Modeling and Simulation of the Communication Networks in.docx

Modeling and Simulation of the Communication
Networks in Smart grid
Yizhou Dong, Ziyuan Cai, Ming Yu, and Mischa Sturer
Dept. of Electrical & Computer Engineering
FAMU-FSU College of Engineering, FL 32310, USA.
[email protected], [email protected], [email protected],
[email protected]
Abstract—A reliable and secure communication network plays
a significant role in Smart grid systems, which aims at
coordinating generation, transmission, distribution, and
consumption parts in power system. The scope of our work
ranges from utility level to end consumption level. The major
difficulties in this work can be summarized as follows: 1)
Performance requirements from the viewpoint of network have
not been clearly defined; 2) Model mapping from power system
to communication networks is not straightforward. 3) Network
performance has not been well-investigated. This paper

proposes a communication network model for a typical
program of smart gird. Moreover, application requirements,
link capacity and traffic settings have been investigated.
Simulation results validate the feasibility of this model and
provide useful network performances which can satisfy both
the non-real-time and real-time application requirements.
Keywords-Smart Grid; Communication Network; Simulation;
Performance; FREEDM; IFM
I. INTRODUCTION
Smart grid becomes to an attractive dominating topic
nowadays in both research universities and industrial
organization. The traditional power communication
infrastructure cannot meet the requirements for our future
power system which the energy will not only generated by
traditional generation facilities but also produced by
distributed facilities and new energy devices. The delivery of
both energy and information must also be end-to-end and
bidirectional. Communication network should interconnect

every device of power system from electricity generation to
end-user consumption, and even more. One view need to be
point out is that, in physical layer, geographic location for
power electricity device and communication network device
can be dissimilar.
NIST published the first definition of Smart Grid in 2009
which represent smart grid standardization in North
American. The networking parts proposed by NIST
emphasize the transformation from traditional power
communication networks to Information and Communication
Technology (ICT), which indicates that both energy and
information transmission must be bidirectional for all levels.
[1] In Europe, European Technology Platform also issued
standards to define smart grid as the target architecture which
enable all users’ connection, including generators,
transmission, and consumers. Other national organizations
and industrial companies also boost the development of
smart grid by provides the recommend standards and

proposals like The German Smart Grid Standardization
Roadmap concentrate their attentions on smart grid’s ICT
infrastructure.
Note that the research progress is developed under The
Future Renewable Electric Energy Delivery and
Management System (FREEDM system) [2], which is power
distribution system motivated by the widespread use of
information network at first. NSF engineering Research
Center established the in 2008 and this system is
headquartered by NCSU and partnering ASU, FSU, FAMU,
MST, RWTH, ETH and more than thirty seven industry
companies. The vision and framework in FREEDM includes
Intelligent Energy Management (IEM), Intelligent fault
management (IFM), Solid State Transformer (SST), Fault
Isolation Device (FID), Reliable and Secure Communication
(RSC), Distributed Gird Intelligence (DGI), i.e. Our present
research goal in this FREEDM project is to evaluate a
feasible communication system and provide corresponding

performance reference by modeling and simulation.
The motivation of our work is that the communication
network model in smart grid has not been clearly
investigated. The difficulty in our work is how to map the
architecture of power system into communication system.
Moreover, the time delay performance for real-time
applications is the major technical problem in our work.
From network view point, what are the FREEDM
communication application requirements? How to implement
it as a reliable and secure communication system? What kind
of model we should use to simulate the FREEDM scenario?
What is the network performance for a typical smart grid
system? Our paper provides some recommendations and
reference for these questions.
This paper is organized as follows. Related work is
reviewed in Section Ⅱ. Scenario formulation, including
network topology model, link capacity and traffic settings, is
proposed by Section Ⅲ. The simulation results are presented

in Section Ⅳ. Section Ⅴ summarize this paper.
II. RELATED WORK
The current research progress for Smart Grid (SG)
Communication Architecture can be divided into three
categories, 1) Survey and Overview for SG communication
infrastructure, 2) Application requirements and supporting
network for SG, 3) Tentative and experimental suggestion
978-1-4577-0653-0/11/$26.00 ©2011 IEEE 2658
for communication network for SG, 4) Security issues for
SG.
In the first category, the common case is to give a big
picture and official background by investigate national and
organizational documents. Such as the NIST report on Smart
Gird [3], SMB smart grid interoperability standards[4], IEC
61850-1 Communication networks and systems in
substations [5] [6].
In the second category, propose a communication

