2011 - Heterogeneous Communication Architecture for the Smart Grid

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2011 - Heterogeneous Communication Architecture for the Smart Grid

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y and large, the current power grid is defined as a
system made of electrical generators, transformers,
transmission, and distribution lines used to deliver
electricity power to eventual users. Smart grid net-
work control and monitoring are very important features in
order to provide distributed generation and storage [1], quali-
ty of service (QoS) [2, 3], and security [1, 4]. Nevertheless,
most of these functions are currently only carried out at high
voltage. In recent years, international organizations, govern-
ments, utilities, and standardization organizations have
become increasingly aware of the need for grid modernization
[5]. The future smart grid must be distinguished by its self-
healing and automation features, taking into account that it
should support thousands of clients and energy providers.
Smart grids must be understood as complex networks of
intelligent electronic devices (IEDs), wired and wireless sen-
sors, smart meters [6], distributed generators, and dispersed
loads that require cooperation and coordination in order to
play their expected role [2, 3]. Needless to say, information
and communication technologies (ICTs), trust management,
and technological integration also play an essential role in this
scenario [1].
In this article, a heterogeneous communication paradigm
based on the requirements of the smart grid network [7] is
proposed in order to support smart grid applications. This
communication paradigm achieves end-to-end integration of
heterogeneous technologies by using the ubiquitous sensor
network (USN) architecture [8] and defining the interoper-
ability with the next-generation network (NGN) as the smart
grid backbone [9]. The framework design must include a
decentralized middleware that has to coordinate all the smart
grid functions [3] (Fig. 1).
This article is organized as follows. First, we introduce
smart grid fundamental topics, and describe the communica-
tions and QoS requirements of smart grids. Second, we dis-
cuss our proposal of a smart grid communication architecture
based on International Telecommunication Union (ITU)
USNs plus NGNs. Then we present the adaptation of the
smart grid communication architecture to the new communi-
cation paradigm proposed in this article. Finally, we conclude
and provide further work.
Components and Communication
Requirements
The change toward the so-called smart grid promises to
change the whole business model, and this concerns utilities,
regulation entities, service providers, technology suppliers,
and electricity consumers. The smart grid requires a broad
array of requirements that are different from those of other
types of networks; for example, very high availability together
with low latency. This transformation toward an intelligent
network is possible by importing the philosophy, concepts, and
technologies from the Internet context [1–5]. According to the
definition in the Strategic Deployment Document (SDD) of
the European SmartGrids Technology Platform, a smart grid
is an electricity network that can intelligently integrate the
actions of all users connected to it (generators, consumers,
and those that do both) in order to efficiently deliver sustain-
able, economic, and secure electricity supplies.
First and foremost, the main component of the smart grid
is the sensor network, which consists of a system of distributed
sensor nodes that interact among themselves and with the
IEEE Network • September/October 2011 30 0890-8044/11/$25.00 © 2011 IEEE
B
B
Agustin Zaballos, Alex Vallejo, and Josep M. Selga, University Ramon Llull
Abstract
The smart grid concept provides a solution to the growing recognition that current
utility grids need an ICT deployment infrastructure based upgrade to allow millions
of potential market players to operate and to cope with distributed generation,
wide-area situational awareness, demand response, electric storage, and efficient
electric transportation. Smart grid deployment is mainly about defining the neces-
sary standards for ICT solutions. The design of the communication network associ-
at ed wi t h t he smart gri d i nvol ves det ai l ed anal ysi s of i t s communi cat i on
requirements, a proposal of the appropriate protocol architecture, the choice of the
most suitable technologies for each case study, and a scheme for the resultant het-
erogeneous network management system. Given the smart grid use cases, this arti-
cle is focused on proposing a heterogeneous communication paradigm for smart
grids based on power line communications and wireless networks. The proposal is
related to the framework of the ITU ubiquitous sensor network architecture using the
ITU next-generation network model. This architecture allows for better management
of the QoS in the smart grid and should facilitate interoperability with other tech-
nologies.
Heterogeneous Communication
Architecture for the Smart Grid
ZABALLOS LAYOUT 9/12/11 12:39 PM Page 30
IEEE Network • September/October 2011 31
infrastructure in order to acquire, process, transfer and pro-
vide information extracted from the physical world. Sensor
nodes could also have processing and routing capabilities
using either a wireless or a wired medium. The processing of
the sensor information should allow the modification of the
electrical grid behavior through intelligent actuators.
