XINGHUO YU, CARLO CECATI,
THARAM DILLON, and
M. GODOY SIMÕES
© INGRAM PUBLISHING
An Industrial Electronics Perspective
T
he power grid is a
massive interconnected network used
to deliver electricity
from suppliers to
consumers and has
been a vital energy
supply. To minimize the impact of climate change while at the same time
maintaining social prosperity, smart
Digital Object Identifier 10.1109/MIE.2011.942176
Date of publication: 23 September 2011
1932-4529/11/$26.00&2011IEEE
energy must be embraced to ensure
a balanced economical growth and
environmental sustainability. Therefore, in the last few years, the new
concept of a smart grid (SG) became
a critical enabler in the contemporary world and has attracted increasing 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 presented to the IEEE Industrial Electronics 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 Gilbert. Since then, a number of great
discoveries and technological developments have been achieved. The
SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 49
The electric grid is a massive interconnected
network used to deliver electricity from
suppliers to consumers.
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 dominating generation mechanism is by
electromechanical generators driven
by heat engines fueled by chemical
combustion or nuclear fission. Traditional 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 renewable energy (RE) sources will have a
greatest discovery of them all was
from Michael Faraday, who discovered the principle of electromagnetic
induction in 1831. At the turn of the
20th century, the inventions and discoveries 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, transmission, and distribution. The electricity
is first generated and then transmitted over long distances to the substations where it is further distributed
to the consumers. The generation
system is driven from several forms
Transmission
Generation
Distribution
Industry
Commercial
Residential
FIGURE 1 – The traditional electric grid.
50 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011
much higher efficiency (estimated to
be about 70%). Data from the Environmental 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 transmission lines that transport electricity for long distances and deliver to
distribution substations where the
voltage is lowered for further distribution to consumers through distribution 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 communication technologies (ICTs) and electronics 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 availability 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 sources. On the other hand, RE from sources such as hydro, biomass, solar,
and geothermal energy and wind is
playing a more important role for
future energy supply. Advanced technologies 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 economically 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
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 economical, 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 energy distribution to optimize the
assets utilization and reduction of
capital expenditure, and improved
management of energy use to reduce losses with embedded generation technologies.
More broadly, smart energy encompasses a wide range of research and
development issues such as industry
sector-wide standardization, policy
framework and reform, operational
technologies and systems (e.g., control systems, grid security and stability, fault detection and prediction,
data and communication, demand
management, self-healing grids, and
long distance energy supply), information and social technologies and
systems for carbon mitigation, gridto-customer integration, customer
behaviors, cross-sector large-scale
modeling, and optimization [2].
The Concept of SGs
The term SG refers to electricity networks that can intelligently integrate
the behavior and actions of all users
connected to it, e.g., generators, customers, and those that do both—to
efficiently deliver sustainable, economical, and secure electricity supplies. In the United States, the meaning
of SG is much broader, referring to a
means to transform the electric industry from a centralized, producercontrolled network to one that is less
Energy
Security
centralized and more consumerinteractive, by bringing the philosophies, concepts, and technologies
that enabled the Internet to the utility
and the electric grid [42]. In China, SG
refers to a more physical networkbased approach to ensure energy
supply is secure, reliable, more responsive, and economic in an environmentally sustainable manner [43].
In Europe, SG refers to a broader society participation and integration of all
European countries in an RE-based
system [44]. A vision of an SG is illustrated in Figure 2. The National Institute of Standards and Technology
(NIST) provides a conceptual model
as shown in Figure 3, which defines
seven important domains: bulk generation, transmission, distribution, customers, service provider, operations,
and markets. In the United States, the
importance of SG is currently considered 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 traditional role of central generation,
transmission, and distribution is
Demand
Management
Conventional
Power Plants
Thermal Plant
Nuclear Plant
Solar
Panels
Smart
Energy
Storage
Appliances
Commercial
Consumers
Smart Grid
Microgrid
Residential
Communication
Storage
Industry
Electric Vehicle
Greenhouse
Gas Reduction
Information and
Communication
Technology
RE
Wind Farm
Solar Panel
FIGURE 2 – The future electric grid.
SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 51
Computer
Operations
Service Provider
Markets
Generation
Customer
Distribution
Transmission
FIGURE 3 – NIST conceptual model of SGs.
transformed by aggregation of distributed 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 generation paradigm.
Key Issues in SGs
There are several technical challenges facing SGs: intermittency of RE
Residential or Commercial Small DR
Small Hydro DR
Wind Turbine DR
Photovoltaic
Array
Solar Water
Heating
Heat Pump
Fuel Cell
Interconnecting
Hardware
Central
Generation
Transmission
Distribution
Traditional Loads
Local Generator
Static
Switch
Interconnecting
Hardware
Industrial DR
FIGURE 4 – A microgrid architecture.
52 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011
Traditional
Loads
Microturbine
generation that affects electricity
quality; large-scale networks of small
distributed generation mechanisms,
e.g., photovoltaic (PV) panels, batteries, 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 availability 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 transmission 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 distributed generation).
A more significant issue is how to
use ICT, electronics, and other advanced technologies to enhance the
efficiency of energy use. This includes
new technologies (e.g., smart meters
and telecommunication technologies)
for sensing, transmission, and processing information relating to grid
conditions, which are vital for timely
monitoring and controlling the network 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 components such as source, loads,
and storage units can be controlled 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 distribution level. It estimates demand
The term SG refers to electricity networks that
can intelligently integrate the behavior and
actions of all users connected to it.
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 estimations heavily rely on weather predictions 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 functionality to achieve a more efficient
grid. Mechanisms such as load
shedding and dynamic pricing can
help reduce total demand. Another approach to limiting demand
peaks is automatic demand dispatch, which consists of delaying
the use of some loads in time.
SG as a multidisciplinary field
presents many challenges and opportunities for industrial electronics research and development, which are
concerned with the application of
electronics and electrical sciences.
These applications enhance the industrial and manufacturing processes,
addressing the latest developments
in intelligent and computer control
systems, robotics, factory communications and automation, flexible manufacturing, 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 electronics, intelligent systems and control,
and IT infrastructure.
Power Electronics
The technology of power electronics is fundamental in SG development because they will have a deeper
penetration of renewable and alternative 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 installed in residential and commercial
applications (typically with power
range of a kilowatt), and many countries 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 configurations and must be compensated
by integration with energy storage. A
power electronic converter is always
needed to allow energy storage during surplus of input power and compensation 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 supplied by the inverter, the power factor may fall out of the prescribed
limits allowed by the utility, and possibly the inverter must supply reactive 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 thermodynamical cycles and large rotating machines; therefore, an ac/ac conversion
SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 53
Embedded generation and siting generators
close to the point of consumption are key
considerations in reducing wasted energy.
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 architecture is shown, where Figure 7(a)
shows a typical dc link integration
very commonly used when dc sources
(PVs, fuel cells, and batteries) are integrated. Figure 7(b) shows a typical ac
link integration, where turbines and
rotating machines are integrated
ac Source
PC
Alternative
Energy Source
QS
PS
QL
PL = PS + PC
PC
dc/dc + dc/ac
Converter
QC
ac Load
FIGURE 5 – Active and reactive power balance for alternative energy conversion.
Local Heat Recovery
Sunlight
dc/dc
Conversion
Storage
Natural Gas
Wind
Hydro
ac/ac
Conversion
Synchronous or
Asynchronous
Fossil Fuel
ac/ac
Conversion
Synchronous
FIGURE 6 – Integration of several sources of energy into the grid.
54 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011
Interconnection
Hydrogen
Islanded
Operation
Utility
Grid
through the utility line frequency,
and Figure 7(c) shows a highfrequency ac link integration, where
fast response and decreased system
size can be achieved. When interconnected 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 sources, 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 inverter can be the interface with local
generation. There are other converters for intermediate stages, necessary
for adapting the energy produced by
the source in such a way that both the
energy source and the inverter operate at their highest efficiency.
Power converters for SG integration and particularly inverters present a higher complexity when
compared with those used in industrial or stand-alone RE systems
because they have to efficiently manage 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 distortions, thus reducing the size of lowpass filters. These functions require
new functions not commonly available in standard converters.
