed

to the consumers. The generation

system is driven from several forms

of energy, such as high-density energy

sources of coal, gas, and oil, as well as

diffusible renewable sources such as

hydro, dispatchable biomass, solar

energy, and wind. Presently, the domi-

nating generation mechanism is by

electromechanical generators driven

by heat engines fueled by chemical

combustion or nuclear fission. Tradi-

tional fossil fuel power plants have a

very low efficiency, i.e., from source

(coal) to the end user, approaching an

overall 30% (thermodynamical cycles

have a limited efficiency and there are

several other losses, including the

transmission and distribution losses),

whereas local generation from renew-

able energy (RE) sources will have a

much higher efficiency (estimated to

be about 70%). Data from the Environ-

mental Investigation Agency (EIA)

International Energy Statistics 2010

supports that 63% electricity in the

United States comes from fossil fuel

combustion, while in China, it is

more than 70%, with most developed

countries within the same range.

The transmission system is usually

composed of higher voltage trans-

mission lines that transport electric-

ity for long distances and deliver to

distribution substations where the

voltage is lowered for further distri-

bution to consumers through distri-

bution networks.

Need for Smart Energy

Smart energy refers to making energy

use more efficient by utilizing the

integration of advanced technologies

such as information and communica-

tion technologies (ICTs) and elec-

tronics and material engineering aimed

at maintaining an environmentally

sustainable system. Smart energy is

needed for a number of reasons. The

primary reason is the limited avail-

ability of non-RE sources such as coal,

gas, and oil on Earth. It is estimated

that Earth has only a few decades of

supply 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 is

playing a more important role for

future energy supply. Advanced tech-

nologies are needed to make these

energy supplies more reliable and

secure [1]. While it is predicted that

RE will be the major future energy

supply in the long run, non-RE will

continue to be the dominant energy

source for the middle and short term

because they are still more economi-

cally feasible with higher energy

density and easy access for its use.

However, government incentives and

larger-scale deployment are making

RE more affordable. The secondary

reason to move toward RE is related

to pollution concerns; almost all

energy production and usage involves

pollution to the environment and

social costs that are usually hidden

from 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 and

oil yields carbon dioxide (which causes

global warming), nitrous oxide (which

causes smog that is harmful to the

elderly), and particulate or dust air

(which increases the risk of lung

cancer).

All of these reasons require us to

think seriously about how to ensure

environmental sustainability while

maintaining needed economic growth.

Smart energy is about taking a holistic

approach in dealing with efficient

energy supply and demand from eco-

nomical, environmental, and social

perspectives. For example, there are

many strategies being developed on

how to improve efficiency with less

waste and better quality of service. It

also requires a paradigm change in

dealing with energy supply and

demand, e.g., new technologies to

harvest and use RE, improved en-

ergy distribution to optimize the

assets utilization and reduction of

capital expenditure, and improved

management of energy use to re-

duce losses with embedded genera-

tion technologies.

More broadly, smart energy encom-

passes a wide range of research and

development issues such as industry

sector-wide standardization, policy

framework and reform, operational

technologies and systems (e.g., con-

trol systems, grid security and stabil-

ity, fault detection and prediction,

data and communication, demand

management, self-healing grids, and

long distance energy supply), informa-

tion and social technologies and

systems for carbon mitigation, grid-

to-customer integration, customer

behaviors, cross-sector large-scale

modeling, and optimization [2].

The Concept of SGs

The term SG refers to electricity net-

works that can intelligently integrate

the behavior and actions of all users

connected to it, e.g., generators, cus-

tomers, and those that do both—to

efficiently deliver sustainable, eco-

nomical, and secure electricity sup-

plies. In the United States, the meaning

of SG is much broader, referring to a

means 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 technologies

that enabled the Internet to the utility

and the electric grid [42]. In China, SG

refers to a more physical network-

based approach to ensure energy

supply 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 all

European countries in an RE-based

system [44]. A vision of an SG is illus-

trated in Figure 2. The National Insti-

tute of Standards and Technology

(NIST) provides a conceptual model

as shown in Figure 3, which defines

seven important domains: bulk gener-

ation, transmission, distribution, cus-

tomers, service provider, operations,

and markets. In the United States, the

importance of SG is currently consid-

ered as equivalent to what was taken

for the Eisenhower Highway System

(envisioned in the 1950s to transform

the transportation infrastructure in

the 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 in

a microgrid architecture as shown in

Figure 4 [3]. In the microgrid, some

feeders can have sensitive loads that

require local generation. Intentional

islanding from the grid is provided

by static switches that can separate

them in less than a cycle. When the

microgrid is connected, power from

local generation can be directed to

the feeder with noncritical loads or

be sold to the utility if agreed or

allowed by net metering. In addition,

a microgrid can be designed for the

requirements of end users, a stark

difference 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 –A microgrid architecture.

