1.
INTERNSHIP REPORT ON SMART GRID
Author:
Kamaldeep Singh
Aravind Avvar
Supervisor:
Prof. Matti Latva-aho
Prof. Premanandana Rajatheva
1
Report on Smart GridsReport on Smart Grid VisionReport on Smart Grid's VisionReport on Smart Grid's Vision

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ABSTRACT
In this report we analyze the background work required to cognize on
Smart Grid technology and how it can be effectively implemented. The main
objective of this report is to consider about the channel models, different
ways of measurement, standards and tools used to optimize and allocate
the resources Smart Grid. Firstly we report briefly the definition of Smart
Grid and how data is communicated in Smart Grids.Secondly we study on
the different ways of measuring and optimizing the allocation of resources.
Finally we study about the present standards and how well are they imple-
mented in the real system. At last we conclude with the present scenario in
Smart Grids and how we can improve on the present implications.
Key words: Power Line Communications, Smart Grid Technology, Stan-
dards, Research Areas in Smart Grids.
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Contents
1 INTRODUCTION 6
1.1 What is Smart Grid . . . . . . . . . . . . . . . . . . . . . . . 6
1.2 Data Communication on Smart Grid . . . . . . . . . . . . . . 8
2 CHANNEL MODELS IN SMART GRID 11
2.1 Channels used in Smart Grids . . . . . . . . . . . . . . . . . . 11
2.2 FSK System for Smart Utility Network . . . . . . . . . . . . . 12
2.2.1 Communication Network Architecture . . . . . . . . . 13
2.2.2 Power Line Intelligent Metering Evolution . . . . . . . 14
3 POWER FLOW MANAGEMENT IN SMART GRID 16
3.1 CDMA Channel Model in Smart Grid . . . . . . . . . . . . . 16
3.2 Smart wires (SW) . . . . . . . . . . . . . . . . . . . . . . . . 18
4 TOOLS USED IN SMART GRID 20
4.1 Simulations Tools used in Smart Grid . . . . . . . . . . . . . 20
5 OPTIMIZATION IN SMART GRID 24
5.1 Optimization Models for Energy Reallocation in a Smart Grid 24
6 RESOURCE ALLOCATION IN SMART GRID 26
6.1 Cost Aware Grid Implementation . . . . . . . . . . . . . . . . 26
6.2 Game Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
7 STANDARDS USED IN SMART GRID 29
7.1 IEEE P2030 Standard . . . . . . . . . . . . . . . . . . . . . . 29
7.1.1 IEEE P1901- Broadband over the power line Networks 31
7.1.2 Next Generation Service Overlay Network IEEE P1903 32
8 SMART GRID DEVELOPMENT VISION 34
9 CONCLUSION 36
10 REFERENCES 37
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4.
FOREWORD
This Internship report is based on the practical training which we performed
in the department of CWC at the University of Oulu. The purpose of this
Internship is to do the literature review on Smart Grids and to understand
its functionality.
Oulu, 15th June 2011
Kamaldeep Singh
Aravind Avvar
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5.
LIST OF ABBREVIATIONS
FSK Frequency Shift Keying
CDMA Code Division Multiple Access
IEEE Institute of Electrical and Electronics Engineers
AMI Advance Metering System
OFDMA Orthogonal Frequency Division Multiple Access
PLC Power Line Communications
MIMO Multiple Input Multiple Output
MISO Multiple Input Single Output
3GPP 3rd Generation Partnership Project
OFDM Orthogonal Frequency Division Multiplexing
IDMA Interleave Division Multiple Access
Wimax Worldwide Interoperability for Microwave Access
SUN Smart Utility Networks
CPE Customer-premises equipment
EPS Electrical Power System
QOS Quality of Service
OSI Open System Interconnection
BPL Broadband over Power Lines
CTP Capacitated Transshipment Problem
IP Internet Protocol
BER Bit Error Rate
QPSK Quadrature Phase Shift Keying
BPSK Binary Phase Shift Keying
DPSK Differential Quadrature Phase Shift Keying
SCADA Supervisory Control and Data Acquisition
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Chapter 1
INTRODUCTION
This chapter gives a brief outlook of the Smart Grid and its functionality.
Data communication on the Smart Grid is explained in detail.
1.1 What is Smart Grid
Smart Grid is a vision that how to generate, distribute and consume en-
ergy. In addition to this, it is how to overcome the shortcomings of today’s
electricity grids. The main goal is to mitigate the impact of disruptions of
the energy supply as well as to enhance security and reliability of energy
infrastructure. The operation and meaning of the smart grid can be best
understood with the figure given below.
Figure 1.1: Smart Grid[26]
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The topology of Smart Grid is shown in the fig. 1.1. It mainly consists
of domains, interfaces and its distribution. There have been radical changes
in a way to generate, distribute and consume energy. The electric grids were
originally designed to distribute the electricity from small number of gener-
ators to millions of the consumers based on the concept that supply must
follow the demand [28]. This phenomenon required additional generators
to be instantly being available to balance the increase in demand and also
the installed generation capacity is sufficient to satisfy the demand at the
peak time. In addition, consumers are charged for electricity on a per unit
basis however the real cost of generating the electricity varies throughout
the day [27]. Clearly, because of the inefficiencies in the previous electricity
grid system, a more dynamic and smarter grid system was paramount.