Renewable and alternative energy 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 affected by input and output energy
fluctuations and by conversion
efficiency, changing with the
quantity of energy at input/output terminals. The converter has
to operate in continuous tracking of the input/output quantities and a subsequent real-time
adjustment of the converter parameter ensuring the highest energy transfer. This requires two
or more conversion stages (typically ac/dc and/or dc/dc and/or
dc/ac in wind, hydro, and geothermal 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 interconnected with maximum power
n
point tracking (MPPT) to optimize the energy transfer.
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.
dc Link
dc Loads
60-Hz Grid
Rotatory
Generation
Stationary
Generation
Rotatory
Storage
Stationary
Storage
ac Loads
(a)
ac Link
ac Loads
and VAR
Compensators
60-Hz Grid
Rotatory
Generation
Stationary
Generation
Rotatory
Storage
Stationary
Storage
ac Loads
(b)
HFAC Link
VAR
Compensators
60-Hz Grid
HF or
60-Hz
Rotatory
Generation
Stationary
Generation
HF or
60-Hz
Rotatory
Storage
Stationary
Storage
ac Loads
HFAC Loads
(c)
FIGURE 7 – Energy integration with (a) dc link, (b) ac link, and (c) HFAC link.
SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 55
An agent is a software entity that can represent
and control an actuator component, such as a
source, a storage unit, or a load.
n
n
n
n
n
n
n
High reliability: The continuity of
service is a major issue when delivering energy.
Synchronization capabilities: All
power sources connected with
the grid have to be fully synchronized, thus ensuring high efficiency
and eliminating failures, and therefore, standards such as IEEE 1547
[45] should be incorporated in
the power electronic interfaces.
Electromagnetic interferences (EMIs)
filtering: The quality of the energy injected on the grid must
respect electromagnetic compatibility (EMC) standards.
Smart metering: The converter
between the local source/load and
the grid must be capable of tracking the energy consumed by load
or injected on the grid transmitting. Real-time information must
be passed to an automatic billing
system capable of taking into account parameters as the buy/sell
energy in real time at the best
economic conditions and informing the owner of the installation
of all required pricing parameter
decisions.
Communication: Intelligent functioning of SGs depends on their
capability to support communications at the same time that
power flows in the systems. Such
functions are fundamental for overall system optimization and for
implementing sophisticated dispatching strategies [11].
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 monitoring, communication systems,
and reconfiguration systems.
Extra intelligent functions capable of making the user interface
friendly and accessible anywhere
through Internet-based communications.
SG systems require power converters with functional controls for
smart power generation with possibility 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). Therefore, the primary intent of a smart
inverter is to enable efficient interconnection and economical operation of dispersed installations to the
utility grid interacting with smart
metering, incorporation of smart
appliances, provision of pricing information and/or some control options
to the consumers, and information
exchange for a fully networked system 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 information and control options.
Converters: Generation
from Solar Energy
PV cells are dc sources where the current depends on the sunlight intensity
and voltage depends on temperature.
Those cells are arranged in series
and/or in parallel, achieving the desired level of voltage and current. A
dc/dc converter provides the necessary voltage boost and regulation
(under control of an MPPT algorithm) 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
56 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011
energy extraction. The dc/dc converter can be either with or without a
transformer; the latter is inserted for
providing galvanic insulation and output 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 suitable high-frequency transformers for
high power (typically limited to 20
kVA applications). However, highfrequency transformers are common
in low to medium power applications
(such as residential inverters and dc
power supplies). Line frequency transformers may be used in grid interface,
but there are power electronic topologies 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 converters are complex to control, particularly when a wide input voltage
variation may occur as in PV applications because the resonance phenomena are strictly connected to the
values of the so-called resonant tank
while the input voltage variations can
be contrasted by varying the operating frequency.
The output of a dc/dc converter
is applied to a PWM inverter with grid
synchronization capabilities, necessary for correct synchronous operations followed by a tight low-pass
filter, necessary for respecting EMC
standards. Phase shifting among the
distinct generators is usually addressed by a phase-locked loop (PLL)
used for a correct generation of the
ac voltage by the inverter, thus avoiding 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
topology consists of a number of Hbridges connected in series, each one
with its own generator, obtained by a
group of cells [14], [15]. The advantages 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 consists of the use of low-power separate
converters, one for each panel or for a
small group of panels, directly producing the desired output voltage
level. In this case, advantages may be
derived from an improved sun energy
conversion with reduced losses (output currents depend of the output
voltage level) and lower wiring costs.