52 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011

generation that affects electricity

quality; large-scale networks of small

distributed generation mechanisms,

e.g., photovoltaic (PV) panels, bat-

teries, wind and solar, and plug-in

hybrid electric vehicles (PHEVs),

which result in high complexity.

Another important characteristic

of power usage is that the peak of

electricity usage is normally around

30% above the average electricity

usage, which means reducing peaks

would result in an increased capacity

of energy supply, allowing the avail-

ability of future growing energy

needs while delaying building more

new power generation plants. One

important concept can be defined as

wasteless, i.e., finding the bottleneck

of unnecessary waste. For example,

energy use for electricity transmis-

sion and distribution may take up to

14% of the input energy generated.

Therefore, embedded generation and

siting generators close to the point of

consumption are key considerations

in reducing wasted energy (such a

concept is usually defined as distrib-

uted generation).

A more significant issue is how to

use ICT, electronics, and other ad-

vanced technologies to enhance the

efficiency of energy use. This includes

new technologies (e.g., smart meters

and telecommunication technologies)

for sensing, transmission, and pro-

cessing information relating to grid

conditions, which are vital for timely

monitoring and controlling the net-

work to ensure efficient energy

supply, security, and safety of the

network and demand management to

meet the customer needs.

To address the above issues, the

following technological advances are

required:

n Distributed control: Control needs

to be distributed, enabling lower

communication needs if grid com-

ponents such as source, loads,

and storage units can be con-

trolled locally or can make some

decisions by themselves [4], [5].

n Demand prediction: This technology

already exists at the transmission

level but is very rare at the distri-

bution level. It estimates demand

on a given portion of the grid a few

hours or days in advance.

n Generation prediction: Generation

can be estimated, mostly for RE

resources such as solar panels

and wind turbines. These estima-

tions heavily rely on weather pre-

dictions and are indispensable to

be able to schedule the use of

non-RE sources by utilities and

to integrate intermittent energy

sources.

n Demand response: Reducing peak

demand is an essential function-

ality to achieve a more efficient

grid. Mechanisms such as load

shedding and dynamic pricing can

help reduce total demand. An-

other approach to limiting demand

peaks is automatic demand dis-

patch, which consists of delaying

the 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 are

concerned with the application of

electronics and electrical sciences.

These applications enhance the in-

dustrial and manufacturing processes,

addressing the latest developments

in intelligent and computer control

systems, robotics, factory communi-

cations and automation, flexible man-

ufacturing, data acquisition and signal

processing, vision systems, and power

electronics. Therefore, the authors are

next presenting some of their views on

the future developments in three key

research themes in IES that are

directly 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 deeper

penetration of renewable and alterna-

tive energy sources, which require

power converter systems. Typically,

a power converter is an interface

between SG and local power sources

[6]. Moreover, they are required by

several subsystems involving energy

storage or harmonic compensation

interconnecting areas or separated

grids [7].

Primarily, RE such as solar (PV)

and wind play a significant role as the

main sources for SG, while minihydro,

geothermal, dispatchable biomass,

tidal, and even hydrogen-based fuel

cells can also be incorporated. RE

sources are increasingly being in-

stalled in residential and commercial

applications (typically with power

range of a kilowatt), and many coun-

tries are already incorporating a

significant portfolio in distributed

energy, with expected growth during

the next few years [8]. However, the

intermittent nature of RE affects the

output characteristics of generator

and converter sets (i.e., their voltage,

frequency, and power); hence, they

cannot be used in stand-alone config-

urations and must be compensated

by integration with energy storage. A

power electronic converter is always

needed to allow energy storage dur-

ing surplus of input power and com-

pensation in case of lack of input

power. Figure 5 shows the effect that

a power converter must consider

absorbed power by the load versus

power injected into the grid. The ac

load 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 prescribed

limits allowed by the utility, and pos-

sibly the inverter must supply reac-

tive power in addition to the active

power. Through converters, several

sources of energy can be integrated

to the grid as shown in Figure 6. Fossil

fuel 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.

SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 53

is necessary. Wind, hydro, and natural

gas usually require rotating machines

as well, but a large storage unit must

compensate their intermittency [9].