This vision has led to the innovation of smart grids, which are improved
electricity grids where household and businesses act as both generators as
well as consumer of electricity, and were the information and electricity flow
together in network.Smart Grid is an electricity delivery system with com-
munication facilities and information facilities for the efficient and reliable
grid operation with improved customer satisfaction and cleaner environ-
ment. Smart Grids are also called Green Communication because it is na-
ture friendly technology. By using the Two-way communication capabilities
of the smart meter one can enhance the current power system. Smart Grid
will allow the flow of energy from consumers to outside network depending
on demand and supply conditions. Features of smart grid Includes real time
monitoring and exchange of the Information [28]. Consumers can adjust
electric supply according to their needs, cost, power, level of reliability and
environmental Impact.
Smart Grids will remove any hindrance in economic growth and facili-
tates the delivery of the energy from the renewable sources of energy like
wind, sun and water. There would be faster detection of outages and black-
outs and rapid system restoration will improve the security and reliability
of the grid [30]. Moreover, Grid will be less vulnerable to potential attacks
and threats. The current electric power grid is outdated and cannot support
the increase in the energy consumption. Hence, it is expected that smart
grids which are more reliable, secure, economic, efficient and environmental
friendly grids will replace the old power grids.
The backbone of the Smart Grids is the Advance Metering Infrastruc-
ture (AMI) consisting of the Smart Meters and the communication network
which has capability to monitor and repair the faulty network in the real
time [1].The utilization structure of smart grid needs to be analyzed before
implementation of the AMI. The utilization or the component structure of
the smart gird is shown in the below figure.
The fig. 1.2 describes the utilization structure of grid from the generation
to the consumption. The figure mainly explains the component utilization.
The vision of the Smart Grid is to research, develop and demonstrate a
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Figure 1.2: Smart Grid Structure[29]
two-way electricity network that will meet the increasing energy demands.
Electricity power Grids must be:
• Flexible: Grid should easily adapt to the changing and challenging
environment.
• Accessible: Grid must be accessible to all users and should have high
efficiency local generation with zero or low carbon emission.
• Reliable: Grid must assure improved quality of supply and security
and must adapt itself with increase in demand without any hazards
and uncertainties.
• Economic: Grid is best valued through innovations and efficient energy
management.
1.2 Data Communication on Smart Grid
The smart grid system requires high speed sensing of the date from all the
sensor nodes within few power cycles [1]. The AMI is employ the meters
on the grid and which are used to provide all the vital information to the
master head end within very short duration of time. The two head end
and the meter are located on the different parts of the network. Orthogo-
nal Frequency Division Multiple Access (OFDMA) based communication is
used over low voltage power line in CENELEC band A and B [3]. In Or-
thogonal Frequency Division Multiple Access channel model time varying
and frequency selectivity power grid channels and noise is undertaken. AMI
provides an ability to use electricity more efficiently and monitor and repair
the networks in the real time.
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The multiuser communication over the low voltage undergoes various
challenges such as large number of sensors, time varying circuits, high back-
ground noise, and varying Grid topologies [18]. The channel model views
current grid configuration as a Multiple Input Multiple Output/Multiple In-
put Single Output (MIMO/MISO) channel and use the channel information
to develop on OFDMA based transrecievers.
The time variation of the loads represent the complex frequency depen-
dent, switching behavior in the CENELEC band of the residential and com-
mercial powered equipment’s. The communication is established between
the head end and the meter [3]. Not only the channel frequency selectivity
causes the fading but switching on/off of loads also causes fading. This is
due to the time varying behavior of the circuit elements. Time varying loads
causes non-linear behavior [18]. However, non-linearity changes slowly. We
can use quasi static approximation and Fourier analysis for time varying and
stochastic impedance.
Monte Carlo simulations can be used to estimate various parameters
like mean, correlation etc. OFDMA system have been in use for various
wireless system includes Wimax and 3rd Generation Partnership Project
(3GPP) for optimizing the simultaneous use of available bandwidth for the
data transmission from the mobile station to the base station [2].
A unique subset of subcarrier is assigned to each user in an OFDMA
system to simultaneously transmit the data.
Figure 1.3: Smart Grid Architecture[25]
In OFDM based systems the available bandwidth is divided into number
of sub bands and each sub band is assigned to different users [3]. The Low
voltage power lines are used in the real time communication, and hence can
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be used in the smart grid monitoring systems [5]. Any system functionality
can be best understood with the layered architecture. The architecture of
the present smart grid is shown in the fig 1.3. It analyzes each function
of the grid to match with the definite layer so that the total automation
doesn’t need any human intervention.
The fig.1.3 describes layer architecture from the bulk generation to trans-
mission, distribution and finally end customer who is going to get the ser-
vice. The physical layer basically constitutes the generation, distribution.
The next layer is communication network layer and it takes cares of the net-
working functionality in smart grid. The next layer which is most important
layer i.e. communication network security layer which take security in to
consideration for the network functionality. Each layer is interdependent on
each another for the efficient smart grid implementation. In this chapter,
we reviewed the various definitions on Smart Grids and how the data is
communicated in Smart Grids.
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Chapter 2
CHANNEL MODELS IN
SMART GRID
We will analyze the use of Channel Models in Smart Grids. There are
many Channels Models used in Smart Grid. But we will mainly concentrate
and narrow down to the main Communication Channel Models employed in
present Smart Grids.
2.1 Channels used in Smart Grids
Power Line Communications (PLC) plays an important role in Smart Grids
for its cost efficiency [13]. Number of methods has been used to deal with
the challenges in PLC like selectivity fading and impulsive noise.
One of the methods namely Orthogonal Frequency Division Multiplex-
ing Interleave Division Multiple Access (OFDM-IDMA), which can be used
to solve the problems caused by the frequency selective channel and im-
pulse noise. There are two major problems in the PLC that are frequency
selectivity which is caused by the Reflections generated by the impedance
discontinuities [11]. The second problem is noise which consists of back-
ground noise and impedance noise. The impedance noise is caused due to
the switching behavior of transient elements.
