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High performance communication architecture for smartdistribution power grid in developing nations
Aryadevi Remanidevi Devidas1,2,3,4 • Maneesha Vinodini Ramesh1,2,3,4 •
Venkat Prasanna Rangan1,2,3,4
� Springer Science+Business Media New York 2016
Abstract In a smart distribution power grid, cost efficient
and reliable communication architecture plays a crucial
role in achieving complete functionality. There are differ-
ent sets of Quality of Services (QoS) requirements for
different data packets transmitting inside the microgrid (a
regionally limited smart distribution grid), making it
challenging to derive optimal communication architecture.
The objective of this research work is to determine the
optimal communication technologies for each data packet
based on its QoS requirement. In this paper, we have
proposed an architecture for a smart distribution power grid
with Cyber Physical System enabled microgrids, which
accommodate almost all functional requirements of a smart
distribution power grid. For easy transition towards optimal
communication architecture, we have presented a six-tier
communication topology, which is derived from the
architecture for a smart distribution power grid. The opti-
mization formulations for each packet structure presented
in this paper minimize the overall cost and consider the
QoS requirements for each packet. Based on the simulation
results, we have made recommendations for optimal
communication technologies for each packet and thereby
developed a heterogeneous communication architecture for
a microgrid.
Keywords Smart grid � Microgrid � Communication
architecture � Heterogeneous network � Linearprogramming � Probability of link error � WiFi � Zigbee
1 Introduction
In developing nations, approximately 1.4 billion people
live without electricity [1]. The absence of an effective grid
infrastructure and the problems associated with the existing
power grid act as a significant barrier to energy access for
the population in developing nations. The existence of off-
grid communities in the developing nations demands an
electrical grid infrastructure for smaller areas that may
work in an autonomous mode.
Rapid increase in the cost, lack of availability of fossil
fuels, and inability to swell the generation capacity
according to the electricity demand are some of the other
crucial concerns in the power sector. This lack of power
generation leads to peak time outages and blackouts, which
are common nowadays in the developing nations. This in
effect, creates a major barrier to a nation’s economic sta-
bility and growth. Hence, to reduce this imbalance between
generation capacity and electricity demand, large-scale
inclusion of renewable energy sources is an integral step to
be considered in designing a future electrical system. The
integration of these renewable energy sources will lead to
an imbalance in power flow through the distribution grid
[2].
Current distribution systems already suffer from insta-
bility due to lack of real-time knowledge about power
& Aryadevi Remanidevi Devidas
aryadevird@am.amrita.edu
Maneesha Vinodini Ramesh
maneesha@am.amrita.edu
Venkat Prasanna Rangan
venkat@amrita.edu
1 Amrita Center for Wireless Networks and Applications
(AmritaWNA), Amritapuri, India
2 Amrita School of Engineering, Amritapuri, India
3 Amrita Vishwa Vidyapeetham, Ettimadai, India
4 Amrita University, Ettimadai, India
123
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DOI 10.1007/s11276-016-1400-2
generation, consumption, losses in transmission, and
insufficient real-time demand-supply management. In
addition, line faults and power thefts affect the perfor-
mance and the energy balance of the microgrid. Thus, it is
necessary to implement autonomous microgrid patches
integrated with an effective, continuous, real-time moni-
toring and management scheme to achieve a stable reliable
distribution grid [3]. To achieve this objective, a CPS
enabled infrastructure is proposed to integrate and manage
distributed renewable energy resources, to continuously
monitor the different electrical and physical parameters for
real-time detection of anomalies, and to manage other
resources using multi level computation and networking
techniques. One of the objectives of the proposed method is
to minimize the mismatch between the electricity demand
and electricity generation, thus making each of the
microgrid energy sustainable thereby the entire distribution
grid.
The effectiveness of the proposed infrastructure depends
on the communication architecture that will enable real-
time communication between different elements and sec-
tions of the microgrid. A highly reliable, secure, scalable
and cost effective communication network is critical for the
next generation power grid [4, 5]. The proposed CPS
enabled microgrid consists of intelligent wireless modules
that are attached to different electrical components to
enable a two way communication between the consumers
and different generating units inside the microgrid and also
enables real time monitoring and control of different units
by the utility. The two-way communication enables a
nearly real-time mutual agreement in energy generation
and energy demand between the electricity suppliers and
consumers, which eventually contributes to energy sus-
tainability inside the microgrid. Moreover, it enables the
detection of a line fault or unnoticed power theft. One of
our major objectives is to develop an optimal communi-
cation architecture that is capable of improving the relia-
bility of the grid by integrating the power and information
flow through the microgrid.
In this work, optimal communication architecture for the
microgrid has been proposed based on the parameters to be
monitored, the amount of data to be transmitted, the fre-
quency of data transmission, the maximum transmission
distance between the grid elements, and the overlay net-
work cost. The communication parameter requirements are
different for the communication between different intelli-
gent agents in the microgrid. Optimal communication
technology has been chosen for each communication link
in the microgrid based on the communication requirement
of the types of packets flowing through each link. As
developing nations require smart distribution power grids,
the overlay communication network for microgrid patches
should be cost effective. Hence, we designed the overlay
communication architecture for the microgrid patches in
such a way that it minimizes the cost while considering the
constraints on parameters such as a communication delay,
channel bandwidth, and packet latency. The networking
module dynamically chooses the appropriate topology for
the data transmission. This module also enables seamless
data transmission in the heterogeneous network with dif-
ferent communication technologies such as Cellular, WiFi,
and Zigbee [6, 7]. The communication module sets the
communication technology for each link based on the
performance requirements associated with the link. To
summarize, the major contributions of this paper are:
1. A multilayer architecture for smart distribution power
grid that is composed of several microgrid patches
consisting of intelligent devices throughout the distri-
bution grid considering all the major functional
requirements of a microgrid. But most of the previous
research works were considered only a subset of
functional requirements identified in this work.
2. Design and simulation of an adaptive and optimal
communication topology for a smart distribution
power grid that allows building an optimal communi-
cation overlay network.
3. Identification of the major functionalities required for
the microgrid and the intelligent devices involved for
enabling those functionalities.
4. Formulation of optimization equations and their solu-
tions for developing the optimal communication over-
lay network for distribution grids integrated with
microgrids, based on the dynamic Quality of Service
(QoS) requirements for each of the functionalities.
The rest of the paper is organized as follows: Sect. 2 dis-
cusses the related work and Sect. 3 describes the archi-
tecture of a smart distribution grid with a CPS enabled
microgrid with a multilevel six tier communication topol-
ogy. Section 4 describes the information packet flow inside
the microgrid, and Sect. 5 and 6 present the heterogeneous
communication requirement as well as the optimization of
the best communication technologies for a microgrid.
Section 7 gives the performance evaluation and Sect. 8
gives the final recommendations about the optimal com-
munication technology. Section 9 concludes the paper.