Another important smart grid component is the smart meter
[6], which is the bridge between user behavior and power con-
sumption metering. Moreover, an enhanced distribution man-
agement system (DMS) is required in order to analyze, control
and provide enough useful information to the utility. The
smart grid is also composed of legacy remote terminal units
(RTUs) that can perform sensor network gateway functions
acting as intermediate points in the medium voltage network.
The sensor network gateway is the bridge between the sensor
network itself and the back-end system. Therefore, it provides
wired/wireless interfaces to other sensor nodes as well as
wired/wireless interfaces to existing ICT infrastructures.
Advanced metering infrastructure (AMI) consists of smart
meters, data management, communication network and appli-
cations. AMI is one of the three main anchors of smart grids
along with distributed energy resource (DER) and advanced
distributed automation (ADA). Last but not least, a geo-
graphic information system (GIS) and a consumer informa-
tion system (CIS) usually contribute with tools and important
processes. All the information recollected and processed by
DMS must be reported to a supervisory control and data
acquisition system (SCADA).
Smart grid networks will manage real-time information and
will collect information from established IEDs for control and
automation purposes. This kind of data network is not exempt
from the growing needs of QoS [2, 3]. Smart grids need to
communicate many different types of devices, with different
needs for QoS over different physical media. IEDs can have
very different QoS necessities depending on the function car-
ried out. For example, real-time communications are required
in the case of fault detection, service restoration or quality
monitoring; periodic communications are used in Automatic
Meter Reading systems (AMR); bulk data transfers are useful
to read logs and energy quality information [2, 3, 6].
The IEDs involved in these processes can be situated in dif-
ferent locations due to the pursued decentralized architecture.
For example, electrical substation elements are connected to
the substation’s Ethernet network; sensors can be installed
along electrical cables communicated through wireless sensor
standards, for example based on IEEE 802.11s. Communica-
tions from the control center to energy meters and between
substations can be carried out via a high variety of technolo-
gies such as narrowband power line communications (NB-
PLC), Universal Mobile Telecommunications System
(UMTS), general packet radio service (GPRS), broadband
PLC (BPL), or WiMAX.
Today, different standard communication protocols at vari-
ous voltage levels and for different kinds of equipment are
used. The medium and low voltage communication assets are
characterized by economically limited ICT infrastructures.
Therefore, standardized, open information models and com-
munication services for all data exchanges are needed in this
case. Due to these circumstances, smart grids will be support-
ed by a highly heterogeneous data network with strict QoS
constraints. One of the most important specifications required
for smart grids is that which refers to necessary communica-
tions. A framework for management of end-to-end QoS for all
communications in the grid will be a must in the future. In
fact, a suitable communication infrastructure enables the elec-
tric system to increase its efficiency to a much greater extent
than automation without communication capacities could ever
do [7].
In this article, a communication paradigm based on IP is
proposed for the smart grid, since it is the most widely used
protocol for communications. Furthermore, several promising
standards have recently appeared for smart grids that base
their communications on IP. An appropriate starting point for
further standards development would be the harmonization of
IEC 61850 standards as they address communications for
DER and ADA.
Figure 1. Heterogeneous network integration.
PHY layer
PHY
SAP
MAC
MAC
SAP
LLC
Convergence
layer
Bridging
PHY layer
PHY
SAP
MAC
MAC
SAP
LLC
PHY layer
WIMAX architecture
Wireless sensor network (WSN)
architecture
Power line communication
(PLC) reference model
PHY
SAP
Privacy
sublayer
MAC CPS
MAC
SAP
Service
specific CS
CS
SAP
Middleware functions: QoS, security, filtering, etc.
IEEE
802.1x
P
L
C

m
a
n
a
g
e
m
e
n
t

p
l
a
n
e
W
S
N

m
a
n
a
g
e
m
e
n
t

p
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a
n
e
ZABALLOS LAYOUT 9/12/11 12:39 PM Page 31
IEEE Network • September/October 2011 32
Smart Grid as an USN
Nowadays, nearly each new application that is added to the
power grid involves the deployment of a new ICT infrastruc-
ture. As a consequence, there is a fragmented map of applica-
tions and communications, which makes it difficult to improve
the quality of supply and smart grid management in general.