OFDM systems are used to convert the frequency selective channel into
the frequency flat channels so that use of the complicated equalizers is
avoided. However, OFDM is not able to handle the busty errors caused
by the impulsive noise in PLC. Interleavers can be used to separate the sub-
sequent affected symbols. Hence, to get the better performance than OFDM
systems, a new system have been proposed which is known as OFDM-IDMA
which is enhanced version of OFDM systems.
OFDM-IDMA is interleave division multiple access uses different chip
level interleaving sequences in contract to the differential spreading sequences
in OFDM systems to distinguish different users [11] .If the interleaving se-
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quences are treated as spreading codes then IDMA can regarded as a special
case of the CDMA.
The performance of OFDM-IDMA is better than the OFDMA because
of the following reasons:-
• Use of the spreader of long length in OFDM-IDMA enables the col-
lection of multipath diversity provided by the PLC channels while
OFDMA fails to collect multipath diversity.
• Spreader couples with interleaver in OFDM-IDMA to alleviate the
effect of impulsive noise by averaging the impulse affected subcarriers
with number of unaffected subcarriers.
OFDM is a feasible solution for converting the frequency fading channels
into the flat fading channels over and IDMA alleviates the effect of impulsive
noise by averaging a number of sub-carriers.
2.2 FSK System for Smart Utility Network
In parallel to the Smart Grids, Smart Utility Networks generally known as
SUN are also gaining popularity these days. SUN is the networking system
that is used in the utility services such as electricity, water, gas, so as to
cover the information from millions of supported nodes across the diverse
geographical environment. SUN design is called IEEE 802.15.4g [4]. SUN
system supports a large number of nodes within the network therefore it
takes into account the homogenous co-existence among its devices. Ho-
mogenous co-existence is handled by the physical layer and medium access
control sub layer.
Advantage of low duty cycle is that it provides technical strength, low
power consumption and good co-existence capabilities but it reduces the
data rate. There is a long silent period in between the two consecutive
signal transmissions. The silent period enables features such as power saving
and effective multiple access [4]. Application of Low duty systems is in the
impulse radios and the spread spectrum radio, both related to the ultra-
wideband technology. Low duty systems are also used extensively in several
specifications of the wireless personal area networks.
The Utility meters are connected through a wireless channel to the data
collectors and further data collectors are further connected to the utility
provider control center servers through the main station.
In SUN systems the data flows from the end nodes to the utility providers
facilitating billing data collection, load assessment and other relaxed mea-
surement [4]. On the other hand, the SUN also facilitates control and man-
agement of utilities services such as service connection/disconnection, service
monitoring and load balancing.
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There are two types of devices in the network, the coordinator capable
devices and the normal devices. In a network cluster, coordinator capable
device are used to manage the network timing and resources, while the other
devices become network nodes. In a cluster, devices may be formed in a star
or tree cluster formulation. Multiple clusters can be joined through the net-
work coordinator capable devices from the respective clusters. This enables
a topology that extends to complex multicluster architecture, supporting
mesh and peer to peer networks [4]. The data collection from the customer
side and to implement back to their components needs intelligence and that
is done with the smart meter system. The following figure shows the imple-
mentation of data collection and distribution in smart meter system.
Figure 2.1: Analysis of Data Collection and Distribution[29]
The fig. 2.1 analyzes the data collection and distribution form the smart
metering system. The data from the CPE smart meter collects the data
and sends it to the EPN agent which transfers it to the EPN edge collection
point. The data is transferred to many EPN edge points before it reaches
the smart grid. The grid analyzes the data and production and distribution
is done depending on the utilization.
2.2.1 Communication Network Architecture
An IP-centric heterogeneous and integrated communication network may
be used to meet the communication demands of Smart Grid applications
that can included in different power grid segments and in multiple network
technologies [25]. The integrated IP network supports data communication
required for controlling and managing applications such as smart metering,
automated demand response, rapid inter-substation response, and distri-
bution automation, synch phasors, SCADA systems, EVs and micro grid
connectivity. The communication network is also expected to support other
utility enterprise traffic.
To provide more reliable services to the consumers there is a need of
robust and real time communication between the remote points of the net-
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work and the control room [17]. One way to achieve this is to use the
existing power line infrastructure as the communications medium, a process
generally known as PLC. Though PLC is not a new concept, advancements
in modulation performance and the ever decreasing cost of implementing
modems in hardware now means that a network wide multi-point to point
network without the need for expensive line traps is possible.
2.2.2 Power Line Intelligent Metering Evolution
The Power Line Intelligent Metering Evolution(PRIME) architecture for the
implementation of metering system in smart grid is shown in the following
figure [17]. It basically constitute exchange agent which gets the information
from the different consumption points. The exchange will implement the
smart metering system. The exchange agent will route the data accordingly
based on the consumption.
Figure 2.2: Intelligent Metering System[30]
The fig. 2.2 shows the implemented power line metering system which
intelligently can take decision depending on the utilization Power line Intel-
ligent Metering Evolution is one of the power line communication technolo-
gies, which is used in smart metering applications. PRIME calls for a new
public, open and non-proprietary telecommunications architecture that will
support the new AMM functionality and enable the building of the elec-
tricity networks of the future, or Smart Grids. The PRIME PHY / MAC
specifications are open, publicly available. PRIME employs OFDM modu-
lation in the CENELEC A band (9 - 95 kHz), and achieves data rates from
21 kbps to 128 kbps at the PHY layer.