infrastructure in conceptual level and evaluate both basic
applications and advanced applications are their major works
[7] [8]. A bunch of features and characteristics of smart gird
network have been raised like data digitalization,
expandability and adaptability, Intelligence, Sustainability
and Customization [9]. Requirements for different levels in
smart grid system have been distinguished. Various
communication configuration depends on application have
been proposed such as phasor measurement units (PMUs)
[10], advanced metering infrastructure (AMI). Related
applications includes some basic application like smart meter,
monitoring control and also some advanced application like
security video surveillance, automatic distributed control
[11].
In the third category, few tentative evaluations mentioned
smart grid traffic profiles and most of them are estimated by
standards or collected from residential power user. [12]
Security issue is fairly significant for smart grid and an

increasing amount of papers focus on this field. For this
paper, security issue is not our key concern.
All in all, most of the work is still in exploratory level and
there is no verified authoritative communication
infrastructure at present. In addition, only a few works have
step into modeling and simulation part for the intergral Smart
Gird communication system.
III. SENARIO FORMULATION
In the view point of power system, smart grid can be
classified into four levels: Generation, Transmission,
Distribution and Consumption. Consider the range of
FREEDM project. We are focusing on the power delivery
from distribution substation and local utility to end
consumers. In order to implement the communication
network in FREEDM system, some specific features should
be included in the model such like Intelligent energy
management (IEM), Intelligent fault management (IFM),
Distributed Renewable Energy Resource (DRER),
Distributed Energy Storage Device (DESD).
It is very important to provide enough evidence to verify
the feasibility and credibility of communication network
topology. So the follows illustrate our investigation and
research for Network Model, Link Capacity and Traffic
Settings.

A. Network Topology Model
As mentioned before, our network model mainly
considerate in the scope of our project, range from Control
Center level to home network level, which is from WAN
level to LAN level in terms of network perspective. In the
background of FREEDM project, IEM and IFM are typical
future communication network devices. Due to its
importance, we promoted the node model in our topology to
illustrate their impact for communication network
performance.
Traditional CCs
(SCADA)
IFM/IEDs IFM IEM/MDMS.. ...
WAN
NAN
Substation level
Control Center level Smart CCs
(DGI)

Neighbor level IFM/RTU IEM/Relay
Home level IEM/SM IEM/SM...
Fig. 1. Network hierarchical structure
As shown in the Fig.1, we propose totally four levels of
network. Firstly, Control Center level represents the current
power grid control center like SCADA system and also
future smart grid center control. The Substation level refers
to current equipment in substation or future equipment in
FREEDM for wide area control, for instance, the IEM and
IFM are exactly in this level. The Neighbor level describes
relay devices or region equipment in a certain zone and the
Home level refers to the equipment in resident area.
The IFMs can be deployed together with RTU and
substations, or standalone in the Substation level. The IEM
can be deployed together with smart meters, the relay nodes
(i.e., the roadside data forward equipment) and the meter
data management system (MDMS) in Substation level.

From the physical distance view point, the Control
Center level is usually far away from Substation level. Thus
we can implement the transmission part between these two
levels as a WAN. However, the range for Neighbor level and
Home level is restricted in a small region. Therefore, we use
NAN to denote the Neighbor level and use LAN to
implement the simulation topology.
2659
Fig. 2. Simulation scenario topology
We provide our network topology model for OPNET
simulation in Fig.2.
In the Control Center level, Billing center represent the
center to collect and analysis the metering data from MDMS.
Control center act as a commander and he take the
responsibility to order the specified control information for
dedicated equipment. WAN1 and WAN2 refers to the routers

in the WAN between Control Center level and Substation
level. In the Substation level, we use MDMS model to
collect all the data information from Home level, which not
merely provide the accumulation function but also can
process the data from sub-layer. In addition, IFM in
Substation level is also considered. In Neighbor level, we use
a LAN model, a server model and a corresponding router to
indicate the relay for a specific region. The server model can
be regarded as IFM or IEM. Inside of the LAN model, each
workstation denotes a device in Home level.
One issue need to be explained in this topology is that we
use two-layers in Neighbor level. Due to the consideration of
the different distance and layers in Neighbor level, some of
the relay region can be positioned as upper-layer relays, and
also the others can be regarded as lower-layer relays.
Table I Node model used in simulation
Name Node model Description
BillingCenter,

Controlcenter,
MDMS
ppp_wkst Workstation for
PPP link
WAN slip8_gtwy_adv Gateway model for
SLIP
Substation,
Relay
ethernet2_slip8_gtw
y_adv
Gateway model for
interconnect
Ethernet and SLIP
IFM, IEM ethernet_server_adv Ethernet server
model
LAN Eth_switched_lan_a
dv
Ethernet LAN mode