2 Related work
Sidhu et al. [8] provide a wireless network design capable of
real-time delivery of physical measurements from the
transmission line to the control center. They have formulated
an optimization problem with the objective of minimizing
the installation and operational costs while satisfying the
end-to-end latency and band-width constraints of the data
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flows. They propose a hybrid hierarchical network archi-
tecture composed of a combination of wired, wireless and
cellular technologies that can guarantee low cost real-time
data monitoring. They have also formulated a placement
problem to find the optimal location of cellular enabled
transmission towers. The authors have chosen three wireless
network design communication technologies: namely, zig-
bee, cellular and optical fiber for the transmission line
monitoring in the smart grid. They have not given any jus-
tification onwhy other wireless or wired technologies are not
used for the design. Although the allowed delay limit varies
with the type of data, the authors did not consider the type of
data that is being transmitted between towers, between the
control center and the tower, and between the substation and
the control center. They also have not considered the factors
affecting the link reliability. The proposed method is an
offline planning approach. Hence, challenges in real
deployment (such as time varying interference) have not
been considered in the paper.
Fateh et al. [9] and Hammoudeh et al. [10], have pre-
sented five communication architectures and viable tech-
nologies for deployment within the distribution section of
the smart grid. The author has considered only the data
transmission from the consumers’ houses to the substation.
However, the author has not considered the other possible
data transmissions in the distribution section in the smart
grid. Simulations or deployments that validate the results
have not been presented in this work. One of the major
challenges in developing the communication architecture
for a smart grid is the identification of suitable communi-
cation technologies for smart grid communication infras-
tructure [11]. In [11], authors describe the potential smart
grid applications and wireless technologies that can be used
for each smart grid application. However, the authors have
not identified an optimal communication technology for
each smart grid application. Most of the research papers
focus on the smart grid communication architectures with
particular attention to metering data communication from
smart meters to control stations as in [12–15].
Lo and Ansari [16] developed an overlay multi-tier
communications infrastructure for a distribution grid in
which entire households are grouped into microgrids in tier
1, sets of microgrids form tier 2, coupled clusters of
microgrids form tier 3, and the distribution network form
tier 4. In the architecture, communication inside a smart
building, which is required for energy demand manage-
ment, has not been considered. The authors offer some
suggestions for the communication technology for each
tier, but they have not given optimal communication
technologies for each tier.
Lu et al. [17] investigated different communication sce-
narios for a smart grid and general communication require-
ments for the smart grid. The authors adopt Distributed
Network Protocol Version 3 (DNP3) over TCP/IP to estab-
lish digital connection between devices in the smart grid. The
results shown in [17] reveal that DNP3 over TCP/IP cannot
satisfy communication requirements, especially in time-
critical scenarios in a smart grid. Lu et al. [17] presented only
one type of packet and have not considered different packets
to ensure efficient functioning of the smart grid and its
communication requirements. Separate packets for data and
control information are important to consider while design-
ing a communication infrastructure for a smart grid.
Abdrabou [18] have proposed a multi-hop wireless network
with a cellular frequency-reuse structure, which is claimed to
be used by dense low-voltage distribution networks for
active monitoring and control purposes. Yu et al. [19], have
proposed a cognitive radio-based hierarchical communica-
tions infrastructure for a smart grid. However, in both of the
papers, [18] and [19] the authors haven’t mentioned how
their proposed communication architecture can be overlaid
on top of the power grid structure. Moreover, they have not
shown the cost effectiveness for their proposed communi-
cation architecture.
Khan et al. [20] investigate the introduction of wireless
communication in the smart grid network. Cognitive radio
(CR) network is a promising communication technology
for the smart grid as CR can maximize the spectrum uti-
lization. CR based architectures, communication networks
and its applications, CR based spectrum sensing, CR based
routing, and MAC protocols are the main survey area of
this paper, which also discusses the future challenges and
opportunities of the CR based smart grids. Amin et al. [21]
discuss the key challenges for smart grid implementation
and deployment which are security and interoperability. It
also raises the problem of how to provide security and
efficiency for a globally connected widely dispersed
heterogeneous network for sensing, communication and
control. This paper introduces a real time monitoring of
demand response and cost for consumers, but they did not
implement a real-time control method.
Xu et al. [22] presents a method to analyze the power
quality of the smart grid system. Intelligent Electronic
Device (IED) based on an IEC61850 standard is used to
simplify the communication network architecture and
manage the power quality of the system. This system
identifies the power quality disturbance, its localization,
and power quality assessment. Monitoring the terminal, the
communication networks and the monitoring center are the
three parts of the distributed power quality monitoring
system. Power PC, DSP and FPGA are the processors used
for the system. Takagiwa et al. [23] proposed service-ori-
ented network architecture for a smart grid based on a
wired optical network. However, this solution imposes a
significant cost for the installation of the wired communi-
cation overlay network.
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3 Architecture for smart distribution gridwith CPS enabled microgrid
Even though a smart grid can provide effective solutions
for many problems in an electrical grid, it is still in its
fledgling state. This is because it is almost impossible to
transform the entire grid consisting of generating section, a
transmission section and a distribution section to be smart
due to the network complexity, dynamic behavior changes
of each of the entities, real-time information collection,
aggregation and dissemination through highly location
dependent entities, and real-time control of different enti-
ties based on real-time information [24, 25]. Even if we
consider the distribution section alone, it is difficult to
make it smart due to the interdependence between different
sections of the power grid infrastructure, information flow
through a complex network of energy procurement and
distribution entities, and a very large spatial network of
entities distributed and organized with respect to the load
flow characteristics. The complexity of the spatial distri-
bution of the entities proliferates as the substations dis-
tribute the power using distribution lines that are based on a
dynamic load management principle. Based on location
dependent distribution requirements, these distribution
lines are split at the distribution transformer into secondary
distribution lines that deliver the energy to the secondary
consumers. The real-time control operations inside such a
distribution section of the grid are too complex. The
complete network displays a progression of never-ending,
self-similar characteristics in large to small scale sections
of the network. Hence, we considered a multilayer
approach of integrating multiple self-similar microgrids to
form the distribution grid in order to make a better smart
distribution grid. It can be described as multilayer archi-
tecture for the distribution grid containing microgrid pat-
ches with a Microgrid Control Station (MCS) is shown in
Fig. 1. We now present ten major design considerations for
developing the proposed architecture.
3.1 Power generation-consumption imbalance
Grid operators in the load dispatch center, substation and
section office have very diminutive control over today’s
system [26]. The operator’s primary task is to utilize the
complete power generated and provide enough power to
satisfy the customer demands in their region of operation.