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There are two basic communication scenarios, one is where we cannot
afford to have delays such as control signals in the power system opera-
tions currently carried out by Supervisory Control and Data Acquisition (
SCADA) system, the other is some delay can be allowed. Application of
wireless and wire line access in these areas should be carefully considered.
The other critical aspect is the communication security. With the ad-
vent of smart meters, ’always on’ security is essential as opposed to ’on off’
security provided for E-commerce applications. Universal, intelligent and
multifunctional devices controlling power distribution and measurement will
become the enabling technology of the ICT-driven Smart Grid. Agents can
be used for acquiring and monitoring data, support decision making, rep-
resent devices and controls etc. They act autonomously and communicate
with each other across open and distributed environments.
In this chapter, we made a study on the present channel models available
and deployed in smart grids for efficient functionality.
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Chapter 3
POWER FLOW
MANAGEMENT IN
SMART GRID
In recent years, there has been an increased demand for more efficient ways
of managing the power distribution in electricity networks; in particular it
is desired to reduce the wasteful electricity consumption in order to reduce
costs and the adverse effect of electricity generation on the environment.
3.1 CDMA Channel Model in Smart Grid
In order to meet the changing requirements, more sophisticated methods of
measuring and controlling the power consumption are desirable. More, so-
phisticated networks, sometimes known as Smart Grids, have been proposed,
which may include features such as a capability to turn off certain house-
hold appliances or factory processes at times of peak demand [25]. These
Smart Grids may use sophisticated meters, sometimes known as Smart Me-
ters, capable of intermittently measuring power consumption in near real
time, and of indicating energy prices to consumers. However, such meters
are typically located at the premises of a customer or provider, and measure
the amount of electrical power flow as a total of all devices located in the
premises [25]. This means that power flows relating to individual devices at
a given premises, or a group of devices distributed across multiple premises,
cannot easily be measured, particularly in view of the relatively high cost
of smart meters making it prohibitive to install a separate meter at each
power consuming and/or providing unit to be measured. There is provided
a method of controlling electricity power within an electricity distribution
network, the electricity distribution network comprising a measured node,
the measured node being arranged to access data store storing data indica-
tive of one or more predefined power flow patterns, in which a power unit is
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electrically connected to the electricity distribution network and is arranged
to consume electric power from/or provide electric power to the electricity
distribution network such that a change in consumption and/or provision of
electricity distribution network such that a change in consumption and/or
provision of electric power by the power unit results in a change in power
flow in the network.
The method comprises of controlling the power flow to and from the
power unit in accordance with a control sequence, such that the consumption
and /or provision of power by the power unit results in a power flow having
a said predefined power flow pattern, and a characteristic of the power flow
resulting from the unit is measured by the measurement node.
By controlling the power flow at the power unit according to a predefined
power flow pattern, a measurement node in a network to which the unit is
connected having the access to the pattern can detect and measure the
power flow resulting from the power unit, allowing the power flow to be
remotely detected and measured. Further, since the method requires only
that the power flow to and/or from a power unit to be controlled, it does
not require complicated and expensive measuring equipment, such as smart
meters [25]. Each of a distributed group said power units is connected to
the electricity distribution network, each of which having an associated said
power flow control device, and the method comprises using the power flow
control devices to control the power flow to and/or from the plurality of
units in accordance with the control sequence, such that the consumption
and/or provision of power by the plurality of power units is coordinated to
collectively provide a power flow having the predefined power flow pattern
and a characteristic measurable by the measured node.
By providing a group of, perhaps distribute, power units with the same
control sequence, so that they collectively provide a combined power flow
according to the predefined pattern, the combined power flow resulting from
group can be measured [25]. In some embodiments, a plurality of the groups
is connected to the network, and the method comprises controlling the power
flow to and/or from each of the groups according to different control se-
quences, such that the power flow patterns resulting from the said groups
mutually orthogonal, or quasi orthogonal, such that a power flow character-
istic associated with each of the power flow patterns can be measured at the
measurement node independently of each of the other patterns.
By using orthogonal power flow patterns, power flow from multiple groups
of devices can be measured simultaneously[25]. There is provided a method
of controlling the electricity flow in an electricity distribution network, the
electricity distribution network comprising a plurality of distributed groups
of power units, each of said power units being arranged to consume and
/or provide electricity associated with the electricity distribution network,
wherein each power unit in a given group is arranged to be controlled by
a control sequence assigned to the group, the control sequence controlling
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power consumption and/or provision by each unit of the group according to
a predefined pattern, resulting in a power flow pattern and each of the mea-
surement nodes being arranged to measure a characteristic of power flowing
in the network according to power consumption of one or more group.
The distribution network is 105 which are distributing the energy to 108
networks which has different types of consumption units with the CDMA
spreading code separation. The CDMA code is used to separate the groups
and users in the smart grid.
3.2 Smart wires (SW)
SW is a technology which enables to realize low cost transmission line mon-
itoring and power flow control in meshed networks. SW allows to utilities
increased power transfer in meshed networks by increasing average line uti-
lization. Georgia Tech has developed the SW technology which converted
existing transmission line to a smart asset, able to monitor and regulate
its power flow, thereby shifting excess power to underutilized lines in the
network [7]. The smart wire circuit schematic constitutes the power line
where it is received by the step down transformer. The smart wire has a
control circuit which controls the flow of electricity in the network. The
circuit schematic of the smart wire is shown in the below figure.
Figure 3.1: Smart Wire Circuit Schematic[7]
The fig. 3.2 describes the circuit schematic of SW when it connected.
The simplest version of the technology, SW, monitors line current and takes
autonomous action. As current builds up on SW, the modules autonomously
take action, gradually increasing the impedance of the line by sensing line
current and comparing it against a reference current based on the line ca-
pacity.
