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

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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

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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

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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

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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.

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