If the total power generated is not fully utilized, it could
lead to a voltage drop in the grid, causing the grid to
become unstable. The substation level operators receive the
information about the total power consumption of the
consumers and total power available for their operating
region or area. But they lack continuous monitoring,
analysis, and decision-making capabilities for multiple
substations to provide real-time balance in power genera-
tion-consumption at substation level. Hence to automate
total grid monitoring and decision-making between sub-
stations, an intelligent device such as Smart Control Station
(SCS) is introduced.
3.2 Sub-optimal or faulty decisions due
to the variation in the capability of operators
From the Operational Technology (OT) point of view, in a
traditional distribution utility control center have Supervi-
sory Control And Data Acquisition (SCADA), a Distribu-
tion Management System (DMS), and an Outage
Management System (OMS) are available, which deal with
’fault’ and ’outages’ as well as day-to-day operations.
These systems are situated at the load dispatch centers and
are mainly stand-alone in nature with limited or no inte-
gration with one another. There are no such control systems
in the lower levels of the power grid, i.e., at the power grid
distribution side. As a result, numerous manual interven-
tions are required from the control center operator to co-
relate the incidents in these stand-alone systems and
understand the situation well enough to make a proper
decision. Due to these manual interventions, time is wasted
in arriving at a conclusive decision for situations needing
immediate attention. This decision making capacity also
varies from operator to operator, and the decision is
entirely driven by the operator’s understanding of the
sequence of events. The result is that sometimes sub-op-
timal/faulty decisions are made. Any inaccurate conclusion
may result in further loss of time and money to the utility.
So despite having the information, today’s utility control
center operations depend on the operator’s capability to
analyze the data and develop an accurate decision. For this
reason we cannot limit the control and monitoring
Fig. 1 Architecture stage 1
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measures only at the higher levels of the power distribution
network. It should be maintained at all levels of the dis-
tribution grid. Thus, intelligent devices such as a Smart
Control Station (SCS) and a Microgrid Controlling Station
(MCS) are introduced to automate grid monitoring, data
collection and analysis, decision-making, visualization, and
database storage at all levels of the distribution grid.
3.3 Consumer aware real-time service variability
based on the energy availability
At the substation, the operators adjust the power con-
sumption generation balance either by borrowing power
from other substation or by shutting down any of the power
line loads for a specific duration. Shutting down of some
power line loads without prior intimation to the consumers
questions the serviceability of the existing grid setup. This
point outs the necessity of a communication and control
entity at the consumer side as well as at the operator side.
In order to make the consumer side smarter, Smart Con-
sumer Nodes (SCN) has been introduced.
The intelligent entity SCN in the smart homes or
buildings helps to monitor the power consumption and
power generation of the consumers inside the microgrid.
SCNs provide the opportunity to Microgrid Control Station
(MCS) to control the consumers energy usage in real-time
and to make automatic bills. SCNs play a major role in the
dynamic power management inside the microgrid by
adjusting the consumption and generation of electricity in
smart buildings whenever a request comes from MCS.
Thus SCNs together with MCS contribute in maintaining
power quality inside the microgrid and also in detecting
and localizing the power thefts and line faults.
3.4 Effective management of the day-to-day
operation of distribution network
The way today’s power distribution control center manages
the day-to-day operation of the distribution network is lar-
gely ineffective, due to the lack of integration of the various
control systems (such as SCADA and DMS) on a common
platform. To ensure uninterrupted power supplies to the end
customers, proper integration of these control systems can
improve management and facilitate the following:
(a) Real-time event correlation
(b) Predictive analytics and forecasting capabilities
(c) Condition based monitoring
(d) Automated fault and outage management
(e) Enhanced demand management to empower an
operator with information for better decision making
and responding to situations needing immediate
attention in a timely and correct manner
In our proposed architecture, we have integrated the control
systems for the microgrid in a common platform that
resides in the MCS. For the distribution grid, the common
platform resides in the SCS. The Smart Transformer Nodes
(STN), together with the MCS and Smart Control Station
(SCS), ensure proper power management between the
microgrids, the microgrid clusters, and the main grid.
The architecture of Fig. 1 enhanced with incorporating
SCN, STN and SCS as shown in Fig. 2. The MCS asso-
ciated to each microgrid controls the entire functionality of
the microgrid. All other intelligent entities inside the
microgrid communicate with the MCS for local decision-
making. The MCS will act as local aggregator and the SCS
will act as global aggregator. The MCS can receive and
send data using a communication module, can store data
using a smart database module, and can allow the con-
sumer and the utility to visualize the relevant data using
two separate visualization modules. The data received by
the MCS can be viewed by the authority, and a constrained
subset of that data can be viewed by consumers (e.g. real-
time consumption details, their bill amounts, real-time
tariff changes, and total consumption per month). The
electric grid parameter values received at the controlling
station are stored in the smart grid distribution system
database.
STNs positioned at the next higher level to the MCS,
manage the supply-demand between the microgrid clusters
below. SCS, the main grid controller, collects all the local
information, provides global decisions, and informs the
decisions to both the local intelligent entities and the higher
level entities.
3.5 Prioritized power delivery
Sometimes the substation has to shut down the distribution
grid of an entire area due to over consumption and power
line faults. But shutting the power of an entire area can
cause critical infrastructures such as hospitals to suffer.
One of the solutions for this issue is to provide selective
routing of power based on the real time supply-demand
values and the consumer classification such as high
Fig. 2 Enhanced architecture integrated with SCN, STN, and SCS
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priority, medium priority and low priority. To achieve this,
real-time grid monitoring, data collection and analysis,
decision-making, and real-time control of different power
grid entities are required. This is achieved through intelli-
gent devices such as a Smart Control Station (SCS), a
Microgrid Controlling Station (MCS), and a Smart Trans-
former Node (STN). The STN, together with the MCS and
the SCS, enables the dynamic selective routing of power
based on the classification of the customers and real-time
supply-demand statistics.
3.6 Automatic line fault detection
Operators generally know which lines are in service and
when relays have to be opened to protect lines against
faults, but they have restricted control capabilities. If a line
fault occurs in the distribution line, the substation gets the
information about the faulty line. Most of the time, con-
sumers inform the operators about such situations over the
telephone. If the fault is persistent, then the maintenance
personnel come to the faulty line locations and correct the
problem. Until that time, the entire line will be discon-
nected from the main grid, and the consumers not receive
power. This issue again questions the serviceability of the
existing grid utility. Localization of the fault is also a hectic
task for maintenance personnel. In order to give the best
service in case of line faults, the fault detection, and
localization should be performed preferably in \20 ms
[27] and only a minimum number of consumers should be
impacted. Hence for monitoring the line faults in our
proposed distribution grid architecture, we have introduced
Smart Distribution Nodes (SDNs).
3.7 Power theft detection
Power theft is considered as one of the pressing problems
of the existing power grid. Power theft is the unauthorized
utilization of power. It contributes to the demand-genera-
tion imbalance and economic losses to the utility. In the
existing power grid, the operators do not have any mech-
anism to know about the theft in real-time. They may only
know it by analyzing monthly or bi-monthly power con-
sumption data collected from the consumers. For moni-
toring the power thefts in real time, the Smart Distribution
Nodes (SDNs) can be used.