The heart of each module is a ’single-turn transformer’(STT) coupling
the line current with control circuitry, along with a fast acting switch that
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inserts the leakage impedance of the STT in series with the transmission
line when the switch is closed. When the switch is open, the leakage and
magnetizing impedances of the STT are inserted in series.
The SW modules are self-powered using the line current and do not
require communications among the devices or to a central control center.
The module operates at line potential and does not connect to the ground,
eliminating isolation issues.
We have analyzed different ways of measuring power which would be
essential for the implementation of smart grids.
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Chapter 4
TOOLS USED IN SMART
GRID
In this chapter, we will analyze Tools that are utilized in Smart Grids. The
Tools used in Smart Grids are done by Simulation as it requires lot of cost
for its implementation. We will also study, the methods used for evaluation
of performance of particular Modulation Technique.
4.1 Simulations Tools used in Smart Grid
There are many parameters which should be taken in to consideration while
modeling and simulating power line communication models. To simulate
most likely integrated /hybrid communication architecture consisting both
PLC and wireless connections is needed. This leads to rather challenging
mathematical and simulation models. Furthermore, user mobility models
giving different scenarios of plug-in electric vehicle charging is also important
given the predictions of high level of penetration over the coming years.
Weather forecasting models to optimize the network in a predictive manner
for wind, solar or hydro energy production will be needed for fostering the
implementation of smart grids with renewable energy resources [6]. The
other aspects such as distribution analysis tools, market models, building
models, renewable resource models and also simulation models for more
theoretical research also play a crucial role.
Communication network model are used by information technology com-
panies and national defense researchers and application developers for com-
munication network design, engineering, and planning. Some of the commu-
nication models for designing communications models are Qualnet, Opnet,
Washington State University [6]. One of the most important enabling com-
ponents of Smart Grid is reliable communications infrastructure that links
together many elements of the grid. The design of communication model is
quite very prominent in the implementation of the smart grid.
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The next step is how to distribute and analyze the smart grid resources.
This can be done with the help of Distribution Engineering Analysis Tool.
Dynamic analysis tools are used primarily by utilities, ISOs and RTOs for
transmission system engineering and planning, including offline studies of
dynamic stability issues and the production of nomograms describing stabil-
ity limits. The dynamic analysis tool helps us to determine the distribution
point of view and analyze it much more effectively. Some of the tools which
are presently used are PSCad (Manitoba HVDC Research), SIM power sys-
tems (The Math works). Renewable resource models are used by utility
planners and operators, researchers, and investors to analyze resource avail-
ability and energy output for wind and solar generation thereby the sys-
tem become much more efficient. Some of the models used for analysis are
LEAP, BCHP Screening Tool, energy PRO, Solar Advisor Model (SAM),
TRNSYS16 [19].Market Models study market design and consumer impact
issues, Transmission companies, market operators: to analyze system and
market performance. Some generation companies study market models to
analyze corporate strategies.
Research-Oriented Simulation Environments is used for analysis of dis-
tribution and smart grid assets, controls, and operational strategies, to in-
vestigate the technical and economic potential of smart grids, developing
and analyzing operational strategies, control algorithms, market/incentive
structures, and communication requirements. The research oriented simu-
lation environments allow us for determining the requirements of the smart
grids which would be useful for design of smart grids [19]. co-simulation
environment would allow engineers to assess the reliability of using a given
network technology to support communication-based Smart Grid control
schemes on an existing segment of the electrical grid; and conversely, to
determine the range of control schemes that differing communications tech-
nologies can support. It helps us to analyze and compare different strategies
of technologies before the implementation of the actual desired grid. In this
report we described simulation of different modulation techniques so as to
determine the performance.
To assess the performances of modulation schemes for PLC is to de-
velop a channel model that attempts to accurately describe the power line
communication channel.
One of the first channel models to gain widespread acceptance was made
by Zimmerman and Dostert. In this model, the multipath effects are re-
solved by attributing a weighting factor, attenuation portion and delay to
each path. The model is verified for simple networks but loses accuracy as
the number of paths increases [19]. To resolve this problem, the modulation
scheme is directly implemented within the ATP-EMTP software environ-
ment using the native FORTRAN based models language. The modulated
signal can be injected into the network at any point using any coupling
scheme. The extracted signal is exported to MATLAB and demodulated.
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Synchronization algorithms allow the simulation to be ’free running’ in the
sense that a frame sent from any node can be demodulated by any other
node without additional user intervention. The main idea of the simulation
is to evaluate the performance of modulation schemes employed in power line
communication channels. The simulation setup is split into three domains:
1) ATP-EMTP domain, where the network model and the inductive
coupler is constructed and simulated.
2) ATP Models domain, where the modulator is simulated in FORTRAN.
3) MATLAB domain, where demodulation and post processing takes
place.
The overall simulation scheme facilitates the simulation of OFDM mod-
ulation on any ATP- EMTP network model [19]. Within ATP-EMTP, one
may replicate network events such as fault transients or switching surges to
study the effect on the communication link. Furthermore, the scheme al-
lows the noise inherent to the power line to be incorporated in the model. A
number of modulator can be considered simultaneously, giving the user an
indication on how time domain multiplexing schemes operate on the power
line channel. The main disadvantage of the presented simulation scheme is
the uncertainty in the accuracy of the line model at high frequencies.