B. Link Capacity
Link capacity is a critical factor in simulation scenario
which needs to meet the requirements for both application
requirements and also the practical using.
As illustrated in network topology model, the
communication networks between the Control Center level
and Substation level are WANs, such as the connections of
dedicated fibers or leased wired lines. For the practical
implementation in companies, VPN or other kinds of private
wired links is being used. Nevertheless, in order to simplify
our network topology, we only regard it as point-to-point
link model to denote this sort of lines. Similarly, we use
point-to-point link to implement the links in backbone
network for Substation level and Neighbor level. But for
Home level, we can regard it as the access part and we
usually use Ethernet link to formularize the links in LAN.
The following provide the link model and link capacity
we use in our simulation for different levels.

Table II link model used in simulation
level Link model Link capacity
(Mbps)
Control Center
level
DS3 44.736
Substation level T1 1.544
Neighbor level
and Home level
10BaseT 10
C. Traffic Settings
Traffic settings for simulation is largely depend on
application requirements and project background. We
consider three kinds of main existing power grid application,
i.e., advanced metering infrastructure (AMI), substation
automation and fault information management.
Advanced metering infrastructure (AMI) is the main
application for power grid no matter it’s a traditional power

system or a future smart gird. In our scenario, we generate
traffic model from all the meters in Home level and transmit
to MDMS in Substation level. After aggregated and
processed in substation, then forward it to BillingCenter in
Control Center level. Data for metering is a large amount of
information transmitted in the network and usually this
application can be regarded as background traffic which is
mostly the basic prerequisite for network performance
problem.
Substation automation (SA), which is an application
requirement can rapidly response to real time events with
appropriate actions to avoid great damage cause by
equipment failure, power disturbance and natural accidents.
We develop traffic demands in our scenario to simulate the
control commands from control center in Control Center
level to each IFM device. Comparing to AMI, substation
automation traffics basically are small amount control
information, but the time delay requirement is more

demanding.
Fault information management application for FREEDM
request the IFM device monitor the work condition for
crucial power device and report it to upper-layer’s control
center. For every half circle of one waveform, IFM need to
sample it sixteen times and transmit these fault management
2660
information to upper control center.
Table III Traffic settings
Traffic type Traffic rate
(kbps)
Uplink/d
ownlink
Proto
col
AMI(part1) 20 Uplink UDP
AMI(part2) 100 Uplink UDP

SA 5 Downlin
k
TCP
IFM 10 Uplink TCP
IV. SIMULATION RESULTS
Our main concern and purpose for this simulation is to
give primary data-type results rather than theoretical analysis
for smart grid communication network performance, thereby
provide a big picture with more detail information for
reference.
In the simulation section, we use OPNET Modeler 16.0A
PL4 to investigate the performance of FREEDM
communication network under various traffic settings and
different packet size. The parameters and values are given in
Table 1, 2 and 3for topology set up and traffic configuration.
The topology dimension for this simulation is
and the network topology shown in Fig. 2.
The performance metrics we mainly concern are defined

as follows:
1) Maximum top-bottom delay for control data: The
maximum time latency since a packet is transmitted
from control center to smart meter in Home level.
2) Maximum delay for metering: The maximum time
latency since a packet is transmitted from smart
meter to MDMS in Substation level.
3) WAN link maximum utilization: The maximum ratio
of the data rate of a certain link to the link capacity in
WAN network.
4) WAN bandwidth efficiency: The ratio of the total
consumed bandwidth to the whole bandwidth in
WAN network.
5) Packet loss for control data: The ratio of the number
of control packets unsuccessfully delivered to the
total number of packets send out by a source node.
6) Packet loss for metering data: The ratio of the
number of metering data packets unsuccessfully
delivered to the total number of packets send out by
a smart meter.
7) End-to-End packet delay: The packet latency for a

dedicate end-to-end flow traffic.
We develop three main scenarios for different
performance evaluation. In our first scenario, simulations are
conducted to examine the performance impact by adding
smart meter nodes.
Fig. 3. Maximum top-bottom delay for control data and
Maximum delay
for metering.
Fig. 3 shows that the maximum delay for metering hold
at zero until the numbers of nodes increase to 200 and with
that metering sharply goes up. However, the time latency for
control information sends from Control Center level to Home
level remains as zero no matter how many meter nodes we
configure. We need to make it clear that for the point with
200 meter nodes, the traffic setting for each node is 20kbps
as mentioned before, thus the data rate for uplink from
Neighbor level to Substation level roughly reach to 1Mbps
which is close to the maximum link capacity of T1 link. As a