The SDNs placed on the top of the distribution poles
measure the voltage and current flow of that point in the
line. Through communication with the descendants of the
SDN (other SDNs or SCNs), the SDN detects and localizes
the power thefts and line faults. The SDN consists of a
voltage and current sensor, a processing module to take
decisions based on the data from the descendants and its
sensors, a communication module to transfer sensor data
and decisions to the next nearby SDN and MCS, and a
circuit breaker or relay to isolate the faulty grid line
whenever required. SDNs with power rerouting mecha-
nisms through tie lines ensure the supply of electricity to
the consumers in the cases of line faults as well, thereby
adding self-healing capability to the microgrid. The
architecture is further enhanced by incorporating SDNs and
such an improved architecture is shown in Fig. 3.
3.8 Automatic billing system
The billing system in an existing power grid is a manual
billing system, where a person from the utility center visits
each household and collects the meter reading to deliver
the billing receipt. Then the consumers go to any nearby
utility center and pay the bill amount. Due to such an
antiquated billing system, consumers are unable to get real-
time information about their consumption. Hence, they are
unable to adjust their consumption according to real-time
supply-demand.
Real-time consumption information is also helpful for
the utility that experiences huge losses due to consumers
tampering with the electric meters. For real-time knowl-
edge of power consumption by consumers and to flag
tampering of the device, an intelligent entity should be at
each load side inside the buildings or houses. The SCNs,
together with the IDs and the MCS, give automatic billing,
as well as, dynamic peak hour decision-making capabilities
to the power grid system.
3.9 Integrating renewable energy generators
In the existing power grid, often the electricity demand
exceeds the electricity generation. This causes a demand-
generation imbalance and contributes to the instability of
the power grid. Hence, it is highly recommended to have
renewable energy penetration to the existing grid. This
necessitates a Renewable Energy Generator (REG) inte-
grates with each and every home or building. The REG
integration enables buildings and houses to have dual roles:
that of consumer and producer. Thereby they become
prosumers. If renewable generators are distributed in the
Fig. 3 Enhanced architecture integrated with SDN
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grid, it is necessary to monitor the generation of each
generator. Renewable Energy Intelligent Devices (REID)
are included in each and every smart home and smart
building in order to provide the power generation and to
balance the grid.
Each microgrid has multiple distributed energy genera-
tion units (DEG units) as well as storage units for making it
energy sustainable. These DEG units make use of renew-
able energy sources such as solar panels or wind turbines to
harvest and store energy in battery banks. A Renewable
Energy Intelligent Device (REID) is connected for con-
trolling the storage and supply of power from these gen-
eration units. Thus, the co-ordinated effort of the MCS, the
REIDs and the SCNs makes the microgrid energy sus-
tainable, at least for small durations. Hence the previous
architecture is further enhanced by introducing IDs and
REIDs, and the enhanced architecture is shown in Fig. 4.
3.10 Integrating multiple microgrids at different
operational modes
The functions of a microgrid can be categorized based on
its dependency on the main grid into three operational
modes: islanded mode, partially connected mode, and fully
connected mode. In an islanded mode, the microgrid
functions totally independently from the main grid. In a
partially connected mode, the contribution of electricity to
the microgrid is divided among the main grid generators
and the microgrid generators. In a fully connected mode,
the main grid provides electricity to the microgrid but
cannot control any functions of the microgrid. Thus,
microgrid consumers depend on the main grid generators
only for electricity. Depending on the requirements of our
system, one or several microgrids can be connected to the
main grid and the microgrids can borrow power from other
microgrids as well. Figure 5 is more picturesque repre-
sentation of the fully developed smart grid architecture.
The Micro Grid Relay (MGR) situated at the Point of
Common Coupling (PCC) as shown in Fig. 5 ensures the
seamless switching of the microgrid from a partially or
fully connected mode to islanded mode and vice versa.
Usually, the microgrid will be in grid-connected mode. If
any electrical disturbance or fault occurs in the main grid,
the microgrid can go to islanding mode thereby separating
it from the rest of the grid. This will help the microgrid to
remain unaffected by the voltage or frequency instability,
and can continue normal operation by consuming power
from the Distributed Energy Generation (DEG) units. Thus,
these intelligent devices help to ensure the quality of power
delivered inside the microgrid. The major functional
requirements of each microgrid are:
1. Monitoring and control of power generation per
consumer
2. Monitoring and control of energy usage per
consumer
3. Monitoring and control of distributed power gener-
ation of the microgrid
4. Monitoring and control of distributed energy con-
sumption of the microgrid
5. Maintaining energy sustainability inside a microgrid
6. Seamless switching from grid-connected mode to
islanded mode
7. Prevention of disruption in electricity supply
8. Detection and localization of faults
9. Detection and localization of power theft
10. Self-healing capability
11. Maintaining power quality
To achieve these functional requirements of the microgrid,
intelligent entities such as Smart Consumer Nodes (SCN),
Smart Distribution Nodes (SDN), Renewable Energy
Fig. 4 Enhanced architecture integrated with IDs and REIDs
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Intelligent Devices (REID), Smart Transformer Nodes
(STN), MicroGrid relays (MGR) and a Microgrid Con-
trolling Station (MCS) are integrated with the power grids
as shown in Fig. 5. Table 1 shows the comparison of
architectures based on the functional requirements of
microgrids.
4 Information packet flow inside the microgrid
The type of information, frequency of information transfer,
size of information, and participants involved in the
information flow characterize the information flow within
each tier and between tiers. Table 2 and Fig. 6 show all of
Fig. 5 Architecture for smart distribution grid with CPS enabled microgrid patches
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the different types of information packets, flowing between
entities in the smart grid architecture. These are elaborately
described in the rest of the section. The information flow in
tier 2 mainly consists of the data transfer from SCN to
MCS of a microgrid. From the SCN of a smart building,
three types of information have as to be transferred to
MCS. They are:
1. The hourly energy consumption information for billing
2. The next hour demand
3. Next hour generation, if the building wants to give
power to the microgrid
Every 1 h this information is to be transferred to the MCS
as one data packet called a Consumer Data Packet (CDP).
We have assumed that the renewable energy sources are
associated to smart buildings. Hence we haven’t considered
any packet flow from the REID directly to the MCS.