This fact usually results in the redundancy of infrastructure
and functions in the grid. In this sense, one of the main objec-
tives of smart grid development is to deploy a unique integrat-
ed system through which all the applications can take
advantage of the same strengths.
Although a similar approach is being discussed in ITU
Telecommunication Standardization Sector (ITU-T) Question
25/16, “Framework of USN applications and services for
smart metering (F.USN-SM)” for AMI, to the best of our
knowledge, our approach is the first proposal of a holistic net-
work architecture based on USNs with the aim of integrating
all the communications requested by smart grid applications
in a single system. There are many applications that can use
the USN, where information and knowledge are developed by
using context awareness. They can be classified as [8]:
• Detection: for example, detection of temperatures exceeding
a particular threshold, intruders, and brush fires
• Tracking: for example, the tracking of items in supply chain
management, plug-in electrical vehicles (PEVs) in intelli-
gent transport systems, and workers in dangerous work
environments such as offshore platforms
• Monitoring: for example, monitoring of inhospitable envi-
ronments such as volcanoes and the structural health of
buildings
Figure 2 shows our proposal of the schematic model of the
smart grid USN. In fact, it is the adaptation of the ITU’s USN
model [8] to the smart grid context, using specific applications
of this domain as USN work at ITU remains fairly generic at
this stage. In the first level, there are different sensor net-
works, which transmit and collect information regarding the
surrounding environment. This information is collected by the
Figure 2. Schematic layers of a USN architecture applied to the smart grid.
USN
access
DMS AMI
Distributed
generation
Demand
response
Supervision
and vigilance
Outage
management
Applications
Sensor networks
USN middleware
NGN Gateway
Gateway Gateway
Gateway
Gateway
ZABALLOS LAYOUT 9/12/11 12:39 PM Page 32
IEEE Network • September/October 2011 33
access network, which can facilitate communication with a
control center or external entities. Then it is transported via a
data network. This ICT infrastructure can be considered a
new kind of access network from the point of view of the
NGN model.
Before reaching the applications, large volumes of data are
collected and processed in the software called USN middleware.
Finally, information reaches the application platform. The USN
application level is a technology platform to enable the effective
use of a USN in a particular industrial sector or application
(e.g., real-time control and automation functions) [7].
Paradigm for Smart Grid Communication
Architecture
Smart Grid’s USN Access Network Level
Along with the previously stated aspects, it is clear that the
ICT infrastructure is crucial in the smart grid; notwithstand-
ing, there is no single technology that can solve all the needs
by itself. In this section, the candidate technologies for the
smart grid communication network are presented, as well as
the network management model based on USNs.
Due to the nature of the power grid, PLC is apparently the
most suitable technology for the communication network,
especially when much of the smart grid infrastructure is
underground or in enclosed places that are not readily acces-
sible. However, as a technological option it presents some
drawbacks, both technically and economically. For example, in
North America, PLC-based AMI systems are generally not
preferred because there are only one to three customers per
transformer, so most PLC technologies for communications
are deemed too costly, compared to Europe, where there are
around 100–300 customers per transformer [6, 7]. Thus, our
proposal employs a combination of PLC and wireless tech-
nologies.
Access USN Baseline Technology — PLC is a suitable candi-
date technology for the USN sensor network, USN access net-
work, and even for NGN [7]. This technology uses the power
grid for transmitting data. It can be divided into BPL and NB-
PLC. NB-PLC is being used for electric company communica-
tions, meter reading [6], and home automation. NB-PLC usu-
ally uses frequencies up to 150 kHz in Europe and 450 kHz in
the United States, and delivers bit rates from 2 to 128 kb/s.
On the other hand, BPL gives the opportunity to communi-
cate at higher bit rates and can be used in in-home LANs and
access networks [7]. Common nominal bandwidth values of
BPL are from 10 to 300 Mb/s, although new systems are offer-
ing higher bandwidths.
The characteristics of the PLC medium make it especially
difficult to ensure a given QoS. Some of the problems that
PLC technology has to overcome are: unpredictable frequency
and time dependence of impedance, attenuation and transmis-
sion characteristics, impulse and background noise and their
wide variability, limited bandwidth, and harmonic interfer-
ence. The variability of the channel is especially troublesome
for QoS because it can suddenly bring the bandwidth down.