An energy storage system may be connected 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 machine) 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 include an active rectifier, rather than
a simple diode bridge, resulting in
improved efficiency of the conversion 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 implementing a back-to-back converter.
Dc/dc (if present) and the dc/ac conversions 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 aerodynamics [17]. Multilevel converters
appear very interesting and promising, 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
WoT is a flexible and mobile framework that
creates a network among the different devices
by deploying sensors.
dc/ac stages [18], [19]. Matrix converters can also be considered for ac/
ac applications [20].
Flexible Alternating Current
Transmission Systems
Flexible alternating current transmission systems (FACTS) have been
developed over the past two decades, 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 displacement of either current or voltage and to reduce the apparent line
impedance. FACTS also make the system more reliable by reducing transient line disturbances such as glitches
and voltage sags and more intelligent
because power flow can be completely controlled with power converters such as static synchronous
compensators (STATCOMs), unified
power flow controller (UPFC), and
various pulsewidth modulated cascaded 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 important for large distributed generation 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 converters specifically designed for FACTS
would be incorporated within the
already existing power converters
for the SG.
Intelligent Systems
and Control
SGs are highly complex, nonlinear
dynamical networks by nature that
present many theoretical and practical challenges. Monitoring and control are the key issues that need to be
addressed to make SG more intelligent and equipped with self-healing,
self-organizing, and self-configuring
capabilities. This requires much more
efficient information (signal) sensing,
transmission, and synthesis. The existing technologies for monitoring,
assessment, and control were predominantly developed in the 1960s,
and the grid operations are rather
reactive, with a number of critical
tasks performed by human operators 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 operation decision in a timely fashion and
2) how to present the information to
users in a most compelling and informed way to help users make highlevel operation decision without bogging 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 compounded by the distributed nature of
RE, real-time performance is a bottleneck in deriving just-enough and
just-in-time information for SG to
operate efficiently. The intermittent
availability of RE requires consideration of the entire operation regime to
deal with the associated problems
such as storages and variable power
quality [23]. The bidirectional electricity flow in the SG due to penetration of a large number of small
generation systems and versatile
usages also pose challenges. Traditional state-space modeling and
SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 57
(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.
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 network (CN) theory [26], which originated 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 collection of nodes and links without relying heavily on the dimensionality of
the system. Typical complex networks include regular networks, random networks, small-world networks,
and scale-free networks as shown in
Figure 8. Such a theory has found its
application in power network vulnerability analysis [27], [28]. How to
embed the CN theory into the sensing,
modeling, analysis and control design
to bring out fast and reliable controllers 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 decision-making for SG operations and
control. New methods are needed to
automate monitoring, assessment,
and control of grid operations to
meet economical, social, and environmental requirements. The key
tasks involved in SG include fault and
stability diagnosis, reactive power
control, distributed generation for
emergency use, network reconfiguration, system restoration, and demand
side management analysis [22]. This
requires advanced technologies to
enable intelligent real-time monitoring, assessment, and control of SG
through ICT.
These challenges require significant research in assessing whether
existing theories and tools are adequate and what the limitations are.
Furthermore, a new generation of
58 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011
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 timeseries 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 analysis 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 automation of operations at the lower operational levels, but also high-level
decisions to take consideration of
macro economical and social requirements. 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 explanatory 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
Editor Layer
Variable
Membership
Editor
If–Then
Rule
Editor
Discourse Layer
Discourse
Semantics
Data Layer
Actual
Results/
Cases
Explanation
Knowledge Base
Output 1
Output 2
Input
Fuzzifier
Sensor/User
Layer
Inference
Engine
Meta
Consequent
Output 3
System Layer
Output n
Output Layer
Real-World Layer
Manual
Operations,
Sensors, Etc.
Data Set,
Anecdote
Reference,
Cases, Etc.