An error in the Consumer Data Packet will affect the
billing and also the energy sustainability effort inside the
microgrid. Thus, data reliability is a critical parameter for
this data packet. Maximum tolerable data packet latency is
\1 h. The communication technology determines the
transmission ranges, the type and number of nodes
involved in the CDP transmission. The nodes involved in
the the CDP transfer can be the SCN, the SDNs and the
MCS in which the SCN and the MCS are the source and
destination, respectively, and the SDNs are the intermedi-
ate nodes which forward the packet to the destination. The
data reliability is directly related to the link reliability,
assuming there are no cyber threats. The data packets can
erroneous due to the effects of wireless interference and
path loss. Data packet errors result in retransmission of the
data packet. This can again cause an increase in commu-
nication costs.
The SCN also transmits the data regarding the power
drawn from the microgrid or power injection to the
microgrid, periodically, to the SDN connected to it for the
power theft and line fault check. Such a data packet is
called the Theft- Fault Detection Packet (TFDP). All SDNs
also transmit the TFDPs to their neighborhood SDN.
Hence, both the SCNs and the SDNs are involved in TFDP
transmission and reception. The frequency of TFDP
transfer depends on the frequency of anomalous data
detected from the microgrid. The maximum transmission
distance of TFDP is equal to the maximum pole-to-pole
distance (around 40 m) [28]. Data reliability is one of the
important factors for TFDPs, since an error in abnormality
detection may lead to wrong decisions. The data latency is
not a critical parameter for the TFDP because of two
reasons:
1. Only single-hop transmission is possible for the TFDP
as we need to detect the fault or theft in the power grid
link between the SCN and the SDN or between two
SDNs.
2. As the TFDP is time stamped, the SDN can compare it
with the measured power value at the same time, which
can be stored in the memory of the SDN.
If the SDN detects a power theft or line fault, then it will
send the Theft-Fault Packet (TFP) to the MCS. The TFPs
Table 1 Comparison of architectures based on functional requirements of microgrid
Architectures in
literature
Functional requirements
Monitoring
and control
of power
generation
per
consumer
Monitoring
and control
of energy
usage per
consumer
Monitoring
and control
of distributed
power
generation of
the
microgrid or
grid
Monitoring
and control of
distributed
energy
consumption
of the
microgrid or
grid
Maintaining
energy
sustainability
inside a
microgrid or
grid
Detection
and
localization
of faults
Detection
and
localization
of power
theft
Maintaining
power
quality
SmartC2Net [33] No Yes No Yes No No No No
Architectures—
direct connect,
local access
aggregators,
interconnected
local access
aggregators, mesh,
internet cloud [10]
No Yes No Yes No No No No
USN architecture
[13]
Yes Yes Yes Yes Yes No No Yes
Distributed MDMS
architecture [32]
No Yes No Yes No No No No
Wireless Netw
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contain the power value lost and the theft or fault detection
flags with time stamps and the source and destination
identifications. The most critical parameter to be consid-
ered when sending the TFP is the packet latency. The
preferred data packet latency for the TFP is less than one
second. The Microgrid Control Station (MCS) will send the
control messages to the SCNs in smart buildings and to the
SDNs on top of the distribution poles. The control packet
sent to the SCNs [Control Packet to Building (CPB)]
include,
1. Control information regarding generation
2. Control information regarding consumption
3. Warning information in case of non-payment of a bill
or in the case of crossing the consumption threshold
In the case of an emergency or maintenance, the control
messages (CPSDN) (Control Packet to SDN), are sent to
the SDNs to switch-off the power to a small portion of the
microgrid. The data latency and the data loss need to be
minimized for the CPB and the CPSDN. The data latency
for the control messages such as the CPB and the CPSDN
should be set at the lowest value. It is assumed that all the
critical decisions are taken locally. The MCS will send the
Control Packet to the Microgrid Relay (CPMGR) regarding
the isolation from the main grid or the connection to the
main grid. For the CPMGR, latency may not be a major
factor because the Microgrid Relay (MGR) is placed so
close to the MCS. But data loss can result in wrong deci-
sions regarding the operational modes of the microgrid.
5 Heterogeneous communication requirements
One of the main goals of the proposed smart distribution
grid architecture in Fig. 5 is to minimize the down time of
the distribution power grid while maintaining its energy
sustainability through reliable and real-time communica-
tion between different intelligent entities. To achieve this,
it is necessary to have in place an efficient communication
architecture for the smart distribution grid. The reliability
and robustness of a communication system can be guar-
anteed by its packet delivery ratio and bit error rate,
whereas real-time communication can be achieved by
minimizing the delay. For a smart distribution grid, tradi-
tional cellular network alone can not be used for smart
communication architecture for three reasons:
1. The power grid might exist in sparse cellular coverage
regions. In such regions, we may require alternate
communication technologies.
2. As the cellular network uses a licensed spectrum for
data transmission, additional cost is incurred. The large
size of the power grid also contributes to the additional
communication cost.
3. For a smart grid, a vast number of intelligent
communication entities placed throughout the grid
need to communicate with each other and with other
devices. This may cause congestion in the existing
cellular network.
Hence in order to cater to the different information packet
delivery with respected to the latency consideration, spatial
distribution of source and destination nodes, a heteroge-
neous and hierarchical communication network is required
for smart grid.
Fig. 6 Data packet flow diagram
Table 2 Packet types exchanged between intelligent devices inside a microgrid
Packet
type
Description Functionalities of each packet
CDP Consumer data packet Current energy consumption and next hour energy generation—consumption information
TFDP Theft-fault detection
packet
Power theft and line fault check. Data sends between two poles continuously to analyze the anomalies
TFP Theft-fault packet Packet sends only after the identification of power theft and line fault
CPB Control packet to
building
Control information for power consumption generation and warning information for non-payment and
over consumption
CPSDN Control packet to SDN Control information for power flow in small area under a single microgrid
CPMGR Control packet to MGR Control information for grid connected mode and island mode
Wireless Netw
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6 Heterogeneous architecture: using the rightcommunication technology for microgrid
Most microgrid functionalities are ensured through the
communication of spatio-temporal electrical or physical
parameters between the intelligent modules. The intelligent
devices, placed at specific locations of the microgrid, vary
their level of participation to achieve each functional
requirement. They generate different types of packets to
achieve different functionality. The QoS requirement var-
ies for each type of packet. These multiple packets must
flow through each communication link and satisfy the QoS
requirements per data packet. Hence, it is challenging to
determine an optimal communication technology for each
communication link in a microgrid.
The number of major microgrid components (such as
smart buildings, distribution poles, renewable energy gen-
erators and batteries) vary depending upon the size of the
microgrid and the power grid structure. In addition, the
number of intelligent devices integrated to the microgrid
varies based on the size of the grid. Hence, the microgrid
needs a cost effective optimal overlay communication
network. The mathematical formulation of the problem
statement is explained below.