Sunlight, hydrogen, and sometimes

natural gas require dc/dc conversion,

with integration to the ac grid through

inverters, while most of the time using

batteries to compensate for their

intermittency. Figure 6 also shows the

needs of islanded operation and the

required needs for disconnecting and

connecting to the grid in accordance

to the real-time needs. In Figure 7, a

distributed generation system archi-

tecture is shown, where Figure 7(a)

shows a typical dc link integration

very commonly used when dc sources

(PVs, fuel cells, and batteries) are inte-

grated. Figure 7(b) shows a typical ac

link integration, where turbines and

rotating machines are integrated

through the utility line frequency,

and Figure 7(c) shows a high-

frequency ac link integration, where

fast response and decreased system

size can be achieved. When intercon-

nected with distribution systems,

these small, modular generation

mechanisms can form a new type of

power system called the microgrid,

and when associated with control and

intelligence, can be called an SG [3].

Depending on the available sour-

ces, inverters, rectifiers, and dc/dc

converters are required. A rectifier

might be a front end for an electric

grid connected to a load or an in-

verter can be the interface with local

generation. There are other convert-

ers for intermediate stages, necessary

for adapting the energy produced by

the source in such a way that both the

energy source and the inverter oper-

ate at their highest efficiency.

Power converters for SG integra-

tion and particularly inverters pre-

sent a higher complexity when

compared with those used in indus-

trial or stand-alone RE systems

because they have to efficiently man-

age bidirectional power flow as well

as critical situations. They must be

capable of either absorbing (in a

controlled manner) energy from the

grid for supplying the local load or

injecting the surplus of the locally

produced energy into the grid [10].

Moreover, they must be capable of

mitigating fluctuations and distor-

tions, thus reducing the size of low-

pass filters. These functions require

new functions not commonly avail-

able in standard converters.

Renewable and alternative en-

ergy systems require the following

specifications:

n High efficiency: Obviously, only a

negligible part of the power should

be dissipated during conversion.

This requirement is severely af-

fected by input and output energy

fluctuations and by conversion

efficiency, changing with the

quantity of energy at input/out-

put terminals. The converter has

to 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 PC

PSQS

QLQC PL = PS + PC

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

54 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011

adjustment of the converter pa-

rameter ensuring the highest en-

ergy transfer. This requires two

or more conversion stages (typi-

cally ac/dc and/or dc/dc and/or

dc/ac in wind, hydro, and geo-

thermal generators).

n Optimal energy transfer: All RE

sources are energy constrained

and as such they need algorithms

to achieve the maximum power

point. Usually, PV arrays and wind

generators must be intercon-

nected with maximum power

point tracking (MPPT) to opti-

mize the energy transfer.

n Bidirectional power flow: In almost

all cases, the power converter

has to be able to indifferently

supply 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

StationaryGeneration

RotatoryStorage

dc Link

ac LoadsRotatory

Generation

StationaryStorage

60-Hz Grid

ac Loads

ac Loadsand VAR

Compensators

60-Hz Grid

StationaryStorage

StationaryGeneration

RotatoryStorage

ac Link

ac LoadsRotatory

Generation

(a)

(b)

(c)

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

SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 55

n High reliability: The continuity of

service is a major issue when de-

livering energy.

n Synchronization capabilities: All

power sources connected with

the grid have to be fully synchron-

ized, thus ensuring high efficiency

and eliminating failures, and there-

fore, standards such as IEEE 1547

[45] should be incorporated in

the power electronic interfaces.

n Electromagnetic interferences (EMIs)

filtering: The quality of the en-

ergy injected on the grid must

respect electromagnetic compat-

ibility (EMC) standards.

n Smart metering: The converter

between the local source/load and

the grid must be capable of track-

ing the energy consumed by load

or injected on the grid transmit-

ting. Real-time information must

be passed to an automatic billing

system capable of taking into ac-

count parameters as the buy/sell

energy in real time at the best

economic conditions and inform-

ing the owner of the installation

of all required pricing parameter

decisions.

n Communication: Intelligent func-

tioning of SGs depends on their

capability to support communi-

cations at the same time that

power flows in the systems. Such

functions are fundamental for over-

all system optimization and for

implementing sophisticated dis-

patching strategies [11].

n Fault tolerance: A key issue for the

SG is a built-in ability of avoiding

propagation of failures among the

nodes and to recover from local

failures. This capability should be

managed 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 anywhere

through Internet-based communi-

cations.