At the moment, there are several ongoing standardization
processes for PLC. The IEEE standardization process by the
P1901 working group is aimed at standardizing both in-home
and access networks for seamless interaction with smart grid
applications. On the other hand, Study Group 15 of the ITU’s
Standardization Sector is working in G.hn and G.hnem speci-
fications. These standards will comprise home network and
access network aspects.
Besides PLC communication protocol, several access tech-
nologies must be integrated into the resulting smart grid
architecture. Each utility has its own communication policy,
either subcontracting an Internet service provider (ISP) for its
communications necessities or deploying a private network.
Some easy options to integrate are briefly outlined in the fol-
lowing:
• WIMAX (Worldwide Interoperability for Microwave
Access): IEEE 802.16 is a standard technology for wireless
wideband access. Among its advantages, the ease of instal-
lation is by far the most important aspect. WIMAX sup-
ports either point-to-multipoint or mesh topologies. In
mesh topologies, it is not necessary that all the nodes are
connected to the central node. In this way, active nodes
periodically announce MSH-NCFG messages (mesh net-
work configuration), which contain information about the
Figure 3. Communications network proposed.
CPE PLC
(customer premises
equipment)
PLC repeater
EMR
CPE PLC
HAN mesh network
PLC in-home
IEEE 802.15.4g
HAN mesh network
PLC in-home
IEEE 802.15.4g
EMR
(electricity meters room)
PPC
(power protection
cabinet)
HE PLC
(head end)
Transformation centre
CPE WiMAX /
IEEE 802.22 To NGN
ZABALLOS LAYOUT 9/12/11 12:39 PM Page 33
IEEE Network • September/October 2011 34
base station identifier and the channel in use.
• IEEE 802.11s: A draft from IEEE 802.11 for mesh networks
that defines how wireless devices can be connected to cre-
ate ad hoc networks. The implementation should be over
the physical layer in the IEEE 802.11 a/b/g/n. A combina-
tion of IEEE 802.11n and IEEE 802.11s could be also a
feasible solution for USNs.
• IEEE 802.22: It uses the existing gaps in the TV frequency
spectrum between 54 and 862 MHz. The development of
this standard is based on the use of cognitive radio tech-
niques in order to give broadband access in areas with low
population and which are difficult to reach.
USN Sensor Network Technology — Most wireless traditional
systems use point-to-point or point-to-multipoint technologies.
Mesh networks are an alternative to these topologies. There
are several reasons to think that a mesh network is appropri-
ate for the smart grid’s sensor network [7]. Firstly, it is easy to
add new nodes in the network thanks to the self-configuration
and self-organization capabilities. Furthermore, a mesh net-
work is a robust network as there will almost always be an
alternative path to the destination. This links with the strin-
gent requirements regarding reliability.
Given the large scenario in which the smart grid is going to
be deployed, different technologies will be needed in order to
cover all the area. Some technologies based on IEEE 802.15.4
are presented as wireless communication candidate technolo-
gies that work within mesh networks.
• IEEE 802.15.4: It defines the medium access control (MAC)
and physical (PHY) layers in low-rate personal area net-
works (LR-PANs). In 2008, the Smart Utility Networks
(SUN) Task Group 4g (TG4g) was created within the
802.15 group. The role of TG4g is to define new physical
layers to provide a global standard that facilitates very large
scale process control applications such as the utility smart
grid.
• IEEE 802.15.5: This is the WPAN mesh standard approved
in March 2009. This working group was established in order
to define a mesh architecture in PAN networks based on
IEEE 802.15.4. There are different proposals regarding
routing in LR-WPAN networks. Nevertheless, these algo-
rithms are not fully optimal.
In the upper layers, there may be communication protocols
such as Zigbee or 6LoWPAN. The 6 Low-power Wireless Per-
sonal Area Network (6LoWPAN) is a work group belonging
to the Internet Engineering Task Force (IETF), which works
over the methods that allow to use IPv6 protocol over the
base of IEEE 802. 15. 4 in 30 kbytes of sensor memory.
Although 6LoWPAN can work with different topologies, it
normally works with mesh networks. On the other hand, Zig-
bee specifies a bundle of high level communication protocols
to be used in low consumption digital radio. It is also based
on the IEEE 802.15.4 standard. Although ZigBee is not
designed to work over IP, the ZigBee Alliance, through the
ZigBee Smart Energy group, announced the ZigBee IP proto-
col in order to fulfill the needs of the power market.