Let IDk;t be the intelligent devices in a microgrid,
where k indicates the number of intelligent device and t
indicates the type of intelligent device. Communication
links have to be established between the different IDk;t in
order to achieve different functional requirements of the
microgrid. Let Li;j represents the direct link between the
ith and jth intelligent devices and Tcom represents the
available communication technologies. Based on the
constraints of the packet types, Pi;jt flowing through each
link Li;j and the microgrid cost limit Cm;g, a Tcom has to be
chosen for each Li;j . Thus the problem definition can be
written as,
Find:
1. Optimal Tcom for each Li;j Subject to:
(a) Cm;g
(b) Constraints (Pi;jt ); 8t
6.1 Optimization formulations
The microgrid implementation must be cost-effective to be
implemented in the rural areas. Cost is, in fact, the most
critical parameter to be minimized. For the CDP trans-
mission, SCNs are the sources and MCS is the destination.
Depending on the chosen technology for CDP transmis-
sion, the number of hops the packet takes will vary for a
fixed sized microgrid. Thus, the optimization problem for
the communication infrastructure for CDP transmission can
be formulated as, Minimize:
f ðCj;gÞ ¼ n� Ic þ ct � n� ðNr þ 1Þ þ n� d ð1Þ
Subject to:
blink � nSCN �Bt ð2Þht ¼ D� rt ð3Þn ¼ ðht � 1þ nSCNÞ ð4Þ
Nr þ 1� 1
1� phtð5Þ
0� p� 1 ð6ÞðNr þ 1Þ½llink þ t þ r� � ht � nSCN � L ð7Þ
The objective is to minimize the overall costs which
include installation, communication and maintenance as
shown in Eq. (1). Constraint in Eq. (2) restricts the flow
bandwidth based on the chosen technology. Equation (3)
gives the hop count which depends on the maximum dis-
tance between the source and the destination. Equation (4)
gives the total number of intelligent devices in the micro-
grid that have participated in the data packet transfer.
Equation (5) limits the number of retransmissions accord-
ing to the probability of link error and the hop count.
Constraint in Eq. (6) limits the value of probability of link
error, p, between 0 and 1. Constraint in Eq. (7) restricts the
packet latency with regard to the link latency, transmission
latency and reception latency. Serial packet forwarding is
taken for latency constraint in Eq. (7), as it gives the worst
case latency value. The optimization problem for the CDP
communication link is a nonlinear programming (NLP)
problem, since the objective function and all the constraints
are non-linear functions of the design variables. We have
used LINGO 14 software to solve the NLP problem [29]
(Table 3).
The TFDP transmission happens only between SDN and
its direct descendants. For TFDP transmission, the hop
count is one as it is peer to peer communication. Hence the
packet latency for TFDP transmission is dependent on the
number of descendants attached to a SDN. Thus, the
optimization problem for the communication infrastructure
for TFDP transmission can be formulated as, Minimize:
f ðCj;gÞ ¼ n� Ic þ ct �� Nr þ 1ð Þ þ d½ � ð8Þ
Subject to:
blink �Bt ð9Þn ¼ nSCN þ nSDNð Þ ð10Þ
Nr þ 1� 1
1� pð11Þ
0� p� 1 ð12Þ
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Nr þ 1ð Þ llink þ t þ r½ � �MaxðndesÞ� L; ndes � n ð13Þ
The objective function shown in Eq. (8) minimizes the
three components of the cost factors: installation, com-
munication, and maintenance. Constraint in Eq. (9) limits
the flow bandwidth of the TFDP. Equation (10) gives the
total number of SCNs and SDNs in the microgrid. Con-
straint in Eq. (11) gives the number of packet retransmis-
sion limits depending up on the probability of link error.
Equation (12) gives the range of variation of the proba-
bility of link error. Constraint in Eq. (13) limits the TFDP
latency which depends on the number of packet retrans-
missions Nr and the maximum number of descendants
attached to a SDN (MaxðndesÞ). The optimization formu-
lation for TFDP is linear.
The TFP transmission occurs between SDN and MCS,
only when an anomaly such as power theft or line fault is
detected at the SDN. Since TFP is a time critical data
packet, the allowed packet latency is very less. Thus the
optimization problem for the communication infrastructure
for TFP transmission can be formulated as,
Minimize:
f Cj;g
� �¼ ht � Ic þ ct �� Nr þ 1ð Þ þ d½ � ð14Þ
Subject to:
blink �Bt ð15Þ
ht ¼D
rtð16Þ
Nr þ 1� 1
ð1� pÞðhtÞ
!
ð17Þ
0� p� 1 ð18ÞðNr þ 1Þ llink þ t þ r½ � � ht � L ð19Þ
The objective of the optimization formulation for TFP in
Eq. (14) minimizes the overall cost which depends on the
number of hops the packet takes to reach the MCS. The TFP
packets are generated at SDNs when a theft or fault is
detected. Constraint in Eq. (15) limits the flow bandwidth.
Equation (16) gives the hop count that the packet should take
to reach the destination. Constraint in Eq. (17) limits the
number of retransmissions of the packet which depends on
the probability of link error and the hop count. Constraint in
Eq. (19) restricts the packet latency which depends on the
number of retransmissions and the hop count. As TFP is a
delay critical packet, with minimum latency (\1 s), the
packet has to reach MCS. Since the CPB transmission is in
the opposite direction of the CDP transmission, the same
optimization formulation for CDP as shown from Eq. (1) to
(7) is applicable for the CPB transmission. In the same
manner, the optimization formulation for TFP as shown from
Eq. (14) to (19) is applicable for the CPSDN transmission.
As MGR is placed so close to MCS, any low range wireless
technology is enough for the CPMGR transmission. Power
line communication (PL Commn.) is also a good option for
CPMGR transmission [30].
Table 3 Description of
notations used in this research
paper
Notation Description
d Maintenance cost for an intelligent device
ct Cost of communication for a technology
Nr Number of retransmissions
Ic Installation cost of an intelligent device
llink Data latency experienced in the link
T Delay for transmitting a packet from an intelligent device
R Delay for receiving the packet from an intelligent device
ht Hop count for a communication technology
D Maximum distance between source and destination
C Maximum allowed communication cost for the data packet transmission
rt Communication range based on the technology
nSCN Number of smart buildings in microgrid
p Probability of error in communication link
blink Flow bandwidth for the data packet
Bt Maximum flow bandwidth of the technology
L Latency allowed for the data packet to reach its end destination
ndes Number of descendants to a SDN in microgrid
nSDN Number of SDNs in microgrid
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7 Performance evaluation
7.1 Simulation environment
We consider a microgrid with equal distribution of poles
and with a maximum of 40 m pole-to-pole distance. The
maximum distance between a smart building or SCN and
the MCS is taken as 240 m. The length of Consumer Data
Packet (CDP) generated by each smart building is taken as
10 kbps.
For Zigbee technology the connectivity range is taken as
40 m, for WiFi the connectivity range is taken as 120 m,
and for Cellular it is taken as 10 km.The flow bandwidth
for Zigbee is taken as 250 kbps, for WiFi it is taken as
11 Mbps and for Cellular it is taken as 75 Mbps. The
maximum latency allowed for CDP is 1 h. We have used
LINGO 14 software to optimize the cost for the commu-
nication link with respect to the QoS metric.