FIGURE 9 – An industrial decision support framework.
process of the tool. There is a need
for an effective explanation to significantly improve the usability of such
tools. It is obvious that neither traditional knowledge-based systems nor
quantitative-based machine learning
algorithms are directly applicable,
because they focus on providing general recommendations and lack a mechanism to deal with problem-specific
tuning. Operational staff need to continuously 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 reflection-in-action support [31].
Since distribution systems were
not designed for bidirectional power
flow, the current state-of-the-art distribution 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 infrastructure. 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 interesting solution for this challenge. An
agent is a software entity that can
represent and control an actuator
component, such as a source, a storage unit, or a load. Agents can communicate 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 control functions in the distribution network is introduced in [32], which
focuses on three main aspects of distributed state estimation, voltage
coordinated control, and power flow
management. By providing a high
level of efficiency, flexibility, and intelligence, this concept creates an important 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 components connected through a vast
and geographically extended network. SG systems exhibit the following features: 1) a large-scale network
structure; 2) many of the controls
are embedded in the system, with
some having scope for variable structure tuning; future control designs,
which must allow for and enlist
where possible these existing controls; 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 system 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 mechanism, which can provide a metaview
to coordinate local controllers [34].
The nature of such a complex network poses new challenges for the
SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 59
existing control theory. Control of
large-scale systems has been researched for many years. A common
philosophy is to use a decentralized
approach that considers the largescale systems as a collection of interconnected subsystems, with a decomposition that is derived directly from
the physical description of the problem and leads to a natural grouping of
state variables. For ill-coupled subsystems, this allows the control to be formulated based on local states and
feedback while considering global
influence [35]. There has been extensive research on the control of largescale 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 dimensionality and complexity. A new way of
thinking is to consider the connectivity and topological structures as factors based on the CN theory to
overcome the dimensionality and
complexity problem [26], which
would simplify the modeling and control 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 components in SG have switching elements,
e.g., converter controls and power
systems stabilizers. How to make use
of CN theory in a large-scale distributed, switching-based control system,
and available intelligent discontinuous controllers [29] is another area
worth exploring.
IT Infrastructure
IT infrastructure is the backbone enabler for SG to be aware of what is
going on, deciding best strategies for
monitoring and control and responding to demand side responses while
keeping the grids to operate efficiently, cost less, and neutralize the
negative impact on environments.
This can be achieved by smart twoway communication (smart link) and
devices (e.g., smart meters). A platform for information exchange is
needed that enables smart appliances and smart meters to exchange
the information between them as
shown in Figure 10. The cyber-physical systems (CPSs) can offer such a
platform that allows for both the
digital information as well as traditional energy (for example, electricity) to flow through a two-way smart
infrastructure.
Utility Grid
Smart Link
(Price)
Smart Gateway
Deliver
On-Demand
Provision
Smart Link
(Consumption)
Smart Meters
Utility Provider
Smart Storage
Smart Devices
FIGURE 10 – Smart link between the utility grid and smart gateway.
60 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011
Cyber–Physical Systems
CPS was defined by the National Science Foundation (NSF) as physical
and engineered systems whose operations are monitored, coordinated,
controlled, and integrated by a computing 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 information system that is tightly coupled with the physical process and
environment through a massive
number of geographically distributed devices [38]. As networked information 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 integrating physical processes and environments to support operations and
management in the physical world.
The scale of such information systems
ranges from microlevel, embedded
systems to ultralarge systems of systems. 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 mechanisms for exchanging the information. This can be achieved by using
the Internet of things or Web of things
(WoT) computing paradigm as a dynamic global network infrastructure
with self-configuring capabilities based
on standard and interoperable communication protocols. Here, physical
and virtual things have identities,
physical attributes, virtual personalities, 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
The WoT framework for CPS has
five layers: device, kernel, overlay,
context, and application programming interface (API). Underneath the
WoT framework is the cyber–physical
interface (e.g., sensors, actuators) that
interacts with the surrounding physical 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, understand, and control the physical world
using these data to perform mission
time-critical tasks. The WoT-based
CPS architecture is shown in Figure 11.
devices by deploying sensors, thus
turning them into smart devices. Such
wireless sensing technologies can assist 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 distributed application. REST specifically
introduces numerous architectural
constraints to the existing Web services architecture elements to: 1)
simplify interactions and compositions between service requesters
and providers and 2) leverage the
existing World Wide Web (WWW)
architecture wherever possible.