SG systems require power con-

verters with functional controls for

smart power generation with possi-

bility of supplying power to local

loads as well as to the utility. A utility

could also request an SG user to

provide voltage support at the point

of common coupling (PCC). There-

fore, the primary intent of a smart

inverter is to enable efficient inter-

connection and economical opera-

tion of dispersed installations to the

utility grid interacting with smart

metering, incorporation of smart

appliances, provision of pricing infor-

mation and/or some control options

to the consumers, and information

exchange for a fully networked sys-

tem enabled by massively deployed

sensors. Traditionally, voltage sags

in distribution systems are corrected

using utility operated capacitor

banks. However, with the advent of

smart inverters, these services may

also be managed by the customer.

This represents one of the tenets of

the SG initiative, i.e., enabling active

participation of consumers in the

demand 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 intensity

and voltage depends on temperature.

Those cells are arranged in series

and/or in parallel, achieving the de-

sired level of voltage and current. A

dc/dc converter provides the neces-

sary voltage boost and regulation

(under control of an MPPT algo-

rithm) necessary for extracting the

highest power from the sun. These

algorithms vary the duty cycle

attempting to maintain fixed output

and at the same time highest PV

energy extraction. The dc/dc con-

verter can be either with or without a

transformer; the latter is inserted for

providing galvanic insulation and out-

put voltage-level amplification. The

presence of a transformer reduces the

overall efficiency due to copper and

core losses and increases the cost of

the residential applications.

For medium-high power, there are

limitations on the availability of suita-

ble high-frequency transformers for

high power (typically limited to 20

kVA applications). However, high-

frequency transformers are common

in low to medium power applications

(such as residential inverters and dc

power supplies). Line frequency trans-

formers may be used in grid interface,

but there are power electronic topolo-

gies specifically designed to avoid

transformers or magnetic components.

Recently, there has been a significant

interest in the use of resonant and

quasi-resonant dc/dc converters in PV

generation systems, because of their

high efficiency and reduced switching

losses [12], [13]. However, these con-

verters are complex to control, partic-

ularly when a wide input voltage

variation may occur as in PV applica-

tions because the resonance phenom-

ena are strictly connected to the

values of the so-called resonant tank

while the input voltage variations can

be contrasted by varying the operat-

ing frequency.

The output of a dc/dc converter

is applied to a PWM inverter with grid

synchronization capabilities, neces-

sary for correct synchronous opera-

tions followed by a tight low-pass

filter, necessary for respecting EMC

standards. Phase shifting among the

distinct generators is usually ad-

dressed by a phase-locked loop (PLL)

used for a correct generation of the

ac voltage by the inverter, thus avoid-

ing current circulations due to a

phase shift among the inverter and

the grid.

Recently, an increasing interest

has been found in new topologies,

which may allow improvements in the

conversion process, such as cascaded

H-bridge multilevel converter for

dc 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.

56 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011

topology consists of a number of H-

bridges connected in series, each one

with its own generator, obtained by a

group of cells [14], [15]. The advan-

tages are better utilization of solar

cells and output voltage waveform,

achieving a significant reduction of

the output filter and an increase in the

efficiency of PV energy conversion

because of their improved utilization.

Another interesting approach con-

sists of the use of low-power separate

converters, one for each panel or for a

small group of panels, directly pro-

ducing the desired output voltage

level. In this case, advantages may be

derived from an improved sun energy

conversion with reduced losses (out-

put currents depend of the output

voltage level) and lower wiring costs.

An energy storage system may be con-

nected in parallel at the inverter input

terminal for reducing the impact of PV

energy 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, usually

consisting of a cascade ac/dc rectifier,

dc/dc converter (useful for dc link

voltage regulation and control), and

dc/ac converter. Modern WECS in-

clude an active rectifier, rather than

a simple diode bridge, resulting in

improved efficiency of the conver-

sion process and for the generator

itself, which can operate closer to its

optimum conditions than using the

simple diodes. In this case, dc/dc

conversion 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 those

used in PV converters except that

usually WECS produce higher power

levels (up to 10 MVA) and the MPPT is

designed to optimize the turbine aero-

dynamics [17]. Multilevel converters

appear very interesting and promis-

ing, but, different from the previous

case, the source is unique; therefore,

other topologies such as neutral point

clamped or the flying capacitor may

be 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 been

developed over the past two deca-

des, to increase the efficiency of

transmission lines through the use of

power converters, which provide

continuous injection of lead or lag

currents to maintain the right dis-

placement of either current or volt-

age and to reduce the apparent line

impedance. FACTS also make the sys-

tem more reliable by reducing tran-

sient line disturbances such as glitches

and voltage sags and more intelligent

because power flow can be com-

pletely controlled with power con-

verters such as static synchronous

compensators (STATCOMs), unified

power flow controller (UPFC), and

various pulsewidth modulated cas-

caded topologies employing insulated

gate bipolar transistors (IGBTs) at

high-voltage levels [21]. FACTS have

been typically applied to transmission

lines, but they have also become im-

portant for large distributed genera-

tion applications, such as wind farms

or large central solar systems, and it

is expected that FACTS technology is

to be further applied to distribution

systems that will be redesigned in the

near future for the SG. It is expected

that those functions in charge of

STATCOMs, UPFC, and other convert-

ers specifically designed for FACTS

would be incorporated within the

already existing power converters

for the SG.