7.2 Simulation parameters
The QoS metrics of communication architecture are
bandwidth, delay, throughput, data rate, packet loss, and
error rate. We have studied the variation of cost for
transmitting each type of packet with respect to the prob-
ability of error, the bandwidth consumes (which depends
on the number of SCNs connected) and the delay experi-
ence (which depends on the maximum distance between
source and destination) for the three communication tech-
nologies: Zigbee, WiFi and Cellular. The probability of
link error has been varied up to 0.9. The number of SCNs
and SDNs in a microgrid has been varied up to a maximum
of 25 and 100 respectively. The maximum distance
between the SCN and MCS has been varied up to a max-
imum of 2.2 km. The distance between source and desti-
nation for each type of data packet has been varied up to a
maximum of 5 km.
7.3 Simulation and discussion
Initially, a simulation study was performed to understand
the impact on cost with respect to the dynamic changes
experienced in probability of error for different commu-
nication technologies such as Zigbee, WiFi and Cellular.
Figure 7 shows the simulation results.
The installation cost is only a one-time cost, whereas the
communication cost will increase as the number of data
packet transmissions increases. Hence, we need to include
the communication cost in the overall cost for Consumer
Data Packet (CDP) transmission. For simulation, we have
considered the microgrid for a maximum of 25 smart
buildings. Zigbee and WiFi have less cost compared to
Cellular, but they will reach the infeasibility point as the
probability of link error moves towards one. In each of the
graphs, the infeasibility point represents the violation of
one of the constraints in the optimization formulation.
Hence, after reaching the infeasibility point, we cannot use
the technology for the packet transmission. Zigbee can be
used until the probability of link error reaches 0.3, and
WiFi can be used until the probability of link error reaches
0.8. The cost is less for WiFi compared to Zigbee and
Cellular. Thus, analysis of the cost versus probability of
link error for CDP points to WiFi as a better option for
transmission. Due to the high cost of Cellular, we are not
considering Cellular for further simulation process.
Figures 8 and 9 show the variation in the cost with an
increase in the number of smart buildings or SCNs in the
microgrid indicating different probabilities of link error in
CDP transmission with Zigbee and WiFi respectively. With
Zigbee, the microgrid having a probability of link error,
0.4, can accommodate up to twenty smart buildings
whereas the microgrid having probability of link error, 0.5,
can accommodate only up to five smart buildings. When
the probability of link error is 0.6, the microgrid can
accommodate only one smart building. Thus, with Zigbee,
as the probability of the link error increases, the number of
SCN decreases.
With WiFi, the microgrid can accommodate 25 smart
buildings when the probability of link error is between 0
and 0. 8. If the probability of link error is 0.9, then the
microgrid with WiFi can accommodate only 10 smart
buildings for CDP transmission. Thus, to accommodate
more smart buildings with less communication network
cost, WiFi is a good option for CDP transmission if the
probability of link error is well below one.
The variation of cost with maximum distance (D) be-
tween SCN and MCS for different probabilities of link
error shown in Fig. 10. For the same range of D, the cost is
much higher for Zigbee than WiFi. Moreover, Zigbee
Fig. 7 Cost versus probability of link error for different technology
for CDP transmission
Wireless Netw
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reaches the infeasibility point faster than WiFi for the same
probability of link error. Hence, WiFi is the best option for
CDP transmission.
The TFDP transmission occurs in one-hop between any
of the SCNs in the microgrid and its descendants. The
maximum allowed packet latency for the TFDP is 1 min.
We have solved the optimization formulation for the TFDP
(Eqs. (8) to (13)). Since the TFDP transmission is one hop
there is no point in considering the Cellular owing to its
high networking cost. Figure 11 shows the variation of cost
with the variation of the maximum number of SDNs in the
microgrid for TFDP transmission using both WiFi and
Zigbee. They both give the same plots for different prob-
abilities of link error, and they both are infeasible when the
probability of link error exceeds 0.7.
Figure 12 shows the variation of cost with an increase in
the probability of link error for both Zigbee and WiFi. Both
Zigbee and WiFi show the same cost for the TFDP trans-
mission. We have considered another parameter which is
the power consumption for the selection of communication
technology for the TFDP transmission. In the case of the
TFDP, the communication devices are placed in each and
every distribution pole and also in each smart building. All
those intelligent devices are powered by the microgrid. If
they are continuously sending the TFDP, then the energy
consumption of intelligent devices will increase and can
affect the sustainability of the microgrid. That is why we
have selected power consumption as a parameter to choose
between Zigbee and WiFi for the TFDP transmission.
Zigbee is a lower power consuming technology than WiFi
[31]. Thus Zigbee is a good option for TFDP transmission.
The Theft-Fault Packet (TFP) is sent from any SDN of
the microgrid to the MCS, upon detecting a theft or a fault.
The packet latency that the TFP can afford is small, as the
packet contains critical data. We took the packet latency
for the TFP as 5 s for simulation of the equation from (14)
to (19). These results satisfy all the QoS metric parameters
as the constraints and all the conditions provided for the
TFP packet. Figures 13, 14 and 15 show the variation of
cost with variation of the maximum distance between the
source and destination for the TFP transmission using
Zigbee, WiFi and Cellular respectively.
For TFP transmission using Zigbee, the cost varies
exponentially with the increase in the probability of a link
Fig. 8 Cost versus number of SCNs in the microgrid with different
link error probability for CDP transmission using Zigbee
Fig. 9 Cost versus number of SCNs in the microgrid with different
link error probability for CDP transmission using WiFi
Fig. 10 Cost versus maximum distance between SCN and MCS in
the microgrid with different link error probability for CDP using
Zigbee and WiFi
Fig. 11 Cost versus number of SDNs in microgrid different link error
probability for TFDP using Zigbee and WiFi
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error as shown in Fig. 13. When the distance (D) between
SDN and MCS reaches 160 m as the infeasibility point is
reached for TFP transmission using Zigbee. At this infea-
sibility point, the packet latency constraint is violated.
Infeasibility point has reached for p ¼ 0:1, at around
120 m of D. Infeasibility point has reached for p ¼ 0:2 and
p ¼ 0:3, at around 80 m of D. Infeasibility point has
reached for p ¼ 0:4, after D exceeds 40 m. Zigbee is
infeasible for TFP transmission when the probability of a
link error exceeds 0.5.
WiFi becomes infeasible for TFP transmission when the
probability of a link error exceeds 0.8. The infeasibility
point is reached close to 450 m when the probability of link
error is zero as shown in Fig. 14. Infeasibility points are
found at around 400, 320, 240, 200, 160, 120 and 120 m
for the probability of link errors, p ¼ 0:1, p ¼ 0:2, p ¼ 0:3,
p ¼ 0:4, p ¼ 0:5, p ¼ 0:6 and p ¼ 0:7 respectively. For
TFP transmission using Cellular, we see a constant cost for
each probability of a link error from p ¼ 0 to p ¼ 0:8.