Conclusions for the Smart Grid USN — Figure 3 shows our
proposal for the USN access and sensor communication net-
works.
Regarding metropolitan/wide area networks, wireless wide-
band technology has been proposed for low populated areas
due to its easy deployment. In this way, WIMAX will work
from the core to the high/medium voltage substations and
PLC from these substations up to the homes.
With regard to the home area network (HAN), some suit-
Figure 4. Extended NGN architecture with OSE.
Policy enforcement
OSE
Application support functions and service support functions
Core transport
functions
Edge
functions
Access
functions
Service user
profiles
UNI
Media
Access
transport
functions
Media handling
functions
Gateway
functions
Interworking
with service
creation
environment
Service
management
Service stratum
Other
networks
End-user
functions
NNI
Transport functions
ANI
Transport user
profiles
NACF
Transport stratum
RACF
Service control
functions
Service
discovery
Service
development
support
Service
registration
Service
composition
Service
coordination
Transport control functions
Other applications provider
Control
except
OSE
Management
Control
for OSE
M
a
n
a
g
e
m
e
n
t

f
u
n
c
t
i
o
n
s
ZABALLOS LAYOUT 9/13/11 12:37 PM Page 34
IEEE Network • September/October 2011 35
able technologies, such as 6LoWPAN, IEEE 802.15.5, and
ZigBee, have been studied. ZigBee is the most currently
extended and mature technology, and the one that has pre-
sented the most smart grid related applications until now.
Finally, the combination of PLC and ZigBee/IEEE 802.15.4g
provides a new concept of home and substation automation
with outside interaction. It has to be said that in some cases
not all these elements will be present in the network, but all
of them must be integrated into the policy base management.
Smart Grid’s USN NGN Level
For this purpose, ITU-T Recommendation Y.211 defines a
generic end-to-end architecture for the QoS resource control
in NGNs. The aim of this architecture is to provide QoS man-
agement of new end-to-end services and multimedia commu-
nications through diverse NGNs, even though enhancements
of the functionalities to support advanced services are still
being discussed. It is also important to mention the Open Ser-
vice Environment (OSE) capabilities of ITU’s NGN model [9]
because OSE capabilities allow the creation of enhanced and
flexible services based on the use of standard interfaces, as
well as the reuse, portability, and accessibility of services (Fig.
4).
According to ITU, an NGN is a packet-based network in
which service-related functions are independent of the under-
lying transport-related technologies. It supports generalized
mobility, which will provide users with consistent and ubiqui-
tous provision of services. The ITU architecture for the QoS
resource control in NGNs has been developed summarizing
the local efforts of different agents in their respective fields:
the Third Generation Partnership Project (3GPP), DSL
Forum, WiMAX Forum, and European Telecommunications
Standards Institute — Telecom and Internet Converged Ser-
vices and Protocols for Advanced Networks’ (ETSI-TISPAN’s)
generic access network architecture.
The QoS control management is achieved by the central-
ized management of QoS through policy-based network man-
agement (PBNM) with protocols such as Common Open
Policy Service for Provisioning (COPS-PR), from the
Resource and Admission Control Functions entity (RACF)
Figure 5. Middleware interaction.
Open API
USN middleware
Sensor network
directory service
Substation monitoring
Home and building monitoring
Distributed
generation monitoring
Security
manager
Security
manager
Integrated conrtol
centre
Substation control and
maintenance
application
Customer
monitoring
application
Distributed
generation control
application
GIS
BBDD
Sensor network
common interface
Sensor network
monitor
Security
manager
Context-aware
rule engine
Sensing data
mining processor
Event
processor
ZABALLOS LAYOUT 9/12/11 12:39 PM Page 35
IEEE Network • September/October 2011 36
(Fig. 4). The RACF carries out the resource control of the
transport subsystem within access and core networks. RACF
uses reference points to manage the negotiated QoS through
the session signaling and the flow control at a network level.
Our NGN proposal for the smart grid’s USN is based on
the use of ITU NGN architectures for the high-level manage-
ment of the smart grid’s data network, including the accep-
tance of traffic streams and QoS management [7]. Since the
architecture has to work over a heterogeneous network, which
consists of wireless and PLC nodes, the communication
descriptors (e.g., QoS parameters or security constraints) must
be mapped between these technologies in order to obtain suit-
able end-to-end communications. This mapping scheme must
be carried out by using, for example, our proposed communi-
cation broker incorporated into the USN middleware [7].