Intelligent Systemsand ControlSGs are highly complex, nonlinear

dynamical networks by nature that

present many theoretical and practi-

cal challenges. Monitoring and con-

trol are the key issues that need to be

addressed to make SG more intelli-

gent and equipped with self-healing,

self-organizing, and self-configuring

capabilities. This requires much more

efficient 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 rather

reactive, with a number of critical

tasks performed by human opera-

tors based on the presented raw data

and past experiences [23]. There are

two questions: 1) how to automate

the acquisition of useful operation

information to make informed opera-

tion decision in a timely fashion and

2) how to present the information to

users in a most compelling and in-

formed way to help users make high-

level operation decision without bog-

ging down into unnecessary waste of

time in understanding rather raw

data. This all becomes more critical as

the information available will grow

exponentially with more sensors/

meters installed.

Dealing with Network Complexity

With increasing complexity com-

pounded by the distributed nature of

RE, real-time performance is a bottle-

neck in deriving just-enough and

just-in-time information for SG to

operate efficiently. The intermittent

availability of RE requires considera-

tion of the entire operation regime to

deal with the associated problems

such as storages and variable power

quality [23]. The bidirectional elec-

tricity flow in the SG due to penetra-

tion of a large number of small

generation systems and versatile

usages also pose challenges. Tradi-

tional state-space modeling and

WoT is a flexible and mobile framework that

creates a network among the different devices

by deploying sensors.

SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 57

control methodologies may not be

suitable for such tasks. A paradigm

shift may be needed in the way the

network is dealt with. One promising

methodology is the complex net-

work (CN) theory [26], which origi-

nated from the graph theory and can

be used in combination with existing

methods and tools to simplify the

analysis and design so that timely

response is possible. The essence of

this theory is to study the subject

system from the aspects of structure

and dynamical function of a collec-

tion of nodes and links without rely-

ing heavily on the dimensionality of

the system. Typical complex net-

works include regular networks, ran-

dom networks, small-world networks,

and scale-free networks as shown in

Figure 8. Such a theory has found its

application in power network vulner-

ability analysis [27], [28]. How to

embed the CN theory into the sensing,

modeling, analysis and control design

to bring out fast and reliable control-

lers is challenging.

Information Sensing

and Processing

The deployment of a large quantity of

smart meters requires fast real-time

data sensing, transmission, and

synthesis to make it usable for deci-

sion-making for SG operations and

control. New methods are needed to

automate monitoring, assessment,

and control of grid operations to

meet economical, social, and envi-

ronmental requirements. The key

tasks involved in SG include fault and

stability diagnosis, reactive power

control, distributed generation for

emergency use, network reconfigura-

tion, system restoration, and demand

side management analysis [22]. This

requires advanced technologies to

enable intelligent real-time monitor-

ing, assessment, and control of SG

through ICT.

These challenges require signifi-

cant research in assessing whether

existing theories and tools are ad-

equate and what the limitations are.

Furthermore, a new generation of

tools may be needed, such as those

based on the CN theory to deal with

problems associated specifically

with SG. For example, the rolling out

of advanced metering infrastructure

(AMI) makes it possible to acquire

real-time information of energy use,

connect RE to grids, manage power

outages and faster restoration, fault

detection, and early warning. How

to fast process an extremely large

volume of signals and sensors,

retrieving required information,

identifying operation patterns, and

control of power systems is an open

question. Data-mining technologies

may be suitable for dealing with the

huge dimensions of data sets, but

they are unable to deal with the time-

series nature of the metering data in a

timely fashion. Time series analysis

methods may be suitable for dealing

with temporal nature of the metering

data. However, they are unable to deal

with the huge dimensionality of the

data sets. Bringing these two schools

of thoughts together will give rise to

efficient and effective data sensing,

processing, and synthesis methods

for SGs. For example, data stream anal-

ysis can be an effective technology

[25] and may become a significant tool

in 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-level

decisions to take consideration of

macro economical and social require-

ments. Decision support is also a key

in making SG more responsive to user

demands. A typical decision support

framework shown in Figure 9 is a

knowledge-based meta-fuzzy system,

incorporating expert systems and

extended fuzzy systems including a

new meta-fuzzy logic mechanism and

a discourse semantics as an explana-

tory mechanism [30]. One challenge

is to overcome the lack of decision

transparency to the end users in the

current decision-support systems

and avoid a ‘‘black box’’ system,

which inhibits users to apply them

because they are not allowed to

access 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.