result, delay for metering data occurs due to the increase of
queuing delay for routers. Moreover, owing to the different
priority requirement of diverse applications, control
information for substation automation undoubtedly maintains
a higher priority. Thus it is reasonable to configure UDP
protocol for metering data and TCP for control signal. After
all, even when the numbers of meter node is relatively high,
our simulation result shows that we can still keep the delay
for control information at a very small level which means our
work result can meet the requirements for power system.
Fig. 4. WAN link maximum utilization and WAN bandwidth
efficiency
Fig. 4 illustrate the maximum link utilization and
bandwidth efficiency for WAN by the increment of node
number. Note that the link maximum utilization here is
obtained by choose the highest utilization ratio from all the
links. The reason is that the links between Substation level
and Neighbor level undertake heaviest load in this network.

However, for the most parts of this network, bandwidth
efficiency remains to a very low level because of the
unemployment of most link resource especially for
2661
downlink.
Fig. 5. Packet loss ratio for control data and meter data
Fig.5 indicates the packet loss ratio for two main types
of traffic flow in our network by increase the node number. It
can be seen that packet ratio for control information is
always stay as zero which is desirable for our application
requirement for substation automation. But for data
information transmits from smart meters, packet loss appears
when node number keeps increase.
In our second scenario, we investigate the impact of
offered load for control traffic on the corresponding time
delay. Fig.6 shows the corresponding result for this part. The

packet size in this scenario all keeps at 1500Byte and also fix
the node numbers. Two traffic classes are categorized by
different hop counts. When we increased the data rate of the
control traffics, the flow delay in the figure appears a slow
linear ascending before 500kbps. After keep increasing the
offered load, the results show a sharp nonlinear rise in
control traffic delay because the increasing load leads to the
queuing delay. The delay performance for these two traffics
is similar. This results show us the queuing is also apply to
this model and 500kbps in this scenario is the turning point.
Figure 6 queuing influence the control traffic delay
In our third scenario, we fix our meter node numbers as
50 and also the traffic data rate for uplink metering data. We
mainly focus on the relationship between delay performance
for control information and control information packet size.
Due to the security concern in smart grid, the usual way to
implement security issue in simulation is to configure the

corresponding security protocol and add relevant overhead in
packet.
Due to the hierarchy structure of network, IFM/IEM
can be deployed at Substation level and Neighbor level. We
can divide our control information from control center to
each IFM/IEM into three categories: control center to
substation, control center to relay level-1, and control center
to relay level-2. The partition here is mainly base on the hop
number, for example, the hop number for CC-sub is 3 or 4,
CC-relay1 is 4 or 5, and CC-relay2 is 5 or 6.
Fig. 7. End-to-End delay for three traffics
Fig.7 shows end-to-end delay for these three traffic
categories by varying the packet size of downlink control
packets. With the increasing packet size, delay linearly
increase and growth rate is primarily depends on the hop
number. One significant drop at the point which packet size
is 1500Byte is due to the fact that TCP protocol validates the

flow control function by adaptively change the mechanism.
Thus 1500Byte packet size for TCP in our scenario is the
maximum one. The maximum end-to-end delay show in fig.
7 is 18.275ms which is produced by a top-bottom control
traffic pass through six routers. Generally, for a 60Hz power
system, the requirement for teleprotection is close to 8ms
which is a requirement we cannot reach for an arbitrary
topology and traffic. However, this result also give us the
hint that we can limit the hop number and configure a certain
packet size, thus we can achieve the most demanding
requirement in smart gird application.
V. CONCLUSION
This paper has proposed the communication network
model for smart grid project – FREEDM, especially from
utility level to consumption level. The model provide a
validated pursuable topology compared to other theoretical
models. Moreover, this paper investigate and summary the
application requirements and corresponding traffic demands.

To our knowledge, this is the first proposal for FREEDM
communication network architecture.
The simulation results for first scenario evaluate the
performance metrics by varying the node number of smart
meters, i.e. WAN link utilization, time delay, and packet loss
ratio. It reasonably illustrate that control data send by Control
center level has minor time delay which can meet the
requirements for Substation Automation. However, delay for
2662
upload metering data is not independent of node number. In
addition, various packet sizes for security concern indeed
have influence on the delay for control information. The
second scenario shows us the queuing when we alter the
offered load for control information. The result in the third
scenario provides a useful data for researchers to achieve the
application requirements especially for high-priority control
command by configure the packet size and hop number.

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