Figure 4.1: BER vs Cyclic Prefix Length of OFDM system [6]
The outcome of the OFDM simulation is found that for an 11 KV rural
overhead networks, channel is extremely frequency selective. For frequency
domain differential PSK, the BER varies depending on the phase rotation
between the adjacent subcarriers. The multipath channel component de-
grades BER is also frequency dependent. Positioning on the network was
observed to affect the BER less than the frequency provided the cyclic prefix
exceeded the RMS delay spread of the channel [19]. The BER curves for the
three different modulation techniques DQPSK, D8PSK, DBPSK of OFDM
can be seen in the shown graphs.
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Figure 4.2: Comparison of Modulation Schemes with OFDM in Smart Grid
[6]
The graph 5.2 shows the plot of BER vs. the cyclic prefix of OFDM
system when implemented in Smart Grids. From the above figure we can
see that higher the cyclic prefix lower is the bit error rate. Even frequency
has also some effect on the performance on the system as it is evident from
the graph.
We studied on the tool deployed in smart grids for their implementation.
These tools are very useful while analyzing for particular topology, technique
or environment.
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Chapter 5
OPTIMIZATION IN
SMART GRID
In this chapter we study on the performance of smart grid and to optimize
the parameters which would contribute to increase it.
5.1 Optimization Models for Energy Reallocation
in a Smart Grid
A Smart Grid is a fully automated electrical distribution and generation
system that is networked, instrumented and controlled. A Smart Grid is a
important system, in which the devices are addressable with digital meth-
ods such as (IP) addresses (Internet Protocol). Many components are also
equipped with processors and sensors that are capable of carrying out in-
telligent actions. The energy produced in the grid can be conventional or
non-conventional like distributed Energy renewable resources [8].
Self-Healing is very much important and needed in today’s new technolo-
gies. Smart Grid should have the ability to take corrective decision to carry
on autonomously without human intervention. When there is a fault state
in the smart grid, the grid should dynamically adapt to the change and
maintain the same power by dynamic algorithms or either quality issues.
Some of the common examples of failure are power outage, poor quality of
power supply and service disruptions. The topology of the Smart Grid as a
network of nodes representing demand sites, supply sources and junctions,
all connected that represent transmission lines. Failures affect the capability
of certain supply sources to meet the demands for energy at certain sites [9].
The main criteria of optimization is to ideally design the grid in such
a way that it does not cause any outage at supply site by maximizing the
cost effectiveness, overall efficiency and reliability of the system. The mod-
els which we design should possess reliability, cost-effectiveness, availabil-
ity, and uncertainty and consumer preference. The basic modeling template
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used while formulating a problem is the Capacitated Transshipment Problem
(CTP). The uncertainty of is modeled with the integer linear programming
framework using chance-constrained programming methods. The optimiza-
tion models have objective functions that optimize a utility function, and
constraints that ensure feasibility of the resource allocations. The agent-
based simulation provides a realistic means of evaluating the performance
of the integer linear programming solutions that would function in a smart
grid when it is on state. The agent-oriented simulation of Smart grid oper-
ation is used to test and evaluate optimization parameters. In constructing
a Smart Grid self-healing model, there are multiple issues. Some pertain to
the physical infrastructure, such as the generators, buses, relays, and trans-
mission lines. Others constitute the cyber information infrastructure they
are related to communication, IP protocols etc.
We concentrate here on the physical issues which are needed to be taken
in to consideration while maximizing the output with minimum resources.
Distributed Device Control Functions: All the devices which are con-
nected should be able to be accessed remotely and can be monitored re-
motely. The best example of such remote monitoring is the ability of circuit
tripping if the input voltage is beyond the threshold.
Selective Load Control: The ability to switch selectively for customers
under undesirable condition and switch on under desirable conditions is very
much important. It also helps to increase the efficiency of the system. This
allows customer also to manage their energy consumption according to their
usage.
Micro-grid Islanding: The customer cluster constitutes small scale power
generators such as solar arrays, fuel cell and wind farms. The cluster is
termed as a micro-grid. This micro-grid disconnects itself when there is
some issue with the main grid and it connects back when it is in normal
condition [10]. The above mentioned conditions seem quite small but it
affects a lot in the efficiency, cost effectiveness and reliability of the smart
grid.
In this chapter we analyzed different ways to optimize the parameters
for better performance.
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Chapter 6
RESOURCE ALLOCATION
IN SMART GRID
Resource allocation is very essential part of the grid which has direct in-
fluence on the performance of the grid. The resources should be properly
utilized as has lot of effect on the cost function.
6.1 Cost Aware Grid Implementation
Resource Allocation is important issue which maximizes the utility function
and helps us use our resources effectively. It brings high performance and
swift flow in the smart grid. The resource allocation problem is modeled as
Knapsack problem and design of the resource allocation is mainly to reduce
the turnaround time of the grid workflow [14].
The linear programming models which are described in the optimization
of smart grid form the edifice for making intelligent decision making in the
grid. The parameter Grid workflow turnaround is the execution time of the
service offered. The service offered by the grid has also some cost factor
which needs to be taken in to consideration. More services means shorter
turnaround time for allocated grid service. The final outcome expected
from the return of investment need proper resource management. There are
currently three alternative methods [12]: 1. Hierarchical 2. Abstract Owner
3. Market Model
We can analyze grid workflow as M/M/C queuing network. The flow
of service from the starting till the end of grid is critical path and has
the average longest execution time. The service average execution time is
very much critical service. This average execution time can be reduced by
increasing the number of abstract owners which is a constraint for cost [15].
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6.2 Game Theory
Game theory can be used as a potential solution to the above mentioned
optimization and resource allocation problems. It helps us to analyze the
equilibriums of the energy infrastructure. Learning and control theory in
game theory allow us to optimize the usage and storage profile of the total
grid. It also focuses on the system dynamics where all the agents in the sys-
tem are given an opportunity to get electricity whenever and wherever they
want. Game theory takes to consideration of the market model while design-
ing efficiently utilizing the resources. The operators take peak demands as
their prior importance so that the design, development and implementation
become efficient with reduced cost [21].