58 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011

process of the tool. There is a need

for an effective explanation to signifi-

cantly improve the usability of such

tools. It is obvious that neither tradi-

tional knowledge-based systems nor

quantitative-based machine learning

algorithms are directly applicable,

because they focus on providing gen-

eral recommendations and lack a mec-

hanism to deal with problem-specific

tuning. Operational staff need to con-

tinuously access new information, as

well as assess and reflect on their own

practice for decision-making. They

also require knowledge of decision

heuristics and practice-based reflec-

tion-in-action support [31].

Since distribution systems were

not designed for bidirectional power

flow, the current state-of-the-art dis-

tribution systems have very limited

smart behavior capabilities, and it is

expected that in the near future the

distribution systems will have a

major redesign in their infrastruc-

ture. Making a grid smarter requires

the ability for it to take into account

all the available information as part

of the decision-making process.

Recently, the approach of multiagent

systems (MASs) is shown as an inter-

esting solution for this challenge. An

agent is a software entity that can

represent and control an actuator

component, such as a source, a stor-

age unit, or a load. Agents can com-

municate and interact with each

other and their environment. This

allows them to cooperate or compete

toward local and/or global goals. A

MAS is thus a group of agents, each

of them with a given intelligence

capacity, forming a kind of distributed

intelligent system. An application of

MAS technology to enable active con-

trol functions in the distribution net-

work is introduced in [32], which

focuses on three main aspects of dis-

tributed state estimation, voltage

coordinated control, and power flow

management. By providing a high

level of efficiency, flexibility, and intel-

ligence, this concept creates an im-

portant element of the SG. In addition

to the new control methods such as

MAS, new functionalities will need to

emerge and be supported by future

control systems [33].

Control Systems

SG systems are extremely complex

with large numbers of diverse com-

ponents connected through a vast

and geographically extended net-

work. SG systems exhibit the follow-

ing features: 1) a large-scale network

structure; 2) many of the controls

are embedded in the system, with

some having scope for variable struc-

ture tuning; future control designs,

which must allow for and enlist

where possible these existing con-

trols; 3) the overall control scheme

has a hierarchical structure; 4) the

available control actions are already

largely physically determined and

have diverse timing, cost and priority

for action; 5) the control goals are

multiobjective with local and global

requirements, which vary with sys-

tem operating state, e.g., normal and

insecure states in power systems;

and 6) there is a need for a high level

of distributed global control mecha-

nism, which can provide a metaview

to coordinate local controllers [34].

The nature of such a complex net-

work poses new challenges for the

Knowledge Base

ActualResults/Cases

VariableMembership

Editor

If–ThenRuleEditor

Input FuzzifierInferenceEngine

MetaConsequent

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.

SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 59

existing control theory. Control of

large-scale systems has been re-

searched for many years. A common

philosophy is to use a decentralized

approach that considers the large-

scale systems as a collection of inter-

connected subsystems, with a decom-

position that is derived directly from

the physical description of the prob-

lem and leads to a natural grouping of

state variables. For ill-coupled subsys-

tems, this allows the control to be for-

mulated based on local states and

feedback while considering global

influence [35]. There has been exten-

sive research on the control of large-

scale systems in a decentralized way

and its applications in large-scale

power systems [36]. However, most

decentralized control methods rely

on modeling the systems with full

states, which is not feasible in very

large-scale network systems such as

SG because of their huge dimensional-

ity and complexity. A new way of

thinking is to consider the connectiv-

ity and topological structures as fac-

tors based on the CN theory to

overcome the dimensionality and

complexity problem [26], which

would simplify the modeling and con-

trol tasks. Some exploitation of this

idea has been seen in related areas

such as pinning control of complex

networks (taking advantage of the

topological structure of the network

to simplify the analysis and control

design) [37]. Many control compo-

nents in SG have switching elements,

e.g., converter controls and power

systems stabilizers. How to make use

of CN theory in a large-scale distrib-

uted, switching-based control system,

and available intelligent discontinu-

ous controllers [29] is another area

worth exploring.