Realization of WoT-Based
CPS Architecture
To realize the SG framework by using 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 communication: A key element to facilitate
WoT-based architecture is the
ability to deploy sensors at different devices with flexibility
and mobility using WSN technology, resulting in 1) reduced installation, integration, operation,
and maintenance costs, 2) speedy
installation and removal, 3) mobile
and temporary installations,
CPS Node
Actuators
y
x
y
x
CPS Node
WoT Overlay
CPS Node
WoT Overlay
WoT Kernel
WoT Overlay
WoT Kernel
WoT Device
WoT Kernel
WoT Device
WoT Device
CPS Event
Physical
Environment
WoT API
WoT Context
CPS Mashups
WoT Overlay
CPS Event
CPS
Desktops
CPS Node
Sensors
WoT Overlay
WoT Kernel
WoT Device
CPS Users
CPS Developers
FIGURE 11 – Reference architecture of CPS.
SEPTEMBER 2011 n IEEE INDUSTRIAL ELECTRONICS MAGAZINE 61
4) real-time and up-to-date energy consumption and information services available at anytime,
anywhere, and 5) enhanced visualization, foresight, and forecasting capabilities.
n Common embedded platform for
information exchange: The following features must be investigated
when developing the WoT architecture: context independence,
service node, or a resource model;
accessibility; data exchange; location transparency; contracts; plug
and play; and automation.
n Representation of events: Various
events such as meter reading,
meter control, meter events, customer 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 information representation, composition of data from multiple sensors
based upon the requirements laid
by a particular application scenario, decomposition of complex
functionality into aggregations of
simpler sensors, semantics enrichment during the sensor composition
phase to support automatic sensor discovery, selection, and composition should be defined.
Thus, the provision of IT infrastructure for SG poses important
architectural, device structure, and
software and system abstraction challenges, which are expected to be
addressed over the next few years.
Discussion and Conclusions
In this article, we have introduced
some background and basic concepts
of SGs. We have presented some
future research and development challenges 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 supposed 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 rotating machines (wind turbines,
microhydro generators, cogeneration
systems, etc.), a number of direct PV
power injection systems, consumer
loads, and power-electronic compensators such as localized STATCOMs.
A holistic design approach can be
taken to subdivide a global optimization task into subtasks for local clusters so that a global control strategy
can be formed and converters can be
designed to respond to coordinated
local subtasks to enable a global control that is distributed and hierarchical. We hope this article serves the
purpose of inspiring researchers and
practitioners to become further involved in this exciting frontier of SG.
Acknowledgment
We would like to acknowledge assistance from Prof. Elizabeth Chang and
Dr. Omar Hussain for discussions about
this article and Dr. Ajendra Dwivedi
for assistance in drawing the figures.
Biographies
Xinghuo Yu (x.yu@rmit.edu.au) is
the director of Platform Technologies Research Institute at Royal Melbourne Institute of Technology (RMIT)
University, Australia. He has published more than 380 refereed papers
in technical journals, books, and conference proceedings. He is the vice
president of planning and development of the IES, an IEEE IES Distinguished Lecturer, and chair of the
IES Technical Committee on SGs. He
started his SG research from a project on detection of leakage currents
on distribution networks with Australian 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 Engineers 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
62 IEEE INDUSTRIAL ELECTRONICS MAGAZINE n SEPTEMBER 2011
and drives at the University of
L’Aquila, Italy. For the last 15 years,
he has been a member of the organizing 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-inchief of IEEE Transactions on Industrial 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 professor 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 forecasting 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 implemented for remote sites under
the Smart Camp ARC project. He is
a Life Fellow of the IEEE and a fellow 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 Simões (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 research and education activities in the
development of intelligent control
for high-power-electronics applications in renewable- and distributed-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 management of smartgrid applications
since 2002 with his NSF Career
Award for ‘‘Intelligent-Based Performance Enhancement Control of
Micropower Energy Systems.’’ He is
a Senior Member of the IEEE.
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