The main advantage of the communication broker architec-
ture for communication control is that the requester node
simply needs to specify the parameters for the communication
broker. Internetworking among different network technolo-
gies is crucial, in both smart grids and NGNs, to support suit-
able end-to-end communications. The communication broker
function is aware of those mappings and decides whether the
network has sufficient resources when a new request is gener-
ated, and it can also reconfigure the intertechnology parame-
ter mappings on demand.
Smart Grid’s USN Middleware Level
By definition, USN’s NGN only performs data transport.
However, due to the fact that ITU’s NGN model has been
chosen by the authors, it also performs additional functions.
In our case, there are many common functions between the
NGN and the middleware, particularly concerning NGN’s
Figure 6. Use of COSMOS messages in the AMR use case.
Periodic
readings are
stopped
Frequency
Init. communication
AMR application
Middleware
Sensor
network
ReqConnCtrl
ConnResCtrl
Continuous Cmd
(Snid, frequency, time)
CmdActionReq
(command to stop, end)
ConnReqCtrl
SensingValue Rpt (Snid, kW, time)
FinishRpt
SensingValue Rpt (Snid, kW, time)
FinishRpt
CmdActionRes (result)
(..........)
(..........)
AuthReqCtrl
AuthResCtrl
Authentication process
Metering programming
Early ending indication
Electric consumption processing
Electric consumption processing
Result indication
(..........)
(..........)
Frequency
ZABALLOS LAYOUT 9/12/11 12:39 PM Page 36
IEEE Network • September/October 2011 37
OSE capabilities. In fact, the middleware is a group of logical
functions and business intelligence that can be implemented
through the OSE. If the OSE does not include the appropri-
ate function, it has to be independently implemented at the
middleware level.
Figure 5 shows an example of the process carried out when
a smart grid application wants to reach the sensor network
through the middleware in order to collect data. It is based
on the scheme proposed in Fig. 2. It shows the architecture
with the applications on the top, the proposed middleware
model in the middle, and sensor networks at the bottom.
According to the illustration, it appears that there are three
active applications which work independently but which are
controlled by a control center. If each application has direct
access to the sensor network, each application developer
should know the details of each sensor network and their
interfaces. However, when using the USN middleware, each
application developer only needs to know how to use the
open application programming interface (API). All the appli-
cations always have to communicate with the middleware and
the middleware has to exchange information appropriately
with sensor network.
In this article, the reference chosen for the middleware is
ETRIS’s Common System for Middleware of Sensor Net-
works (COSMOS) [10], which is in its ITU standardization
process. Figure 6 shows an example of a use case using COS-
MOS interface between the middleware and the sensor net-
work. The communication between the applications and the
middleware is shown in a conceptual way, as it is carried out
through the open API. The case shown in Fig. 6 is related to
AMR. First, the connection and authentication process must
conclude. Next, the AMR application configures periodic
meter reading through the ContinuousCmd command. In this
way, the sensor network will periodically send the energy con-
sumption value to the middleware. In this process, the appli-
cation can interrupt the programming introduced by issuing
the CmdActionReq command.
Conclusions
This article provides a general but complete view of the cur-
rent ICT status for smart grids. In essence, the smart grid is
a totally automated energy transport network, which is able
to guarantee bidirectional power and information flows
among generation plants, final users, and applications inter
alia. It goes without saying that the development of the
smart grid will mean a drastic change in power use and
administration. On one hand, customers will become active
actors in energy management and will be able to control
their consumption. Moreover, they will have new applica-
tions, both inside and outside their homes, that will provide
them with a higher quality of life. On the other hand, utili-
ties will be able to control demand peaks and manage the
grid efficiently, from generation to distribution. For these
reasons, thei r communi cati on i nfrastructure must be
improved.
At the moment, the definition of smart grids is a key
objective for many countries and many entities are focusing
on it, such as the SmartGrids and IntelliGrid platforms,
NIST, EPRI, and the IEEE. Standards of communication
protocols, information representation models, modules
interfaces, and processes are crucial if smart grids are to
succeed. A communication paradigm architecture is present-
ed in this article after its communication needs and require-
ments have been anal yzed. When studyi ng ITU’ s USN
concept and NGN, it is obvious that the need for interaction
between different levels and the middleware should be met
in order to unify network management. USN middleware
has been extended for security, mobility, and QoS parame-
ters negotiation and configuration in order to cooperate, in
a dovetailed manner, with NGN management tools like
NGN OSE.