IT InfrastructureIT infrastructure is the backbone ena-

bler for SG to be aware of what is

going on, deciding best strategies for

monitoring and control and respond-

ing to demand side responses while

keeping the grids to operate effi-

ciently, cost less, and neutralize the

negative impact on environments.

This can be achieved by smart two-

way communication (smart link) and

devices (e.g., smart meters). A plat-

form for information exchange is

needed that enables smart applian-

ces and smart meters to exchange

the information between them as

shown in Figure 10. The cyber-physi-

cal systems (CPSs) can offer such a

platform that allows for both the

digital information as well as tradi-

tional energy (for example, electric-

ity) to flow through a two-way smart

infrastructure.

Cyber–Physical Systems

CPS was defined by the National Sci-

ence Foundation (NSF) as physical

and engineered systems whose oper-

ations are monitored, coordinated,

controlled, and integrated by a com-

puting and communication core.

Since its inception, CPS has been

applied in multiple disciplines such

as embedded systems and sensor

networks. More specifically, CPS can

be considered as a networked infor-

mation system that is tightly cou-

pled with the physical process and

environment through a massive

number of geographically distrib-

uted devices [38]. As networked in-

formation systems, CPS involves

computation, human activities, and

automated decision-making enabled

by ICT. More importantly, these

computations, human activities, and

intelligent decisions are aimed at

monitoring, controlling, and inte-

grating physical processes and envi-

ronments to support operations and

management in the physical world.

The scale of such information systems

ranges from microlevel, embedded

systems to ultralarge systems of sys-

tems. This thus breaks the boundary

between the cyber and the physical

by providing a unified infrastructure

that permits integrated models

addressing issues from both worlds

simultaneously.

To realize the CPS architecture in

the SG, we need a special-purpose

dedicated infrastructure, which should

have wireless sensors connected to

the Internet–real-time and secure

several protocol-exchange mecha-

nisms for exchanging the informa-

tion. This can be achieved by using

the Internet of things or Web of things

(WoT) computing paradigm as a dy-

namic global network infrastructure

with self-configuring capabilities based

on standard and interoperable com-

munication protocols. Here, physical

and virtual things have identities,

physical attributes, virtual personal-

ities, and use intelligent interfaces and

are seamlessly integrated into the

information network [39]. WoT is a

flexible and mobile framework that

creates 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.

60 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011

devices by deploying sensors, thus

turning them into smart devices. Such

wireless sensing technologies can as-

sist in using the energy efficiently in a

number of ways. The building block for

having a WoT-based communication

platform is representational state

transfer (REST), which is a specific

architectural style [40] based on the

architecture of the Web and the HTTP

1.1 protocol, which has become the

most successful large-scale distrib-

uted application. REST specifically

introduces numerous architectural

constraints to the existing Web serv-

ices architecture elements to: 1)

simplify interactions and composi-

tions between service requesters

and providers and 2) leverage the

existing World Wide Web (WWW)

architecture wherever possible.

The WoT framework for CPS has

five layers: device, kernel, overlay,

context, and application program-

ming interface (API). Underneath the

WoT framework is the cyber–physical

interface (e.g., sensors, actuators) that

interacts with the surrounding physi-

cal environment. The cyber–physical

interface is an integral part of the CPS

that produces a large amount of data.

The WoT framework allows the cyber

world to observe, analyze, under-

stand, and control the physical world

using these data to perform mission

time-critical tasks. The WoT-based

CPS 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 to

be addressed are as follows [41]:

n IP addressable things and smart

gateways: When a bidirectional

communication link exists between

the providers and consumers, the

information exchanged between

the various smart devices and

smart meters has to be regulated

through a smart gateway.

n Flexibility in wireless communica-

tion: A key element to facilitate

WoT-based architecture is the

ability to deploy sensors at dif-

ferent devices with flexibility

and mobility using WSN technol-

ogy, resulting in 1) reduced in-

stallation, integration, operation,

and maintenance costs, 2) speedy

installation and removal, 3) mobile

and temporary installations,

CPS Developers

CPS Users

WoT API

WoT Context

WoT 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.

SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 61

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 for

information exchange: The follow-

ing features must be investigated

when developing the WoT archi-

tecture: context independence,

service node, or a resource model;

accessibility; data exchange; loca-

tion transparency; contracts; plug

and play; and automation.

n Representation of events: Various

events such as meter reading,

meter control, meter events, cus-

tomer data synchronization, and

customer switching need to be

defined. These complex events

should be decomposed into an

aggregation of simpler events.

n Abstraction of suitable events:

Abstraction of the smart device

information for event and infor-

mation representation, composi-

tion of data from multiple sensors

based upon the requirements laid

by a particular application sce-

nario, decomposition of complex

functionality into aggregations of

simpler sensors, semantics enrich-

ment during the sensor composition

phase to support automatic sen-

sor discovery, selection, and com-

position should be defined.

Thus, the provision of IT infra-

structure for SG poses important

architectural, device structure, and

software and system abstraction chal-

lenges, which are expected to be

addressed over the next few years.

Discussion and ConclusionsIn this article, we have introduced

some background and basic concepts

of SGs. We have presented some

future research and development chal-

lenges and opportunities in the SG in

three related but distinct focal areas

as pertinent to IES. It should be

emphasized that future developments

in these three focal areas are not sup-

posed to stand alone and need to be

integrated. For example, an SG can be

framed as a series of loosely coupled

microgrid clusters, with each cluster

possibly including one or more ro-

tating machines (wind turbines,

microhydro generators, cogeneration

systems, etc.), a number of direct PV

power injection systems, consumer

loads, and power-electronic compen-

sators such as localized STATCOMs.

A holistic design approach can be

taken to subdivide a global optimiza-

tion task into subtasks for local clus-

ters so that a global control strategy

can be formed and converters can be

designed to respond to coordinated

local subtasks to enable a global con-

trol that is distributed and hierarchi-

cal. We hope this article serves the

purpose of inspiring researchers and

practitioners to become further in-

volved in this exciting frontier of SG.

AcknowledgmentWe would like to acknowledge assis-

tance from Prof. Elizabeth Chang and

Dr. Omar Hussain for discussions about

this article and Dr. Ajendra Dwivedi

for assistance in drawing the figures.

BiographiesXinghuo Yu (x.yu@rmit.edu.au) is

the 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 papers

in technical journals, books, and con-

ference proceedings. He is the vice

president of planning and develop-

ment of the IES, an IEEE IES Distin-

guished Lecturer, and chair of the

IES Technical Committee on SGs. He

started his SG research from a pro-

ject on detection of leakage currents

on distribution networks with Austra-

lian utilities in 2005, funded by the

Australian Research Council. He is a

Fellow of the IEEE and also a fellow

of the Australian Computer Society

(ACS) and the Institution of Engi-

neers Australia (IEAust). His research

interests include variable structure

and nonlinear control, complex and

intelligent systems, and industrial

applications.

Carlo Cecati (c.cecati@ieee.org)

is a professor of industrial electronics

and drives at the University of

L’Aquila, Italy. For the last 15 years,

he has been a member of the organ-

izing committees of numerous IECON

and ISIE and an active member of

the IES. He is a cochair of the IES

Committee on SGs and a member of

the Committee on RE Systems and

the Committee on Power Electronics.

Since 2009, he has been coeditor-in-

chief of IEEE Transactions on Indus-

trial Electronics. He is a Fellow of the

IEEE. His research interests cover

several aspects of power electronics,

electrical drives, RE, and SGs.

Tharam Dillon (tharam.dillon@

cbs.curtin.edu.au) is a research pro-

fessor at the Digital Ecosystems and

Business Intelligence Institute, Curtin

University of Technology, Australia.

He has published more than 800

papers in international conferences

and journals, eight authored books,

and six edited books. He developed

the most widely used methods for

load forecasting, system price fore-

casting in deregulated systems, and

medium-term economic production

planning for hydrothermal systems.

This work led to his work in SG. A

variant of this is already being im-

plemented for remote sites under

the Smart Camp ARC project. He is

a Life Fellow of the IEEE and a fel-

low of ACS and IEAust. His research

interests include Web semantics,

ontologies, Internet computing, CPS,

neural nets, software engineering,

and data mining and power systems

computation.

M. Godoy Simoes (msimoes@

mines.edu) received the Ph.D. degree

from the University of Tennessee,

Knoxville, in 1995. He is currently

with the Colorado School of Mines,

where he has been establishing re-

search and education activities in the

development of intelligent control

for high-power-electronics applica-

tions in renewable- and distribute-

d-energy systems. He was a past

chair for the IAS IACC and cochair

for the IES Committee on SGs. He

has been involved in activities

related to the control and manage-

ment of smartgrid applications

since 2002 with his NSF Career

62 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011

Award for ‘‘Intelligent-Based Per-

formance Enhancement Control of

Micropower Energy Systems.’’ He is

a Senior Member of the IEEE.

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