Game theory model decision based on distributed decision making pro-
cess. Thereby the roles of the customers are end players in the game. These
customers play the game in such a way to maximize their energy consump-
tion with reduced costs. They make the strategies for distribution of energy
consumption depending on their usage. Each customer has their own util-
ity function where they try to maximize their utility function and naturally
resulting in better smart grid systems. Each customer while playing their
game tries to account for preferences ’subscriber preferences’. An optimiza-
tion problem can be formulated to maximize the utility of all subscribers
by reducing the energy cost. From the operator point of view he can deter-
mine the pattern of preferences of the customer depending on his usage and
design it appropriately. The energy consumption can also change among
different users. Each user has different utility function which is determined
by adopting from the concept of microeconomics.
The game theory also provides flexibility to determine when the devices
have to interact with the main grid or to make decision when to get con-
nected. We can determine when the agent is connected to the main grid
and when it gets disconnected we can create a storage profile depending on
the connection thereby minimizing the cost of unnecessary production. This
storage profile know the total consumption of the customer and intelligently
maintain a particular strategy to maximize the parameters which we would
like and minimize the cost factor and other factors [18].
Game theory provide strategies to reduce peak demand sites to satiate
with the energy generation and consumption, load management, load shift-
ing technologies by storage profiles. In game theory we use distributed load
management profile to control the power demand. This can be done with
dynamic pricing algorithms with a focus on real time interaction among
subscribers. Optimal values of energy consumption optimal price can be
advertised by the operator. We can find distributed energy consumption
solutions based on congestion games which finally lead us to Nash equilib-
rium solution. The optimality criteria designed when implemented in reality
need to be adjusted depending on the implementation. The application of
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coalition formation in smart grid systems allows us to minimize the cost of
the whole systems [21].
In this chapter we analyzed the different way to allocate resources as per
the requirement based on different models.
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Chapter 7
STANDARDS USED IN
SMART GRID
In this chapter, we will concentrate on the different standards that are de-
signed for smart grids. Some of the standards we studied are IEEE P2030,
P1901, and P1903. These standards play a very important role in the de-
sign of grid. IEEE Standard 2030 Guide for Smart Grid Interoperability of
Energy Technology and Information Technology operation with the Electric
Power System (EPS) and End-Use Applications and Loads. The first and
foremost thing to analyze where we need a standard and why we need it.
The reason why we need a standard is to maintain good Quality of Service
(QOS) and make each manufacturer understand the minimum requirements
for the implementation. The reason where we need standard is analyzed in
the following fig..
7.1 IEEE P2030 Standard
Figure 7.1: IEEE P2030 Standard Implementation[27]
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30.
The fig. 4.1 shows the requirement of standards required for implementa-
tion of IEEE P2030 Standard as it specifies interoperability. In recent years,
there has been an increased demand for more efficient ways of managing the
power distribution in electricity networks; in particular it is desired to re-
duce the wasteful electricity consumption in order to reduce costs and the
adverse effect of electricity generation on the environment. In order to meet
the changing requirements, more sophisticated methods of measuring and
controlling the power consumption are desirable. More, sophisticated net-
works, sometimes known as Smart Grids, have been proposed, which may
include features such as a capability to turn off certain household appliances
or factory processes at times of peak demand. These Smart Grids may use
sophisticated meters, sometimes known as Smart Meters, capable of inter-
mittently measuring power consumption in near real time, and of indicating
energy prices to consumers [16]. The three main components energy, in-
formation, communication are very vital in the design of smart grid. They
form basis for increasing the efficiency of the system.
Figure 7.2: Interoperability of components in Grid[28]
The fig. 4.2 shows the need for interoperability in the components in
Smart Grid. Energy Information and Communication are major compo-
nents in the implementation of the Smart Grid.
Why we need interoperability?
Interoperability is very much important while dealing on broad range of
networks. Interoperability the ability of multiple networks, devices and com-
ponents to communicate and operate together effectively, securely, without
user intervention [29]. The new systems and infrastructure that have been
evolved from the last decade of years are interoperable for better services.
Smart Grid deployment needs lot of planning and analysis to sustain to
the changes after implementation of the system. For this sustainability it
needs to be interoperable and understand the other technologies so as to
adapt and become smart. The final aspect of interoperability is backward
compatibility and smart grid should be able to cope with the previous and
present standards to become more reliable and efficient. Standards create
platform for the devices and grid for communication irrespective of the lo-
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cation of the device and the service provider [26]. The introduction of new
technologies and standards has to be properly secured with proper cyber
security technologies in order to prevent any breach in the smart grids. The
secured means of utilization allows providing more efficient smart grids and
better consumption with smarter networks.
We describe here some of the present standards that are available in
the present day market. So any research or development should take in to
consideration the present standards for interoperability for efficient smart
grid networks.
P2030 Standard Scope and Purpose This standard provides understand-
ing and defines smart grid interoperability of the electric power system with
end-use applications and loads [26]. Smart grid is a combination of en-
ergy technology, Information technology, communication technology which
together work for the energy generation, transmission, delivery and com-
munication flow among the components. This standard mainly addresses
Interconnection and intrafacing frameworks and strategies with design defi-
nitions, providing guidance in expanding the current knowledge base. This
knowledge base is very much required for the architectural design and pro-
duction of the efficient electric system. It provides basic knowledge on the
interoperability issues of electric power system with end user taken in to
consideration. It tries to integrate three main domain groups of technol-
ogy which are required for implementation of smart grid technology. They
are information, communication and energy technology. It aims to achieve
seamless operation of smart grid technology with the help of the above de-
scribed motives. The interoperability of IEEE P2030 can be best understood
by the corresponding fig.4.3
The fig. 4.3 shows the interoperability standard of IEEE which basically
constitutes the operation of Smart grid with the interoperability of different
standards.