Cellular becomes infeasible when the probability of a link
error exceeds 0.8 as shown in Fig. 15. Thus, Cellular
technology is a better option for TFP transmission if D is
large (Table 4).
8 Final recommendations based on simulationresults
Based on the simulation results and discussions presented
in Sect. 7, we have arrived at some conclusions regarding
the optimal communication technologies for each packet.
For CDP, WiFi is the best candidate for the probability of
link error values ranging from 0 to 0.5 with respect to the
Fig. 12 Cost versus probability of link error for TFDP using Zigbee
and WiFi
Fig. 13 Cost versus maximum distance between source and destina-
tion for TFP using Zigbee
Fig. 14 Cost versus maximum distance between source and destina-
tion for TFP using WiFi
Fig. 15 Cost versus maximum distance between source and destina-
tion for TFP using cellular
Table 4 Optimal communication technology for each packet
Type of packet Communication technology
CDP (ID to SCN) WiFi
CDP (SCN to MCS) WiFi
TFDP ZigBee
TFP Cellular
CPMGR Power-line communication
CPB WiFi
CPSDN Cellular
Wireless Netw
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overall cost. Also, WiFi can accommodate a greater num-
ber of SCNs or smart buildings compared to Zigbee for
even larger link error probabilities.
For the TFDP, WiFi and Zigbee give the same cost
values for different link error probabilities ranging from 0
to 0.7. When the link error probability exceeds 0.7, both
Zigbee and WiFi hit the infeasibility point. In that case, we
have introduced low power consumption as the deciding
parameter and recommended Zigbee.
The best communication technology option for TFP is
ZigBee if the distance between source and destination and
the link error probability are small. WiFi becomes infea-
sible for TFP transmission when the link error probability
exceeds 0.7. Cellular is the best option for TFP transmis-
sion if cost is not a concern for packet transmission.
For CPB transmission, the same technology used for
CDP transmission can be used. Similarly, for CPSDN, the
same technology used for TFP transmission can be used.
For CPMGR, any short-range communication technology
is a better option. As wireless communication technologies
experience link unreliability, power line communication is
a good candidate for CPMGR transmission. Figure 16
shows all the results regarding optimal communication
technologies for transmission of the different data packet in
the microgrid.
9 Conclusion
In this paper, we have presented a smart distribution grid
architecture with CPS enabled microgrid patches and a six
tier communication topology. We have identified different
information packet flows in the microgrid to achieve the
most important functionalities of the microgrid. The
optimization formulations presented in this paper mini-
mize the cost for different packets with respect to prob-
ability of link error, maximum distance between source
and destination, and number of SDNs that lead to the
optimal communication architecture for microgrid. Based
on the simulation results, the recommended communica-
tion architecture is heterogeneous using communication
technologies such as Zigbee, WiFi, Cellular and power
line communication.
Acknowledgements The authors would like to express gratitude for
the immense amount of motivation and research solutions provided
by Sri. Mata Amritanandamayi Devi, The Chancellor, Amrita
University. The authors would also like to acknowledge Dr.
P. Kanakasabapathy, Dr. P. Ushakumari, Ms. Nibi K. V. for their
valuable contributions to this work. This work is partially funded by
the Indigo Energy program under FP7, with the project titled as
‘‘Stabiliz-Energy (Stabiliz-E)’’ under ‘‘DST/MRCD/New Indigo/Sta-
biliz-e/2014/(G)’’. This work is also supported by TATA Consultancy
Services under the TCS Research Scholar Program.
Fig. 16 Microgrid with optimal communication technologies
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Aryadevi Remanidevi Devidasreceived the M.Tech. degree in
wireless networks and applica-
tions from Amrita University,
India. She is currently a Ph.D.
candidate in Amrita Center for
Wireless Networks and Appli-
cations, Amrita University,
India. Her Ph.D. thesis focuses
on dynamic energy management
strategies in CPS enabled
micro-grids that make use of
optimal communication overlay
network for micro-grid.
Maneesha Vinodini Rameshreceived the Ph.D. degree in
computer science and engineer-
ing from Amrita University, in
2009, where she serves as the
Director of Amrita Center for
Wireless Networks and Appli-
cations and as a Professor of
Computer Science and Engi-
neering. She is the Editor of the
Ad Hoc Networks Elsevier and
has served as Program Chair for
ACWR 2011. She has more than
63 papers, including several
journals and best paper awards,
to her credit all in the area of Wireless Sensor Networks. She has
given invited talks at several eminent universities all over the world.
She is also the Co-Principal Investigator of the European Commission
funded WINSOC (Wireless Sensor Networks with Self Organization
Capabilities for Critical and Emergency Applications) Project, and
Principal Investigator of 8 internationally recognized projects funded
by different organizations from all over the world. In 2012, she
received NABARD Award for Rural Innovation—2nd prize from the
Honorable Finance Minister, Government of India for her research
activities benefited to the rural community.
Venkat Prasanna Rangan was
appointed by AMMA as the
Vice Chancellor of Amrita
Vishwa Vidya peetham in 2003.
Amrita Vishwa Vidyapeetham
is a young and dynamic
University established by its
Chancellor, Sri Mata Amri-
tanandamayi Devi, popularly
called AMMA all over the
world and one of the foremost
humanitarian leaders of the
world today. Previously, he
founded and directed the Mul-
timedia Laboratory and Internet
and Wireless Networks (WiFi) Research with the University of Cal-
ifornia, San Diego, (UCSD) where he served as a Professor of
Computer Science and Engineering for 16 years. He is an interna-
tionally recognized pioneer of research in Multimedia Systems and
Internet E Commerce. In 1993, he founded the first International
Conference on Multimedia: ACM Multimedia 93, for which he was
the Program Chairman. This is now the premier world-wide confer-
ence on multimedia. He also founded the first International Journal on
Multimedia:ACM/Springer-Verlag Multimedia Systems, which is
now the premier journal on Multimedia. He is also a Fellow of ACM
since 1998, the youngest to achieve this international distinction. He
has been awarded the NSF National Young Investigator Award in
1993, The NCR Research Innovation Award in 1991, and The Pres-
ident of India Gold Medal in 1984. In July 2000, Internet World
featured him on its cover page and named him as one of the top 25
Stars of Internet Technologies. In August 2012, Silicon India ranked
him as one of the ‘‘50 Indians Who Redefined Entrepreneurship in the
Last 65 Years of Independence.’’ He has over 85 publications in
International (mainly IEEE and ACM) Journals and Conferences, and
also holds 22 US patents.
Wireless Netw
123
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