IP plus IEEE 802.15.4g-based sensor networks are pro-
posed as the USN bottom level using a mesh configuration
over heterogeneous technologies. PLC technology will play an
important role in smart grid essential communications but has
to be complemented by wireless communication protocols.
The resulting communication architecture must be able to
integrate whichever technology may be considered relevant by
any smart grid actor. Internetworking between different net-
work technologies is also very important. Heterogeneous net-
work management is an active research area that must evolve
in order to be applied in smart grids.
In conclusion, several trends and technology designs have
been clearly presented in this study of the problem. The most
important consideration is that ITU USN/NGN have been
successfully adapted and applied to smart grid communication
architecture. This work allows us to create a unified ICT
framework capable of comprehensibly supporting the strin-
gent communication requirements of smart grids.
Acknowledgment
We would like to thank EU Seventh Framework Program
project INTEGRIS (ICT-Energy-2009, number 247938) and
La Salle (URL) for their support, especially L. Kinnear for
the linguistic reviews of the article.
References
[1] A. Zaballos et al., “Survey and Performance Comparison of AMR over PLC
Standards,” IEEE Trans. Power Delivery, vol. 24, no. 2, 2009, pp. 604–13.
[2] EPRI, D. Von Dollen, “Report to NIST on the Smart Grid Interoperability Stan-
dards Roadmap,” 2009.
[3] V. Pothamsetty and S. Malik, “Smart Grid Leveraging Intelligent Communica-
tions to Transform the Power Infrastructure,” Cisco rep., 2009.
[4] Y. Kim et al., “A Secure Decentralized Data-Centric Information Infrastructure
for Smart Grid,” IEEE Commun. Mag., vol. 48, no. 11, 2010, pp. 58–65.
[5] A. R. Metke and R. L. Ekl, “Security Technology for Smart Grid Networks,”
IEEE Trans. Smart Grids, vol. 1, 2010, pp. 99–107.
[6] T. M. Chen, “Smart Grids, Smart Cities Need Better Networks,” Editor’s
Note, IEEE Network, vol. 24, no. 2, 2010, pp. 2–3.
[7] INTEGRIS FP7 Project “INTelligent Electrical Grid Sensor Communications”
ICT-Energy-2009 call (number 247938); http://fp7integris.eu.
[8] ITU-T, “Ubiquitous Sensor Networks (USN),” ITU-T Technology Watch Briefing
Report Series, no. 4, 2008.
[9] ITU-T Rec. Y.2234, “Open Service Environment Capabilities for NGN,”
2008.
[10] J. Wook Lee et al., “COSMOS: A Middleware for Integrated Data Process-
ing over Heterogeneous Sensor Networks,” ETRI J., vol. 30, no. 5, 2008, pp.
696–706.
Biographies
AGUSTIN ZABALLOS ([email protected]) received his M.S. degree in electron-
ics engineering from University Ramon Llull (URL), Barcelona, Spain, in 2000,
where he is currently pursuing a Ph.D. degree in computer science. He has been
an assistant professor in the Department of Computer Science at URL since 1999
and project manager of the R&D Networking Area since 2002. His research is
focused on real-time routing protocols in smart grids and sensor networks.
ALEX VALLEJO ([email protected]) received his M.S. degree in electronics
engineering from URL in 2001, and his Ph.D. degree in telecommunications
engineering from URL in 2010. Currently, he is the manager director at Malpas
IT and a part-time professor at URL. His research interests include the manage-
ment of communication networks, smart grids, next-generation networks, and
intelligent systems applied to networking systems.
JOSEP M. SELGA ([email protected]) received his M.S. degree from the Poly-
technic University of Madrid, Spain, in 1971 and his Ph.D. degree in telecom-
munications engineering from the Polytechnic University of Catalonia, Barcelona,
Spain, in 1985. Currently, he is a professor at URL. He has been manager of
Telecommunications and Control Systems of the power utilities ENHER and
ENDESA, and President of the Technical/Regulatory Working Group of the PLC
Forum. His main research interest is on computer networking and the smart grid.
ZABALLOS LAYOUT 9/12/11 12:39 PM Page 37

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