7.1.1 IEEE P1901- Broadband over the power line Networks
The P1901 is IEEE working group of the Broadband over power line net-
works. The draft published by this group on 1st Feb 2011, mainly address
the Medium access control and physical layer specifications of the Broad-
band over power line networks.
This standard aims to develop communication devices which work at
speeds greater than 100 MBPS over the electric power system. The devices
are termed as Broadband over power line devices BPL. The transmission
frequencies are below 100 MHZ and they are for both used for first/last mile
wireless solution for wireless local area network and for distribution points.
This standard defines how these devices are interoperable for all classes
of BPLS devices. The standard will take in to consideration of the necessary
security questions to ensure the privacy between communicating users and
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Figure 7.3: IEEE P2030 Interoperability Standard[28]
allow the use of BPL for security sensitive services. This standard is limited
to the physical layer and the medium access sub-layer of the data link layer,
as defined by the International Organization for Standardization (ISO) Open
Systems Interconnection (OSI). Purpose of this Standard:
High speed communication links use new modulation techniques and new
media which are open, and locally shared by several BPL devices. With-
out an independent, openly defined standard, BPL devices serving different
applications will not co-operate with one another and provide unacceptable
service to all parties. The main idea of this standard is fair existence of the
BPL devices without getting separated from the main domain. The imple-
mentation of this standard will provide with the interoperability with neigh-
boring protocols, such as bridging for seamless interconnection via 802.1.
The standard also complies with EMC limits set by national regulators, so
as to ensure successful co-existence with wireless systems [30].
7.1.2 Next Generation Service Overlay Network IEEE P1903
Next Generation Service Overlay Network IEEE P1903 describes a frame-
work of Internet Protocol (IP)-based service overlay networks and specifies
context-aware, dynamically adaptive services. Some of the services are using
locally derived information to discover, organize, and maintain traffic flow
in the network within a specified local area network. One way is to develop
network structures, routing and forwarding schemes based on the needs and
capabilities of network structures depending on the customers [30]. The fig.
4.4 shows the overlay network of IEEE P1903 standard which uses different
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Figure 7.4: Overlay Network IEEE P1903[26]
forwarding schemes depending on the load of customers. It describes the
components of overlay network IEEE P1903. It shows the options available
for the networks to switch their load accordingly depending on the customers
and network availability.
We have studied different standards which are from IEEE for the better
implementation of the Smart Grid.
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Chapter 8
SMART GRID
DEVELOPMENT VISION
In this chapter we study on the future expectations of the smart grid devel-
opment in different countries. We study the outcome of grid development
in the future.
The vision shows the new technologies while retaining the flexibility to
adapt to the future developments .Network technologies will increase the
power transfer and will reduce the energy loss and this will improve the
quality of services .Advances in the simulation tools will greatly assist to
convert the innovation into the practical application which is beneficial for
both consumers and utilities. Development in the communication, metering
and business system will open up the opportunity at every level on the
system to increase the market size for technical and commercial field [22].
The development vision of the smart grid can be assessed based on the
utilization and its functionality. The below figure shows the smart grid
vision in future.
The figure 8.1 shows the futuristic vision of Smart Grid which basi-
cally constitutes the infrastructure automation utility and data. It needs
to support applications from operator to customer which require efficient
structure.
Smart grids are systems which are complicated and composed of intricate
design that incorporate consumer interactions and decision points. That is
the reason why it makes it difficult for design and development of smart
gird. Smart grids are implemented in many countries so development and
demonstration needs to be discussed in global context. But the deployment
is treated to be regional as we need to take in to consideration lot of local
factors which decide efficient deployment. The reason why it needs to be
discussed regional because of the infrastructure, demand growth, generation
and market structures [23].
Many countries are motivated by economic, security and environmental
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Figure 8.1: Smart Grid Vision[26]
factors to choose their own priorities while implementing smart grid tech-
nologies. These countries analyze different approaches to assess the impact
of potential smart grid deployment [24]. Some of the regional characteristics
which countries will be taking in to consideration are
• Industry, residential load prevalence or the deployment of electric ve-
hicles.
• Status of existing and planned new transmission and distribution net-
works.
• Current and planned mix of supply, including fossil, nuclear and re-
newable generation.
• Current and future demand and sectoral make-up of demand, such as
manufacturing.
• Ability to interconnect with neighboring regions.
• Regulatory and market structure.
• Climatic conditions and resource availability.
We analyzed on the different ways to implement the smart grid in the
near future with the operator and customer taking in to consideration.
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Chapter 9
CONCLUSION
In this report we have studied about the Smart Grid technology. It in-
cludes the communication on the grid, channel model, resource allocation,
optimization, standards and distribution. We have analyzed that current
grids are outdated, inefficient and overburdened. Smart grids are actually
designed to optimize the efficiency and stability. We have studied about the
data communication in Smart Grids and have seen that the communication
in the grid is mainly done using OFDMA technology. We have analyzed how
these channel models are used in practical systems in different applications.
We even analyzed how to perform power flow measurement in Smart Grid
using CDMA technology. We then analyzed how we can do simulations to
improve the efficiency of Smart Grid performance. We then analyzed pa-
rameters which can optimize and allocate resources in Smart Grids. Finally,
we understood the concept behind the present implemented standards in
Smart Grids.
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Chapter 10
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